Microorganisms and Mental Health 3031243323, 9783031243325

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Current Topics in Behavioral Neurosciences 61

Jonathan Savitz Robert H. Yolken Editors

Microorganisms and Mental Health

Current Topics in Behavioral Neurosciences Volume 61

Series Editors Mark A. Geyer, Department of Psychiatry, University of California San Diego, La Jolla, CA, USA Charles A. Marsden, Queen’s Medical Centre, University of Nottingham, Nottingham, UK Bart A. Ellenbroek, School of Psychology, Victoria University of Wellington, Wellington, New Zealand Thomas R. E. Barnes, The Centre for Mental Health, Imperial College London, London, UK Susan L. Andersen, Medfield, MA, USA Martin P. Paulus, Laureate Institute for Brain Research, Tulsa, OK, USA Jocelien Olivier, GELIFES, University of Groningen, Groningen, The Netherlands

Current Topics in Behavioral Neurosciences provides critical and comprehensive discussions of the most significant areas of behavioral neuroscience research, written by leading international authorities. Each volume in the series represents the most informative and contemporary account of its subject available, making it an unrivalled reference source. Each volume will be made available in both print and electronic form. With the development of new methodologies for brain imaging, genetic and genomic analyses, molecular engineering of mutant animals, novel routes for drug delivery, and sophisticated cross-species behavioral assessments, it is now possible to study behavior relevant to psychiatric and neurological diseases and disorders on the physiological level. The Behavioral Neurosciences series focuses on translational medicine and cutting-edge technologies. Preclinical and clinical trials for the development of new diagnostics and therapeutics as well as prevention efforts are covered whenever possible. Special attention is also drawn on epigenetical aspects, especially in psychiatric disorders. CTBN series is indexed in PubMed and Scopus. Founding Editors: Emeritus Professor Mark A. Geyer Department of Psychiatry, University of California San Diego, La Jolla, USA Emeritus Professor Charles A. Marsden Institute of Neuroscience, School of Biomedical Sciences, University of Nottingham Medical School Queen’s Medical Centre, Nottingham, UK Professor Bart A. Ellenbroek School of Psychology, Victoria University of Wellington, Wellington, New Zealand

Jonathan Savitz • Robert H. Yolken Editors

Microorganisms and Mental Health

Editors Jonathan Savitz Laureate Inst for Brain Research Tulsa, OK, USA

Robert H. Yolken Psychiatry and Behavioral Sciences Dept Johns Hopkins School of Medicine Baltimore, MD, USA

ISSN 1866-3370 ISSN 1866-3389 (electronic) Current Topics in Behavioral Neurosciences ISBN 978-3-031-24332-5 ISBN 978-3-031-24333-2 (eBook) https://doi.org/10.1007/978-3-031-24333-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The hypothesis that microorganisms play a causal role in the development of psychiatric disorders has a long history, dating back to at least the late nineteenth century where it received support from leading researchers such as Emil Kraepelin (Yolken and Torrey 2008). The finding in 1913 by Noguchi and Moore that the serious psychiatric disorder known as general paresis of the insane was in fact a CNS infection caused by Treponema pallidum demonstrated that an infectious agent could cause psychiatric symptoms and led the way to an eventual cure of this previously untreatable disease (Kaplan 2010). The 1919 influenza pandemic sparked further interest in the role of microorganisms when increased rates of schizophrenia and other psychiatric disorders were observed post-infection (Menninger 1994; Kepinska 2020). The hypothesis was revived in the 1980s with prominent reports of herpesvirus infections in the cerebrospinal fluid (CSF) of individuals with schizophrenia. The recent Covid-19 pandemic has also highlighted the fact that viral infections can lead to a wide range of neuropsychiatric complications. Although there have been some differences among individual studies, a large body of evidence has now accumulated linking specific microbial infections with severe mental illnesses such as schizophrenia, bipolar disorder (BD), and major depressive disorder (MDD). Moreover, the recent elucidation of how microorganisms interact within the intestinal tract and affect brain function through the gut-brain axis has shown that organisms can alter behavior without actually replicating within the CNS. An increased understanding of neurodevelopment and neuroinflammation has further provided mechanistic insight into the ways in which microbial organisms, either singly or in combinations with other microorganisms or host factors, can contribute to the etiopathogenesis of psychiatric disorders. The current volume provides an updated review of the role of microorganisms in mental health and disease. We start with a summary of the typical behavioral manifestations of sickness and how this evolutionary-conserved response to infection may relate psychiatric symptoms (Robert Dantzer, The University of Texas MD Anderson Cancer Center). Sørensen and Benros (Copenhagen University) then

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provide an overview of the epidemiological evidence linking inflammation and depression while Maree Webster (Johns Hopkins University) reviews the evidence for inflammation in postmortem samples with severe mental illness. The next two chapters take a neurodevelopmental perspective. Buka, Lee, and Goldstein (Brown University and Harvard University) review two decades of their epidemiological work on the link between prenatal infections and the risk for schizophrenia and psychotic disorders while important methodological challenges and sources of variation in animal models of maternal immune activation are covered by Urs Meyer (University of Zurich). It is not always appreciated that microorganisms may have beneficial as well as pathological effects. Dawud, Holbrook, and Lowry (University of Colorado, Boulder) highlight the importance of microbial signals in immunoregulation and how the absence of key commensal microorganisms can predispose to inflammation in the modern world. Dickerson, Jeste, and colleagues (Johns Hopkins University and the University of California, San Diego) provide a high-level overview of the emerging relationship between the human microbiome and mental health and future opportunities for intervention. Continuing this theme, Kim, Zisman, and Holingue (Johns Hopkins University) discuss the link between the microbiome, intestinal disturbances, and autism. Finally, Severance (Johns Hopkins University) reviews the role of both commensal and pathogenic fungi in the microbiome and how they may impact psychiatric disorders. In the last section of the volume, we turn our attention to specific viruses, bacteria, and parasites. Ford and Savitz (Oklahoma State University and Laureate Institute for Brain Research) review the literature on the relationship between premature aging and cytomegalovirus (CMV) infection while Zheng and Savitz review neuroimaging studies of CMV and their implications for psychiatric disorders. Marques, Nimgaonkar, and colleagues (University of Pittsburgh) present data from a humaninduced pluripotent stem cell culture to model the pathogenic mechanisms potentially linking herpes simplex virus infections with cognitive impairment. The recent Covid-19 pandemic has focused attention on the neuropsychiatric complications of coronaviruses. Dickerson, Severance, and Yolken present interesting new data indicating that this association is likely not limited to the SARS Coronaviruses which cause COVID-19 but may also be associated with exposure to the endemic non-SARS coronaviruses. Delaney, Murray, and Fallon (Columbia University) review the literature on Borrelia burgdorferi, the causative agent of Lyme disease, and neuropsychiatric symptoms and their treatment. Jianchun Xiao (Johns Hopkins University) discusses the literature on the relationship between the neurotropic protozoan, Toxoplasma gondii, and behavioral changes in both animal models and human participants. Most human diseases are inherently complex in origin and involve multiple genetic, epigenetic, and environmental influences. However, despite advances in genetic and metabolic therapies infections remain among the most treatable of human diseases. In the final chapter, Savitz and Yolken discuss the therapeutic implications of this area of research for the treatment of psychiatric disorders. In the more than 100 years since the elucidation of the infectious origin of neurosyphilis, the prevention and treatment of infection arguably remains the

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avenue of research most likely to lead to practical interventions for the prevention and treatment of mental disorders. Tulsa, OK, USA Baltimore, MD

Jonathan Savitz Robert H. Yolken

References Yolken RH, Torrey EF (2008) Are some cases of psychosis caused by microbial agents? A review of the evidence. Mol Psychiatry 13(5):470–479 Kaplan RM (2010) Syphilis, sex and psychiatry, 1789-1925: part 2. Australas Psychiatry 18(1):22– 27 Menninger KA (1994) Influenza and schizophrenia. An analysis of post-influenzal “dementia precox,” as of 1918, and five years later further studies of the psychiatric aspects of influenza. 1926. Am J Psychiatry 151(6 Suppl):182–187 Kepinska AP, Iyegbe CO, Vernon AC, Yolken R, Murray RM, Pollak TA (2020) Schizophrenia and Influenza at the Centenary of the 1918-1919 Spanish Influenza Pandemic: Mechanisms of Psychosis Risk. Frontiers in Psychiatry 11:72

Contents

Evolutionary Aspects of Infections: Inflammation and Sickness Behaviors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Robert Dantzer

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The Immune System and Depression: From Epidemiological to Clinical Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nina Vindegaard Sørensen and Michael Eriksen Benros

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Infections, Inflammation, and Psychiatric Illness: Review of Postmortem Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maree J. Webster

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Infections During Pregnancy and Risks for Adult Psychosis: Findings from the New England Family Study . . . . . . . . . . . . . . . . . . . . . . . . . . . Stephen L. Buka, Younga Heather Lee, and Jill M. Goldstein

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Sources and Translational Relevance of Heterogeneity in Maternal Immune Activation Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urs Meyer

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Evolutionary Aspects of Diverse Microbial Exposures and Mental Health: Focus on “Old Friends” and Stress Resilience . . . . . . . . . . . . . . Lamya’a M. Dawud, Evan M. Holbrook, and Christopher A. Lowry

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The Microbiome and Mental Health Across the Lifespan . . . . . . . . . . . . 119 Faith Dickerson, Amanda Hazel Dilmore, Filipa Godoy-Vitorino, Tanya T. Nguyen, Martin Paulus, Adrian A. Pinto-Tomas, Cristofer Moya-Roman, Ibrahim Zuniga-Chaves, Emily G. Severance, and Dilip V. Jeste Influences of the Immune System and Microbiome on the Etiology of ASD and GI Symptomology of Autistic Individuals . . . . . . . . . . . . . . 141 Amanda Kim, Corina R. Zisman, and Calliope Holingue ix

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Fungal Forces in Mental Health: Microbial Meddlers or Function Fixers? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Emily G. Severance Effect of Cytomegalovirus on the Immune System: Implications for Aging and Mental Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Bart N. Ford and Jonathan Savitz Effect of Cytomegalovirus Infection on the Central Nervous System: Implications for Psychiatric Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Haixia Zheng and Jonathan Savitz Herpesvirus Infections in the Human Brain: A Neural Cell Model of the Complement System Derived from Induced Pluripotent Stem Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Ernesto T. A. Marques, Matthew Demers, Leonardo D’Aiuto, Priscila M. S. Castanha, Jason Yeung, Joel A. Wood, Kodavali V. Chowdari, Wenxiao Zheng, Robert H. Yolken, and Vishwajit L. Nimgaonkar Non-SARS Coronaviruses in Individuals with Psychiatric Disorders . . . 265 Faith B. Dickerson, Emily G. Severance, and Robert H. Yolken Neuropsychiatric Symptoms and Tick-Borne Diseases . . . . . . . . . . . . . . 279 Shannon L. Delaney, Lilly A. Murray, and Brian A. Fallon Behavioral Changes Induced by Latent Toxoplasmosis Could Arise from CNS Inflammation and Neuropathogenesis . . . . . . . . . . . . . . . . . . 303 Jianchun Xiao Therapeutic Implications of the Microbial Hypothesis of Mental Illness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 Jonathan Savitz and Robert H. Yolken

Evolutionary Aspects of Infections: Inflammation and Sickness Behaviors Robert Dantzer

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Neuroimmune Mechanisms of Sickness Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3 Evolutionary Aspects of Sickness Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 4 The Adaptive Value of Sickness Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Abstract Sickness behavior was conceptualized initially as the behavioral counterpart of the fever response to infectious pathogens. It helps to raise body temperature to its higher setpoint and to maintain it at this new level and it has the additional benefit of enabling a weakened organism to protect itself from other dangers. The discovery of the behavioral effects of proinflammatory cytokines produced by activated immune cells provided a cellular and molecular basis to this phenomenon. The administration of cytokines or cytokine inducers like lipopolysaccharide to healthy rodents allowed to reveal the similarities and differences between inflammation-induced sickness behavior and the fever response. It also led to the understanding of how the inflammatory response that is triggered at the periphery can propagate into the brain and induce the behavioral manifestations of sickness. At the behavioral level, the demonstration that sickness behavior is the expression of a motivational state that reorganizes perception and action in face of a microbial pathogen just like fear in face of a predator appeared at first glance to strengthen the adaptive value of this behavior. However, all aspects of sickness behavior are not always favorable for the organism. This is the case for anorexia that is beneficial in the context of bacterial infection but detrimental in the context of viral infection. In addition, studies of sickness behavior in natural conditions revealed that like any other defensive behavior, sickness behavior requires trade-offs between its survival R. Dantzer (*) Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 1–14 https://doi.org/10.1007/7854_2022_363 Published Online: 4 May 2022

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benefits for the sick individual and the costs incurred especially in the context of gregarious groups. Thanks to these studies, evidence is emerging that sickness behavior is much more variable in its expression than initially thought, and that part of this variability depends not only on the pathogen and the social context in which the infection develops but also on individual factors including species, sex, age, nutrition, and physiological status. Keywords Cytokines · Evolution · Fitness · Innate immunity · Sickness behavior

1 Introduction While I was studying veterinary pathology at the School of Veterinary Medicine in Toulouse, France, in the mid 1960s, I was struck by the archetypal description of what sick animals look like. The image was always the same whatever the animal species. At the beginning of their infectious episode, sick animals are febrile and show lethargy, depression, anorexia, and reduction in grooming. They isolate themselves from their conspecifics and have no appetite even for the type of food they normally favor. Of course, the experienced veterinarians whom we all aspired to become had to go beyond these non-specific symptoms of infection and search for those clinical signs that are specific of the infectious disease itself. The behavior of sick animals was not interesting by itself as our ultimate objective was to identify the disease in order to treat it in the best possible way. Still, what was intriguing for me at the time was that the conceptualization of this non-specific behavioral pattern of sickness was not without analogy with the discourse on the non-specific activation of the pituitary-adrenal axis that is characteristic of the stress response as defined by Hans Selye. This idea of sickness behavior as a stereotyped response to infection remained with me for a long time but without any real willingness to address it on my part. I had enough on my plate with my research work on stress and well-being. The game changing event occurred in the late 1980s when molecular biologists were able to characterize the inflammatory mediators that are released by activated innate immune cells and mediate the development of infection-induced fever and activation of the hypothalamic-pituitary-adrenal response. After getting enough recombinant human interleukin-1β (IL-1β) from Glaxo I was finally able to demonstrate for the first time that this proinflammatory cytokine mediates the development of sickness behavior as well (Dantzer and Kelley 1989; Kent et al. 1992; Tazi et al. 1988). Before their molecular structure was elucidated IL-1β and other proinflammatory cytokines were known as endogenous pyrogens, i.e., the intermediate factors that the host needs to produce in order to develop a fever response to an infectious agent. Sickness behavior was first conceptualized in the context of the fever response (Hart 1988). Fever is the result of an increase in the set point for the regulation of body temperature, meaning that the febrile individual must increase its heat production and at the same time drastically decrease its heat losses. One cannot

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develop a fever if one has to engage in the expensive activity of foraging or searching for a sexual partner. Increased sleepiness and lethargy suppress all activities directed toward the environment. Huddling minimizes body area exposed to ambient air while reduced grooming minimizes heat losses. In other words, it made sense to claim that sickness behavior was the necessary behavioral counterpart of the energetic requirements of the fever response to infection. The objective of this chapter is to present the mechanisms of sickness behavior, show its universality across animal species equipped with an innate immune system, and discuss the validity of the claim that because of its association with the fever response it is always adaptive for the host.

2 Neuroimmune Mechanisms of Sickness Behavior As previously mentioned, sickness behavior is characterized by lethargy, sleepiness, anorexia, and social isolation. In the terms used by Hart, “animals that are acutely ill with systemic protozoan, bacterial or viral infections are typically described as depressed and lethargic with little interest in eating food and drinking water. A little later in the course of a disease they commonly show signs of dehydration along with indications that they have lost interest in grooming since they develop rough hair coats. These behavioral signs generally accompany a fever response and, together with the occurrence of fever, are recognized by animal handlers and veterinarians as signs that an animal is sick or is becoming sick with an infectious disease”. However, this does not mean that all animals that develop these signs are always afflicted with an infectious disease as similar behavioral alterations can be observed in several other conditions, such as poisoning or dehydration. Infection-induced sickness behavior can be identified more specifically by its dependency on the mechanisms that mediate the recognition of infectious agents by innate immune cells, the production of proinflammatory cytokines at the site of infection, and the propagation of this immune message to the brain. Innate immune cells recognize pathogen-associated molecular patterns (PAMPs) which correspond to specific molecular motives of infectious agents. This is the case, for instance, for lipopolysaccharide (LPS) that is the major component of the outer layer of the membrane of Gram-negative bacteria. PAMPs are recognized by patternspecific receptors that are expressed by innate immune cells, such as monocytes, macrophages, neutrophils, dendritic cells, and epithelial cells (Li and Wu 2021). Toll-like receptors (TLRs) are a well-known example of pattern recognition receptors. They are composed of an extracellular domain, a transmembrane domain, and an intracellular effector domain that recruits adaptor molecules to activate downstream signaling pathways. For example, upon binding LPS TLR4 recruits the adaptor myeloid differentiation factor (MyD88) that ultimately signals via the transcription factor nuclear factor-kappa B. This leads to the production of proinflammatory cytokines such as IL-1, tumor necrosis factor, and IL-6. Not all pattern recognition receptors are located at the cell membrane. Some receptors are

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located intracellularly. They are important for the recognition of molecular motives of infectious agents that invade cells. This is the case for nucleotide oligomerization domain (NOD)-like receptors and retinoic acid-inducible gene-I (RIG-I)-like receptors. NOD1, for instance, recognizes a component of the cell wall of Gram-negative bacteria whereas NOD2 recognizes single-stranded ribonucleic acid of viruses. Upon binding to their ligands, NOD-like receptors are recruited into the plasma membrane and endosomal membrane where they initiate signal transduction. RIG-Ilike receptors recognized double-stranded RNAs and their activation results in the increased expression of type I interferons. Activation of pattern recognition receptors leads to the production of cytokines via multiple signaling pathways. The NFκB signaling pathway mediates the innate immune response and its interaction with the adaptive immune response. It promotes the transcription of most inflammatory genes other than interferons for which IRF-3 downstream of TLR3, the TLR4-TIR domain and RIG-I are the key elements. Mitogen-activated protein kinase (MAPK) signaling relays the effects of inflammatory cytokines on their cellular targets. The inflammasome is a multi-protein complex that is assembled in the cytoplasm by pattern recognition receptors and results in the activation of caspase-1 which is necessary for maturation of cytokines of the IL-1 family. Most inflammatory cytokines act as autocrine or paracrine communication signals in the local environment in which they are produced. IL-6 is an exception. It is released into the general circulation and acts on hepatocytes to promote the production and release of acute phase proteins such as C-reactive protein. The way cytokines ultimately act in the brain to induce sickness behavior has been studied mainly in rats, guinea pigs, and mice, and has been the subject of much debate (Dantzer et al. 2000). This debate was fueled initially by the use of the intravenous route to inject endogenous pyrogens. The predominant view at the time was that macrophages release endogenous pyrogens into the general circulation. Endogenous pyrogens cannot enter the blood-brain barrier. Instead, they activate macrophage-like cells in circumventricular areas, those brain regions that lack a fully formed blood-brain barrier. There, they induce the formation of secondary messengers in the form of prostaglandins E2 that can diffuse freely through the blood-brain barrier to reach neurons of the thermoregulatory center of the hypothalamus. However, the discovery that at least part of the effects of cytokines on the brain are relayed by sensory nerves conflicted with this theory (Wan et al. 1994; Watkins et al. 1994; Bluthe et al. 1994). Cytokines activate directly or indirectly sensory neurons that innervate the site of inflammation. This neural form of information is then transmitted to the brain via the primary and secondary projections of these neurons (Konsman et al. 2002). As a typical example, proinflammatory cytokines released in the abdominal cavity by LPS injected via the intraperitoneal route activate the sensory branch of the vagus nerves that project to the nucleus tractus solitarius at the level of the brain stem. This neural activation then propagates to other brain areas such as the parabrachial nucleus, paraventricular and supraoptic nuclei of the hypothalamus, central amygdala, bed nucleus of the stria terminalis, and periaqueductal gray. This neural pathway intersects with a humoral pathway that is

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dependent on the passage of pathogen-associated molecular patterns in the general circulation. By recruiting macrophages in circumventricular areas this humoral pathway is at the origin of a slow wave of production of proinflammatory cytokines that progress into the brain parenchyma by active diffusion, recruiting other macrophage-like cells “en passant.” This secondary wave of cytokines originating from circumventricular areas would be responsible ultimately for the production of proinflammatory cytokines by microglial cells which are the resident macrophages in the brain parenchyma. It has been proposed that the neural pathway traces the path for the effects of these locally produced cytokines by sensitizing the brain areas to their activity (Dantzer et al. 2000). However, this hypothesis has not yet been tested. Another pathway is represented by the recruitment of inflammatory monocytes to the brain vasculature by microglia (DiSabato et al. 2021). This would allow the trafficking of these peripheral innate immune cells into the parenchyma of those brain regions in which the blood-brain barrier is compromised. This pathway appears to be activated in chronic stress conditions such as the repeated social defeat paradigm (Weber et al. 2017). We do not yet fully understand what happens when the immune message propagates to the brain. Several neuronal effects of cytokines are indirect and relayed by the synthesis and release of inflammatory mediators such as prostaglandin of the E2 series, radical oxygen species, or nitric oxide. The main cellular source of prostaglandins is represented by endothelial cells and perivascular cells along small venules. Prostaglandins act on various prostaglandin receptors that are distributed throughout the brain in neuronal circuits that have been found to mediate the effects of inflammatory cytokines on fever, sleep, anorexia, activation of the hypothalamic-pituitary-adrenal axis, and hyperalgesia (Saper et al. 2012). However, other mechanisms could also be at play as IL-1β, when it is expressed in the brain, can act directly on neurons by increasing or decreasing their excitability depending on the amount of IL-1β present (Nemeth and Quan 2021). Microglial cells produce inflammatory cytokines in the brain either in response to inflammatory stimuli taking place in the brain or in response to peripheral inflammation relayed to the brain by the mechanisms described above. The role of microglia in the development of sickness behavior has been studied mainly using pharmacological tools. Administration of minocycline, a second generation antibiotic that acts as a down regulator of microglia activation, attenuated the effects of LPS administered peripherally on mood (O'Connor et al. 2009). In the same manner, minocycline treatment of mice exposed to repeated social defeat, a stressor procedure that induces microglia activation and monocyte recruitment in the brain, abrogated these effects together with the cognitive deficits presented by mice submitted to the social stressor (McKim et al. 2016). Interestingly, this intervention did not prevent the persistence of social avoidance behavior that was apparent in stressed mice. However, a more radical intervention consisting of depleting brain microglia by various modalities of intervention not only failed to abrogate inflammation-sickness in mice but actually enhanced its expression probably by unmasking an inhibitory role of microglia on the immune activity of astrocytes (Vichaya et al. 2020). More recently, specific activation of microglia in the mouse

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dorsal striatum by chemogenetic tools was shown to induce anhedonia and aversion. These effects were mediated by IL-6 signaling in microglia and Cox1-dependent production of prostaglandins acting on EP1/2 receptors in striatal medium spiny neurons (Klawonn et al. 2021). Conversely, chemogenetic inhibition of microglia abrogated the development of aversion in LPS-treated mice. The emphasis on the neuroimmune mechanisms of sickness behavior has led researchers to neglect the fact that this behavior is by necessity associated with important alterations in energy metabolism that are not only dependent on the requirements of the fever response as initially thought but also by those of activated immune cells. The proliferation of immune cells and production of proinflammatory cytokines necessitate a reprogramming of their cellular metabolism from oxidative phosphorylation to aerobic glycolysis. The molecular mechanisms of this metabolic reprogramming have been elucidated thanks to advances in immunometabolism (O'Neill et al. 2016). Whatever the details of these mechanisms it is important to remember that aerobic glycolysis generates only four molecules of adenosine triphosphate, the currency for energy metabolism in the organism, for one molecule of glucose instead of 36 for oxidative phosphorylation. The high increase in energy metabolism at the site of inflammation can only be sustained if other parts of the organism including the brain and skeletal muscles consume less energy. This means that sickness behavior is probably dictated at least in part by energy repartition considerations (Straub 2014) or by the delicate balance between defense and tolerance metabolic programs within the organism (Wang et al. 2019). We will come back to this notion in the section discussing the adaptive value of sickness behavior.

3 Evolutionary Aspects of Sickness Behavior The exact mechanisms by which proinflammatory cytokines act in the brain are likely to depend on the species. What is important for the identification of sickness behavior as a component of the defense response of the organism to an infection is that this behavior should not only occur during the course of an infection, but its development should be dependent on the production and release of inflammatory mediators by innate immune cells. As mentioned in the previous section, the mechanisms of infection-induced sickness behavior have been mainly studied in laboratory rodents. However, infection-induced sickness behavior is also present in other species including insects, fish, reptiles, and worms (Lopes et al. 2021). As a typical example, Kirsten and colleagues recently demonstrated that zebrafish inoculated with formalin-inactivated Aeromonas hydrophila bacterin developed a reduction in their locomotor activity, social preference, and exploratory behavior toward a novel object, and these changes in behavior were associated with an upregulation of the expression of proinflammatory cytokines in their brains (Kirsten et al. 2018a). This allowed these authors to conclude that the divergence in cytokine profile they observed between individual zebrafishes differing in their response to novelty and to social stimuli (Kirsten et al. 2018b) was probably driven by neuroimmune

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interactions. When the microbial agent invades the brain like it was described in zebrafish infected with the tilapia lake virus, the behavioral signs of infection were even more severe with signs of neuropathology developing over a background of sickness behavior (Mojzesz et al. 2021). The innate immune response to microbial pathogens in drosophila involves two pathways. The Toll pathway regulates production of antimicrobial peptides against fungi and Gram-positive bacteria. The immune deficiency (IMD) pathway responds to Gram-negative bacteria (Takeda et al. 2003) by also producing antimicrobial peptides. The ability of antimicrobial peptides to act on the nervous system has mainly been studied in the context of sleep. Increased sleepiness in drosophila is a well-characterized response to microbial infection and to other forms of cellular stress. In Caenorhabditis elegans, antimicrobial peptides act as somnogens, signaling across tissues to promote sleep by activating sleep neurons in response to peripheral tissue injury (Sinner et al. 2021). In Drosophila melanogaster, the antimicrobial peptide nemuri can function both at distance when produced in damaged peripheral tissues and locally as it is expressed also in the brain where its enhanced production following sleep deprivation promotes restorative sleep (Toda et al. 2019). In general, mechanisms regulating sleep during sickness are partially distinct from those regulating healthy sleep as demonstrated by elegant experiments in C. elegans and drosophila. Cellular stress induced by infection or other environmental insults is associated with the release of epidermal growth factor (EGF) or EGF-like peptides that signal to central neuroendocrine cells. These neurons release RFamide peptides that induce anorexia, movement quiescence, and elevated arousal threshold (Davis and Raizen 2017). Like in vertebrates, cooperation between the immune system and the nervous system in invertebrates involves a variety of mechanisms that allow animals to adapt their behavior to the presence of pathogens that coexist with them (Montanari and Royet 2021). Although the behavioral effects of proinflammatory cytokines in non-human primates have been less well studied than in laboratory rodents, there is evidence that they are able to develop the whole range of sickness behaviors observed in other animal species. For instance, intravenous injection of IL-1α in juvenile rhesus monkeys rapidly induced sleep-like inactivity and decreased their behavioral and vocal responses to broadcasted calls from conspecifics (Friedman et al. 1995). Monkeys injected with a high dose of IL-1α showed signs of agonistic behavior when challenged by a human experimenter (Friedman et al. 1996). Similar signs of inactivity and increased sleepiness were observed in response to IL-1β in monkey pairs with evidence of enhanced huddling behavior (Reyes and Coe 1996). This tendency to increased social contact was observed also in monkeys injected with LPS and is in contrast with the social withdrawal seen in rodents in response to the same treatment (Willette et al. 2007). All these behavioral changes were associated with increased circulating levels of IL-6 although its role in the development of sickness behavior was not specifically addressed. As studies of sickness behavior in humans are very disparate in terms of research design, modality of immune stimulation, and selection of the endpoint under study, a meta-analysis of the human sickness behavior was conducted recently (Shattuck and

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Muehlenbein 2016). Depressed affect and fatigue were the most commonly reported symptoms, and they showed an association with IL-6 and IL-1β. Focusing on LPS-induced sickness behavior, Lasselin and colleagues confirmed that reduction in food intake and alterations in sleep patterns are part of the picture (Lasselin et al. 2020). Other objective behavioral changes include increased frequency of moans and sighs, increased yawning, reduced walking speed, and changes in the willingness to expend effort to get a reward. Self-report assessments provide clear evidence for fatigue, reduction in appetite, and reduced social interest, together with an increase in negative mood as well as in state anxiety. All these effects develop within 2–3 h after intravenous injection of LPS and are over by 6 h. Experiments specifically designed to address the impact of LPS on social experiences revealed that inflammation increases sensitivity to negative, threatening social experiences, and in contrast increases sensitivity to positive, socially rewarding experiences (Moieni and Eisenberger 2018). The behavioral effects of immune stimulation targeting TLR3 or intracellular DNA sensors and mimicking a viral infection have been less studied than those targeting TLR4 and mimicking a Gram-negative bacteria infection. Acute peripheral administration of polyriboinosinic:polyribocytidylic acid (poly I:C), a synthetic double-stranded RNA that mimics a viral infection, induces a dose-dependent typical sickness behavior in mice and rats, with decreased locomotor activity, feeding and burrowing, and hyperthermia or hypothermia depending on the dose (Cunningham et al. 2007). Although there was no evidence of tolerance when the treatment was repeated, most experiments on poly I:C make use of only acute injections. The study of the behavioral effects of poly I:C is complicated by the existence of different molecular weight forms of this compound which do not have the same ability to activate the type I interferon response (McGarry et al. 2021). In addition, there is evidence that the reduction of voluntary wheel running in response to poly I:C, a model of inflammation-induced fatigue, is independent of its ability to induce IFNβ (Matsumoto et al. 2008). The anorexic response to poly I:C was blocked by genetic deletion of TLR3 or TRIF when the compound was injected at the periphery but not when it was injected into the lateral ventricle of the brain (Zhu et al. 2016). The central effect of poly I:C was blocked by genetic deletion of MyD88, suggesting that the mechanisms of sickness induced by a neurotropic virus could differ from those of sickness induced by a non-neurotropic virus. Interferons have been studied mainly in the context of anxiety disorders and depression. The behavioral effects of these cytokines will not be detailed in the present chapter. Casual observations confirm that acute administration of interferons induces symptoms of sickness although they have not been studied systematically. Chronic administration of interferon-α induced anxiety-like behavior and decreased environmental exploration in rhesus monkeys (Felger et al. 2007). Locomotor activity was decreased only in dominant monkeys whereas subordinate monkeys exhibited increased locomotor activity. Three monkeys out of eight developed persistent huddling behavior which was interpreted as a sign of depression-like behavior.

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The effects of inflammation on learning and memory need to be mentioned despite the fact that they are not really part of the description of sickness behavior. Although it is usually assumed that proinflammatory cytokines negatively interfere with learning and memory, there is a need for some nuance. Administration of LPS to rats, for instance, abrogated acquisition of a fear response conditioned to the context but had no effect on the fear response to the distinctive cues paired with electric shocks (Pugh et al. 1998). Administration of LPS or IL-1β to rodents does not induce reliable effects on spatial reference memory measured in the Morris Water Maze and it is difficult to separate the observed effects from the impact of sickness on performance (Cunningham and Sanderson 2008). Furthermore, the conditioned taste avoidance induced by LPS or IL-1β attests that inflammation does not prevent the association of taste cues with sickness (Tazi et al. 1988; Cross-Mellor et al. 2004).

4 The Adaptive Value of Sickness Behavior As mentioned in the introduction, sickness behavior is considered traditionally to be a highly organized defense strategy that is adopted by a sick individual to facilitate defense against an invading pathogen (Hart 1988). This notion implies that sickness behavior is part of a motivational system that reorganizes the host priorities in face of a microbial infection in order to maximize its fitness just like fear in face of a physical danger (Aubert 1999). However, this does not guarantee that sickness behavior is always adaptive for the host. In the same way that the increased vigilance of a fearful individual toward all possible danger objects compromises its ability to benefit from other opportunities offered by its environment, the survival benefits of sickness behavior in terms of conservation of metabolic energy for the immune system come with important costs represented by increased risk of predation, reduction in social engagement and reproductive opportunities, and diminished territorial defense (Adelman and Martin 2009). Detailed observations of acute infections in wild vervet monkeys showed that sick individuals were actually twice as likely to receive aggression from conspecifics and six times more likely to become injured than healthy individuals (McFarland et al. 2021). In addition, fevers did not influence the time spent socializing with conspecifics, suggesting that social isolation is not a constant feature of sickness behavior and, by extension, that sickness behavior does not necessarily decrease the risk of disease transmission within the group. These original findings indicate clearly that there is a need to assess more in depth the trade-off between the survival benefits of sickness behavior and the costs incurred especially in the context of gregarious groups. This is the object of a specialized discipline known as ecoimmunology (Demas and Carlton 2015). Some of the issues revolving around the adaptive value of sickness behavior are the same as those that have been addressed already for the fever response. It is clear that there is no simple answer to the question of the usefulness of treating fever, sickness behavior, and the underlying inflammatory response by antipyretics, in the

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same way that it is not possible to prove that fever and sickness behavior are beneficial under all circumstances (Kluger et al. 1996; Harden et al. 2015). The matter is made even more complicated by the fact that it is not possible to generalize from one type of infection to another one. For instance, there is evidence that one of the most common signs of sickness behavior, anorexia, is protective in the case of the bacterial sepsis that develops in mice infected with Listeria monocytogenes while nutritional supplementation is detrimental (Wang et al. 2016). Similar findings have been reported for invertebrates as well (Wing and Young 1980). However, nutritional supplementation protects against mortality from influenza, a viral infection, whereas blocking glucose utilization is lethal in this condition (Wang et al. 2016). This observation has been interpreted to suggest that distinct inflammatory responses are coupled with specific metabolic programs that support either resistance resulting in elimination of the pathogen or tolerance consisting of living with the pathogen and minimizing the tissue damage it can cause. In the case of a bacterial infection, ketogenesis is the predominant metabolic program while glucose utilization predominates in the case of a viral infection. Not surprisingly, resistance to pathogen infection via fever and sickness behavior has been the most commonly studied response in immunopathology whereas much less is known about tolerance and the way it interacts with resistance to ultimately determine health (Schneider and Ayres 2008). Whether tolerance is associated with lack of sickness behavior or a form of sickness behavior that promotes tolerance has not yet been addressed. Social distancing is another feature of sickness behavior that has received some attention (Stockmaier et al. 2021). Sick individuals isolate from the social group either passively because of their physical weakness or the avoidance response they trigger in healthy members of the group, or actively, by isolating themselves from other members of the group. This behavior appears a priori to have survival value not for the sick individuals themselves but for the group in which they belong as it minimizes the risk of dissemination of the pathogen. However, the value of social isolation as a strategy to fight infection cannot be generalized. We have seen earlier that feverish vervet monkeys do not isolate from other members of the group in natural settings. Social immunity in insects – the fact that insects living in group develop the capability of mounting collective anti-pathogen defenses (Meunier 2015) – work against social isolation as the best strategy to fight infection. Of note, social isolation of the sick individual by its conspecifics requires the ability to recognize sickness cues emanating from the infected individual and their use to trigger avoidance responses, a competence that is probably not shared to the same extent across different species. In addition, the recognition of sickness in infected individuals should not always trigger an avoidance response by the rest of the group, otherwise there would be no possibility of caregiving (Dantzer 2021). Another important question in terms of adaptation is for whom sickness behavior is advantageous. So far, we have focused on the adaptive value of sickness behavior for the host or for the group in which it belongs. However, it is easy to imagine that the sickness response can be exploited by the pathogen to its advantage. If the sickness induced by the infectious pathogen spares locomotor activity, its dissemination will be favored as the host continues to be active instead of developing

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lethargy and socially isolating itself. This type of strategy appears to be exploited by Salmonella typhimurium. This Gram-negative bacterium does not cause anorexia as it has the ability to inhibit inflammasome activation and IL-1β maturation in the small intestine, therefore limiting transmission of the immune message from the gut to the brain (Rao et al. 2017). This allows the infected host to still engage in foraging, which increases the risk of transmission of the pathogen to new hosts thanks to the fecal shedding of the infectious agent.

5 Conclusion What we know about sickness behavior has been acquired mainly through very wellcontrolled laboratory experiments consisting of exposing genetically homogenous individuals not to microbial pathogens of which the infectious nature and virulence can vary depending on the stock but to specific pathogen-associated molecular patterns. Most of the work has been done on lipopolysaccharide that targets TLR4 and partly TLR2. This has allowed us to better understand the mechanisms of sickness behavior. We now view this form of behavior as an adaptive response to the sensing of inflammatory mediators by the brain. Its postulated function is to serve to maximize the efficacy of the inflammatory response by favoring reallocation of metabolic energy to the immune system. However, evidence is emerging that sickness behavior is much more variable in its expression than initially thought, and that part of this variability depends not only on the pathogen and the social context in which the infection develops but also on individual factors including species, sex, age, nutrition, and physiological status. In addition, although the mechanisms that lead to the development of sickness behavior have been well characterized at least in the context of TLR4 signaling, much less is known about the mechanisms that are responsible for its dissipation once the pathogen has been cleared or the organism has shifted its strategy from defense to tolerance. The persistence of some aspects of sickness behavior once the infection has been cleared at least in appearance is still an important issue in the context of pathologies such as the post-sepsis syndrome or its likely family member, long Covid.

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The Immune System and Depression: From Epidemiological to Clinical Evidence Nina Vindegaard Sørensen and Michael Eriksen Benros

Contents 1 Why Study the Immune System and Depression? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 The Immune System and the Central Nervous System (CNS) . . . . . . . . . . . . . . . . . . . . . . . . 2 Infections and Depression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Autoimmune Disorders and Depression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Immune Findings Related to Depression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Blood-Based Immune Findings Associated with Depression . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 CSF Based Immune Findings Associated with Depression . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Brain Imaging of Microglial Activation Related to Depression . . . . . . . . . . . . . . . . . . . . . . 4.4 The Gut-Immuno-Brain-Axis in Depression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 (Immuno-)Genetics of Depression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Immune Hypotheses of Depression Pathogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 The Obstacles of Immune System Research Concerning Depression . . . . . . . . . . . . . . . . 6.2 Immunotherapy for Depression? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Depression is a frequent mental disorder with a substantial contribution to years lived with disability worldwide. In the search for new treatment targets, the immune system’s contribution to the pathogenesis of depression has received increased attention as immune activation has been associated with depression in various epidemiological and case-control studies. Epidemiological studies have shown that immune exposures such as severe infections and autoimmune disorders increase the risk of depression. Furthermore, immune system activation has been indicated in case-control studies of depression revealing higher levels of key proinflammatory cytokines among patients with depression than healthy controls, N. V. Sørensen and M. E. Benros (*) Biological and Precision Psychiatry, Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Hellerup, Denmark Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 15–34 https://doi.org/10.1007/7854_2022_369 Published Online: 17 June 2022

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particularly in blood and to some extent in the cerebrospinal fluid. Moreover, brain imaging studies indicate increased microglial activity during depression, and gut microbiota studies have documented alterations of gut microbiota composition to be associated with depression. Based on findings from animal and human studies, several immune-mediated molecular mechanisms have been suggested to underlie the association between increased immunological activity and depression. However, the research is challenged by the heterogeneity of the depression diagnosis and – to some extent – the precision of currently available technology for immune biomarker quantification, particularly regarding the assessment of low-grade neuroinflammation. Nonetheless, an enhanced understanding of the complex interactions between the immune system and the brain in the context of depression could pave the way for precision medicine approaches with immune-modulating treatment as a promising additional option in the treatment of depression. Keywords Biomarkers · Cerebrospinal fluid · Depression · Immunology · Leukocytes

Abbreviations BBB CNS CRP CSF EBV HLA IgG IL NMDAR NLRP3 TNF-ɑ TSPO WCC

Blood-brain barrier Central nervous system C-reactive protein Cerebrospinal fluid Epstein–Barr virus Human leukocyte antigen Immunoglobulin G Interleukin N-methyl-D-aspartate receptor NOD-, LRR- and pyrin domain-containing protein 3 Tumor necrosis factor-ɑ 18-kDa translocator protein White cell count

1 Why Study the Immune System and Depression? Depression is estimated to affect 264 million people worldwide with a lifetime prevalence estimated to 14.6% for depression in high-income countries (Bromet et al. 2011; Spencer et al. 2018). Depression is one of the leading causes of years lived with disability (Spencer et al. 2018) and treatment outcomes need improvement as up to 50% of the patients have to change treatments, indicating limitations of treatment efficacy (Gronemann et al. 2021). Since the 1950s, with the pivotal

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discovery of antidepressants targeting the monoamine systems, only one new drug target has achieved approval for depression treatment by the U.S. Food & Drug Administration (FDA), with the noncompetitive N-methyl-D-aspartate receptor (NMDAR) antagonist Ketamine targeting the glutaminergic system (Hillhouse and Porter 2015; Corriger and Pickering 2019). The immune system’s involvement in the pathogenesis of subgroups of depression could provide new alternative treatment targets taking advantage of the numerous immunomodulating drugs already approved and available for other inflammatory conditions. As readily available alternatives, several drugs targeting the immune system are already approved for the treatment of various immunological disorders (e.g., non-steroidal anti-inflammatory drugs for rheumatoid arthritis (Atzeni et al. 2021) and minocycline for infections (Garrido-Mesa et al. 2013)) and currently immune modulatory medications are under development for the treatment of an increasing number of diseases (Melief 2021). The current diagnosis of depression is based on symptoms and phenotyping with none of the depression symptoms being pathognomonic of depression (Malhi and Mann 2018). Moreover, many depression symptoms frequently co-occur in other psychiatric disorders (e.g., psychotic disorders) and medical conditions (e.g., inflammatory disorders or infections). Infection and activation of the immune system can induce sickness behavior (Miller and Raison 2016; Kelley et al. 2003), with many symptoms overlapping with symptoms of depression. Sickness behavior is driven largely by increases in pro-inflammatory cytokines (Kelley et al. 2003). From an evolutionary perspective, the response to infections with sickness behavior can have evolved out of behavior promoting host and community survival during episodes of infections and explain the symptom overlap between diseases with known increased immunological activity (autoimmune disorders and infections) and depression (Miller and Raison 2016). The variation of depression symptoms has been proposed to result from heterogeneity in mechanisms underlying depression, hereby implying that depression could be divided into subtypes based on the genesis and/or pathogenesis (Lynch et al. 2020). Hence, the identification of an immunological depression subtype, and biomarkers thereof, could renew the treatment options of depression and pave the way for more personalized and precise – and hereby improved – treatment than available today.

1.1

The Immune System and the Central Nervous System (CNS)

Resident within the CNS, microglia cells from the innate immune defense are found in a surveillant state during normal circumstances within the brain (Kettenmann et al. 2013). However, also peripherally derived immune cells (macrophages, dendritic cells, and T-cells) survey the brain (Ousman and Kubes 2012). The discovery of peripheral immune surveillance of the brain was pivotal, since the brain until

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approximately 30 years ago, due to the blood-brain barrier (BBB), had been considered immune-privileged without direct interaction to the peripheral immune system (Carson et al. 2006). The BBB is a term introduced to describe the unique microvasculature of the CNS (Daneman and Prat 2015) that tightly regulates the molecular exchange between the periphery and CNS necessary to maintain CNS homeostasis (Abbott et al. 2006). Variating dysfunction of the BBB has been observed during numerous CNS pathological circumstances (Kadry et al. 2020), whereof many of these circumstances (e.g., multiple sclerosis (Haase and Linker 2021), stroke (Shi et al. 2019), and neurodegenerative disorders (Ransohoff 2016)) are associated with a pro-inflammatory response within the CNS – termed neuroinflammation (DiSabato et al. 2016). Neuroinflammation can be divided into acute and chronic, where chronic neuroinflammation is likely most relevant in understanding most CNS diseases (Streit et al. 2004) – including depression. Due to the BBB, neuroinflammation is not always reflected in blood samples, cerebrospinal fluid (CSF) markers of neuroinflammation are therefore used in diagnostics of several neurological CNS disorders (Costerus et al. 2018). Since the CSF is the material closest to the brain that can be collected without significant side effects (by a lumbar puncture), CSF analysis is the “gold standard” in diagnostics of neuroinflammation and Fig. 1 provides an overview of some of the potential biomarkers of CNS inflammation related to depression already measurable in CSF.

2 Infections and Depression A large-scale nationwide study revealed an association between severe infections and depression by finding hospital contacts for infections to increase the subsequent risk of unipolar depression by 63% (Benros et al. 2013). Moreover, the study showed that the risk of depression increased in a dose-response manner with the number of prior infections and in a temporal manner with the risk of depression being highest with the temporal proximity to the last infection. Furthermore, a meta-analysis of 16 infectious agents and their association to depression revealed association to herpes simplex virus 1, varicella-zoster virus, Borna disease virus, Chlamydia trachomatis, and Epstein Barr virus (EBV) (Wang et al. 2014). EBV is of particular interest since a 40% increase in the risk of a depression after infectious mononucleosis (whereof approximately 90% of cases is caused by EBV) has been revealed (Vindegaard et al. 2021) with prolonged fatigue as a shared core symptom of infectious mononucleosis (Rea et al. 2001) – and depression. The interest to the link between infections and depression further sparked when the COVID-19 pandemic caused by the SARSCoV-2 virus struck the world (Guan et al. 2020). An early systematic review indicated an increased risk of depressive symptoms after the SARS-CoV-2 infection (Vindegaard and Benros 2020). A recent large-scale study with 6 months follow-up found the risk of mood disorders to be 79% higher after COVID-19 infection as compared to influenza and 41% higher as compared to other respiratory tract infections (Taquet et al. 2021).

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Inflammation

Normal conditions Neuttroph Neu phil

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Fig. 1 Heuristic model of immune biomarkers associated with depression measurable in cerebrospinal fluid (CSF). An overview of interactions between the immune system of the central nervous system and the periphery at normal and inflammatory conditions. The blood is separated from the central nervous system by the blood-brain barrier (BBB). Under normal conditions, the barrier is tightly regulated and only a few peripheral immune cells cross it. However, during inflammation, an increase in pro-inflammatory cytokines activates both peripheral immune cells and the resident microglial cells that further produce pro-inflammatory cytokines (such as IL-6, IL-8, and tumor necrosis factor (TNF)-alpha). BBB impairment can be measured by increased levels in serum/CSF

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3 Autoimmune Disorders and Depression Autoimmune disorders have in a nationwide study been shown to increase the risk of depression by 46% (Benros et al. 2013). Autoimmune disorders are characterized by the immune system reacting against the body’s own tissue (Rose 2017). Molecular mimicry – that antigens on microorganisms resemble host epitopes and hereby induce an autoimmune response – is well described to be involved in the pathogenesis of at least some autoimmune disorders (Wang et al. 2015). Similar to depression, autoimmune disorders are more frequent among women than men (Desai and Brinton 2019) and progress fluctuant with symptom flare-up (Torres-Aguilar et al. 2019). Some autoimmune disorders, including multiple sclerosis and systemic lupus erythematosus, are classified as immune-mediated inflammatory diseases and are suggested to have imbalanced inflammatory cytokines as central to their pathogenesis (Kuek et al. 2007). In immune-mediated inflammatory diseases, there is substantial evidence of frequent depression and depressive symptoms (Magyari and Sorensen 2020; Moustafa et al. 2020) and there is an overlap in symptoms between autoimmune disorders and depression including fatigue, poor concentration, and sleep disturbances (Magyari and Sorensen 2020). Several autoimmune disorders have been suggested to potentially induce the production of CNS-reactive antibodies that are pathogenic when the BBB is dysfunctional (Chen et al. 2009). CNS-reactive – primarily antineuronal – autoantibodies as contributors to neuropsychiatric pathophysiology have received increasing attention (Pollak et al. 2020), since the pivotal discovery of anti-NMDAR encephalitis in 2007 (Dalmau et al. 2011). The anti-NMDAR encephalitis is characterized clinically by psychiatric symptoms and additional neurological symptoms over the course of clinical development (Herken and Prüss 2017). Anti-NMDAR encephalitis is caused by antineuronal autoantibodies directed against the NMDAR (Dalmau et al. 2008) and is the most frequent type of autoimmune encephalitis (Granerod et al. 2010). Cohort studies have found depressive symptoms to be among the initial symptoms in 19–29% (Herken and Prüss 2017; Restrepo-Martinez et al. 2020) of the anti-NMDAR encephalitis cases and at case report level depression has also been reported as the only symptom of anti-NMDAR encephalitis (Moldavski et al. 2021). Antineuronal antibodies have been found in blood of patients with depression; however, also appear in blood of healthy controls (Kruse et al. 2015; Steiner et al. 2014; Dahm et al. 2014). Moreover, animal studies have indicated that anti-NMDAR antibodies need to reach the brain to be pathogenic (Hammer et al. 2014); questioning the importance of antineuronal antibodies in blood when the BBB is intact (Ehrenreich 2017). Thus, studies of antineuronal antibodies in the CSF are of utmost importance as they reflect antibodies that can react with CNS tissue,

Fig. 1 (continued) albumin ratio and CSF protein. Antineuronal antibodies (IgG) can cause neuroinflammation through intrathecal production by plasma cells or by entering the brain through a compromised BBB

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however, studies comparing CNS-reactive autoantibodies in CSF from patients with depression to healthy controls are lacking (Lang and Prüss 2017).

4 Immune Findings Related to Depression 4.1

Blood-Based Immune Findings Associated with Depression

Despite CSF being state of the art in investigations of neuroinflammation, alterations in the peripheral immune system related to depression are also of interest. A largescale study of 73,131 participants found elevated C-reactive protein (CRP) levels to be associated with an increased risk of depression (Wium-Andersen et al. 2013) and this is supported by two meta-analyses revealing higher CRP and interleukin-6 (IL6) levels to be associated with future depressive symptoms (Mac Giollabhui et al. 2020; Valkanova et al. 2013). Furthermore, elevated CRP levels are associated with increased all-cause mortality when newly diagnosed with depression (Horsdal et al. 2017). Systemic inflammation is associated with BBB dysfunction (Varatharaj and Galea 2017) and higher levels of blood cytokines (most consistently IL-6) have repeatedly been found associated with depression in meta-analyses (Kohler et al. 2017; Howren et al. 2009; Goldsmith et al. 2016; Osimo et al. 2020). The most pronounced pro-inflammatory response in the blood is observed in the acute phase of depression and a decrease in, e.g., IL-6 levels is observed following treatment (Goldsmith et al. 2016). Blood CRP – a very sensitive marker of inflammation (Sproston and Ashworth 2018) – has additionally been found elevated in patients with depression compared to healthy controls (Howren et al. 2009) and to be associated with symptom severity in women (Köhler-Forsberg et al. 2017). Regarding the composition of immune cells in the blood, a meta-analysis found a higher overall leukocyte count in depression compared to controls (Zorrilla et al. 2001). This meta-analysis was published 20 years ago and due to the technological evolvement of immune cell subtyping and the amount of literature published, an updated review is an important need. Within the past decade, an interest in the ratio between neutrophils and lymphocytes in the blood of patients with various brain disorders has been seen (Bi et al. 2021; Hasselbalch et al. 2018; Kara et al. 2021) and a meta-analysis found increased neutrophil-lymphocyte-ratio in patients with depression compared to healthy controls (Mazza et al. 2018).

4.2

CSF Based Immune Findings Associated with Depression

Alterations in a variety of CSF biomarkers have been associated with depression, and CSF cytokines and chemokines (signaling molecules central in inflammatory

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processes) have been increasingly investigated in relation to depression. A metaanalysis of CSF studies found higher levels of IL-6, tumor necrosis factor (TNF)-α and IL-8, among patients with depression as compared to healthy controls (Enache et al. 2019). IL-6 is a potent pro-inflammatory protein with a key function in acute inflammation (Tanaka et al. 2014); however, is also involved in chronic inflammation and autoimmunity (Tanaka et al. 2014; Gabay 2006). TNF-α is also a potent key pro-inflammatory cytokine promoting chronic inflammation through diverse effects (Varfolomeev and Vucic 2018), such as stimulation of IL-8 production by the endothelia, important to leukocyte diapedesis (Middleton et al. 1997). However, the complex cytokine interactions in the CSF and CNS are not yet understood in detail in the context of depression. Several other neuroinflammatory biomarkers quantifiable in CSF are already widely used for diagnostics e.g. in neurology (Costerus et al. 2018) and their association to depression has been investigated to some extent. A general marker of CNS pathology is the direct measurements of peripheral white cell count (WCC) in CSF. A meta-analysis found no significant difference in CSF WCC between patients with unipolar depression and healthy controls (Mousten et al. 2022), as the two largest case-control studies included found no significant differences between groups (Hattori et al. 2015; Omori et al. 2020), however, CSF WCC was not a primary outcome of these studies and still needs to be investigated in detail, e.g. correlation to symptom severity. A large case-only study including 125 patients with depressive symptoms and indications of neuroinflammation revealed elevated WCC among 4.0% of patients with depression (Endres et al. 2016). A case-control study comprising 106 patients with recent-onset depression compared to 106 healthy controls found a 18% higher CSF WCC among the patients relative to the healthy controls and the difference between patients with severe depression to be 43% relative to healthy controls (Sørensen et al. 2022). However, even larger case-control studies investigating WCC alterations in CSF in detail are lacking, as such studies could include e.g. patients with treatment resistent or recurrent depression. Moreover, detailed CSF immune cell composition is still unexplored in the context of depression. BBB dysfunction can be caused by or be closely related to neuroinflammatory conditions (Kadry et al. 2020) with the most reliable marker of BBB dysfunction being CSF/serum albumin ratio and with CSF total protein as a less specific alternative (Hegen et al. 2016). A meta-analysis of CSF/serum albumin ratio and CSF total protein found higher levels among patients with affective disorders compared to healthy controls (Orlovska-waast et al. 2019) and a meta-analysis of patients with unipolar depression compared to healthy controls has reported increased CSF total protein among patients (Mousten et al. 2022). This indicates BBB dysfunction to be plausible among patients with depression; however, the clinical impact remains unresolved as a study of 106 patients with depression found no significant differences in CSF/serum albumin ratio or CSF total protein as when compared to healthy controls (Sørensen et al. 2022). The association between autoimmune disorders/immune-mediated inflammatory diseases and depression is well-established (Benros et al. 2013); however,

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immunoglobulin G (IgG) index – a reliable marker of intrathecal IgG synthesis (Simonsen et al. 2020) – is still somewhat unexplored in the context of depression with only one case-control study (from 1999) revealing no significant differences when comparing patients with depression to healthy controls (Hampel et al. 1999) and likewise a study of 106 patients with recent onset depression, however, without any somatic co-morbidity found none to have increased IgG index (Sørensen et al. 2022). In the light of the associations of depression and autoimmune disorders/immune-mediated inflammatory diseases, IgG index and additional markers of autoimmune and/or B-cell function – especially measured in largescale studies of CSF – are lacking in the exploration of the immune system’s impact on depression.

4.3

Brain Imaging of Microglial Activation Related to Depression

Microglia cells are central in the neuroinflammation (Streit et al. 2004) and their activation can be investigated by imaging of the 18-kDa translocator protein (TSPO) by position emission tomography (Turkheimer et al. 2015). Even though this method is not readily available in the clinical setting, a meta-analysis found five out of six studies to report elevated TSPO levels in the anterior cingulate cortex and temporal cortex of patients with major depressive disorder compared to healthy controls (Enache et al. 2019), however, there have been reported inconsistencies in TSPOimaging results caused by methodological difficulties in measuring low-grade inflammation by TSPO tracing (Nettis et al. 2020) that remains to be resolved. Furthermore, when reviewing studies of microglia morphology associated with depression only four out of eight post-mortem studies found differences between patients and controls (Enache et al. 2019). Nevertheless, this area of research is of high importance due to the central role of microglia in neuroinflammation.

4.4

The Gut-Immuno-Brain-Axis in Depression

A relatively recent hypothesis of depression pathogenesis is related to the gutimmuno-brain-axis. The gut microbiota – all of the microbes in the gut – is estimated to consist of trillions of organisms, and has an important role in the training of the immune system (Lynch and Pedersen 2016), and has thus been suggested to affect the brain via several pathways (Fan and Pedersen 2021) summarized in the gutimmuno-brain-axis. A systematic review on gut microbiota and severe mental disorders revealed challenges in microbiota research due to the amount of confounding factors and heterogeneity in designs of previous studies (Vindegaard et al. 2020). The largest study to date included 156 patients with depression

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compared to 155 healthy controls and found several alterations of the gut microbiota to be related to the depression (Yang et al. 2020), whereas a recent meta-analysis revealed how gut microbiota perturbations are associated with transdiagnostic patterns and a large inconsistency in findings from gut microbiota studies, where investigations of gut microbiota function are warranted (e.g., tryptophan metabolism) (Nikolova et al. 2021).

4.5

(Immuno-)Genetics of Depression

A twin study of depression estimated the heritability of depression to 38% (Kendler et al. 2006) and a sibling cohort study of depression found some depressive symptoms to correlate between siblings (Korszun et al. 2004). However, depression is a polygenic disorder (Ripke et al. 2013) and current polygenic risk scores have been estimated to only explain 1.5–3.2% of the depression risk (Howard et al. 2019). A meta-analysis of genome-wide association studies (GWAS) included a total of 246,363 cases and 561,190 controls identified 102 independent variants associated with depression and among them found the lin-28 homolog b gene (LIN28B; that is implicated in immune response (Wei et al. 2016)) to have genome-wide significance (Howard et al. 2019). Of other immune-related genes, a recent genome-wide association analysis including 135,458 major depression cases and 344,901 controls identified 44 risk variants related to depression, whereof some were related to the immune system (Wray et al. 2018). A study combined GWAS with human brain proteomics and identified the P2RX7 gene to be associated with depression (Wingo et al. 2021), and this could link immune system and depression as P2RX7 encodes for the P2X7-receptor involved in the regulation of the NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) inflammasome important to immune function (Franceschini et al. 2015). The human leukocyte antigen (HLA) gene – found to be associated with autoimmune disorders (Bodis et al. 2018) – has been investigated in association with depression; however, a recent genome-wide association study found no significant associations between depression and HLA genes in a cohort of 18,511 depression cases compared to 19,645 controls (Nudel et al. 2019).

5 Immune Hypotheses of Depression Pathogenesis Several factors associated with depression have been suggested to induce low-grade inflammation, including: (chronic) infections, autoimmune disorders, chronic general medical conditions, unhealthy lifestyle (e.g., diet, inactivity), obesity, chronic stress, gut microbiota dysbiosis, sleep disturbances, psychological stressors and thereby potentially increase the risk of depression. Systemic inflammation can cause BBB impairment and enhance neuroinflammation by the influx of peripheral cytokines and leukocytes to the brain (Varatharaj and Galea 2017). As the

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hypothesis of reduced monoamine availability is important to the current understanding of the depression pathogenesis (Boku et al. 2018), the mechanisms underlying the reduction of available monoamines (e.g., serotonin (5-hydroxytryptamine)) are of particular interest and, while not fully understood, might differentiate between subgroups of patients with depression (Willner et al. 2013). The kynurenine pathway stands central in the current understanding of how immune-related molecular mechanisms can underlie monoamine system alterations involved in depression pathogenesis since it constitutes a concurrent catabolic pathway of tryptophan – the precursor of serotonin (Troubat et al. 2020). The switch from serotonin to kynurenine production can be immune regulated (e.g. via the NLRP3 inflammasome) (Savitz 2020) and has been suggested to reduce serotonin availability (Beurel et al. 2020). Furthermore, it has been hypothesized that sustained activation of the kynurenine pathway, activated by the immune system, can be a key contributor to depression (Brown et al. 2021). Figure 2 provides a heuristic model of hypothesized mechanisms of the immune system’s contribution to depression pathophysiology. Both the kynurenine pathway and NLRP3 inflammasome implications in depression pathogenesis are reviewed in detail elsewhere (Savitz 2020; Brown et al. 2021; Kaufmann et al. 2017) and it stands clear that more research is needed to understand the complex interactions underlying the immune systems’ contribution to depression pathogenesis.

6 Perspectives 6.1

The Obstacles of Immune System Research Concerning Depression

Despite strong epidemiological and clinical associations between the immune system and depression, findings in this area have yet not led to the implementation of new treatment options. There appear to be two major obstacles in resolving the mechanisms underlying these associations between the immune system and depression. First, the heterogeneity of the depression diagnosis (Malhi and Mann 2018) complicates the identification of new treatment options. The heterogeneity can be overcome by large-scale studies with detailed phenotype data for studying subtypes of depression. Subtyping based on the following categories has been proposed: symptoms, pathogenesis, time of onset, gender, and treatment response (Baumeister and Parker 2012), and in the pathogenesis category a subtype characterized by markers of (chronic) inflammation (Kunugi et al. 2015). Second, the available technology may still be too inaccurate to measure low-grade neuroimmunological alterations associated with depression. Considering the complexity of the immune system and its complex impact on depression pathophysiology, advancing technology and accuracy of measurements will hopefully lead to a more detailed understanding of the link between immunity and depression than today.

TNF-a

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Fig. 2 The biological framework of some of the mechanisms linking the immune system and depression. Systemic inflammation is hypothesized to cause an influx of pro-inflammatory cytokines (e.g. interleukin (IL)-6 and tumor necrosis factor (TNF)-ɑ), peripheral inflammatory cells (Varatharaj and Galea 2017), metabolites of the kynurenine pathway, and hypothalamic–pituitary–adrenal (HPA) axis hormones potentially favoring the kynurenine pathway (Brown et al. 2021) (a). Increased pro-inflammation can induce an increase in damage-associated molecular patterns (DAMPs) (b) that via the Toll-like receptor (TLR)

WBCs

HPA axis hormones

Systemic inflammation

26 N. V. Sørensen and M. E. Benros

activate Nuclear Factor-κB (NF-κB) (Kelley et al. 2019) (c) lead to an increase in the pro-inflammatory cytokines IL-6 and TNF-ɑ (Visentin et al. 2020) (d) further promoting inflammation. Furthermore, NF-κB activation increases IL-1β and IL-18 by activation of the NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3-inflammasome) (e, f) (Visentin et al. 2020). Tryptophan (TRY) is a precursor for serotonin (g), however, can also be catabolized through the kynurenine pathway (h) (Visentin et al. 2020). The shift from metabolizing TRY to serotonin to catabolization through the kynurenine pathway has been suggested to be induced by inflammation (Troubat et al. 2020) (i, j). The kynurenine pathway promotes further inflammation and cell damage by (among other derivate effects) the production of reactive oxygen species (k, l). One of the end downstream products of the kynurenine pathway is kynurenic acid, an antagonist of the N-methyl-D-aspartate receptor (NMDAR) leading to reduced calcium influx (m) (Brown et al. 2021). Sustained antagonism of NMDAR has been suggested to cause glutamate hypofunction and to be related to depression pathophysiology (Brown et al. 2021)

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6.2

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Immunotherapy for Depression?

Besides the reduction of stigma related to depression, a detailed understanding of depression genesis and pathogenesis is also highly important to improve the current treatments. A comprehensive meta-analysis indicated an effect of several antiinflammatory agents on depressive symptoms and clinical depression (KohlerForsberg et al. 2019). However, future RCTs of anti-inflammatory treatment of depression should be large enough to allow for possible stratification by inflammatory status (Miller and Pariante 2020) in identifying relevant immune-related subgroups with the high potent effects of immunomodulating treatments. Studies need also to be adequately powered to determine the effective doses as well as to identify relevant diagnostic and prognostic biomarkers.

7 Conclusion There is evidence of an association between an activated immune system and depression, mainly relying on epidemiological, preclinical, case-only, and casecontrol studies; however, research into this association has not yet led to new implemented treatment options. Immune-mediated molecular mechanisms underlying depression need to be further validated and the complex interactions between the immune system and the brain need further exploration in the context of depression. Immunomodulating medicaments could be promising treatment options for depression; however, the detailed understanding of the immune system in the depression pathogenesis is challenged by the heterogeneity of depression diagnosis and the inaccuracy of low-grade immune biomarker quantification, particularly in the CSF. Large-scale investigations or subtyping (e.g., by inflammatory markers) could enhance the understanding of the immune system’s impact on depression paving the way to a more personalized and more effective treatment than available today. Acknowledgments The present work was funded by an unrestricted grant from The Lundbeck Foundation (grant number R268-2016-3925).

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Infections, Inflammation, and Psychiatric Illness: Review of Postmortem Evidence Maree J. Webster

Contents 1 Evidence for Infection in the Postmortem Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Evidence for Inflammation in the Postmortem Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Schizophrenia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Mood Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Heterogeneity of Immune/Inflammation Evidence in Postmortem Psychiatric Brains . . . . . 4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract While there is an abundance of epidemiological evidence implicating infectious agents in the etiology of severe mental illnesses, postmortem studies have not yet detected an increased incidence of microbial nucleic acid or proteins in the brains of people with mental illness. Nevertheless, abnormally expressed immune and inflammatory markers have consistently been found in the postmortem brain of patients with schizophrenia and mood disorders. Some of these abnormalities may be the result of an infection in utero or early in life that not only impacted the developing immune system but also the developing neurons of the brain. Some of the immune markers that are consistently found to be upregulated in schizophrenia implicate a possible viral infection and the blood brain barrier in the etiology and neuropathology of the disorder. Keywords Bipolar disorder · Brain · Major depressive disorder · Schizophrenia Many large epidemiological studies have consistently shown associations between a broad range of infectious agents including viruses, bacteria, and parasites, and the major mental illnesses, particularly schizophrenia and major depression (Arias et al. 2012; Khandaker et al. 2012, 2013; Wang et al. 2014; Köhler-Forsberg et al. 2019; M. J. Webster (*) Stanley Medical Research Institute, Rockville, MD, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 35–48 https://doi.org/10.1007/7854_2022_362 Published Online: 4 May 2022

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Burgdorf et al. 2019). The risk of developing a major mental illness is increased with exposure to infection in utero, as well as during childhood and adolescence. Most people exposed to these infectious agents experience a latent form of the disease and remain asymptomatic. Many of these infectious agents are neurotrophic but remain dormant in sensory nerve ganglion while others can persist in lymphocytes, monocytes, and epithelial cells. However, the microbes can enter the brain through various routes if reactivated when a person is stressed or immune compromised. While people with severe mental illness show no signs of active infections, the latent microbe may reside in neurons or other brain cells and thereby disrupt the normal development and/or function of the brain.

1 Evidence for Infection in the Postmortem Brain While microbial nucleic acid sequences have been detected in the postmortem brain of people asymptomatic for infection at their time of death (Yolken and Torrey 1995; Conejero-Goldberg et al. 2003; Hobbs 2006), most studies have not found an increased incidence of microbial RNA or DNA in the brain of people with mental illness (Yolken and Torrey 1995; Conejero-Goldberg et al. 2003; Hobbs 2006; Taller et al. 1996). Although, one study did detect an increased number of cases with HHV-6 DNA and protein in cerebellar Purkinje cells in major depressive disorder and bipolar disorder as compared to unaffected controls and schizophrenia (Prusty et al. 2018). Searching for microbes in the human brain is technically challenging but recent advances in high-throughput sequencing technologies and the increasing availability of large postmortem cohorts are likely to greatly enhance our chances of detecting them if indeed they are still in the brain. Unfortunately, the infection may have occurred many years prior to the onset of the disorder and the postmortem samples can only be examined many decades after that, so the microbe may no longer be present or detectable. The brain is not as “immune privileged” as once perceived, and there are many “immune” proteins expressed in the CNS. These proteins not only have a role in the inflammatory response but also play modulatory roles in activity-dependent refinement of connections, synaptic transmission, synaptic plasticity, and homeostasis, particularly during brain development (Deverman and Patterson 2009; Garay and McAllister 2010). Multiple cytokines, major histocompatibility complex (MHC) proteins, and the complement proteins all have such pleiotropic effects in the CNS. An infection during brain development could disrupt the expression of these proteins that then impact brain development or leave a signature of “immune” abnormalities in the CNS. In addition, the top genetic GWAS locus for schizophrenia susceptibility is in the MHC region of the genome, thus a polymorphism in any one of the genes in this location may impact “immune” expression and alter the developing brain. Moreover, an early environmental insult such as an infection could interact with this genetic vulnerability to permanently impact the regulation of these “immune” genes and brain development. Whatever the mechanism, abnormally

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expressed inflammatory markers have consistently been detected in the postmortem brain of patients with schizophrenia and mood disorders and may be the result of an early infection that impacted not only the developing immune system but also the developing neurons of the brain (Horvath and Mirnic 2014).

2 Evidence for Inflammation in the Postmortem Brain Postmortem brain research has provided molecular evidence for immune/inflammation pathogenesis in several major mental illnesses, particularly schizophrenia and major depression. However, there are no neuropathological signs of an active infection, or of encephalitis or any changes typical of an autoimmune disorder of the CNS in these disorders. There is no astrogliosis, microglial nodules, diffuse brain necrosis, or demyelination that may be expected with a typical infection or autoimmune disorder. The immune/inflammation related changes appear more subtle, but a consistent picture is emerging that implicates the immune system in the etiology of at least a subset of these patients.

2.1

Schizophrenia

While schizophrenia is not a classic neuroinflammatory disorder in that there is no evidence for astrogliosis and inconsistent and limited evidence for microglial activation (Najjar et al. 2013; van Kesteren et al. 2017; Snijders et al. 2020; De Picker et al. 2021) or lymphocytic infiltration (Bogerts et al. 2017; Sneeboer et al. 2020; Schlaaff et al. 2020) there are nevertheless many astrocytic and microglial markers dysregulated in schizophrenia (Najjar et al. 2013; van Kesteren et al. 2017; Najjar and Perlman 2015; Trepanier et al. 2016; Sakai et al. 2016; Ramaker et al. 2017; Shao and Vawter 2008; Toker et al. 2018). Systematic reviews (Najjar et al. 2013; van Kesteren et al. 2017; Najjar and Perlman 2015) and a quantitative meta-analysis (Trepanier et al. 2016) of the immune-related neuropathology data conclude that, despite considerable variability across studies, there is consistent evidence to show immune and inflammation related molecules are generally upregulated. Numerous microarray studies (review (Horvath and Mirnic 2014), also (Shao and Vawter 2008; Lanz et al. 2019)) and meta- and mega- analysis of microarray data (Misty et al. 2013a, b; Bergon et al. 2015; Hess et al. 2016; Fillman et al. 2014) as well as RNA-sequencing studies of the brain (Fillman et al. 2013; Hwang et al. 2013; Kim et al. 2016a, b; Chang et al. 2017; Gamazon et al. 2019; Wang et al. 2018; Gandal et al. 2018a; Carlström et al. 2021) have consistently shown upregulated expression, and dysregulated co-expression modules, of genes in immune- and inflammation related pathways from multiple cohorts and many different brain regions in schizophrenia. Proteomic studies also implicate dysregulated immune-related proteins in schizophrenia (Martins-de-Souza et al. 2009a, b; Harris et al. 2012). Given these

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immune abnormalities in the brain one may expect to find the microglia, the resident immune cells of the CNS, to be activated or reactive. While the neuropathology studies on microglia are inconsistent (Najjar et al. 2013; van Kesteren et al. 2017; De Picker et al. 2021; Najjar and Perlman 2015; Trepanier et al. 2016) a more recent meta-analysis of neuropathology studies (Snijders et al. 2020) and an RNA-sequencing meta-analysis from a large postmortem cohort show that microglial-related transcripts are downregulated in schizophrenia but co-occur with the upregulation of networks of inflammatory-related transcripts (Gandal et al. 2018a). Indeed, evidence is emerging to indicate that the microglia may be in a more quiescent state and perhaps unable to respond appropriately to maintain brain homeostasis, at least in the frontal cortex of a subset of people with schizophrenia (Murphy and Shannon Weickert 2021). While the numbers and sets of immune-related markers found to be dysregulated differs in each study, there is a subset of genes that are consistently upregulated across many studies e.g. guanylate-binding protein 2 (GBP2), interferon-induced transmembrane proteins (IFITM), Serpin Family A Member 3 (SERPINA3), cytokines IL1B, IL6, and the scavenger receptor CD163 (Trepanier et al. 2016; Kim et al. 2016b; Wu et al. 2012; Cai et al. 2018; Siegal et al. 2014; Volk et al. 2015; Zhang et al. 2016; Merikangas et al. 2022). These tend to be expressed in vascular endothelial cells, and perivascular macrophages and astrocytes implicating the blood-brain barrier and a possible abnormal interaction with the peripheral immune system in schizophrenia (Hwang et al. 2013; Kim et al. 2016b; Cai et al. 2018; Siegal et al. 2014; Volk et al. 2015; Purves-Tyson et al. 2021; Harris et al. 2008; Murphy et al. 2020). GBP2 is involved in the innate immune system and specifically in the defense against viral infection (Braun et al. 2019). IFITM1,2, & 3 have all been found upregulated in schizophrenia in multiple different brain areas and are known to defend against many different viruses by preventing cytosolic entry of viruses and restricting an early step in viral replication (Ren et al. 2020). SERPINA3 is a serpin peptidase inhibitor, induced by cytokines, including IL6, that has anti-inflammatory and antioxidant activities (Sánchez-Navarro et al. 2020). The upregulation of SERPINA3 in the perivascular astrocytes at the blood brain barrier in schizophrenia (Murphy et al. 2020) indicates that the astrocytes may be mounting a compensatory protective response to the infectious agent that is causing the increase in IFITM in the endothelial cells. The increase in IL6 and the increase in CD163 positive macrophages that are found in the perivascular region may also be in response to the same agent. The fact that the microglia are not mounting a traditional inflammatory reaction indicates that the immune response may be chronic and/or from a past exposure and the microglia are “exhausted” (Murphy and Shannon Weickert 2021). Interestingly, one of the most replicated and consistent neuropathology findings in schizophrenia are the deficits in GABAergic interneurons (Dienel and Lewis 2019). In the frontal cortex there is an inverse correlation between the higher cytokine levels, including IL6 in schizophrenia and the GABAergic neuron markers (Fillman et al. 2013). There is also an inverse correlation between the higher IFITM mRNA levels in the endothelial cells and markers of GABAergic neurons (Siegal et al. 2014). Likewise, in the hippocampus, the immune/inflammation related

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co-expression modules of gene expression are inversely correlated with GABAergic markers (Hwang et al. 2013; Kim et al. 2016a). Moreover, one of the immune-related modules was specifically enriched for genes related to “response to virus,” suggesting that the activated module of immune/inflammation response by viral infection is not only associated with schizophrenia, but also with decreased GABAergic cell density (Kim et al. 2016a). Together the data indicate that the immune changes in the brain, perhaps caused by an environmental factor such as an infection, may lead to the altered neuronal circuitry. Whether the immune signature in the brain is a response to the effects of a current or chronic/latent infection or whether it is a signature remaining from an earlier developmental insult is a topic of much ongoing research. A useful animal model to study the effects of early infections on developing fetal brain is to inject a pregnant animal with polyinosinic:polycytidylic acid (poly I:C) to mimic viral infection or lipopolysaccharide (LPS) to mimic a bacterial infection. Maternal immune activation in utero leads to higher cytokine levels in fetal brain (Meyer et al. 2006) that may persist to a varying extent postnatally (Purves-Tyson et al. 2021; Garay et al. 2013) and affect the development of neural circuits, including the GABAergic interneurons (Meyer et al. 2008; Richetto et al. 2014). While MIA did not specifically lead to increased levels of IFITM mRNA in the adult mouse brain (Volk et al. 2015) immune stimulation of neonatal mouse pups did increase IFITM in hippocampus (Ibi et al. 2013) and immune stimulation in adult animals also leads to increases in IFITM mRNA and cytokines in the frontal cortex, similar to changes observed in schizophrenia (Volk et al. 2015). Moreover, a chronic brain inflammation mouse model with increased IL6 levels throughout life leads to decreases in GABAergic markers and increased IFITM in both cortex and hippocampus (Takao et al. 2013). These studies indicate that immune activation, even in the absence of an actual microbe, and even in the more mature animal, is sufficient to induce the antiviral defense molecules that could then have deleterious effects on the GABAergic circuitry. Interestingly, many of these ubiquitously upregulated immune transcripts are also upregulated in the midbrain in schizophrenia (Purves-Tyson et al. 2021, 2020), where most dopamine-releasing neurons are located. The upregulated immune transcripts were replicated in the midbrain of the MIA mouse model (PurvesTyson et al. 2021) indicating that immune activation in the developing brain could also be contributing to the dopaminergic abnormalities that are a hallmark of schizophrenia. The top GWAS-implicated gene complement component C4, which lies inside the major histocompatibility complex (MHC) region of the genome (Sekar et al. 2016), is also consistently found to be overexpressed in the brain of subjects with schizophrenia (Gamazon et al. 2019; Wang et al. 2018; Carlström et al. 2021; Sekar et al. 2016; Gusev et al. 2018; Gandal et al. 2018b). As part of innate immunity, the complement system marks pathogens for destruction but within the developing CNS it also marks synapses for elimination. The pleiotropic effects of these dysregulated immune-related molecules, that also regulate brain development, plasticity, and homeostasis, may be more important to the pathophysiology of schizophrenia than

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immune activation per se. However, infection at vulnerable times during development in genetically susceptible individuals could permanently disrupt the normal expression of these pleiotropic immune molecules and thereby increase vulnerability to subsequent “stress” on brain development and function.

2.2

Mood Disorders

There are less postmortem brain studies that show consistent evidence for neuroimmune activity in the mood disorders than in schizophrenia. Reviews of glial pathology show a consistent loss of astrocytes and a decrease in astrocytic markers in major depression (Najjar et al. 2013; Mechawar and Savitz 2016; Enache et al. 2019) but no consistent changes for bipolar disorder (Najjar et al. 2013; Toker et al. 2018; Mechawar and Savitz 2016; Kotzalidis et al. 2015). There is inconsistent evidence for dysregulation of microglia in the mood disorders (Sakai et al. 2016; Mechawar and Savitz 2016; Enache et al. 2019; Sneeboer et al. 2019). Any upregulation of microglia that has been observed is more pronounced in those who died by suicide (Sakai et al. 2016; Mechawar and Savitz 2016; Enache et al. 2019; Brisch et al. 2021). There is no consistent direction of change in the immune-related markers that have been measured in the brain of subjects with major depressive disorder (Enache et al. 2019), or bipolar disorder (Mechawar and Savitz 2016; Kotzalidis et al. 2015; Giridharan et al. 2020). There have been multiple metaanalyses of microarray data (Gandal et al. 2018b; Omar et al. 2019; Elashoff et al. 2007; Wu et al. 2019) as well as RNA-seq studies (Ramaker et al. 2017; Kim et al. 2016a; Gandal et al. 2018a; Elashoff et al. 2007; Wu et al. 2019; Pacifico and Davis 2016) from several brain areas that have found immune changes in the brain in mood disorders. Findings indicate general dysregulation of immune-related modules of gene co-expression in major depressive disorder (Khandaker et al. 2013; Gandal et al. 2018b; Pantazatos et al. 2017; Mahajan et al. 2018) and bipolar disorder (Kim et al. 2016a; Omar et al. 2019; Elashoff et al. 2007; Pacifico and Davis 2016) but with no consistent direction of change. However, in bipolar disorder, modules of gene co-expression that appear more specific for microglial are downregulated (Ibi et al. 2013; Zandi et al. 2022), whereas astrocyte-related modules are upregulated (Ramaker et al. 2017; Gandal et al. 2018a). In contrast to schizophrenia, where immune molecules are consistently upregulated, it appears that in mood disorders there is no clear direction of dysregulation. However, in major depressive disorder astrocytes and astrocytic markers are downregulated whereas in bipolar disorder the astrocyte-related co-expression modules are upregulated, similar to schizophrenia. Moreover, co-expression modules of microglia related genes are downregulated in both schizophrenia and bipolar disorder. While a distinct pattern of immune-related changes may eventually emerge for each diagnostic group, no consistent set of dysregulated markers has emerged to indicate a possible cause of the immune/inflammation related changes in the brain of those with mood disorders. Several non-mutually

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exclusive causal factors, including physical and psychological stress, autoimmunity and pathogens could be contributing to the immune/inflammation related changes in the brain, however, to date no clear candidate has emerged. The lack of consistent findings in the expression of immune markers in the mood disorders may be attributed to a variety of factors including heterogeneity of clinical phenotypes within the diagnostic groups and small sample size.

3 Heterogeneity of Immune/Inflammation Evidence in Postmortem Psychiatric Brains The variability in the sets of dysregulated molecules between the different studies within each diagnostic group could be attributed to different methodologies used, different brain regions examined, and factors intrinsic to human postmortem studies (van Kesteren et al. 2017; Sneeboer et al. 2019) such as sample size, PMI, pH, agonal state, psychotropic medications, age, and sex (Labonté et al. 2017; CabreraMendoza et al. 2020). Average age of the cohort is important because aged normal controls have an elevated immune-related gene expression profile, so including them in a study (Birnbaum et al. 2018) will mask the upregulated expression that is found in schizophrenia (Kim et al. 2018; Sabunciyan 2019). Medications are also a confounding factor. The lifetime antipsychotic dose often positively correlates with the level of immune markers. Thus, the question is whether the antipsychotics are contributing to the higher immune signal or whether those with high immune signal are the more severely ill patients who therefore require a higher dose of medication. The latter is more likely since antipsychotics can have anti-inflammatory effects (Capuzzi et al. 2017; Marcinowicz et al. 2021) and exposing nonhuman primates, rodents (Lanz et al. 2019; Siegal et al. 2014; Volk et al. 2015), or endothelial cell cultures (Cai et al. 2018; Purves-Tyson et al. 2021) to antipsychotics does not change the level of the immune markers of interest. Perhaps the more important factor emerging to explain the heterogeneity of results for immune/inflammation related markers is the heterogeneity within the patient phenotypes. Clinical features such as comorbidities (Cabrera et al. 2019), stage of illness (De Picker et al. 2021; Mahajan et al. 2018), and suicidality (Trepanier et al. 2016; Sakai et al. 2016; Mahajan et al. 2018) can all contribute to heterogeneity. Moreover, it is now apparent that within each diagnostic group there is a subgroup of patients that have a particularly high immune signature in the brain (Bogerts et al. 2017; Schlaaff et al. 2020; Fillman et al. 2014, 2013; Carlström et al. 2021; Zhang et al. 2016; Catts et al. 2014). Within schizophrenia there are 40–46% of patients (Fillman et al. 2013, 2014; Purves-Tyson et al. 2021) and 30% within bipolar disorder (Fillman et al. 2014), that have a particularly high inflammatory signature in the brain. Statistical clustering methods have also successfully identified a “high immune” subgroup within schizophrenia based on peripheral immune markers (Martinuzzi et al. 2019) as well as brain transcriptomics (Carlström et al.

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2021). What contributes to this elevation remains to be determined but it is possible that an earlier exposure to infection has dysregulated the immune system or left an immune signature that effects the pleiotropic functions of immune molecules, that then impact brain development. Determining retrospectively if the subjects with the higher inflammatory signature in the postmortem studies were exposed to a prior infection is generally not possible given the current clinical data that are available but will be important to gather in the future to determine why a subgroup of patients consistently has higher levels of immune markers. Interestingly, serum antibody levels for several infectious agents have been measured in the Stanley Medical Research Institute Array Collection of 105 brains by Dr. Robert Yolken using ELIZA methodology. Preliminary analyses show that when grouping the 105 samples according to “high” or “low” inflammation signature as per the immune clustering of markers from the frontal cortex (Fillman et al. 2014) then we find the CMV and HSV2 serum levels are elevated in the “high inflammation” group as compared to the “low inflammation” group (data available at www.sncid. stanlyresearch.org). This suggests that the cases in the “high inflammation” subgroup are also the cases that were exposed to virus earlier in life. Thus, stratification based on prior exposures to infectious agents is likely to become increasingly necessary as we endeavor to determine the etiology of the severe mental illnesses.

4 Conclusions While there is no evidence for an ongoing active infection of the CNS in the major mental illnesses, it appears that in schizophrenia immune/inflammation related markers are generally upregulated, whereas in mood disorders there is no consistent direction of changes in marker expression despite clear evidence for dysregulation. Much of the inconsistency could be eliminated in the future by stratifying patients based on clinical phenotypes and known infectious exposures. Some of the abnormalities in the postmortem brain, particularly in schizophrenia may be explained by possible early infection that disrupted brain development. The time of infectious exposure whether in utero, childhood or adolescence, together with genetic susceptibility, may determine the level of impact on the developing brain and thus, the final clinical outcome with different behavioral and cognitive phenotypes. It is possible that an earlier or latent infection anywhere in the body, particularly in genetically susceptible individuals, could induce an inflammatory signature that influences brain connectivity. Overall, it appears that it is dysregulation of immune molecule signaling in the brain rather than classic neuroinflammation, that is impacting brain development, function, and homeostasis in a subset of vulnerable individuals.

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Infections During Pregnancy and Risks for Adult Psychosis: Findings from the New England Family Study Stephen L. Buka, Younga Heather Lee, and Jill M. Goldstein

Contents 1 Setting the Stage: The Continuum of Reproductive Casualty and Collaborative Perinatal Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Initial Serologic Studies: Prenatal Infections and Risk for Psychosis Among Offspring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Maternal Immune Response: A Potential Common Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Beyond Diagnosis to the Impacts of Prenatal Immune Exposures on the Brain . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract For the past 40 years, our team has conducted a unique program of research investigating the prenatal risks for schizophrenia and related adult psychiatric disorders. The New England Family Study is a long-term prospective cohort study of over 16,000 individuals followed from the prenatal period for over 50 years. This chapter summarizes several major phases and findings from this work, highlighting recent results on maternal prenatal bacterial infections and brain imaging. Implications regarding the causes and potential prevention of major psychotic disorders are discussed.

S. L. Buka (✉) Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA e-mail: [email protected] Y. H. Lee Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA Departments of Psychiatry and Medicine, Harvard Medical School, Boston, MA, USA J. M. Goldstein Departments of Psychiatry and Medicine, Harvard Medical School, Boston, MA, USA Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA Innovation Center on Sex Differences in Medicine, Massachusetts General Hospital, Boston, MA, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 49–70 https://doi.org/10.1007/7854_2022_397 Published Online: 15 November 2022

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Keywords Bacterial infections · Cohort studies · Fetal programming · Immune processes · Perinatal risks · Psychosis · Schizophrenia For the past 40 years, our team has conducted a unique program of research investigating the prenatal risks for schizophrenia and related adult psychiatric disorders. This chapter summarizes several major phases and findings from the work on schizophrenia and other psychoses, highlighting recent results on maternal prenatal bacterial infections and brain imaging. We summarize the following phases of our work on this topic: 1. Setting the stage – the continuum of reproductive casualty and Collaborative Perinatal Project. 2. Initial serologic studies: prenatal infections and risk for psychosis among offspring. 3. Maternal immune response – a potential common pathway. 4. Beyond diagnosis to the impacts of prenatal immune exposures on the brain.

1 Setting the Stage: The Continuum of Reproductive Casualty and Collaborative Perinatal Project A major early influence on this work were the seminal studies of epidemiologists Benjamin Pasamanick and Abraham Lilienfeld on the potential role of pregnancy and delivery complications (PDCs) in the etiology of a variety of child neuropsychiatric disorders (e.g., cerebral palsy, epilepsy, reading disorders). Based on a number of case–control studies, they observed that complications during the prenatal period were more common across a range of conditions, with differing strengths of association. Based on this body of work, they proposed (Pasamanick et al. 1956) “... a continuum of reproductive casualty extending from death [which] might descend in severity through cerebral palsy, epilepsy, mental deficiency, and perhaps even to behavior disorder.” Early attempts to investigate this hypothesis for psychiatric disorders employed case–control designs of treated samples with psychiatric disorders and hospital, sibling, or population controls, which had notable methodologic limitations (Buka et al. 1988). Briefly, these included: inappropriate control groups; non-standardized diagnostic procedures resulting in overly broad and indefinite psychiatric groupings; 3) inclusion of a large and heterogeneous set of maternal, obstetric, and infant-based events under the general heading of “PDCs;” and noncomparable obstetric records for the case and control subjects (Buka et al. 1993). Around this time the US National Institutes of Health undertook a major longitudinal investigation – the National Collaborative Perinatal Project (CPP) with the ambitious goals of identifying conditions and mechanisms during the prenatal, perinatal, and early childhood periods which adversely influenced child neurobehavioral development. The Project entailed a single study design which called for the systematic collection of data through the prospective observation

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and examination of thousands of pregnancies through the first 7 years of life. At the conclusion of the study, a total of 55,908 births were enrolled in 12 study sites in the USA between 1959 and 1966. Cohort mothers were intensively studied during pregnancy and their infants were evaluated for physical, behavioral, and cognitive development during the first 7 years of life (Niswander and Gordon 1972; Klebanoff et al. 1998b). In addition to detailed clinical evaluations, approximately 800,000 serum samples were obtained from the mothers and stored in a repository for further analyses. Dr. Mark Klebanoff played a vital role in ensuring the maintenance of this valuable national resource (Klebanoff et al. 1998a). A unique feature of the study was that the data recorded at any given time was not biased by reference to antecedent events or in light of what developed subsequently. As a result, the sample included information on a broad spectrum of pregnancy conditions, age, race, socioeconomic status, demographic characteristics, birth trauma, maternal conditions, among others. In short, participating children included those with known chromosomal, metabolic, and neurological disorders, as well as a cohort of infants without documented or suspected birth defects. Major findings from the CPP have been summarized by Niswander and Gordon (1972), Broman et al. (1975, 1985), and Nichols and Chen (1981). These volumes explore the relative roles of constitutional and environmental events on a variety of birth and early life outcomes. Numerous publications have examined the antecedent factors associated with specific childhood neuropsychiatric disorders (c.f. Torrey et al. (1975). Our prospective investigations of the role of infections, immune mechanisms, and later psychiatric disorders have been conducted with the approximately 17,000 pregnancies enrolled in the New England cohorts of the CPP. This included approximately 4,000 pregnancies enrolled in Providence, RI and 13,000 in Boston, MA. Our first major study (the “Providence 1000”) selected 500 infants born in Providence, RI with moderate perinatal complications and 500 matched comparison subjects. Using standardized psychiatric assessments, the CPP team interviewed these children as adults at a mean age of 23 years (Buka et al. 1993). The generally null results indicated no elevated risk for psychiatric disorder in relation to perinatal complications, with two exceptions. Infants born with conditions suggestive of chronic fetal hypoxia were at marginally elevated risk for both cognitive impairment and psychotic disorders, including schizophrenia. These initial findings led to a considerable body of work over the next 25 years (Buka et al. 1988, 1993, 1999, 2001a, b; Goldstein et al. 2000, 2010; Anastario et al. 2012) largely focused on schizophrenia and other psychotic disorders, with critical early funding from the Stanley Medical Research Institute. Ongoing studies of the combined influence of perinatal complications, infections during pregnancy, and family history of psychosis now include both a “high-risk” study of over 700 original CPP parents with a history of psychiatric treatment and matched unaffected comparison parents and a nested case–control study of approximately 200 CPP offspring diagnosed with schizophrenia, bipolar disorder, and other psychoses, their unaffected siblings and matched unaffected comparison subjects (Buka et al. 2013). The case–control component of the schizophrenia work resulted in the first uses of stored maternal serum samples relative to adult outcomes in

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relation to subsequent psychosis (Buka et al. 2001a, b) – (Sect. 2 below). These schizophrenia projects now include over 1,000 CPP cohort members with and without psychotic disorders and incorporate detailed clinical diagnostic, neuropsychological, structural, and functional imaging procedures (Thermenos et al. 2005; Goldstein et al. 2010; Seidman et al. 2013) (Sect. 3 below).

2 Initial Serologic Studies: Prenatal Infections and Risk for Psychosis Among Offspring As part of the original Collaborative Perinatal Project, the protocol included collection and storage of maternal serum samples throughout pregnancy, as well as umbilical cord serum samples collected at the time of birth. In collaboration with NIH intramural scientists this resource enabled the first direct tests of potential infectious agent risks for later schizophrenia and other psychoses (Buka et al. 2001a). Drawing upon the longitudinal follow-up studies described above, our initial study was a nested case–control study of 27 adults with clinically confirmed schizophrenia (n = 13), schizophreniform disorder (n = 1), bipolar disorder with psychotic features (n = 2), brief psychosis (n = 3), and psychosis not otherwise specified (n = 8); and 54 unaffected controls, matched for sex, ethnicity, and date of birth. Participants were interviewed by a trained diagnostic interviewer using the Structured Clinical Interview for DSM-IV (Buka et al. 2001a). Trained diagnosticians (2 clinical psychologists, 2 adult psychiatrists) then completed best-estimate consensus diagnoses according to the DSM-IV criteria, based on interview data and medical record review (First 1997). For each study participant, a maternal blood sample was obtained from the National Institutes of Health repository for the last collection obtained during pregnancy (usually when the mother was delivered of the neonate). Levels of total IgG, IgM, IgA, and albumin were measured by laser rate nephelometry (Fink et al. 1989). For each sample, the level of immunoglobulin was analyzed in terms of absolute concentrations (measured in milligrams per deciliter) as well as a ratio of the concentration of immunoglobulin to the concentration of albumin (immunoglobulin-albumin ratio) to control for individual differences related to hemodilution that can occur during pregnancy (Ailus 1994) or differential evaporation that might occur during sample storage. Levels of specific IgG class antibodies to cytomegalovirus, rubella virus, Toxoplasma gondii, human parvovirus B19, herpes simplex virus type 1, herpes simplex virus type 2 (HSV-2) virion antigen (HSV-1), and Chlamydia trachomatis were measured by solid-phase enzyme immunoassay (Ashley et al. 1998). Solid-phase enzyme immunoassays were performed for the measurement of IgG antibodies to HSV-2 type-specific glycoprotein gG-2 (Ashley et al. 1998). Immunoassays were performed for the measurement of antibodies to human papillomavirus type 16 using solid-phase viral-like particles cloned and expressed in baculovirus (Viscidi et al. 1997). All samples were analyzed under

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code, with the laboratory performing the studies being unaware of the clinical status of the study individuals. Study results showed significant elevations in maternal serum IgG and IgM class immunoglobulins at delivery among the psychotic case series. There were no significant differences in maternal levels of serum IgA class antibodies, indicating that the results were related largely to a systemic, as opposed to a mucosal, immune response. The differences were apparent when the IgG or IgM levels were expressed as concentrations per milliliter of serum or as immunoglobulin-albumin ratios. These findings indicated that the differences were unlikely explained by differential hemodilution or evaporation of the samples (Ailus 1994). As we commented at the time “IgG and IgM class antibodies are both generated in response to infection” IgM immunoglobulins are generally generated within a few days following systemic infection and are detectable for several months while IgG immunoglobulins are generated 1–3 weeks after initial infection and are detectable for several years. The association of elevated levels of total immunoglobulins and subsequent psychosis thus provided objective confirmation of previous studies that have documented a correlation between the history of clinical signs of infection during pregnancy and the development of schizophrenia in the offspring.” Further, we observed a statistically significant and graded association between maternal levels of IgG antibodies to HSV-2 and the subsequent development of psychosis in the offspring, in both the virion antigen and gG2 HSV-2 assays. However, not all of the increased immunoglobulin levels in the case series could be attributed to HSV-2 infection. Of the 13 cases who had elevated levels of total IgG antibodies (defined as >1 SD above the mean for the control sample), 5 (38%) also had elevated HSV-2 antibody levels. We noted “the relationship between elevated levels of immunoglobulins and increased levels of antibodies to HSV-2 should be addressed in studies of additional populations.” We replicated these results with a considerably larger sample of 200 cases and 554 matched unaffected controls (Buka et al. 2008). From the New England cohorts, we included 89 newly identified cases, not previously analyzed – 33 schizophrenia, 6 schizoaffective disorder-depressed type, 7 other nonaffective psychoses; and 43 affective psychoses, including 14 schizoaffective disorder-bipolar type, 22 bipolar disorder with psychotic features, 4 major depressive disorder with psychosis, and 3 type not specified. In this work we collaborated with Prof. Tyrone Cannon who had conducted similar work with the Philadelphia, PA cohort of the CPP. These investigators used a citywide psychiatric database that identified 339 study offspring who had been treated and diagnosed with some form of psychotic disorder. Psychiatric records were located and reviewed for 144 of these individuals. Six experienced diagnosticians performed the chart reviews. Diagnostic procedures followed DSM-IV criteria and 111 individuals received a confirmed diagnosis of schizophrenia or other psychoses. These included 69 schizophrenia psychoses, including 60 schizophrenia and 9 schizoaffective disorder-depressed type, and 42 affective psychoses, including 7 schizoaffective disorder-bipolar type, 11 bipolar disorder with psychotic features, 21 major depressive disorder with psychosis, and 3 type not specified.

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Using this large case series, we again found a statistically significant association between serologic evidence of HSV-2 infection and the subsequent development of schizophrenia and other psychoses among adult offspring. Offspring of mothers with serologic evidence of HSV-2 infection had 1.6 greater odds for the subsequent development of psychoses (95% confidence interval [CI] 1.1–2.3). This risk was slightly, but not significantly, elevated in relation to schizophrenia (odds ratio = 1.8) rather than affective psychoses (odds ratio = 1.3). As we noted at the time “These effect sizes are comparable with those reported for specific genetic polymorphisms that have been linked to schizophrenia” (Gilbody et al. 2007). The proportion of the sample that was seropositive for prior HSV-2 infection (approximately 25%) was substantially higher than the rate of psychosis. Thus, it is unlikely that a past exposure to HSV-2 would, by itself, impact the developing fetus in a fashion that could result in subsequent psychoses. Instead, we hypothesized that those women with prior HSV-2 infection (as evidenced by elevated IgG antibodies in late pregnancy) who were also exposed to conditions during pregnancy that could result in re-exposure to the virus might be at particularly elevated risk for psychosis among surviving offspring. We identified two variables that have been associated with or serve as proxies for re-exposure to HSV-2: frequency of intercourse (Guinan et al. 1985) and failure to use barrier contraceptives (Vontver et al. 1982). Frequency of intercourse was reported directly by the pregnant women in mid-pregnancy and provided the strongest evidence for effect modification of the association between HSV-2 and offspring psychosis (OR 2.6, 95% CI 1.4–4.6). Use of barrier contraceptives during pregnancy was not assessed. However, a proxy measure of contraceptive practices (history of contraceptive use prior to pregnancy) showed modest evidence of effect modification (OR 2.0, 95% CI 1.2–3.2). Of note, offspring of HSV-2 positive women with a history of prior contraceptive use and who reported a lower frequency of intercourse during pregnancy were at no elevated risk for subsequent psychoses. In contrast, HSV-2 seropositive women without a history of contraceptive use and who reported a high frequency of intercourse during pregnancy were at approximately fourfold increased risk for offspring psychoses. This pattern of results was observed for all race/ethnic groups and study sites. We interpreted these findings in support of the hypothesis that is the potential re-exposure to and/or activation of prior HSV-2 infection that affected subsequent risk for offspring disorder. It is also possible that other lifestyle issues or unmeasured variables that are associated with sexual practices prior to and during pregnancy may account for these results. We also noted that another contemporaneous investigation of this topic using stored maternal sera, Brown et al. (2006) reported no association between HSV-2 and offspring psychoses. Differences in sample demographics, diagnostic composition, and limited sample size may partially explain these seemingly divergent results. For example, in the current investigation, the association between HSV-2 exposure and subsequent psychoses was more pronounced among African American than Caucasian subjects. This may largely be accounted for by the greater prevalence of HSV-2 seropositivity among African Americans in the USA (Kimberlin 2004) and in the current sample. With a base rate of HSV-2 seropositivity of only 7.2%, the

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power to detect differences between Caucasian cases and control subjects in the current sample and with other predominantly white samples is limited.

3 Maternal Immune Response: A Potential Common Pathway We recognized already in this early work that – in light of the multiple conditions during the prenatal period that had been shown to increase risk for schizophrenia – aberrant immune activity might be a common etiologically-relevant pathway. Prior to our findings on HSV-2 (and marginal findings for toxoplasma), there had also been key studies on influenza. Most involved ecologic designs, examining rates of schizophrenia several years after the 1957 A2 influenza epidemic. The initial study on this topic indicated increased rates of schizophrenia among cohorts exposed to this epidemic during the second trimester of pregnancy (Mednick et al. 1994), followed by both positive and negative replications (Brown et al. 2000). As a whole, this body of research suggested that there is a wide range of prenatal and perinatal infections and other environmental insults that could adversely affect infant brain development may increase the likelihood of schizophrenia in later life, most likely in genetically susceptible individuals (Wright et al. 1993; Weinberger 1995; Yolken and Torrey 1995; Jones and Cannon 1998; Torrey and Yolken 1998). To further evaluate this hypothesis, we conducted an early study to investigate the association of a panel of key maternal cytokines (IL-1-β, IL-2, IL-4, IL-8, and TNF) in relation to subsequent psychosis among the adult offspring (Buka et al. 2001b). With the series of 27 cases and 54 controls described above, we found that increased levels of TNF measured in maternal sera at the time of birth were associated with schizophrenia and related psychotic disorders in the offspring. Subjects whose TNF exceeded the 75th percentile of the control sample had an odds ratio of 2.4 for psychosis. Those whose maternal antibodies exceeded the 90th percentile had an odds ratio of 5.7. This pattern was also observed, slightly attenuated, for IL-8. The findings on TNF were replicated later and extended to IL-6 in a much larger sample of cases of psychoses and controls (Goldstein et al. 2014). Using a nested case–control design and prospectively-collected prenatal maternal sera, we investigated the associations between cytokines IL-1β, IL-6, TNF, and IL-10 and chemokine IL-8 and the sex-dependent risk for psychoses (and schizophrenia alone) among 103 cases and 102 healthy controls drawn from the New England CPP. Analyses comparing cases and controls consisted of nonparametric tests and adjusted generalized estimating equation models based on deviant subgroups. While there were no significant differences in cytokine/chemokine levels between cases and controls overall, differences emerged when analyses were stratified by sex and psychosis subtype (bipolar and schizophrenia). Male schizophrenia cases had higher levels of TNF (1.8 pg/mL vs. 3.9 pg/mL, KSa = 1.5, p = 0.02) compared with females with schizophrenia, who had significantly lower levels of TNF compared with controls

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(1.8 pg/mL vs. 4.0 pg/mL, KSa = 1.4, p = 0.04), differences that were not seen in bipolar psychoses. This was consistent with our previous findings since most of the cases in our original study of 27 cases were male. In addition, there were even stronger effects of maternal prenatal exposure to IL-6 and IL-6:IL-10, on psychosis risk. (Note, IL-6:IL-10 is a ratio of the pro-inflammatory cytokine, IL-6, to the anti-inflammatory effects of IL-10, a ratio used to examine the functional effects of IL-6.) Males and females with schizophrenia had higher levels of maternal IL-6 exposure than controls or affective psychoses, but the effect size was much larger and significant for males than females versus controls (males: (2.8 pg/mL vs. 1.3 pg/mL, Z = 2.3, p = 0.02; IL-6:IL-10: 1.5 pg/ mL vs. 0.7 pg/mL, Z = 2.0, p < 0.05; females: (2.1 pg/mL vs. 1.5 pg/mL, Z = 0.6, p = NS. This held when we created a deviant subgroup of the 75th percentile of maternal IL-6 exposure (OR75 = 3.2, 95% CI: 1.1, 9.2). The IL-6 exposure was also significantly associated with the expression of flat affect (Pearson r = 0.47 ( p < 0.05), worse attention (r = 0.48, p < 0.05), lower reading comprehension (r = -0.96, p < 0.001), and lower verbal IQ (r = -0.79, p < 0.10) particularly in these men (Goldstein et al. 2014). Like males with schizophrenia, males with affective psychoses (AP) had significantly higher levels of IL-6:IL-10 than controls (1.5 pg/mL vs. 0.7 pg/mL, Z = 2.1, p = 0.03), in part driven by low maternal IL-10 in AP cases (1.4 pg/mL vs. 2.4 pg/ mL). However, females (in contrast to the males) with AP were not affected by maternal IL-6 exposure. Human TNF is a 233-amino-acid non-glycosylated peptide which plays a central role in the human immune response to infections. We found that elevated levels of maternal TNF were associated with a history of infection during the third trimester of pregnancy, particularly in males. The history of infection during pregnancy was obtained during the cohort study and thus was not subject to recall bias. It is thus likely that maternal infection was one of the principal stimuli of the increased levels of TNF measured in the mothers. This finding was consistent with other studies of maternal TNF during pregnancy. For example, elevated levels of maternal TNF have been associated with chorioamnionitis (Saji et al. 2000), recurrent miscarriage (Mallmann et al. 1991), and preterm labor (Gücer et al. 2001). Increased amniotic fluid levels of TNF have been associated with fetal infection (Baud et al. 1999) and premature rupture of the membranes (Zhang et al. 2000). In addition, increased expression of TNF within the fetal brain has been associated with periventricular leukomalacia (Yoon et al. 1997). In animal models, TNF has been shown to be one of the main links between maternal infection and neonatal brain abnormalities (Urakubo et al. 2001). Abnormalities in TNF have also been associated with schizophrenia, including an association between schizophrenia and polymorphisms in the TNF genes (Boin et al. 2001) and increased levels of TNF in peripheral blood of individuals with first-episode schizophrenia (Theodoropoulou et al. 2001). IL-6 can cross the blood brain barrier at a key period of the sexual differentiation of the brain (Dahlgren et al. 2006), suggesting potential sex effects on brain development. Animal models established that injection of pregnant mice with IL-6 altered offspring behavior and cortical brain regions, although specificity by

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offspring sex was not studied. IL-6, IL-1β, and TNF reduced number and length of frontal cortical dendrites when administered in late 2nd trimester (Marx et al. 2001; Gilmore et al. 2004) and induced prepulse and latent inhibitions in animal models of psychosis (Smith et al. 2007). Likewise, mimicking infection in pregnant rodents through peripheral introduction of polyinosinic:polycytidylic acid (poly I:C; a synthetic, double-stranded RNA molecule that induces an influenza-like immune response), or lipopolysaccharide (LPS; a bacterial cell wall component that also induces an innate immune response), had lasting offspring effects, such as increased levels of IL-6 in amniotic fluid and placenta (Urakubo et al. 2001; Gilmore et al. 2003) and behavioral deficits in offspring (Zuckerman and Weiner 2003). Rodent studies demonstrated specific effects of gestational IL-6 injection on offspring memory/working memory circuitry (Sparkman et al. 2006), deficits found in schizophrenia, particularly, as found in our studies, in men (Goldstein et al. 1998; Abbs et al. 2011), and related to prenatal infection (Brown et al. 2009). This was also consistent with our findings indicating greater expression of negative symptoms (specifically flat affect) and specific cognitive deficits (verbal performance, IQ and attention), particularly in maternally exposed males. There are multiple animal studies consistent with other clinical studies demonstrating abnormal levels of adult IL-6 and TNF levels in schizophrenia compared to healthy controls (Ganguli et al. 1994; Müller et al. 1997; Patterson 2009; Watanabe et al. 2010; Twohig et al. 2011). Our study underscored the importance of the impact of sex and psychosis type for understanding the variability of findings seen across studies in the literature. Although our studies did not identify mechanisms, multiple pathways regulate the effects of cytokines on offspring brain development, with specific mechanisms depending on timing of exposure. These include dysregulation of nerve growth factors, loss of dendritic connections, white matter connectivity, apoptosis, dysregulation of neurotransmitters, and hormonal dysregulation that impacts the healthy sexual differentiation of the brain during fetal development as well as brain aging (Goldstein et al. 2014).

4 Beyond Diagnosis to the Impacts of Prenatal Immune Exposures on the Brain Thus far we have discussed the impact of prenatal immune challenges on psychosis risk and the specific impact by sex and psychosis type. However, we need to understand how maternal immune exposures are transmitted to fetal brain development and what specific brain regions are impacted that are retained and associated with psychosis risk years later. Previous animal literature demonstrated that maternal prenatal immune challenges impacted specific offspring brain circuitry and behavior years later. This included cognitive deficits in memory and working memory circuitries (Sparkman et al. 2006), frontal cortical and hippocampal brain regions (Gilmore et al. 2004; Sparkman et al. 2006), and dysregulation of inhibitory control

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of arousal/or response to negative stress (Smith et al. 2007). However, few studies, aside from our group (Goldstein et al. 2021) and a few others (Gilmore et al. 2004; Ellman et al. 2010; Kalmady et al. 2014), have had the prospective data to map this trajectory in individuals who later onset with psychiatric disorders compared with healthy controls. The New England CPP cohort uniquely allowed us to examine these issues over the last several years in psychoses and major depressive disorder. In two recent studies, we reported on the impact of maternal immune exposures on negative stress response and memory circuitries in psychoses. Repeated and prolonged adverse responses to negative stress have been associated with increased risk for many chronic diseases, including psychiatric disorders. Perturbations in the in utero development of the stress response circuitry have played a key role underlying the developmental origins of disease, including what has been termed prenatal stress models of chronic disease. Brain circuitry involved in the stress response system includes arousal in hypothalamus, amygdala, and brainstem periaqueductal gray, and inhibitory control of arousal by cortical regions (medial prefrontal cortex, orbitofrontal cortex, and anterior cingulate cortex), and the hippocampus. These regions regulate response to negative stress which involves both steroid hormone (e.g., glucocorticoids and gonadal hormones) and immune physiological responses. Immune responses include the regulation of TNF and IL-6 (which we found significantly associated with schizophrenia risk), and whose receptors are densest in the paraventricular nucleus of the hypothalamus, hippocampus, and the pituitary gland. The release of maternal prenatal immune exposures, like TNF and IL-6, can impact fetal development, in part, by stimulating placental production of corticotropin releasing hormone and thereby impacting glucocorticoid function (through the hypothalamic pituitary adrenal (HPA) axis) in the fetus (Zuloaga et al. 2012; Beijers et al. 2014), also referred to as stress response circuitry. This chain of events is thought to have long-term consequences for the offspring’s brain health. In a previous study of ours in 80 adult offspring, equally divided by sex, followed from in utero development to midlife using the New England CPP, we investigated using functional magnetic resonance imaging whether concentrations of in utero pro-inflammatory cytokines in maternal sera (drawn at the start of 3rd trimester) were associated with abnormalities in activity in specific brain regions that regulate negative stress and immune function 45 years later, and whether this differed by offspring sex. Adults were diagnosed with schizophrenia, bipolar psychosis, or were healthy controls. We predicted that adverse concentrations of pro-inflammatory cytokines in maternal prenatal sera would be associated with hyperactivity in offspring’s stress-arousal regions (e.g., hypothalamus), and hypoactivity in inhibitory regions of the stress response (in particular, hippocampus). This was based, in part, on the fact that hippocampus has a negative feedback role on the hypothalamus and both brain regions are most dense with receptors for TNF and IL-6, cytokines we previously found were significantly associated with psychoses risk (Goldstein et al. 2014). Using an fMRI task we had used for over 15 years to induce negative stress response, results showed that exposure to pro-inflammatory cytokines in utero was

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significantly associated with sex differences in brain activity and connectivity of hypothalamus and hippocampus during response to negative stressful stimuli 45 years later (Goldstein et al. 2021). Lower maternal TNF levels were significantly associated with higher hypothalamic activity in both sexes and higher functional connectivity between hypothalamus and anterior cingulate only in men. Higher prenatal levels of IL-6 were significantly associated with higher hippocampal activity in women alone. When examined in relation to the anti-inflammatory effects of IL-10, the ratio TNF:IL-10 was associated with sex-dependent effects on hippocampal activity and functional connectivity with hypothalamus. Further, higher levels of maternal IL-6 were associated with hypoactivity in hippocampus, again suggesting less hippocampal ability to inhibit arousal (negative feedback) in the hypothalamus. Collectively, results suggested that adverse levels of maternal in utero pro-inflammatory cytokines and the balance of pro- to anti-inflammatory cytokines impacted brain development of offspring in a sexually dimorphic manner that persisted across the lifespan, independent of diagnosis. However, the pattern of dysregulation manifested differently in male and female offspring, due, in part, to differential maternal in utero anti-inflammatory responses and the timing of prenatal exposure. New work (described below) extended our work on prenatal maternal immune challenges on offspring brain structure and function, specifically focusing on structural abnormalities in memory circuitry, a critical cognitive abnormality in schizophrenia. Several methods for structural imaging have been proposed to investigate associations between regions within and between brain networks (Labouesse et al. 2015; Chukwurah et al. 2019). Among them, techniques based on covariance modeling have been found to be particularly useful in several neuropsychiatric conditions including Alzheimer’s disease and schizophrenia (Seeley et al. 2009; Abbs et al. 2011; Alexander-Bloch et al. 2013; Liu et al. 2021). Included in this literature were publications from our group that investigated sex differences in the covariance of regions within working memory circuitry by schizophrenia (Abbs et al. 2011), and more recently by sex and reproductive status (Seitz et al. 2019). An assumption underlying this analytic approach is that brain regions included in a functional network are connected through shared neurodevelopmental and functional processes, and thus, it is suitable to analyze these regions as a network, rather than independently. Our group explored volumetric abnormalities within working memory circuitry in early midlife in relation to adult psychosis using covariance structural modeling. A second novel feature of this recent work was a focus on bacterial infections during pregnancy. There has been relatively limited research on bacterial infections, such as urinary tract infection, bacteriuria, and bacterial vaginosis, in relation to subsequent psychosis (Brown et al. 2000; Babulas et al. 2006; Sørensen et al. 2009), despite their high prevalence among pregnant women as a result of physiological changes and immune suppression during pregnancy (Mikhail and Anyaegbunam 1995). Several studies (Cook et al. 1976; Broman 1987; McDermott et al. 2000; Lee et al. 2020b), including our own (Lee et al. 2020b), report on its lasting impact on the offspring’s neurodevelopment in early childhood. Moreover, our recent findings

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suggest that prenatal exposure to bacterial infection may also increase the risk of psychotic disorders in adulthood. In this study, participants who were prenatally exposed to bacterial infection had up to 2 times the risk of being diagnosed with schizophrenia and related psychoses than those unexposed (adjusted odds ratio [aOR] = 1.8, 95% confidence interval [CI] = 1.2–2.7) with additional variations by severity of infection and offspring sex (Lee et al. 2020a). For example, the effect of multisystemic bacterial infection (aOR = 2.9, 95% CI = 1.3–5.9) was nearly twice that of less severe localized bacterial infection (aOR = 1.6, 95% CI = 1.1–2.3). Furthermore, male participants were significantly more likely than female participants to develop psychosis after maternal exposure to any bacterial infection during pregnancy (sex interaction p = 0.018). Recent work then extended our studies of bacterial infection on psychosis risk to its impact on specific brain circuitry regulating memory. NEFS offspring were recruited at the time of 46–53 years of age and completed clinical, cognitive, and neuropsychological assessments and functional and structural magnetic resonance imaging (MRI). We identified a subset consisting of 168 NEFS participants (53% female) who were scanned using a high-resolution T1 sequence on a 1.5T whole body scanner. Participants in psychotic and control groups were well matched in terms of offspring sex, maternal race and ethnicity, maternal education, season of birth, parental history of mental illness, parental socioeconomic status, and age at MRI scan. Brain regions of interest included dorsolateral prefrontal cortex, hippocampus, parahippocampus, inferior parietal lobule, superior parietal lobule, and caudal anterior cingulate cortex. When comparing volumetric differences by adult psychosis and offspring sex, male cases had significantly smaller hippocampi than did male controls and female cases, consistent with our previous findings (Goldstein et al. 2002) as well as those of others (Exner et al. 2008; Adriano et al. 2012). With respect to prenatal exposure to bacterial infection, exposed participants had larger hippocampi compared to those unexposed, especially in the right hemisphere, which was also consistent with previous hippocampal findings (Ellman et al. 2010). We then compared the covariance matrices of the volumetric measures in the working memory circuitry by adult psychosis status using the Box’s M test (Box 1949) and Jennrich’s test (Jennrich 1970). As shown in Table 1, we found evidence Table 1 Box’s M and Jennrich’s tests for overall differences in covariance and correlation matrices by adult psychosis and prenatal bacterial infection

Average Left hemisphere Right hemisphere Average Left hemisphere Right hemisphere

Box’s M Jennrich p χ2 χ2 Adult psychosis 30.90 0.08 33.74 24.70 0.26 22.76 39.46 0.01 38.70 Prenatal bacterial infection 31.70 0.06 22.24 40.09 0.01 19.84 29.11 0.11 27.30

p 0.00 0.09 0.00 0.10 0.18 0.03

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Table 2 Pearson correlation coefficients between right hemisphere volumes of regions of interests (ROIs) among psychotic cases and controls Adult psychosis Case Control Test statistic Pearson correlation and 95% bias-corrected and accelerated (BCa) bootstrap confidence interval HIPP-paraHIPP 0.19 (-0.23, 0.57) 0.11 (-0.05, 0.25) Z = 0.38, p = 0.70 HIPP-sPAR -0.11 (-0.47, 0.27) -0.11 (-0.24, 0.03) Z = 0.02, p = 0.98 HIPP-iPAR 0.57 (0.25, 0.77) 0.04 (-0.14, 0.20) Z = 2.70, p = 0.01 HIPP-cACC 0.27 (-0.19, 0.64) 0.01 (-0.18, 0.17) Z = 1.19, p = 0.23 HIPP-DLPFC 0.26 (-0.26, 0.64) 0.03 (-0.14, 0.19) Z = 1.03, p = 0.30 paraHIPP-iPAR -0.69 (-0.86, -0.44) -0.05 (-0.21, 0.09) Z = 3.55, p < 0.01 paraHIPP-sPAR 0.01 (-0.37, 0.49) -0.02 (-0.17, 0.15) Z = 0.16, p = 0.87 paraHIPP-cACC -0.14 (-0.44, 0.10) -0.08 (-0.25, 0.09) Z = 0.26, p = 0.79 paraHIPP-DLPFC 0.09 (-0.50, 0.55) -0.19 (-0.35, -0.03) Z = 1.26, p = 0.21 sPAR-iPAR -0.18 (-0.50, 0.23) -0.08 (-0.24, 0.09) Z = 0.44, p = 0.66 sPAR-cACC 0.24 (-0.16, 0.57) -0.14 (-0.27, 0.01) Z = 1.69, p = 0.09 sPAR-DLPFC -0.17 (-0.63, 0.38) -0.08 (-0.26, 0.13) Z = 0.41, p = 0.68 iPAR-cACC 0.27 (0.00, 0.57) -0.11 (-0.27, 0.09) Z = 1.73, p = 0.08 iPAR-DLPFC 0.07 (-0.51, 0.56) -0.09 (-0.23, 0.05) Z = 0.71, p = 0.48 cACC-DLPFC -0.07 (-0.35, 0.30) 0.01 (-0.13, 0.15) Z = 0.35, p = 0.73 HIPP hippocampus, paraHIPP parahippocampus, sPAR superior parietal cortex, iPAR inferior parietal cortex, cACC caudal anterior cingulate cortex, DLPFC dorsolateral prefrontal cortex

suggesting differences by adult psychosis status, particularly in the right hemisphere (Box’s M test: χ 2 = 39.46, p = 0.01; Jennrich’s test: χ 2 = 38.70, p = 0.00). Post-hoc analyses of the correlation coefficients in the right hemisphere revealed that covariance differences by adult psychosis were most highly associated with relationships involving hippocampus, parahippocampus, and inferior parietal lobule (see Table 2). Among psychotic cases, the average volume of the inferior parietal lobule had a strong negative correlation with that of the parahippocampus (Pearson’s correlation coefficient (r) with 95% confidence limits: 0.57 [0.25, 0.77]) and moderate positive correlation with hippocampal volume (r = -0.69 [-0.86, -0.44]). Lastly, we compared the covariance matrices by prenatal exposure to bacterial infection and found evidence implying differences in the covariance matrices in the right hemisphere (Box’s M test: 29.11, p = 0.11; Jennrich’s test: χ 2 = 27.30, p = 0.03). Posthoc analyses of the correlation coefficients in the right hemisphere revealed that covariance differences by prenatal exposure to bacterial infection were associated with relationships between parahippocampus, superior parietal lobule, and dorsolateral prefrontal cortex (see Table 3). Among exposed individuals, the volume of the parahippocampus showed a moderate positive association with that of the superior parietal lobule (r = 0.41 [0.05, 0.67]) and strong negative association with the volume of the dorsolateral prefrontal cortex (r = -0.76 [-0.86, -0.57]), whereas unexposed individuals did not.

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Table 3 Pearson correlation coefficients between right hemisphere volumes of regions of interests (ROIs) among exposed and unexposed individuals Prenatal bacterial infection Exposed Unexposed Test statistic Pearson correlation and 95% bias-corrected and accelerated (BCa) bootstrap confidence interval HIPP-paraHIPP 0.09 (-0.32, 0.41) 0.04 (-0.12, 0.19) Z = 0.26, p = 0.80 HIPP-sPAR 0.05 (-0.36, 0.52) -0.14 (-0.28, 0.01) Z = 0.91, p = 0.36 HIPP-iPAR 0.17 (-0.38, 0.55) -0.01 (-0.20, 0.19) Z = 0.87, p = 0.38 HIPP-cACC -0.26 (-0.58, 0.08) 0.00 (-0.17, 0.17) Z = 1.32, p = 0.19 HIPP-DLPFC -0.05 (-0.40, 0.34) 0.05 (-0.16, 0.20) Z = 0.50, p = 0.62 paraHIPP-iPAR 0.22 (-0.12, 0.56) -0.07 (-0.26, 0.09) Z = 1.44, p = 0.15 paraHIPP-sPAR 0.41 (0.05, 0.67) -0.09 (-0.22, 0.04) Z = 2.54, p = 0.01 paraHIPP-cACC 0.02 (-0.37, 0.44) -0.09 (-0.28, 0.09) Z = 0.52, p = 0.60 paraHIPP-DLPFC -0.76 (-0.86, -0.57) -0.16 (-0.33, 0.03) Z = 4.04, p < 0.01 sPAR-iPAR 0.04 (-0.23, 0.35) -0.08 (-0.23, 0.08) Z = 0.56, p = 0.57 sPAR-cACC -0.06 (-0.36, 0.26) -0.07 (-0.21, 0.07) Z = 0.05, p = 0.96 sPAR-DLPFC -0.22 (-0.52, 0.22) -0.19 (-0.36, -0.01) Z = 0.11, p = 0.91 iPAR-cACC -0.08 (-0.30, 0.24) -0.15 (-0.28, 0.00) Z = 0.32, p = 0.75 iPAR-DLPFC -0.34 (-0.60, 0.05) -0.06 (-0.19, 0.08) Z = 1.42, p = 0.15 cACC-DLPFC -0.03 (-0.45, 0.31) 0.03 (-0.12, 0.17) Z = 0.29, p = 0.77 HIPP hippocampus, paraHIPP parahippocampus, sPAR superior parietal cortex, iPAR inferior parietal cortex, cACC caudal anterior cingulate cortex, DLPFC dorsolateral prefrontal cortex

As in preclinical studies, hippocampus is connected with the inferior parietal lobule, dorsolateral prefrontal cortex (Seltzer and Van Hoesen 1979), and anterior cingulate cortex (Barbas and Pandya 1989; Sesack et al. 1989). Additionally, the inferior parietal lobule is directly connected with the dorsolateral prefrontal cortex and the anterior cingulate cortex (Makris et al. 2005), and the parahippocampus is connected with the hippocampus and other cortical areas (Petrides et al. 1993). Since the hippocampus provides important input to the dorsolateral prefrontal cortex (Goldman-Rakic et al. 1984) and given that neonatal hippocampal lesions induce post-pubertal changes in prefrontal cortex (Bertolino et al. 1997) mimicking aspects of schizophrenic pathophysiology, it has been hypothesized that the interaction between these two regions might be particularly disturbed in schizophrenia (Fletcher 1998; Meyer-Lindenberg et al. 2005). Abnormalities associated with prenatal maternal immune exposure found here are also consistent with our previous clinical brain imaging work on sex-dependent structural abnormalities in volumes in schizophrenia compared with healthy controls (Goldstein et al. 2002) and sex-dependent memory circuitry covariance abnormalities found in schizophrenia (Abbs et al. 2011).

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5 Conclusions The work summarized above, in concert with other large prospective cohorts across the globe, has confirmed that complications during the pre- and perinatal periods are among the most replicated and established risks for schizophrenia. Meta-analyses indicate that individuals with schizophrenia are about twice as likely to have experienced obstetric complications compared with healthy controls (Geddes and Lawrie 1995; Cannon et al. 2002). Early estimates suggested that as much as 20% of current rates of schizophrenia and related psychoses could be attributed to such complications (Geddes and Lawrie 1995), suggesting potential opportunities for prevention. Early studies also underscored the great variety of conditions during pregnancy – viral exposures, bacterial exposures, psychosocial stressors, under nutrition, urban residence – that were reported to increase subsequent rates of schizophrenia. These exposures may have a common final pathophysiology through dysregulation of the maternal immune system. Serologic investigations by our group and others have both identified specific viral exposures (e.g., herpes simplex virus type 2 (HSV-2), influenza A2) and potential common pathways through enhanced maternal immune activity. Recent work demonstrated the long-term impacts of heighted maternal immune levels on brain structure and function, sex differences, and relevance to schizophrenia and other major mental disorders. It is unlikely that either perinatal complications or heightened immune levels in pregnancy are independent sufficient causes of schizophrenia. Most individuals exposed to obstetric complications do not develop schizophrenia (Buka et al. 1993). Thus, obstetric complications appear insufficient to cause schizophrenia, raising questions as to the added influence of genetic risk and, in particular, whether genetic risk for schizophrenia results in a developing brain that is more vulnerable to insult via obstetric complications (Forsyth et al. 2013). It is notable that recent large genome-wide association studies found that a sizable proportion of genetic risk loci involve genes that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium 2014), including sex-dependent immune gene expression (Sekar et al. 2016; Blokland et al. 2022). An intriguing recent study using placental tissue demonstrated striking evidence of such interactions, suggesting that individuals with high polygenic risk for schizophrenia largely evidenced increased likelihood of illness when also experiencing serious obstetrical complications – with a five-fold greater likelihood of developing schizophrenia in comparison with individuals with similarly high genetic risk but no history of serious obstetrical complications (Ursini et al. 2018). However, an initial replication attempt did not confirm these placental gene*obstetric complication findings (Vassos et al. 2022). Schizophrenia is ubiquitous worldwide with a high global burden (Charlson et al. 2018). The research summarized in this chapter has both attempted to clarify the etiology and to identify potential strategies for the eventual prevention of this severe

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disorder by targeting immune pathways across the lifespan, beginning in prenatal development

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Sources and Translational Relevance of Heterogeneity in Maternal Immune Activation Models Urs Meyer

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Planned and Unplanned Sources of Model Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Methodological Control Over Model Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Benefits of Model Variability for Translational Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Enhancing Bidirectional Translational Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract The epidemiological literature reporting increased risk for neurodevelopmental and psychiatric disorders after prenatal exposure to maternal immune activation (MIA) is still evolving, and so are the attempts to model this association in animals. Epidemiological studies of MIA offer the advantage of directly evaluating human populations but are often limited in their ability to uncover pathogenic mechanisms. Animal models, on the other hand, are limited in their generalizability to psychiatric disorders but have made significant strides toward discovering causal relationships and biological pathways between MIA and neurobiological phenotypes. Like in any other model system, both planned and unplanned sources of variability exist in animal models of MIA. Therefore, the design, implementation, and interpretation of MIA models warrant a careful consideration of these sources, so that appropriate strategies can be developed to handle them satisfactorily. While every research group may have its own strategy to this aim, it is essential to report the methodological details of the chosen MIA model in order to enhance the transparency and comparability of models across research laboratories. Even though it poses a challenge for attempts to compare experimental findings across laboratories, variability does not undermine the utility of MIA models for translational research. In fact, variability and heterogenous outcomes in U. Meyer (✉) Institute of Pharmacology and Toxicology, University of Zurich-Vetsuisse, Zurich, Switzerland Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 71–92 https://doi.org/10.1007/7854_2022_398 Published Online: 29 October 2022

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MIA models offer unique opportunities for new discoveries and developments in this field, including the identification of disease pathways and molecular mechanisms determining susceptibility and resilience to MIA. This review summarizes the most important sources of variability in animal models of MIA and discusses how model variability can be used to investigate neurobiological and immunological factors causing phenotypic heterogeneity in offspring exposed to MIA. Keywords Animal model · Autism · Infection · Inflammation · Maternal immune activation · Neurodevelopmental disorders · Poly(I:C) · Resilience

1 Introduction Infectious or non-infectious maternal immune activation (MIA) during pregnancy is a transdiagnostic environmental risk factor for various psychiatric and neurological disorders with neurodevelopmental etiologies (Brown and Meyer 2018; Gumusoglu and Stevens 2019; Meyer 2019). While MIA has long been recognized to contribute to the etiology of schizophrenia and autism spectrum disorder (Buka et al. 2001; Chess 1971; Mednick et al. 1988), the recent Zika virus epidemic and the current Covid-19 pandemic have greatly increased the awareness of the possible neurodevelopmental sequelae of this early-life adversity (Edlow et al. 2022; Schuler-Faccini et al. 2022). A number of pathophysiological processes have been implicated in the association between MIA and subsequent brain disorders, including inflammatory responses and oxidative stress taking place in maternal and fetal compartments, activation of maternal stress response systems, temporary microand/or macronutrient deficiencies, and disruption of placental functions (Bilbo et al. 2018; Meyer 2013, 2019). All these processes can affect somatic cell development and change the offspring’s neurodevelopmental trajectories, which in turn can lead to the emergence of behavioral and cognitive disturbances later in life. In addition, epigenetic modifications have recently been identified as a molecular mechanism by which MIA can induce long-term changes in brain functions (Labouesse et al. 2015; Richetto et al. 2017; Richetto and Meyer 2021), including generation-spanning effects on behavior and gene transcription (Weber-Stadlbauer et al. 2017, 2021). Despite the increasing evidence for significant health consequences, however, the effects of MIA on the offspring are heterogeneous in both male and female offspring. Indeed, while some offspring of MIA-exposed mothers develop central nervous system (CNS) dysfunctions, there is a substantial degree of resilience to MIA, which determines the extent to which offspring are protected from developing neurodevelopmental sequelae (Meyer 2019; Mueller et al. 2021). The risk-resilience dichotomy is noticeable when considering that infectious or non-infectious MIA is relatively common during pregnancy. For example, the prevalence of respiratory tract infections is estimated to be as high as 50% during pregnancy, while febrile

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episodes and urinary tract infections occur in approximately 17–21% of pregnant women (Collier et al. 2009). Likewise, allergic diseases affect approximately 18–30% of women in the childbearing age (Pali-Schöll et al. 2017), with asthma accounting for approximately 4–8% of these cases (Kwon et al. 2003). In spite of their relatively frequent occurrence, however, infectious or non-infectious immunopathologies cause lasting CNS disorders in only a comparatively small portion of exposed offspring (Hornig et al. 2018; Jones et al. 2017; Mahic et al. 2017). This heterogeneity is also mirrored by the variable effects of MIA in animal models (Weber-Stadlbauer and Meyer 2019). Animal models of MIA have originally been developed with the aim to support human epidemiological studies implicating MIA in the etiology of psychiatric disorders and are now widely used as experimental tools to study neurobehavioral and molecular dysfunctions in relation to a broad spectrum of MIA-induced pathologies (Boksa 2010; Brown and Meyer 2018; Careaga et al. 2017; Meyer 2014; Meyer et al. 2005). These models provide preclinical platforms allowing to explore causal relationships, identify underlying neurobiological mechanisms, and, ultimately, develop novel therapeutic interventions and preventative strategies against MIA-induced brain disorders. Like in many other model systems (Kafkafi et al. 2018; Richter 2017; Voelkl et al. 2020), however, the specificity of the effects induced by MIA in laboratory animals is influenced by a number of factors, leading to substantial heterogeneity in MIA-associated phenotypes (Weber-Stadlbauer and Meyer 2019). While this heterogeneity can introduce challenges in terms of reproducing and comparing the experimental outcomes of MIA across independent research laboratories (Kentner et al. 2019), model variability per se is not an undermining characteristic of MIA models. In fact, model variability offers unique opportunities for new (and sometimes unexpected) discoveries, granted that researchers are aware of the potential sources of variability and are open to novel methodological approaches and ways of data analysis. This review summarizes the most important sources of variability in animal models of MIA and discusses how model variability can be used to investigate neurobiological immunological factors causing phenotypic heterogeneity in offspring exposed to MIA.

2 Planned and Unplanned Sources of Model Variability It has long been recognized that a number of methodological factors can influence the severity and/or specificity of the effects in animal models of MIA (Table 1). Many of these factors, including choice of animal species, genetic background of the animals, type and dosing of the immunogen, and timing of MIA, are inherent characteristics of the experimental design. Thus, these factors represent planned or intentional sources of model variability, which can be controlled relatively easily by the experimenter. The rationale underlying the implementation of these types of model variability is manifold, including (1) the evaluation of species-specific effects of MIA by comparing its consequences in different species such as mice (Meyer

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Table 1 Sources of variability in maternal immune activation models and opportunities for translational research Source of variability Species

Animal strain and genetic background

Immunogen type

Dosing

Route of administration

Examples – Mouse – Rat – Spiny mouse – Ferret – Pig – Non-human primate – Different mouse or rat strains – Genetically modified animals – Virulent pathogens (e.g. influenza virus, Zika virus, Toxoplasma gondii) – Natural or synthetic immunogens (e.g. poly(I:C), LPS, staphylococcal enterotoxin) – Experimental allergens (e.g. ovalbumin) – Local inflammatory agents (e.g. turpentine oil) – Individual cytokines or chemokines (e.g., IL-1β, IL-6, IL-17A) – Titration of virulent pathogens – Dose-responses of natural or synthetic immunogens (e.g., poly (I:C) or LPS) or cytokines and chemokines – Intraperitoneal (i.p.) – Intravenous (i.v.) – Subcutaneous (s.c.) – Intramuscular (i.m.) – Intranasal (i.n.)

Possible reason for variability – Species-specific differences in immune responses and susceptibility to immunogens – Species-specific physiology and behavior

Opportunities of variability for new discoveries – Comparison and possible generalization of MIA effects across species

– Differing immune responses and susceptibility to immunogens – Strain-specific physiology and behavior – Activation and recruitment of distinct arms of the immune system – Different kinetics of immune response – Variations in the persistence and clearance of the immunogen

– Identification of gene-environment interactions

– Different doseresponse curves depending on species, animal strain, animal vivarium, and animal hygiene status

– Identification of sub- and suprathreshold effects

– Activation and recruitment of distinct immune organs – Different doseresponse curves depending on route of administration – Variations in the persistence and clearance of immunogen

– Comparison of the effects induced by localized versus systemic immune responses

– Identification of convergent and divergent effects induced by distinct immunogens

(continued)

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Table 1 (continued) Source of variability Timing and chronicity of exposure

Examples – Single exposure at specific stages of gestation – Repeated exposure during parts of or during the entire gestational period

Possible reason for variability – Differing immune responses resulting from immunological and hormonal fluctuation across gestation – Differing placental and fetal immune responses as a function of the progression of gestation

Age of offspring

– Cross-sectional studies in neonatal, juvenile, adolescent, adult or aged offspring – Longitudinal studies spanning different ages in the same offspring

– Age-specific manifestation of pathology

Sex of offspring

– Inclusion of one sex only – Inclusion of males and females – Different vendors of immunogen (e.g. Sigma or InvivoGen for poly(I: C)) – Different batches of immunogen from same vendor – Different sources, types or subtypes of virulent pathogens (e.g. influenza A (H1N1) and influenza A (H3N2) viruses)

– Sex-specific manifestation of pathology

Vendor or batch of immunogen

– Variable molecular constitution of immunogen (e.g. poly(I: C) enriched for highmolecular or low-molecular weight) – Variable potency of different batches or sources of non-virulent immunogens – Variable potency of different types or subtypes of virulent pathogens – Variable potency of same type or subtype of virulent pathogens obtained from different sources

Opportunities of variability for new discoveries – Identification of prenatal windows with maximal susceptibility to immune-mediated pathologies – Identification of differential long-term effects depending on disruption of different neurodevelopmental programs – Identification of age-specific manifestation of pathology – Identification of interactive effects between prenatal immune activation and innate maturational events – Identification of sex-specific manifestation of pathology – Identification of cellular and molecular mechanisms underlying differential immune responses to variable molecular constitution of immunogens or to different types or subtypes of virulent pathogens

(continued)

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Table 1 (continued) Source of variability Contamination of immunogen or vehicle

Examples – Contamination of poly(I:C) powders with LPS – Use of nonpyrogen-free vehicle solutions

Animal housing

– Differences in the basic type of animal caging systems (e.g. open cage system or individually ventilated cage system) – Differences in the degree of caging enrichment – Group caging versus isolated caging – Variable number of cage mates

Vendor or source of animals

– Unknown or variable litter affiliation – Different hygiene or microbiome status of animals obtained from different vendors

Breeding

– On-site breeding versus purchase of timed-pregnant animals – Single or group housing of pregnant and reading dams

Possible reason for variability – Unpredictable or variable immune response resulting from contamination – Unintended induction of immune responses in vehicletreated control animals – Differences in cage microenvironment (e.g., intra-cage levels of oxygen (O2), carbon dioxide (CO2) and ammonia (NH3)) and opportunities for physical activity (e.g. climbing, rearing, nest building) – Variable exposure to inter-cage olfactory and acoustic cues – Differences in the levels of perceived stress resulting from social hierarchies – Chronic stress resulting from overcrowding or social isolation – Incomplete randomization of dams from different litters to experimental groups – Variable immune response resulting from differences in hygiene or microbiome status of animals – Unpredictable influence of stress exposure during shipment of timed-pregnant animals – Differences in maternal care directed towards individual pups

Opportunities of variability for new discoveries – No explicit opportunities for fundamental discoveries

– Identification of moderating or exacerbating effects of social factors (e.g. social hierarchies, social isolation) on effects induced by MIA – Identification of the influence of housing factors (e.g., enriched environment) on effects induced by MIA

– Identification of the role of the microbiome in MIA-induced pathologies

– Examination of the influence of maternal care on the effects of MIA – Examination of the influence of maternal stress on the effects of MIA (continued)

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Table 1 (continued) Source of variability Hygiene status

Examples – Existence of (undetermined) pathogenic or non-pathogenic viruses, bacteria, mycoplasma, ecto- and endoparasites, and helminthes in animal facility or colony

Circadian rhythm

– Induction of MIA during the dams’ lightor dark-phase – Testing during the animals’ light- or darkphase

Between-litter variation

– Use of limited or multiple numbers of litters

Possible reason for variability – Unpredictable or variable immune response resulting from (undetermined) pathogens in animal facility or colony – Sub-chronic or chronic immune exposures resulting from (undetermined) pathogens in animal facility or colony – Differing immune responses depending on induction of MIA during the dams’ light- or darkphase – Basal differences in the animals’ behavioral performance during light-phase versus dark-phase – Masking or amplifying effects of MIA resulting from differences in the animals’ behavioral performance during light-phase versus dark-phase – Litter-to-litter variation in maternal (e.g., maternal immunity or endocrinology), antenatal (e.g., in utero physiology) and postnatal (e.g., maternal physiology and behavior) factors – False-positive or false-negative findings resulting from litter-tolitter variation – Inflation of effects when using multiple offspring from a limited number of litters (multiparous species)

Opportunities of variability for new discoveries – No explicit opportunities for fundamental discoveries

– Identification of interactions between effects induced by MIA and physiological or molecular processes determining the circadian rhythm

– Identification of susceptible versus resilient mothers – Identification of factors conferring a differential susceptibility to MIA

(continued)

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Table 1 (continued) Source of variability Within-litter variation

Examples – Multiparous species such as mice or rats

Possible reason for variability – Variable fetal effects depending on uteroplacental positioning – Within-litter individualization resulting from social hierarchies – Stochastic epigenetic variability and subsequent variation in gene transcription during brain development and maturation

Opportunities of variability for new discoveries – Stratification of offspring according to phenotypic resilience or susceptibility, or according to clusters of phenotypes – Identification of factors contributing to within-litter individualization (e.g., exposure to variable social factors such as social hierarchies) – Identification of epigenetic processes shaping the effects of MIA

The table summarizes known sources of variability and outlines the possible reasons for each source. This table also highlights the opportunities offered by the various sources of variability. Modified from Weber-Stadlbauer and Meyer (2019)

et al. 2005; Shi et al. 2003), rats (Crum et al. 2017; Zuckerman et al. 2003), spiny mice (Ratnayake et al. 2012), pigs (Antonson et al. 2017), ferrets (Li et al. 2018), and non-human primates such as rhesus monkey (Bauman et al. 2019; Careaga et al. 2017; Machado et al. 2015); (2) the identification of sub- and supra-threshold effects of MIA through manipulations of immune stimulus intensity and chronicity (Meyer et al. 2005; Mueller et al. 2018; Murray et al. 2019); (3) the examination of possible interactions between MIA and specific genetic risk factors (Ayhan et al. 2016; Lipina et al. 2013) or additional environmental adversities such as psychological trauma (Giovanoli et al. 2013, 2016); (4) the comparison of the effectiveness of different immunogens to alter brain and behavioral development (Arsenault et al. 2014; Glass et al. 2019; Harvey and Boksa 2012; Meyer 2014; Missig et al. 2020; Shi et al. 2003); and (5) the investigation of critical prenatal periods through manipulations of the maternal immune system at distinct gestational stages (Nakamura et al. 2022; Meehan et al. 2017; Meyer et al. 2006, 2008; Richetto et al. 2017). Substantial variability can also arise from unplanned or unintentional factors (Table 1). This type of model variability is often recognized only once the experimental readouts of primary interest have been collected and compared with other existing data. Because the precise sources of unplanned model variability are less obvious and controllable, their identification requires systematic investigation. However, with the increasing use of MIA models in preclinical research, along with the resulting challenges in comparing findings across independent laboratories (Kentner et al. 2019), a number of factors contributing to unplanned model variability have

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recently been identified. These include variability in the immunogenicity of different batches and/or vendor-specific variations of immunogens (Careaga et al. 2018; Kowash et al. 2019; Mueller et al. 2019), inherent baseline differences of individual animals to immune-stimulating agents (Estes et al. 2020), differential susceptibility of isogenic animals obtained from different breeding facilities (Kim et al. 2017), influences of the precise type of caging system (Mueller et al. 2018), and presence of marked within-litter variation in MIA models using multiparous species such as rats and mice (Haddad et al. 2020; Mueller et al. 2021). These sources of unplanned model variability were initially not predicted by a-priori assumptions, but were recognized as such through systematic experimental investigations. Now that they have been shown to influence the outcomes of MIA in laboratory animals, these additional sources of variability can be taken into account when designing and implementing MIA models. Other intrinsic factors such as age (Piontkewitz et al. 2011; Richetto et al. 2014; Vuillermot et al. 2010) and sex (Bitanihirwe et al. 2010; Missig et al. 2020) of the offspring may also constitute unplanned sources of model variability, which require additional consideration in the experimental design of MIA models. Because of the notable popularity and utility of MIA models, it is expected that their continuous use will lead to the identification of various other sources of unplanned model variability. For example, the diurnal light cycle during which animals are tested has a known impact on behavioral functions per se (Richetto et al. 2019), and therefore, testing during the light versus dark phase may influence the expected outcomes of MIA in laboratory animals. Another likely but yet unexplored source of variability relates to differences in the hygiene status of the animal facility, in which MIA models are implemented. Since the primary experimental manipulation in MIA models is an intervention targeting the immune system, lab-to-lab variations in the hygiene status of breeding and/or testing facilities could be another relevant source of unplanned model variability.

3 Methodological Control Over Model Variability Like for any other experimental model system, a meaningful translation of MIA models requires comparability and reproducibility (Brown and Meyer 2018; Kentner et al. 2019). The consideration and incorporation of known sources of model variability (Table 1) into experimental designs is a pivotal first step to minimize the inconsistency and unpredictability of MIA models. Some methodological variations causing heterogeneity in the outcomes of MIA are easily recognizable (e.g., animal species or type of immunogen) and can be readily taken into account during the design of the model. Other possible sources of variability, however, may be less obvious and are more difficult to control. To illustrate the latter, the effectiveness of a given immunogen to induce brain and behavioral abnormalities may follow differential dose-response curves depending on the animal facility and its prevailing hygiene status (Mueller et al. 2018) and may further be affected by variations in

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the immunogenicity of distinct immunogen batches or vendor-specific variations of the agent used to induce MIA (Careaga et al. 2018; Kowash et al. 2019; Mueller et al. 2019), or by individual baseline differences in the reactivity to the immune-activating agent (Estes et al. 2020). It is, therefore, recommendable that researchers working with MIA models establish their own dose-response curves to identify sub- and supra-threshold effects in their primary readouts of interest, rather than simply relying on doses that were found to be effective in other research laboratories. Ideally, such initial dose-response studies would involve the concomitant assessment of the immunogen’s potency and efficacy, be it through the quantification of certain immune factors such as cytokines (Mueller et al. 2018), or through the analysis of behavioral responses such as maternal sickness behavior (Kolmogorova et al. 2017). In view of the possibility that different batches and/or variations of specific immunogens can vary in terms of their immunogenicity (Careaga et al. 2018; Kowash et al. 2019; Mueller et al. 2019), it is also advisable to ascertain the molecular constitution and potency of the chosen immunogen for each newly acquired batch of the immunogen before starting the actual experimental study. The existence of between-litter and within-litter variation poses another challenge for MIA models, especially when implemented in multiparous species such as rats and mice. While both types of variation have been reported in the MIA literature (Estes et al. 2020; Haddad et al. 2020; Mueller et al. 2021), there is currently no consensus as how to best address this challenge. Because between-litter variation can result from baseline differences of individual animals to immune-stimulating agents (Estes et al. 2020) and differences in litter size (Salari et al. 2018), this type of variation can be curtailed through assessing the animals’ baseline immunoreactivity before pregnancy and matching the size of animal litters, respectively. These measures do not, however, fully account for the noticeable within-litter variation that exists in MIA models using multiparous species (Haddad et al. 2020; Mueller et al. 2021; Vasistha et al. 2020). In fact, a recent large-scale, whole-litter phenotyping approach conducted using a viral-like MIA model in mice, which included over 150 offspring of multiple MIA-exposed and control dams, revealed that the presence or absence of overt behavioral dysfunctions in MIA-exposed offspring was largely driven by within-litter rather than between-litter variability (Mueller et al. 2021). The stronger contribution of within-litter variability observed by Mueller et al. (2021) is consistent with the findings from another study using the same MIA model in mice, which revealed larger within-litter than between-litter variability in the context of MIA and disruption of cortical interneuron development (Vasistha et al. 2020). However, the factors determining within-litter variability in MIA models remain largely unknown and warrant further investigations. As reviewed elsewhere (WeberStadlbauer and Meyer 2019), there are several plausible candidates in this regard, including uteroplacental positioning causing varying immune responses and hormonal exposures during fetal brain development (Rosenfeld 2015; Wiebold and Becker 1987), stochastic epigenetic variability and subsequent variation in gene transcription during brain development and maturation (Richetto and Meyer 2021),

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and de novo rearrangements in the chromosomal DNA caused by retrotransposable elements (Richardson et al. 2014). Whatever precise mechanisms involved, the existence of marked within-litter variability in MIA models using multiparous species has important implications for experimental study designs. For example, picking only one or two pups out of a given litter can readily lead to a high probability of biased sampling, which in turn may lead to spurious findings that are unrepresentative of the full litter’s data. While whole-litter testing would be the most rigorous way to avoid such confounds, the available findings suggest that including approximately half of a given litter can already minimize the probability of biasing the outcomes to a great extent (Haddad et al. 2020; Mueller et al. 2021). Therefore, it is recommended that at least half of an average litter should be tested for an accurate representation of the effects of MIA in multiparous species. Additionally, if a specific readout of interest test is known to produce skewed data in littermates, more animals per litter should be tested to minimize sampling bias (Haddad et al. 2020). Attention should also be directed toward the possible influence introduced by caging effects. Besides the recently discovered impact of different caging systems per se (Mueller et al. 2018), animals that are co-housed in a particular cage usually develop a social relationship whereby one (or a sub-group) predominates over the others (Howard 2002). Such social hierarchies are relevant for (but not exclusive to) male mice and are influenced by intra-cage factors such as cage enrichment (Körholz et al. 2018; McQuaid et al. 2018). Importantly, cage-associated factors can shape individualization, thereby amplifying within-litter variability (Howard 2002; Körholz et al. 2018). While dominant animals explore and commandeer relevant cage microhabitats, subordinates often fail to secure a base for themselves and spend much of their time withdrawing from contact with dominant animals (Howard 2002; Körholz et al. 2018, McQuaid et al. 2018). These hierarchy-related cage factors may change the levels of perceived stress in subordinate animals, which in turn may be a central driving force for within-litter individualization. Whether, or to what extent, within-litter individualization resulting from intra-cage factors contributes to variability in MIA models remains unknown. This question clearly warrants examination, especially when considering previous findings showing that MIA can interact with stress during peripubertal maturation to shape the nature and/or severity of adult behavioral and neurochemical abnormalities (Giovanoli et al. 2013, 2016). Taken together, there are several strategic control measures that can be implemented to handle the known sources of variability in MIA models. Because some sources of variability may remain indefinable and difficult to approach, however, it is pivotal for the field to report the methodological details of the chosen MIA model (Kentner et al. 2019). Adhering to recently established reporting guidelines (Kentner et al. 2019) will readily improve transparency and comparability of MIA models across laboratories. In addition, well-organized community efforts, coupled with improved data and metadata sharing, will facilitate the identification of novel sources of unplanned variability in MIA models and will have a key role in promoting satisfactory ways to approach them.

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Despite the multiple types and sources of variability (Table 1), however, a number of phenotypes in MIA models are highly reproducible within and between different research laboratories, and even across different species (reviewed in: Careaga et al. 2017; Kentner et al. 2019; Meyer 2014; Weber-Stadlbauer and Meyer 2019). While reproducibility against the background of methodological variability in MIA models may appear paradoxical, systematic variation in experimental conditions (i.e., heterogenization) can actually maximize rather than minimize reproducibility (Richter 2017; Richter et al. 2010; Voelkl et al. 2020). Indeed, methodological standardization can generate spurious results by increasing test sensitivity at the expense of external validity (Richter 2017; Richter et al. 2010; Voelkl et al. 2020). It will be important to determine whether such methodological heterogenization has contributed to the reproducibility and robustness of some phenotypes in MIA models.

4 Benefits of Model Variability for Translational Research While model variability can be challenging with regard to comparing and reproducing experimental findings across research laboratories, it also offers unique opportunities for translational research. First of all, the multitude of immunogens that are currently used in MIA models (Table 1) allows researchers to examine possible convergence between the pathological effects induced by different immuneactivating agents and their down-stream signaling pathways. Making use of this type of planned model variability has already led to the identification of both common and dissimilar effects of MIA when induced by distinct immunogens (Arsenault et al. 2014; Brown and Meyer 2018; Glass et al. 2019; Harvey and Boksa 2012; Meyer 2014; Missig et al. 2020; Shi et al. 2003). However, the majority of current MIA models are based on maternal exposure to nonvirulent, immune-activating agents, such as the viral mimetic, poly(I:C), or the bacterial endotoxin, lipopolysaccharide (LPS) (Brown and Meyer 2018; Meyer 2014; Weber-Stadlbauer and Meyer 2019). Although this experimental approach offers some clear advantages (Meyer et al. 2009), including minimal biosafety requirements and stringent control over the intensity and duration of the (innate) immune response, it does not reproduce the full spectrum of immune responses normally induced by infectious pathogens. Although infection-triggered innate immune responses seem to be a crucial contributing factor to many associations involving MIA (Choi et al. 2016; Shi et al. 2003; Smith et al. 2007), it is unlikely that distinct pathogens mediate the adverse effects of maternal infection on offspring through the same immune responses and pathophysiological mechanisms (Meyer 2019). To fully appreciate and approach this complexity, the field would benefit from an extension of experimental approaches that make use of prenatal exposure to distinct virulent and nonvirulent agents (Brown and Meyer 2018). Indeed, because different immunogens can induce a distinct set of neuroimmune abnormalities across brain development, expanding the repertoire of infectious and non-infectious stimuli in MIA models will help explaining why not all

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infectious pathogens have the same potential to increase neuropsychiatric disease risk (Brown and Derkits 2010; Buka et al. 2001). Moreover, a closer examination of the commonalities and differences between the mediating factors and outcomes of distinct MIA models will allow researchers to identify the neuroimmunological pathways that are associated with differing long-term deficits in brain structure and function. The use of cross-species approaches in MIA models helps minimizing the risk for over-interpreting or over-simplifying the findings obtained in a certain animal species or strain (Careaga et al. 2017). Although the majority of MIA models have been developed in rodent species, mostly in rats and mice (Weber-Stadlbauer and Meyer 2019), some have been extended to species that are evolutionarily and ethologically closer to humans, including rhesus monkeys (Bauman et al. 2019; Careaga et al. 2017; Machado et al. 2015). The latter species exhibit greater similarity to humans in terms of genetics, immunology, neurobiology, and behavior, and compared with rodents, it is also more comparable to humans in terms of placental physiology, gestational timelines, pre- and postnatal brain development, and cortical architecture (Careaga et al. 2017). Thus, the inclusion of species that are more similar to humans, such as the rhesus monkey, can aid in interpreting the outputs of rodent MIA models in terms of what they might mean for pathological symptoms in humans, thereby enhancing the cross-species transfer of information and translatability to the clinical condition in humans. Phenotypic variability within a particular type of MIA model also offers ample opportunities for translational research. As discussed above, such variability can stem from inherent or experimentally induced litter-to-litter variation and, in the case of multiparous species, from within-litter individualization. Even within welldesigned and well-controlled MIA models, such variability can be quite large and may minimize or even nullify simple group differences, especially when the experimental groups are considered as being homogenous entities (Estes et al. 2020; Haddad et al. 2020; Mueller et al. 2021; Vasistha et al. 2020). When extending the classical approach of simple group comparisons to more refined analyses, however, the existence of between- and within-litter variability can offer unique opportunities for new discoveries. For example, between-litter variation can be used to examine why some pregnancies are more susceptible to MIA than others (Estes et al. 2020) and to assess the predictive potential of specific immunological markers in individual pregnancies in determining the degree of brain and behavioral deficits in the offspring (Lins et al. 2018). Likewise, within-litter variability can be used to identify factors that contribute to susceptible and resilient phenotypes after exposure to MIA. To illustrate this opportunity, unsupervised cluster analysis of experimental data collected in a large cohort of >150 MIA and control offspring has revealed the existence of subgroups with dissociable behavioral, transcriptional, neuroanatomical, and immunological profiles even under conditions of genetic homogeneity (Mueller et al. 2021). These findings suggest that phenotypic variability in MIA models can be used to advance our understanding of the variable neurodevelopmental effects induced by MIA in human populations and to identify new molecular targets for improved, biomarker-guided interventions.

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Taking maximal advantage of variability in MIA models requires researchers to open up to new (or modified) methodological approaches and ways of data analysis. For example, identifying susceptible and resilient mothers and/or offspring, and unraveling the mechanisms underlying this dissociation, necessitates the use of relatively large numbers of litters and whole-litter testing approaches. Furthermore, the identification of pathological entities that go beyond predefined treatment groups requires advanced statistical methods, such as unsupervised clustering approaches, principal component analyses, and perhaps even machine learning. The latter offers a powerful and unbiased approach in many areas of behavioral sciences (Valletta et al. 2017), which often entail diverse, complex, and high-dimensional data sets exhibiting nonlinear dependencies and unknown interactions across multiple variables. Like in other research domains (Valletta et al. 2017), the datasets obtained in MIA models may fail to conform to the assumptions of many classical statistical methods, and therefore, machine learning might offer an additional approach to extract new knowledge from these data.

5 Enhancing Bidirectional Translational Validity Despite the continued progress in modeling MIA-related risk factors in animals, there remain challenges regarding how to best approach the bidirectional translation between the findings derived from human epidemiology and basic science using animal models (Brown and Meyer 2018). As graphically summarized in Fig. 1, the translatability between epidemiology and basic science can readily be enhanced by deconstructing neuropsychiatric outcomes of MIA into pathophysiologically defined phenotypes that are identifiable in humans and animals and that evaluate the interspecies concordance regarding interactions between MIA and other diseaserelevant factors. With a few exceptions, epidemiological studies of MIA have generally aimed at establishing associations between infectious, inflammatory, or other immune exposures and risk of certain psychiatric disorders, the latter of which are defined by the current nosologic system. On the other hand, most animal models of MIA are singlefactor models, in which the isolated effects of MIA-related exposures are investigated with respect to behavioral, cognitive, neuroimaging, and neurophysiologic phenotypes in the offspring. For practical reasons, these models are often implemented in rodent species and are based on artificial immune-activating agents (e.g., synthetic double-stranded RNA) that do not require stringent biosafety precautions. The outcome of these epidemiological and basic science approaches is often a lack of analogy, resulting in a translational gap that can undermine their translational validity (Fig. 1a). To overcome these limitations and to maximize the bidirectional translational validity of MIA research, human epidemiology and basic science would require modifications of research concepts and the addition of supplementary research modules (Fig. 1b). Specifically, the objective of assessing the effects of MIA in

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Fig. 1 Contribution of epidemiological and basic science studies to translational research of maternal immune activation (MIA) in neurodevelopmental and psychiatric disorders. (a) Schematic illustration of the prevailing research approaches in epidemiology and basic science. Most epidemiological studies of MIA aim to establish associations between infectious or inflammatory exposures and risk of specific disorders, the latter of which are defined by the current nosologic system. On the other hand, most animal models of MIA are single-factor models, in which the isolated effects of MIA-related exposures are investigated with respect to behavioral, cognitive, neuroimaging, and neurophysiologic phenotypes in the offspring. For practical reasons, these models are often implemented in rodent species and are based on artificial immune-activating agents (e.g., synthetic double-stranded RNA) that do not require stringent biosafety precautions. The outcome of these epidemiological and basic science approaches is often a lack of analogy, resulting in a translational gap that can undermine their translational validity. (b) Schematic illustration of epidemiological and basic science approaches that can maximize the bidirectional translational validity through the modification of research concepts and the addition of supplementary research modules. In these alternative approaches, the objective of assessing the effects of MIA-related exposures in epidemiological studies is complemented or even replaced by (1) attempts to explore their effects on specific behavioral, cognitive, neuroimaging, and neurophysiologic phenotypes, which are free of nosologic constraints; (2) the concomitant study of genetic and epigenetic factors; and (3) the establishment of multifactorial animal models that incorporate genetic or epigenetic risk factors and MIA-related exposures that involve epidemiologically established infectious pathogens and other immune factors, such as inflammatory mediators. In addition, cross-species comparisons involving animal species with advanced cortical development are used to further enhance the bidirectional translatability between epidemiologic and basic science studies. Modified from Brown and Meyer (2018)

human epidemiology could be complemented or even replaced by attempts to explore its consequences on specific behavioral, cognitive, neuroimaging, and neurophysiologic phenotypes, which are free of nosologic constraints, along with the concomitant study of genetic and epigenetic factors. At the same time, the bidirectional translational validity of MIA research would be enhanced by the use of multifactorial animal models that incorporate genetic or epigenetic risk factors and MIA-related exposures that involve epidemiologically established infectious

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pathogens and other immune factors, such as inflammatory mediators. In addition, cross-species comparisons involving animal species with advanced cortical development will further enhance the bidirectional translatability between epidemiologic and basic science studies. Prenatal exposure to infectious or inflammatory adversity may be viewed as a general vulnerability factor for developmental disturbances, rather than a diseasespecific risk factor. Indeed, this exposure alters neurobiological and behavioral functions that cannot be simply mapped onto a particular diagnostic phenotype (Brown and Meyer 2018). This view is compatible with the Research Domain Criteria system, which capitalizes on biological determinism to explain the pathogenesis of distinct psychiatric symptoms and focuses on endophenotypes rather than nosologic entities (Insel et al. 2010). Shifting the research focus to classification of neurobiological outcomes rather than nosologic entities likely minimizes strict disease-to-model correspondence, which is still a major challenge for translational studies in the context of MIA and beyond (Brown and Meyer 2018). The findings from animal models of MIA could facilitate the selection of biobehavioral outcomes to be investigated in corresponding human epidemiological studies and vice versa. Hence, there is a continuous need to build shared discovery platforms that encourage greater cross-fertilization between human epidemiology and basic research using animal models of MIA.

6 Concluding Remarks The epidemiological literature reporting increased risk for neurodevelopmental and psychiatric disorders after prenatal exposure to infectious or non-infectious MIA is still evolving, and so are the attempts to model this association in laboratory animals. Epidemiological studies of MIA offer the advantage of directly evaluating human populations but are limited in their ability to uncover pathogenic mechanisms. Animal models, on the other hand, are limited in their generalizability to psychiatric disorders but have made significant strides toward discovering causal relationships and biological pathways between MIA and neurobiological phenotypes. With the proposed modifications of research concepts and the addition of supplementary research modules (Fig. 1), the continual integration of epidemiological and basic science studies is expected to advance our understanding of the developmental, cellular, and molecular mechanisms involved in the precipitation of increased risk for chronic brain disorders beyond nosologic boundaries and to help to establish interventions that can attenuate or even prevent the emergence of MIA-induced pathologies. For those of us who work with animal models of MIA, it is important to appreciate that the design, implementation, and interpretation of these models require a careful consideration of multiple types of variability (Table 1), so that we can develop appropriate strategies to handle them. While every research group may have its own strategy to this aim, it is essential to report the methodological details of

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the chosen MIA model in order to enhance transparency and comparability of models across laboratories. Even though model variability poses a challenge for attempts to compare experimental findings across research laboratories, it does not at all undermine the utility of MIA models for translational research. In fact, variability and heterogenous outcomes in MIA models offer ample opportunities for new discoveries and developments in this field, including the identification of disease pathways and molecular mechanisms determining susceptibility and resilience in relation to immune-mediated neurodevelopmental disorders and mental illnesses. Acknowledgments UM receives financial support from the Swiss National Science Foundation (Grant No. 310030_188524 and 407940_206399) and the University of Zurich.

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Evolutionary Aspects of Diverse Microbial Exposures and Mental Health: Focus on “Old Friends” and Stress Resilience Lamya’a M. Dawud, Evan M. Holbrook, and Christopher A. Lowry

Contents 1 2 3 4

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Global Incidence and Prevalence of Common Mental Health Disorders . . . . . . . . . . . . . . . . . 95 A Need for more Effective Therapies with a More Rapid Onset of Action . . . . . . . . . . . . . . 96 A Need for Approaches to Increasing Stress Resilience: Strategies for Prevention of Common Mental Health Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5 Inflammation as a Risk Factor for Common Mental Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.1 Inflammation as a Risk Factor for Anxiety Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.2 Inflammation as a Risk Factor for Mood Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.3 Inflammation as a Risk Factor for Trauma and Stressor-Related Disorders . . . . 100 6 The Increasing Incidence and Prevalence of Inflammatory Disease in Modern Urban Societies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 7 Urban vs. Rural Upbringing and Mental Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

L. M. Dawud and E. M. Holbrook Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA C. A. Lowry (*) Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Rocky Mountain Mental Illness Research Education and Clinical Center (MIRECC), Rocky Mountain Regional VA Medical Center (RMRVAMC), Aurora, CO, USA Department of Physical Medicine and Rehabilitation, University of Colorado Anschutz Medical Campus, Aurora, CO, USA Military and Veteran Microbiome: Consortium for Research and Education (MVM-CoRE), Aurora, CO, USA Center for Neuroscience, University of Colorado Boulder, Boulder, CO, USA Center for Microbial Exploration, University of Colorado Boulder, Boulder, CO, USA inVIVO Planetary Health, Worldwide Universities Network (WUN), West New York, NJ, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 93–118 https://doi.org/10.1007/7854_2022_385 Published Online: 11 August 2022

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8 Hypothetical Frameworks Highlighting the Importance of Exposures to Diverse Microbial Environments to Mental Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 The Hygiene Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 The “Farm Effect” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 The Biodiversity Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 The Disappearing Microbiota Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 The “Old Friends” Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract The prevalence of inflammatory disease conditions, including allergies, asthma, and autoimmune disorders, increased during the latter half of the twentieth century, as societies transitioned from rural to urban lifestyles. A number of hypotheses have been put forward to explain the increasing prevalence of inflammatory disease in modern urban societies, including the hygiene hypothesis and the “Old Friends” hypothesis. In 2008, Rook and Lowry proposed, based on the evidence that increased inflammation was a risk factor for stress-related psychiatric disorders, that the hygiene hypothesis or “Old Friends” hypothesis may be relevant to psychiatric disorders. Since then, it has become more clear that chronic low-grade inflammation is a risk factor for stress-related psychiatric disorders, including anxiety disorders, mood disorders, and trauma- and stressor-related disorders, such as posttraumatic stress disorder (PTSD). Evidence now indicates that persons raised in modern urban environments without daily contact with pets, relative to persons raised in rural environments in proximity to farm animals, respond with greater systemic inflammation to psychosocial stress. Here we consider the possibility that increased inflammation in persons living in modern urban environments is due to a failure of immunoregulation, i.e., a balanced expression of regulatory and effector T cells, which is known to be dependent on microbial signals. We highlight evidence that microbial signals that can drive immunoregulation arise from phylogenetically diverse taxa but are strain specific. Finally, we highlight Mycobacterium vaccae NCTC 11659, a soil-derived bacterium with anti-inflammatory and immunoregulatory properties, as a case study of how single strains of bacteria might be used in a psychoneuroimmunologic approach for prevention and treatment of stress-related psychiatric disorders. Keywords Anxiety · Darwinian medicine · Depression · Gut-brain axis · Hygiene hypothesis · Microbiome · Microbiota · Microbiota-gut-brain axis · Old friends · Posttraumatic stress disorder

1 Introduction In this chapter, we focus on the potential role of exposures to diverse microbial environments in promotion of stress resilience, particularly in the context of prevention and treatment of stress-related psychiatric disorders, including anxiety

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disorders, mood disorders, and trauma- and stressor-related disorders such as posttraumatic stress disorder (PTSD) (American Psychiatric Association 2013). One approach to prevention of stress-related mental health disorders is to identify modifiable risk factors (Insel and Scolnick 2006). In this review, we outline evidence supporting the hypothesis that: (1) inappropriate inflammation is a risk factor for the development and persistence of symptoms of stress-related psychiatric disorders; and (2) exposures to diverse microbial environments, including non-pathogenic bacteria found in nature, can induce anti-inflammatory and immunoregulatory responses and thus regulate the inflammatory response to day-to-day stressors and traumatic events, in turn promoting resilience to stress. A failure of immunoregulation, which is defined as a balanced expression of regulatory T cells (Treg) and effector T cells, may be involved in contributing to an overreactive inflammatory stress response thus predisposing individuals to the development of stress-related psychiatric disorders (Langgartner et al. 2018), particularly in urban environments with reduced exposures to diverse microbial environments (Böbel et al. 2018). Promoting stress resilience by increasing contact with microbial “Old Friends,” i.e., microorganisms with anti-inflammatory and immunoregulatory properties, may provide an alternative strategy for prevention and treatment of stress-related psychiatric disorders.

2 Global Incidence and Prevalence of Common Mental Health Disorders Common mental health disorders include anxiety disorders, depressive disorders, and trauma- and stressor-related disorders, such as PTSD that are classified in ICD-10 as: “neurotic, stress-related and somatoform disorders” and “mood disorders” (Patel and Kleinman 2003; World Health Organization 1992; National Collaborating Centre for Mental Health (UK) 2011). Anxiety and mood disorders are the most prevalent forms of common mental health disorders and substantially contribute to the global burden of disease (Whiteford et al. 2013). The World Health Organization surveyed mental health conditions in 2015 and found an estimated 3.6% of the global population was suffering from anxiety disorders and 4.4% of the global population was suffering from depression, with women being more likely to be affected by these disorders than men (World Health Organization 2017). Depression is ranked as the single largest contributor to global disability while anxiety disorders are ranked as the sixth largest contributor to global disability (World Health Organization 2017). Subsequent to the onset of the COVID-19 pandemic, the global prevalence and burden of anxiety and mood disorders has increased further, particularly among young persons, and again with a stronger impact on females (Santomauro et al. 2021). While previously classified as an anxiety disorder, and thus included among the common mental health disorders, PTSD is now classified in the Diagnostic and

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Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) as a trauma- and stressor-related disorder (American Psychiatric Association 2013). PTSD is particularly prevalent among military Veterans. Since October of 2001, approximately 2.7 million U.S. troops have been deployed in recent conflicts (Wenger et al. 2018). Findings suggest that approximately 20% of returning service members meet criteria for PTSD or associated mental health conditions (Tanielian et al. 2008). In the USA, many Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) Veterans are resistant to engaging in conventional mental health treatments (Kim et al. 2010; Kim et al. 2010), highlighting the importance of exploring alternative interventions (Williams et al. 2011). Moreover, a substantial proportion of individuals are not significantly helped by traditional treatments. Non-response rates in outcome studies for PTSD are often as high as 50% (Schottenbauer et al. 2008). Similar non-response rates have been described in non-military populations (Stein et al. 2006, 2009).

3 A Need for more Effective Therapies with a More Rapid Onset of Action First-line psychotherapies for generalized anxiety disorder (GAD), as one example of an anxiety disorder, include cognitive behavioral therapy (CBT), cognitive therapy (CT), and applied relaxation, while first-line pharmacotherapies for GAD include selective serotonin reuptake inhibitors (SSRIs) and serotonin and noradrenaline reuptake inhibitors (SNRIs) (Anxiety and Depression Association of America 2015). Meanwhile, first-line psychotherapies for major depressive disorder include psychotherapy, including CBT, interpersonal psychotherapy (IPT), and problemsolving therapy (PST), while first-line pharmacotherapies for major depressive disorder include SSRIs, SNRIs, bupropion, mirtazapine, and a number of newer agents (Anxiety and Depression Association of America 2020). However, several limitations affect the efficacy and feasibility of these treatments. These limitations include, but are not limited to, delayed onset of action, adverse effects that impair quality of life, relapse risk upon withdrawal, and non-adherence (Andrews et al. 2011; Freedman 2010; Li et al. 2012; Lin et al. 1995; Mathew et al. 2012; Papakostas and Fava 2009; Rush et al. 2006b). First-line therapies for the treatment of PTSD are evidence-based cognitive behavioral psychotherapies, for example, CBT and eye movement desensitization and reprocessing (EMDR) (Resick et al. 2017; Department of Veterans Affairs DoD 2017; American Psychological Association 2017; Watkins et al. 2018). These interventions are effective in reducing PTSD symptoms; however, not all persons respond with complete recovery (Steenkamp et al. 2020). For example, approximately two-thirds of US Veterans who complete these treatments continue to meet diagnostic criteria for PTSD (Steenkamp et al. 2020; Stein et al. 2006, 2009) and there are high dropout rates from first-line psychotherapies for PTSD (Kehle-Forbes et al. 2016; Steenkamp et al. 2020).

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First-line pharmacotherapies for PTSD include SSRIs (Martin et al. 2021). Unfortunately, pharmacotherapies also have important shortcomings. Indeed, only about half of patients respond to SSRIs and more than a third of SSRI-treated patients fail to respond, do not reach full remission, or even develop SSRI resistance (Bernardy and Friedman 2015; Golden et al. 2002; Rush et al. 2006a; Kemp et al. 2008). Furthermore, most patients who initially respond to SSRI treatment fail to maintain therapeutic gains over time. In particular, Veterans affected by PTSD are generally resistant to SSRI therapy (Schnurr et al. 2007; Prigerson et al. 2001; Friedman et al. 2007).

4 A Need for Approaches to Increasing Stress Resilience: Strategies for Prevention of Common Mental Health Disorders While it is important to pursue novel, fast acting interventions, including pharmacological interventions, for treatment of stress-related psychiatric disorders, there is also a need for novel approaches to prevention of these common mental health disorders (Insel and Scolnick 2006). When considering approaches to prevention of stress-related psychiatric disorders, one could argue that a reasonable strategy would be to target risk factors for these disorders, focusing on modifiable risk factors (Fig. 1). One factor that seems to increase risk of developing stress-related psychiatric disorders is chronic low-grade inflammation (Rohleder 2014). In the next section, we briefly consider the evidence that inappropriate or excessive inflammation is a risk factor for development of stress-related psychiatric disorders.

5 Inflammation as a Risk Factor for Common Mental Disorders Increasing evidence suggests that inflammation plays an important role in determining risk of development of stress-related psychiatric disorders, including anxiety disorders, mood disorders, and trauma- and stressor-related disorders such as PTSD. A connection between increases in cytokine activation, low-grade background inflammation, and stress-related psychiatric disorders has been observed in numerous studies implying a role for chronic low-grade inflammation in both the risk of development of stress-related psychiatric disorders and the persistence of symptoms (Capuron and Dantzer 2003; Dantzer et al. 1998, 1999; Miller and Raison 2016; Michopoulos et al. 2017; Flux and Lowry 2023; Rohleder 2014).

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Fig. 1 Risk factors for stress-related psychiatric disorders include: (1) genetic predisposition; and (2) environmental influences, including adverse childhood experiences (ACEs), and exposures to diverse microbial inputs. Microbial inputs can be either proinflammatory or anti-inflammatory/ immunoregulatory (i.e., resulting in a balanced expression of regulatory T cells (Treg) and effector T cells). A failure of immunoregulation can lead to chronic low-grade inflammation and increased risk of stress-related psychiatric disorders. Figure created with biorender.com

5.1

Inflammation as a Risk Factor for Anxiety Disorders

Anxiety disorders such as GAD, panic disorder (PD), and phobias (agoraphobia, social phobia, etc.) have been shown to be associated with chronic low-grade inflammation. For example, heightened proinflammatory markers, such as C-reactive protein (CRP), have been demonstrated in individuals diagnosed with anxiety disorders (Michopoulos et al. 2017; Vogelzangs et al. 2013; Copeland et al. 2012; Bankier et al. 2008). Other studies have found increased circulating proinflammatory cytokines, such as tumor necrosis factor (TNF), interleukin (IL) 1

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beta, and IL-6 among individuals diagnosed with GAD and PD (Vieira et al. 2010; Hoge et al. 2009; Michopoulos et al. 2017; Brambilla et al. 1994; Zou et al. 2020). In line with these findings, there have been reports of high incidences of anxiety disorders and increased levels of emotional reactivity in individuals with inflammatory disease, including asthma, allergies, and autoimmune disorders (Lowry et al. 2016; Stanhope et al. 2022; von Mutius and Vercelli 2010; von Mutius et al. 1994). The weight of evidence is consistent with the hypothesis that inappropriate inflammation plays a role in determining risk of anxiety disorders (Haroon et al. 2012; Michopoulos et al. 2017). Interestingly, lower levels of interferon gamma (IFNγ), a proinflammatory cytokine, have been found in both GAD and PD patients (Tukel et al. 2012; Vieira et al. 2010). Decreased IFNγ secretion from isolated peripheral blood mononuclear cells (PBMCs) from GAD patients could reflect decreased exposures to diverse microbial environments. For example, administration of the soil-derived bacterium, Mycobacterium vaccae NCTC 11659, or the type strain of M. vaccae, M. vaccae ATCC 15483, increases IFNγ and Th1 signaling in mice (Gong et al. 2020; Lahey et al. 2016; Smith et al. 2020; Zhang et al. 2016). Meanwhile, in humans, immunization with Mycobacterium vaccae NCTC 11659 increases IFNγ responses of PBMCs to subsequent antigen challenge up to a month following treatment (von Reyn et al. 2017). Interestingly, IFNγ expression, possibly arising from meningeal T cells then acting on both microglia and neurons in the brain, is necessary for social behavior (Filiano et al. 2016). Consistent with a potential dysregulation of immunoregulation in individuals with anxiety disorders, lower phytohemagglutinin (PHA)-stimulated secretion of anti-inflammatory cytokines, including IL-2, IL-4, and IL-10 from isolated PBMCs have been documented in GAD patients (Vieira et al. 2010). Consistent with these findings, Hou et al. 2017 reported decreased circulating concentrations of IL-10, as well as higher TNF/IL-10 and TNF/IL-4 ratios (Hou et al. 2017). Altogether, data are consistent with the hypothesis that persons with a diagnosis of GAD have dysregulation of immunoregulation.

5.2

Inflammation as a Risk Factor for Mood Disorders

Among the common mental health disorders, the strongest case can be made for a role for inappropriate inflammation as a risk factor for development of mood disorders. This has been reviewed extensively elsewhere (Capuron and Dantzer 2003; Dantzer et al. 1998, 1999; Miller and Raison 2016; Michopoulos et al. 2017; Flux and Lowry 2023) and thus will not be reviewed in detail here. Evidence supports impaired immunoregulation in persons with a diagnosis of mood disorders. Previous studies have demonstrated lower percentages of Treg in association with lower serum concentrations of the anti-inflammatory cytokines IL-10 and transforming growth factor beta 1 (TGFβ-1) in persons with major depressive disorder (Grosse et al. 2016; Li et al. 2010; Chen et al. 2011; Snijders

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et al. 2016) and increases in percentages of Treg following treatment (Grosse et al. 2016). Based on the weight of existing evidence, induction of Treg has been proposed as one novel intervention for treatment of major depressive disorder, at least in the subset of patients with inflammatory MDD (Ellul et al. 2018).

5.3

Inflammation as a Risk Factor for Trauma and Stressor-Related Disorders

As mentioned above, the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) recategorized PTSD into a new classification of “Traumaand Stressor-Related Disorders.” In this revision of the DSM, the main diagnostic criterion of this class requires a previous exposure to a traumatic or stressful event (American Psychiatric Association; 2013). The DSM-5 describes clusters of specific behavioral symptoms that accompany PTSD including “re-experiencing,” “avoidance,” “negative alterations in cognitions and mood,” and “hypervigilance” (Reber et al. 2016; American Psychiatric Association 2013). PTSD affects 10–30% of individuals who have experienced a traumatic event (VanElzakker et al. 2014) with the greatest likelihood of PTSD occurring due to traumas involving interpersonal violence such as forms of assault, rape, or abuse (Yehuda and LeDoux 2007). According to the National Center for PTSD, about 6% of the population will experience PTSD at some point in their lives (National Center for PTSD 2022). About 8% of women develop PTSD sometime in their lives compared with about 4% of men (National Center for PTSD 2022). Approximately 60% of men and 50% of women experience at least one traumatic event throughout their lifetime (National Center for PTSD 2022). This suggests that there is an underlying vulnerability to developing PTSD that affects a percentage of the population. Not everyone develops PTSD following a traumatic event; individual variability based on genetic, epigenetic, and environmental factors contributes to how susceptible an individual is to developing trauma- and stressor-related disorders (Reber et al. 2016; American Psychiatric Association 2013). While PTSD places a significant burden on the individual and society at large due to negative social and psychiatric consequences, it is also correlated with negative somatic consequences including autoimmune disorders, metabolic syndrome, pulmonary disease, and cardiovascular diseases that may be due to underlying mechanisms of low-grade systemic inflammation (Babson et al. 2015; Dennis et al. 2016; Lindqvist et al. 2014; Speer et al. 2018; Wolff et al. 2011). A transdiagnostic meta-analysis (i.e., a metaanalysis that quantitatively integrates the literature on the relationship of inflammatory biomarkers to trauma exposure and related symptomatology) found that trauma exposure was associated with increased circulating CRP and circulating proinflammatory cytokines, including IL-1β, IL-6, and TNF (Tursich et al. 2014), suggesting that trauma exposure may be causal for chronic low-grade inflammation.

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Conversely, studies indicate that increased biomarkers of inflammation, including increased circulating CRP concentrations, predict future risk of developing PTSD (Eraly et al. 2014; Schultebraucks et al. 2020). Finally, persons with a diagnosis of PTSD have a higher risk of developing an autoimmune disorder. Specifically, Veterans diagnosed with PTSD have a significantly higher risk for diagnosis with any of the autoimmune disorders alone or in combination (i.e., thyroiditis, inflammatory bowel disease, rheumatoid arthritis, multiple sclerosis, and lupus) considered individually compared with Veterans with no psychiatric disorders (O’Donovan et al. 2014). This is suggestive of a broad failure of immunoregulation and an inability to suppress inappropriate inflammation in Veterans with a diagnosis of PTSD. Consistent with these findings, those with a PTSD diagnosis have a decreased proportion of peripheral Treg cells (Sommershof et al. 2009), and Treg abundance can be increased following successful treatment using narrative exposure therapy (NET) (Morath et al. 2014). Recent studies have demonstrated that persons with a diagnosis of PTSD, relative to healthy controls, have elevated circulating concentrations of lipopolysaccharide (LPS) and lipopolysaccharide-binding protein (LBP) (Voigt et al. 2022). LPS is a component of the outer membrane of gram-negative bacteria, while LBP is induced by prolonged elevation of LPS and is considered a biological marker of “leaky gut,” a condition where bacteria and other microorganisms within the lumen of the gut can translocate across the gut mucosa into the body and systemic circulation. Together, these data are consistent with the hypothesis that PTSD is associated with a dysregulated microbiome-gut-brain axis (Hemmings et al. 2017; Loupy and Lowry 2019; Malan-Muller et al. 2018, 2022) and that comprehensive therapy would benefit from stabilizing the dysregulated microbiome and gut mucosal barrier.

6 The Increasing Incidence and Prevalence of Inflammatory Disease in Modern Urban Societies In 2002, Jean-François Bach published an article in the New England Journal of Medicine reporting an alarming increase in the incidence of immune disorders in the 50-year period from 1950–2000 (Bach 2002). Included were increases in autoimmune disorders, including Crohn’s disease (a form of inflammatory bowel disease (IBD), multiple sclerosis, and type 1 diabetes, as well as asthma, citing data from a number of contemporary original research articles (Pugliatti et al. 2001; Tuomilehto et al. 1999; Dubois et al. 1998; Farrokhyar et al. 2001). These historical trends are consistent with a gradient of the incidence of asthma and atopy based on rural vs. urban living, with, for example, the Amish (who maintain traditional farming practices, including use of large animals for farm work) having the lowest prevalence, Swiss farmers (who have adopted modern farming practices, including the use of tractors instead of animals for farm work) having intermediate levels of prevalence, and Swiss non-farmers having the highest prevalence (Holbreich et al.

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2012). These differences have persisted in subsequent studies and mechanistic studies point toward an important role of innate immune signaling, leading the authors to conclude “These findings suggest that in the Amish, intense and presumably sustained exposure to microbes activates innate pathways that shape and calibrate downstream immune responses” (Stein et al. 2016).

7 Urban vs. Rural Upbringing and Mental Health Although there are many potential confounds, meta-analysis suggests that individuals living in urban areas have an increased risk of developing any psychiatric disorder, and specifically an anxiety disorder or a mood disorder, relative to individuals living in rural areas (Peen et al. 2010; Stamper et al. 2016). This relationship led Böbel et al. (2018) to conduct a study to determine if differences were evident in inflammatory immune responses to a psychosocial stress exposure (the Trier Social Stress Test (TSST)) in healthy young persons that were either: (1) raised on a farm with farm animals for the first 15 years of life; or (2) raised in a city in the absence of daily contact with animals. The two groups did not differ in early life or perceived life stress. However, even though rural participants reported higher levels of anxiety before and after the TSST, urban participants responded with greater stress-induced increases in circulating PBMCs and prolonged increases in circulating IL-6, a proinflammatory cytokine. These data are consistent with the hypothesis that not only does the transition to an urban lifestyle involve increased risk of immune disorders, such as allergic asthma, but that it also involves increased risk of stressrelated psychiatric disorders in which psychosocial stress and exaggerated inflammation are thought to be important risk factors (Rohleder 2014).

8 Hypothetical Frameworks Highlighting the Importance of Exposures to Diverse Microbial Environments to Mental Health A number of hypotheses have been put forward to explain the increasing incidence and prevalence of inflammatory disease in modern urban societies. These include the hygiene hypothesis (Strachan 1989, 2000), the “Farm Effect” (von Mutius 2022), the biodiversity hypothesis (von Hertzen et al. 2011; Haahtela 2022), the disappearing microbiota hypothesis (Blaser and Falkow 2009; Blaser 2015), and the “Old Friends” hypothesis (Rook et al. 2004). What all of these hypotheses share, however, is that they propose, in one manner or another, that reduced exposures to diverse microbial ecosystems are responsible for increases in noncommunicable diseases in modern urban environments. Here we will briefly describe each of these hypotheses in turn, then focus on the “Old Friends” hypothesis, the context in which the most

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research has been done to evaluate the role of diverse microbial exposures in promotion of stress resilience and mental health.

8.1

The Hygiene Hypothesis

Initially, the term “hygiene hypothesis” was based on observations made by David Strachan, then at the London School of Hygiene and Tropical Medicine, in which a decrease in hay fever was observed in children with multiple older siblings in first world countries (Strachan 1989). This led Strachan to the conclusion that an excess of hygiene and decreases in childhood illness exposures were the causes of the increase in hay fever seen in first world countries (Strachan 1989; Rook and Lowry 2022; Rook et al. 2014a). Media and journalists latched onto this phrase as it was a simplistic concept that determined our excess cleanliness was the cause of our allergies and autoimmune disorders (Rook and Lowry 2022). While hay fever was not a new concept and was first described in the tenth century by Abu Bakr Al-Razi who called it “rose fever,” a connection between wealth, urbanization, and hay fever was observed in the nineteenth century by Dr. Charles Blackley (1873; Azizi 2010; Bungy et al. 1996). Blackley noticed that hay fever was more prevalent in wealthy and urbanized people compared to common people who did not live in the city (Blackley 1873; Rook and Lowry 2022). With this connection and Strachan’s suggestion that allergic diseases were prevented by infections in early childhood, the hygiene hypothesis was developed and initially focused on allergic disorders without taking into consideration humanity’s evolutionary history and dependence on microorganisms during the hunter-gatherer phase (Rook and Lowry 2022). These early childhood infections, which were largely not present during the hunter-gatherer phase of human evolution, are known as crowd infections, which became prevalent during urbanization. Crowd infections either elicit immunity or kill the host, which is why they cannot persist in hunter-gatherer groups but are common in urban environments. Crowd infections are not protective against chronic inflammatory disorders and instead have been shown to worsen them. Hygienic practices have been shown to decrease rates of crowd infections, as repeatedly emphasized by public health agencies during the COVID-19 pandemic, and thus a focus on negative consequences of hygiene, as proposed in the hygiene hypothesis, is somewhat misleading from the perspective of public health.

8.2

The “Farm Effect”

Subsequent studies demonstrated that rural upbringing confers protection against allergic asthma (von Mutius 2022). The protective effect of rural upbringing against allergic asthma is so highly replicated, it is referred to as simply the “Farm Effect” (von Mutius 2022; Genuneit 2012).

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The Biodiversity Hypothesis

The biodiversity hypothesis states that current deficits in the exposure to natural environments and microbial diversity of individuals living in Westernized civilizations have unintended adverse health consequences (von Hertzen et al. 2011; Haahtela 2022; von Hertzen et al. 2015). Lack of exposure to microbial biodiversity especially during early development causes a deficiency in immunoregulatory circuits and leads to increased risk of developing allergic asthma later in life (von Hertzen et al. 2011, 2015; Haahtela 2022).

8.4

The Disappearing Microbiota Hypothesis

The disappearing microbiota hypothesis (Blaser and Falkow 2009; Blaser 2015) postulates that the important factor in increasing prevalence of modern allergic and metabolic diseases might not be decreased exposures to environmental microorganisms but instead could reflect the loss of ancestral microorganisms through vertical transfer, i.e., from one generation to the next, which in turn affects human physiology and disease risk. Notable losses include Helicobacter pylori, and losses of microbiota due to antibiotic use.

8.5

The “Old Friends” Hypothesis

The “Old Friends” hypothesis was formulated by Professor Graham Rook and colleagues in 2004 as a revision of the hygiene hypothesis (Rook et al. 2004). It provides a useful hypothetical framework for explaining the increases in inflammatory diseases in modern urban societies, in part because it highlights the importance of immunoregulation, indicated by a balanced expansion of regulatory T cells (Treg) and effector T cell populations, which are known to be driven by microbial signals. It is also useful in that it takes the focus away from hygiene (i.e., the idea that we are “too clean”), particularly at a time when personal hygiene is important to avoid transmission of communicable disease, including COVID-19, and instead emphasizes the importance of exposures to diverse microbial environments with microbial signals that can drive immunoregulation. Immunoregulation is driven mainly by organisms with which mammals co-evolved, including: (1) the commensal microbiota, which have been altered by the Western lifestyle, including a diet that is commonly low in microbiota-accessible carbohydrates (Sonnenburg and Sonnenburg 2014; von Hertzen et al. 2015); (2) pathogens associated with the “old infections” that were present throughout life in evolving human hunter-gatherer populations (Atherton and Blaser 2009); and (3) organisms from the natural environment with which humans were inevitably in daily contact with (and,

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consequently, had to be tolerated by the immune system) (Rook et al. 2014a). Immunoregulation is thought to be compromised in modern high-income settings due to reduced contact with these three categories of organisms (Rook et al. 2014a; Ohnmacht et al. 2015; Sefik et al. 2015). Failure of immunoregulation, attributable to reduced exposure to the microbial environment within which the mammalian immune system evolved, is thought to be one factor contributing to recent increases in stress-related and chronic inflammatory disorders in high-income countries (Sonnenburg and Sonnenburg 2014; Atherton and Blaser 2009; Rook et al. 2014a). Finally, and directly relevant to the thesis of this chapter, data from both preclinical and clinical studies are consistent with the hypothesis that inadequate immunoregulation also increases risk for development of stress-related psychiatric disorders (Raison et al. 2010; Rook et al. 2013, 2014a; Rook and Lowry 2008), an idea first put forward by Rook and Lowry in 2008 (Rook and Lowry 2008). Figure 2 illustrates the three categories of “Old Friends,” while Fig. 3 illustrates potential mechanisms underlying induction of immunoregulation by microbial signals.

8.5.1

The Phylogenetically Broad But Strain-Specific Nature of Microorganisms That Induce Immunoregulation

Commensal microbes are initially transmitted by mothers and other family members and play an important role in the development of mammalian organ systems including the gut, immune system, and brain (Rook et al. 2014a, b). Germ-free mice have severely deficient numbers of regulatory T cells (Ohnmacht et al. 2015; Sefik et al. 2015). The immunoregulatory potential of single strains of bacteria is highlighted by the fact that inoculation of germ-free mice with single strains of bacteria is sufficient to restore percentages of Rorγ+ Helios Treg (a distinct population of Tregs in the mouse colon that constrains inflammatory responses) to levels found in specified pathogen-free (SPF) mice. This induction mapped to a broad, but specific, array of individual bacterial species, meaning that bacteria from widely different phyla were capable of inducing these immunoregulatory effects, but that the effects were strain-specific. We have yet to understand the “code” that enables one bacterial strain to induce immunoregulatory responses, while other closely related strains cannot do so. This remains an important objective for future studies. Given that we do not yet understand fully what enables specific strains of bacteria to drive immunoregulation, a reasonable strategy might be to promote high diversity of the gut microbiota, in hopes that one or more strains could provide the bacterial signals that drive immunoregulation. A number of physiological variables and lifestyle factors that are positively or negatively associated with diversity of the gut microbiota have recently been identified (Manor et al. 2020).

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Fig. 2 Potential sources of the three categories of “Old Friends” and how they interact with the immune system to induce anti-inflammatory and immunoregulatory effects. The three categories of “Old Friends” are: (1) harmless environmental microorganisms found in mud, untreated water, and fermenting vegetable material that have been depleted during the transition from a rural to an urban lifestyle; (2) organisms that form part of the co-evolved human microbiota (including commensal microorganisms); and (3) “Old Infections,” i.e., infections present in early man that usually do not sterilize or kill the host and that have also been depleted since urbanization. (A) The soil in this diagram depicts a diverse set of microorganisms that live within it, an environment that is rare to find in an urban environment compared to a rural one, thus depicting the first category of “Old Friends.” Here, a person harvesting lettuce they have grown is agitating the soil enough to form soil particulates that they eventually breathe in, exposing themselves to “Old Friends.” (B) One broad subset of soil microbes, Actinobacteria, are commonly found in the upper airway, depicted by mycobacteria from the genus Mycobacterium being inhaled into the nasal cavity (Macovei et al. 2015; Kim et al. 2022). Dendritic cells “sample” the contents of the nasal lumen by extending pseudopods that allow the cell to phagocytize a bacterium. The dendritic cell will then digest it and CD103+, CCR7+ dendritic cells migrate to a nearby lymph node via a lymphatic vessel to present processed antigens of the bacterium to lymphocytes. (C) Dendritic cell sampling is a common theme of the innate immune system – in the lumen of the small intestine, home to part of the gut microbiome, dendritic cells undergo a similar process of phagocytizing microorganisms in the lumen, digesting them, and ultimately presenting the processed antigens to lymphocytes. Unlike in the upper airway, dendritic cells in the small intestine mostly sample microorganisms that form part of the co-evolved human microbiota – the second category of “Old Friends.” The commensal microbes in the small intestine are especially influenced by whether a person was raised in an urban/ rural environment as well as diet. (D) Animals that humans are in close contact with, such as dogs, can also influence the composition of the human microbiota. Commensal microbes from a dog’s skin microbiome can be transferred to a person’s skin, where they can colonize to form part of the person’s skin microbiome (Song et al. 2013); further, dogs can expose their owners to “Old Friends” by bringing microbes found in mud and untreated water into the house. (E) The last category of “Old Friends” is depicted by Helicobacter pylori infecting the epithelial cells of the stomach. Unlike a regular infection that produces a robust inflammatory response, if H. pylori is tolerated by the dendritic cells and lymphocytes of the immune system, then an anti-inflammatory and

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Fig. 3 A dendritic cell transports a phagocytized “Old Friend” to a lymph node and presents it to naïve T cells, resulting in differentiation of naïve T cells into regulatory T cells. (A) A mature CD103+, CCR7+ dendritic cell migrates to a lymph node via an afferent lymph vessel. An “Old Friend” is bound to TLR2 and is ultimately phagocytized and bound to MHC II for presentation to a lymphocyte. (B) Activation of a naïve T cell in the presence of IL-10, TGF-β, and IL-18 secreted from a dendritic cell results in differentiation of the naïve T cell to a FoxP3+ regulatory T cell. (C) A FoxP3+ regulatory T cell migrates from the lymph node to tissue via an efferent lymph vessel. Abbreviations: CCR, C-C chemokine receptor; CD, cluster of differentiation; IL, interleukin; MHC, major histocompatibility complex; TCR, T cell receptor; TGF-β, transforming growth factor beta; TLR, toll-like receptor; Treg, regulatory T cell. Not to scale. Figure created with biorender.com

8.5.2

Immunoregulatory Strategies for Prevention of Stress-Related Psychiatric Disorders: Soil-Derived Mycobacterium vaccae NCTC 11659 as a Case Study

Ellul and colleagues recently proposed a path toward induction of Treg to promote psychoneuroimmune resilience with the intention of developing an immunotherapy approach to treatment of major depressive disorder. The strategy proposed involves use of low-dose interleukin 2 (IL-2), which induces Treg and inhibits inflammatory Th17 lymphocytes (Ellul et al. 2018). Another approach might be use of bacterial strains that have demonstrated anti-inflammatory and immunoregulatory effects (for  ⁄ Fig. 2 (continued) immunoregulatory response (i.e., characterized by a balanced expression of regulatory and effector T cells) arises instead (Arnold et al. 2012; Lundgren et al. 2005). Abbreviations: DC, dendritic cell; IL, interleukin; TGF-β, transforming growth factor beta; Treg, regulatory T cell. Not to scale. Figure created with biorender.com

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Table 1 PTSD-relevant findings following immunization with Mycobacterium vaccae NCTC 11659 PTSD symptom based on DSM-5 Intrusions Avoidance (avoiding people, situations, circumstances resembling or associated with the event)

Negative alterations in mood and cognition

Alterations in arousal or reactivity (hypervigilance for threat, exaggerated startle response, irritability, difficulty concentrating, sleep problems)

Effects of M. vaccae NCTC 11659 in rodent models N.A. Decreased stress-induced anxiety-like defensive behavioral responses (avoidance) (Amoroso et al. 2019a, b; Frank et al. 2018; Reber et al. 2016; Loupy et al. 2021) Promotion of proactive behavioral responses to stress (Amoroso et al. 2019a, b; Frank et al. 2018; Reber et al. 2016; Loupy et al. 2021) Antidepressant-like behavioral effects (Lowry et al. 2007; Siebler et al. 2018) Prevention of surgery-induced microglial priming (Fonken et al. 2018; Frank et al. 2018; Frank et al. 2019) and cognitive impairment (Fonken et al. 2018) Enhanced fear extinction in fear-potentiated startle (Fox et al. 2017; Hassell et al. 2019; Loupy et al. 2019) Enhanced fear extinction in models of stressinduced exaggeration of cued fear paradigms (Hassell 2019) Prevention of stress-induced cortical hyperarousal (Bowers et al. 2019) Prevention of stress-induced sleep and behavioral impairments (Bowers et al. 2021)

recent review, see Sterrett et al. 2022; Flux and Lowry 2020, 2023; Loupy and Lowry 2019; Lowry et al. 2016). We have been studying the potential of one such bacterial strain, M. vaccae NCTC 11659, in rodent models in order to test the hypothesis that this strain has potential as an intervention for prevention and treatment of stress-related psychiatric disorders. Although not yet tested in clinical trials for stress-related psychiatric disorders, it has been studied in numerous clinical trials for other conditions (for review, see Amoroso et al. 2021). M. vaccae NCTC 11659 induces regulatory T cells in mice and rats and has shown particular promise for promotion of stress resilience effects in a number of stress models, consistent with prevention of a PTSD-like syndrome (Table 1).

9 Conclusions Interventions that increase immunoregulation and attenuate chronic low-grade inflammation have potential for prevention and/or treatment of stress-related psychiatric disorders in which a failure of immunoregulation and the resulting chronic

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low-grade inflammation are recognized as risk factors. Increased exposure to immunoregulatory “Old Friends” may provide a novel and promising strategy to promote stress resilience for the purposes of prevention and treatment of stress-related psychiatric disorders, including anxiety disorders, mood disorders, and traumaand stressor-related disorders, such as PTSD.

10

Future Directions

Although evidence strongly supports the hypothesis that exposures to “Old Friends” can promote stress resilience, the mechanisms involved are not completely understood, particularly in the context of the microbiome-gut-brain axis signaling mechanisms. Thus, future studies should identify mechanisms involved, which will facilitate development of novel interventions. Acknowledgements This work is supported by NIH T32 HL149646. Dr. Christopher A. Lowry is supported by the National Center for Complementary and Integrative Health (grant numbers R01AT010005 and R41AT011390), the Colorado Office of Economic Development and International Trade (OEDIT) Advanced Industries Accelerator Program (grant number CTGG1-20203064), the Department of the Navy, Office of Naval Research Multidisciplinary University Research Initiative (MURI) Award (grant number N00014-15-1-2809), and an anonymous donor through Benefunder. Conflict of Interest CAL serves on the Scientific Advisory Board of Immodulon Therapeutics, Ltd., is a cofounder and Chief Scientific Officer of Mycobacteria Therapeutics Corporation, and is a member of the faculty of the Integrative Psychiatry Institute. The remaining authors have no conflict of interests to report.

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The Microbiome and Mental Health Across the Lifespan Faith Dickerson, Amanda Hazel Dilmore, Filipa Godoy-Vitorino, Tanya T. Nguyen, Martin Paulus, Adrian A. Pinto-Tomas, Cristofer Moya-Roman, Ibrahim Zuniga-Chaves, Emily G. Severance, and Dilip V. Jeste Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 2 Schizophrenia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 3 Substance Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

F. Dickerson (*) Sheppard Pratt, Baltimore, MD, USA Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA e-mail: [email protected] A. H. Dilmore Biomedical Sciences Graduate Program, University of California, San Diego, CA, USA Sam and Rose Stein Institute for Research on Aging, University of California, San Diego, CA, USA e-mail: [email protected] F. Godoy-Vitorino Department of Microbiology and Medical Zoology, University of Puerto Rico School of Medicine, San Juan, PR, USA e-mail: fi[email protected] T. T. Nguyen Sam and Rose Stein Institute for Research on Aging, University of California, San Diego, CA, USA Department of Psychiatry, University of California, San Diego, CA, USA e-mail: [email protected] M. Paulus Laureate Institute for Brain Research, Tulsa, OK, USA e-mail: [email protected] A. A. Pinto-Tomas and C. Moya-Roman University of Costa Rica, San Jose, CR, USA e-mail: [email protected] I. Zuniga-Chaves Department of Bacteriology, Microbial Doctoral Training Program, University of WisconsinMadison, Madison, WI, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 119–140 https://doi.org/10.1007/7854_2022_384 Published Online: 11 August 2022

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4 Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Suicidality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Loneliness and Wisdom in Older Adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Longevity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Introduction: The combined genetic material of the microorganisms in the human body, known as the microbiome, is being increasingly recognized as a major determinant of human health and disease. Although located predominantly on mucosal surfaces, these microorganisms have profound effects on brain functioning through the gut–brain axis. Method: The content of the chapter is based on a study group session at the annual meeting of the American College of Neuropsychopharmacology (ACNP). The objective was to discuss the emerging relationship between the human microbiome and mental health as relevant to ACNP’s interests in developing and evaluating novel neuropsychiatric treatment strategies. The focus is on specific brain disorders, such as schizophrenia, substance use, and Alzheimer’s disease, as well as on broader clinical issues such as suicidality, loneliness and wisdom in old age, and longevity. Results: Studies of schizophrenia indicate that the microbiome of individuals with this disorder differs from that of non-psychiatric comparison groups in terms of diversity and composition. Differences are also found in microbial metabolic pathways. An early study in substance use disorders found that individuals with this disorder have lower levels of beta diversity in their oral microbiome than a comparison group. This measure, along with others, was used to distinguish individuals with substance use disorders from controls. In terms of suicidality, there is preliminary evidence that persons who have made a suicide attempt differ from psychiatric and non-psychiatric comparison groups in measures of beta diversity. Exploratory studies in Alzheimer’s disease indicate that gut microbes may contribute to disease pathogenesis by regulating innate immunity and neuroinflammation and thus influencing brain function. In another study looking at the microbiome in older adults, positive associations were found between wisdom and alpha diversity and negative associations with subjective loneliness. In other studies of older adults, here

E. G. Severance Stanley Neurovirology Laboratory, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA e-mail: [email protected] D. V. Jeste Sam and Rose Stein Institute for Research on Aging, University of California, San Diego, CA, USA Department of Psychiatry, University of California, San Diego, CA, USA Department of Neurosciences, University of California, San Diego, CA, USA e-mail: [email protected]

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with a focus on longevity, individuals with healthy aging and unusually long lives had an abundance of specific microorganisms which distinguished them from other individuals. Discussion: Future studies would benefit from standardizing methods of sample collection, processing, and analysis. There is also a need for the standardized collection of relevant demographic and clinical data, including diet, medications, cigarette smoking, and other potentially confounding factors. While still in its infancy, research to date indicates a role for the microbiome in mental health disorders and conditions. Interventions are available which can modulate the microbiome and lead to clinical improvements. These include microbiome-altering medications as well as probiotic microorganisms capable of modulating the inflammation in the brain through the gut–brain axis. This research holds great promise in terms of developing new methods for the prevention and treatment of a range of human brain disorders. Keywords Aging · Beta diversity · Mental health · Microbiome · Microorganisms · Probiotics

1 Introduction The human body harbors microbial ecosystems that interact with human physiological processes, including those of the immune system (Castro-Nallar et al. 2015). The microbiota refers to the collection of bacteria, archaea, viruses, fungi, protozoa, and other microbes that are present in mucosal sites in the body. The microbiome refers to the combined genetic material of these microorganisms and the multitude of additional physiological functions that are available to but not encoded by the human host. Through the use of nucleic acid sequencing techniques and translational experiments in animal models, the microbiome has been studied to determine its role in human health and disease. Associations between the microbiome and several neurological and psychiatric disorders have been found in this rapidly expanding area of research (Mitrea et al. 2022). The human microbiota are most densely populated in the gastrointestinal tract. The gastrointestinal tract, commonly referred to as the gut, is estimated to contain more than 20–100 trillion microorganisms which comprise more than a thousand distinct bacteria species. In addition to bacteria, the gut contains substantial numbers of viruses, fungi, and other microorganisms and all of the functions encoded in the genetic material of these microbiota (Gill et al. 2006; Sender et al. 2016). It is estimated that the microbiome contains at least 100 times as many genes as have been identified in the human genome (Gill et al. 2006). The gut–brain axis, also known as the gut–immune–brain axis, is the bidirectional pathway that facilitates signaling between microbes in the gut and the immune and nervous systems (Morais et al. 2021). There are several pathways that provide the biological bases for this communication between microbiota and the brain as shown

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Pathogens Inflammation Stress Toxins Diet Genes

GUT

Microbiome Archaea Bacteria Fungi Parasites Viruses

Tryptophan metabolism

SCFAs

Immune

YYY -antibodies YYY -complement -cytokines

Dysbiosis

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• CRH • ACTH • Cortisol

Vagus nerve

Fig. 1 The gut microbiome and biological pathways connecting the gut and brain. The microbiome is shaped by numerous environmental factors as well as genes, and the brain relies on a healthy gut environment for certain key pathways shown here. When the microbiome becomes imbalanced and dysbiosis occurs, these pathways are also disrupted and result in altered brain functioning. Based on material from Dinan et al. (2015) and Severance et al. (2014)

in Fig. 1. First, immune responses elicited by bacterial products, coupled with alterations of the intestinal barrier, allow these products to enter the bloodstream. Second, circulating bacterial metabolites such as short-chain fatty acids (SCFAs) or tryptophan which is metabolized to kynurenine can have direct and indirect effects on the brain. Third, the hypothalamic–pituitary–adrenal (HPA) axis can be influenced by the microbiota and can in turn activate microglia (Salavrakos et al. 2021). Fourth, microbiota and their products have a direct connection to the central nervous system through the vagus nerve (Ahn and Hayes 2021). All of these processes have been more extensively studied in animal models than in humans. The microbiome is quantified through the use of complex laboratory and data analytic techniques. Most studies of the human microbiome are based on fecal samples that are obtained using a variety of collection methods (Tang et al. 2020). Samples may also be taken from the oral pharyngeal cavity from the back of the throat or from the mouth. In some cases, samples have been obtained from internal parts of the intestinal tract through endoscopy. Once collected, samples are generally analyzed by means of nucleic acid extraction followed by sequencing techniques. The most common techniques employ PCR-based amplification of conserved regions such as the 16 s ribosomal region of bacteria and the ITS 1-2 internal transcribed space rRNA region of fungi. Alternately, sequencing can be performed in unamplified nucleic acids in the technique known as shotgun sequencing (Wensel et al. 2022). Advantages and limitations of the different approaches are discussed below in the section on schizophrenia. In either case nucleic acids are matched to taxa through the use of a number of available algorithms and databases. In the case of

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bacteria and eukaryotic organisms, the taxa may be identified at the phylum, family, genus, or species level. These data may then be used to determine alpha diversity, characterizing the number and distribution of taxa within individual samples, and beta diversity, the dissimilarity between pairs of samples calculated by a distance matrix. There are multiple measures that have been developed to quantify both alpha and beta diversity (Qi et al. 2021). The genetic material may also be used to determine the number or abundance of individual taxa. The metabolic products of the microorganisms can be identified by their direct measurement in body fluid when full microbial genes have been sequenced, or by the prediction of these metabolites based on shotgun or rRNA sequencing results when full sequencing has not been done. In either case, functional metabolic pathways may be inferred. More details about these procedures may be found in the references (Esvap and Ulgen 2021). The purpose of this chapter is to highlight recent progress in establishing relationships between the human microbiome and mental health. The chapter is not intended to provide an exhaustive review of studies on the microbiome in psychiatric disorders and mental health conditions but to selectively highlight six areas of research in this expanding field. The chapter focuses on specific disorders such as schizophrenia, substance use, and Alzheimer’s disease, as well as on broader conditions such as suicidality, loneliness and wisdom in old age, and longevity. The content of the chapter is based on presentations made at a study group session at the annual meeting of the American College of Neuropsychopharmacology (ACNP) held in San Juan, PR, on December 7, 2021.

2 Schizophrenia Schizophrenia is a serious mental illness that affects over 20 million people worldwide (Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017, 2018). While some affected individuals recover after illness onset, the typical illness course consists of ongoing psychotic symptoms and periodic exacerbations that persist throughout life (Jauhar et al. 2022). People with schizophrenia have, on average, a 15- to 20-year shorter lifespan than the general population, resulting in considerable medical and personal costs to the society (Saha et al. 2007; Jin and Mosweu 2017). Premature mortality in schizophrenia is believed to be caused by accelerated biological aging, evidence for which includes higher incidence of co-occurring cardiac, metabolic, and gastrointestinal diseases (Nguyen et al. 2018). The biology underlying the increased risk for these conditions is poorly understood and is attributed to several different factors, including increased inflammation, neural abnormalities, medications, and gastrointestinal issues (Khandaker et al. 2015; van Erp et al. 2018; Severance et al. 2015). The gut microbiota–brain axis provides a framework to demonstrate how these diverse processes may be connected in diseases like schizophrenia.

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Fig. 2 Functional pathways related to trimethylamine-N-oxide (TMAO) reductase and Kdo2-lipid A biosynthesis that were altered in schizophrenia. (a) The log ratios of KEGG Orthologs TMAO reductase relative to the bottom 20% of KEGG Orthologs (i.e., those most associated with schizophrenia), were significantly lower in schizophrenia by a Wilcoxon test (Nguyen et al. 2021b). (b) The log ratios of Kdo2 – lipid A biosynthesis pathway relative to the bottom 20% of pathways (i.e., those most associated with schizophrenia) were significantly lower in schizophrenia (Nguyen et al. 2021b). Functional measures such as these give insight into the biological pathways that may be depleted in the schizophrenia gut microbiome

In the past few years, several groups have reported on differences in the gut microbiota between persons with schizophrenia and non-psychiatric comparison subjects (NCs). Overall, a majority of the studies did not report a significant difference in alpha diversity between these two groups but did note a significant difference in beta diversity (Nguyen et al. 2021a; Murray et al. 2021). Persons with schizophrenia also are reported to have higher relative abundances of Megasphaera and lower loads of Haemophilus, Sutterella, Coprococcus, Ruminococcus, Bacteroides, and Roseburia in the gut than (NCs) (Nguyen et al. 2021a; Murray et al. 2021). While the functional roles of these taxa are unclear, Sutterella and Ruminococcus are known to have higher abundance in the gut microbiome of children with autism, and some species of Roseburia are used as probiotic organisms (Wang et al. 2013; Tamanai-Shacoori et al. 2017). Probiotics will be discussed in a subsequent section. The study of the microbiome in schizophrenia is gradually shifting from these broad diversity and taxonomic comparisons to functional analyses that seek to characterize consequences of the differences in the gut microbiome in people with schizophrenia. As shown in Fig. 2, Nguyen et al. identified functional pathways related to trimethylamine-N-oxide (TMAO) reductase and Kdo2-lipid A biosynthesis that were altered in schizophrenia (Nguyen et al. 2021b). This suggested specific molecular processes that could be impacted by changes in the gut microbiome in schizophrenia. However, this study relied on bioinformatic predictions of bacterial

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metabolic capacity from 16S rRNA sequencing data. Shotgun metagenomic sequencing experiments can partly address the limitations of these 16S sequencing experiments, since they sequence all microbial DNA instead of only the amplified 16S gene, allowing for investigation of function without relying on bioinformatic predictors. Furthermore, shotgun metagenomics is not limited to bacteria or archaea like 16S sequencing, but also allows for characterization of the microbiome in organisms other than bacteria. This is important, because other microbes, such as Herpes Simplex Virus Type 1, Epstein Barr virus, the fungus Candida albicans, and the protozoan Toxoplasma gondii have been reported to impact cognition and other traits in schizophrenia (Dickerson et al. 2021). Shotgun metagenomic sequencing has been used in a few recent studies of the microbiome in schizophrenia. A group studying treatment-naive schizophrenia patients found that methanogenesis, gamma-aminobutyrate (GABA) shunt, and transport system of manganese, zinc, and iron were more common functions in the gut microbiome in schizophrenia patients than in NCs (Zhu et al. 2020). These results are meaningful as they demonstrate the translation of shotgun metagenomics findings into in vivo microbiome-associated behavioral changes. A separate group of investigators found that schizophrenia patients had a higher abundance of microbial peptidases and lower abundance of enzymes for carbohydrate catabolism, despite having a diet lower in proteins (Liang et al. 2022). This suggests that the gut of people with schizophrenia shifts toward protein fermentation. These studies have thus further characterized the functional consequences of the altered gut microbiome in schizophrenia. While the study of the schizophrenia gut microbiome is intriguing, there are several limitations to the reported findings. As stated above, many publications are limited to 16S rRNA investigations, which are limited in terms of the microbiota that they can capture and in the functional conclusions that can be made. Furthermore, most studies are limited in scope to the gut microbiome. While the gut is a rich source of microbial diversity and is part of the well-established gut–brain axis, the oral and pharyngeal microbiome is also known to show significant differences between schizophrenia patients and NCs (Yolken et al. 2021a). A majority of the studies are also limited to cross-sectional sampling, even though it is known that the microbiome varies temporally within a given individual (Flores et al. 2014). Finally, most microbiome cohorts do not reflect the ethnic and cultural diversity of the patients in the world, limiting the variability in microbial diversity they capture (Abdill et al. 2022). Although research examining the gut microbiome in schizophrenia is still in early stages, there is growing evidence that their gut microbiome is functionally different from that of NCs. Future investigations that study the gut microbiome longitudinally with shotgun metagenomics and direct measurement of microbial products will be exciting and may elucidate new biomarkers for schizophrenia and promising novel targets for therapeutic intervention.

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3 Substance Use Substance use disorders (SUDs) are highly prevalent, affecting more than 20 million individuals in the US (Substance Abuse and Mental Health Services Administration 2019). Misuse of substances is associated with adverse physical and mental effects and impaired social functioning. The effect of substance use on the microbiome is an emerging area of research. Although there is evidence that immune responses in the periphery and the central nervous system are altered by exposure to drugs of abuse, the contributions of neuroimmune interactions to substance use behaviors are just beginning to be appreciated. Brain-immune system interactions in substance use disorders are much more complex and important than previously understood (Hofford et al. 2018). In contrast to studies with alcohol use disorder, changes of bacterial diversity of intestinal microbiota in individuals with SUDs have not been clearly characterized (Xu et al. 2017). There is some research in opioid users, suggesting that the gastrointestinal microbiome may be involved in tolerance to pain-relieving effects of these drugs (Akbarali and Dewey 2017). In animal studies, intermittent, but not sustained, morphine treatment is associated with microglial activation, hyperalgesia, and impaired reward response and some of these effects are mimicked by depletion of the gut microbiota via antibiotic treatment (Lee et al. 2018). Others have reported that morphine administration results in a shift in the gut microbiome and metabolome within 1 day (Wang et al. 2018). Chronic use of morphine reduces pathogen clearance and induces bacterial translocation across the gut barrier in animal studies (Banerjee et al. 2016). However, it is unclear how opioids modulate the gut microbiome and metabolome. Opioid modulation of gut homeostasis in mice suggested that medical interventions to ameliorate the consequences of drug use/abuse will provide potential therapeutic and diagnostic strategies for opioid-modulated intestinal infections (Wang and Roy 2017). In addition, lifestyle characteristics of substance users, e.g., receptive anal intercourse, methamphetamine use, and marijuana use can have profound effects on microbiome variation (Fulcher et al. 2018). More recently, some researchers have proposed that there is a bidirectional relationship between gut microbiota and opioid-related behaviors (Ren and Lotfipour 2020). Specifically, chronic morphine intake induces dysbiosis, increased intestinal permeability, and a probable neuroinflammation, whereas cocaine induces a dysbiosis. Taken together, there is a possibility that the gut microbiome can be a moderator of the effects of opioid agonists and thereby increase the liability to continued drug-taking. These drugs can also be modulated by the microbiome and affect the behavioral response to stimulant drugs (Salavrakos et al. 2021). These bidirectional interactions have led some investigators to propose that modulating the gut microbiome in SUDs could result in the design of new therapies based on opposing the effects of abused drugs on the host’s commensal bacterial community (Angoa-Perez and Kuhn 2021). Thus, there is accumulating evidence for a gut–immune–brain communication in the pathogenesis of SUDs (Lucerne and Kiraly 2021).

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In a recent study, Paulus et al. examined the oral microbiome of individuals attending a substance use treatment program who had been recently abstinent (Kosciolek et al. 2021). The oral cavity is the second-largest microbial habitat after the gut; the microorganisms here differ but overlap with those found in the lower intestinal tract. The oral microbiome provides a readily accessible diverse and adaptive environment to examine the effects of health conditions and their impacts on microbiota. In this study, participants were a sub-sample (N ¼ 177) of the Tulsa 1000 (T-1000) project (Victor et al. 2018). Samples from 123 substance users and 54 healthy comparison subjects were analyzed using 16S rRNA marker gene sequencing to characterize the oral microbiome. The substance users differed significantly from the healthy comparison subjects on the unifrac distance, a phylogenetic-based measure of beta diversity, but did not differ in alpha diversity. Using a machine learning approach, microbiome features combined with sociodemographic variables successfully categorized group membership with 87–92% accuracy, even after controlling for external factors such as smoking or alcohol consumption. In addition, individuals with SUDs with relatively lower beta diversity also reported higher levels of negative reinforcement experiences associated with their primary substance of abuse, i.e., they reported use of substances due to aversive conditions associated with non-use. Taken together, these findings support the notion that the oral microbiome could be used as a way of indexing group membership. However, more work is needed to determine whether it also contains information about drug use severity or risk for substance use relapse. The fact that oral microbiome diversity was related to the degree to which negative reinforcement processes determine substance use provides initial evidence of a link with substance use severity. Several previous investigations have found that negative reinforcement processes are important for extreme and prolonged substance and alcohol use disorders (Koob 2013).

4 Alzheimer’s Disease Alzheimer’s disease (AD) is the most common cause of dementia, affecting around 6.2 million Americans aged 65 and older (Alzheimer’s Disease Facts and Figures 2021). The symptoms of AD progress slowly over several years and include memory loss and impaired cognitive abilities (Kukull and Bowen 2002). Several pathological changes have been identified in the postmortem brains of patients, including activated inflammatory cells, extracellular beta-amyloid fibrillar plaques and neurofibrillary tangles (composed of intraneuronal hyperphosphorylated microtubule-associated protein tau) (Walsh and Selkoe 2004; Hardy and Selkoe 2002). Recent advances in gut microbiome research suggest that microbes could be contributing agents of the disease, regulating innate immunity and

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Fig. 3 Schematic representation of the gut–brain axis representing the influence of the microbiome in Alzheimer’s disease onset. Microglial activation is involved in the progression of the disease

neuroinflammation, thus influencing brain function (Shen and Ji 2019; Kowalski and Mulak 2019). Gut bacteria can contribute to the integrity of the blood-brain barrier (BBB), affecting neural development, neurotransmitter synthesis, and microglial activity (Parker et al. 2020), all of which can contribute to the onset and progression of the disease as shown in Fig. 3. During bacterial dysbiosis – imbalance of good gut microbes – bacterial-induced cytokines, neurotransmitters, and certain bacterial metabolites – can cross the BBB and cause a neural inflammatory cascade (Forlenza and Forlenza 2018). Indeed, bacterial lipopolysaccharides have been discovered in amyloid plaques in the brain of Alzheimer’s patients (Zhan et al. 2018). In turn, amyloid plaques have also been correlated to high blood levels of lipopolysaccharides (Marizzoni et al. 2020). Increases in microbial-associated molecules such as LPS resulting from dysbiosis play a key role in innate immunity and can activate neuroinflammatory routes in the brain’s resident macrophages, the microglia. Germ-free mice have more immature, less functioning microglia than mice with a microbiota, and a lower amyloid burden (Harach et al. 2017). Acetate, a gut microbiota-produced short-chain fatty acid, drives microglia maturation and can modulate disease progression (Erny et al. 2021).

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Genetic factors are also associated to Alzheimer’s disease. One such molecule with functional polymorphisms is apolipoprotein ε4 (APOE4) which acts in the transport and metabolism of lipids, the maintenance of cholesterol homeostasis and contributes to cascading events dependent on beta-amyloid (de Chaves and Narayanaswami 2008). In humans, APOE exists in three different isoforms in humans (apoE2, apoE3 and apoE4), and APOE4 homozygous and heterozygous people are at an increased risk of Alzheimer’s and cardiovascular disease (Liu et al. 2013). Studies have found that transgenic mice with different APOE genotypes have significantly different gut bacterial abundances. APOE4 animals had lower dominance of Firmicutes and higher levels of Prevotellaceae and Clostridium (Tran et al. 2019). Recently, APOE4 was also identified as a risk factor for severe COVID-19 and post-COVID mental fatigue (Magusali et al. 2021; Kurki et al. 2021). It is unclear if these participants had gut dysbiosis; however, other studies have associated compositional alterations of gut microbiome in patients with long-term complications of COVID-19 (Liu et al. 2022). Diet is one of the most important modulators of the gut microbiota. As ApoE4 carriers are more prone to high cholesterol levels, changes in the dietary habits such as a low cholesterol and high-fiber diet may lower Alzheimer’s progression while modulating the gut microbiome (Fritsch et al. 2021). Little is known about the relationship between the ApoE4 allele with intestinal dysbiosis in humans. As life expectancy increases, the number of persons suffering from Alzheimer’s will rise, putting a greater strain on society and healthcare. Mechanistic studies linking dietary interventions, and gut microbiota in different ethnic groups, and Apo E4 allele combinations, are needed to develop direct interventions aimed at impeding AD progression.

5 Suicidality Suicide is a leading cause of death and accounts for more than 700,000 deaths worldwide (Stone et al. 2021). A vast majority of persons who die by suicide have a diagnosable psychiatric disorder such as major depression, bipolar disorder, or schizophrenia (Nordentoft et al. 2011). There are multiple additional risk factors for death by suicide, the strongest of which is a history of a previous suicide attempt. Other risk factors that have been identified include a history of physical or sexual abuse, behavioral traits of impulsivity and aggressiveness, co-occurring substance use, and feelings of hopelessness. However, the ability to predict suicide with accuracy at the individual level based on clinical factors remains elusive. The association between biological markers and suicide behaviors has been the focus of recent investigations, especially markers of immune dysregulation. It is known that inflammation can trigger depressive symptoms and conversely that many individuals with mood disorders have evidence of immune activation (Brundin et al.

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2017). In addition, persons with autoimmune disorders are at high risk for depression (Ludvigsson et al. 2011). There is evidence that inflammation may be associated not only with a proclivity for a mood disorder but specifically with suicidal behavior. Studies have found an association between a suicide attempt history and the level of pro-inflammatory cytokines such as IL-6, which are cell signaling molecules involved in the immune response, possibly mediated by alterations in the kynurenine pathway (Black and Miller 2015; Bryleva and Brundin 2017). The gastrointestinal tract may be the most important source of this immune activation as evidenced by an increased level of gastrointestinal symptoms in individuals with depressive episodes and suicide attempts (Huang et al. 2021) as well as increased levels of markers of gastrointestinal inflammation (e.g., antibodies to dietary yeast proteins) and bacterial translocation (i.e., “leaky gut”) in individuals with depression and suicidal behaviors (Ohlsson et al. 2019; Dickerson et al. 2017). In addition, recent studies have identified alterations in the microbiome in this population. There have been several studies documenting alterations in the microbiome in individuals with major depressive disorder (Sanada et al. 2020). However, there has been little study of the association between alterations in the microbiome and suicidal behavior. This issue has implications for treatment as the microbiome may be altered through interventions such as probiotics, prebiotics, and other immunomodulatory agents. Dickerson et al. are investigating the oral, pharyngeal, and fecal microbiome in hospitalized psychiatric patients who had a recent suicide attempt and those who did not have a suicide attempt, and also in individuals without a psychiatric disorder. In this study, DNA was extracted from the samples and then the microbial DNA was amplified using primers directed at the bacterial 16 s RNA gene and characterized by high throughput sequencing using previously described methods (Yolken et al. 2021a). In an interim analysis, suggestive differences in beta diversity measures were found in the composition of the microbiome among the three groups. Further studies are underway with a larger sample and with the addition of blood-based immune markers and clinical measures. Ongoing studies employing metagenomic sequencing will also include an analysis of viral, fungal, and protozoal components of the microbiome (Yolken et al. 2021b). Potential therapeutic interventions for suicidal behavior involving the microbiome include probiotic microorganisms, which are non-pathogenic anaerobic bacteria such as Lactobacilli and Bifidobacteria, which have been shown to modulate the immune response in humans and which confer a health benefit (Noonan et al. 2020). Probiotics are hypothesized to operate in the gastrointestinal tract by enhancing the epithelial barrier and the competitive exclusion of pathogenic microorganisms. Prebiotics, which may also be therapeutic, are oligosaccharide or polysaccharide starches that promote the growth of beneficial bacteria in the gut (Berding and Cryan 2022). These supplements offer potential as adjunctive treatments for the prevention of suicidal behaviors in high-risk populations but have not been tested in clinical trials with this outcome. Additional potential interventions include compounds which contain both prebiotic and probiotic components, termed synbiotics (Hofmeister et al. 2021). The microbiome can also be modulated through

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fecal transplantation, an approach which is under study for a range of psychiatric disorders (Chinna Meyyappan et al. 2020). An increased understanding of the role of the gastrointestinal microbiome might thus lead to new methods for the management of suicidal behaviors and the prevention of the serious morbidity and mortality associated with suicide.

6 Loneliness and Wisdom in Older Adults Loneliness, or distress due to the subjective negative perception that one is socially isolated, has become a major public health problem in recent decades (Cacioppo and Cacioppo 2018). Subjective loneliness and objective social isolation (i.e., measurable dearth of social relationships) are associated with high rates of serious physical and mental illnesses, a weakened immune system, and premature death (National Academies of Sciences Engineering, and Medicine, Division of Behavioral and Social Sciences Education Health 2020). Loneliness and social isolation are especially concerning among older adults, a rapidly growing sector of the US population. Approximately one-quarter of adults over the age of 65 report feeling socially isolated. There is evidence to suggest that the COVID-19 pandemic exacerbated loneliness, with both homebound and non-homebound older adults experiencing increased loneliness and social isolation during the pandemic (Ankuda et al. 2022). Therefore, understanding the biological and social determinants of loneliness in older adults is of considerable importance. Research by Jeste and colleagues suggests that wisdom should be a critical component of interventions to combat chronic loneliness. This group has published several studies showing strong negative correlations between loneliness and wisdom (Lee et al. 2019). Although wisdom is traditionally considered to be a construct related to religion or philosophy, empirical research over the last few decades has demonstrated that it is a measurable human trait and may be partially biologically encoded (Abdellaoui et al. 2019). Wisdom is multifaceted and is defined by its affective, cognitive, and reflective dimensions (Jeste and Lee 2019). The affective or compassionate dimension, characterized by pro-social behaviors and attitudes, is the most predictive of loneliness. The biological underpinnings of loneliness and wisdom are particularly interesting from a microbiome perspective. Over the last few years, there has been an explosion of research into the gut–immune–brain axis, a biological network that facilitates signaling between microbes in the gut and the nervous system (Sherwin et al. 2019). Beyond the association between the microbiome and specific diseases, the gut–immune–brain axis has also sparked research into the relationship between the microbiome and social behaviors. Specifically, microbes may produce molecules that drive social behaviors via the nervous system and/or social behaviors may facilitate the spread of microbes to others, as shown in Fig. 4 (Morais et al. 2021; Nguyen et al. 2020). While characterization of the former pattern is limited to animal models, it is well appreciated that the gut, skin, genital, and oral microbiota are

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Fig. 4 Microbiota and social relationships. Wisdom and loneliness are associated with the gut microbiome. The relationship between the microbiome and psychosocial state, such as loneliness or wisdom, is hypothesized to be bidirectional (Morais et al. 2021; Nguyen et al. 2020). Specifically, microbes can be shared between individuals [represented by solid arrows] in a social setting (or not shared, in the case of social isolation) and an individual’s gut microbiome can affect their willingness to socialize with others. These relationships are hypothesized to be mediated by the gut–brain axis, represented by the dashed arrows

largely shared among people living together (Song et al. 2013). The perplexing but growing study of the association between social behaviors, the brain, and gut microbiota suggests a need to examine the relationship between the gut microbiome and loneliness and wisdom. Nguyen and colleagues performed 16S rRNA amplicon sequencing on DNA extracted from stool samples in a cohort of 184 community-dwelling adults between the ages of 28 and 97 years (Nguyen et al. 2021c). These participants also completed validated self-report measures of loneliness, wisdom, compassion, social support, and social engagement. Results indicated that wisdom, compassion, social support, and social engagement were positively associated with within-subject (alpha) diversity, while loneliness was negatively correlated with such diversity. Furthermore, wisdom explained a significant percentage of variance in between-subject (beta) diversity. These exploratory analyses point to a possibly bidirectional relationship between the gut microbiome and loneliness and wisdom. Further analyses are planned with updated study designs and analysis techniques. These preliminary findings have several limitations from a study design perspective. The study was biased toward college-educated non-Latinx Whites, as this is the primary demographic of the sampling location. Furthermore, the cross-sectional analysis did not consider fluctuations in loneliness or in gut microbial diversity over time. On the microbiome side, the analysis only included 16S rRNA amplicon sequencing, and therefore, could not capture microbes outside of bacteria and archaea, nor had the resolution to identify specific gene families or microbes at the species level. Also, the investigation was limited to the gut microbiome; while the gut microbiome is interesting from a gut–brain axis perspective, the microbiota from other body sites could also have meaningful associations with loneliness and/or

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wisdom. Specifically, the skin and oral microbiota may have interesting associations with these characteristics, since they have a large overlap among individuals in regular contact (Song et al. 2013). In addition, objective metrics were not collected on the number of social contacts, which could help tease apart the constructs of loneliness and social isolation. Continued study of the gut microbiome and loneliness and wisdom is necessary to inform the biological implications of loneliness. Specifically, conducting longitudinal studies of loneliness and wisdom in diverse populations that sample the microbiome from multiple body sites will provide better insight into the relationship between the microbiome and social behaviors, and will likely provide new directions for this emerging field.

7 Longevity Life expectancy has been increasing throughout the world, especially since the mid-twentieth century. Despite this steady progress, there are wide disparities in life expectancy among and within countries due to variations in the quality of medical care and in the standard of living and other environmental factors, as well as due to genetic and other individual differences among people (Peterson et al. 2018). While life expectancy is the average age at death for persons born in the same year, longevity refers to especially long-lived members of a population. Understanding what enables persons to remain healthy at an older age and to have an unusually long life span may lead to strategies that can be applied to other persons in the population. Recent studies provide evidence of a correlation between microbiome composition and healthy aging. When compared to their younger peers, long-lived individuals have higher microbial alpha diversity in their gut microbiomes, with a higher production of short-chain fatty acids such as butyric acid, and reduced presence of Faecalibacterium, Bacteroidaceae, and Lachnospiraceae, but more abundance of Akkermansia species (Badal et al. 2020). Correspondingly, a decrease in Akermansia muciniphila in the intestine may lead to the thinning of the mucosa that results in a weakening of the intestinal barrier function, which facilitate toxins and pathogens’ invasion (Zhang et al. 2019). Different studies have explored the potential role of Akkermansia as a marker of dysbiosis since this bacterium decrease has been associated with gut microbiota alteration (Lopetuso et al. 2020) and with the severity of appendicitis and inflammatory bowel disease (Xu et al. 2020). Interestingly, A. muciniphila has been shown to increase life span in mice (Cerro et al. 2022). One limitation of the microbiome studies is the lack of representation of certain geographical areas, such as Central America, where very few studies have been conducted and none have considered older populations. The Nicoya Peninsula in Costa Rica is classified as a longevity hotspot, popularly known as Blue Zone (Rosero-Bixby et al. 2013). Compared to other Blue Zones circa 2005, Nicoyan males aged 60 and over have the lowest death rates in the population, similar to

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Okinawa’s men from another Blue Zone; female mortality rates in this Costa Rican region are also low but are slightly higher than in Okinawa (Rosero-Bixby et al. 2013). Therefore, this region offers a unique opportunity to analyze whether the microbiome patterns associated with healthy aging are somewhat universal. Preliminary data from Pinto and colleagues suggests higher alpha microbial diversity in Centenarians from the Nicoya Peninsula when compared to their descendants. The researchers also detected an increased richness in the elders over aged 80 in Nicoya compared to a population near the country’s capital in the same age range. Moreover, several potential beneficial microbes were identified in the Nicoya cohort, such as Akkermansia, Bifidobacterium, Christensenella, and Faecalibacterium. Some of the bacteria that were found to be abundant in the Centenarians in the Blue Zone now are being tested as next-generation probiotics for therapeutic use. A. muciniphila has also been proposed as a novel candidate for the development of food or pharmacological supplements, with beneficial effects on health, by reducing the development of metabolic disorders associated with obesity, diabetes, hepatic and cardiovascular diseases (Cani and de Vos 2017). These investigations are just getting underway and offer promise for the potential application of microbiome research for therapeutic use.

8 Discussion The topics in this chapter provide a partial overview of ongoing studies about the microbiome in several psychiatric disorders and health conditions. The studies also provide pointers about how such research in this area may lead ultimately to new methods of assessment and treatment. There are numerous challenges to achieving the goal of applying the results of microbiome research to clinical care. The methods used to study the microbiome are varied and not yet standardized. Studies differ in terms of methods of sample collection; for example, the studies of suicidality and substance use were focused on the oropharyngeal microbiome vs. the gut microbiome that was the focus of research in the other topic areas. The microbiome measures also vary across studies and include different calculations of alpha and beta diversity. In addition, a wide range of individual microorganisms have emerged as having reduced or increased abundance in different mental health conditions and the identification of these taxa is based on different research methods. For example, In the case of Alzheimer’s disease, associated taxa have been found in animal models of other disorders; those identified in schizophrenia are varied and some overlap with those found in other psychiatric disorders. Much further work remains to determine how the identified taxa and their metabolites map onto pathways that may play a role in the pathophysiology of mental health conditions. Additional challenges include consideration of the many other factors which contribute to the composition of the microbiome, and which confound the association of the microbiome with mental health conditions. As noted earlier, these factors include diet, use of antibiotics, and substance use; of further relevance to psychiatric

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populations are tobacco smoking, use of antipsychotic and antidepressant medications, and the level of physical activity (Antinozzi et al. 2022; Patangia et al. 2022; Gill et al. 2022). There are also significant differences among persons based on geography and ethnicity that need to be taken into account (Gupta et al. 2017). Despite these methodological challenges, even early research on the microbiome can lead to hypotheses that may be tested in clinical trials. As noted in the sections on suicidality and longevity, trials of probiotics have been proposed but have not yet been performed to target these outcomes. Trials of probiotics have been carried out for other psychiatric conditions (Nikolova et al. 2021; Dickerson et al. 2014, 2018), but more studies are needed to determine the optimal make-up of the probiotic supplements and their effectiveness for the clinical issue under study as well as the mediating effects on the microbiome. Trials of probiotics and related products such as prebiotics and synbiotics have the advantage of being relatively low risk, inexpensive, and acceptable to participants (Zuccotti et al. 2008). In addition, probiotics may be studied as interventions to prevent relapse or symptom exacerbation as well as for symptom reduction (Dickerson et al. 2018). Future studies of the microbiome in mental health conditions would benefit from standardization of sample collection, storage procedures, and the methods of data analysis to help in terms of obtaining reproducible results. It would be further helpful for researchers to share samples, extracted DNA, and sequencing data in order to harmonize their studies. In addition, it will be important to systematically address the potential effects of diet and medications, both psychiatric and somatic, on the microbiome in study populations. While still in its infancy, research to date indicates a role for the microbiome in the etiopathogenesis of mental health disorders and conditions. Interventions are available which may modulate the microbiome and lead to clinical improvement. This research offers great promise in terms of developing new methods for assessment and treatment. Acknowledgments This work was supported by the Stanley Medical Research Institute grant # 07-1690 (FD); National Institutes of Health grants # U54GM133807 (FG-V), 2U54MD007600 (FG-V), 5P20GM103475-17 (FG-V); National Institute of Mental Health grants # 2 R01 MH094151-08 (DVJ), 1 R01 MH115127-02 (DVJ), 3 R01 MH115127-03S1 (DVJ), K23 MH118435 (TTN), and by the Sam and Rose Stein Institute for Research on Aging at University of California San Diego (DVJ).

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Influences of the Immune System and Microbiome on the Etiology of ASD and GI Symptomology of Autistic Individuals Amanda Kim, Corina R. Zisman, and Calliope Holingue Contents 1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Background on Autism Spectrum Disorder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 The Intersection of the Immune System and Microbiome on Autism Etiology . . . . . . . . . . . 3.1 Maternal Immune Activation and Neurodevelopmental Outcomes in Offspring . . . 3.2 The Microbiome Modulates the Impact of the Immune System on Neurodevelopment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Other Potential Factors Involved in the MIA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Links Between Microbiome/Microbial Interventions and GI Symptoms in ASD . . . . . . . . . 4.1 Observational Studies Linking Microbiome Composition with GI Symptoms . . . . . 4.2 Experimental Studies Linking the Microbiome with GI Symptoms . . . . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Caveats and Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Autism Spectrum Disorder is a developmental condition associated with impairments in communication and social interactions, and repetitive and restricted behavior or interests. Autistic individuals are more likely to experience gastrointestinal (GI) symptoms than neurotypical individuals. This may be partially due to dysbiosis of the gut microbiome. In this article, we describe the interaction of the microbiome and immune system on autism etiology. We also summarize the links between the microbiome and gastrointestinal and related symptoms among autistic A. Kim Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA C. R. Zisman Department of Psychology, Pennsylvania State University, University Park, PA, USA C. Holingue (*) Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 141–162 https://doi.org/10.1007/7854_2022_371 Published Online: 17 June 2022

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individuals. We report that microbial interventions, including diet, probiotics, antibiotics, and fecal transplants, and immune-modulating therapies such as cytokine blockade during the preconception, pregnancy, and postnatal period may impact the neurodevelopment, behavior, and gastrointestinal health of autistic individuals. Keywords Autism · Etiology · Gastrointestinal · Immune · Microbiome

1 Objectives In this paper, we set out to accomplish two objectives. Our first objective is to describe the intersection, or interaction, of the microbiome and immune system on neurodevelopment and autism etiology more specifically. About 80% of the immune system is in and around the intestinal mucosa (Critchfield et al. 2011) and microbes produce both pro- and anti-inflammatory cytokines (Heberling et al. 2013). Gut microbiota and the immune system both influence neurodevelopmental processes (Doenyas 2018). Studies of germ-free mice show that gut microbiota colonization modulates basic neurodevelopmental processes, including neurogenesis, neuronal differentiation and survival, myelination, formation, and integrity of the blood–brain barrier, development and maturation of microglia, expression of neurotrophins, neurotransmitters and their receptors, apoptosis, and synaptic pruning (Sharon et al. 2016). Further, colonization with diverse microbiota in early life is critical for proper immune regulation and development (Slattery et al. 2016). As Vuong and Hsiao reviewed, gut microbiota demonstrate bidirectional interactions with the immune system (Vuong and Hsiao 2017). Thus, there is a clear rationale for studying the interplay between the microbiome and immune system in ASD etiology. Several excellent articles review the growing literature linking the microbiome and immune system with neurodevelopment (Vuong and Hsiao 2017; Careaga et al. 2017; Meltzer and Van de Water 2017; Onore et al. 2012; Gebrayel et al. 2022; Saurman et al. 2020; Davies et al. 2021; Liu et al. 2019). However, review articles on the intersection of these systems on autism etiology are considerably sparser. Of note, Paysour et al. (2019) provide a concise and excellent review of the interaction of maternal immune activation and the microbiome on the risk of autism-related disorders. In addition, Doenyas (2018) reviews the common mechanisms by which gut microbiota and inflammation may influence synaptic and connectivity neuronal properties during development, and how early-life probiotics may modulate neurodevelopment. Our second objective is to summarize the links between the microbiome and gastrointestinal and related symptoms among autistic individuals. The GI microbiota is an important factor in physiological homeostasis. It plays a vital role in the metabolism and maintenance of immune homeostasis and may influence the central nervous system (CNS) activities through neural, endocrine, and immune pathways (Sampson and Mazmanian 2015). Research has shown that the microbiome of

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autistic children tends to differ from those of neurotypical or non-autistic children. However, the microbiome alterations between autistic and non-autistic individuals are inconsistent across studies, with the exception of somewhat distinguishable patterns in Prevotella, Bifidobacterium species, Firmicutes at a phylum level, and Clostridiales clusters (including Clostridium perfringens) (Ho et al. 2020). There are many possible reasons for these inconsistencies across studies, including small sample sizes, different comparison groups, failure to control for confounders, and variations in experimental and analytic techniques (Saurman et al. 2020). However, another important consideration is that the heterogeneity of microbiome profiles in ASD reflects the heterogeneity of ASD, including GI symptoms and diet. A systematic review conducted by Davies et al. focused on how the microbiome may alter the presentation of ASD, including GI symptoms. The review suggests that gut dysbiosis may increase intestinal permeability, leading to more severe GI symptoms and a systemic inflammatory response. The review by Davies et al. highlights the need for future studies to understand the exact level of contribution of altering the gut microbiome on the clinical manifestations of ASD (Davies et al. 2021). As described further below in this chapter, empirical microbiome research is beginning to help us to better understand how the microbiome affects gastrointestinal (GI) symptoms, how prebiotics can alleviate these GI symptoms, and how fecal samples can help us further investigate the microbiome, including among autistic individuals (Vuong and Hsiao 2017; Saurman et al. 2020; Ho et al. 2020). The immune system functions as a defense mechanism against disease- or illnesscausing pathogens. A regulated immune system consists of pro- and antiinflammatory signaling; however, a dysregulated immune system may experience harmful inflammation when it responds to physiological changes initiated by pathogens. Allergies, asthma, and autoimmune disorders are all associated with dysfunction in the immune system (Masi et al. 2015). An increasing body of evidence indicates a strong inflammatory state associated with ASD (Croonenberghs et al. 2002; Siniscalco et al. 2018). This state of inflammation is often linked to the dysfunction of the immune system (Brigida et al. 2017). A growing amount of evidence suggests that a subgroup of autistic individuals has some form of immune system dysregulation (Croonenberghs et al. 2002; Mead and Ashwood 2015). Identifying these subgroups and linking cellular immunophenotypes to different symptoms may be crucial to defining a specific group of patients with immune dysfunctions as a major etiology underlying behavioral symptoms and co-occurring conditions (Masi et al. 2015; Gładysz et al. 2018; Ashwood et al. 2011; Inga Jácome et al. 2016). However, most of the literature thus far has focused on links between the immune system and core features of ASD, namely social and communication impairments (Ashwood et al. 2011; Mostafa and Al-Ayadhi 2011; Grimaldi et al. 2018; Emanuele et al. 2010), which are outside the scope of this paper.

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2 Background on Autism Spectrum Disorder Autism Spectrum Disorder (ASD) is a developmental condition associated with impairments in communication and social interactions, and repetitive and restricted behavior or interests (American Psychiatric Association D, Association AP 2013). Across the United States, ASD has a cumulative prevalence of about 2.8% of children between 3 and 17 years old (Xu et al. 2019; Newschaffer et al. 2007). Male children have a significantly higher prevalence of ASD, with a male to female ratio of about 4.2 to 1 (Maenner et al. 2021). The etiology of ASD is still largely unknown. However, various biological and environmental factors have been indicated in ASD development. While ASD is a highly heritable condition with heritability estimates ranging from 38% to 90% (Sandin et al. 2017), the inheritance process is still unclear, despite over 100 candidate genes being identified and investigated (Bacchelli and Maestrini 2006). In addition to etiologic genetic factors, there are a variety of pre- and peri-natal environmental factors implicated in ASD etiology, including parental age, environmental chemicals, air pollution, prenatal maternal bacterial or viral infections, and prenatal maternal medication use (Lyall et al. 2017). While these factors have all been identified as potential etiological components of ASD, much of this work is observational in nature (i.e., causality is not certain), and the pathways from these environmental exposures to ASD development are mostly unknown (Lyall et al. 2017). To explain the heterogeneity of etiological hypotheses, researchers are beginning to fully evaluate the potential of a gene-by-environment interaction in ASD with some promising results (Volk et al. 2014; Schmidt et al. 2011; Mazina et al. 2015). Compared to the general population, autistic individuals have higher rates of co-occurring medical and mental health disorders (Croen et al. 2015; Lai et al. 2019). A recent meta-analysis indicated that rates of almost every mental health condition are increased in individuals on the spectrum compared to neurotypical individuals, with particularly high rates of attention-deficit hyperactivity disorder, and anxiety disorders (Lai et al. 2019). In addition to higher likelihood of mental health conditions, autistic individuals have a significantly higher risk of medical conditions, including autoimmune diseases, gastrointestinal disorders, diabetes, obesity, seizures, sleep-wake disorders, and various other chronic conditions (Croen et al. 2015). The underlying etiological factors which may cause high comorbidity rates are still largely unknown, though the gut–brain axis may be involved (Tye et al. 2019). It is well established in the literature that gastrointestinal (GI) symptoms have an increased prevalence in autistic compared to neurotypical individuals (Marler et al. 2017). A review of the literature regarding GI symptoms found a median prevalence of almost 47% of any GI symptom (i.e., diarrhea, constipation, etc.) (Holingue et al. 2018), indicating this is a widespread experience in the autistic community, which can result in significant distress (Holingue et al. 2021). GI symptoms have also been found to be associated with repetitive behaviors and stereotypies, though we contend

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that these behaviors may be coping behaviors for anxiety (Chakraborty et al. 2021). Treatments for GI symptoms often include restrictive diets and probiotics, which show moderate success in at least some individuals on the spectrum (Madra et al. 2020).

3 The Intersection of the Immune System and Microbiome on Autism Etiology 3.1

Maternal Immune Activation and Neurodevelopmental Outcomes in Offspring

The genetic and environmental causes of ASD and related neurodevelopmental conditions have yet to be fully elucidated. However, growing evidence has established the prenatal immune environment as crucial for etiology over the last five decades (Careaga et al. 2017; Estes and McAllister 2015; Fox-Edmiston and Van de Water 2015; Patterson 2011). The suggestion of a link between maternal infection and ASD dates back to the 1964 rubella pandemic, in which the incidences of autism and schizophrenia increased from less than 1% among the unexposed population to about 13% and 20%, respectively (Estes and McAllister 2016; Patterson 2009). Subsequent studies of other infectious agents have also shown an elevated likelihood of neuropsychiatric and neurodevelopmental conditions, though some studies have failed to replicate these associations (Careaga et al. 2017; Meltzer and Van de Water 2017; Estes and McAllister 2016). Cytokines have also been extensively linked with ASD or related neurodevelopmental outcomes in both animal and human studies (see Metzer and Van de Water for an excellent review (Meltzer and Van de Water 2017)). Collectively, this diverse set of studies suggests that maternal response to infectious agents is critical rather than a specific infectious agent (Meltzer and Van de Water 2017). Strong evidence for maternal immune response as a critical player in the etiology of neurodevelopmental conditions also comes from the animal literature. A growing body of animal studies has replicated this association and elucidates how maternal immune activation leads to neurodevelopmental changes in offspring. Further, this literature demonstrates that microbial and immune interventions have the potential to modify offspring neurodevelopment and behavior, as will be described in the next section of this paper. In the later 1990s and early 2000s, researchers found that maternal immune activation in pregnant rodents, via infection or a pro-inflammatory adjuvant, led to schizophrenia-like and ASD-like behaviors in offspring (Careaga et al. 2017; Shi et al. 2003; Fatemi et al. 1998a, b, 2000, 2002). The MIA model consists of a direct infection, via exposure to an infectious agent such as influenza, or administration of a pro-inflammatory adjuvant, often the double-stranded RNA and its mimic, polyinosinic acid:polycytidylic acid [Poly(I:C)], to a pregnant rodent. Consequently, immunological, neurodevelopmental, and behavioral changes are

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observed in the offspring (Meltzer and Van de Water 2017; Gilmore et al. 2005; Meyer et al. 2005; Urakubo et al. 2001; Zuckerman and Weiner 2005), which are used to generate insights about neuropsychiatric and neurodevelopmental conditions in humans. We note that, in this manuscript, we describe the MIA model in the context of autism specifically, but MIA has relevance to many other conditions. Indeed, Careaga and others have aptly stated that prenatal immune insult may be a primer for the altered trajectory of fetal brain development, and the resulting phenotype likely depends on other genetic and environmental factors (Careaga et al. 2017; Harvey and Boksa 2012; Meyer et al. 2011; Meyer 2014). In the now seminal studies, Shi and Colleagues (2003, 2009) infected pregnant mice (BALB/C and C57BL/6 strains) with either a single dose of human influenza or poly(I:C) and observed abnormal behavioral responses in the offspring, including deficits in prepulse inhibition in the acoustic startle response, deficits in exploratory behavior in open-field and novel-objects tests, as well as changes to cerebellar development. Malkova et al. (2012) expanded on these studies by injecting pregnant mice three times with poly(I:C) (using a quarter dose of that used by Shi and colleagues) and found lower rates and reduced total length of ultrasonic vocalizations among male offspring, as well as socio-behavioral changes, deemed similar to the autism symptoms of reduced sociability and increased repetitive behaviors, as measured by isolation test, three-chamber test, and marble burying (see Kaidanovich-Beilin et al. (2011)). This sort of experiment has been replicated and expanded on numerous times since (Careaga et al. 2017; Meltzer and Van de Water 2017; Paysour et al. 2019; Estes and McAllister 2016). Subsequent work has elucidated the immunological mechanism by which the MIA leads to these changes in the offspring. Notably, Choi et al. (2016) found that activation of the IL-17α pathway in the fetal brain, induced by IL-17α into the fetus, or poly(I:C) or IL-6 injection into the pregnant mother, results in MIA-associated phenotypes in the offspring. Further, Lammert et al. (2018) confirmed that blockage of IL-17α using monoclonal anti-mouse IL-17α neutralizing antibody prevents MIA-associated outcomes in the offspring of MIA mothers (specifically in the Tac mouse, which will be elaborated on below). Research in the last 5 years is beginning to show how this immune response affects the brain of MIA-exposed offspring. For example, Yim et al. (2017) have demonstrated that cortical abnormalities from MIA are preferentially located in a region that encompasses the dysgranular zone of the primary somatosensory cortex. In fact, activation of pyramidal neurons in this cortical region induces MIA-associated behavioral abnormalities in wild-type animals, i.e., those not exposed to MIA. Moreover, reduction of neuronal activation rescues these phenotypes among MIA-exposed offspring. Kim et al. (2022) have recently shown that IL-17α leads to postnatal alterations in the chromatin landscape of naive CD4+ T cells, which may help explain why children exposed to inflammation during gestation have an increased likelihood of both inflammatory diseases as well as neurodevelopmental conditions. In fact, the authors found that the maternal gut microbiota mediated this link between IL-17α and chromatin remodeling (Kim et al. 2022).

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While MIA exposures are quite common in pregnancy, neurodevelopmental conditions like ASD are comparatively rare in terms of incidence. Therefore, other factors must be at play. The microbiome has emerged as one such key factor.

3.2

The Microbiome Modulates the Impact of the Immune System on Neurodevelopment

Hsiao and colleagues (Hsiao et al. 2013a, b) were among the first to demonstrate the interplay between the microbiota and immune system on offspring outcomes. As expected, this team showed that offspring of MIA mice, as modeled by poly(I:C), showed behavioral abnormalities. Further, the offspring had defects in intestinal integrity (i.e., permeability) and alterations in the gut microbiome (Hsiao et al. 2013a). However, oral treatment of the offspring with the human commensal Bacteroides fragilis corrected the gut permeability, altered microbial composition, and improved the defects in communicative, stereotypic, anxiety-like, and sensorimotor behaviors (Hsiao et al. 2013a). B. fragilis was used in this study because it had previously been shown to correct GI pathology in mouse models of colitis (Mazmanian et al. 2008) and protect against neuroinflammation in mouse models of multiple sclerosis (Ochoa-Repáraz et al. 2010). In a separate publication, Hsiao et al. (2013b) showed that MIA offspring had an altered serum metabolomic profile. They treated naive mice with a metabolite (4EPS) known to be increased by MIA and restored by B. fragilis. Administration of this metabolite led to behavioral abnormalities in the offspring, suggesting that metabolites from gut bacteria may be responsible for the behavioral changes in offspring. In 2017, Kim and Colleagues (2017) carried out work that further cemented the importance of the microbiome in modulating these offspring outcomes. As described above, previous research demonstrated that in pregnant rodents, IL-17α produced by Th17 helper cells induce behavioral and cortical abnormalities in the offspring (Choi et al. 2016). However, it was unclear whether other maternal factors were needed to promote these MIA-associated phenotypes and how MIA leads to T cell activation and subsequent IL-17α increase. Accordingly, Kim et al. treated wild-type C57BL/6 mice with vancomycin, a broad-spectrum antibiotic, before either saline or poly(I:C) administration. Pretreatment of poly(I:C) injected mothers with vancomycin prevented the development of behavioral abnormalities among MIA-exposed offspring. Further, the offspring failed to develop cortical patches. The team found that vancomycin treatment led to a reduction in the proportion of Th17 cells in the small intestine of the pregnant rodent and a subsequent decrease in IL-17α levels in the maternal plasma. This suggested that vancomycin affects commensal bacteria, specifically segmented filamentous bacteria, critical for the MIA-associated response in offspring. Importantly, Kim et al. (2017) also found that segmented filamentous bacteria presence in the maternal small intestine and immune effects of poly(I:C) are needed

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during pregnancy, not during post-natal nursing, to generate MIA-associated behavioral phenotypes in the offspring. In sum, when accompanied by strong signaling for IL-17α in the mother, microbiota-specific gut Th17 cells in pregnant rodents are sufficient to produce neurodevelopmental abnormalities in the offspring. Accordingly, women with gut microbial communities that promote excessive TH17 cell differentiation and who are exposed to inflammation during pregnancy may be more likely to give birth to autistic children (Kim et al. 2017). Th17 cells are a subset of CD4+ T cells and are critical for human responses against extracellular bacteria and fungi, play a role in gut homeostasis, and have been linked to inflammatory and autoimmune diseases (Choi et al. 2016; Wilke et al. 2011; Osokine and Erlebacher 2017). Lammert et al. (2018) also published work supporting the joint importance of the microbiome and immune systems on neurodevelopment. To do so, Lammert and colleagues took advantage of the established differences in intestinal microbial profiles and subsequent immune response differences between C57BL/6 mice from the Jackson Laboratory (Jax) and Taconic Biosciences (Tac) (Kim et al. 2017; Ivanov et al. 2009; Sivan et al. 2015). Specifically, Tac mice have a skewed T cell response toward IL-17α production by the commensal segmented filamentous bacteria (Ivanov et al. 2009). First, Lammert and colleagues treated C57BL/6 Jax and Tac mice with either Poly(I:C) or saline (control). Poly(I:C) injection led to MIA-associated outcomes in the offspring of Tac, but not Jax, mice. Tax mice offspring had reduced frequency and shorter ultrasonic vocalizations, lack of preference for novel mouse over a novel object, and increased marble burying, together representing social and communication deficits and repetitive behavior according to the MIA model of autism. The authors performed a fecal transplantation experiment to confirm whether these MIA-induced differences were due to microbiota differences or genetic drift between Tac and Jac mice. They co-housed Jax mice with Tac mice for at least 2 weeks before MIA induction. Offspring of these co-housed Jax mice demonstrated the same neurodevelopmental changes as the Tac mice. As expected, the conventionally housed Jax mice did not. The researchers confirmed that co-housing led to SFB colonization in the co-housed Jax mice, similar to the levels found in the Tac mice. Together, this shows that the prenatal microbiome environment is critical for the development of these neurodevelopmental outcomes in the context of MIA exposure. Lastly, Lammert and colleagues found that changes in the microbiota composition of co-housed Jax dams also led to increased secretion of Il-17α following poly(I:C) injection, which suggests that the maternal microbiota may impact inflammatory cytokine production when exposed to MIA. Accordingly, the blockade of IL-17α during gestation in the MIA-exposed mothers prevented the Tac and the co-housed Jax offspring from developing the neurodevelopmental abnormalities (Lammert et al. 2018). Most recently, based on the above extant literature and related studies (Tanabe et al. 2008), Wang and colleagues (Wang et al. 2019) hypothesized that oral probiotics administered during pregnancy could be an effective strategy in mitigating the offspring neurodevelopmental outcomes in the context of MIA. The researchers found that using a probiotic compound (containing Bifidobacteria

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bifidum, Bifidobacteria infantis, Lactobacillus helveticus, fructooligosaccharides, and maltodextrin) prevented the autism-like behaviors in the offspring induced by MIA. Further, the increases in IL-6 and IL-17α in the maternal serum and fetal brains, typically associated with MIA, were prevented. Parvalbumin positive (PV+) neuronal loss and decrease in the γ-aminobutyric acid (GABA) levels in the prefrontal cortex of adult offspring were also prevented. Interestingly, the behavioral deficits in offspring of MIA-exposed dams were slightly, but not entirely, improved through cross-fostering during the postnatal period with mothers treated with oral probiotics (Wang et al. 2019). Importantly, these animal experiments demonstrate that there are multiple critical windows (preconception, pregnancy, and offspring early-life) relevant for understanding etiology and intervention in the context of maternal immune activation and the microbiome. Lammert et al. (2018) showed that preconception microbiota transplantation could influence the neurodevelopment of the offspring of MIA-exposed dams. Wang et al. (2019) demonstrated that microbial interventions (e.g., oral probiotics) during pregnancy have the potential to modulate MIA-associated outcomes in offspring. Cross-fostering during the postnatal period led to a slight improvement in neurodevelopmental outcomes. Further, Kim et al. (2017) found that the presence of SFB in the maternal small intestine and a pro-inflammatory stimulus are needed during pregnancy, not the post-natal nursing window, to generate MIA-associated behavioral phenotypes in the offspring. However, Hsiao and Colleagues (2013a, b) showed that oral treatment of MIA-offspring with human commensal Bacteroides fragilis corrected the gut permeability, altered microbial composition, and improved defects in behavior. Further, treatment of naïve mice with bacterial metabolite 4EPS, which is increased by MIA and restored by B. fragilis, led to behavioral abnormalities. More research is needed to understand how the timing of the MIA and other exposures influence the heterogeneity of offspring outcomes.

3.3

Other Potential Factors Involved in the MIA Model

As noted by Comer 2020, MIA is a risk factor for both schizophrenia and ASD, yet some of the neurological features observed in these conditions seem to be opposing. Schizophrenia is characterized by a substantial loss of gray matter, resulting in hypoconnectivity between the anterior hippocampus and prefrontal cortex (Blessing et al. 2020; Comer et al. 2020; Vita et al. 2012), while ASD seems to be associated with hyperconnectivity (Comer et al. 2020; Supekar et al. 2013). Given the heterogeneity of neurodevelopmental responses to MIA, there must be other factors at play. An excellent review on this topic was written by Meyer in 2019 (Meyer 2019). Potential sources include the specificity of infectious pathogens, immune mediators, the intensity of MIA, the timing of MIA, maternal diet (also see Ruskin et al. 2017; Vuillermot et al. 2017), gestational diabetes mellitus, maternal stress, the maternal

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gut microbiome, of course, postnatal factors, and family history and genetic background (Meyer 2019). Osokine and Erebach also point to the placenta as a potential regulator of Il-17α travel from maternal plasma, where it is produced by Th17 cells, to the fetal brain (Osokine and Erlebacher 2017). Of note, Tsukada and colleagues provide an elegant review of the molecular mechanisms relevant to the placenta that underly models of neurodevelopmental conditions in MIA (Tsukada et al. 2019). In MIA animal models, important experimental factors may influence MIA response, including differences in immunogen manufacture, the timing of immunogen administration, dosage, route of administration, housing conditions, the timing of cage changes, and mouse strain used (Comer et al. 2020; Careaga et al. 2018; Kentner et al. 2019; Kowash et al. 2019). Sex is likely also an important modifier of the MIA-neurodevelopment link. Indeed, a growing number of studies now highlight the sex-dependent effects of MIA on the neurodevelopmental, neuropathological, and behavioral outcomes of rodent offspring (Ruskin et al. 2017; Xuan and Hampson 2014; Haida et al. 2019). We invite the reader to read a salient review on this topic by Ardalan et al. (2019). The possibility that sex modifies the impact of MIA on offspring outcomes is especially relevant given that in humans, sex is often related to prevalence, age of onset, and progression of neurodevelopmental conditions including ASD (BaronCohen et al. 2011). Further, both animal and human studies are beginning to link sex with the microbiome and gut–brain axis more broadly (see Kim et al. 2020; Holingue et al. 2020a). Together, this suggests that sex, and the associated biological correlates of sex, may be critical players in the interaction between the immune system, microbiome, and neurodevelopmental and behavioral outcomes in offspring. We expand briefly on one factor that may contribute to the heterogeneity of MIA responses: genetics. ASD has a strong genetic and environmental basis, and considering the interaction between genetics and environment is essential in elucidating the etiology of conditions like ASD (Newschaffer et al. 2007; Lyall et al. 2017; Chaste and Leboyer 2022; Newschaffer et al. 2002; Abrahams and Geschwind 2008; Nardone and Elliott 2016). In terms of epidemiologic literature, work by Mazina and Colleagues (2015) found that among 1,971 children with an ASD diagnosis from the Simons Simplex Collection, there was a significant interaction between the presence of copy number variants and history of maternal infection during pregnancy on autistic symptomatology, i.e., individuals with both copy number variants and maternal infection history had higher rates of social-communication impairments and repetitive/restricted behaviors. Of note, there was no significant interaction between copy number variants and prenatal infections on cognitive and adaptive functioning in this sample, leading the authors to suggest the effects of this interaction are specific to ASD rather than global neurodevelopment (Mazina et al. 2015), However, this needs to be further studied. Similarly, Schwarter et al. (2013) found that strain (C57BL/6J versus BTBR T+ tf/J) also modified the impact that MIA had on mice offspring. Strain-specific interactions were found in social approach, ultrasonic vocalizations, repetitive grooming, and marble burying behaviors. Further, persistent dysregulation of the adaptive immune system was observed in the BTBR mice only, suggesting that this

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may be mediating the link between strain and MIA-induced offspring outcomes. However, another possibility is that the Disrupted in Schizophrenia 1 (Disc1) gene may be involved, as BTBR mice are an inbred strain derived from a mouse that lacks the Disc1 gene. Indeed, prior research has shown that transgenic mice with a mutant form of this gene have increased symptoms of anxiety, depression, social behavior deficits, schizophrenia-related behaviors, and changes to the cytokine profile, when born to MIA-exposed dams (Abazyan et al. 2010; Lipina et al. 2013). In addition, Morais et al. (2018) found that MIA in NIH Swiss mice versus C57BL6/J mice had differential effects on offspring, specifically, anxiety and depressive-like symptoms, endocrine response to stress and gut permeability, and vasopressin receptor 1a mRNA expression in the hypothalamus. This highlights the importance of genetic background on offspring outcomes in the context of MIA. The exact mechanism by which genetic background interacts with MIA is not known from this study, though the authors hypothesized that hypothalamic Avpr1a, which codes for a protein that acts as a receptor for arginine vasopressin, may be partially responsible; further investigation into AVP signaling in the context of the MIA model is needed. Other possible factors driving these strain differences include intestinal permeability and microbial composition, particularly in light of the previously reported findings by Kim et al. (2017). Lastly, gut microbiota products and inflammatory cytokines may influence host genetic expression directly, though it has been shown in cancer but not ASD literature (Hullar and Fu 2014). Thus, it is biologically plausible that gut microbiota metabolites and cytokines may influence the expression of ASD-susceptibility genes, which has critical implications for our understanding of gene-environment interactions (Doenyas 2018).

4 Links Between Microbiome/Microbial Interventions and GI Symptoms in ASD 4.1

Observational Studies Linking Microbiome Composition with GI Symptoms

While most research to date has focused on microbiome differences associated with ASD diagnosis itself, a growing body of literature highlights how microbiome composition and diversity are associated with phenotypes among autistic individuals. This section presents data on links between microbiome and GI symptoms, though we note when behavioral changes are also identified in these same studies. Strati et al. characterized the bacterial and fungal gut microbiota communities in 40 autistic individuals and 40 neurotypical controls. In addition to identifying microbiome differences between the two groups, the authors also found that constipation was associated with different bacterial patterns in autistic compared to neurotypical individuals. Specifically, autistic individuals with constipation had

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higher bacteria levels belonging to Escherichia/Shigella and Clostridium cluster XVIII, compared to non-autistic individuals without constipation (Strati et al. 2017). Next, Kong et al. studied 20 autistic individuals and 19 neurotypical family members as controls. Given the clinical heterogeneity in ASD, the authors sought to explore associations between microbiome markers and clinical indicators in ASD. Indeed, they identified links between the microbiome and the presence of allergies, abdominal pain, and abnormal dietary habits in autistic individuals (Kong et al. 2019). Together, these studies (Strati et al. 2017; Kong et al. 2019) provide preliminary evidence for using the microbiome as markers of co-occurring conditions, especially GI conditions, within autistic adults. In animal models, commensal clostridia interact with intestinal mucosa to regulate serotonergic pathways (Reigstad et al. 2015; Yano et al. 2015). Therefore, Luna and colleagues evaluated whether microbiome-neuroimmune profiles from rectal biopsy specimens and blood differed across the following groups: autistic children with functional GI disorders, neurotypical children with functional GI disorders, and neurotypical children without abdominal pain. The authors found that the autistic children with functional GI disorders had a significant increase in several mucosaassociated Clostridiales and decreases in Dorea, Blautia, and Sutterella. Further, when stratified by abdominal pain, multiple organisms in this group correlated significantly with cytokines, namely IL-6, IL-1, IL-17α, and interferon-γ. Il-6 and tryptophan release from fecal mucosal biopsy specimens were highest in autistic children with functional GI disorders, while serotonergic metabolites were increased in the broad group of children with functional GI disorders. Lastly, pro-inflammatory cytokines were significantly correlated with tryptophan and serotonin and several Clostridiales taxa previously associated with ASD. In sum, this study identified distinct microbial signatures in the mucosa of autistic children with functional GI disorders that correlated with cytokine and tryptophan homeostasis. Given the strong links between the microbiome and many environmental factors, taking into account potential confounders is critical. Yap et al. (2021) recently published work including 247 children (ages 2–17), including 99 autistic children, 51 paired non-autistic siblings, and 97 unrelated non-autistic children. In this large metagenomics study, the authors found that autism itself was not strongly associated with the gut microbiome. In fact, only one taxon (Romboutsia timonensis) was associated with an autism diagnosis. However, robust links were identified between the microbiome and dietary habits, stool consistency, and age. The connection between a less diverse microbiome and a less diverse diet is especially noteworthy, given that restricted interests (a core trait of ASD) are associated with a less diverse diet. This highlights the importance of adjusting for confounding factors and suggests that microbial differences in autistic individuals may be driven by diet (Yap et al. 2021).

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Experimental Studies Linking the Microbiome with GI Symptoms

Moving beyond observational studies, experimental studies also show convincing links between microbial modulation and co-occurring conditions among autistic individuals, especially GI symptoms. Inoue et al. conducted a study focusing on using a prebiotic dietary water-soluble fiber obtained from hydrolyzed guar gum on a group of autistic children with constipation. The study found that this prebiotic significantly increased the frequency of defecation per week and altered the gut microbiota. This study demonstrates that dietary supplementation with partially hydrolyzed guar gum to autistic children with constipation may improve constipation and symptoms associated with gut microbiome dysbiosis (Inoue et al. 2019). Grimaldi et al. investigated the use of exclusion diets (gluten-free casein-free) and a 6-week prebiotic intervention (galacto-oligosaccharide content Bimuno®) in autistic children. Following the exclusion diet, children had significantly lower abdominal pain and bowel movement scores, and changes in microbiome composition, namely lower abundance of Bifidobacterium species and Veillonellaceae family and higher presence of Faecalibacterium prausnitzii and Bacteroides species. Significant correlations were also identified between the bacterial populations and fecal amino acids. The prebiotic intervention following the exclusion diet. Changes included a significant increase in the Lachnospiraceae family, fecal and urine metabolites, and improvements in anti-social behavior, measured by the Autism Treatment Evaluation Checklist (Grimaldi et al. 2018). Another study led by Arnold et al. (2019) conducted a randomized pilot trial using Visbiome, a probiotic containing species Lactobacillus and Bifidobacterium. The Visbiome formulation was found to have health benefits in autistic children with GI symptoms. Results showed significant improvement in GI complaints within the probiotic group compared with the placebo group. Sanctuary et al. also conducted a study to assess the tolerability of a probiotic (Bifidobacterium) in combination with a bovine colostrum product, a source of prebiotic oligosaccharides. The study found that the combination treatment was well-tolerated by the participants in their cohort, alongside a reduction in the frequency of GI symptoms and a reduction in the frequency of atypical behaviors (Sanctuary et al. 2019). Lastly, Kang et al. conducted Microbiota Transfer Therapy (MTT) to evaluate its impact on the gut microbiota composition and GI and ASD symptoms of a small cohort of autistic children (Kang et al. 2017). MTT consists of a 2-week antibiotic treatment, a bowel cleanse, and an extended fecal microbiota transplant. Scores on the Gastrointestinal Symptom Rating Scale decreased by about 80% at the end of the intervention. Evaluation of MTT showed that the overall bacterial diversity and the abundance of Bifidobacterium, Prevotella, and Desulfovibrio increased post-MTT. Kang et al. conducted a follow-up evaluation (Kang et al. 2019) of this cohort 2 years after the MTT was completed and found that the improvements in GI symptoms (and changes in ASD-related symptoms) were maintained even after the completion of the treatment. Such findings demonstrate the potential effectiveness of MTT as a

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therapy for the treatment of GI symptoms in autistic children. A major limitation of this study is the lack of a placebo-control arm. Randomized, blinded, placebocontrolled trials are necessary to confirm the effect of these therapies on GI symptoms (Kang et al. 2017, 2019).

5 Conclusions 5.1

Caveats and Considerations

There are several caveats and considerations that are essential for the interpretation of the literature reviewed in this article. First, much of the research regarding the etiology of ASD is based on animal models. Studying the intersection of microbial, immune, and other factors on neurodevelopment is considerably more difficult in humans than in animals. Therefore, the MIA model has provided an opportunity to study the effects of prenatal immune challenges in a controlled environment, which cannot be done with humans. It is intended to produce an animal with changes in behaviors that are related to core features of the disease or condition being studied, or in this case, early-onset deficits in social behavior and communication, repetitive behaviors, or restricted interests, as these are core features of ASD in humans (Careaga et al. 2017; American Psychiatric Association D, Association AP 2013; Silverman et al. 2010). Yet, there are limitations to these models, including concerns about construct validity (i.e., etiologic relevance of the model to humans), face validity (i.e., the resemblance of model outcome measures to human features), and predictive validity (i.e., the response of the model to therapeutic agents used in humans) (Careaga et al. 2017; Patterson 2011; Nestler and Hyman 2010; Tordjman et al. 2007; Willner 1984). Careaga et al. provide an excellent review of these issues (Careaga et al. 2017). Next, it is essential to consider the complexity and heterogeneity of autism etiology and phenotypes. For example, in the context of autism etiology, microbiome modification of the immune response is likely not the only mechanism by which the immune system and microbiome interact to influence offspring outcomes. For instance, Foley et al. showed that brief prenatal exposure to elevated levels of microbial products (PPA/LPS) could induce changes in offspring social behavior across the life course of rats (Foley et al. 2014). Importantly, the immune system is one of many systems that may mediate the effects these microbial products have on the offspring. Other mechanisms include neurotransmitter synthesis/release, G-coupled receptor activation, oxidative stress, altered lipid profiles, mitochondrial dysfunction, and epigenetic alterations. Critically, these have been previously associated with neurodevelopmental conditions including ASD (Foley et al. 2014; Frye et al. 2013; Inoue et al. 2012; MacFabe 2012, 2013). In addition, maternal immune activation can exert effects through other mechanisms (e.g., oxidative stress, gene expression) beyond triggering an immune response in the offspring (Doenyas 2018). Similarly, there are likely other

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immune-related processes, besides maternal immune activation, that are at play in the etiology of autism. Notably, both the animal and human literature show there is the possibility of a distinct, maternal autoantibody-driven subset of ASD (see Metzer and Van de Water (2017) for more details). We also note that while research on the microbiome has generated a lot of excitement and interest in terms of its potential to decrease the burden of co-occurring conditions among autistic individuals, as a field, we must prioritize understanding not only the efficacy but also the safety of microbial interventions (Daliri et al. 2018; Harrington et al. 2021).

5.2

Implications

First, in terms of the etiology of autism, animal studies demonstrate that the interaction of the maternal and early-life microbiome may modulate neurodevelopmental outcomes, especially in the context of maternal immune activation. Indeed, Holingue et al. found that among women who received an antibiotic during pregnancy, influenza in trimester two was not associated with ASD, though among women who were exposed to an antibiotic, influenza in trimester two was associated with an increased likelihood of the child being diagnosed with ASD, even after adjustment for child sex, childbirth year, maternal age, gestational age, C-section delivery, and low birthweight. Though this finding needs to be replicated, it suggests antibiotic use may modify the influence maternal immune activation has on the likelihood of autism in the child (Holingue et al. 2020b). Second, both diet and GI symptoms/stool patterns are linked to microbiome composition. Indeed, as discussed by Yap and colleagues, these factors may be driving the microbiome differences identified in other ASD literature (Yap et al. 2021). However, we note that the lack of microbiome difference between autistic and non-autistic individuals, after adjustment for diet and GI symptoms, does not minimize the possibility that the maternal or infant microbiome is involved in the etiology of autism. Further, it potentially implicates the microbiome in the pathogenesis of gastrointestinal symptoms. Because of the sparseness of the literature, we were not able to review the joint influence of the microbiome and immune systems on phenotypes in autistic individuals. However, understanding the intersection of these systems on phenotypic heterogeneity is critical, as it is for etiology. More original research is needed to elucidate the interplay between the microbiome and immune system among autistic individuals. Due to the broad range of phenotypical variability of autistic individuals, a more comprehensive evaluation of the integration of microbiome, metabolomics, immunology, genetics, and other environmental factors is needed. This has implications for the prediction, etiology, and treatment of co-occurring conditions in ASD. In sum, microbial interventions, including diet, probiotics, antibiotics, and fecal transplants, and immune-modulating therapies such as cytokine

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blockade during the preconception, pregnancy, and postnatal period may impact the neurodevelopment, behavior, and gastrointestinal health of autistic individuals.

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Fungal Forces in Mental Health: Microbial Meddlers or Function Fixers? Emily G. Severance

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 The Microbiome and Gut-Brain Axis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Inflammation, Dysbiosis, and the Psychiatric Microbiome . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Overview of Fungal Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Pathogenic Fungi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 The Psychiatric Mycobiome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 DNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Immunoassays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Changing the Mycobiome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Fungi and the CNS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In the mental health field, the gut-brain axis and associated pathways represent putative mechanisms by which gastrointestinal (GI) microbes and their gene products and metabolites can access and influence the central nervous system (CNS). These GI-centered investigations focus on bacteria, with significant information gaps existing for other microbial community members, such as fungi. Fungi are part of a complex and functionally diverse taxonomic kingdom whose interactions with hosts can be conversely deadly and beneficial. As serious sources of morbidity and mortality, fungal pathogens can quickly turn healthy microbiomes into toxic cycles of inflammation, gut permeability, and dysbiosis. Fungal commensals are also important human symbionts that provide a rich source of physiological functions to the host, such as protection against intestinal injuries, maintenance of epithelial structural integrities, and immune system development and regulation. Promising treatment compounds derived from fungi include antibiotics, probiotics, and antidepressants. Here I aim to illuminate the many attributes of fungi as they are E. G. Severance (*) Stanley Division of Developmental Neurovirology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 163–180 https://doi.org/10.1007/7854_2022_364 Published Online: 12 May 2022

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applicable to overall improving our understanding of the mechanisms at work in psychiatric disorders. Healing the gut and its complex ecosystem is currently achievable through diet, probiotics, prebiotics, and other strategies, yet it is critical to recognize that the success of these interventions relies on a more precisely defined role of the fungal and other non-bacterial components of the microbiome. Keywords Fungus · Infection · Microbiota · Mycobiome · Psychiatry · Yeast

1 Introduction 1.1

The Microbiome and Gut-Brain Axis

Humans host a community of interacting microorganisms that hail from different higher order taxonomic groups and kingdoms: Archaea, bacteria, fungi, protozoa, and viruses. These microbes encode millions of unique non-human genes, embodying an immense collective of biological function known as the microbiome (Gilbert et al. 2018). The microbiome is most dense in the GI tract, where it actively engages in essential host activities such as digestion, vitamin and amino acid synthesis, nutrient absorption, metabolism, fiber fermentation, immune system development, maturation and protection, and hormone production and regulation (Dinan and Cryan 2017; El Aidy et al. 2014). Next generation genomic sequencing initiatives in conjunction with transcriptomics, proteomics, and metabolomics are revealing the taxonomic identities and relative compositions of many of these microbes, the diverse functions of microbially-expressed gene products and the inferred metabolic pathways in which these genes and microbes interact in the human host (Gilbert et al. 2018). The concept of a gut-brain axis under control by a realm of resident microbes and their novel molecular and cellular pathways makes the microbiome an intriguing source of untapped therapeutic potential for many diseases including complex brain disorders. The gut-brain axis is typically described as a two-way signaling pathway that connects the CNS with the enteric nervous system, thus linking peripheral intestinal functions with CNS-based biochemical and cognitive functions. Direct innervations of the gut-brain axis occur via the vagus nerve and other spinal afferents and efferents (Dinan and Cryan 2017; Erny et al. 2015). Certain neurotransmitters such as serotonin are predominantly found in the GI tract, where it is estimated that 95% of the body’s supply is generated. While gut secretory, signaling, and other beneficial functions have been primarily attributed to serotonin, this neurotransmitter may also contribute in a negative context to GI inflammation. The remaining 5% of the body’s serotonin is produced in the brain, where its effects on mood have long linked serotonin insufficiencies to depression and anxiety (Banskota et al. 2019). Short chain fatty acids (SCFAs) such as acetate, propionate, and butyrate, are products of microbial fermentation of dietary fiber, which also have widespread positive GI,

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metabolic and immune effects for the host. These compounds can cross the bloodbrain barrier (BBB) and may have promising therapeutic roles in stress reduction based on results from experimental models and clinical studies (González-Bosch et al. 2021; Joseph et al. 2017; Dalile et al. 2019). In reality, the gut-brain axis is more likely a complex, multi-directional network that incorporates many other systems, such as the circulatory, endocrine, and immune systems (Dinan and Cryan 2017; Boem and Amedei 2019; Powell et al. 2017).

1.2

Inflammation, Dysbiosis, and the Psychiatric Microbiome

Exposure to any number of environmental stressors can shift a microbiome out of balance and lead to a disease state. A low-level pervasive inflammation comorbid to psychiatric disorders is being rigorously reexamined in the context that this inflammation is related to imbalances of the gut-brain axis (Severance et al. 2012, 2014, 2020; Frank et al. 2021; Maes et al. 2012; Nguyen et al. 2019; Ohlsson et al. 2019). A dysbiotic microbiome can fuel a pathogenic, pro-inflammatory cycle resulting in compromised blood-gut and blood-brain endothelial barriers, a leaky gut, and a brain exposed to a variety of circulating gut microbial toxins, metabolites, and systemically circulating immune factors. Activation of the brain’s glial machinery to tag and remove material recognized as foreign or insufficient can lead to structural alterations and faulty neurotransmission (Severance and Yolken 2020a). The redundancy in the gut, circulation, and brain of classic immune pathway components such as those of the complement system reconciles how GI inflammation can have consequences such as aberrant synaptic pruning in the brain (Severance and Yolken 2020b; Severance et al. 2018, 2021; Nimgaonkar et al. 2017). Disruptions in the microbiome are evidenced by disease-associated shifts in microbial inventories. Current microbiome studies of psychiatric disorders continue to document case–control differences in major bacterial taxa and inferred metabolic pathways, as well as biochemical brain and behavioral changes in rodents subjected to human-to-rodent microbiome transplant experiments (Li et al. 2020; CastroNallar et al. 2015; Dickerson et al. 2017; Yolken et al. 2015; Shen et al. 2018; Xu et al. 2019; Zheng et al. 2019; Zhu et al. 2019, 2020; Vindegaard et al. 2019; Nguyen et al. 2021). Modest improvements of psychiatric, cognitive, and other symptoms with probiotics, dietary changes, and other GI adjunctive treatments suggest that these dysbioses may be correctable (Severance et al. 2018; Minichino et al. 2020; Dickerson et al. 2014, 2018; Tomasik et al. 2015; Ghannoum et al. 2021). Thus, identifying the sources of this inflammation is critical for the design of effective treatments. Some have speculated that any GI condition comorbid to psychiatric disorders may be simply the result of medication; however, studies of unmedicated first-episode case–control cohorts and of the US military indicate that gut-related immune alterations can be detected prior to symptom onset and before medication is administered (Li et al. 2013; Weber et al. 2018).

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Although our knowledge about the microbiome is expanding at unprecedented rates for such an emerging field, this nascency has fostered a narrow focus taxonomically. Most studies of the microbiome focus on bacteria, while other organisms such as fungi likely equally contribute to inter- and intra-kingdom dynamics of resident commensals, but are much less well-studied (Ghannoum et al. 2010; Suhr and Hallen-Adams 2015; Enaud et al. 2018; Musumeci et al. 2022; Forbes et al. 2018).

2 Overview of Fungal Basics The fungi dwell in a kingdom of eukaryotic taxa that includes microscopic yeasts and spores as well as some macroscopic organisms (certain molds, mushrooms). Fungi are so grouped based on chitin-containing cell walls, hyphae filamentous growth structures, a heterotrophic means of acquiring carbon, and an ecological role as organic matter decomposer and environmental nutrient cycler (Baron 1996; Buckley 2008). Humans have harnessed fungal properties and products toward a variety of dietary, medical, and religious endpoints for over 5,000 years (Buckley 2008; Chang and Buswell 2022). Mushrooms, for example, are direct food sources, and other fungi are used for food-related processes such as bread leavening and beer fermentation (both Saccharomyces cerevisiae, aka Baker’s and Brewer’s yeasts) (Buckley 2008; Legras et al. 2007). Ingestion of mushrooms for food inevitably led to the discovery of the psychedelic nature of certain species. Cultivated in prehistoric and later cultures for its spiritual benefit, one active psychedelic ingredient is now known to be psilocybin, a chemical currently being studied for antidepressant effects (Hodge et al. 2022; Nichols 2020). Penicillium spp. of fungi not only contribute the active ingredients to blue cheeses, but these species are also medically important, having given rise to the first modern antibiotic, penicillin (Buckley 2008; Banjara et al. 2015). Another Saccharomyces species, S. boulardii, is routinely used in probiotic preparations which have the goal of harmonizing the gut microbiome (Ghannoum et al. 2021; Musumeci et al. 2022).

3 Pathogenic Fungi Thus, on the one hand, while fungi have a long history of benefits to humans, fungal species are also quiescent and rampant colonizers of the human body, acting as symbionts during good health and becoming potent pathogens in disease (Baron 1996; Chin et al. 2020; MacAlpine et al. 2022). Fungal diseases represent significant sources of morbidity and mortality, with per year medical costs estimated to exceed $7 billion (CDC 2022). In immunocompetent healthy individuals, fungal–bacterial interactions are stable and are reflected by a homeostatic, balanced microbiome; however, during bacterial dysbioses or species depletion with antibiotics, potentially

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Table 1 Common fungal taxa associated with human diseases and conditions Fungus Aspergillus fumigatus

Fungal disease or condition Aspergillosis, respiratory

Blastomyces dermatitidis Candida albicans Candida auris Candida glabrata Coccidioides spp.

Blastomycosis, respiratory

Cryptococcus neoformans Histoplasma spp. Malassezia spp. Pneumocystis jirovecii Saccharomyces cerevisiae

Candidemia, mouth, throat, GI, vagina Candidemia GI Coccidioidomycosis (valley fever), respiratory Cryptococcosis, respiratory Histoplasmosis, respiratory Skin, GI Pneumocystis pneumonia, respiratory GI

Presumed role/threat Opportunistic/ environmental Community/ environmental Opportunistic/hospital Opportunistic/hospital Opportunistic Community/ environmental Community/ environmental Community/ environmental Dietary Opportunistic Dietary

Adapted from (CDC 2022)

pathogenic yeast infections can occur (MacAlpine et al. 2022; Kim and Sudbery 2011; CDC 2019). Studies of fungal responses to antibiotics give a glimpse of bacterial-fungal dynamics and how a dysbiotic bacterial microbiome can result in dominance and overgrowth by an unchecked opportunistic pathogen such as Candida albicans (Forbes et al. 2018; Underhill and Braun 2022). C. albicans is the most well-studied human fungal commensal and is a diploid, polymorphic yeast that inhabits mucosal surfaces including human respiratory, GI, and genitourinary tracts (Ghannoum et al. 2010; Suhr and Hallen-Adams 2015). The pathogenicity of C. albicans ranges in severity from mouth, throat, and reproductive tract infections to a systemic invasive candidiasis affecting the circulatory system, bones, and brain (Kim and Sudbery 2011). The rise in fungal-containing biofilms, treatment resistance, and in the numbers of immune-compromised and vulnerable populations who may be particularly susceptible, such as COVID-19 patients, makes fungal diseases such as Candidiasis and especially the emerging strain Candida auris so alarming (CDC 2019, 2022; Pappas et al. 2018; Pristov and Ghannoum 2019). Many common fungal genera that may contribute to human diseases are listed in Table 1. DNA sequencing and culture-based studies allow anatomical site-specific inventories of fungal taxa to be constructed and demonstrate the presence of fungi on virtually all mucosal surfaces and skin. In investigations of oral mycobiota from healthy individuals, Candida, Cladosporium, Aureobasidium, Saccharomycetales, Aspergillus, and Fusarium were found to be the prevalent genera of fungi (Ghannoum et al. 2010). In studies of skin, Malassezia species are often found to dominate both healthy and diseased skin mycobiomes (Abdillah and Ranque 2021). In studies of the lung mycobiome in disease, the most commonly isolated taxa were

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C. albicans, Aspergillus spp., Penicillium, Cryptococcus, and Eurotium; whereas the healthy lung tended to be dominated by Aspergillus spp., and other environmentallyderived fungi, although with some variation among studies (Forbes et al. 2018; Nguyen et al. 2015). Mycobiome studies of the GI tract have raised the interesting issue regarding if a particular fungus is an active colonizer of the host or if it is just passing through as part of the diet and digestive process. For example, a survey of the fecal mycobiome detected not only known human symbionts (Candida, Cryptococcus, Malassezia, and Trichosporon), but also environmental fungi (Cladosporium spp.), and foodassociated fungi (Debaryomyces hansenii, Penicillium roqueforti) (Enaud et al. 2018). Thus, the respiratory mycobiome may be similarly muddled by the intake of multi-sourced environmental fungi (vs commensals). Collectively, the mammalian GI tract may contain as many as 50 or more different fungi genera, with the most common coming from Candida, Saccharomyces and Cladosporium (Underhill and Iliev 2014). Although the relative order of abundance may change from study to study, Candida and Saccharomyces tend to dominate the gut mycobiomes, which as we mentioned is quite interesting given that C. albicans is a fungal commensal and S. cerevisiae is obtained through ingestion. The degree to which externally-derived, dietary yeasts such as S. cerevisiae, Debaryomyces hansenii, Penicillium roqueforti, colonize the gut in an immunocompetent host is not clear (Enaud et al. 2018; HallenAdams and Suhr 2017). S. cerevisiae antibodies are also used as biomarkers of GI inflammation to help diagnose Crohn’s disease and to understand the fungal component of other inflammatory bowel and related diseases including ulcerative colitis and irritable bowel syndrome (Cimická et al. 2022; Gao and Zhang 2021; Torres et al. 2020).

4 The Psychiatric Mycobiome 4.1

DNA Sequencing

Establishing a causal role of the microbiome in human diseases is a challenge. Studies to date take on a number of different approaches that typically involve direct High Throughput Sequencing (HTS) of mucosal samples or indirect measures of exposures using antibody-based immunoassays. HTS studies are valuable because they require no a priori knowledge of which species to target. There are two major types of HTS: one that PCR-amplifies a conserved locus across a specific taxonomic group (e.g., 16S rRNA for bacteria or ITS rRNA for fungi); and a second type, known as metagenomic sequencing that provides sequences of every DNA specimen in a sample. Captured sequences are analyzed with bioinformatic algorithms designed to characterize inter-group differences in specific taxa or community diversity patterns (Jovel et al. 2016). These sequencing data in turn can guide the direction of translational studies that examine the functional relevance of specific taxa or groups of taxa in germ-free and microbiota transplantation animal models.

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Sequencing studies of the fungal mycobiome in psychiatric disorder cohorts are few and generally of low sample sizes. One of the first HTS investigations of a psychiatric microbiome was a metagenomics study of oropharyngeal samples, which found amongst numerous changes in bacterial taxa, an elevated abundance of the fungi, C. dubliniensis, in people with schizophrenia compared to controls (CastroNallar et al. 2015). In a study of the fecal mycobiome, Candida spp. were also elevated in patients with depression compared to controls, with a diverging pattern of corresponding decreases for Penicillium. In extended pathway analyses that are applied to ascertain the functional relevance of the sequences captured, the bacteria-fungal correlation network was found to be disrupted (Jiang et al. 2020). In another study of the fecal mycobiome, a drug-naïve, first-episode schizophrenia population had a greater abundance of the fungal taxa, Chaetomium, as compared to controls. This fungus is primarily an environmental mold that resides in soil, air, and plant debris, but is also found in serious wounds and infections in immunecompromised individuals. This study also documented altered levels of a different soil mold of the genera, Trichoderma, but this time lower abundances were reported for schizophrenia compared to controls. Species of this fungal genera generally are opportunistic plant symbionts, but, interestingly also include the species responsible for common house mold, Trichoderma longibrachiatum. This mold can produce small toxic peptides that interfere with ion channel functioning in humans. Pathway inferences based on the sequences from this study showed a significant enrichment of the bacterial-fungal correlation network (Zhang et al. 2020).

4.2

Immunoassays

Our knowledge of the psychiatric mycobiome has largely been shaped by blood biomarker studies that demonstrate elevated exposures to fungal taxa and dominant colonization by certain known aggressive opportunistic pathogens like C. albicans. In a study of individuals with schizophrenia, mood disorders, and controls, C. albicans antibody levels were greater in males with schizophrenia, were associated with GI disturbances in males with schizophrenia and females with bipolar disorder, and were associated with deficits in cognitive functioning in females with schizophrenia and bipolar disorder. In a cohort of antipsychotic-naïve and medicated patients, C. albicans antibody levels were not a function of medication effects (Severance et al. 2016). In studies of the dietary yeast, S. cerevisiae, antibodies were significantly elevated in people with schizophrenia and bipolar disorder compared to non-psychiatric controls. Antibody levels were not associated with medication in bipolar disorder, but in schizophrenia, individuals who were antipsychoticnaïve had elevated antibodies, suggesting that this type of medication may have immunosuppressant properties (Severance et al. 2012). In schizophrenia and bipolar disorder, anti-S. cerevisiae antibodies were associated with antibodies to food antigens and in bipolar disorder, these antibodies were also associated with GI symptoms. These Candida and Saccharomyces data indicate fungal-related GI

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inflammation and dysbioses as comorbidities in schizophrenia and bipolar disorder, raising the possibility that treatment with gut microbiome modulators such as probiotics may be helpful adjunctive therapeutics. Because fungal taxa may be involved in the dysbioses, an antifungal therapeutic prong should be added to research studies that investigate microbiome modulation therapies.

4.3

Changing the Mycobiome

In a pilot placebo-controlled clinical trial of 65 outpatients diagnosed with schizophrenia, probiotic treatment was found to ameliorate GI functioning compared to patients receiving placebo (Dickerson et al. 2014). Results from a follow-up proteomics study revealed corresponding changes in immune-related proteins indicative of probiotic-mediated improvement of GI epithelial and immune pathologies (Tomasik et al. 2015). This research group then examined if probiotic corrections of GI and psychiatric symptoms depended on whether C. albicans was an active member of the functional, or rather, dysfunctional microbiome (Severance et al. 2017). In males, probiotic treatment significantly reduced C. albicans but not S. cerevisiae antibodies over the 14-week study period. The highest reports of bowel discomfort occurred in C. albicans-seropositive individuals receiving the placebo. There were trends toward improvement of psychiatric symptoms with probiotic treatment only in those who were seronegative for C. albicans. To further investigate this association in a larger cohort of 384 males with schizophrenia, the authors found that C. albicans seropositivity was associated with worse psychiatric symptoms. In a study of fecal microbiota transplantation (FMT) in ulcerative colitis, the presence of Candida dominance before FMT helped identify those who were responsive to FMT, and low Candida post-FMT was associated with improvement of disease severity (Leonardi et al. 2020).

5 Fungi and the CNS Earlier in this book chapter, I introduced the complement system as an immune pathway by which gut microbiome instability, inflammation, and dysbiosis in the gut could result in aberrant synaptic pruning anomalies in the CNS. C. albicans was implicated in a study of the complement C4 gene in schizophrenia to determine if certain C4 haplotypes might predispose individuals to an increased risk of pathogen and other microbe-related exposures associated with the disorder (Severance et al. 2021). The study uncovered extensive C4 haplogroup associations with microbial markers in schizophrenia and very few in controls. Although exposure to Toxoplasma gondii, a protozoan parasite that enters its mammalian host through the GI tract, showed the strongest association with a specific C4 haplotype in schizophrenia

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(a C4A-long doublet), C. albicans was also significantly associated with the C4 haplotype (a C4A-singlet) in the disorder. There are also other routes by which fungi might be either directly or indirectly neuropathogenic. Indeed, this is an important topic as the prevalence of CNS fungal infections is on the rise even in those who are immunocompetent (Kably et al. 2022). Invasion of the brain by fungal infections is a particular concern for people with weakened immune systems, as fungi such as C. albicans, C. neoformans, and A. fumigatus are presumed to cross the BBB and initiate an immune response by resident glia (Snarr et al. 2020). In post-mortem studies of Alzheimer’s disease, amyotrophic lateral sclerosis and Parkinson’s disease, fungi including Alternaria, Botrytis, Candida, Cladosporium, Fusarium, and Malassezia have been found in either CSF or brain tissue samples (Pisa et al. 2015; Alonso et al. 2015a, b; Phuna and Madhavan 2022). Among the mechanisms hypothesized to promote plaques in Alzheimer’s disease, Candida spp. may form fungal glial granulomas with amyloid precursor protein, whereas Malassezia spp. likely propagate a neuroinflammatory state through activation of helper T-cells 1 and 17 (Phuna and Madhavan 2022). Lending further insight regarding operative pathogenic CNS mechanisms are results from studies in a mouse model, where the authors induced a state of low-grade candidemia. In this experimental setting, C. albicans could rapidly cross the BBB and cause a transient cerebritis that was associated with short-term memory deficits, amyloid protein deposition, cytokine production, and activation of microglial cells (Wu et al. 2019). Yeasts and other fungi may also bring about CNS effects through indirect mechanisms. C. albicans produces a range of toxic breakdown products and bioactive peptides that are capable of crossing the blood-gut and blood-brain barriers, activating apoptotic pathways associated with neuronal function (Semon 2014), and manufacturing neurotransmitters including noradrenaline (by Saccharomyces spp) and serotonin (by Candida spp) (Dinan et al. 2014). Extracellular vesicle research is an up-and-coming field, and experimental studies of Candida species indicate that these vesicles may provide a transport mechanism for fungal products such as virulence factors, membrane-associated and cytoplasmic proteins, and secretory factors (Karkowska-Kuleta et al. 2020). This mode of inter-fungi and fungi-host communication has also been studied in S. cerevisiae, C. neoformans, M. sympodialis, and others (de Toledo et al. 2019). These EVs can also cross the BBB and activate microglia and astrocytes and interestingly can aid neuronal functions in health, but in a stressful environment, can bring to the brain oxidative and inflammatory signals that are harmful to neurons (Kaur et al. 2021). Certain psychiatric disorders may have neurodevelopmental origins, and we have not yet addressed a possible role of fungal infections in this regard. A large literature base supports that exposure to infections and resulting immune system activation during pregnancy predisposes offspring to biochemical and behavioral brain pathologies in animals and psychiatric disorders in humans (Severance and Yolken 2020a; Khambadkone et al. 2020; Estes and McAllister 2016; Knuesel et al. 2014; Khandaker et al. 2013; Patterson 2011; Brown et al. 2009; Buka et al. 2008). The degree to which antenatal C. albicans yeast infections has immediate impacts on the

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course of pregnancy, such as preterm birth, vs. has more long-term psychiatric effects on offspring post-natally, has not been well-addressed. Because many pathogens have been implicated in the neurodevelopmental etiology of such disorders as schizophrenia, it seems possible that exposure to a yeast pathogen during pregnancy may be a risk for some individuals. For example, a maternal-yeast infection has been associated with an increased risk for childhood epilepsy (Andersen et al. 2012). There are also other non-immune pathways by which fungal-associated interrupted neurodevelopment could result in disorders such as schizophrenia. For example, unregulated apoptosis during fetal development could produce widespread cellular and other structural and functional brain deficits with especially critical effects on synaptic pathologies (Semon 2014; Glantz et al. 2006; Margolis et al. 1994). As mentioned earlier, C. albicans and certain foods such as malt, cocoa, and beer produce bioactive cyclic dipeptides, and this class of peptide has been found to activate apoptotic pathways in experimental cancer models (Brauns et al. 2004). Thus, the inappropriate activation of apoptotic pathways during critical neurodevelopmental timepoints could result in synaptic pathologies consistent with schizophrenia (Semon 2014).

6 Conclusions The taxonomic grouping known as fungi is extremely functionally diverse and bestows on the host numerous benefits as well as challenges as diagrammed in Fig. 1. Understanding the precise set of circumstances which cause any given fungal species to transform from a normal productive member of the microbiome into one that bears extreme pathogenicity remains elusive. Any number of the usual environmental suspects such as exposure to antibiotics, toxins, or other sources of stress will in theory bring about such a conversion, but natural host variation ensures that one individual’s extreme response to one environmental factor is not always matched by another’s. Further challenging is that disorders like schizophrenia and mood disorders already have high etiological, diagnostic, and pathophysiological heterogeneities and occur in populations of people who themselves are differentially subjected to an abundance of socioeconomic, lifestyle, and other environmental factors. Current limitations impacting all microbiome studies regardless of the taxonomic focus, therefore, are these highly heterogeneous forces that mold the human structure and function into vastly individualized forms, coupled with experimental drawbacks such as small sample sizes and unstandardized study designs. These technical limitations prevent the declaration of causal over correlative claims regarding an altered microbiome specific to psychiatric disorders. Nevertheless, even for a newly emerging field, the sequencing and antibody studies reported here quite strongly support a fungal contingent relevant to psychiatric disorders and related putative mechanisms. In spite of the early stage of research, it is reasonable to start formulating and testing treatment plans that may incorporate how significant microbial dysbioses containing fungal elements will be identified and controlled in the future.

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Fungal Benefits to Host

Fungal Challenges to Host

• Food source & food processing roles • • Mushrooms, Blue cheese (Penicillium) • • Brewer’s & baker’s yeast (S. cerevisiae) • • Source of therapeutics • • Antibiotics (Penicillium) • •

Probiotics (S. boulardii) Antidepressants (psilocybin)

• • •

Protect microbial diversity Maintain GI health Develop immune system

• Aid gut microbiome functions

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Commensals turn pathogenic (Candida) Pathogenic environmental spp. (Cryptococcus) Unclear role of diet-derived spp. (S. cerevisiae) Infections range from minor to systemic • • •

Circulatory system Bones Brain

• CNS Infections on the rise • Treatment resistance on the rise

Fungal Considerations in Psychiatry • • • • •

Candida & dietary Saccharomyces associated with psychiatric disorders Fungal species and products can directly cross the BBB Fungal overgrowth contributes to toxic cycle of dysbiosis, inflammation, brain access Fungal pathogens associated with complement pathway activation Possible neurodevelopmental effects of exposure during pregnancy

Fig. 1 A diversity of roles for fungi and the mycobiome in mental health. Fungi and the mycobiome have a diversity of functions that contribute to human health and disease. Benefits to the host include the role of fungal species and products as sources of food and medicine, as well as basic contributors to gut microbial functioning in times of good health. Fungal presence in the human host becomes challenging during states of compromised immunity, imbalanced microbomes, and insufficient therapeutics with which to effectively treat these infections. As relevant to psychiatric issues, fungal species can be beneficial in terms of providing potentially therapeutic remedies such as probiotic and antidepressant compounds. This promising benefit may not outweigh the challenges faced by the host in the presence of extreme dysbiosis and potently pathogenic fungi that are able to access the brain

Antifungal drug development itself faces a number of challenges especially in light of increased prevalence of drug-resistant strains of Candida and the rise of Candida infections in general (Pappas et al. 2018). Antifungal drugs fall into various mechanistic types and such groups include inhibitors of ergosterol synthesis, fungal RNA biosynthesis, cell wall biosynthesis, and fungal membrane sterol regulation (Burchacka et al. 2022). The currently most effective treatments of candidiasis are echinocandins that target the fungal cell wall and azoles that interfere with ergosterol in the fungal membrane. Broader antifungal treatment categories undergoing active research include antimicrobial peptides, combinational therapy, immunotherapy, metals and nanoparticles, natural compounds, and repurposed drugs (Bandara and Samaranayake 2022). An interesting treatment-related field targets the ability of C. albicans to self-regulate its own virulence through interconversion of its growth states, a process known as budded-to-hyphal transition (BHT). BHT is triggered by environmental variables such as availability of nutrients, high temperature, pH, and other stressors. The signaling apparatus controlling BHT is a promising system with many possible targets that when inhibited could have potent antifungal implications. Interestingly, clozapine, a current frontline treatment of schizophrenia, is a BHT inhibitor (Midkiff et al. 2011). The use of checkpoint inhibitors to treat invasive

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fungal infection like candidiasis may also be a future applicable treatment option (Mellinghoff et al. 2022). This field of research that straddles the disciplines of microbiology, neuroscience, and immunology and unifies the many molecular and cellular pathways guiding the gut-brain axis holds much promise for the future of treating mental health problems. Unlike with efforts to correct complex developmentally-constructed dysfunctional neurocircuitry or indelible deficiencies designed by our DNA, the microbiome is highly malleable through interventions such as diet, probiotics, prebiotics, and FMT. Furthermore, microbiome modifying treatments do in fact shift fungal dynamics (Ghannoum et al. 2021; Severance et al. 2017; Leonardi et al. 2020). Inter-Kingdom relationships and relative roles of other non-bacterial components of the microbiome will be critical to future studies which must inevitably address the whole microbiome and all taxa contained therein. Acknowledgments Dr. Severance receives research funding from the Stanley Medical Research Institute.

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Effect of Cytomegalovirus on the Immune System: Implications for Aging and Mental Health Bart N. Ford and Jonathan Savitz

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 HCMV Infection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Cellular Tropism, Viral Entry, and Initial Immune Activation . . . . . . . . . . . . . . . . . . . . . . 2.3 The HCMV Life Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Host Cell Immune Evasion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 HCMV Disrupts Toll-Like Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 HCMV Interferes with Interferons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 HCMV Encodes an IL-10 Homolog That Supresses Immunity . . . . . . . . . . . . . . . . . . . . . 3.4 MHC Expression and Antigen Presentation is Inhibited by HCMV . . . . . . . . . . . . . . . . 4 Impact on the Immune System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Clinical Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Aging and Immunosenescence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Vaccine Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Disease Susceptibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Relevance to Mental Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Stress Is a Risk Factor for Psychiatric Illness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Stress Is a Risk Factor for HCMV Infection and Reactivation . . . . . . . . . . . . . . . . . . . . . . 6.3 Inflammation Is Implicated in the Etiology of Psychiatric Disorders . . . . . . . . . . . . . . . . 6.4 Inflammation Predisposes to HCMV Reactivation and Vice Versa . . . . . . . . . . . . . . . . . 6.5 Is HCMV an Overlooked Co-Factor in the Genesis of Psychiatric Illness? . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Human cytomegalovirus (HCMV) is a major modulator of the immune system leading to long-term changes in T-lymphocytes, macrophages, and natural B. N. Ford (*) Department of Pharmacology and Physiology, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA e-mail: [email protected] J. Savitz Laureate Institute for Brain Research, Tulsa, OK, USA Oxley College of Health Sciences, The University of Tulsa, Tulsa, OK, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 181–214 https://doi.org/10.1007/7854_2022_376 Published Online: 25 July 2022

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killer (NK) cells among others. Perhaps because of this immunomodulatory capacity, HCMV infection has been linked with a host of deleterious effects including accelerated immune aging (premature mortality, increased expression of immunosenescence-linked markers, telomere shortening, speeding-up of epigenetic “clocks”), decreased vaccine immunogenicity, and greater vulnerability to infectious diseases (e.g., tuberculosis) or infectious disease-associated pathology (e.g., HIV). Perhaps not surprisingly given the long co-evolution between HCMV and humans, the virus has also been associated with beneficial effects, such as increased vaccine responsiveness, heterologous protection against infections, and protection against relapse in the context of leukemia. Here, we provide an overview of this literature. Ultimately, we focus on one other deleterious effect of HCMV, namely the emerging literature suggesting that HCMV plays a pathophysiological role in psychiatric illness, particularly depression and schizophrenia. We discuss this literature through the lens of psychological stress and inflammation, two well-established risk factors for psychiatric illness that are also known to predispose to reactivation of HCMV. Keywords Aging · Cytomegalovirus · Immunity · Immunosenescence · Inflammation · Psychiatric disorders · Stress · Vaccine

1 Introduction Human Cytomegalovirus (HCMV) is a beta-herpesvirus that can establish lifelong infections and notably alters the architecture of the host immune system. HCMV’s ~235 kB linear, double-stranded DNA genome is packaged in an icosahedral capsid, surrounded by a tegument matrix, and encased in a lipid envelope containing viral glycoproteins (Liu and Zhou 2011). Its genome is divided into a unique long (UL) and a unique short (US) region flanked by repeating sections (Van Damme and Van Loock 2014). Serology studies estimate a prevalence of 83% (95% CI: 78–88) in the global population, but seropositive rates vary by region (Zuhair et al. 2019). The risk of infection increases with age. An estimated 36.3% of U.S. children between the ages of 6 and 11 are seropositive, while 90.8% of adults over 80 are seropositive (Staras et al. 2006). Other major factors that contribute to varying rates of seroprevalence in population-based studies are sex, race/ethnicity, socioeconomic status, HIV status, and daycare attendance (Dowd et al. 2009; Bate et al. 2010; Lachmann et al. 2018; Hoehl et al. 2020) Congenital infections can result in morbidity or lifelong disabilities, most notably neurodevelopmental retardation, microencephaly, and hearing loss (Kenneson and Cannon 2007; Crough and Khanna 2009). HCMV is perhaps the most important pathogenic contributor to complications following solid organ and hematopoietic stem cell (HPC) transplantation (Harvala et al. 2013; Selvey et al. 2017). Even with prophylactic antiviral treatment, the immunocompromised state following transplantation can lead to HCMV viremia from primary infection, reinfection, or reactivation

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of latent infection (Ljungman et al. 2017). As such, HCMV can cause pneumonia, gastrointestinal disease, hepatitis, retinitis, encephalitis, nephritis, cystitis, myocarditis, pancreatitis, and other end organ disease in transplant recipients (Ljungman et al. 2017). In people living with human immunodeficiency virus (HIV), coinfection with HCMV can give rise to similar complications (Gianella and Letendre 2016) and more rapid progression to AIDS (Webster et al. 1989; Detels et al. 1994). Thus, HCMV is considered to be an important public health issue, and several vaccines are currently in development (Gugliesi et al. 2020). Nevertheless, HCMV infection often goes unnoticed. In immunocompetent adults, primary infection induces a mild febrile illness, or none at all. This does not mean that HCMV is harmless to most individuals. Negative consequences arise over time as a result of chronic infection in which the latent virus periodically reactivates and is held in control by persistent surveillance and action of immune effector cells (Walter et al. 1995; Lim et al. 2020). HCMV has evolved a large number of immune evasion strategies that involve manipulation of the host genome and microenvironment by viral gene products in a manner that has been likened to a conductor tuning an orchestra (Dell’Oste et al. 2020). This interplay has long-lasting effects on the phenotypic makeup of host T-cells, natural killer (NK) cells, monocyte/macrophages, and dendritic cells (DCs). In particular, HCMV is associated with an accumulation of antigen-specific, late-differentiated cells, a hallmark of advanced age (Rölle and Brodin 2016; Pangrazzi and Weinberger 2020; Ford et al. 2020). In this chapter, we will discuss how HCMV subverts and influences the immune system during initial infection, lytic replication, and latency and describe the longterm effects on the immune system. We then address some of the clinical implications of HCMV with respect to immunosenescence, vaccine response, and disease susceptibility. Finally, will briefly discuss the potential bidirectional interaction between HCMV and psychiatric disease. That is, evidence that psychological stress and mental health factors alter the function of the adaptive immune system leading to increased susceptibility to infection and weakened control of HCMV as well as evidence that HCMV may contribute to the genesis of several psychiatric disorders.

2 HCMV Infection 2.1

Transmission

Outside of tissue transplantation and placental transfer, the most common method of HCMV transmission is exchange of bodily fluids. Infants and young children in the pre-school years (0–6 years old) are known to shed virus in their saliva and urine at a high rate (Murph et al. 1986; Noyola et al. 2005; Cannon et al. 2011; Watanabe et al. 2019; Alain et al. 2020). Thus, daycare attendance increases the likelihood of HCMV infection (Adler 1985). HCMV viral shedding prevalence was higher in children attending day care (51.9%) than those seeking emergency medical care (21.7%), and day care capacity was positively correlated viral shedding (Grosjean

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et al. 2014). This suggests that being in close quarters supports HCMV viral shedding in children and day care centers are important reservoirs for HCMV transmission (Pass et al. 1990). Sexual contact is another opportunity for transmission since virus is shed in semen and cervical mucus (Handsfield et al. 1985; Yang et al. 1995). An association between increased sexual activity and HCMV seropositivity has been found in some (Fowler and Pass 2006; Staras et al. 2008; Dowd et al. 2009) but not all studies (Foxworth et al. 2014; Patrick et al. 2014). The increased risk of infection with sexual activity may be specific to females (Staras et al. 2008; Dowd et al. 2009). Women are more likely to be seropositive for HCMV overall. Worldwide the prevalence ratio for HCMV infection in women compared to men is 1.13 (95% CI: 1.11–1.14) (Cannon et al. 2010). Fomite transmission of HCMV is also possible. Lab analysis has shown that the AD169 strain of HCMV remained viable for 1–3 h on dry, non-absorbent surfaces and for up to, but possibly exceeding, 6 h on wet, absorbent surfaces including foods such as crackers (Stowell et al. 2012). HCMV remains viable on human hands for several minutes and can be transferred to surfaces with reduced viability (Stowell et al. 2014).

2.2

Cellular Tropism, Viral Entry, and Initial Immune Activation

HCMV can infect a broad range of cell types (Myerson et al. 1984; Sinzger et al. 2008). The predominant targets are fibroblasts, epithelial, endothelial, and smooth muscle cells, but other cell types such as leukocytes, neurons, and brain pericytes are also susceptible (Sinzger et al. 2008; Alcendor et al. 2012). Myeloid progenitor CD34+ HPCs, CD14+ monocytes, and macrophages are important in the latency/ reactivation cycle and for dissemination of the virus (see below) (Min et al. 2020). Viral envelope proteins are able to utilize various host cell proteins to gain access depending on the tissue type (Compton et al. 1993; Gredmark et al. 2004; Vanarsdall and Johnson 2012; Farrell and Stevenson 2019; Stein et al. 2019; Gerna et al. 2019; Elste et al. 2020; Murray et al. 2020). HCMV cell entry is dependent on glycoprotein complexes that form attachment and fusion receptors facilitating envelope fusion with the cell membrane (Isaacson et al. 2008). Importantly, the host cell immune response is triggered by these initial interactions (Boyle et al. 1999; Song et al. 2001). Within hours of cell entry, the immune regulating transcription factors, nuclear factor kappa B (NFκB), specificity protein 1 (SP1), and phosphatidylinositol-3 kinase (PI3K) are activated (Zhu et al. 1998; Simmen et al. 2001; Browne et al. 2001).

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The HCMV Life Cycle

The replication cycle of HCMV is typical of other beta-herpesviruses and characterized by three phases of gene expression, immediate-early (IE), early (E), and late (L) (Ye et al. 2020). Upon entry to the cell, viral tegument proteins facilitate translocation of the viral genome into the nucleus and stimulate IE gene transcription (Kalejta 2008). IE genes prepare the cellular environment for viral genome replication and virion assembly. Apoptosis and antiviral interferon (IFN) response is blocked by several IE gene products and tegument proteins (Stinski and Meier 2011). IE86 holds the cell cycle in a G1/S transition state favorable for genome replication by stabilizing p53 (Stinski and Meier 2011). E genes are expressed roughly 8 h post-infection. These gene products continue to create a favorable environment for viral replication by modulating control of cellular DNA synthesis and altering the immune response. Other E genes are directly involved in HCMV genome replication (White and Spector 2011). L phase genes control the formation of the viral capsid, packaging of viral DNA, and eventual egress of new virions (Anders et al. 2011). Like other members of the herpesvirus family, HCMV is never fully cleared by the immune system, but typically persists as a lifelong infection by establishing non-virulent latency. Initially infected epithelial and endothelial cells upregulate cell-adhesion molecules and downregulate tight junction molecules, which promotes transmigration of monocytes (Bentz et al. 2006). During transmigration, the monocytes become infected themselves and thus disseminate the virus to various bodily organs (Myerson et al. 1984; Bentz et al. 2006). When HCMV reaches CD34+ HPCs in the bone marrow, a major reservoir for lifelong infection is established (Goodrum et al. 2012). CD34+ HPCs and their infected myeloid progeny do not produce lytic infection but remain in the latent phase (Zhu et al. 2018). HCMV replication is initiated during monocyte or DC activation in the periphery and the virus can then spread to other tissues (Hargett and Shenk 2010; Reeves and Sinclair 2013). During latency, HCMV actively evades immune detection by selective gene expression, but does not undergo viral replication (Reeves and Sinclair 2010). Besides immune evasion, latency-associated genes also suppress apoptosis (Poole et al. 2015), dampen intracellular signaling pathways (Smith et al. 2021), and downregulate replication-phase HCMV genes (Ye et al. 2020). The major immediate early promoter (MIEP) is silenced epigenetically, halting robust expression of the major IE genes that initiate the replication cycle (Reeves and Sinclair 2010; Groves et al. 2021). When the MIEP, or alternate major IE promoter regions, are reactivated, so too is the replication cycle, leading to viral reproduction and spread (CollinsMcMillen et al. 2020).

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3 Host Cell Immune Evasion In this section, we will examine several molecular mechanisms of immune evasion employed by HCMV with a focus on those that target immune processes known to be independently disrupted by psychological stress. In later sections, we will provide data indicating that stress-related psychological disorders may promote susceptibility to HCMV infection and/or pathology by working synergistically with HCMV immune evasion.

3.1

HCMV Disrupts Toll-Like Receptors

HCMV envelope proteins stimulate the innate immune response upon initial encounter with the host cell. Toll-like receptors (TLRs) are membrane-spanning pattern recognition receptors that detect microbial-associated molecules. Bound TLRs initiate immune response signaling pathways such as pro-inflammatory transcription factors NFκB and activator protein (AP)-1, or antiviral type I IFN response genes (Barbalat et al. 2009; Oliveira-Nascimento et al. 2012). The TLR-1/TLR-2 heterodimer is the primary PAMP sensor of HCMV, recognizing both gB and gH glycoproteins and is enhanced by CD14 expression (Compton et al. 2003; Boehme et al. 2006). TLR-3 detects double-stranded RNA, which is created by both RNA and DNA viruses, and TLR-4 detects lipopolysaccharide and other glycoproteins (Park et al. 2019). HCMV viral gene products disrupt TLR activity in several ways. HCMV microRNA miR-UL112-3p down regulates expression of TLR-2 (Landais et al. 2015). Viral proteins US7 and US8 are active in blocking TLR-3 and TLR-4 signaling (Park et al. 2019). US7 promotes ubiquitination and proteosome degradation by binding TLR-3 and TLR-4 to Derlin-1 and Sec61β (Park et al. 2019). US8 blocks chaperone protein UNC93B1 interaction with TLR-3 and targets TLR-4 to lysosome degradation (Park et al. 2019). In patients with major depressive disorder (MDD), TLRs 3, 4, 5, and 7 were over expressed, while TLRs 1 and 6 were under expressed (Hung et al. 2014). Since the TLR-1/2 complex initiates the antiviral response to HCMV, a subset of MDD patients may be more susceptible to infection at the cellular level. To our knowledge, however, this possibility has not be tested experimentally.

3.2

HCMV Interferes with Interferons

The predominant molecular antiviral defense mechanism is the Type I IFN response, in which the primary effector molecules, IFNα and IFNβ, stimulate the expression of various IFN stimulated genes (ISGs) (Boyle et al. 1999; Boehme et al. 2004; Netterwald et al. 2004). Many PRRs induce this response through various signaling

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cascades that converge on IFN response factor (IRF) 3 and IRF7, although other IRFs can also promote a type I response (McNab et al. 2015). IRF3 promotes transcription of IFNB and IFNA4, which upregulate IRF7. IRF7 promotes a second wave of IFNα production as well as positively feeding back on the action of IRF3 (McNab et al. 2015). IFNα and IFNβ bind to the heterodimeric receptor complex, IFNAR1/IFNAR2. The canonical activation pathway utilizes Janus kinase (JAK)1 and tyrosine kinase (TYK)2 phosphorylation of signal transducer and activator of transcription (STAT)1/STAT2 dimers, but other STAT (signal transducer and activator of transcription) molecules, multiple mitogen-activated protein kinase (MAPK) pathways, and/or the PI3K:mTOR pathway may also be induced. The end result is the upregulation of hundreds of ISGs that are involved in antiviral effector functions and other cellular responses to infection. HCMV is a potent type I IFN stimulator but has many ways to shield itself from the response. Cyclic guanosine mono-phosphate-adenosine monophosphate (cGAMP) synthase (cGAS) is activated by cytosolic DNA. cGAMP binds stimulator of IFN genes (STING), which recruits TANK-binding kinase 1 (TBK1) and IRF3. Phosphorylation of TBK1 and IRF3 induces the type I IFN response (Paijo et al. 2016). The cGAS-STING axis is a key sensor of HCMV and activator of IFNα/β. HCMV protein UL31 directly interacts with and disassociates DNA from cGAS (Huang et al. 2018). UL83 also blocks cGAS from interacting with STING (Biolatti et al. 2018b). UL82 (pp71) and US9 bind STING and disrupt its ability to form activation complexes with TBK1 and IRF3 (Fu et al. 2017; Choi et al. 2018). IFNα/β expression is also diminished by UL83 (pp65), which disrupts nuclear translocation of IRF3 and IRF1 (Browne and Shenk 2003; Abate et al. 2004; Marshall and Geballe 2009; Biolatti et al. 2018a). IFNγ inducible protein 16 (IFI16) is another cytosolic DNA sensor that can dimerize and initiate a type I IFN response to HCMV (Gariano et al. 2012). When UL83 binds IFI16, it interacts with NFκB to initiate transcription from the MIEP (Biolatti et al. 2018a). Meanwhile, UL44 inhibits IRF3 and NFκB binding to antiviral gene promotor regions (Fu et al. 2019). Relevant to mental health, multiple types of stress-related factors—including social isolation, posttraumatic stress, and eudaimonic well-being, among others—have been associated with a “conserved transcriptional response to adversity” characterized by overexpressed inflammatory genes, and under expressed type I IFN genes (Cole et al. 2015; Kohrt et al. 2016; Boyle et al. 2019). Therefore, adversity-associated suppression of antiviral immunity may enhance HCMV’s ability to evade the immune response.

3.3

HCMV Encodes an IL-10 Homolog That Supresses Immunity

UL111A is an HCMV viral gene that is expressed in lytic and latent phases. Alternate splicing of the transcript gives rise to several isoforms of a homolog to

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human IL-10 (cmvIL-10) (Poole et al. 2020). IL-10 is a cytokine that provides regulatory feedback to immune activation. Importantly for HCMV infection, a major function of IL-10 is to suppress the activity of antigen-presenting cells (APCs) including monocytes, macrophages, and DCs by downregulating major histocompatibility complex (MHC) expression, co-stimulatory cell surface molecules, and pro-inflammatory cytokines (IFNγ, tumor necrosis factor (TNF), IL-1β, IL-6) (Mittal and Roche 2015). cmvIL-10 binds the IL-10 receptor with identical affinity as human IL-10, thus activating the Jak1-STAT3 pathway that culminates in its anti-inflammatory effects. In vitro, cmvIL-10 blocks lipopolysaccharide-induced activation of DCs (Chang et al. 2009), prevents NFκB signaling in monocytes (Nachtwey and Spencer 2008), and inhibits proliferation of PBMCs (Spencer et al. 2002). A study in rhesus macaques found that cmvIL-10 (rhesus CMV strain) had profound impact on the magnitude and kinetics of the primary immune response with long-lasting consequences for the humoral and T-cell memory response (Chang and Barry 2010).

3.4

MHC Expression and Antigen Presentation is Inhibited by HCMV

HCMV viral immunity is mediated primarily by CD4+ and CD8+ T-cells. Cytotoxic CD8+ T-cells recognize viral peptides presented on MHC class I molecules and initiate killing of the infected cell. Helper CD4+ T-cells recognize MHC class II presentation of viral peptides by APCs—primarily monocyte/macrophages, DCs, and B-cells. An early T-cell escape strategy is to block MHC-I expression normally stimulated by initial binding of viral envelop proteins to cell surface molecules (Song et al. 2001). The IE gene, US3, encodes an endoplasmic reticulum (ER)-resident glycoprotein that retains protein-loaded MHC-I in the ER (Ahn et al. 1996). Early phase genes US2 and US11 mediate proteosome degradation of MHC-I heavy chains, and US6 interferes with peptide loading by transporters associated with antigen processing (TAP) in the ER lumen (Wiertz et al. 1996; Jones and Sun 1997; Ahn et al. 1997). US2 is also able to initiate degradation of human leukocyte antigen (HLA)-DRα and DMα, two essential MHC-II proteins (Tomazin et al. 1999). US3 binds HLA-DR, blocking attachment of the invariant chain and preventing MHC-II complex assembly (Hegde et al. 2002). The type II IFN, IFNγ, promotes MHC-II expression even in non-APC cells such as fibroblasts and epithelial cells by upregulating the MHC class II transactivator gene (CIITA) (Lim et al. 2020). Independent of US2 and US3, HCMV inhibits expression of CIITA in Langerhans cells and a myeloid progenitor cell line which decrease transcription of HLA-DR (Lee et al. 2011; Sandhu and Buchkovich 2020). INFγ mediated gene expression is further inhibited by UL23’s binding of N-myc interactor protein and prevention of nuclear translocation of STAT activators (Feng et al. 2018).

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Psychological stress could conceivable create a favorable environment for HCMV through excessive glucocorticoid production by chronic hypothalamicpituitary-adrenal (HPA) axis activation. Glucocorticoid signaling negatively moderates the inflammatory and cellular immune response, in part by down regulation of MHC II expression and upregulation of IL-10 gene expression in immune cells (Celada et al. 1993; Spencer and Deak 2017; Shimba and Ikuta 2020). Additionally, IL-10 is also produced by the hypothalamus and pituitary glands and promotes corticotropin-releasing factor and corticotropin (Smith et al. 1999). Thus, stress may contribute to HCMV pathology through synergistic functions of cmvIL-10 and human IL-10 and by inhibition of MHC II expression, although more research is needed to test this model.

4 Impact on the Immune System For a supposedly benign virus, HCMV infection has a remarkable impact on the immune system driving the largest antigen-specific immune response of any known microbe within the vascular system (Klenerman and Oxenius 2016; Moss 2019). This effect may be partly due to the litany of immune evasion strategies employed by the virus, which results in a complex interplay with the host. The immune impact can be most clearly seen in the twin study of Davis and colleagues, which examined the sources of non-genetic variability in 204 different parameters of the immune system (Brodin et al. 2015). Remarkably, they showed that relatively young monozygotic twins discordant for HCMV infection had greatly reduced correlations in more than 50% of the measured parameters, especially, the frequencies of effector CD8+ and gamma-delta T-cells, serum concentrations of IL-6 and IL-10, and cell signaling in response to stimulation with IL-6 and IL-10 (Brodin et al. 2015). Classically, HCMV infection is associated with an expansion of effector memory CD8+ and CD4+ T-cells that are specific for HCMV and a concomitant decline in naïve T-cells. In fact, depending on the duration of latent infection, up to 10% of effector memory CD4+ cells and 50% of effector memory CD8+ cells become targeted at HCMV antigens, a phenomenon that has been termed “memory inflation” (Khan et al. 2004; Almanzar et al. 2005; Sylwester et al. 2005; Klenerman and Oxenius 2016). These HCMV-specific T-cells exhibit characteristics that are often observed in the context of cellular senescence, such as decreased expression of co-stimulatory receptors, CD27 and CD28, and increased expression of CD57, KLRG1, and CD45RA (Herndler-Brandstetter et al. 2012; Klenerman and Oxenius 2016; Ford et al. 2020). The HCMV-specific CD28- CD4+ cells secreting IFNγ and TNF are thought to have cytotoxic properties as they express granzyme B and perforin. However, they lack molecules for secondary lymphoid organ homing (e.g., CCR7 and CD62L), suggesting that they play a direct role at sites of infection (Pawelec et al. 2005; van den Berg et al. 2019a). Recent work also indicates that approximately one-third of HCMV-specific CD4+ cells express the programmed cell death protein (PD-1) receptor, a marker of “exhaustion” (Parry et al. 2021). These

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cells retained potent cytotoxic activity but secreted less T-helper 1 (Th1) cytokines after antigen engagement (Parry et al. 2021). The HCMV-specific CD8+ cells are thought to be fully differentiated and to show high cytotoxic potential, expressing granzyme B and perforin (Appay et al. 2002; van den Berg et al. 2019a). Like the CD4+ cells, they tend to be negative for markers of lymphoid organ homing (van den Berg et al. 2019a). HCMV infection also impacts NK cell phenotypes. The non-classical MHC class I molecule, HLA-E is expressed at low levels on the surface of normal cells (Iwaszko and Bogunia-Kubik 2011). Interaction with the NK cell receptor, NKG2A, produces inhibitory signals that block cytotoxic activity. If HLA-E expression is lost, as is the case in tumor cell line K562 (Garson et al. 1985), the loss of inhibitory signals tilts the balance towards NK-mediated killing. Conversely, when HLA-E is over expressed, binding to activating receptor NKG2C increases and also initiates NK cell cytotoxicity (Wada et al. 2004). Overexpression of HLA-E is observed in HCMV-infected cells (Prod’homme et al. 2012; Rölle et al. 2014). Consistent with this phenomenon, HCMV infection is also associated with an expansion of memory-like NKG2C+ CD57+ FcεRIγ- NK cells, a specialized cellular subset that demonstrates antibody-dependent cellular cytotoxicity and releases pro-inflammatory cytokines such as IFNγ (Rölle and Brodin 2016; Semmes et al. 2020). NKG2C is usually expressed on a minority of NK cells which appear to undergo a clonal expansion akin to that typically observed in lymphocytes during HCMV infection. These cells have been hypothesized to prime the immune system and protect against HCMV as well as other viral infections through high IFNγ production as a result of epigenetic remodeling of the IFNγ locus (Luetke-Eversloh et al. 2014; Rölle and Brodin 2016; Semmes et al. 2020). Myeloid-lineage cells play a special role in the latency, reactivation, and internal spread of HCMV (Min et al. 2020). CD34+ hemopoietic cells (HPCs) are bone marrow resident progenitors of CD14+ monocytes. Classical CD14+ CD16- monocytes circulate in the blood with a half-life of approximately 1.6 days, although non-classical (CD14lo CD16+) and intermediate (CD14+ CD16+) monocytes have longer lifespans of 4–7 days (Patel et al. 2017a). Exogenous pathogenic stimulation and/or endogenous cytokine signals can initiate monocyte differentiation to macrophages or DCs (Goudot et al. 2017). The short lifespan of monocytes is a “problem” for HCMV, and hence the virus extends the lifespan of these cells by blocking apoptosis as well as promoting their differentiation into longer-lived macrophages, which can support viral replication (Stevenson et al. 2014). HCMV also establishes latency in CD34+ HPC cells (Zhuravskaya et al. 1997; Streblow and Nelson 2003; Wills et al. 2015; Collins-McMillen et al. 2018). CD14+ monocytes derived from these infected progenitor cells circulate in the blood and infiltrate the tissues, disseminating the virus throughout the body including the brain parenchyma (Kosugi et al. 2002; Bentz et al. 2006). When monocytes are activated and differentiate into macrophages or DCs, HCMV is reactivated and enters the lytic phase of its lifecycle. Macrophages are heuristically divided into M1 (classical, IFNγ-activated) and M2 (alternative, IL-4-activated) subtypes. M1-like polarized macrophages have a

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high level of phagocytic activity and secrete pro-inflammatory mediators such as IL-6, IL-12, IFNγ, and TNF, which induce a Th1 immune response and facilitate complement-mediated phagocytosis (Nikitina et al. 2018). M2-like macrophages, in contrast, promote a Th2 immune response by secreting cytokines like IL-4 and IL-10, and are thought to play a greater role in homeostatic processes and cellular repair. HCMV has evolved to alter the biological function of infected macrophages to promote changes that support the virus life cycle. HCMV is thought to reprogram macrophages so that they express both M1 and M2-associated genes, although they are more M1-like on the phenotypic spectrum (Stevenson et al. 2014). While the M1-like phenotype facilitates the spread of the virus through inflammation, the expression of M2-like genes attenuates the extent of the host antiviral response (Stevenson et al. 2014; Nikitina et al. 2018). Moss has postulated that the memory inflammation co-occurring with HCMV infection upregulates Th1 CD4+ and CD8+ inflammatory responses, thus increasing the inflammatory capacity to respond to infectious challenges and modulating the quality of the immune response to heterologous antigens (Moss 2019). While potentially evolutionarily advantageous under certain conditions early in life, this predilection for inflammatory responses may become counterproductive as the individual ages because it augments “inflamm-aging” (Moss 2019). As will become clear below, this “balancing selection” model is played out to some extent in the literature on vaccine immunogenicity and disease susceptibility in the context of HCMV infection.

5 Clinical Relevance 5.1

Aging and Immunosenescence

Longitudinal studies of elderly cohorts led to the notion of an immune risk phenotype (IRP), a cluster of immune parameters that predict future mortality (Ferguson et al. 1995; Pawelec et al. 2001; Wikby et al. 2005). The main characteristics of the IRP are high levels of CD8+ cells versus CD4+ cells, leading to an inversion of the CD4+/CD8+ cell ratio as well as an attenuation of mitogen-stimulated lymphoproliferative responses (Effros 2004; Pawelec et al. 2005). It was established fairly quickly that people with an HCMV infection were more likely to have an IRP such that a positive HCMV serology predicted earlier mortality in both octogenarians and nonagenarians (Olsson et al. 2000; Wikby et al. 2002). Several other studies have since confirmed that elderly individuals who are HCMV positive die earlier than their seronegative counterparts with hazard ratios in the order of 1.2–1.4 (Strandberg et al. 2009; Wang et al. 2010; Roberts et al. 2010; Simanek et al. 2011; Gkrania-Klotsas et al. 2013; Savva et al. 2013). Mortality from cardiovascular disease may be an important component of this association – a meta-analysis of prospective studies calculated a hazard ratio of 1.30 for HCMV (Wang et al. 2017).

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The link between HCMV and mortality risk may be accounted for by inflammation. A composite measure of circulating IL-6 and TNF concentrations reportedly mediated the association between anti-HCMV immunoglobulin G (IgG) antibody titers and cardiovascular disease risk in a population-based study of older Latinos (Roberts et al. 2010). Further, the accumulation of cytotoxic CD57+CD28- CD4+ cells expressing the vascular endothelium homing marker, CX3CR1, was hypothesized to be a driver of cardiovascular disease in the context of dual HCMV/HIV infection (Chen et al. 2020). Nevertheless, there are some negative findings including a meta-analysis of five longitudinal cohorts that failed to detect a significant relationship between HCMV and all-cause or cardiovascular mortality (Chen et al. 2021). However, in this study, individuals with IgG antibody titers in the highest quartile did show a non-significant trend towards increased mortality relative to seronegative controls (HR 1.13) and seropositive individuals in the lowest antibody quartile (HR 1.18), findings in line with the small effect size reported in previous work. It is possible that the strength of the relationship between HCMV and mortality risk is dependent on the stringency of statistical adjustment as the models in the Chen et al. paper included age and sex as well as education, body mass index (BMI), smoking status, number of comorbidities, and C-reactive protein (CRP). In addition to associations with IRPs and mortality risk, there is also indirect evidence implicating HCMV in immunosenescence. Telomeres, the protective nucleotide sequences that cap the ends of chromosomes, have been demonstrated to shorten with age, and this shortening is associated with accelerated cellular senescence, risk of mortality, and age-related diseases (Aubert and Lansdorp 2008). Several studies have reported a cross-sectional association between HCMV infection and reduced length of telomeres in leukocytes (Spyridopoulos et al. 2009; Rizzo et al. 2013; Aiello et al. 2017; Lin et al. 2021). Moreover, in the Whitehall study, healthy HCMV-positive participants aged 53–76 years showed a greater reduction in telomere length over the 3 year follow-up than HCMV negative individuals (Dowd et al. 2017). Although other pathogens may also impact telomere length, HCMV seems to have the largest effect. Aiello and colleagues examined the link between infection with chronic pathogens and telomere length in a large sample (N ¼ 1,708) of individuals aged 20–49 years from the National Health and Nutrition Examination Survey (NHANES) (Noppert et al. 2020). Individuals who were seropositive for HCMV, herpes simplex virus 1 (HSV-1), and H. pylori had significantly decreased telomere length compared to their counterparts with a low pathogen load. The mechanism underlying this HCMV-associated shortening of leukocyte telomeres is still unclear. What is known is that the capacity for increasing telomerase expression tends to be lost in differentiated lymphocytes (Valenzuela and Effros 2002), and as noted above, HCMV infection is associated with a significant clonal expansion of highly differentiated T-cells. Indeed, kidney transplant patients who acquired a primary HCMV infection showed a sudden and persistent drop in lymphocyte telomere length (van de Berg et al. 2010). Various other types of aging indices or “clocks” have been developed. Using a methylation-based clock, Hurme and colleagues examined the association between epigenetic aging and HCMV infection in both nonagenarians and young controls

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(Kananen et al. 2015). Participants who were HCMV positive had a greater epigenetic age in both samples, showing a 2.5 year relative increase in the younger sample and a 6-year increase in the elderly sample. A more recent study utilized methylation markers around the ELOVL2 gene in PBMCs to obtain an index of epigenetic aging in a sample of individuals aged 60–90 years and found that HCMV-positive participants had a significantly older epigenetic age (65 years) compared with HCMV negative individuals (59 years) (Poloni et al. 2021). The effect of HCMV on methylation levels of ELOVL2 had actually been reported previously (Bacalini et al. 2017), but these authors had argued that methylation of ELOVL2 is a surrogate marker of cell replication and failed to detect a correlation with longevity or mortality in their sample.

5.2

Vaccine Response

Vaccine immunogenicity tends to decline in the elderly (Goodwin et al. 2006; Sasaki et al. 2011). Moreover, individuals who fail to express the major co-stimulatory CD28 receptor on their T-cells due to a homozygous mutation, respond poorly to diphtheria, tetanus, and human papilloma virus vaccines (Béziat et al. 2021), while higher percentages of CD57+CD28- lymphocytes have been associated with reduced antibody titers following influenza vaccine in elderly participants (Trzonkowski et al. 2003). Thus, if HCMV does accelerate aging of the immune system and lead to an expansion of CD28- cells, one would expect to see a decrease in vaccine efficacy in people who test seropositive for HCMV. On balance, the literature is suggestive of a weak effect in this direction. Most papers have focused on the influenza vaccine. A recent meta-analysis of 13 such studies reported a non-significant trend for a negative effect of HCMV infection on IgG titers (pooled OR of 0.65), although results were heterogeneous and there was evidence of publication bias (van den Berg et al. 2019b). There was also a weak negative correlation between HCMV antibody levels and influenza antibody levels after vaccination, which as the authors note, could be illustrative of a cumulative effect of multiple HCMV reactivations over time on vaccine responses or a spurious association driven by an average increase in HCMV titers with chronological age (van den Berg et al. 2019b). Nevertheless, other studies have challenged the hypothesis that T-cell diversity is compromised by HCMV-induced memory inflation (Lindau et al. 2019), and it has been postulated that the proportion of B-cell lineages that respond to influenza vaccination may even be increased (de Bourcy et al. 2017). In line with these data, Davis and colleagues argue that HCMV enhances influenza vaccine immunogenicity in an age-dependent fashion (Furman et al. 2015). Whereas no significant effect of HCMV was observed in older individuals aged 61–80 years, in young participants (20–30 years), HCMV infection was associated with an elevation in IL-13 and IFNγ, higher CD8+ pSTAT1 and pSTAT3 responses to IL-6, and higher influenza antibody titers post the trivalent inactivated influenza vaccine (Furman et al. 2015).

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With respect to other types of vaccines, the data are also mixed. HCMV seropositivity was negatively associated with vaccine response to an experimental Ebola vaccine (Bowyer et al. 2020) as well as the pneumovax23 pneumococcal vaccine in both healthy individuals and individuals with chronic kidney disease who were over the age of 65 years (Wall et al. 2021). Another study showed that HCMV seropositive healthy participants showed reduced IFNγ secretion by NK cells one week post an experimental dual vaccine against HCV and HIV, suggesting a weakened vaccine-induced Th1 response (Woods et al. 2021). In contrast, humoral immune responses to the DTaP-Hib-HBV vaccine were found to be similar in congenital HCMV-infected, postnatal HCMV-infected and HCMV-uninfected infants at seven months age (Pathirana et al. 2021). Another study reported that although HCMVpositive individuals had more terminally differentiated CD4+ and CD8+ cells and their NK cells showed a reduced activation profile, they nevertheless produced a normal T-cell response to the ChAdOx1 SARS CoV-2 vaccine (Sharpe et al. 2022). Similarly, in patients awaiting lung transplantation who received a live-attenuated herpes zoster vaccine (Zostavax), HCMV seropositivity was associated with increased frequencies of CD28- T-cells but not altered vaccine immunogenicity (Wang et al. 2021).

5.3

Disease Susceptibility

As humans age, new, naïve T-cell production declines, and expansion of memory T-cells fills the gap (Pawelec et al. 2009; Palmer 2013). Repeated viral activation leads to oligoclonal expansion of late-differentiated HCMV-specific memory cells (Ford et al. 2020). Thus, in HCMV-positive individuals, the T-cell compartment becomes more devoted to the control of HCMV and consequently could be less equipped to respond to novel pathogens because of the loss of naive cells or the maintenance of anti-HCMV clones at the expense of memory cells to other pathogens (Pawelec et al. 2001). Below, we summarize evidence to suggest that HCMV infection may compromise immunity to other infectious agents. Nevertheless, whether this phenomenon is related to memory inflation and a reduction in naïve T-cells is still unclear. HCMV seropositivity was shown more than 30 years ago to negatively affect the natural history of HIV infection and the survival of HIV-positive patients (Webster et al. 1989). Multiple studies have since confirmed that elevated HCMV antibody levels are associated with HIV disease progression (Sinicco et al. 1997; Kovacs et al. 1999; Robain et al. 2001; Deayton et al. 2004; Patel et al. 2017b; Isnard et al. 2021). One mechanism underpinning this phenomenon may be related to the activation of inflammatory pathways. HCMV IgG titers have been positively correlated with sCD14 and CRP concentrations in people living with HIV (Patel et al. 2017b). These data may explain why HCMV infection is an established risk factor for the development of inflammation-related disease comorbidity in the context of HIV infection (Lichtner et al. 2015; Schnittman and Hunt 2021). Another possibility is

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that HCMV directly transactivates HIV gene expression—for instance, one of the genes coded for by the MIEP can activate the HIV long terminal repeat (LTR), which encodes the HIV transcriptional promoter (Ho et al. 1990; Nardiello et al. 1994; Yurochko et al. 1999). It has also been suggested that HCMV is not directly pathogenic but rather compromises CD4+ cell function, explaining why in HCMV seroconverters, AIDS was reported to occur at higher CD4+ cell counts (Deayton et al. 2004). There are several other examples of HCMV negatively impacting the immune response to infectious agents. In both children and adults, the antiviral immune response to HCMV appears to disrupt the immune response to tuberculosis (TB), increasing the risk of progressing to TB disease (Müller et al. 2019; Stockdale et al. 2020; Martinez et al. 2021; Olbrich et al. 2021). Similarly, HCMV infection is associated with reduced immunity to Epstein-Barr virus (EBV) (Khan et al. 2004). Additionally, there are several studies indicating that HCMV reactivation in critically ill patients with sepsis is associated with increased ventilation times and mortality rates (Kalil 2008; Kalil and Florescu 2011; Imlay and Limaye 2020). Although a recent clinical trial with ganciclovir did not demonstrate a significant effect on IL-6, the primary outcome, the ganciclovir-treated group did better than the placebo group on a number of exploratory outcomes, i.e., higher number of ventilator-free days and shorter duration of mechanical ventilation (Limaye et al. 2017). In contrast, there is emerging evidence that HCMV may be protective under certain circumstances. In a study of over 200 athletes who were followed during their winter training programs, it was found that athletes who were seropositive for HCMV reported fewer symptoms of upper respiratory tract infections compared to their seronegative counterparts (He et al. 2013). The effect was even stronger in the 21% of the cohort who were both HCMV and EBV positive. The authors suggest that the mechanism may be related to the amplified mobilization of CD8+ cells during exercise by HCMV which would improve immune surveillance (He et al. 2013). Regarding non-infectious disease, there is some preliminary evidence that HCMV infection protects against relapse in patients with leukemia (Lönnqvist et al. 1986; Behrendt et al. 2009; Elmaagacli et al. 2011; Ito et al. 2013; Turki et al. 2022). The mechanism underlying this putative phenomenon is unclear, but the possibility has been raised that it may be related to the HCMV-induced priming of NKG2C+ NK cells (Rölle and Brodin 2016). From early in the SARS-COV-2 pandemic, it has been argued that HCMV may contribute to COVID-19 severity (Kadambari et al. 2020). Immunosupressive therapies given to severe COVID-19 patients are a risk factor for opportunistic infections, including HCMV (Abdoli et al. 2021). HCMV-related complications have been described in several case reports of severe COVID-19 patients (Oualim et al. 2020; Molaei et al. 2021; Gozzi-Silva et al. 2021; Maillet et al. 2021; Amundson et al. 2021; Amiya et al. 2021; Shaikh et al. 2021). In 232 COVID-19 patients, hospitalization was associated with increased seroprevalence of HCMV and HSV-1, although this association may have been influenced by demographic differences (Shrock et al. 2020). Conversely, HCMV seropositivity independent of age, race,

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and sex was associated with increased risk of infection (OR ¼ 1.7, 95% CI: 1.24–2.33, p ¼ 0.001) and hospitalization (OR ¼ 2.63, 95% CI: 1.37–5.35, p ¼ 0.005) in a retrospective study of 246 COVID-19 patients and 738 matched controls (Alanio et al. 2022). Likewise, 18 out of 38 mechanically ventilated COVID-19 patients reactivated HSV and/or HCMV viremia during hospitalization and reactivation was associated with longer duration of ventilation (median 9 vs. 23 days) (Le Balc’h et al. 2020). A similar study of COVID-19 intensive care patients found an association between longer stay only with EBV and not CMV or human herpesvirus (HHV)-6 reactivation (Simonnet et al. 2021). However, in this study, only 5 of 34 patients tested positive for HCMV viremia, and all were successfully treated with anti-virals, but the EBV and HHV-6 reactivated patients were not (Simonnet et al. 2021). These results suggest that more research is warranted on the impact of HCMV and immune response to SARS-COV-2, and that HCMV serostatus could be a useful clinical stratification tool for the treatment of COVID-19 patients.

6 Relevance to Mental Health 6.1

Stress Is a Risk Factor for Psychiatric Illness

Multiple systematic reviews and meta-analyses have consistently concluded that stressful life events are an important risk factor for the onset of diverse psychiatric disorders including anxiety disorders (Moreno-Peral et al. 2014), bipolar disorder (Rush 2003; Lex et al. 2017), MDD (Kessler 1997; Kendler et al. 1999; Hammen 2018), obsessive compulsive disorder (Brander et al. 2016), psychosis (Fusar-Poli et al. 2017; Beards et al. 2020; Martland et al. 2020), schizophrenia (Day 1981; Norman and Malla 1993; Holtzman et al. 2013), and postpartum psychiatric illness (Meltzer-Brody et al. 2018). Hazard ratios are large ranging from approximately 3–6, depending on the disorder, but also the number of previous episodes (Kendler et al. 2000).

6.2

Stress Is a Risk Factor for HCMV Infection and Reactivation

Psychosocial stress may increase susceptibility to HCMV infection or hinder the ability to control the virus. In a large occupational cohort, low socioeconomic status (SES) was association with greater odds of HCMV seropositivity and IgG antibody titers were associated with greater depression and anxiety after controlling for confounders (Rector et al. 2014). Analysis of data from the NHANES III found that the odds of HCMV seropositivity decreased with increasing household income

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starting at an early age, widening during adulthood, then converging in later life when the majority of participants were seropositive (Dowd et al. 2009). In a followup analysis, high HCMV IgG antibody titers, a surrogate marker for poor viral control (Glaser and Kiecolt-Glaser 1994), were associated with poverty in HCMVpositive children age 6–16 while controlling for many other health and demographic factors, including BMI, low birth weight, asthma, and others (Dowd et al. 2012). Childhood Trauma Questionnaire scores from adults with MDD were positively associated with the odds of HCMV seropositivity (Ford et al. 2019). Subscale analysis found this effect to be driven by physical and sexual abuse (Ford et al. 2019). Another retrospective analysis found that HCMV serostatus and IgG titers were associated with adverse environmental conditions and family dysfunction (Janicki-Deverts et al. 2014). Although external factors in adverse environments associated with early life stress likely play a role in these findings, we do not think that an increase in exposure fully accounts for the disparities. Exposure to HCMV is common and it is likely that the vast majority of individuals are exposed even if infection is not established. Rather, we hypothesize that the immunosuppressive effects of chronic stress render individuals more susceptible to becoming HCMV infected after exposure. Certainly, the classic experimental studies of Sheldon Cohen and colleagues demonstrated that stress increases susceptibility to both rhinovirus infection and the development of clinical colds (Cohen and Williamson 1991; Cohen et al. 1991, 2012). One possible mechanism for this impairment in antiviral immunity is the reduced expression of antiviral response genes such as type I IFNs observed across and wide range of adverse conditions (Cole 2019). Reactivation of HCMV is also initiated by various stressors. For instance, caregivers to family members with dementia had higher antibody titers against HCMV compared with controls (Pariante et al. 1997). Relative to breast cancer survivors with no or low childhood adversity, those who experienced childhood adversity had more current depressive symptoms, poorer sleep quality, and higher HCMV titers (Fagundes et al. 2013). Spaceflight has also been associated with HCMV viral shedding in urine (Mehta et al. 2000). Greater perceived stress and high HCMV antibody titers were associated with higher proportions of latedifferentiated CD8+ cells in older adults aged 64–92 years (Reed et al. 2019) echoing a similar report in younger cohort (Bosch et al. 2009), although in this study, HCMV antibodies were not measured. The mechanism underlying the link between stress and HCMV infection/ reactivation is thought to be related to sympathetic nervous system (SNS) activity. Epinephrine and norepinephrine produced by the SNS can bind to adrenergic receptors expressed on many different types of immune cells, potentially altering the dynamics of the immune response by interfering with the NFκB signaling cascade (Kolmus et al. 2015). In a monocytic tumor line, norepinephrine or epinephrine binding to the β2-adrenergic receptor reactivated IE gene expression via the CREB/ATF-1 signaling pathway (Prösch et al. 2000). Consistent with these in vitro data, following spikes in circulating catecholamines that coincided with acute myocardial infarction, ten out of ten patients had detectible HCMV IE gene expression in PBMCs, although none showed signs of HCMV disease (Prösch et al. 2000).

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Further, mice treated with a β2-adrenergic receptor agonist were more susceptible to MCMV, while deficiency of this receptor resulted in a better clearance of the virus and less tissue damage (Wieduwild et al. 2020). Interestingly, a clinical study reported that HCMV seropositive individuals showed a reduced antibody response to the influenza vaccine but only when they were also taking a beta-adrenergic blocker (Reed et al. 2017). Conceivably, beta-blockers may predispose to reactivation of HCMV and/or directly amplify the effects of HCMV on the immune system. On a broader level, chronic adrenergic stress may also create an environment that is not conducive to viral control by impairing DC maturation, suppressing CD8+ T-cell function, and inducing differentiation of myeloid-derived suppressor cells (Schmidt et al. 2016; Estrada et al. 2016; Mohammadpour et al. 2018; Iñigo-Marco and Alonso 2019).

6.3

Inflammation Is Implicated in the Etiology of Psychiatric Disorders

Inflammation is increasingly recognized to play a key pathophysiological role in a subgroup of people with psychiatric disorders, particularly mood disorders and schizophrenia (Irwin and Cole 2011; Miller and Raison 2016; Mechawar and Savitz 2016; Savitz and Harrison 2018; Savitz 2019; Pape et al. 2019; Pillinger et al. 2019). The data supporting this hypothesis are derived from a multitude of sources including cross-sectional elevations of inflammatory mediators in the blood, cerebrospinal fluid, and postmortem brain in psychiatric populations versus controls; epidemiological studies showing prospective associations between inflammation and the subsequent onset of psychiatric illness; genome-wide association studies identifying mood disorder risk variants in immune gene regions; naturalistic studies demonstrating that treatment with immune stimulators like IFNα causes depression in approximately one-third of patients; and the arguably successful treatment of some psychiatric patients with anti-inflammatory medications.

6.4

Inflammation Predisposes to HCMV Reactivation and Vice Versa

As stated previously, monocyte differentiation is known to stimulate HCMV replication (Taylor-Wiedeman et al. 1994; Söderberg-Nauclér et al. 2001). In vitro allogenic stimulation of peripheral blood mononuclear cells (PBMCs) from HCMV-positive donors with histo-incompatible PBMCs stimulated IE gene expression within 17 days (Söderberg-Nauclér et al. 1997a). In Concanavalin A stimulated monocyte-derived macrophages, blockage of IFNγ and TNF, but not IL-1, IL-2 or transforming growth factor (TGF)-β, inhibited HCMV viral production, and addition

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of either IFNγ or TNF was sufficient to initiate viral reactivation (Söderberg-Nauclér et al. 1997b). TNF is now known to initiate HCMV transcription by activating NFκB, which in turn, binds to the IE region (Stein et al. 1993; Döcke et al. 1994; Prösch et al. 2000). Preclinical studies show that IL-6 also plays a role in reactivation via ERK-MAPK signaling (Hargett and Shenk 2010; Reeves and Compton 2011), while TNF, IL-1β, IL-18, CD40L, and IL-6 have all been reported to activate AP-1 and/or NFκB, which in turn induce transcription of the IE genes (Liu et al. 2016). Consistent with these data, HCMV reactivation occurs frequently during sepsis and predicts adverse outcomes (Imlay and Limaye 2020; Lambe et al. 2022). Importantly, HCMV reactivation may itself because of inflammation in both immunosuppressed and immunocompetent populations (Simanek et al. 2011; Schnittman and Hunt 2021). In people living with HIV, concurrent HCMV infections have been shown to elicit immune activation that predisposes to inflammatory diseases such as cardiovascular disease and malignancy (Schnittman and Hunt 2021). Consistent with these data, a randomized placebo-controlled trial of valganciclovir in HCMV seropositive people with HIV reduced CD8+ cell activation as well as plasma concentrations of sTNFR2, sCD163, and sCD14 compared with placebo (Hunt et al. 2011; Schnittman and Hunt 2021). Similarly, HCMV increases the risk of coronary artery disease after heart transplantation (Grattan 1989; McDonald et al. 1989), an effect that was mitigated in a clinical trial of ganciclovir (Valantine et al. 1999). HCMV has also been reported to be a trigger of autoimmune diseases such as systemic lupus erythematosus perhaps by inducing inflammation via activation of NFκB and the reprogramming of macrophages (Chan et al. 2008; Guo et al. 2018). Some studies have also reported positive correlations between circulating inflammatory mediators and HCMV serostatus or antibody titers in community samples (McDonald et al. 2004; Schmaltz et al. 2005; Bennett et al. 2012; Li et al. 2017), although these data are less consistent than in medically-ill populations.

6.5

Is HCMV an Overlooked Co-Factor in the Genesis of Psychiatric Illness?

The complex interaction between stress, inflammation and HCMV may explain why multiple studies have reported an association between HCMV seropositivity or antibody titers and mood disorders (Appels et al. 2000; Trzonkowski et al. 2004; Miller et al. 2005; Phillips et al. 2008; Tedla et al. 2011; Jaremka et al. 2013; Rizzo et al. 2013; Rector et al. 2014; Simanek et al. 2014, 2018; Avramopoulos et al. 2015; Prossin et al. 2015; Tanaka et al. 2017; Dickerson et al. 2017, 2018; Gale et al. 2018; Sølvsten Burgdorf et al. 2019; Frye et al. 2019; Coryell et al. 2020) and schizophrenia (Albrecht et al. 1980; Torrey et al. 1982; Kaufmann et al. 1983; Dalman et al. 2008). For a more detailed review, please see the chapters by Zheng and Savitz and Savitz and Yolken in this volume. We hypothesize that in a subgroup of patients,

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stress and/or inflammation leads to the reactivation of HCMV, which in turn further exacerbates any underlying immune activation by altering macrophage, NK cell, and T-lymphocyte function. By analogy, HCMV acts as an accelerant on a smoldering fire. These immune changes likely have negative consequences for brain structure and function. Alternatively, HCMV may infect and damage the brain directly. After controlling for multiple potential confounders, we showed that in participants with MDD, HCMV seropositivity was associated decreased white matter integrity in pathways that connect the occipital lobe to the orbitofrontal cortex via the insula (Zheng et al. 2021a), reduced gray matter volume (GMV) in orbitofrontal and temporal regions (Zheng et al. 2020), and hypoconnectivity between the salience and sensorimotor networks during resting-state fMRI (Zheng et al. 2021b). Similar associations between HCMV infection and reductions in GMV of the hippocampus have also been reported in individuals with bipolar disorder and schizophrenia (Houenou et al. 2014; Andreou et al. 2021). Future research is required to elucidate the mechanistic pathways through which HCMV affects the brain in the context of psychiatric illness as such data could reveal novel approaches for the treatment of psychiatric disorders. Acknowledgments BNF acknowledges support from the Brain and Behavior Research Foundation (30031). JS acknowledges support from the William K. Warren Foundation, the National Institute of Mental Health (R01MH123652) and the National Institute of General Medical Sciences (P20GM121312).

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Effect of Cytomegalovirus Infection on the Central Nervous System: Implications for Psychiatric Disorders Haixia Zheng and Jonathan Savitz

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 CMV in Immune-Naïve and Immunocompromised Populations . . . . . . . . . . . . . . . . . . . . . . . . . . 3 CMV and Mood Disorders (Major Depressive Disorder and Bipolar Disorder) . . . . . . . . . . 4 CMV and Schizophrenia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Potential Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Cytomegalovirus (CMV) is a common herpesvirus that establishes lifelong latent infections and interacts extensively with the host immune system, potentially contributing to immune activation and inflammation. Given its proclivity for infecting the brain and its reactivation by inflammatory stimuli, CMV is well known for causing central nervous system complications in the immune-naïve (e.g., in utero) and in the immunocompromised (e.g., in neonates, individuals receiving transplants or cancer chemotherapy, or people living with HIV). However, its potentially pathogenic role in diseases that are characterized by more subtle immune dysregulation and inflammation such as psychiatric disorders is still a matter of debate. In this chapter, we briefly summarize the pathogenic role of CMV in immune-naïve and immunocompromised populations and then review the evidence (i.e., epidemiological studies, serological studies, postmortem studies, and recent neuroimaging studies) for a link between CMV infection and psychiatric disorders with a focus on mood disorders and schizophrenia. Finally, we discuss the potential mechanisms through which CMV may cause CNS dysfunction in the context of

H. Zheng (*) Laureate Institute for Brain Research, Tulsa, OK, USA e-mail: [email protected] J. Savitz Laureate Institute for Brain Research, Tulsa, OK, USA Oxley College of Health Sciences, The University of Tulsa, Tulsa, OK, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 215–242 https://doi.org/10.1007/7854_2022_361 Published Online: 4 May 2022

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mental disorders and conclude with a summary of the current state of play as well as potential future research directions in this area. Keywords Bipolar disorder · Central nervous system · Cytomegalovirus · Depression · Inflammation · Neuroimaging · Schizophrenia

1 Introduction Human cytomegalovirus (CMV) is a double-stranded DNA virus from the Betaherpes subfamily and is also known as human herpesvirus-5 (HHV-5). CMV is not completely cleared after primary infection but establishes lifelong latent infections. Such infections are evident in approximately 80% of the global general population although there is substantial variation by age, SES, and geographic region (Zuhair et al. 2019). CMV harbors a large number of genes dedicated to evading innate and adaptive immunity in the host, such as attenuating type I interferon production, downregulating natural killer (NK) cell activity, inhibiting major histocompatibility (MHC) class I and II antigen presentation, and interfering with T-cell proliferation (Ye et al. 2020; Patro 2019; Mishra et al. 2019; Griffiths and Reeves 2021). This complex pattern of virus–host interaction may alter host immunity over time (Picarda and Benedict 2018). Remarkably, up to 30% of all CD8+ cells (Sylwester et al. 2005) and 5% of CD4+ cells (Pourgheysari et al. 2007) can become directed against CMV antigens. This age-related expansion in CMV-specific T-cells is accompanied by a decrease in naïve CD8+ and CD4+ cells, raising the possibility that recurrent viral reactivation can negatively impact host immunity to other microbial infections (Pourgheysari et al. 2007; Khan et al. 2002; Vescovini et al. 2007; Chidrawar et al. 2009). Indeed, previous studies have reported that CMV seropositivity is associated with a reduced immune response to influenza virus vaccination in both young and elderly individuals (Frasca et al. 2015; Derhovanessian et al. 2013; Trzonkowski et al. 2003; Wall et al. 2021). More recently, a large sample longitudinal cohort study in South Africa (n ¼ 963, followed up for a median of 6.9 years) reported that infants exposed to CMV infection in the first year were at significantly increased risk of subsequently developing tuberculosis (TB) independent of the probability of exposure to TB (Martinez et al. 2021). However, other studies have reported that CMV may provide protection against heterologous infection and boost vaccine efficacy, perhaps through persistent activation of the immune system (Barton et al. 2007; Furman et al. 2015). While this immune-modulatory capacity of CMV may have minimal effects in healthy populations, it can lead to disease or tissue damage in vulnerable populations such as the immune-naïve, the immunocompromised, and potentially, patients with psychiatric disorders.

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2 CMV in Immune-Naïve and Immunocompromised Populations CMV infections have traditionally been considered to be benign in healthy adults, however, CMV is a well-known cause of pathology in utero. Intrauterine infection can be symptomatic or asymptomatic, the former frequently characterized by disordered hepatic function, hematological abnormalities, and growth restriction (Baldwin and Cummings 2018; Krstanovic et al. 2021; Moulden et al. 2021). CMV is capable of infecting a wide range of cells including astrocytes, neurons, microglia, and neural stem cell precursor cells (Krstanovic et al. 2021; Teissier et al. 2014; Lokensgard et al. 1999). Thus, the brain is a major target in congenital CMV infections. Infection of the developing brain results in a generalized pro-inflammatory response which may explain the widespread histopathological abnormalities observed in about half the cases with symptomatic infection including cortical and cerebellar hypoplasia, microcephaly, calcifications, and ventriculomegaly (Baldwin and Cummings 2018; Krstanovic et al. 2021; Moulden et al. 2021). These neuropathological changes may result in sensorineural hearing loss, seizures, retinitis, vestibular dysfunction, and developmental delays (Baldwin and Cummings 2018; Krstanovic et al. 2021; Moulden et al. 2021). Rare cases of CMV encephalitis can also occur in immune competent children and adults (Rafailidis et al. 2008). CMV can also be pathogenic in immunocompromised adults including people living with HIV (Schnittman and Hunt 2021), patients receiving immunosuppressive medications for bone marrow or organ transplantation (Freeman 2009), and patients with sepsis (Imlay and Limaye 2020). In these individuals, CMV infection may be poorly controlled and can lead to end-organ diseases such as pneumonitis, retinitis, hepatitis, encephalitis, or hemorrhagic colitis (Griffiths et al. 2015; Camargo and Komanduri 2017). While uncontrolled CMV replication can be directly pathogenic, as in the case of intrauterine infections, many of the detrimental effects of the virus are thought to be related to CMV-associated immune activation and inflammation (Schnittman and Hunt 2021; Vasilieva et al. 2020). For instance, when followed longitudianally detection of CMV DNA in peripheral blood mononuclear cells (PBMCs) of HIV patients was shown to be associated with a greater incidence of non-AIDS clinical events related to immune dysfunction (e.g., cardiovascular disease, malignancy). Although less common, CMV can cause neurological disease in people living with HIV. Encephalitis, polyradiculitis, or retinitis generally occurs in patients with CD4+ cell counts below 50 cells/mm3 (Bowen et al. 2016). MRI can be normal in these patients but periventricular hyperintensities in the case of encephalitis, and gadolinium enhancement in the nerve roots in the case of polyradiculitis, may be present (Bowen et al. 2016). More subtle effects on the brain may occur in less immunosuppressed patients. For instance, several studies have reported higher anti-CMV IgG titers to be associated with cognitive deficits in people with HIV receiving antiretroviral therapy (Letendre et al. 2018; Ballegaard et al. 2018; Brunt et al. 2016).

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One important mechanism underlying lytic CMV replication is inflammation. Specifically, tumor necrosis factor (TNF) is known to activate NF-kB, which in turn binds to the immediate early enhancer region of CMV, initiating virus transcription (Docke et al. 1994; Stein et al. 1993; Prosch et al. 2000). Subsequently, IL-6 was also shown to play an important role in reactivation via ERK-MAPK signaling (Reeves and Compton 2011; Hargett and Shenk 2010). More recent work in mice has further expanded this repertoire reporting that several inflammatory mediators, including TNF, IL-1β, IL-18, CD40L, and IL-6 activate AP-1 as well as NF-kB, which in turn induce transcription of the immediate early genes (Liu et al. 2016). However, asymptomatic CMV replication is itself a cause of inflammation in immunocompetent populations, and as noted above, CMV causes inflammationrelated morbidity in immunocompromised patients (Schnittman and Hunt 2021; Simanek et al. 2011). This raises the question of whether CMV may play a role in psychiatric disorders that are associated with inflammation and impaired adaptive immunity such as major depressive disorder (Mechawar and Savitz 2016; Ford et al. 2019; Miller and Raison 2016; Dantzer et al. 2008).

3 CMV and Mood Disorders (Major Depressive Disorder and Bipolar Disorder) Major depressive disorder (MDD) is associated with stress, weakened anti-viral immunity, and inflammation (Miller and Raison 2016; Dantzer et al. 2008; Zorrilla et al. 2001; Evans et al. 2002; Leday et al. 2018; Mechawar and Savitz 2016; Cole 2014). For instance, a subgroup of depressed individuals show elevations in circulating inflammatory markers (Dowlati et al. 2010; Howren et al. 2009; Leighton et al. 2018; Osimo et al. 2019), decreased proliferative response of lymphocytes to mitogens, decreased natural killer cell function and lymphopenia in vitro (Zorrilla et al. 2001), upregulated expression of inflammation-related genes together with down-regulated expression of anti-viral genes (Leday et al. 2018; Cole 2014), poor control of chronic viral infections (Evans et al. 2002), impairment of vaccineinduced immune responses to the varicella-zoster virus (Irwin et al. 2011, 2013), a reduced vaccine-induced antibody response to the hepatitis B virus (Afsar et al. 2009), and a loss of childhood vaccine-induced immunity to measles (Ford et al. 2019). Since inflammation and stress can contribute to CMV reactivation (Docke et al. 1994; Pariante et al. 1997; Glaser et al. 1985; Limaye et al. 2008), some depressed patients may be vulnerable to CMV infection and reactivation. Since 1974, at least 14 observational studies have linked CMV infection with depression (Appels et al. 2000; Rector et al. 2014; Phillips et al. 2008; Trzonkowski et al. 2004; Miller et al. 2005; Dickerson et al. 2017; Simanek et al. 2014, 2019; Burgdorf et al. 2019; Dickerson et al. 2018; Jaremka et al. 2013; Lycke et al. 1974; Coryell et al. 2020; Gale et al. 2018). Specifically, higher prevalence rates of CMV seropositivity have been found in patients with depression than health controls

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(Lycke et al. 1974) and in patients with earlier acute coronary syndromes with a high level of depressive symptoms than a low level of depressive symptoms (Miller et al. 2005). A longitudinal study that included 771 older U.S. Latinos (up to 10 years of follow-up) found that CMV seropositivity is associated with increased risk of depression onset (odds ratio ¼ 1.38), and this risk was even higher in the female group (odds ratio ¼ 1.70) (Simanek et al. 2019). Similarly, a prospective Danish case–control study reported an increased odds ratio of CMV infection among individuals who were subsequently diagnosed with a mood disorder (incidence rate ratios for mood disorders ¼ 1.43) (Burgdorf et al. 2019). Furthermore, elevated anti-CMV IgG antibody titer, which is commonly used as a surrogate marker of CMV reactivation (Iglesias-Escudero et al. 2018; Mehta et al. 2000), has been repeatedly associated with MDD or depressive symptoms (Appels et al. 2000; Rector et al. 2014; Phillips et al. 2008; Trzonkowski et al. 2004; Dickerson et al. 2017, 2018; Simanek et al. 2014; Jaremka et al. 2013; Lycke et al. 1974; Coryell et al. 2020; Gale et al. 2018). Notably, one study reported that one unit increase in CMV IgG titer level was associated with a 26% higher risk of new onset of depression a year later within CMV seropositive subjects. Further, the risk for depression was 3.87 times higher in the individuals with IgG titer levels in the highest quartile group (Simanek et al. 2014). Taken together, these data are consistent with a possible causal link between CMV infection and the onset of depression. On the other hand, negative results have also been reported. For instance, a longitudinal study (11 years follow-up, n ¼ 8,028) found that neither CMV serostatus nor CMV titer level was significantly associated with increased risk of new-onset depressive or anxiety disorders and also found that CMV seropositivity at baseline was associated with significantly reduced risk of developing a new-onset generalized anxiety disorder (odds ratio ¼ 0.43) (Markkula et al. 2020). Similarly, a sizeable population-based survey (n ¼ 6,825 Americans aged 15–39) reported no statistically significant association between CMV seropositivity and MDD (Simanek et al. 2018). However, this study revealed a sex effect. Specifically, CMV seropositivity was associated with lower odds of depression among men (odds ratio ¼ 0.54), while the CMV titer level was associated with increased odds of mood disorders among CMV seropositive women (odds ratio ¼ 1.25) (Simanek et al. 2018). Another study reported higher CMV titers to be associated with hypomanic symptoms but no difference in depressed mood among patients with bipolar disorder (BD) (Prossin et al. 2015). Several other confounders such as age, medication, heterogeneity of the disease, socioeconomic status, and household crowding may also contribute to the contradictory results across different research groups. For instance, it is well known that the seroprevalence rate of IgG against CMV is lower in younger populations than in older populations which are more likely to be exposed to the virus over time. Further investigation considering potential confounders and meta-analyses are warranted to disentangle these contradictory results. Like MDD, BD is strongly associated with immune dysregulation and systemic chronic inflammation (Tsai et al. 1999, 2017; Fernandes et al. 2016; Breunis et al. 2003; Magioncalda and Martino 2021). CMV infection has also been proposed as one candidate for causing immune activation in BD (Yolken and Torrey 1995, 2008;

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Maes et al. 2021). This hypothesis is supported by epidemiological observations of higher anti-CMV IgG or IgM antibody titers in diverse patient populations across different countries relative to healthy controls (Prossin et al. 2015; Tedla et al. 2011; Rizzo et al. 2013; Avramopoulos et al. 2015; Tanaka et al. 2017). Consistent with these findings, a recent large case–control study that included more than 1,200 patients with BD and 745 healthy controls showed higher IgG seropositivity rates (odds ratio ¼ 1.33) and higher IgG titers in the BD group (Frye et al. 2019). Although Rizzo et al. reported no difference on CMV seropositivity rates between patients with BD and controls, they found CMV IgG titers were higher on average in patients with BD relative to controls (Rizzo et al. 2013). A common limitation of these serological studies is that IgG correlates relatively poorly with viral load in infected tissues (Marandu et al. 2019). Further, IgG antibodies cannot distinguish between a recently acquired infection or reactivation of a latent infection. One study measured IgM against CMV, which is considered a sensitive and somewhat specific indicator of recent CMV infection (Lazzarotto et al. 2001), and found CMV IgM level was significantly higher in patients with BD than healthy controls (Tanaka et al. 2017). However, as in the case of the MDD literature, it is important to note that not all studies have reported positive results. For instance, a Dutch study (n ¼ 2,364 BD and n ¼ 5,101 controls) found no significant association between exposure to CMV and BD (Snijders et al. 2019). The above-mentioned National Health and Nutrition Examination Survey also found that CMV seropositivity was associated with a lower risk of BD among males (odds ratio ¼ 0.37) but a higher risk of BD among women (odds ratio ¼ 1.43) (Simanek et al. 2018). As noted above, several lines of evidence (i.e., postmortem histological studies, human cell culture models, human brain-derived cells, and animal models) have demonstrated that almost all cell types in the brain (i.e., myeloid lineage cells, vascular pericytes, endothelial cells of the blood-brain barrier, glial cells, neurons, and neuronal precursor cells) have a certain degree of propensity for CMV infection (Tsutsui et al. 2005; Poland et al. 1990; Alcendor et al. 2012; Fish et al. 1998; Arribas et al. 1996; Luo et al. 2008). Especially, it has been demonstrated that brainderived endothelial cells, vascular pericytes, astrocytes, and neurons are fully permissive for CMV infection and replication (Poland et al. 1990; Alcendor et al. 2012; Luo et al. 2008). Thus, it is conceivable that CMV itself or the inflammation associated with recurrent CMV reactivation may lead to abnormalities of brain structure and function. Following this hypothesis, we previously found a robust and replicable association between CMV seropositivity and brain structure/function in MDD (Zheng et al. 2020, 2021a, b). Specifically, after carefully adjusting for up to 11 potential confounders, we found that relative to CMV negative (CMV-) MDD participants, matched CMV+ participants showed (1) reduced temporal lobe white matter integrity (fractional anisotropy, FA) of the inferior fronto-occipital fasciculus (IFOF), a major tract connecting the orbitofrontal cortex (OFC) and the occipital cortex via the temporal lobe (Zheng et al. 2021a) (see Fig. 1), (2) reduced gray matter volume (GMV) in the OFC and temporal regions (Zheng et al. 2020, 2021b) (see Fig. 2), and (3) reduced functional connectivity between the salience network and sensorimotor

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Fig. 1 CMV is associated with reduced white matter integrity in depression. In two independent samples (n ¼ 303 and n ¼ 249), we consistently observed lower FA in the IFOF in CMV+ versus CMV MDD subjects in both samples. In the Discovery sample, we identified a cluster with significantly lower FA (d ¼ 0.58, pFWE < 0.05) in the right IFOF in CMV+ participants with MDD compared to CMV participants with MDD (a). A significantly lower mean FA in CMV+ participants with MDD at the same cluster was confirmed in the Replication sample (d ¼ 0.45). Exploratory whole-brain voxel-wise analyses revealed the CMV effect in FA of IFOF in MDD was bilateral in both samples (b, see our publication (Zheng et al. 2021a) for details)

network (Zheng et al. 2021b) (see Fig. 3). An association between CMV infection and decreased medial temporal volume (i.e., bilateral dentate gyrus (Andreou et al. 2021) and right hippocampus (Houenou et al. 2014)) has also been reported in patients with BD. Furthermore, in these studies IgG antibody titer was inversely associated with the medial temporal volume in the patients with BD (Andreou et al. 2021; Houenou et al. 2014). Interestingly, all the neuroimaging studies performed to date report that the association between CMV infection and brain abnormalities is limited to patients with mood disorders and is not present or at least not as salient in the healthy controls (Zheng et al. 2020, 2021a, b; Andreou et al. 2021; Houenou et al. 2014). Conceivably, CMV may play an etiological role in mood disorders because of immune dysregulation in this population. Nevertheless, it remains unclear how CMV-associated brain alterations are related to behavioral changes in mood disorders, although three studies have reported an association between elevated IgG and IgM anti-CMV antibody titers and suicide attempts in this population (Dickerson et al. 2017, 2018; Coryell et al. 2020).

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Fig. 2 CMV infection is associated with reductions in gray matter volume (GMV) of orbitofrontal and temporal regions in 3 independent MDD samples. Figure (a) illustrates the smaller cortical regions in the CMV+ group than the CMV group in each of the samples ( puncorrected < 0.05). If a region replicated in at least 2 out of 3 samples, then this region was considered statistically significant and coded in a different color for visualization. Four regions (i.e., temporal pole, fusiform gyrus, orbital sulcus, and the parahippocampal gyrus) were replicated in at least 2 out of 3 different samples. The effect size of the standardized beta coefficient (equivalent to Cohen’s d) ranged from 0.2 to 0.41 of the 4 replicated regions (b). Data adapted from our previous publication with identical methodology but a different brain atlas (Zheng et al. 2020, 2021b)

4 CMV and Schizophrenia The hypothesis that latent viruses such as CMV may be involved in the development of schizophrenia began to evolve and generate research interest in the 1970s (Torrey and Peterson 1973, 1976; Crow et al. 1979; Tyrrell et al. 1979). To date, we are aware of 20 serological studies that test the link between CMV and schizophrenia. It should be emphasized that there is significant heterogeneity in this literature. For example, Yolken et al. reviewed 14 serological studies published between 1973 to 1992 that measured serum CMV antibody levels in patients with chronic schizophrenia (Yolken and Torrey 1995). Lycke et al. reported that relative to controls (n ¼ 140), CMV antibodies were significantly more prevalent in patients with schizophrenia (n ¼ 327) (Lycke et al. 1974). Two additional studies reported a higher level of serum CMV antibodies in schizophrenia as compared to controls, but the difference was not statistically significant (Gotlieb-Stematsky et al. 1981; King et al. 1985a). All other 11 studies reported no differences in the level of antibody titers between patients and controls (Toorey et al. 1978; Cappel et al. 1978; Albrecht et al. 1980; Libikova 1983; King et al. 1985b; Rimon et al. 1986; Schindler et al. 1986; Delisi et al. 1986; Cazzullo 1987; Pelonero et al. 1990; Fux et al. 1992). Later, Torrey et al. summarized an additional five serological studies that used more sensitive laboratory assays, well-matched socioeconomic/geographical variables, and patients with new-onset or first episode of schizophrenia. All of these studies

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Fig. 3 CMV infection is associated with hypoconnectivity between the salience network and sensorimotor network in MDD. In our previous study, we employed a conservative strategy using both anatomical based parcellation (Desikan-Killiany atlas, ROI-to-ROI analyses, a) and network-based parcellation (Seed-to-Voxel analyses, b) and stringent statistical threshold (voxellevel puncorrected < 0.001, cluster level pFDR < 0.05) to investigate the effect of CMV on functional connectivity in MDD sample (n ¼ 99, CMV+: 42). We found that regardless of parcellation method or analytical approach, relative to the CMV group, the CMV+ group showed hypoconnectivity between the salience network and the sensorimotor network with a standardized beta coefficient (equivalent to Cohen’s d) ranging from 0.57 to 0.99 (for detail see our publication (Zheng et al. 2021b))

reported a higher CMV seropositivity rate in schizophrenia versus controls (Torrey et al. 2006). Further, a Swedish national cohort study reported that symptomatic CMV infection of the CNS during childhood (congenital infection not included) confers a higher risk (risk ratio ¼ 16.6) of developing schizophrenia and non-affective psychotic illness (Dalman et al. 2008). However, a recent systematic review and meta-analysis that included 18 relevant articles published between 1950 and 2020 (a total of 2,688 patients and 1,642 controls) found no significant difference in the prevalence of CMV seropositivity or CMV titer level in schizophrenia and other non-affective psychotic disorder versus healthy comparison participants (Moya Lacasa et al. 2021). As in the case of the mood disorder literature, clinical heterogeneity could conceivably contribute to differences across studies. For instance, one study divided patients with schizophrenia into deficit (N ¼ 88, deficit defined as patients with schizophrenia who had primary negative symptoms such as restricted affect and diminished social drive) versus non-deficit schizophrenia (N ¼ 235), and reported that deficit schizophrenia had a significantly higher prevalence of CMV seropositivity than non-deficit schizophrenia (odds ratio ¼ 2.01) (Dickerson et al. 2006). Similarly, another study reported that patients with schizophrenia who were CMV

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IgG positive (n ¼ 52) had significantly higher negative symptoms scores than those who were CMV IgG seronegative (n ¼ 40) (Bolu et al. 2016). Perhaps the most interesting serological finding supporting the CMV hypothesis of schizophrenia was the detection of antibodies against CMV in the cerebrospinal fluid (CSF) of patients with schizophrenia in the early 1980s (Albrecht et al. 1980; Torrey et al. 1982). One study using an enhanced neutralization test found that relative to controls, 68% of patients with chronic schizophrenia (n ¼ 60) had a higher CSF to serum ratio of CMV antibody (Albrecht et al. 1980). Another study using an enzyme immunoassay observed IgM antibodies against CMV in 11% of the schizophrenia sample (n ¼ 178), but none of the healthy controls (Torrey et al. 1982). Following these studies, at least another seven studies attempted to replicate the results (reviewed in Torrey et al. (2006); Orlovska-Waast et al. (2019)). Several studies also found elevated antibody titers against CMV in the CSF of patients with schizophrenia compared to the control group (Leweke et al. 2004; Kaufmann et al. 1983; van Kammen et al. 1984), while others reported conflicting results (i.e., antibody undetectable or no significant difference from controls) (Gotlieb-Stematsky et al. 1981; King et al. 1985a; Rimon et al. 1986; Shrikhande et al. 1985). One possible explanation for these confusing results may be due to the duration of illness and the long-term use of antipsychotic medication. As reported in the investigation conducted by Leweke et al. relative to healthy controls, the increased anti-CMV IgG in the serum and CSF was only found in untreated patients with recent-onset schizophrenia but not in the treated schizophrenia group (Leweke et al. 2004). Furthermore, this study reported a stepwise decrease of IgG level in both serum and CSF from subjects who never received antipsychotic medications, subjects who had received medication in the past, subjects who were receiving treatment at the time of the study, to controls without any psychiatric disorders, suggesting treatment and medication status may affect antibody level in serum and CSF (Leweke et al. 2004). Beyond serological studies using blood or CSF samples, several postmortem brain studies have searched for CMV nucleic acid in patients with schizophrenia (reviewed in (Yolken and Torrey 1995)). While the sensitivities of the hybridization or polymerase chain reaction (PCR) techniques ranged from one viral genome per 10 cells to one viral genome per 833 cells, eight out of nine studies yielded negative results (Aulakh et al. 1981; Taylor and Crow 1986; Carter et al. 1987; Moises et al. 1988; Taylor et al. 1985; Alexander et al. 1992; Taller et al. 1996; Sierra-Honigmann et al. 1995). The only positive study was performed by Moises et al. They detected CMV DNA in the temporal cortex of a person with schizophrenia (23-year-old male) (Moises et al. 1988). It is potentially noteworthy that CMV DNA was also found in one postmortem sample of an individual who had received immunosuppressive therapy as a treatment for rheumatoid arthritis (Taylor and Crow 1986), supporting the notion that CMV can directly infect the brain when host anti-viral immunity is suppressed. Although it is plausible that differences in technology and CMV strains may exist across these studies, the lack of direct evidence for CMV infection in the brain has posed a strong argument against the hypothesis that latent/persistent CMV infection in the brain contributes to the development of schizophrenia. However, all

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these studies were conducted in the 80s or 90s using techniques that are relatively insensitive compared with those currently available (Cassedy et al. 2021). Thus, a new investigation may shed new light on this debate. However, it is also important to note that the a priori probability of detecting CMV transcripts is limited by the fact that a latent CMV infections are usually associated with very low levels of RNA/DNA (Shnayder et al. 2018), and postmortem studies often use old-fixed brain tissue. In addition, the absence of viral material in the postmortem samples does not necessarily rule out the possibility that CMV plays a pathological role only during the early stages of psychiatric disorders or that CMV exerts pathological effects via immune activation without directly infecting the brain. While the evidence for an association between CMV and schizophrenia per se is equivocal, CMV infection may be more likely to have a negative influence on cognitive function in the context of schizophrenia. A study involving 329 patients with schizophrenia reported that exposure to CMV was associated with a slower response and increased errors when performing a cognitive task (i.e., the Trail Making Test, a measure of visual search, working memory, and psychomotor speed) (Shirts et al. 2008). Dickerson et al. also reported that CMV seropositivity was associated with non-statistically significant but lower cognitive function in patients with schizophrenia as measured by the Repeatable Battery for the Assessment of Neuropsychological Status (Dickerson et al. 2003a). Furthermore, CMV IgG titer was found to be associated with smaller right hippocampal volume and lower episodic verbal memory (measured by California Verbal Learning Test) in patients with schizophrenia (Houenou et al. 2014). Consistent with the observations from patients with schizophrenia, a prospective cohort study found that higher levels of IgG against CMV at baseline predicted more-rapid global decline in cognitive function over a period of 4 years in elderly Mexican Americans (Aiello et al. 2006). Similarly, it has been reported that over an average of 5 years of follow-up, CMV seropositivity was associated with a 2.15 times increased risk of developing Alzheimer’s disease among older black and white individuals (Barnes et al. 2015). The association between higher anti-CMV IgG titer and lower cognitive functioning has also been observed in a younger healthy population (mean age 32.8 years, n ¼ 521) (Dickerson et al. 2014). Some studies also found a cumulative effect of joint exposure to CMV and other herpesviruses such as herpes simplex virus type 1 (HSV-1) on cognitive impairments in patients with schizophrenia as well as in healthy subjects (Hamdani et al. 2017; Strandberg et al. 2003; Watson et al. 2013). On the other hand, other studies have argued that HSV-1 infection rather than the CMV exposure might be the primary driver of the cognitive dysfunction (Prasad et al. 2011; Yolken et al. 2011). Nevertheless, both CMV seropositivity and CMV antibody titers are often associated with elevated HSV-1 antibody titers although whether this is due to common exposure or CMV-induced immune dysregulation is unclear (Stowe et al. 2012). Thus, the specificity between CMV infection and cognitive function may warrant further investigation. To date, two clinical trials using the anti-CMV drug, valacyclovir, to suppress viral replication have been performed in patients with schizophrenia (Dickerson et al. 2003b, 2009). In the first trial, oral valacyclovir was prescribed to 65 outpatients

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over 16 weeks along with their usual psychiatric medications. Fifty-eight patients completed the 16 weeks trial, and 21 were CMV seropositive. This study found the 21 CMV seropositive patients showed an improvement in overall schizophrenic symptoms (i.e., positive symptoms, negative symptoms, and general symptoms) (Dickerson et al. 2003b). Unfortunately, this observation did not replicate in their second double-blind and placebo-controlled trial, in which 47 CMV seropositive schizophrenia patients were assigned randomly to valacyclovir or placebo group for 16 weeks of adjunctive treatment (Dickerson et al. 2009). Future trials with more effective anti-CMV medications like valganciclovir or letermovir may be more informative.

5 Potential Mechanisms The blood-brain barrier (BBB) is the protective layer composed of brain microvascular endothelial cells and tight junction proteins that surrounds the basement membrane, pericytes, microglia, astrocytes, and neurons (Abbott et al. 2010). The BBB and these junctions play an essential role in limiting the free movement of large molecules such as virus particles from the blood to the CSF and CNS cells. CMV is capable of infecting myeloid lineage cells, vascular pericytes, endothelial cells of the blood-brain barrier, glia, neurons, and neuronal precursor cells (Tsutsui et al. 2005; Poland et al. 1990; Alcendor et al. 2012; Fish et al. 1998). Thus, one possible pathophysiological mechanism is that CMV particles in the circulatory system reach and infect brain microvascular endothelial cells and gain entrance to the CNS. Second, CMV may compromise tight junctions of the BBB and thus predispose the parenchyma to an immune-mediated pathogenesis. That is, it has been shown in human and animal studies that active and latent CMV infection induces systemic inflammatory responses and the expression of chemokines and cytokines (i.e., monocyte chemoattractant protein-1 [MCP-1], interferon-gamma [IFN-γ]) (Hamilton et al. 2013; Froberg et al. 2001; van de Berg et al. 2010), that are sufficient to downregulate the expression of tight junction protein expression and enhance BBB permeability (Chai et al. 2015; Stamatovic et al. 2005). In addition, one study in elderly adults observed IFN-γ in 80% of CSF samples from CMV seropositive subjects while no IFN-γ could be detected in any of the CSF samples from CMV seronegative subjects, raising the possibility that CMV infection is associated with inflammation in the CNS (Lurain et al. 2013). Once peripheral cytokines enter the brain, the activity of glial cells (i.e., microglia, astrocytes, and oligodendrocyte) can be compromised, leading to axonal demyelination and neurodegeneration (Wohleb et al. 2016; Hammond et al. 2019; Mechawar and Savitz 2016; Glass et al. 2010). Third, CMV could conceivably undermine the host immune response and induce host immunosuppression, which may in turn promote cellular senescence and tissue degeneration (Naniche and Oldstone 2000; Salminen 2021). Early studies have documented that CMV suppresses bone marrow myelopoiesis (Simmons et al. 1990; Torok-Storb et al. 1992), impairs monocyte function (Buchmeier and Cooper

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1989; Carney and Hirsch 1981), and inhibits cytotoxic T-lymphocyte and natural killer (NK) cell activity (Schrier and Oldstone 1986; Schrier et al. 1986). CMV encodes a unique interleukin 10 (IL-10) homolog, which is 27% identical to human IL-10 and can bind to IL-10 receptors and impact host immune signaling and T-cell responses (Slobedman et al. 2009; Kotenko et al. 2000; Klenerman and Oxenius 2016). We also previously reported that CMV+ subjects had lower percentages of both naïve T-cells (CD4+ and CD8+) and higher circulating concentrations of CD27/CD28 effector memory T-cells than CMV subjects (Ford et al. 2020). These immunomodulatory effects may provoke pathological changes in several tissues that are similar to the aging process (i.e., impaired tissue homeostasis), leading to tissue damage and neuropathology (Salminen 2021). Indeed, beyond the aforementioned psychiatric disorders, CMV seropositivity has also been associated with an increased risk of cognitive decline and mortality in the elderly (Savva et al. 2013; Roberts et al. 2010; Wang et al. 2010; Nimgaonkar et al. 2016), as well as adverse health outcomes more generally, including atherosclerosis (Betjes et al. 2007; Nikitskaya et al. 2016), inflammatory bowel disease (Lawlor and Moss 2010; Nowacki et al. 2018; Lv et al. 2017; Beswick et al. 2016), cancer (Joseph et al. 2017; Herbein 2018), multiple sclerosis (MS) (Langer-Gould et al. 2017; Vanheusden et al. 2015; Geginat et al. 2017), vascular dementia (Lin et al. 2002; Firth et al. 2016), and Alzheimer’s disease (AD) (Barnes et al. 2015; Lurain et al. 2013; Readhead et al. 2018; Renvoize et al. 1979; Westman et al. 2014; Bu et al. 2015; Harris and Harris 2015; Itzhaki 2016; Steel and Eslick 2015). Fourth, CMV may trigger autoimmunity and autoimmune-mediated neuroinflammation (Halenius and Hengel 2014; Vanheusden et al. 2017). For example, autoantibodies (i.e., anti-CD13 (Soderberg et al. 1996a, b), antiphospholipid (Mengarelli et al. 2000), and anti-endothelia cell autoantibodies (Varani et al. 2002), etc.) have been repeatedly observed in CMV-infected transplant recipients. In immunocompetent individuals, CMV infection has also been linked with the onset of autoimmune disorders (Halenius and Hengel 2014). Finally, a combination of direct viral invasion and immune-mediated neuropathology may also be possible. For instance, a recent case study discussed a 38-year-old healthy male with an initial diagnosis of CMV-induced CNS infection (e.g., acute transverse myelitis) who 40 days later developed an immune-mediated cerebral white matter demyelination (Daida et al. 2016).

6 Concluding Remarks The question of whether neurotropic latent viruses such as CMV play an etiological role in psychiatric disorders has been studied for nearly half a century. Here, we have surveyed the currently available evidence implicating CMV in the etiology of psychiatric disorders. The extant data are largely cross-sectional and correlational. Taken together, while the overall evidence repeatedly links CMV infection with psychiatric disorders, suggesting that CMV may play an etiological role in mood

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disorders and psychoses, direct or conclusive evidence is still missing. Several pieces of information are urgently needed in order to better establish the precise model of the interaction between CMV infection and psychiatric disorders. First, given the somewhat inconsistent results in the current literature, a thorough systemic review or meta-analyses would be helpful to determine the heterogeneity of existing evidence and estimate the summary effect size of CMV infection on psychiatric disorders. Future population-based prospective studies together with statistical strategies to balance potential confounders are also warranted to provide more convincing conclusions. Second, the precise virological or immunological mechanisms through which CMV infection leads to structural and functional alterations in the brain in the context of psychiatric disorders remain unknown. Experimental manipulations using animal models – particularly non-human primates since human CMV does not replicate efficiently in rodents – may provide a more sophisticated examination or demonstration of the possible mechanisms. The existing evidence in human studies linking CMV infection with brain alterations has generally relied on serological markers such as IgG, which is an indirect measure of viral activity. Future studies that leverage more specific markers of CMV reactivation (e.g., high sensitivity PCR or CMV-encoded microRNAs (Zhou et al. 2020)) and neuroimaging techniques should help the field move toward the goal of improved mechanistic understanding. The ongoing development of CMV vaccines also holds tremendous promise, not just for the prevention of future CMV infections, but also for understanding the role that CMV plays in psychiatric disorders. Third, the potential for reverse causation must be addressed before discussing any therapeutic implications. That is, does CMV reactivation causally drive the disease process in psychiatric disorders or is the observed higher prevalence of CMV in psychiatric populations simply the consequence of immune dysregulation in these patient groups? Innovative approaches should be encouraged to delineate this question. Although it is high risk and high cost, a well-powered randomized and placebo-controlled anti-CMV treatment trial has the potential to deliver promising evidence that could help draw causal conclusions. Another approach worth considering is the application of causal inference tools such as instrumental variable or Mendelian randomization to examine the statistical evidence of causal relations from observational data (Emdin et al. 2017; Sekula et al. 2016; Baiocchi et al. 2014). Instrumental variable and Mendelian randomization seek to find nature’s randomized trial embedded in observational data and use it to estimate causal effects (Emdin et al. 2017; Sekula et al. 2016; Baiocchi et al. 2014). The beauty of these tools is that they are considered unconfounded and test the causal relationship in one direction, thus avoiding the reverse causation issue. While these methodologies depend heavily on certain statistical assumptions and generally require a large sample size, they could provide valuable insights if rigorously performed. In summary, there is no doubt that immune system activation plays an important and complex role in psychiatric disorders. CMV infection could be a targetable source of low-level immune activation and has been repeatedly implicated in psychiatric disorders for nearly 50 years. While much work remains to be done to

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understand the nature of these correlational relationships, given the existence of well-tolerated anti-CMV medications and the ongoing development of vaccines, there is sufficient evidence to suggest that this line of study may lead to actionable interventions that could prevent or treat neuropathology in some patients with psychiatric disorders. Acknowledgments HZ and JS acknowledge support from the William K. Warren Foundation. JS also receives support from the National Institute of Mental Health (R01MH123652) and the National Institute of General Medical Sciences (P20GM121312).

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Herpesvirus Infections in the Human Brain: A Neural Cell Model of the Complement System Derived from Induced Pluripotent Stem Cells Ernesto T. A. Marques, Matthew Demers, Leonardo D’Aiuto, Priscila M. S. Castanha, Jason Yeung, Joel A. Wood, Kodavali V. Chowdari, Wenxiao Zheng, Robert H. Yolken, and Vishwajit L. Nimgaonkar

Ernesto T. A. Marques and Matthew Demers contributed equally to this work. E. T. A. Marques Department of Infectious Diseases and Microbiology, University of Pittsburgh, Pittsburgh, PA, USA Virology and Experimental Therapeutic Laboratory, Institute Aggeu Magalhaes, Recife, PE, Brazil M. Demers Department of Infectious Diseases and Microbiology, Graduate School of Public Health, Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA, USA L. D’Aiuto, J. A. Wood, K. V. Chowdari, and W. Zheng Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA P. M. S. Castanha Department of Infectious Diseases and Microbiology, University of Pittsburgh, Pittsburgh, PA, USA J. Yeung Department of Infectious Diseases and Microbiology, University of Pittsburgh, Pittsburgh, PA, USA Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX, USA R. H. Yolken Division of Neurovirology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA V. L. Nimgaonkar (*) Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA VA Pittsburgh Healthcare system at U.S. Department of Veterans Affairs, Pittsburgh, PA, USA Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 243–264 https://doi.org/10.1007/7854_2022_383 Published Online: 5 September 2022

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Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 HSV-1 and Its Role in Human Pathology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Links Between Herpesviruses and Dementias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Links Between Herpesviruses and Other Forms of Cognitive Decline . . . . . . . . . . . . . . 1.4 The Human Complement Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Special Features of the Complement System in the Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 The Need for Human Brain-Relevant Cellular Models of the Complement System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Using hiPSC-Derived Cells to Model Brain Complement Function . . . . . . . . . . . . . . . . 2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Human iPSCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 iPSC-Derived Neuronal Cell Differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 iPSC-Derived Microglial Cell Differentiation and Characterization . . . . . . . . . . . . . . . . . 2.4 Characterization of hi-M . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Human Fetal Astrocytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 hi-M and hi-N Co-culture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 hi-M, hi-N, and ha-D Co-cultures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 Gene Expression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 Complement Protein Assays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Cellular and Transcriptomic Characterization of Hematopoietic Precursor Cells (HPCs) Derived hi-N and hi-M . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Complement Gene Expression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Detection and Localization of Complement Proteins in hi-N and hi-M . . . . . . . . . . . . . 4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Background: Herpesviruses alter cognitive functions in humans following acute infections; progressive cognitive decline and dementia have also been suggested. It is important to understand the pathogenic mechanisms of such infections. The complement system – comprising functionally related proteins integral for systemic innate and adaptive immunity – is an important component of host responses. The complement system has specialized functions in the brain. Still, the dynamics of the brain complement system are still poorly understood. Many complement proteins have limited access to the brain from plasma, necessitating synthesis and specific regulation of expression in the brain; thus, complement protein synthesis, activation, regulation, and signaling should be investigated in human brain-relevant cellular models. Cells derived from human-induced pluripotent stem cells (hiPSCs) could enable tractable models. Methods: Human-induced pluripotent stem cells were differentiated into neuronal (hi-N) and microglial (hi-M) cells that were cultured with primary culture human astrocyte-like cells (ha-D). Gene expression analyses and complement protein levels were analyzed in mono- and co-cultures. Results: Transcript levels of complement proteins differ by cell type and co-culture conditions, with evidence for cellular crosstalk in co-cultures. Hi-N and hi-M cells have distinct patterns of expression of complement receptors, soluble factors, and regulatory proteins. hi-N cells produce complement factor 4 (C4) and

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factor B (FB), whereas hi-M cells produce complement factor 2 (C2) and complement factor 3 (C3). Thus, neither hi-N nor hi-M cells can form either of the C3-convertases – C4bC2a and C3bBb. However, when hi-N and hi-M cells are combined in co-cultures, both types of functional C3 convertase are produced, indicated by elevated levels of the cleaved C3 protein, C3a. Conclusions: hiPSC-derived co-culture models can be used to study viral infection in the brain, particularly complement receptor and function in relation to cellular “crosstalk.” The models could be refined to further investigate pathogenic mechanisms. Keywords Brain · Brain development · Co-culture · Complement system · iPSC · Microbial infections · Neurological diseases · Viral infection

1 Introduction Much effort has been expended to identify the etiology and pathogenesis of dementias, subtle forms of cognitive decline related to aging, and psychiatric disorders such as psychoses. An etiologic role for microorganisms that infect the brain has been proposed in cognitive impairment observed in such individuals. Though several bacterial and viral pathogens have been investigated, interest has converged on herpesviruses (Prasad et al. 2012; Watson et al. 2013). In the following sections, we initially describe herpes simplex virus type 1 (HSV-1) as it epitomizes many characteristic features of herpesviruses. We next summarize growing evidence linking herpesviruses to dementias and other, more subtle forms of cognitive decline. Finally, we conclude by describing a multi-cellular model of complement function that could yield additional mechanistic information relevant to the viral hypotheses of cognitive dysfunction.

1.1

HSV-1 and Its Role in Human Pathology

HSV-1 is a double-stranded DNA virus that frequently causes primary infections in human mucosal surfaces. Infection of other individuals occurs through oral or sexual transmission (Kriebs 2008), with population prevalence increasing with age to 90% or more in some regions (Prasad et al. 2012). Following initial mucosal infections that can be asymptomatic, virions migrate to sensory ganglia, where they establish dormancy (also called a latent state) that extends through the lifetime of the host (Harkness et al. 2014; Steiner et al. 2007). Reactivation from latency can occur unpredictably with reactivated virions retracing the path through sensory nerves to the original site of infection (Shimomura and Higaki 2011; Steiner et al. 2007). Though oral mucosal surfaces are the most common sites of infection, severe corneal

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keratitis (and blindness due to recurrence) and encephalitis can occur (Harkness et al. 2014; Steiner et al. 2007).

1.2

Links Between Herpesviruses and Dementias

Early analyses indicated elevated prevalence of seropositivity for antibodies directed to herpes virus among individuals diagnosed with dementia compared with appropriate control groups (summarized in Nimgaonkar et al. (2016)). Additionally, several studies have documented the presence of herpes viral proteins and nucleic acids in post-mortem brain tissues from patients with dementia, although the causeand-effect relationship of this association remains uncertain (Wozniak et al. 2009). Naturally, such studies associating pathology observed in patient-derived archival tissues and prior exposure to viral agents have limited interpretability due to the biology and epidemiology of human herpesvirus infections. These issues are discussed in detail in other chapters in this volume.

1.3

Links Between Herpesviruses and Other Forms of Cognitive Decline

A long-standing body of research exists linking herpes viruses – particularly HSV-1 – with psychiatric disorders such as schizophrenia and bipolar disorder (Prasad et al. 2012). A related, albeit smaller literature links HSV-1 infection with “cognitive aging,” i.e., cognitive decline that is noted in relation to aging (Nimgaonkar et al. 2016). The research stems from observations that survivors of HSV-1 encephalitis frequently suffered from cognitive deficits or psychotic syndromes (Hokkanen and Launes 2007). Furthermore, over a dozen studies have indicated a higher prevalence of elevated HSV-1 antibodies in plasma or serum samples from individuals with schizophrenia or bipolar disorder, compared with non-psychotic control individuals. Though others did not detect such differences (Aiello et al. 2008; Barnes et al. 2014; Katan et al. 2013; Nimgaonkar et al. 2016); a recent meta-analysis did indicate a case-control difference with small to medium effect size (Dickerson et al. 2020). Other supportive lines of evidence include brain imaging studies indicating reduced gray matter volumes among HSV-1 seropositive individuals (Prasad et al. 2011; Schretlen et al. 2010), and improvement in cognitive functions among HSV-1 seropositive patients with schizophrenia who received prolonged high dose antiviral medication RCT (Bhatia et al. 2018; Prasad et al. 2013). We analyzed these studies through the prism of nine “viewpoints” articulated in the “Bradford Hill criteria” designed to test for causal links in chronic human diseases (Fedak et al. 2015; Hill 1965). Despite inherent problems in conclusively establishing causal links for chronic disorders, we encountered plausible etiologic

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links between recurrent HSV-1 infections, and “cognitive aging,” particularly among individuals with schizophrenia (Prasad et al. 2012).

1.4

The Human Complement Cascade

One important issue regarding the potential link between viral infection and cognitive impairment/psychiatric disorders is identification of mechanisms by which infection causes these conditions in the absence of clinically apparent encephalitis. The complement system provides a plausible set of pathophysiological mechanisms for this interaction. The complement system is a complex group of functionally related proteins that play a central role in immune responses acting on the entire organism (Merle et al. 2015a; Merle et al. 2015b; Sarma and Ward 2011). The complement system comprises three activation pathways termed the classical, alternative and lectin pathways, that converge into enzymatically cleavage of complement component 3 (C3), forming the C3a and C3b fragments. C3b goes on to form C5 convertase and cleave component 5 (C5) into C5a and C5b. C5b can go on to participate in the membrane attack complex with complement factors C6–C9. Complement factors C3a and C5a act as powerful signaling molecules, recruiting other cells to generate an immune response. The classical and lectin pathways of complement activation are triggered by the interaction of a pattern-recognition molecule with a substrate molecule. In the classical pathway, C1q serves as the pattern-recognition molecule, by binding directly onto surfaces and by interacting with an antibody-antigen immune complex. In the lectin pathway, mannose binding protein (MBL), Ficolin, and some Collectins interact with target complex carbohydrate molecules to activate factors MASP-1 and subsequently MASP-2. Both cascades lead to cleavage of C4, which allows the formation of the C3 convertase C4b2a (Coulthard et al. 2018a; Merle et al. 2015a; Sarma and Ward 2011; Veerhuis et al. 2011). The alternative complement pathway depends on the spontaneous self hydrolysis, of a bond within the C3 protein, a phenomenon commonly called “tickover”. The hydrolyzed C3, the C3b fragment can bind factor B, and in the presence of factor D cleaves factor B to form C3bBb, the alternative pathway C3 convertase. Under normal physiologic conditions host membrane complement regulatory molecules deactivate the convertase, however when bound to surfaces of an infectious agent the cascade proceeds. In addition, the alternative pathway forms a feedback loop that further increased the Classical and Lectin pathways (Coulthard et al. 2018a; Merle et al. 2015a; Sarma and Ward 2011; Veerhuis et al. 2011).

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Special Features of the Complement System in the Brain

The complement system has unique functions in specialized compartments, like the central nervous system (CNS). Findings from human and animal studies indicate that microglia and astrocytes are abundant sources of complement C3 in CNS, whereas microglia are the primary source of C1q (Veerhuis et al. 2011). Neurons and astrocytes both produce and secrete C4, while microglia do not. The complement system also plays an important role in neurodevelopment (Coulthard et al. 2018a; Magdalon et al. 2020; Reemst et al. 2016). Synapse elimination, a crucial process in peripheral (Colman and Lichtman 1993; Thompson 1985) and central nervous system development, has been shown to depend on proteins of the complement pathway. The complement proteins are key molecules initiating the engulfment of synapses and sometimes whole axons by microglial cells (Chung et al. 2016; Fernandez et al. 2019; Johnson and Stevens 2018; Schafer et al. 2012; Sipe et al. 2016; Stephan et al. 2012; Stevens et al. 2007; Xu and Henkemeyer 2009). This engulfment is necessary for central nervous system development, and its disruption has been linked to several neurological disorders, including schizophrenia and autism (Comer et al. 2020; Neniskyte and Gross 2017; Prasad et al. 2016; Wang et al. 2018a). Microglia are key participants in this process, by phagocytosing synapses and axons (Reemst et al. 2016; Schafer et al. 2012; Stephan et al. 2012). Differentiation and migration of CNS cells also involve complement proteins. C3a and C5a, cleavage products of C3 and C5 known as anaphylatoxins, are known to be important immune cell chemotactic agents. In the brain, they are involved in neurogenesis and neuronal migration, as evidenced by both gene knockout and pharmacological studies (Coulthard et al. 2017; Coulthard et al. 2018b; Gorelik et al. 2017; Gorelik et al. 2018; Magdalon et al. 2020). The lectin pathway appears to be a determinant of migration of cells to the neural crest. Knockdown of collectin 11 (COLEC11) causes severe developmental defects in zebrafish, and COLEC11 and MASP-1 mutations have been associated with human developmental disorders (Magdalon et al. 2020; Rooryck et al. 2011). C3a is also necessary for neural crest cell migration (Carmona-Fontaine et al. 2011). However, some of these studies appear to be contradictory, thus more work remains to be done. Alterations in complement function have been reported among patients with schizophrenia (SZ) (Veerhuis et al. 2011). Complement gene variation has also been associated with risk for SZ (Colman and Lichtman 1993; Johnson and Stevens 2018; Thompson 1985). Prior genome-wide association studies (GWAS) indicate that elevated gene copy number (GCN) of C4A, a major C4 isotype, increases risk for SZ (Thompson 1985). C4A GCN is strongly correlated with C4A transcript

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levels (Stevens et al. 2007; Thompson 1985). In human post-mortem brain (PM) samples, C4A transcript levels are also increased by ~40% in 5 brain regions of patients with SZ compared with controls (Thompson 1985). Based on these data, it has been proposed that elevated C4 protein levels lead to increased synaptic pruning (Thompson 1985), a putative pathogenic process in SZ (Schafer et al. 2012).

1.6

The Need for Human Brain-Relevant Cellular Models of the Complement System

Most of the work to understand the interactions and effects of complement molecules has been conducted in small animal models (Coulthard et al. 2018a; Magdalon et al. 2020). However, there are significant differences between mouse and human complement systems. For instance, humans have two isoforms of complement C4 (C4A and C4B) that are not encoded in rodents (Law et al. 1984; Schifferli and Paccaud 1989). Naturally, a human in vitro model of the CNS would be impactful in elucidating the complement system’s role in the brain.

1.7

Using hiPSC-Derived Cells to Model Brain Complement Function

Human-induced pluripotent stem cells (hiPSCs) can be differentiated into neuraland glia-like cells in vitro (Bitar and Barry 2020; D'Aiuto et al. 2014a; Dolmetsch and Geschwind 2011; Suzuki and Vanderhaeghen 2015). These cells offer the advantages of unlimited expansion, possessing human genetic backgrounds, and relative ease of experimental manipulation. Differentiation of hiPSC generates a heterogeneous culture of neurons, with cells that stain for ß-III Microtubulin, MAP2, VGLUT1, CUX2, and calbindin; neural progenitor cells (NPC), marked by staining for Nestin, PAX6, and SOX1; radial glial cells, characterized by expression of GFAP, S100β, and VIMENTIN (D'Aiuto et al. 2018). We therefore evaluated the potential of an adherent co-culture system of hi-NPC-derived neural cells (hi-N) and hiPSC-derived microglial cells (hi-M) in studying the complement system. Hi-N and hi-M cells were derived separately from the same iPSC cell line and co-cultured with or without primary culture human astrocyte-like cells (ha-D). Secretion of soluble complement factors into culture medium was estimated using ELISA, and global transcription was assayed using RNAseq. Similarity to human CNS cell types was estimated using the CIBERSORT software deconvolution algorithm (Veerhuis et al. 2011) (see Fig. 1). Our findings show that hi-N and hi-M cells have distinct patterns of complement-related gene transcript and protein expression. Neither type of cell alone can form a C3 convertase, however when combined they express all the necessary components of C3 and C5 convertases. In addition, the expression levels of several factors are changed suggesting that complement molecules may mediate a crosstalk between these cells in co-cultures.

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Fig. 1 RNAseq results. (a) CIBERSORT analysis shows co-cultures contain gene expression signatures similar to neurons (45%) and microglia (26%), as well more similarity to astrocytes and endothelial cells than hi-N culture. (b) Expression level of different complement genes in hi-N, hi-M, hi-NM Co-cultures and ha-D from co-culture. (c) Expression of complement regulatory genes in hi-N, hiM, Co-cultures and ha-D

2 Methods 2.1

Human iPSCs

Human-induced pluripotent stem cell (iPSC) lines SC0000019 and SC0000020 from Rutgers University cell and DNA repository were used for generation of microglial and neuronal cell lines in this study. Rigorous quality control guidelines were followed, as published previously (D'Aiuto et al. 2012).

2.2

iPSC-Derived Neuronal Cell Differentiation

Human iPSCs were differentiated as previously described (D'Aiuto et al. 2014b). Briefly, hiPSCs were grown to confluence in monolayer 6-well culture plates, then incubated for 5 days in neural progenitor selection medium consisting of Dulbecco’s

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minimum essential medium/F12 medium (DMEM/F12) supplemented with 0.5
N2 supplement (GIBCO), 1
Glutamax (Gibco), 1
nonessential amino acids, and 1
antibiotic-antimycotic (Gibco). After 5 days, cells were cultured in expansion medium, consisting of a similar formulation with 1
N2 supplement and 20 ng/mL recombinant FGF2 (ThermoFisher). Thus, hiPSC were induced to form neural rosettes, which were harvested manually, allowed to aggregate in low attachment, replated on new matrigel plates and then harvested manually again to purify neural progenitor cells (hi-NPCs). NPCs were expanded for up to six passages, then switched to neural induction medium for 3 days and neural maintenance medium for a minimum of 2 weeks. Neural maintenance medium consists of Neurobasal (Gibco), 1
B27 Supplement, 1
Glutamax, 1
Antibiotic/antimycotic, and 10 ng/mL BDNF. Neural induction medium consists of neural maintenance medium plus a cocktail of three small molecules: 1 μM dorsomorphi-N, 10 μM forskolin, and 3 μM CHIR 99021.

2.3

iPSC-Derived Microglial Cell Differentiation and Characterization

hiPSCs were differentiated into microglial-like cells as published (McQuade et al. 2018). First, hiPSCs were seeded in 12-well plates on matrigel at a density of 16–40 colonies per well and treated with StemDiff hematopoietic media according to protocol provided by STEMCELL Technologies. After 12 days of treatment, non-adherent cells were collected and assayed for expression of CD34 and CD43 by flow cytometry (FC) to validate differentiation into hematopoietic stem cells (HSC). Briefly, cells (5.0
105–1.0
106) were incubated in 0.5% BSA plus FcR Blocking Buffer (Miltenyi) at 4 C to block, then for 10 min with fluorescent antibodies (REAffinity, Miltenyi) to stain. The cells were then fixed in 1% paraformaldehyde and counted on a BD LSRII Fortessa flow cytometer. Isotype controls were added to rule out non-specific binding, and samples were tested with and without FcR blocking reagent to reduce Fc-receptor binding. If cell cultures expressed both CD34 and CD43 (>85%), they were used for further differentiation. These cells were transferred to matrigel-coated 6-well plates, incubated in differentiation medium (DMEM/F12, 2
B27, 2
ITS-G supplement, 0.5
N2 supplement, 1
Glutamax, 1
nonessential amino acids, 400 μM monothioglycerol and 5 μg/mL additional insulin supplemented with a cocktail of100 ng/mL IL-34, 50 ng/ mL TGFβ1, and 25 ng/mL M-CSF). On alternate days, cells were supplemented with 1 mL per well differentiation medium with freshly thawed cytokine supplement. At intervals of 12 days, 6 mL medium was removed from each well and centrifuged at 350
g to pellet cells, and cells resuspended in 1 mL fresh medium and returned to the well. After 25 days, differentiation medium was additionally supplemented with CD200 and CX3CL1, both at 100 ng/mL. On day 28, microglia-like cells were harvested for characterization and co-culture experiments.

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Characterization of hi-M

To check for presence of CD45 and CD11b, 5.0
105–1.0
106 cells were incubated in 0.5% BSA plus FcR Blocking Buffer (Miltenyi) at 4 C to block, then for 10 min with fluorescent antibodies (REAffinity, Miltenyi) to stain. Cells were then fixed in 1% paraformaldehyde and counted on a BD LSRII Fortessa flow cytometer. Isotype controls were used to rule out non-specific binding, and samples were tested with and without FcR blocking reagent to rule out Fc-receptor binding. To stain for microglial markers C1Q, P2RY12, Iba1, and CD18, cells were incubated overnight on matrigel-coated coverglass, fixed for 20 min at room temperature in 4% paraformaldehyde, permeabilized for 20 min at room temperature in 0.2% Triton X-100, blocked for 1 h at 4 C in 10% goat serum (LifeTech) with Fc Block (BD Pharmingen), then stained overnight at 4 C with primary antibody. After overnight incubation, cells were washed 3
in PBS, incubated with fluorescent secondary antibody (AlexaFluor, ThermoFisher) for 1 h at 4 C, washed 3
in PBS, counterstained with Hoechst 33342 and mounted on microscope slides with SlowFade Gold mounting medium (ThermoFisher). Cells were imaged with a Leica DiaPlan DM5500 microscope. Phagocytosis was examined by incubating cells with one of three fluorescently labeled targets; Hylite 488-labeled Amyloid-ß 1–42 peptide (Anaspec), AlexaFluor 488-labeled streptococcus aureus bioparticles (ThermoFisher), or AlexaFluor 488-labeled zymosan bioparticles (ThermoFisher). Cells were incubated at 37 for 2 h, counterstained with antibody to CD18 (RnDSystems), fixed post-staining and imaged.

2.5

Human Fetal Astrocytes

The primary culture was comprised of a human astrocyte cell line (Gibco, Catalog number N7805100). The cells were tested for the astrocyte-specific marker glial fibrillary acid protein (GFAP). They were cultured according to the manufacturers’ specifications and subsequently passaged (https://www.thermofisher.com/order/ catalog/product/N7805100).

2.6

hi-M and hi-N Co-culture

To co-culture hi-M and hi-N, cells were differentiated per the above protocols and combined in monolayer. Hi-NPCs were first seeded on 12-well plates on matrigel and allowed to differentiate to hi-N for 2 weeks, at which point hi-M were added. The number of hi-M used was equal to one half the original seeding population of hi-NPCs. Co-cultures were maintained in neurobasal medium (Gibco) supplemented

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with B27, Glutamax, and BDNF (10 ng/mL). After 2 weeks, media was collected for protein measurements.

2.7

hi-M, hi-N, and ha-D Co-cultures

To culture hi-M, hi-N, and ha-D cells together, transwell membrane plates were used. Hi-NPCs were seeded on the upper membrane of the transwell and differentiated to hi-N as previously described. After 4 weeks, previously frozen hi-M were seeded on the membrane and allowed to mix with the hi-N cells. At the same time, ha-D cells were added to the bottom compartment of the plate, separated from the transwell membrane. Co-cultures were maintained in neurobasal medium (Gibco) supplemented with B27, Glutamax, and BDNF (10 ng/mL). After 2 weeks RNAs were purified for RNA sequence analysis.

2.8

Gene Expression Analysis

RNA was extracted from cell cultures using the RNeasy kit (Qiagen) and sequenced using the TruSeq Stranded Total RNA kit (Illumina) with a read depth of 40–50 million fragments per library. Following QC, reads were mapped to human genome construct GRCh38/hg38 using sequence and annotation provided by Ensembl (release 82). Subsequent analysis of interactions between complement genes was conducted using normalized transcripts per kilobase million (TPM) expression values. Cell-type-specific gene expression was examined using an externally generated and validated reference dataset (Wang et al. 2020) and CIBERSORT software (Newman et al. 2015). CIBERSORT uses a reference input matrix of gene expression signatures, which are collectively used to estimate the relative abundance of cell types of interest. Linear support vector regression (SVR) is employed to deconvolve the mixture. The reference panel included expression data from astrocytes, endothelial cells, microglia, excitatory and inhibitory neurons, and oligodendrocyte cell cultures developed by Wang et al. (2020) based on data generated from scRNAseq generated by Darmanis et al. (2015), as well as data from the PsychENCODE Consortium (Wang et al. 2018b) and Olah et al. (2018).

2.9

Complement Protein Assays

A commercial quantitative ELISA assay was used to measure C4 in culture media (Assaypro), and in-house ELISA assays were used to measure concentrations of C1q, C3, CFH, CFI, CFD, C5, C5a, and C3a. Forty-eight hours prior to collection,

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medium was removed from cultures and monolayers were washed with one volume DMEM/F12. Fresh medium was added and cell culture was resumed. After 48 h medium was harvested, centrifuged at 10,000
g for 5 min and supernatants collected and frozen at 80 C until analysis. All assays were performed for hi-N, hi-M, and co-cultures in quadruplicate and in two separate experiments separated by 2 months.

3 Results 3.1

Cellular and Transcriptomic Characterization of Hematopoietic Precursor Cells (HPCs) Derived hi-N and hi-M

Neuronal (hi-N) and microglia (hi-M) cells were generated as described in the Methods section. The hi-N cells’ differentiation method and cellular characterization have been published previously (D'Aiuto et al. 2014b). Hematopoietic precursor cells (HPCs) derived from hiPSCs consistently express CD34 and CD43, as determined by flow cytometry. HPCs cultures expressing CD34 and CD43 cells markers in over 85% of cells were differentiated into microglia. After differentiation, hi-M consistently expressed high levels of both CD11b (85%+) and CD45 (70%+), as determined by flow cytometry (data not shown). Microglia/macrophage markers Iba1 (AIF1), CD18, as well as the microgliaspecific marker P2RY12, were consistently detectable via immunostaining. P2RY12 was robustly expressed in nuclei, which has been reported in microglia in or adjacent to tumors but not typically in healthy brain tissue (Zhu et al. 2017). TMEM119, another specific microglial marker, could not be detected in any of our microglia-like cells, which is consistent with other groups’ findings for in vitro microglia culture (Bohlen et al. 2017; Gosselin et al. 2017; Hasselmann and Blurton-Jones 2020). The cells resembled macrophages morphologically when grown in monoculture, consistent with the original description (Abud et al. 2017; Gosselin et al. 2017; McQuade et al. 2018). Co-cultures were prepared as described in methods; hi-N and hi-M mixed, either in a 12-well plate for two-cell culture or on a transwell membrane, separated from ha-D cells beneath, for three-cell culture. Hi-M integrated into hi-N cultures sufficiently to not be visible with a brightfield microscope within 24 h. Integrated hi-M did maintain expression of Iba1 and the morphology changed to resemble macrophages with elongated processes. We extracted RNA from hi-N and hi-M monocultures, as well as hi-N/M co-cultures for mRNAseq. We then examined TPM values and used the CIBERSORT analysis algorithm to impute cell type representation in each set. Hi-N monoculture expression profiles show a mix of neurons, primarily inhibitory and endothelial cells. Hi-M monocultures are composed almost exclusively of cells that resemble microglia. Co-cultures of hi-N and hi-M appear to diversify somewhat, showing

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more contribution from endothelial cells and astrocytes, and complete loss of excitatory neurons. These data indicate that the HPCs derived hi-N, hi-M cells are excellent representations of neurons and microglia types of cells, respectively (Fig. 1a).

3.2

Complement Gene Expression Analysis

The transcript levels of genes encoding complement soluble factors, receptors, and regulatory molecules were quantified using normalized TPM expression values. The transcripts of hi-N, hi-M and hi-N and the co-culture expressed several genes encoding soluble elements of the complement cascade with unique patterns of complement system-related genes. The hi-N monocultures expressed significant levels of mRNA encoding the soluble factors (C1r, C1s, MASP1, MASP2, C4, CFB, CFD, and C5) and regulatory proteins (C1-INH, CFI, VTN, CLU, and FHL-1) (Fig. 1b, c and Fig. 2). The hi-M monocultures expressed some of the same soluble complement cascade components (C1r, C1s, MASP2, CFD, C5, and C1-INH (Serping)) and additional ones such as C2, C3, C1q, CFP (Properdin), that is also expressed by hi-N. Notably, the hi-N did not express C2 or C3, thus they are unlikely to form either the classic (C4bC2a) or the alternative (C3bBb) C3-convertases. Separately, hi-M cells did not express C4 and CFB, also precluding the formation of the classic or alternative C3-convertases in these cells. However, in hi-M and hi-N co-cultures, transcripts of both C3-convertases are detected and some mRNAs appeared to have increased expression in the co-cultures such as complement factor I (CFI), a molecule that participates in de-activation of the C3 convertases and production of iC3b and iC4b. CFI mRNA was present in low levels in each of the monocultures but when both cells were co-cultured, CFI levels increase dramatically. Over 2,000 other transcripts not related to the complement system also had altered expression patterns (expression TPM > ¼1, FC > ¼2, FDR corrected p < ¼0.05), suggesting that when hi-N and hi-M are co-cultured, they can modify their expression patterns. No significant levels of C6, C7, C8, or C9 transcripts were found in the cell cultures, indicating that hi-N, hi-M and combined cultures cannot form the membrane attack complex. In addition, we did not detect MBL transcripts (A figure with the soluble cascade proteins and the cultures expressing them is in Fig. 2). We next analyzed the expression of the complement proteins associated with the cellular plasma membrane. Hi-N cells expressed the C1q receptors gC1qR, cC1qRCD91, but did not express C1q. It is notable that hi-M produces C1q, suggesting that C1q receptors in hi-N cells might be engaged in the co-cultures. The proteaseactivated receptor-1 (PAR1), a member of the G protein-coupled receptors is expressed in hi-N cells. It has been proposed that PAR1 is a putative receptor for the C4 cleavage product, the C4a fragment (Wang et al. 2017). The role of C4a binding on PAR1 in neurons still unknown. In addition, hi-N expresses the complement regulatory molecule CD46 that together with CFI, which is expressed in high

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Fig. 2 This figure display the components of the complement cascade. The black arrows indicate the direction of the cascade, black line with stomp indicate it inhibits the reaction. Green arrow indicate the factors that increase expression in co-cultures. Balloon colors indicate the cell type that expresses the complement factor, Blue hi-N, Yellow Hi-M; Green both cells, and Purple only in cocultures. (a) Soluble complement factors produced by hi-N, hi-M and Hi-NM co-culture. (b) Membrane factors – hi-N. (c) Membrane factors – hi-M

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amounts in the co-cultures, can inactivate the C3-convertases and form iC3b and iC4b. CD59 is also present in hi-N cultures (Figs. 1 and 2b). Hi-M cells expressed the same set of C1q complement receptors (gC1qR, cC1qRCD91); however, they express a very distinct group of receptors (Fig. 2c). It includes the G-protein-coupled receptors C3aR, C5aR, C5L2 and the complement CR3, CR4 receptors that bind to C3b, iC3b, C4b and iC4b. It expresses the regulatory protein CD55, also known as decay-accelerating factor DAF, that can deactivate the C3-convertase. The expression of CD44, a co-receptor and the CR3/CR4 receptors that are associated with phagocytosis are increased in the co-cultures as compared to the hi-N MNC and Hi-M monocultures. Importantly, some molecules that have an effect on the complement system were also altered. The co-cultured cells have elevated expression of 2–8 sialyltransferases III (ST8SiaIII) and V (ST8SiaV) enabling the increase on sialylation of complex carbohydrates. Sialyltransferase increases expression of GT1a, GQ1b, and other sialylated glycolipids on cell surfaces. These sialylated sugars have an important role inhibiting the alternative complement activation and on neuronal development. In addition, there was an increase in the expression of phosphatidylserine translocases, enzymes that exposes phosphatidylserine to the outside membrane layer (TMEM30A, TMEM16D and F). The presence of phosphatidylserine on the outer surface can attract C1q binding to the membrane region.

3.3

Detection and Localization of Complement Proteins in hi-N and hi-M

• Expression and localization of C1q in hi-M and hi-N co-culture: Hi-M cells were integrated into hi-N cultures and stained for C1q. The integrated hi-M cells expressed maintained expression of C1q (Fig. 3). • Expression of C3, C4, and C3a in hi-N, hi-M, and co-culture: The C3, C4, and the C3 cleavage fragment C3a were investigated in the monoculture and co-culture supernatants (Fig. 4). As expected, C4 was detected in the hi-N but not hi-M monocultures. In the co-culture, the C4 levels remain high. C3 was only detected in hi-M cells and in co-cultures. Interestingly the C3 protein levels greatly increased in the co-cultures. C3a was not detected in hi-N, but it was detected in hi-M and in the greatest amount on the co-cultures, compatible with the higher amounts of C3 found.

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C1Q

Fig. 3 Hi-M cells continue to express C1Q in co-culture with hi-N Fig. 4 ELISA for soluble complement factors. Culture medium harvested after 48 h from hi-N MNCs, hi-M, or a co-culture of the two. Levels of C4, C3 and C3a were assessed by ELISA

4 Discussion We aimed to design a cell culture model to study the functions of the complement system in the brain to facilitate an improved understanding of the pathogenic mechanisms linking herpes virus infections with cognitive impairment. The models were designed particularly to focus on complement function in the human central nervous system. The hi-N and hi-M were characterized by expression of specific protein markers of neuronal and microglia cells, by transcriptomic CIBERSORT analyses and by immunofluorescence microscopy; together these analyses indicated that these hiPSC-derived cells are legitimate representations of neuronal and microglial cells. Our initial studies also indicate the feasibility of co-culturing hiPSC-derived neuronal and microglial cells to mimic complement activation in the human brain.

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The complement system is linked to the immune response to herpesviruses, particularly to HSV-1. For example, both the antibody-mediated and phagocytic immune response to HSV-1 are dependent on complement system activation (Da Costa et al. 1999; Van Strijp et al. 1989). Additionally, both primary infection and reactivation involve viral mechanisms evading complement-mediated antiviral activity (Verzosa et al. 2021). Furthermore, activation of the complement system has been described in herpesvirus encephalitis as a component of pathways determining latency and reactivation following the resolution of viral infection (Eriksson et al. 2016; Kapadia et al. 2002). Numerous associations have been made tying HSV-1, complement, and neurologic/psychiatric disorders. The complement pathway has been linked to schizophrenia and other psychiatric disorders by genetic analyses through the measurement of complement components in affected individuals (Nimgaonkar et al. 2017; Severance et al. 2021). The complement system is also speculated to play a central role in mechanisms linking herpesviruses to the etiopathogenesis of Alzheimer’s Disease (Harris and Harris 2018). Currently, there is ongoing development of pharmacological modulators of individual complement components and overall complement function with the goal of treating brain disorders (Schartz and Tenner 2020). Focus on the complement system may consequently lead to a new understanding of the link between infections and psychiatric disorders, providing the framework for new methods of diagnosis and therapy. The hiPSC-derived cells morphologically resembled those observed in vivo. We have previously described similarities in morphological features and IC staining of hi-N in relation to neuronal cells in human post-mortem brain samples (D'Aiuto et al. 2014b). The hi-M cells show features of human microglia, as reported previously (McQuade et al. 2018). In support, the hi-M cells expressed both CD11b and CD45. A higher proportion of hi-M expressed CD45 than in prior studies; this is typically interpreted as either a neuroinflammatory response or infiltration of peripheral macrophages (Hopperton et al. 2018; Rangaraju et al. 2018). The hi-M did not resemble ramified microglia found in vivo, consistent with the original report describing differentiation from hiPSCs (McQuade et al. 2018), whereas ramified morphology was only reported in cells implanted into brains of adult mice (Abud et al. 2017). The hi-N and hi-M had the expected patterns of neuronal and microglial cells. These initial analyses indicate the feasibility of such cell model. Based on our gene expression analyses, there were notable changes in the RNA expression pattern of hi-M and hi-N co-cultures with ha-D cells separated by a semipermeable membrane. Neither the hi-M nor the hi-N cells alone expressed two essential mRNAs to form the classic (C4bC2a) or the alternative (C3bBb) C3-convertases; however, when cultured together they could generate both types of C3-convertases, suggesting that the activation of the classical and the alternative pathways is feasible under these conditions. The specialization of soluble complement factor production by different types in the brain cells suggests paracrine effects of the CNS complement system are closely regulated and only occur when specific cell types are in proximity.

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The expression of C1q was observed in hi-N + hi-M co-cultures. C1q can activate the classic pathway by binding directly to plasma surface molecules – such as phosphatidylserine, DNA, heparin and calreticulin – triggering a specialized form of opsonization. These mechanisms may be similar to those described for clearing apoptotic cells. Notably, the levels of expression of the C1q-associated proteases (C1r, C1s) and C4 increased significantly in the hi-M + hi-N co-cultures compared with either the hi-M or the hi-N cultures. The increased transcription of these proteases could augment the ability to form C1qrs complex and cleave C4 to form C4a and C4b. Properdin (CFP) expression was also significantly increased in the hi-M + hi-N co-cultures, indicating the potential for greater activity of an alternative pathway amplification loop. In sum, the specific patterns of expression of complement proteins in the cell cultures demonstrated that complement system has the potential of forming a C3- and C5-convertase. The dynamic relationship between hiPSC-derived neurons, microglial cells, and astrocytes suggest our model is suitable for studying the human complement system relevant to the CNS. Our results are consistent with other hi-PSC-derived models that examined the role of complement proteins in synaptic pruning (Guttikonda et al. 2021). The detection of complement proteins C4, C3 and C3a in the cell culture supernatant also supports the relevance of our cellular model. C4 was undetectable following approximately 2 weeks’ differentiation of hiPSCs into hi-N, indicating that C4 is likely to be a product of differentiated cultures, but not neural progenitor cells. The concentration of C3 in the supernatant fluid in the co-culture increased more than two-fold relative to hi-M monocultures, while the increase in C3a was more modest. This result points toward either a decrease in convertase activity due to the detected increase of CFI and CFH expression levels in the co-culture or by an increase in uptake of C3a by C3aR. The model described here requires further refinement and analyses in view of some limitations. We utilized commercially purchased ha-D cells that were passaged prior to use; the gene expression data provide limited support for their resemblance to human astrocytes. Further characterization of these cells is necessary. Additionally, it is necessary to conduct additional co-culture studies utilizing hiPSC-derived astrocytes from the same donor used to derive the hi-M and hi-N. Further analyses of complement protein products within cell extracts and in supernatant fluid would be desirable. Neither the monocultures nor the co-cultures expressed MBL or Ficolin, indicating that the Lectin pathway is unlikely to be activated.

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Non-SARS Coronaviruses in Individuals with Psychiatric Disorders Faith B. Dickerson, Emily G. Severance, and Robert H. Yolken

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Study Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Immunoassay Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Demographic and Clinical Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Statistical Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Background: The pandemic caused by severe acute respiratory syndromeCoronavirus-2 (SARS-CoV-2) has highlighted the importance of coronaviruses in human health. Several seasonal, non-SARS Coronaviruses are endemic in most areas of the world. In a previous study, we found that the level of antibodies to these seasonal Coronaviruses was elevated in persons with a recent onset of psychosis. In the current study, the level of antibodies to seasonal Coronaviruses was compared between individuals with psychiatric disorders and a non-psychiatric comparison group. Methods: Participants (N ¼ 195) were persons with a diagnosis of schizophrenia, bipolar disorder, major depressive disorder, or without a psychiatric disorder. Each participant had a blood sample drawn from which were measured IgG antibodies to the spike proteins in four non-SARS Coronaviruses, 229E, HKU1, NL63, and OC43, using a multiplex electrochemiluminescence assay. Linear regression models were employed to compare the levels of antibodies between each psychiatric group F. B. Dickerson (*) Stanley Research Program, Sheppard Pratt, Baltimore, MD, USA e-mail: [email protected] E. G. Severance and R. H. Yolken Stanley Division of Developmental Neurovirology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 265–278 https://doi.org/10.1007/7854_2022_386 Published Online: 11 August 2022

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and the comparison group adjusting for demographic variables. Logistic regression models were employed to calculate the odds ratios associated with increased levels of antibodies to each seasonal Coronavirus based on the 50th percentile level of the comparison group. Results: The schizophrenia group had significantly increased levels of antibodies to the seasonal Coronaviruses OC43 and NL63. This group also had increased odds of having elevated antibody levels to OC43. The major depression group showed a significantly lower level of antibodies to Coronavirus 229E. There were no significant differences between any of the psychiatric groups and the comparison group in the levels of antibodies to seasonal Coronaviruses 229E or HKU1. Conclusions: The elevated level of antibodies to OC43 and NL63 in the schizophrenia group indicates increased exposure to these agents and raises the possibility that Coronaviruses may contribute to the etiopathology of this disorder. The causeand-effect relationship between seasonal Coronaviruses and psychiatric disorders should be the subject of additional investigations focusing on longitudinal cohort studies. Keywords Antibodies · Coronavirus · Infection · Pandemic · Psychiatric disorders · Schizophrenia

1 Introduction Coronaviruses are a diverse group of enveloped positive-strand RNA viruses with outer envelopes that have distinct crown-like morphologies. Coronaviruses infect humans and a wide range of animals. The recent Covid-19 pandemic caused by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2 has highlighted the importance of coronaviruses in human health. The role of SARS-CoV-2 in psychiatric disorders is discussed in other chapters of this book. Other epidemic Coronaviruses which caused serious manifestations in infected humans include SARS-CoV-1 which caused a serious but geographically contained outbreak in 2002–2004 and MERS-Cov (Middle East respiratory syndrome Coronavirus), first identified in Saudi Arabia in 2012 and which causes serious disease with a high rate of mortality but which is largely isolated to the Middle East. In addition to these epidemic viruses, there are also a number of endemic seasonal Coronaviruses which are highly prevalent in the USA and most other areas of the world but which cause mostly self-limited respiratory diseases. Non-SARS respiratory infections occur from Group I (229E and NL63) and Group II (OC43 and HKU1) Coronaviruses. The seasonal Coronaviruses 229E and OC43 were first described in the 1960s (McIntosh et al. 1967; Hamre and Procknow 1966; Tyrrell and Bynoe 1965), whereas NL63 and HKU1 were more recently discovered and first described in 2004–2005 (van der Hoek et al. 2004; Woo et al. 2005).

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Fig. 1 Maximum-likelihood tree of coronavirus ORF1b amino acid sequences. PHEV ORF1b (GenBank accession no. DQ011855) was compared to other coronaviruses on the amino acid level. Group 1 includes human coronavirus 229E (HCoV-229E, AF304460), human coronavirus NL63 (HCoV-NL63, AY567487), porcine epidemic diarrhea virus strain CV777 (PEDV, AF353511), and porcine transmissible gastroenteritis virus Purdue strain (TGEV, AJ271965). Group 2 includes human coronavirus OC43 (HCoV-OC43, AY391777), bovine coronavirus Mebus strain (BCoV, U00735), murine hepatitis virus Penn 97-1 strain (MHV-Penn97-1, AF208066), and murine hepatitis virus A59 (MHV-A59, NC_001846). Group 3 includes avian infectious bronchitis virus Beaudette strain (IBV-Beaudette, M95169), avian infectious bronchitis virus BJ strain (IBV-BJ, AY319651) and avian infectious bronchitis virus LX4 strain (IBV-LX4, AY338732). Human coronavirus HKU1 (HCoV-HKU1, NC_006577) and SARS coronavirus Frankfurt-1 strain (SARS-CoV, AY291315) are shown. The out group includes equine Berne torovirus (EToV, X52374). The scale bar represents the genetic distance (nucleotide substitutions per site) (Vijgen et al. 2006)

The four human non-SARS seasonal Coronaviruses currently recognized are designated as OC43, NL63, HKU-1, and 229-E. There are also many other Coronaviruses that infect domestic and wild animals. The presumed animal sources of these viruses as well as the epidemic Coronaviruses SARS1 and MERS are shown in the Fig. 1. The evolutionary relationship between the human and related animal coronaviruses is depicted in Fig. 2 (Vijgen et al. 2006). In light of the genomic similarities, it is likely that the human seasonal Coronaviruses originated from Coronaviruses infecting domesticated and wild animals. Of particular interest in this regard is OC43 since it is highly homologous to bovine coronaviruses and may have originated from a recent transmission from cows to humans and caused a serious pandemic around 1890 known as the “Russian Flu.” In addition to causing death and social disruption, many cases of the “Russian Flu” were associated with psychiatric manifestations (Stefano 2021).

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Fig. 2 Summary diagram of the animal groups representing natural hosts and the putative intermediate hosts for four seasonal Coronaviruses and two epidemic Coronaviruses known to infect humans

Presumably the current strains of OC43 represent genetic variations of the “Russian Flu” virus resulting in less serious symptoms. Studies relating to OC43 suggest that the other seasonal Coronaviruses might have originally caused serious epidemic diseases, as well. It will be of great interest to see if the SARS-Cov-2 viruses causing the current pandemic will evolve into less virulent strains similar to the current seasonal Coronaviruses. Infections with the seasonal Coronaviruses occur largely during the winter months. The prevalence of the viruses varies by geographic region. These viruses are common causes of self-limited respiratory illness in children and adults. Analysis from samples taken from children with acute respiratory infections in the USA indicate that the most prevalent seasonal Coronaviruses vary from year to year with, for example, OC43 being the seasonal Coronavirus most prevalent in 2015

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and 2017, while NL63 and HKU1 were the most prevalent seasonal Coronaviruses identified in 2016 (Killerby et al. 2018; Li et al. 2016). While largely associated with respiratory symptoms, seasonal Coronaviruses, particularly OC43, have been linked to cases of meningoencephalitis and febrile seizures in children (Principi et al. 2010). Furthermore, RNA from Coronaviruses OC-43 and 229E have been found in brain tissues obtained from individuals with multiple sclerosis (Cristallo et al. 1997). Also, numerous animal Coronaviruses have been found to infect the brain and cause pathological changes (Cowley and Weiss 2010). We previously developed a solid phase enzyme immunoassay for the sensitive and specific measurement of antibodies to the human seasonal Coronaviruses (Severance et al. 2008). In a previous study, we employed this assay to examine the prevalence of seasonal Coronaviruses in individuals with a recent onset of psychosis (Severance et al. 2011). We found that levels of IgG antibodies to Coronaviruses HKU1 and NL63 were significantly higher in the patient group (Severance et al. 2011). In a subsequent study, we also found evidence of increased levels of antibodies to NL63 in individuals with mood disorders (Okusaga et al. 2011). The purpose of the current study was to employ a newly developed chemiluminescence assay to compare IgG antibody levels to the non-SARS seasonal Coronaviruses 229E, HKU1, NL63, and OC43 among persons with a diagnosis of schizophrenia, bipolar disorder, and major depressive disorder and persons without a psychiatric disorder.

2 Methods 2.1

Study Participants

Participants were individuals diagnosed with schizophrenia, bipolar disorder, major depressive disorder or persons without a psychiatric disorder who were enrolled during the period April 2018–September 2021 in the Stanley Research Program at Sheppard Pratt in Baltimore, MD USA for a study of the association between infection, immunity, and psychiatric disorders. All participants provided written informed consent after the study procedures were explained. The study was approved by the Sheppard Pratt local institutional review board. The inclusion criterion for individuals with schizophrenia was a diagnosis of schizophrenia, schizophreniform disorder, or schizoaffective disorder. The inclusion criterion for individuals with bipolar disorder was a diagnosis of bipolar disorder including bipolar I disorder, bipolar II disorder, or bipolar disorder not otherwise specified. Those with major depressive disorder had either a single or recurrent episode. The psychiatric participants were recruited from inpatient and day hospital programs of Sheppard Pratt and from affiliated psychiatric rehabilitation programs. The diagnosis of each psychiatric participant was established by the research team

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including a board-certified psychiatrist and based on the Structured Clinical Interview for DSM-IV Axis 1 Disorders (First et al. 1996) and available medical records. The inclusion criterion for the individuals without a psychiatric disorder was the absence of a current or past psychiatric disorder as determined by screening with the DSM-IV Axis I Disorders, Non-patient Edition (First et al. 1998). Persons in this group were recruited from posted announcements at local health facilities and universities in the same geographic area where the psychiatric participants were recruited. Participants in all groups met the following additional criteria: age 18–65 (except the non-psychiatric controls who were aged 20–60); proficient in English; absence of any history of intravenous substance abuse; absence of intellectual disability by history; absence of HIV infection; absence of serious medical disorder that would affect cognitive functioning; absence of a primary diagnosis of alcohol or substance use disorder per DSM-IV criteria.

2.2

Immunoassay Measures

Each participant had a blood sample drawn from which were measured IgG antibodies to the spike proteins in four non-SARS Coronaviruses: 229E, HKU1, NL63, and OC43 using a multiplex electrochemiluminescence assay. The performance characteristics of these assays have been previously described (Li et al. 2022). The assay (V-PLEX COVID-19 Coronavirus Panel 2) was purchased from Meso Scale Diagnostics, Rockville, Md. and performed following the manufacturer’s instructions. Results were recorded in arbitrary units and further analyzed as described below.

2.3

Demographic and Clinical Measures

Demographic and background information was obtained by interview during the study visit, during which the blood sample was drawn. A review of body systems was conducted to identify comorbidities including a respiratory disturbance; respiratory disturbances cited included upper respiratory infections, asthma, and seasonal or other allergies. Body Mass Index (BMI) was calculated based on height and measured weight. Participants were also asked about their current cigarette smoking. All participants were individually administered the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) (Randolph et al. 1998). The individuals with psychiatric disorders were interviewed and rated on the Brief Psychiatric Rating Scale (BPRS) (Overall and Gorham 1962). Their medication data were recorded from the clinical chart. Maternal education was used as a proxy for premorbid socioeconomic status.

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Statistical Analyses

Demographic and clinical characteristics among groups were compared with Chi-square analyses for dichotomous variables and one-way analysis of variance for linear variables. The levels of antibodies to each seasonal Coronavirus in individuals with schizophrenia, bipolar disorder, and major depressive disorder were compared to those of the non-psychiatric comparison group using linear regression models employing age, sex, and race as covariates. Logistic regression models were employed to calculate the odds ratios associated with elevated levels of antibodies to each of the seasonal Coronaviruses defined as having a fluorescent value greater than or equal to the 50th percentile level of the non-psychiatric group. In the case of significant associations that were found between the level of antibodies and a diagnostic group, additional covariates were tested in the model in order to rule out these variables as confounders including cigarette smoking and presence of a respiratory disorder. When multiple samples were available from the same individual, only the first sample was analyzed. An alpha of 0.05, 2-tailed, was employed to indicate a significant effect. Due to the exploratory nature of the study, corrections for multiple comparisons were not performed. All analyses were performed with STATA version 16 (STATA Corp LP, College Station, Texas, U.S.A.).

3 Results The sample consisted of 195 persons: 37 with a diagnosis of schizophrenia, 64 with bipolar disorder, 61 with major depressive disorder, and 33 in the non-psychiatric comparison group. Characteristics of the study participants are displayed in Table 1. The groups differed significantly on all of the descriptive variables except for BPRS score among the psychiatric groups. We first determined whether the quantitative levels of antibodies to the seasonal Coronaviruses differed among the diagnostic groups adjusted for relevant covariates. The schizophrenia group had significantly increased levels of antibodies to OC43 (coefficient ¼ 3282.9; 95% CI 1110.3, 5455.6; p ¼ 0.003) compared with the non-psychiatric comparison group (Fig. 3). The schizophrenia group showed significantly increased levels of the NL63 Coronavirus variant (coefficient ¼ 7532.8; 95% CI 1203.2, 13862.4; p ¼ 0.020 (Fig. 4). On the other hand, the major depression group showed a significantly lower level of antibodies to Coronavirus 229E (Fig. 5). None of the groups differed in terms of antibodies to Coronaviruses HKU1 (Fig. 6). The individuals with bipolar disorder did not differ in terms of antibodies to any of the seasonal Coronaviruses. We also determined the odds of having elevated levels of antibodies to the seasonal Coronaviruses defined as having levels greater than the 50th percentile of

33.6  11.5 20 (31%) 51 (80%) 14.9  2.61 14.6  2.5 79.5  13.0 45.7  8.8 29.4  6.8 7 (11%) 20 (32%) 45 (70%) 33 (52%) 52 (81%)

39.5  12.0 29 (78%) 18 (49%) 11.8  2.5 12.9  2.2

71.1  11.6 47.6  8.4 32.6  6.7 9 (24%) 21 (57%)

33 (89%) 19 (51%) 17 (46%)

Bipolar disorder N ¼ 64

6 (10%) 10 (16%) 2 (3%)

88.3  14.0 46.4  8.1 29.6  6.9 0 (8%) 12 (20%)

30.5  11.0 36 (40%) 39 (64%) 14/0  2.1 13.9  2.7

Major depressive disorder N ¼ 61

b

a

Continuous variables presented as mean  SD Categorical variables presented as N (%) c Significant difference among groups, p < 0.001 d Significant difference among groups, p  0.01 e Almost all of the non-Caucasians were African American f n ¼ 50 in the bipolar disorder group; n ¼ 12 in the major depressive disorder group; n ¼ 32 in the non-psychiatric group g n ¼ 63 in the bipolar disorder group and n ¼ 12 in the major depressive disorder group h Significant difference among groups, p < 0.05 i n ¼ 35 in the schizophrenia group, n ¼ 63 in the bipolar disorder group, n ¼ 32 in the non-psychiatric group

Demographic variables Agec Malec Caucasiand,e [p ¼ 0.01] Education, yearc Maternal educationc Clinical variables RBANS cognitive scorec,f BPRS symptom totalg Body mass indexh,i Respiratory disorder Current smokerc Psychiatric medications Antipsychoticc Antidepressantc Mood stabilizer

Schizophrenia N ¼ 37

Table 1 Characteristics of study participantsa,b

– – –

91.4  8.8 – 27.8 (7.2) 0 1 (3%)

36.7  13.2 10 (30%) 19 (58%) 16.3  2.3 14.8  3.0

Non-psychiatric group N ¼ 33

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Fig. 3 Levels of antibodies to coronavirus variant OC43 in fluorescence units, by diagnostic group showing scatterplot and median values. Ctr non-psychiatric comparison group, Scz schizophrenia group, Bp bipolar disorder group, Mdd major depressive disorder group Fig. 4 Levels of antibodies to seasonal Coronavirus NL63 in fluorescence units, by diagnostic group showing scatterplot and median values. Ctr non-psychiatric comparison group, Scz schizophrenia group, Bp bipolar disorder group, Mdd major depressive disorder group

the non-psychiatric controls adjusted for relevant covariates (Fig. 7). Individuals with schizophrenia had increased odds of having elevated levels of antibodies to seasonal Coronavirus OC43 (OR ¼ 5.78, 95% CI 1.72–19.45, p ¼ 0.005). The major depressive disorder group had significantly reduced odds for elevated levels of antibodies to the seasonal Coronavirus 229E (OR ¼ 0.369; 95% CI 0.150, 0.919; p ¼ 0.030). There were no differences in the odds of increased antibodies to seasonal Coronavirus NL63 or seasonal Coronavirus HKU1 among the psychiatric groups. The levels of antibodies to the seasonal coronaviruses were not significantly altered by Covid vaccination.

274 Fig. 5 Levels of antibodies to seasonal Coronavirus 229E in fluorescence units, by diagnostic group showing scatterplot and median score. Ctr non-psychiatric comparison group, Scz schizophrenia group, Bp bipolar disorder group, Mdd major depressive disorder group

Fig. 6 Levels of antibodies to seasonal Coronavirus HKU1 in fluorescence units, by diagnostic group showing scatterplot and median score. Ctr non-psychiatric comparison group, Scz schizophrenia group, Bp bipolar disorder group, Mdd major depressive disorder group

Fig. 7 Odds ratios of antibody levels >50% of levels in the comparison group for each seasonal Coronavirus by psychiatric group with 95% confidence intervals. The dotted line represents OR ¼ 1

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4 Discussion We found that individuals with schizophrenia had increased levels of antibodies to the seasonal Coronaviruses OC43 and NL63 as compared to individuals without a psychiatric disorder. This increased level of antibodies to OC43 was manifested both by the analysis of antibody level as a continuous variable as well as by the increased odds of having antibody levels greater than the 50th percentile of the comparison group. This increased level of antibodies could not be explained by available demographic or clinical variables as determined by regression models. Significant increases in levels of antibodies to OC43 were not noted in the individuals with the other psychiatric disorders included in the study. Elevated levels of IgG antibodies to viral antigens are generally a manifestation of an increased level of past exposure to the virus. Reasons for the increased exposure to seasonal Coronaviruses in individuals with schizophrenia are not known with certainty. Individuals with schizophrenia are more likely than the comparison group to undergo hospitalization, live in group homes, or experience homelessness, all factors which can increase exposure to respiratory viruses. Reasons for the increased levels of exposure to OC43, in particular, as compared to other seasonal Coronaviruses are not known with certainty but may be related to the differential biological or epidemiological properties of this virus (Hulswit et al. 2019; Corman et al. 2018). The fact that OC43 has also been associated with serious CNS infections in humans and causes immunopathological changes in the brains of experimentally infected animals (Butler et al. 2006) raises the possibility that this increased level of exposure may contribute to symptoms or cognitive impairments in individuals with schizophrenia (Butler et al. 2006; Singer et al. 2021; Nilsson et al. 2020). The potential role of OC43 in the pathogenesis of schizophrenia should be the subject of additional investigations. This study extends our previous findings which indicated that antibodies to all four seasonal Coronaviruses were found elevated in individuals with the recent onset of psychosis as compared to individuals without a psychiatric disorder (Severance et al. 2011). Differences between the results of the current and past study may be due to temporal differences which are known to occur among the seasonal Coronaviruses, as well as differences in the populations which were tested, and the assays which were employed. It is also of note that there is genetic variation among circulating strains of OC43 and that these variants might have altered biological properties (Vijgen et al. 2005). Previous exposure to seasonal Coronaviruses may also have consequences for the clinical response to epidemic Coronaviruses such as SARS2, the cause of the COVID-19 worldwide pandemic. While some studies have suggested some degree of protection due to cross-reactivity of antibodies or T-cells (Wratil et al. 2021; Loyal et al. 2021), others have not found significant degrees of cross-protection from previous exposure to seasonal Coronaviruses (Anderson et al. 2021). On the contrary, one recent study reported that previous exposure to seasonal Coronaviruses such as OC43 increases the susceptibility to symptomatic infection with SARS-2 as

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well as clinical severity of the illness. The mechanism of this association is unclear but might be related to incomplete or aberrant immune responses generated by nonneutralizing cross-reactive epitopes (Wratil et al. 2021). We also found decreased levels of antibodies to a seasonal Coronavirus in individuals with major depressive disorder. This finding is consistent with other studies indicating a decreased immune response to other infectious antigens such as measles in individuals with major depression (Ford et al. 2019). The reasons for this decrease are not known with certainty but may be related to decreased activity of effector lymphocytes or other arms of the adaptive immune system (Maes et al. 2021). The effects of these decreased levels on susceptibility to infection with pandemic Coronaviruses or response to immunization is not known but should be the subject of additional investigations. Our studies demonstrate increased levels of antibodies to seasonal Coronaviruses in individuals with schizophrenia, indicating an increased level of exposure to these viruses. The cause-and-effect relationship between seasonal Coronaviruses and the etiopathogenesis of schizophrenia and other serious psychiatric disorders should be the subject of additional investigations focusing on longitudinal studies of cohorts. The availability of new pharmacological interventions capable of the prevention and treatment of a broad range of Coronavirus infections (Li et al. 2021; Ordonez et al. 2021) might lead to new methods for the management of serious psychiatric disorders. Acknowledgments Supported by the Stanley Medical Research Institute.

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Neuropsychiatric Symptoms and Tick-Borne Diseases Shannon L. Delaney, Lilly A. Murray, and Brian A. Fallon

Contents 1 Lyme Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Diagnostic Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Post-Treatment Lyme Disease Syndrome (PTLDS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Risk Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Mechanisms of Persistent Illness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Neuropsychiatric Symptoms and Lyme Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Acute-Onset Neuropsychiatric Symptoms in Children After Lyme Disease . . . . . . . . . . . . . . 5 Neuropsychiatric Symptoms and Non-Lyme-Related Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Treatment Approaches for Persistent Medical and Neuropsychiatric Symptoms Associated with Lyme Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Antibiotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Psychotropics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Other Pharmaceuticals and Supplements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Immune Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Illustrative Case: Neuropsychiatric Symptoms in a Child with Multiple Infections . . . . . . . 8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In North America, Lyme disease (LD) is primarily caused by the spirochetal bacterium Borrelia burgdorferi, transmitted to humans by Ixodes species tick bites, at an estimated rate of 476,000 patients diagnosed per year. Acute LD often manifests with flu-like symptoms and an expanding rash known as erythema migrans (EM) and less often with neurologic, neuropsychiatric, arthritic, or cardiac features. Most acute cases of Lyme disease are effectively treated with antibiotics, but 10–20% of individuals may experience recurrent or persistent symptoms. This chapter focuses on the neuropsychiatric aspects of Lyme disease, as these are less S. L. Delaney (✉), L. A. Murray, and B. A. Fallon Lyme and Tick-Borne Diseases Research Center at Columbia University Irving Medical Center, New York, NY, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 279–302 https://doi.org/10.1007/7854_2022_406 Published Online: 14 December 2022

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widely recognized by physicians and often overlooked. Broader education about the potential complexity, severity, and diverse manifestations of tick-borne diseases is needed. Keywords Borreliosis · Lyme disease · Neuropsychiatric symptoms · PANS · PTLDS

1 Lyme Disease 1.1

Background

In North America, Lyme disease (LD) is primarily caused by the spirochetal bacterium Borrelia burgdorferi (Bb), transmitted to humans by Ixodes species tick bites (Burgdorfer 1989). Early LD often manifests with flu-like symptoms and a characteristic expanding rash known as erythema migrans (EM). Disseminated infection may lead to neurologic features, arthritis, or carditis (Centers for Disease Control and Prevention, National Center for Emerging and Zoonotic Infectious Diseases (NCEZID) D of VBD (DVBD) 2022a). The majority of acute LD cases are effectively treated with 10–28 days of antibiotic therapy (Centers for Disease Control and Prevention, National Center for Emerging and Zoonotic Infectious Diseases (NCEZID) D of VBD (DVBD) 2022b). LD is the most common vector-borne illness in the USA (Centers for Disease Control and Prevention, National Center for Emerging and Zoonotic Infectious Diseases (NCEZID) D of VBD (DVBD) 2022a). In a review of commercial insurance claims between 2010 and 2018, the Centers for Disease Control (CDC) determined that LD was newly diagnosed and treated in 476,000 patients per year (Kugeler et al. 2021). Although LD occurs most commonly in the Northeast, Mid-Atlantic, and Upper Midwest regions, LD has been reported in all states (Centers for Disease Control and Prevention 2022). Even urban areas are impacted: Bb-laden ticks have been identified in New York City and Chicago, presumably due to bird migration (Hamer et al. 2012; Daskalakis 2017). One study showed that in Long Island, NY, an adult Ixodes scapularis tick had a 57% chance of carrying Bb (Sanchez-Vicente et al. 2019).

1.2

Diagnostic Challenges

The CDC emphasizes that LD diagnosis requires both careful clinical assessment and judicious laboratory testing (Centers for Disease Control and Prevention, n.d.). However, clinical ambiguity in some cases and the known limitations of diagnostic tests have led to conflicting opinions about diagnosis and management. In the 1990s,

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this fueled strident debates between academic researchers and community physicians; this disagreement has been referred to as the “Lyme wars” in the popular press (Fallon and Sotsky 2018). While CDC surveillance criteria focus on core verifiable signs of LD – invaluable for epidemiologic surveillance – these criteria were mistakenly interpreted as the sole manifestations of LD by many clinicians. It is well-documented that LD can have protean manifestations, such that some cases would not meet CDC surveillance criteria. In fact, LD became known as the “new great imitator,” competing with syphilis for that title (Pachner 1988). LD diagnostic testing has several limitations. First, since Bb resides only transiently in the bloodstream (Liang et al. 2020), direct detection of the pathogen is challenging. Thus, indirect detection of Bb via host antibody response is usually performed. However, since antibodies develop over a period of weeks after infection, antibody tests are often negative in early LD. Conversely, since antibodies may persist for months or years after infection, antibody tests cannot differentiate active vs. resolved LD. Since the mid-1990s, the CDC has endorsed a two-tier testing method for LD (Centers for Disease Control and Prevention, n.d.). First, a sensitive enzyme immunoassay (EIA) is performed, followed by a western immunoblot for specimens yielding positive or equivocal results. Most recently, the CDC has modified these recommendations to allow a second FDA-cleared EIA to be used in place of the western immunoblot (Mead et al. 2019). The two-tier testing algorithm is specific, but insufficiently sensitive: studies indicate a sensitivity of about 35–50% in early LD and 70–90% in early neurologic LD (Wormser et al. 2013; Aguero-Rosenfeld et al. 2005; Marques 2015; Dressler et al. 1993). A second challenge in diagnostic testing is inter-laboratory variability in immunoblot results (Fallon et al. 2014). One laboratory may interpret an immunoblot as positive, while another may interpret an immunoblot from the same serum sample as negative. This is obviously problematic when clinicians rely exclusively on one laboratory for LD serologic testing. A third challenge is that some laboratories have novel assays or lab-specific criteria for immunoblot interpretation that have not yet been fully validated. Finally, although most often negative serologic testing accurately conveys absence of exposure to Bb, seronegative LD is well-documented in the literature (Lawrence et al. 1995; Liegner et al. 1997; Liegner 1993; Dattwyler et al. 1988). In one series of studies, patients whose spinal fluid initially tested negative on standard antibody testing were subsequently shown to test positive when the Bb antibodyantigen immune complexes were dissociated, thereby freeing the antibodies for detection (Schutzer et al. 1990, 1999). A European study demonstrated that 15% of patients with central nervous system LD, confirmed by antibodies to Bb in spinal fluid, had tested negative for Bb antibodies in serum (Knudtzen et al. 2017).

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2 Post-Treatment Lyme Disease Syndrome (PTLDS) 2.1

Background

PTLDS is a provisional diagnosis describing symptom persistence with functional impairment despite recommended antibiotic treatment for LD (Aucott 2015). Common symptoms include pain, fatigue and/or cognitive problems that emerge within the first 6 months after diagnosis and treatment. Many patients with PTLDS also report peripheral sensory abnormalities, which has been associated with small fiber neuropathy and autonomic dysfunction (Novak et al. 2019). One recent study estimates that two million people in the USA have PTLDS (DeLong et al. 2019). PTLDS inflicts a significant economic burden: patients with the diagnosis have $3,798 higher total health care costs and 66% more outpatient visits over a 12-month period than patients without the diagnosis (Adrion et al. 2015). Moreover, an online study of patients ages 25–54 with persistent Lymerelated symptoms revealed reduced employment (45.9% vs. 81.0% in the general population), with many receiving disability (24%) (Johnson et al. 2014). Despite this, National Institutes of Health (NIH) funding for LD lags significantly behind other infectious diseases that are far less prevalent in the USA, such as malaria, HIV, and West Nile virus (Locke 2019). PTLDS has received increasing attention due to recognition of “long-hauler syndromes” after COVID-19. Indeed, the clinical phenotype of brain fog, mood dysregulation, autonomic dysfunction, and fatigue is common to both PTLDS and “long COVID-19” (Owens 2022). Research advances in pathogenesis and treatment of PTLDS may prove valuable for long COVID and other postinfectious syndromes (Aucott and Rebman 2021).

2.2

Risk Factors

Many factors may increase the risk of developing PTLDS. These include the virulence of the Borrelia strain, genetics of the human host, medical or psychiatric comorbidities, and the cumulative burden of prior illness and trauma (Mustafiz et al. 2022). A consistently reported risk factor for PTLDS is delayed diagnosis and treatment. One study reported that 59% of participants with PTLDS experienced delayed diagnosis or misdiagnosis of LD (Rebman et al. 2017). Although 10–20% of patients may still develop PTLDS even if acute LD is rapidly diagnosed and treated (Marques 2008), rates of PTLDS are higher if treatment is delayed (Aucott 2015). The most common obstacle to a timely diagnosis is not seeing or recognizing EM. One study indicated that over 50% of patients with symptoms suggestive of LD but without EM may be misdiagnosed with a non-Lyme illness (Aucott et al. 2009). Even if EM is present, misdiagnosis may still occur; case reports document instances of EM misdiagnosed as cellulitis (Miles and Mansuria 2021) or an insect bite

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(Schutzer et al. 2013). Many clinicians believe that EM nearly always presents as a “bull’s eye” lesion with partial central clearing. In fact, EM manifests as a “bull’s eye” in only about 20% of cases (Aucott et al. 2012). More commonly, EM appears as a homogenous expanding red or pink rash with central redness (Smith et al. 2002; Tibbles and Edlow 2007).

2.3 2.3.1

Mechanisms of Persistent Illness Persistent Infection

Bb has evolved to evade the immune system in multiple ways. Tick saliva contains chemokine-inhibitory evasin protein that reduces immune cell migration to the infection site (Hayward et al. 2017). Bb produces nearly a dozen lipoproteins facilitating complement system evasion (Skare and Garcia 2020). Moreover, an in vitro study identified the presence of Bb “persisters” not killed by standard antibiotics (Sharma et al. 2015). Bb persisters have also been identified in dogs (Straubinger et al. 1997), mice (Hodzic et al. 2008) and monkeys (Embers et al. 2012). In humans, Bb persistence after antibiotic therapy has been noted in case reports (Strle et al. 1996; Fallon et al. 1997). Post-mortem case studies of patients with a history of LD and dementia report evidence of spirochetes in the brain (Waniek et al. 1995; Gadila et al. 2021). However, it remains unclear in the published cases studies whether the persistent Bb was quiescent or causing disease.

2.3.2

Immune Dysregulation

Ongoing immune reactivity evidenced by elevated IL-6 (Soloski et al. 2014) and CCL-19 (Aucott et al. 2016) has been observed among patients with PTLDS. The acute phase reactant C-reactive protein (CRP) has been reported as elevated in some studies of antibiotic-refractory Lyme arthritis and PTLDS (Uhde et al. 2016). In contrast, CRP levels were not elevated in a cohort of myalgic encephalomyelitis/ chronic fatigue syndrome (ME/CFS) patients, who often present with similar symptoms as PTLDS patients (Uhde et al. 2018). Ongoing immune activation may arise due to slow clearance of peptidoglycan, a component of Bb shed during growth (Steere 2020). Autoimmunity is a known mechanism of postinfectious Lyme arthritis (Strle et al. 2017; Lochhead et al. 2021). Patients with neurologic LD have been reported to more often have antibodies to cardiolipin and gangliosides (García Moncó et al. 1993), potentially contributing to symptoms (Garcia-Monco et al. 1995). These findings led to hypotheses that autoimmunity contributes to PTLDS as well. There is homology between certain Bb outer surface proteins and neuronal tissue, potentially leading to cross-reactivity and neurologic symptoms (Garcia-Monco et al. 1995; Sigal 1993; Raveche et al. 2005; Alaedini and Latov 2005). Elevated antineuronal antibodies have been observed among patients with persistent LD

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symptoms, comparable to levels seen in patients with systemic lupus erythematosus (Chandra et al. 2010). Repeat Bb infections have been associated with increased antineuronal antibodies, including anti-lysoganglioside GM1, anti-tubulin, and antiD1R (Fallon et al. 2020). Another study of cytokines in patients with early LD showed that high IL-23 was associated with the development of PTLDS, and antibody response to the endothelial cell growth factor autoantigen correlated directly with IL-23 levels (Strle et al. 2014).

2.3.3

Altered Brain Functioning

In a study of 40 patients with persistent LD symptoms, 70% had abnormal brain single-photon emission computerized tomography (SPECT) findings (n = 28); 96% of those abnormal scans evidenced heterogenous hypoperfusion (Fallon et al. 1997). After antibiotic treatment, 41% of these scans were interpreted in a semi-quantitative method as clinically improved. Of note, however, heterogenous hypoperfusion on SPECT is not specific to LD. In a masked SPECT study comparing 20 patients with a history of LD to 14 diseased controls with ME/CFS, Creutzfeldt-Jakob disease, or cerebral vasculitis, no differences were observed in heterogeneous hypoperfusion findings between groups. In perhaps the best-conducted SPECT study of LD, cerebral blood flow was quantifiably measured in 13 patients with well-documented Lyme encephalopathy. Reduced cerebral perfusion was observed in all 13 patients compared to healthy controls, particularly in the frontotemporal white matter, basal ganglia, frontal cortex, and cingulate gyrus. When reassessed 6 months after 1 month of IV ceftriaxone treatment, perfusion significantly improved in all 13 patients. Although perfusion deficits are not specific to LD, this study supports the value of SPECT imaging in detecting benefits of IV ceftriaxone on cerebral blood flow (Logigian et al. 1997). Positron emission tomography (PET) has also been used to assess brain blood flow, brain metabolism, and inflammation in LD. In a study of 35 patients with Lyme encephalopathy and 17 healthy controls, fully quantified assessments of cerebral blood flow and metabolism were obtained. The LD group showed bilateral reduction in blood flow and metabolism in gray and white matter brain regions, particularly in the temporal, parietal, and limbic areas, relative to the control group. The LD group also evidenced a significantly diminished ability to enhance cerebral blood flow after a hypercapnic challenge, suggesting vascular compromise or reduced metabolic demand (Fallon et al. 2009). In the first study to examine cerebral inflammation in LD, PET imaging was conducted among 12 patients with persistent post-treatment Lyme symptoms and 19 controls. Radiotracer [11C]DPA-713 was used to detect the mitochondrial 18 kDa translocator protein, which is increased in expression by activated microglia and reactive astrocytes. Patients in the LD group demonstrated higher [11C]DPA-713 binding in eight brain regions relative to the controls, suggesting that microglial activation may be involved in the pathophysiology of persistent symptoms (Coughlin et al. 2018).

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3 Neuropsychiatric Symptoms and Lyme Disease Neurologic manifestations of LD may emerge from the central and/or peripheral nervous system, and may include meningitis, cranial neuritis, and/or radiculoneuritis. Later neurologic sequelae may be more insidious: one study of patients with chronic neurologic Lyme disease indicated that neurologic symptoms may emerge as early as 1 month to as late as 14 years after initial infection, with a median time of 16 months from EM to peripheral nervous system symptoms and 26 months from EM to central nervous system symptoms (Logigian et al. 1990). This underscores the need for clinician and patient education about delayed neurologic symptoms. Early studies of neurologic LD reported irritability, anxiety, and depression (Logigian et al. 1990; Betman et al. 1993; Reik et al. 1979; Pachner and Steiner 2007). While these mood changes and cognitive problems are the most common neuropsychiatric manifestations of LD, less common manifestations include psychosis, bipolar-like mood swings, anorexia, and obsessive-compulsive disorder (Fallon and Neilds 1994). Neuropsychiatric symptoms related to LD may present before or after typical medical symptoms. In well-documented case reports of mania (Pasareanu et al. 2012), OCD (Pachner 1988), Tourette syndrome (Riedel et al. 1998), and psychosis (Hess et al. 1999) associated with LD, psychiatric symptoms resolved after antibiotic therapy. Studies assessing rates of depression in LD have been mixed: some have shown elevated rates of depression after LD (Hassett et al. 2008; Doshi et al. 2018; Tager et al. 2001), but others have not (Dersch et al. 2015; Kalish et al. 2001; Schmidt et al. 2015). One cross-sectional study reported that hospitalized patients with neuroborreliosis had increased rates of cognitive problems and depressive disorders compared to hospitalized patients with Lyme arthritis (Oczko-Grzesik et al. 2017). Another study showed that chronic symptoms, including depression, were more common in neuroborreliosis patients (50%) than controls (16%) (Vrethem et al. 2002). Conversely, another study did not find increased depressive symptoms among patients with EM 6 months after treatment (Rebman et al. 2017). Rates of suicidal ideation among people with PTLDS have been estimated at 20–43% (Doshi et al. 2018; Tager et al. 2001; Bransfield 2017). A psychiatrist who specializes in LD conducted a retrospective chart review of 253 patients with confirmed or probable LD in his outpatient psychiatric practice, and found that 43% disclosed suicidal ideation (Bransfield 2017). Differentiating a neuropsychiatric disease due to an infectious trigger from a primary psychiatric disorder can be difficult. Functional brain imaging and neuropsychological testing can facilitate differential diagnosis (Fallon et al. 1997; Keilp et al. 2019). Other considerations when evaluating whether a psychiatric disorder is primary or secondary to LD include: (1) concurrence with non-psychiatric symptoms, such as joint pain, headaches, light or sound sensitivity, and/or neuropathy; (2) psychiatric symptoms (e.g., mood lability, marked irritability, severe and prolonged panic attacks) are atypical for the individual; (3) poor response to

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psychiatric medications that are normally effective; (4) lack of psychological precipitant; (5) lack of family psychiatric history; or (6) new-onset psychiatric illness in a patient 40 years or older (Fallon et al. 1997). Often, patients with LD and neuropsychiatric symptoms do not show objective deficits on CSF, EEG, or MRI studies, but do show objective cognitive deficits on neuropsychological testing (Fallon et al. 1997). Memory may be particularly affected in neurologic LD (Logigian et al. 1990; Krupp et al. 1991). However, memory deficits also occur among patients with major depression who do not have LD. One study compared the neurocognitive profile of adults with PTLDS to adults with major depression. This study found that the PTLDS and major depression groups had similar deficiencies in processing speed. However, the PTLDS group had greater memory impairment than the depression group, and the depression group had greater attentional impairment than the PTLDS group. Among the PTLDS patients with memory impairment, language fluency deficiencies were also observed. These data suggest that memory and language fluency impairments may form part of the clinical profile differentiating PTLDS from major depression (Keilp et al. 2019). The above studies, though controlled, had limitations, such as small sample size or referral bias. To address these limitations, a retrospective cohort study of mental disorders and LD was conducted using the entire population of Denmark over a 22-year period. Researchers used Danish registries of hospital-based diagnoses on all citizens to assess the prevalence of any mental disorder and suicidal behavior among people with hospital contact for LD (n = 12,616) vs. people without hospital contact for LD (n = 6,933,221). Patients with a pre-LD mental disorder diagnosis were excluded. This study revealed that a hospital-based diagnosis of LD was associated with a subsequent 28% higher rate of any mental disorder, a 42% higher rate of affective disorder, a twofold higher rate of suicide attempts, and a 75% higher rate of death by suicide. Incidence of mental disorders was highest among those with more than one episode of LD, and during the period closest to the hospital contact for LD (Fallon et al. 2021). Conversely, another Danish study of neurologic LD showed no increase in hospital-based diagnosis of psychiatric disorder or hospitalizations after a positive spinal fluid antibody test for LD. However, the spinal fluid-positive group did have a significantly higher rate of prescriptions for psychiatric medications in the year following their LD diagnosis. It is possible that neuropsychiatric symptoms in this group were treated in the community, precluding their inclusion in the hospitalbased registry (Tetens et al. 2021). There is a paucity of literature on neurocognitive deficits in children with LD. One controlled study showed that children with LD had significantly more cognitive and psychiatric disturbances than healthy controls (Tager et al. 2001). Other studies of children with LD have not shown cognitive deficits (Adams et al. 1994, 1999).

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4 Acute-Onset Neuropsychiatric Symptoms in Children After Lyme Disease The diagnosis of Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS) describes the abrupt, dramatic onset of OCD and/or tics, neurologic abnormalities, and comorbid neuropsychiatric symptoms (e.g., separation anxiety, mood instability, urinary frequency or incontinence, behavioral regression, poor handwriting) associated with group A Streptococcus (GAS) infection among children ages 3–12 years. Etiologic triggers of autoimmune neuropsychiatric conditions are theorized to include not only Streptococcus but also Mycoplasma pneumoniae and Bb. Thus, the diagnostic term pediatric acute-onset neuropsychiatric syndrome (PANS) was developed to encompass other infectious triggers of sudden onset neuropsychiatric disorders (Frankovich et al. 2015). The pathophysiology of PANS/PANDAS is likely diverse, ranging from persistent infection to postinfectious autoimmunity. Studies have reported an elevated rate of antineuronal antibodies (anti-lysoganglioside, anti-tubulin, and anti-dopamine-D1 and D2 receptors) among patients with PANS/PANDAS (Cunningham 2014). PANDAS is theorized to be similar to Sydenham’s chorea, whereby GAS antibodies cross-react with neuronal proteins in the basal ganglia (Kirvan et al. 2003). One mouse model of PANDAS suggests recurrent GAS infections produce neuroinflammation directly by activating Th17 lymphocytes and indirectly by opening the blood-brain barrier to permit entry of cross-reactive antibodies to the basal ganglia (Xu et al. 2021; Platt et al. 2020). Antibodies may bind specifically to striatal cholinergic interneurons and alter their activity (Frick et al. 2018). Although the relationship between PANS and LD has not yet been studied, clinicians have observed new-onset neuropsychiatric symptoms in children after Bb infection. One early case report described a 12-year-old boy who experienced multiple episodes of right knee swelling. Serologic testing confirmed Lyme arthritis, and he was treated with doxycycline 100 mg BID for 30 days. Two months later, he experienced depression, social withdrawal, decreased food consumption, compulsive exercising, and significant weight loss. He was psychiatrically hospitalized and diagnosed with anorexia nervosa. He was subsequently transferred to a medical unit. Because serum and CSF antibodies titers to Bb were elevated, he was treated with IV penicillin 20 million U daily for14 days. His neuropsychiatric symptoms resolved over the following weeks (Pachner 1988). Another early case report described an 18-year-old girl with sudden-onset severe anxiety, depersonalization, and panic attacks. Serologic testing revealed a positive Lyme ELISA and western blot. Subsequent lumbar puncture revealed IgG antibodies to Bb in CSF. Neurologic LD was diagnosed, and 6 weeks of IV ceftriaxone was prescribed. The patient experienced 80% symptom improvement within 3 months of treatment (Fallon and Neilds 1994). Neuropsychiatric symptoms may be complicated by multiple infectious triggers. One case report described a 7-year-old girl who developed a dramatic decline in cognitive functioning, dysgraphia, difficulty with social cues, anxiety, fatigue,

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nighttime awakening, chills, joint and muscle pain, obsessions and compulsions, and aggression over a 3-week period. Six months prior to the neuropsychiatric disorder onset, the child had been diagnosed and treated for multiple episodes of strep pharyngitis. The diagnosis of PANDAS was made. Further work-up revealed a positive Lyme ELISA, indeterminate Lyme western blot (negative by CDC standards but positive by in-house lab criteria), and positive M. pneumoniae IgG titer. Over a 2.5 year period, the patient received both oral and IV antibiotics. A 3-month course of intravenous immunoglobulin (IVIg) appears to have contributed most to her gradual resolution of symptoms (Cross et al. 2021). Children with PANS are often in crisis. Parents bemoan, “We lost our kid.” Children often do not understand what is happening to them; some may describe, “there’s a monster in me.” Families often consult multiple specialists who may diagnose psychological stress, anxiety, or conversion disorder. Although concurrent support from mental health providers is often essential, standard psychiatric treatment may be insufficiently effective if the underlying infection or immune-mediated disorder is not also treated. Ambiguity about treatment approaches may cause conflict between parents and clinicians or between the parents themselves, who may disagree on appropriate treatment and parenting of the sick child. Siblings of the sick child also report significant stress.

5 Neuropsychiatric Symptoms and Non-Lyme-Related Diseases Even less is known about persistent neuropsychiatric symptoms related to nonLyme-related illnesses. At our consultation clinic for patients with chronic unexplained symptoms and suspected tick-borne illness, serum antibodies targeting Borrelia miyamotoi, a newly recognized tick-borne pathogen in the relapsing fever family, are relatively common (~26%) (Delaney et al. 2020). Symptoms of B. miyamotoi disease are similar to LD, except the former does not produce a pathognomonic rash. As a result, patients who suspect they are infected with Bb but actually are infected with B. miyamotoi are correctly told they do not have LD, but testing for B. miyamotoi disease is often not considered by clinicians. As a result, the patient remains in a state of confusion and without the benefit of antibiotic therapy. In our clinic, 98% of patients with serum antibodies to B. miyamotoi had never been previously tested for B. miyamotoi disease (Delaney et al. 2020). Clinicians also may not be aware that transmission of B. miyamotoi from tick bite to human can occur much rapidly than Bb (Han et al. 2019); a tick attachment of less than 24 h may result in B. miyamotoi transmission. Whether B. miyamotoi infection can trigger neuropsychiatric symptoms has not yet been studied. Another tick-borne disease, babesiosis, has been reported to cause mood lability and depression (Vannier et al. 2015). Persistent Babesia microti infection has been documented in immunocompromised and immunosuppressed hosts (Bloch et al.

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2019), and numerous studies have shown high rates of seroreactivity to Babesia duncani in clinical populations (Prince et al. 2010; Horowitz and Freeman 2019; Scott and Scott 2018). Like B. miyamotoi disease, it is not known whether Babesia species infections can cause persistent neuropsychiatric symptoms. Additionally, Bartonella species have been referred to as “stealth pathogens”; in vitro, they can invade red blood cells and endothelial cells, thereby possibly circumventing and attenuating the host immune response (Merrell and Falkow 2004). Associations between persistent bartonellosis and neuropsychiatric symptoms are emerging. One case report described success of antimicrobial therapy in treating severe neuropsychiatric symptoms in a 14-year-old boy with bartonellosis (Breitschwerdt et al. 2019). Moreover, a case-control study of 17 individuals with psychosis and 13 healthy volunteers revealed that the individuals with psychosis were much more likely to test PCR-positive for Bartonella species than the healthy volunteers (65% vs. 8%, p = 0.002) (Lashnits et al. 2021). This study supports the need for further investigation of the relationship between bartonellosis and psychiatric disorders.

6 Treatment Approaches for Persistent Medical and Neuropsychiatric Symptoms Associated with Lyme Disease 6.1

Antibiotics

While there is considerable controversy about how to treat persistent symptoms related to LD, there is general agreement that initial treatment with doxycycline, amoxicillin, or cefuroxime is usually effective for EM (Centers for Disease Control and Prevention, National Center for Emerging and Zoonotic Infectious Diseases (NCEZID) D of VBD (DVBD) 2022b), while also recognizing that not all patients experience remission with initial treatment. However, guidelines on duration of initial treatment range from 10 to 28 days (Lantos et al. 2021; National Institute for Health and Care Excellence 2018; Cameron et al. 2014). When patients relapse after an initial course of antibiotic therapy, clinicians are faced with the decision of whether to provide an additional course of therapy. The UK’s National Institute for Health and Care Excellence (NICE) guidelines for LD recommend an additional 21-day course of a different antibiotic for patients who relapse or have insufficient response after the initial course of antibiotics (National Institute for Health and Care Excellence 2018). In contrast, guidelines published by the Infectious Diseases Society of America (IDSA), American Academy of Neurology (AAN), and American College of Rheumatology (ACR) do not recommend additional antibiotic treatment, except in cases of persistent moderate to severe Lyme arthritis for which a 2–4 week course of intravenous antibiotic is recommended (Lantos et al. 2021). The limitation shared by all guidelines is that there have been

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too few high-quality studies to determine optimal treatment for individuals with persistent Lyme-related symptoms. The few studies that have been done have not selected people based on the amount of prior antibiotic therapy. Thus, scientific evidence is lacking upon which to base decisions about whether to provide additional courses of antibiotic therapy for patients who have had, for example, one or two prior courses. While the largest controlled clinical trial of repeated antibiotic therapy in the USA found no benefit to repeat treatment (Klempner et al. 2001), two smaller studies did report positive findings. One study of 55 previously treated adults with persistent severe fatigue associated with LD reported sustained improvement in fatigue at 6 months among patients who received 1 month of IV ceftriaxone compared to patients who received IV placebo (64% ceftriaxone vs. 18% placebo responder rates) (Krupp et al. 2003). Another study of 37 patients with posttreatment Lyme encephalopathy reported sustained improvement after repeated IV antibiotic therapy on the secondary outcome measures of physical function and pain, but not on the primary outcome measure of cognition (Fallon et al. 2008). Notable in the latter study is that this benefit was observed even though participants were already treated with considerable prior antibiotic therapy. Despite the positive findings with repeated antibiotic therapy in these two studies, both had small sample sizes. Moreover, risks related to intravenous antibiotic therapy are of concern. These clinical trials have been reviewed in greater detail elsewhere (Fallon 2012). There is mixed evidence from in vitro and animal studies regarding efficacy of triple-antibiotic combination treatment (Feng et al. 2019) and pulsed-dose antibiotic therapy (Sharma et al. 2015), but these have not yet been evaluated in human studies. The broad-spectrum antibiotic minocycline is effective against Bb, and has also been reported in some studies, but not all, to be an effective treatment for negative symptoms in schizophrenia (Chaudhry et al. 2012), depression related to HIV (Emadi-Kouchak et al. 2016), peripheral and autonomic neuropathy (Syngle et al. 2014), and acute encephalitis (Kumar et al. 2016). Thus, minocycline may be a judicious treatment choice for patients with LD-associated neuropsychiatric symptoms; a controlled trial is needed. The narrow-spectrum antibiotic Hygromycin A was identified as effective against Bb in vitro and in animal models, representing a favorable safety profile and minimal disruption to the gut microbiome compared to antibiotics typically prescribed for LD (Leimer et al. 2021). Trials in humans are planned.

6.2

Psychotropics

Controlled trials have not yet been conducted to assess the potential benefit of psychotropic drugs for neuropsychiatric disorders associated with LD. In the presence of active untreated infection, psychopharmacology may be less effective; indeed, this atypical poor response can be a clue to the mental health professional of an underlying undetected medical problem. If neuropsychiatric symptoms are prominent, psychiatric medication can be given concurrently with antibiotics. When

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symptoms persist, psychotropics should be considered to reduce symptoms and enhance quality of life. These medications can be useful for a wide range of neuropsychiatric symptoms, including insomnia, central and peripheral pain, fatigue, attention disorders, depression, mood lability, and anxiety. A small case series indicated that gabapentin was helpful in reducing chronic neuropathic pain associated with LD (Weissenbacher et al. 2005). Carbamazepine has been reported as helpful in reducing LD-induced hyperacusis (Nields et al. 1999). There has been increasing interest in low-dose naltrexone to manage chronic pain and inflammatory disorders (Younger et al. 2014) including fibromyalgia (Younger et al. 2013; Parkitny and Younger 2017), long COVID-19 (O’Kelly et al. 2022), complex regional pain syndrome (Soin et al. 2021), and other pain disorders (Hatfield et al. 2020; Kim and Fishman 2020). Thus, some clinicians have prescribed low-dose naltrexone for PTLDS patients reporting persistent musculoskeletal pain. Clinical trials have not yet examined the efficacy of low-dose naltrexone for PTLDS.

6.3

Other Pharmaceuticals and Supplements

High-throughput screening of new drugs against Bb in vitro revealed that disulfiram halted growth of Bb persisters in vitro (Pothineni et al. 2016). Positive therapeutic responses have been documented in human case reports and clinical series; this improvement may be due to multiple mechanisms, including a antimicrobial, immune-modulating, and/or dopamine-enhancing effects of disulfiram (Gao et al. 2020), or in some cases a placebo effect. However, disulfiram treatment is associated with significant risks, including the well-known aversive disulfiram-alcohol reaction and other dangerous drug interactions. Less commonly, serious toxic effects to the liver, peripheral nerves, and central nervous system may occur from disulfiram use. One in vitro study demonstrated efficacy of certain botanicals against Bb, most notably Cryptoleptis sanguinolenta and Polygonum cuspidatum (Japanese knotweed) (Feng et al. 2020). Another in vitro study demonstrated that the essential oils clove bud, cinnamon bark, and oregano completely eradicated viable Bb (Feng et al. 2017). Animal studies are needed before translation to human studies.

6.4

Immune Modulation

Increasing evidence supports the hypothesis that for some patients, ongoing Lymerelated symptoms may be due to immune dysregulation rather than persistent infection. In Lyme arthritis that persists despite oral and IV ceftriaxone therapy (“post-infectious Lyme arthritis”), it is increasingly clear that inflammatory and autoimmune mechanisms cause the proliferative synovitis that can last months to

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years; disease-modifying antirheumatic drugs are recommended (Lochhead et al. 2021; Steere and Angelis 2006). Immune dysregulation has also been reported in some studies of PTLDS, as noted in an earlier section of this manuscript. As with postinfectious Lyme arthritis, this has led clinicians to recommend immune-modulating therapies for patients with possible autoimmune-mediated neurologic syndromes triggered by LD (Wong et al. 2022). Intravenous immunoglobulin (IVIg) therapy is a recognized treatment for postinfectious autoimmune neurologic sequelae (Sonneville et al. 2009; Danieli et al. 2021), although little has been published on its use for persistent LD symptoms. IVIg may have a therapeutic effect by multiple mechanisms: restoring the balance of pro-inflammatory and anti-inflammatory cytokines, enhancing catabolism of pathogenic autoantibodies, decreasing B cell antibody production, interfering with autoimmune cell migration across the blood-nerve barrier, interrupting complement activation, and influencing the effect of Fc receptors (Hartung 2008). In a study of 30 patients with neuropathic pain and current serum OspA antibodies who either had a history of LD (n = 22) or received the LYMErix vaccine, all patients showed enhanced epidermal nerve fiber density and improved subjective neuropathic symptoms after 6–12 months of IVIg (Katz and Berkley 2009). One case report described a 63-year-old man with bilateral progressive arm weakness, pain in the extremities and back, and serum and CSF findings confirming Lyme neuroborreliosis. While the CSF infection was resolved after IV ceftriaxone therapy, as evidenced by marked reduction in CXCL13 CSF levels, the arm weakness worsened. This patient with well-documented CNS neuroborreliosis had developed an autoimmune-mediated polyneuropathy. He was subsequently treated with several courses of IVIg, with good response (Rupprecht et al. 2006). Serologic studies have found IgM-GM1 antibodies among patients with neuroborreliosis (García Moncó et al. 1993), suggesting molecular mimicry may have accounted for the multifocal polyneuropathy. Persistent neurologic symptoms have also been treated with plasmapheresis. A case report described a 15-year-old boy with severe ataxia and dysarthria with elevated protein and lymphocytes in the CSF. CSF was tested for many potential causes, all of which were negative, but was not tested for Bb antibodies. Thus, the patient was diagnosed initially with postinfectious cerebellitis and treated with IVIg for 5 days, but without benefit. Serology later returned positive for LD by ELISA and western blot, but the patient’s parents denied consent to conduct a second lumbar puncture to detect intrathecal Bb antibodies. Nevertheless, the patient was prescribed IV ceftriaxone, but after 3 weeks his symptoms worsened. Reconsideration led to the diagnosis of Guillain-Barré syndrome for which a second 5-day course of IVIg was ineffective. Plasmapheresis was then administered for 5 days with marked improvement of neurologic symptoms. While one cannot be absolutely certain this case was due to Lyme neuroborreliosis, the diagnosis is likely given fully-positive serologic markers for LD, elevated protein and lymphocytes in CSF, and negative CSF results for other potential causes (Çelik et al. 2016). Persistent neuropsychiatric symptoms in PANS/PANDAS may also be treated with IVIg or plasmapheresis (Frankovich et al. 2017). A recent open-label trial of

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IVIg for PANS showed greater than 50% improvement in OCD symptoms (Melamed et al. 2021). Another small, double-blinded placebo-controlled trial of IVIg for PANDAS showed that IVIg was safe and well-tolerated, but did not lead to significant symptom improvement relative to placebo (Melamed et al. 2021). Although case reports of immunomodulatory treatment for persistent neuropsychiatric symptoms in the context of LD exist (Cross et al. 2021), controlled clinical trials have not yet been conducted to demonstrate efficacy.

7 Illustrative Case: Neuropsychiatric Symptoms in a Child with Multiple Infections James, a boy from a Lyme-endemic area in the northeastern USA, was well until age 7 when he had a ski-related head injury without loss of consciousness. He subsequently developed headaches, body aches, and behavior changes (separation anxiety, grandiose and magical thinking, and “meltdowns”). Due to positive M. pneumoniae and Streptococcus serologies and recurrent culture-positive streptococcal pharyngitis, he was treated with several antibiotic courses which led to temporary improvement. However, joint and neck pain, noise and light sensitivity, and a neck tic emerged. During symptom exacerbations, James’ behavior was described as impulsive, wild, and hypersexualized, and occasionally accompanied by paranoia and episodic hallucinations. About 6 months after the onset of symptoms, a physician evaluation led to concern about autoimmune encephalitis possibly triggered by either LD (suggested by a prior positive Lyme IgM immunoblot) or Group A Streptococcus infection. The physician prescribed IV ceftriaxone for 1 month and high-dose IVIg over 2 days, with significant improvement in James’ physical symptoms and many of the neuropsychiatric ones. Due to a serologic assay positive for B. duncani infection, the physician also recommended Japanese knotweed, a herbal treatment shown to eradicate B. duncani in vitro (Zhang et al. 2021). At age 9, James was reevaluated due to recurrent neuropsychiatric symptoms including labile mood (angry to happy) and agitation with daily tantrums. Serologic testing revealed an equivocal Lyme C6 peptide ELISA, a near positive Lyme western blot (4 of 10 IgG bands), and elevated M. pneumoniae IgG antibodies. Given the history of new-onset psychiatric symptoms in the context of prior Streptococcus infection and positive serologic titers for other infections, the working diagnosis was PANS. Due to the primary presenting complaint of mood dysregulation which persisted despite multiple prior courses of antibiotic therapy, treatment with the mood stabilizer lamotrigine was provided at low doses with good response. James’ mother noted he was calmer, more positive, and more mature. Use of lamotrigine requires careful monitoring, given the uncommon but serious dermatologic risks, particularly among children less than 12 years of age. Follow-up 4 months later indicated that James’ improvement in psychiatric symptoms was sustained.

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This case demonstrates the challenges faced by clinicians when neuropsychiatric and somatic symptoms occur together in the context of positive or suggestive microbial serologies. The diagnosis of PANS is probable; the prior head injury may have increased the permeability of his blood-brain barrier to circulating antibodies or infection. Mood dysregulation and grandiosity in this case are also characteristic of primary bipolar disorder. However, bipolar disorder most commonly emerges around age 17.3 years (Bolton et al. 2021). Supporting the PANS diagnosis in this case is that the average age of onset is 6.3–7.4 years (Swedo et al. 1998), as occurred with James. For some patients with infection-triggered neuropsychiatric disorders, bipolar features resolve after antibiotic or immunologic therapy, while for others, bipolar disorder requires ongoing management with psychiatric medication. A mood stabilizer was deemed appropriate given the history of labile mood and agitation in a child who had already received considerable prior antibiotic therapy. Treatment with a standard antidepressant, such as a selective serotonin reuptake inhibitor (SSRI), was not chosen because SSRIs without a concomitant mood stabilizer may worsen mood dysregulation in disorders with bipolar features, or in some children with infectiontriggered neuropsychiatric disorders. In one retrospective cases series, adverse behavioral responses were noted in 37% of children with PANDAS who were treated with a SSRI (Murphy et al. 2006). Lamotrigine is a mood stabilizer and anticonvulsant whose mechanism of action involves blocking sodium channels and inhibiting release of glutamate, thereby reducing excitotoxicity in the central nervous system. A study of lamotrigine in the context of mood dysregulation associated with tick-borne infection should be considered. James’ favorable response to lamotrigine highlights the importance of diagnostic re-evaluation and consideration of appropriate treatment with psychiatric medication for children with prominent neuropsychiatric disorders.

8 Conclusion Many clinicians lack knowledge about tick-borne illness, and most are unfamiliar with potential neuropsychiatric manifestations. Broader education about the potential complexity, severity, and diverse manifestations of tick-borne diseases is needed. Large-scale paradigm-shifting public health interventions, similar to measures taken in combating malaria, are needed to reverse the ongoing spread of tick-borne diseases. For those living in Lyme-endemic areas, comprehensive education about tick bite prevention, tick attachment management, EM identification, or summer flu-like illness is needed for parents, teachers, and students. This education also needs to be extended to staff and families attending summer camps in endemic areas. An infection work-up including testing for Lyme and other tick-borne diseases is recommended for individuals with new-onset neuropsychiatric symptoms, especially in endemic areas, and when symptoms are atypical or the individual is not responding to psychiatric medications generally considered effective. Given the

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limitations of diagnostic testing, making use of more than one laboratory for diagnostic assessment may provide more comprehensive data. Assessing the spinal fluid of patients with new-onset severe neuropsychiatric conditions may help identify infections or autoimmune markers of encephalitis that can help guide treatment decisions. For patients with diagnosed tick-borne illness, personalized treatment may be geared toward eradicating infection, modulating the immune response, and/or addressing neuropsychiatric symptoms. Psychological treatment to address the stressors of managing a chronic illness, associated consequences, and the trauma of delayed diagnosis and invalidation from medical providers can also be helpful for many patients. A comprehensive approach to diagnosis and treatment that integrates research and clinical domains can help bridge the gap between basic sciences and the clinical world.

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Behavioral Changes Induced by Latent Toxoplasmosis Could Arise from CNS Inflammation and Neuropathogenesis Jianchun Xiao

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 The Distribution of Tissue Cysts Has the Potential to Disturb Brain-Wide As Well As Specific Regions of the Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 The Toxoplasma Manipulation Hypothesis Could Be a Consequence, Not a Cause, of Parasitism in the Hosts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 The Persistence of Tissue Cysts Requires Continued Immune Surveillance to Prevent Reactivation and Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Tissue Cysts Are Significant Contributors to Behavioral Changes and Neuro-Immune Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Mechanisms Through Which Cyst Presence Is Responsible for Behavioral Changes . . . . 7 Neuropathogenesis Could Arise from Chronic Neuroinflammation, as an Indirect Effect of the Tissue Cysts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Chronic infection with Toxoplasma gondii, a neurotropic parasite, has been linked to multiple behavioral changes in rodents and humans. The pathogenic mechanisms underlying these correlations are not known. I discuss here from animal studies the distribution of tissue cysts, the constant immune surveillance, the critical role of cyst burden, and the time-dependent consequences, which I believe are crucial to explaining the behavioral changes. In line with the brain-wide distribution of tissue cysts and chronic neuroinflammation, infected mice displayed a broad range of behavioral phenotypes. Many studies suggest that behavioral changes in mice are directly associated with tissue cyst presence or cyst burden and the host immune response. Cyst burden may not exert direct effects; however, the mechanisms causing behavioral and neuropathological changes are potentially the consequence of cyst burden over time, such as the neuroinflammation required to control J. Xiao (*) Stanley Division of Developmental Neurovirology, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, MD, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 303–314 https://doi.org/10.1007/7854_2022_370 Published Online: 9 June 2022

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the reactivation of tissue cysts. The reduction of neuroinflammation has proven that neuropathogenesis and behavioral abnormalities can be reversed, at least partially, in infected mice. Overall, Toxoplasma-induced behavioral changes are likely to be an indirect consequence of the host immune response in a parasite burden-dependent manner. Keywords Behavioral changes · Mechanisms · Neuroinflammation · Neuropathogenesis · Tissue cyst · Toxoplasma gondii (T. gondii)

1 Introduction Toxoplasma gondii is a single-celled and obligatory intracellular parasite that influences human health. The parasite has a complex life cycle containing sexual replication in a definitive host and asexual propagation in an intermediate host (Dubey 2008). Although a wide variety of warm-blooded hosts serve as an intermediate hosts, the only known definitive hosts are members of the cat family (Felidae). When Toxoplasma infects an intermediate host, such as rodents or humans, it infiltrates the central nervous system, forms slow-growing cysts inside neurons, and persists indefinitely (Sibley et al. 2009). Toxoplasma seroprevalence is estimated at 10.4%, or approximately 35 million people in the US population (Jones et al. 2018). As one of the most common neurotropic parasites, more and more studies suggest that chronic infection, the most frequent form in humans, has significant clinical ramifications. Not only does chronic infection give rise to life-threatening reactivation in immunocompromised individuals, but the infection has also been linked to multiple behavioral changes and neuropsychiatric disorders in immunocompetent individuals (Xiao and Yolken 2015; Xiao et al. 2018). For example, Toxoplasma seropositivity has been associated with an increased risk for some psychiatric disorders and neurodegenerative diseases such as schizophrenia, bipolar disorder, mixed anxiety, depressive disorder, obsessive-compulsive disorder, suicide attempts, Alzheimer’s disease (AD), and Parkinson’s disease (PD), as reviewed previously (Xiao et al. 2018). How these neuropsychiatric changes arise and whether they are due to parasite manipulation or the host immune response to infection remain under debate. I reviewed studies on the anatomical basis of localization, the requirement of host immune surveillance, the critical role of tissue cysts, and the time-dependent consequences that I believe are crucial to explaining the infectionassociated changes.

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2 The Distribution of Tissue Cysts Has the Potential to Disturb Brain-Wide As Well As Specific Regions of the Network The brain is a heterogeneous structure that varies in neuronal density according to information-processing demands. However, most of the literature on rodents agree that Toxoplasma cysts have a widespread distribution in nearly all brain structures (Berenreiterova et al. 2011; Boillat et al. 2020; Hermes et al. 2008; Vyas et al. 2007). Berenreiterova et al. (2011) systematically mapped the distribution of tissue cysts in the brains of mice with latent toxoplasmosis. They found tissue cysts of the parasite in 54 anatomically defined brain regions, which comprised 92% of the brain. Boillat et al. (2020) developed a novel tool using light-sheet microscopy to precisely map the locations of cysts in the brains of Toxoplasma-infected mice. Using the registration of the imaged brains to the Allen Brain Atlas, they attributed cysts to more than 550 different subregions of the brain, highlighting a widespread localization. These results suggest that Toxoplasma might affect the information processing within a broad range of brain functions, thus affecting a wide range of mouse behaviors. Consistent with the anatomic basis of infection, previous studies have reported various behavioral changes in rodents with latent toxoplasmosis (Worth et al. 2014), such as alterations in motor and learning performance, memory, sociability, dominance, mate choice, anxiety, locomotor activity, and exploration. Similarly, Toxoplasma seropositivity in human studies has been linked to numerous neurodegenerative and psychiatric disorders such as AD, PD, schizophrenia, bipolar disorder, mixed anxiety, depressive disorder, obsessive-compulsive disorder, autism, and suicide attempts (Xiao et al. 2018). Although tissue cysts are distributed throughout the brain, the cyst density is not homogeneous. Some brain regions are more heavily infected than others, such as the amygdala, hippocampus, cortex, and olfactory bulbs (Berenreiterova et al. 2011; Boillat et al. 2020; Hermes et al. 2008; Vyas et al. 2007; Li et al. 2015). Of note, several studies found that the cyst density was the highest in the cortical region (Berenreiterova et al. 2011; Boillat et al. 2020). Consistent with the specific enrichment, Toxoplasma seropositivity has often been associated with alterations in cognitive functioning and overall cognitive decline in humans (Nimgaonkar et al. 2016; Beste et al. 2014; Mendy et al. 2015). The cognition effects of Toxoplasma in animals have been evaluated in tests for spatial, olfactory, and associative learning and memory, where cognitive performance is altered (Kannan and Pletnikov 2012). The causal factors underlying brain colonization during acute infection, including local properties of the blood-brain barrier, regional cytoarchitectural features, the level of metabolism and blood flow, and the presence of compact myelinated bodies as natural barriers to the parasite, have been suggested to contribute interactively to the increased infestation of certain cortical and subcortical regions (Berenreiterova et al. 2011).

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3 The Toxoplasma Manipulation Hypothesis Could Be a Consequence, Not a Cause, of Parasitism in the Hosts The behavioral manipulation hypothesis has received much attention because felid is the only known definitive host for Toxoplasma. It suggests that “Toxoplasma’s ability to convert the rodents’ natural aversion to feline odors into an attraction” is to increase the reproductive fitness of the parasite (Vyas et al. 2007; Kannan et al. 2010; Webster 2007). However, a recent study by Boillat and colleagues (Boillat et al. 2020) has cast doubt on this classic tale. To address the specificity of felid attraction, the authors tested mice in an experimental set-up that included two predator odors (bobcat and fox) and two non-predator odors (guinea pig and mouse). Toxoplasma-infected mice spent more time exploring guinea pig and fox odors, with no particular attraction to bobcat odor. In a more natural setting where mice were allowed to interact with an anesthetized rat, there was a clear attraction toward the nonfeline live predator in infected mice. By testing a range of different behaviors, this study has shown that Toxoplasma infection alters the fear responses of mice in all challenging situations they encounter, regardless of feline predators, nonfeline predators, or other threatening stimuli. Since attractions also occurred on nonfeline predators, it suggests that the “Toxoplasma manipulation hypothesis” is a consequence, not a cause, of parasitism (Boillat et al. 2020; Xiao 2020). In support of this, there are inter-individual variations of tissue cysts in distribution within subregions of the brain among infected mice (Berenreiterova et al. 2011; Boillat et al. 2020), suggesting a random infection and dissemination process, possibly following the anatomic pattern of the vascular system in the brain (Boillat et al. 2020). Indeed, cysts are often found a short distance from blood vessels (Hermes et al. 2008).

4 The Persistence of Tissue Cysts Requires Continued Immune Surveillance to Prevent Reactivation and Disease When Toxoplasma infection progresses to a chronic state, the parasite mainly resides in the brain for extended periods in the form of tissue cysts (Cabral et al. 2016; Melzer et al. 2010). Such dwelling requires a continuing neuroinflammatory response to prevent parasite reactivation and encephalitis (Luft and Remington 1992). However, histopathologic studies revealed that cysts located within neurons are not associated with an inflammatory response (Hermes et al. 2008). In the rare circumstances when neuronal membranes are disrupted or cysts are ruptured, there was a marked invasion of inflammatory cells, predominately mononuclear cells but also neutrophils as well (Hermes et al. 2008; Xiao et al. 2016a). In the brains of chronically infected mice, it is uncommon to find visible free bradyzoites and virtually no free tachyzoites (Hermes et al. 2008). These findings suggest that the ongoing inflammatory process effectively eliminates parasites outside cysts in neurons. These findings are in line with the observation that tissue necrosis or damage

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and neuronal death rarely occur during the chronic phase of Toxoplasma infection (Hermes et al. 2008; Parlog et al. 2014; David et al. 2016). Neuroinflammation is not restricted to the areas surrounding the cysts. Instead, it exerts widespread effects throughout the CNS. In the brain, invasion by Toxoplasma leads to widespread activation of microglia and astrocytes (Hermes et al. 2008; Evans et al. 2014). There are perivascular, leptomeningeal inflammatory cells, CD4+ and CD8+ T cells in the brain parenchyma (Hermes et al. 2008). There is an increased expression of inflammatory mediators, such as chemoattractants (CCL5, CXCL9, and CXCL10) (Wen et al. 2010) and cytokines (TNF, IFN-γ, IL-1β, IL-2, IL-8, IL-12, IL-6, IL-10, TGF-β, and lipoxin A4) (Blader and Saeij 2009; Melo et al. 2011; Dupont et al. 2012). MRI studies reported an increase in ventricle size in chronically infected mice (Hermes et al. 2008). In addition, multiple transcriptomic analyses show an upregulation of immune-specific transcripts in the brains of mice with latent toxoplasmosis (Boillat et al. 2020; Tanaka et al. 2013). It is noteworthy that the genetic background of the host significantly influences the extent of inflammation. However, there are still a few perivascular inflammations in the most resistant mouse strain (Hermes et al. 2008). Although these inflammatory responses are a protective mechanism for controlling Toxoplasma reactivation, it is accompanied by diverse brain pathologies because many of these effector molecules have been implicated in mediating neuropsychiatric disorders (Najjar et al. 2013). For example, microglial cells, a prominent part of the histopathology observed in chronically infected mice, also are a feature of neurodegenerative diseases, including AD, multiple sclerosis, PD, AIDS, dementia, and stroke (Itzhaki et al. 2016; Perry et al. 2010). At the clinical level, the pro-inflammatory cytokine IFN-γ, a key molecule in Toxoplasma-induced neuroinflammation (Suzuki et al. 1988), has been implicated in major depressive disorder, bipolar disorder, schizophrenia, and obsessive-compulsive disorders (Najjar et al. 2013).

5 Tissue Cysts Are Significant Contributors to Behavioral Changes and Neuro-Immune Responses A significant correlation between the degree of behavioral/neurological abnormalities and the magnitude of the parasite burden or inflammatory process has been reported (Boillat et al. 2020; Hermes et al. 2008; Xiao et al. 2016b). Boillat et al. (2020) investigated whether the presence of cysts was responsible for behavioral changes. Using mutant strains defective in producing cysts, they found a strong correlation between cyst burden and behavioral outcomes using elevated plus maze, open-field, and predator avoidance. In mice with higher cyst burdens, our studies found that the infection leads to cortical neurodegeneration (Li et al. 2019), NMDAR autoantibody generation (Li et al. 2018), behavioral changes, altered gene expression, and immune activation (Xiao et al. 2016b). The extent of most changes was directly correlated with levels of MAG1 antibody, which serves as a serologic

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marker for cyst burden (Xiao et al. 2016b, 2021, 2013). These changes were not found in mice with lower cyst burdens or mice that underwent acute infection but did not progress to the chronic stage. Moreover, Hermes et al. (Hermes et al. 2008) observed that mice with the most inflammation had the tiniest brains and most abnormal neurologic and behavioral findings. These results have significantly advanced previous studies that show behavioral changes in rodents are linked to differences in cyst presence (Evans et al. 2014; Afonso et al. 2012). Transcriptome analysis of host brains reveals that the expression of many immune- and neuronal-related genes was mainly determined by cyst burden. Tanaka et al. (Tanaka et al. 2013) have found positive correlations between the numbers of parasites in the infected mouse brains and the expression levels of genes involved in host immune responses. In contrast, genes that had a negative correlation with parasite numbers were those genes that are predicted to be involved in neurological functions, such as small-GTPase-mediated signal transduction and vesicle-mediated transport. In this study, differentially expressed genes were observed between mice exhibiting the clinical signs of toxoplasmosis and those that did not, and in general, higher numbers of parasites in the whole brain were associated with more severe symptoms of toxoplasmosis. Boillat et al. (2020) observed numerous transcriptional and neuronal alterations linked to cyst load. Pathway enrichment analysis revealed that a substantial number of genes related to immunity were upregulated in the brains of infected mice. Downregulated genes were mainly involved in pathways related to neuronal signaling. In particular, there is a link between cyst burden and markers that are associated with neuronal loss, astrocyte activation, pro- and anti-inflammatory cascades, upregulation of apoptotic and excitotoxic pathways, and downregulation of neurotransmitter pathways. In this study, the plasma level of pro-inflammatory cytokines, such as IFN-γ and IL-12/IL-23p40, was also cyst burden-dependent. In the infected brain, we and others had observed increased expression of markers for synaptic remodeling (e.g., C1q) (Hermes et al. 2008; Xiao et al. 2016a) and neuronal cell damage (e.g., FJB) (Li et al. 2019; Haroon et al. 2012), as well as decreased expression of markers for pre- and postsynaptic function (NMDAR, PSD-95) (Parlog et al. 2014; Li et al. 2018). Taken together, these data suggest that the parasite burden-dependent neuro-immune response plays a critical role in modulating host behavior.

6 Mechanisms Through Which Cyst Presence Is Responsible for Behavioral Changes Given the associations between behaviors and cyst burden or inflammatory response, it raises the question of which one is the decisive factor driving behavioral changes. In Toxoplasma-infected rats, tissue cysts were present at the earliest time point (2 weeks postinfection (wpi)), but behavioral responses to cat odors were not attenuated at this time point (Evans et al. 2014). Such behavior did not occur until

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three wpi. In another study, Haroon et al. (Haroon et al. 2012) demonstrate that upon oral infection with Toxoplasma cysts, chronically infected BALB/c mice lost over time their natural fear of cat urine. The predator odor avoidance in infected mice was observed at day 60 but not at day 30 postinfection (Haroon et al. 2012). Interestingly, the number of intracerebral Toxoplasma cysts significantly declined from day 30 to day 60 postinfection, as determined by counting Toxoplasma cysts on brain sections and total brain suspensions. These findings suggest that physical cyst presence alone cannot explain changes in behavior, but the mechanisms through which cyst presence that related to behavior may require time to develop (Evans et al. 2014). Indeed, in vivo experiments showed that neurons harboring Toxoplasma cysts became functionally impaired, as evidenced by a reduction of neuronal activity-dependent thallium uptake, a potassium analog, and the percentage of non-functional neurons increased over time (Haroon et al. 2012). Specifically, the percentage of thalliumnegative cysts was increased from 40% on day 30 to 78% on day 60 postinfection. Moreover, cyst harboring neurons of all brain regions showed a reduced thallium uptake at day 60 postinfection, suggesting that functional inactivation of neurons is not limited to specific neuronal subtypes. Studies from our group have also shown that the degenerative effects and behavioral changes were not found in mice infected but failed to establish persistent infection (e.g., no tissue cysts) (Xiao et al. 2016b; Li et al. 2018, 2019).

7 Neuropathogenesis Could Arise from Chronic Neuroinflammation, as an Indirect Effect of the Tissue Cysts Through measuring neuroinflammatory changes, several studies suggest that neuropathogenesis and behavioral abnormalities in infected mice are likely mediated by neuroinflammation over time, as an indirect effect of the parasite burden. Lang et al. (Lang et al. 2018) observed profound changes in synaptic protein composition in Toxoplasma-infected mice, with multiple proteins such as EAAT2, Shank3, AMPA receptor, and NMDA receptor subunits being downregulated. However, inflammation-related proteins showed an upregulation. When antiparasitic drug sulfadiazine was administrated, it strongly reduced tachyzoite levels and diminished neuroinflammatory mediators. As a result, the treatment resulted in a partially reduced cyst level, despite a sizable number of tissue cysts still in the brain. The treatment resulted in a reduced expression of GFAP, TNF, and IFN-γ in infected mice, whereas a partial restoration of protein levels for EAAT2, Shank3, and GluA2. Their results suggest that the detected synaptic alterations are a consequence of the distinct neuroinflammatory milieu caused by the parasite, which could be reversed by diminishing the inflammatory response. Guanabenz is an FDA-approved drug for hypertension that was recently shown to have activity against chronic toxoplasmosis. However, the effectivity is dependent

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on the mouse strain and the drug delivery method used as well (Martynowicz et al. 2019). Guanabenz can lower brain cyst burden up to 80% in chronically infected BALB/cJ mice when given intraperitoneally but not when administered by gavage or food. In contrast, guanabenz increases cyst burden when given to chronically infected C57BL/6J mice. Regardless of the administration route and mouse model, guanabenz consistently reverses Toxoplasma-induced hyperactivity in latently infected mice. These results suggest that the number of brain cysts is not associated with this hyperactivity behavior. Guanabenz is currently in clinical trials for multiple sclerosis due to its anti-inflammatory property (Way et al. 2015; Takigawa et al. 2016). Indeed, brain examination shows that guanabenz decreases inflammation and perivascular cuffing in infected mice (Martynowicz et al. 2019). The rescue of Toxoplasma-induced hyperactivity is correlated with reduced neuroinflammation but brain cyst burden. The finding suggests that this behavioral change arises from host responses to infection.

8 Conclusion Neuroinflammation is a common feature of neurodegenerative and neuropsychiatric disorders (Guzman-Martinez et al. 2019). Chronic Toxoplasma infection is characterized by a progressive increase in neuroinflammation in the host. A growing number of studies suggest that Toxoplasma-induced behavioral changes could be an indirect consequence of the neuroinflammation in a parasite burden-dependent manner. Dissecting the inflammatory processes that effectively control parasite reactivation should reveal better insights into the mechanisms underlying the behavioral changes observed during latent toxoplasmosis. Moreover, it is conceivable that this state of neuroinflammation could act as a co-factor for individuals who are at risk of developing neuropsychiatric diseases. Such interaction may explain why Toxoplasma seroprevalence is associated with multiple neuropsychiatric disorders. Although the latent form of the parasite is impervious to current drugs, an alternative approach that may result in clinical benefit is to limit neuroinflammation. Acknowledgments This work was supported by the Stanley Medical Research Institute (SMRI).

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Therapeutic Implications of the Microbial Hypothesis of Mental Illness Jonathan Savitz and Robert H. Yolken

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Herpesviruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Herpesviruses Implicated in Psychiatric Illness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Anti-Herpetic Medications and Vaccines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Treatment of Herpesvirus-Induced Inflammation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Antiviral Treatment of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome . . . . 2.5 Treatment of Psychiatric Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Toxoplasma gondii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Toxoplasma gondii and Psychiatric Illness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Treatment of Toxoplasma gondii in the Context of Mental Illness . . . . . . . . . . . . . . . . . . 3.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 The Microbiome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 The Microbiome and Mental Illness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Treatment of Psychiatric Disorders with Probiotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract There is increasingly compelling evidence that microorganisms may play an etiological role in the emergence of mental illness in a subset of the population. Historically, most work has focused on the neurotrophic herpesviruses, herpes simplex virus type 1 (HSV-1), cytomegalovirus (CMV), and Epstein–Barr virus (EBV) as well as the protozoan, Toxoplasma gondii. In this chapter, we provide an umbrella review of this literature and additionally highlight prospective studies that J. Savitz (*) Laureate Institute for Brain Research, Tulsa, OK, USA Oxley College of Health Sciences, The University of Tulsa, Tulsa, OK, USA e-mail: [email protected] R. H. Yolken Stanley Division of Developmental Neurovirology, Johns Hopkins School of Medicine, Baltimore, MD, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Curr Topics Behav Neurosci (2023) 61: 315–352 https://doi.org/10.1007/7854_2022_368 Published Online: 24 May 2022

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allow more mechanistic conclusions to be drawn. Next, we focus on clinical trials of anti-microbial medications for the treatment of psychiatric disorders. We critically evaluate six trials that tested the impact of anti-herpes medications on inflammatory outcomes in the context of a medical disorder, nine clinical trials utilizing antiherpetic medications for the treatment of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) or schizophrenia, and four clinical trials utilizing anti-parasitic medications for the treatment of schizophrenia. We then turn our attention to evidence for a gut dysbiosis and altered microbiome in psychiatric disorders, and the potential therapeutic effects of probiotics, including an analysis of more than 10 randomized controlled trials of probiotics in the context of schizophrenia, bipolar disorder (BD), and major depressive disorder (MDD). Keywords Bipolar disorder · Clinical trial · Cytomegalovirus · Epstein–Barr virus · Herpes simplex virus · Major depressive disorder · Microbiome · Schizophrenia · Toxoplasma gondii

1 Introduction The hypothesis that microorganisms play a causal role in the development of mental illness has a long and venerable history, dating back to at least the late nineteenth century where it was adopted by such luminaries as Emil Kraepelin (Yolken and Torrey 2008). There was additional interest in the role of infections following World War 1 when increased rates of schizophrenia and other psychiatric disorders were noted by Karl Meninger and others to be sequalae of the influenza pandemic (Menninger 1994; Kepinska et al. 2020). The hypothesis was rekindled in the 1980s with prominent reports in Lancet and Science of evidence of herpesvirus infections in the cerebrospinal fluid (CSF) of individuals with schizophrenia. In the interim, a large body of evidence has accumulated linking specific herpesviruses and the apicomplexan protozoan, Toxoplasma gondii, to schizophrenia as well as bipolar disorder (BD) and major depressive disorder (MDD). This literature is succinctly reviewed in the sections below. We also touch on the question of whether a disruption of beneficial microorganisms in the guise of our microbiomes is a key component of the pathophysiology of psychiatric disorders. Unfortunately, as reviewed in the relevant sections below, “the rubber has yet to meet the road” with respect to actionable treatments.

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2 Herpesviruses Herpesviruses are common double-stranded DNA viruses that establish lifelong latent infections, undergo periodic reactivation, and may cause disease in vulnerable populations (Croen 1991). The Herpesviridae which infect humans are divided into 3 groups based on both genomic and phenotypic properties: the alpha group comprises herpes simplex virus 1 (HSV-1 also designated as human herpesvirus 1, HHV-1), herpes simplex virus 2 (HSV-2, also known as HHV-2), and varicella zoster virus (VZV, HHV-3); the beta group comprises cytomegalovirus (CMV), human herpesvirus 6 (HHV-6), and human herpesvirus 7 (HHV-7); and the gamma group is composed of Epstein–Barr virus (EBV, HHV-4) and Kaposi’s sarcomaassociated herpesvirus (HHV-8). Most research has focused on HSV-1, CMV, and EBV which are known for causing mild symptoms in healthy adults but more serious illness in neonates or immunocompromised individuals. In these susceptible populations immune dysfunction leads to a loss of control over viral replication leading directly or indirectly to tissue damage and in more severe cases, end-organ disease and death (Griffiths and Reeves 2021; Griffiths et al. 2015). The pathobiology of these viruses is briefly discussed below followed by a review of their potential roles in human neuropsychiatric disorders. Herpes Simplex Virus Type 1 (HSV-1) is an alphaherpesvirus transmitted by oral or sexual contact. Primary infection generally involves replication in epithelial cells. However, because the virus enters sensory neurons through nerve termini and travels through axons in a retrograde fashion, it can establish latency in neural cell bodies (Nicoll et al. 2012). Viral DNA can persist in the neural cell body in an episomal form, i.e. it is not integrated into the host cell chromosome but rather floats in the cytoplasm (St Leger et al. 2021). Latency is maintained by a complex series of interactions involving both viral and host factors. Latent virus can be reactivated later in life and provide a pool of virus for recurrent infection. Factors known to induce reactivation include stress, immune suppression, aging, and infection with other viruses (Yan et al. 2020). It was previously thought that almost all individuals were exposed to HSV-1 by adulthood. However, the availability of assays specific for antibodies to HSV-1 has indicated lower rates of prevalence. Recent data from the 2015–2016 National Health and Nutrition Examination Survey (NHANES) in the United States indicates that the prevalence of HSV-1 ranges from 27% in individuals 14–19 years of age to 59.7% in individuals 45–50 years of age. Prevalence also varies by sex, race/ ethnicity, and socioeconomic status (McQuillan et al. 2018). HSV-1 was generally thought to be spread by oral-oral contact manifested by oral lesions but more recently has been identified as a common cause of anogenital lesions transmitted through oral genital contact (Ryder et al. 2009). HSV-1 can also be transmitted from an infected mother to her newborn. Reactivation of HSV-1 can rarely result in an overwhelming infection of the CNS manifested as encephalitis. The risk of HSV-1 encephalitis in adults is markedly increased by mutations in a number of genes including ones which control toll-like

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receptors and central nervous system neuron-intrinsic immunity (Lafaille et al. 2019). HSV-1 encephalitis and multiorgan infection can also occur in neonates exposed to mothers with HSV-1 genital infections (James and Kimberlin 2015). HSV-1 encephalitis in both neonates and adults is often fatal in the absence of specific anti-herpesvirus therapy. However, it is becoming increasingly apparent that HSV-1 infection of the brain does not always lead to encephalitis but rather the establishment of a persistent infection held in check by the CNS immune system (Marcocci et al. 2020). Persistent HSV-1 infection of the brain has been found in individuals with Alzheimer’s disease but also in other disorders and individuals without apparent brain diseases, albeit at a lower level (Itzhaki et al. 2020). Cytomegalovirus (CMV) is a betaherpesvirus found worldwide. Primary infection with CMV often occurs during childhood through contact with oral or genital secretions. CMV can replicate in a wide range of cell types including epithelial and mucosal cells (Sinzger et al. 2008). CMV shedding occurs in saliva, urine, semen, cervicovaginal secretions, and breast milk, leading to a variety of transmission routes (Petersen et al. 2021). Primary infection often occurs in infants and young children. Data from NHANES indicates a seroprevalence rate in the United States ranging from 37.5% in 6–11-year-old children to 58.0% in 40–49-year-old adults (Petersen et al. 2021). The overall age-adjusted CMV seroprevalence is 50.4% with substantially higher rates in Hispanics and non-Hispanic black individuals compared to non-Hispanic white individuals (Petersen et al. 2021). Primary infections in immune competent individuals can be asymptomatic or produce non-specific symptoms such as low-grade fevers or a mononucleosis syndrome similar to that caused by EBV. Both primary infection and reactivation of CMV have potentially serious consequences in individuals who are immune compromised due to cancer chemotherapy, HIV infection, immune suppressive therapy, or congenital immunodeficiency conditions (Griffiths and Reeves 2021). Neonates can also become infected after maternal infection with a wide range of consequences including developmental delay and sensorineural hearing loss (Moulden et al. 2021). The resolution of primary infection often leads to the establishment of a lifelong infection characterized by periods of latency and reactivation. Latency can occur in many cell types but there appears to be some tropism for hematopoietic progenitor cells. Latency is maintained by a complex set of interactions many of which remain to be elucidated. The process of latency and reactivation has been characterized as a “Gordian knot, presenting what has proved to be a highly intertwined and nuanced puzzle” (Goodrum 2016). Reactivation of CMV was thought to be of little consequence in immune competent adults (Forte et al. 2020), however recent studies have challenged this concept. For example, reactivation during pregnancy can lead to CMV infection of the neonate although the consequences are generally less serious than those which occur following primary maternal infection. Also, past exposure to CMV has been associated with an increased risk of hypertension (Firth et al. 2016) and other cardiovascular disorders associated with mortality (Simanek et al. 2011). These conditions are probably the consequences of the long-term effects of CMV on the

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immune system. Additionally, exposure to CMV has been associated with cognitive decline in the elderly (Wang et al. 2010; Nimgaonkar et al. 2016) and decreased levels of cognitive functioning in some populations of non-elderly adults (Dickerson et al. 2014a) and children (Vanyukov et al. 2018), including individuals with underlying psychiatric disorders (Andreou et al. 2021a). While the precise mechanisms underlying this association are not known, it is of note that serological evidence of CMV infection of middle age and elderly adults was associated with significant increases in telomere shortening (Dowd et al. 2017) a process which has been associated with cognitive decline and increased risk of Alzheimer’s disease (Lee et al. 2020). Moreover, repeated episodes of CMV reactivation over time may lead to a reduction in naïve T-cells together with an expansion of CMV-specific T-cells, particularly terminally differentiated effector memory cells (Ford et al. 2020). This process may also contribute to a disruption in immunological aging – see chapter by Dr. Ford in this volume. CMV infection of the elderly has also been associated with blunted responses to vaccines raising the possibility that CMV exposure may also increase the risk of other infectious diseases (Nicoli et al. 2022). Epstein–Barr Virus (EBV) is a highly prevalent gamma herpesvirus with worldwide distribution. It is generally transmitted by oral contact. Initial infection results in replication within epithelial cells followed by the infection of B-lymphocytes. Initial (primary) infection generally occurs in childhood or adolescence. Primary infection in infants and young children is usually associated with non-specific respiratory symptoms while infections in older children, adolescents, and young adults are often associated with the symptom complex of fever, adenopathy, and pharyngitis characterized as infectious mononucleosis. The reason for the age-associated response is not known with certainty but might be related to hormonal changes which occur during puberty (Rostgaard et al. 2019). Following the resolution of the acute infection, the virus can establish lifelong latency within B-lymphocytes and epithelial cells, with reactivation subsequently occurring throughout the lifespan. Long-term infection with EBV has been associated with a number of neoplastic diseases in lymphocytes and epithelial cells including B-cell lymphomas, nasopharyngeal carcinomas, and some forms of stomach cancers and lymphoproliferative disorders (Dugan et al. 2019). Long-term infection also results in alterations to the host immune system. EBV has thus also been associated with several autoimmune disorders such as rheumatoid arthritis, systemic lupus erythematosus, and systemic sclerosis (Houen and Trier 2020). Furthermore, the association between EBV and multiple sclerosis, long postulated based on cross-sectional studies, has recently been confirmed by a prospective study of military personnel where the risk of this disorder was found to be increased 32-fold following EBV infection (Bjornevik et al. 2022). The immune response to EBV can be characterized by the measurement of IgG and IgM class antibodies to different viral proteins. Acute infection is characterized by antibodies to EBV early antigen (EA) and IgM antibodies to viral capsid antigen (VCA) while antibodies to EBV nuclear antigen (EBNA) and IgG antibodies to VCA develop later and persist for extended periods of time. Antibodies can also be measured to whole virion antigens as well different viral proteins. Acute infection is

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often also associated with heterophilic antibodies to animal proteins that can be used to establish diagnosis during the acute phase of illness (De Paschale and Clerici 2012).

2.1

Herpesviruses Implicated in Psychiatric Illness

There is a long-standing interest in the role of herpesviruses in psychiatric illness going back at least 50 years. First, many herpesviruses are neurotropic and can cause neurological disease (Griffiths and Reeves 2021; Griffiths et al. 2015; Marcocci et al. 2020; Teissier et al. 2014; Lokensgard et al. 1999; Krstanovic et al. 2021; Poland et al. 1990; Alcendor et al. 2012; Bello-Morales et al. 2020). Second, latent herpesvirus infections can be reactivated by psychological stress (Prosch et al. 2000; Glaser et al. 1985; Pariante et al. 1997; Freeman et al. 2007; Padgett et al. 1998; Cliffe et al. 2015; Chida and Mao 2009) which is intimately associated with the onset of several psychiatric illnesses (Chen et al. 2017; Kendler and Gardner 2016; Keller et al. 2007; Beards et al. 2013). Third, depression has been associated with impairment of the adaptive immune system and viral immunity, raising the possibility that herpesvirus infections are not optimally controlled in this population (Zorrilla et al. 2001; Cole 2014; Leday et al. 2018; Evans et al. 2002; Irwin et al. 2013; Ford et al. 2019a, b). Fourth, inflammatory stimuli can trigger viral reactivation (Prosch et al. 2000; Docke et al. 1994; Stein et al. 1993; Reeves and Compton 2011; Hargett and Shenk 2010; Walton et al. 2014; Mallet et al. 2021; Cuddy et al. 2020) and lytic viral replication can in turn worsen inflammation (Vasilieva et al. 2020; Schnittman and Hunt 2021). This is relevant because a diversity of evidence suggests that inflammation may play a pathophysiological role in a subset of psychiatric disorders (Mechawar and Savitz 2016; Savitz and Harrison 2018; Savitz 2020; Miller and Raison 2016; Irwin and Cole 2011; Pape et al. 2019; Pillinger et al. 2019). Fifth, in the case of mood disorders, there is an obvious parallel between the cycles of viral reactivation and latency, and the relapsing–remitting nature of depressive symptoms. Here, we briefly summarize the literature implicating herpesviruses in psychiatric illness. Mood disorders and schizophrenia have been the predominant focus of research and most studies have concentrated on CMV and HSV-1. For more detailed reviews, please see references (Yolken and Torrey 2008; Torrey et al. 2006) as well as chapters by Drs. Dickerson and Zheng in this volume. At least 20 studies have implicated CMV in mood disorders (Appels et al. 2000; Rector et al. 2014; Miller et al. 2005; Dickerson et al. 2017a, 2018a; Simanek et al. 2014; Simanek et al. 2019; Jaremka et al. 2013; Prossin et al. 2015; Frye et al. 2019; Phillips et al. 2008; Trzonkowski et al. 2004; Coryell et al. 2020; Burgdorf et al. 2019; Tanaka et al. 2017; Avramopoulos et al. 2015; Rizzo et al. 2013; Tedla et al. 2011; Gale et al. 2018). Most of these studies were cross-sectional and reported either a higher frequency of seropositivity to CMV in depressed cases versus controls or a positive correlation between anti-CMV IgG levels and depressive symptoms. However, two prospective studies found that CMV seropositivity was associated with an increased

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risk of depression onset (odds ratio ¼ 1.38) (Simanek et al. 2019) or mood disorders more generally (incidence rate ratio ¼ 1.43) (Burgdorf et al. 2019). The latter study also found that CMV was associated with an increased risk of developing any psychiatric disorder (incidence rate ratio ¼ 1.37) (Burgdorf et al. 2019). Further, a longitudinal analysis of the Detroit Neighborhood Health Study reported that for every one unit increase in CMV IgG antibody level, the odds of incident depression increased by 26% such that participants with IgG antibody titers in the highest quartile had 3.87 times greater odds of incident depression compared to participants in the lower three quartiles (Simanek et al. 2014). The potential link between depression and CMV also receives support from our recent work demonstrating that individuals with MDD who are seropositive for CMV have reduced gray matter volume, decreased white matter integrity, and altered resting state functional connectivity compared to matched CMV negative participants with MDD (Zheng et al. 2021a, b, c). That is, CMV may cause neural abnormalities associated with depressive illness in a vulnerable subset of patients. Nevertheless, despite burgeoning evidence of a role for CMV in depression, it should be noted that not all serological studies are supportive of the link between CMV and mood disorders (Markkula et al. 2020; Simanek et al. 2018; Snijders et al. 2019; Hsu et al. 2016). The link between schizophrenia and CMV is more equivocal. Three studies in the early 1980s reported elevated antibodies against CMV in the CSF of patients with schizophrenia versus controls (Albrecht et al. 1980; Torrey et al. 1982; Kaufmann et al. 1983). However, replication has proven challenging (King et al. 1985; Rimon et al. 1986; Gotlieb-Stematsky et al. 1981; Shrikhande et al. 1985). The serological data are also heterogeneous with several cross-sectional studies reporting a higher frequency of CMV antibodies in individuals with schizophrenia compared to controls but also many negative results. Indeed, a number of meta-analyses or systematic reviews have failed to detect evidence of a statistical association between CMV serostatus and schizophrenia (Moya Lacasa et al. 2021; Arias et al. 2012; Khandaker et al. 2013). Notable among recent positive studies is a large Swedish cohort study which retrieved data on CNS hospital admissions in people between the ages of 0 and 12 and hospitalizations for non-affective psychoses in people aged 14 years or more (Dalman et al. 2008). People hospitalized with a CMV infection were 16.6 times more likely to develop a non-affective psychosis in the future (Dalman et al. 2008). However, it should be noted that in this study, most of the CMV infections were acquired in utero or early in childhood suggesting that age of onset is an important determinant of the risk for later psychiatric illness. Differences in the timing of infection may explain discrepancies in the association between CMV exposure and psychiatric outcomes noted in different populations. Finally, consistent with the possibility that CMV may be damaging the brain in vulnerable populations, CMV antibody titers have been found to be inversely associated with hippocampal volume in individuals with BD or schizophrenia (Andreou et al. 2021b; Houenou et al. 2014). The link between HSV-1 and mood disorders is less impressive than CMV with several studies failing to detect differences between cases and controls in HSV-1 serostatus (Simanek et al. 2014, 2018, 2019; Gale et al. 2018; Markkula et al. 2020;

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Hsu et al. 2016). Nevertheless, there are some notable exceptions. HSV-1 seropositivity was recently associated with depression (OR ¼ 1.29) in a large UK Biobank sample (Ye et al. 2020) and with suicidal behavior (OR ¼ 1.40) in a cohort of Danish blood donors (n ¼ 353 cases and 3,479 controls) (Nissen et al. 2019). Although the evidence for an excess of HSV-1 infections in schizophrenia versus control populations is weak (Arias et al. 2012; de Witte et al. 2015; Thomas et al. 2013), as noted in Sect. 2.5, below, there are compelling data to suggest that HSV-1 infection may contribute to cognitive impairment in the context of schizophrenia. In short, two recent publications, a systematic review of the literature and a metaanalysis that included 9 studies, concluded that HSV-1 has a detrimental impact on neurocognitive function in individuals with schizophrenia with a small to moderate effect size (Tucker and Bertke 2019; Dickerson et al. 2020). Consistent with these data are reports of an association between HSV-1 serostatus and cognitive performance in non-psychiatric samples (Fruchter et al. 2015; Hamdani et al. 2017; Jonker et al. 2014). Additionally, a longitudinal study showed that HSV-1 positive but not HSV-1-negative patients with schizophrenia displayed a loss of gray matter volume over a one-year follow-up period (Prasad et al. 2011). Further, relative to HSV-1 negative participants, HSV-1 positive participants demonstrated increased BOLD responses in regions of the brain such as the prefrontal cortex and hippocampus during a working memory fMRI task (n-back task), suggesting that increased neurobiological effort may be required to achieve parity in working memory performance in seropositive participants (D'Aiuto et al. 2015). Regarding EBV, individuals with schizophrenia were found to have an aberrant response to infection characterized by increased levels of reactivity to EBV-VCA but not to EBNA-1 as well as whole virion antigens. When combined with genetic risk of schizophrenia as measured by polygenic risk scores, individuals with the aberrant response to EBV and increased genetic risk of schizophrenia had greater than 8.5-fold odds of a schizophrenia diagnosis as compared to individuals without these risk factors (Dickerson et al. 2019). A link between EBV infection and schizophrenia is further supported by a study indicating an increased incidence of psychotic experiences in adolescents who were infected with EBV in childhood (Khandaker et al. 2014). EBV infection in children has also been associated with Alice in Wonderland syndrome (Mastria et al. 2016), a perceptual disorder involving visual and somesthetic integration. Individuals with schizophrenia and increased levels of some EBV antibodies have also been found to have decreased levels of cognitive performance particularly in the realm of social cognition (Dickerson et al. 2021). It is also of note that clozapine is able to modulate the in vitro replication of EBV as documented by its ability to inhibit lytic reactivation (Anderson et al. 2019). The need to perform multiple assays for EBV to characterize the immune response may explain why some studies have failed to find associations with schizophrenia (de Witte et al. 2015). An aberrant response to EBV proteins has also been found in individuals with MDD (Jones-Brando et al. 2020) and chronic fatigue syndrome (Loebel et al. 2017) along with deficient T and B cell memory responses (Loebel et al. 2014). EBV has not been previously associated with BD (Snijders et al. 2019)

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but this disorder has not been extensively examined in terms of an aberrant immune response to infection.

2.2

Anti-Herpetic Medications and Vaccines

Because herpesviruses share a common replication cycle and some metabolic pathways not found in humans, a number of medications have been developed which inhibit a broad range of herpes viruses with relatively few effects on host metabolism. The first generation of medications were nucleoside analogs that targeted the viral enzyme, DNA polymerase. The first of these to attain widespread use was acyclovir or its orally administered prodrug valacyclovir, which is initially phosphorylated by the viral enzymes, thymidine kinase (HSV-1 and 2) or UL97 (CMV), and subsequently converted into triphosphates by cellular kinases in order to compete with unsubstituted deoxynucleotide triphosphates (dNTPs) as a substrate for viral DNA polymerase (Coen and Schaffer 2003). Incorporation of these nucleosides into viral DNA terminates DNA elongation, and functionally inactivates the DNA polymerase (Coen and Schaffer 2003). While acyclovir/valacyclovir is more selective for HSV-1 and HSV-2, the nucleoside analogs ganciclovir and its oral prodrug valganciclovir are usually considered to be first-line treatments for most CMV-related diseases. Unfortunately, mutant strains of virus which can no longer phosphorylate these drugs can evolve. This problem, together with the potential for mutagenicity inherent to nucleoside analogs, has led to the development of several new generation medications. One notable example is letermovir, which targets the CMV terminase complex, inhibiting DNA cleavage during encapsidation, thus possessing a superior side-effect profile to nucleoside analogs (Marty et al. 2017). While several nucleoside analogs have been shown to inhibit EBV replication in vitro, there are currently no FDA-approved treatments for EBV (Andrei et al. 2019). One potential candidate, Maribavir, inhibits EBV and CMV protein kinases that are necessary for the phosphorylation of several proteins in virally infected cells. However, phase II and III clinical trials of Maribavir for the prevention of CMV disease in recipients of allogeneic stem-cell transplants have produced mixed findings (Winston et al. 2012; Marty et al. 2011; Maertens et al. 2019; Papanicolaou et al. 2019). While medical treatments for the herpesviruses continue to evolve, the development of vaccines that protect against primary infection would be a gamechanger. Currently, the only anti-herpesvirus vaccine that is approved is for VZV and the need for vaccine prophylaxis remains high. Indeed the National Academy of Medicine has ranked CMV as of the highest priority targets for vaccine development (Stratton et al. 2000). Although anti-CMV vaccine candidates have not to date been able to prevent infection through neutralizing antibodies, some vaccines have shown promise by mitigating CMV disease after organ transplantation, and several candidates, including an mRNA vaccine, are currently in phase II clinical trials (Struble et al. 2021). The most advanced HSV vaccine candidate is the glycoprotein D2

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subunit vaccine by GlaxoSmithKline which consists of the HSV-2 glycoprotein D (gD2-AS04) and the monophosphoryl lipid A (MPL) adjuvant (Krishnan and Stuart 2021). A phase III clinical trial demonstrated 58% efficacy in preventing HSV-1 genital disease but no efficacy against HSV-2 (Belshe et al. 2012). Several vaccines targeting EBV glycoproteins have completed phase I trials but although well tolerated have not been shown to protect against EBV infection (Sun et al. 2021). In sum, while current vaccines do not elicit sufficient humoral immunity against primary infection there is optimism that new technologies such mRNA-based vaccines will eventually bring efficacious products to market. The success of a recently developed glycoprotein-based vaccine which prevents reactivation of VZV and the development of shingles and post-herpetic neuralgia (Cunningham et al. 2016) illustrates the potential of low toxicity vaccines to prevent the long-term complications of a herpesvirus infection.

2.3

Treatment of Herpesvirus-Induced Inflammation

Most studies in this section focus on CMV because the virus is a known cause of inflammation-linked pathology in immunosuppressed populations. Specifically, CMV may trigger graft rejection and reduce the survival of transplant recipients (Rollag et al. 2012) and contribute to medical morbidity in the context of HIV (Schnittman and Hunt 2021) and sepsis (Limaye et al. 2008). Thus, some clinical trials have begun to focus on co-occurring inflammatory diseases or herpes virusmediated inflammatory responses as they can be important determinants of clinical outcome. Hunt and colleagues tested the contribution of asymptomatic CMV replication to systemic inflammation in an 8-week randomized placebo-controlled trial of 900 mg/ day of valganciclovir in CMV positive people with HIV (PWH). Compared to the placebo arm (n ¼ 16) there was a significant reduction in CD8+ (CD38+ HLA-DR+) cell immune activation as well as plasma concentrations of sTNFR2, sCD163, and sCD14 in participants randomized to the valganciclovir arm (n ¼ 14) (Schnittman and Hunt 2021; Hunt et al. 2011). CMV increases the risk of developing coronary artery disease (CAD) after heart transplantation (Grattan et al. 1989; McDonald et al. 1989) and therefore a clinical trial was conducted in which 149 patients were randomized to receive 4 weeks of intravenous treatment with ganciclovir (n ¼ 76) or placebo (n ¼ 73) immediately after transplantation (Valantine et al. 1999). Patients received intravenous infusions at a dose of 5 mg/kg every 12 h for 14 days, followed by 6 mg/kg once daily 5 days per week for 2 weeks. CAD was assessed an average of 4.7 years later and was found to be reduced in the ganciclovir group not treated with calcium blockers compared to the placebo group (Valantine et al. 1999). The VICTOR trial compared oral valganciclovir and intravenous ganciclovir for the treatment of CMV disease in solid organ transplant recipients (Asberg et al. 2007). In a follow-up study focused on immunological markers, no significant effect of treatment was observed but higher baseline plasma

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concentrations of the chemokine, CXCL16, and von Willebrand factor (a marker of endothelial cell activation) were associated with poor clinical outcome 21 days after transplantation (Rollag et al. 2012). Critically ill patients with sepsis were randomized to receive either intravenous ganciclovir (5 mg/kg twice daily for 5 days), followed by either intravenous ganciclovir or oral valganciclovir once daily until hospital discharge (n ¼ 84) or matching placebo (n ¼ 76). Although the two arms did not differ in the primary outcome measure, i.e. plasma concentrations of interleukin 6 (IL-6), ganciclovir improved oxygenation and reduced mechanical ventilation requirements (Limaye et al. 2008). Three studies independently examined the effect of valacyclovir on inflammatory markers in PWH with a positive HSV-2 serology (Andersen-Nissen et al. 2016; Yi et al. 2013; Roxby et al. 2013). Sixty HIV/HSV-2-coinfected adults on suppressive antiretroviral therapy were randomized 1:1:1 to 12 weeks of placebo, low-dose valacyclovir (500 mg twice daily), or high-dose valacyclovir (1 g twice daily) (Yi et al. 2013). Compared to placebo, valacyclovir had no significant effect on activated CD8+ T cells, CRP, IL-6, sICAM-1 as well as other secondary immune outcomes (Yi et al. 2013). Similarly, Roxby et al. reported that valacyclovir had no effect on CD4+ or CD8+ (CD38+ HLA-DR+) cell activation (Roxby et al. 2013). In this trial, 117 pregnant Kenyan women were randomized to receive 500 mg valacyclovir twice daily or matching placebo for over 1 year. In the third study, an 18-week randomized, placebo-controlled crossover trial of 500 mg valacyclovir taken twice daily found that valacyclovir significantly reduced plasma CXCL10 but not 30 additional cytokines and chemokines (Andersen-Nissen et al. 2016).

2.4

Antiviral Treatment of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS) is characterized by unexplained disabling fatigue and a constellation of other symptoms such as sleep and cognitive disturbances, muscle pain, and tender lymph nodes. Given the partial overlap in symptoms between psychiatric disorders like depression and ME/CFS, the therapeutic use of antiviral medications for ME/CFS may be instructive for psychiatric disorders. Since many individuals have a history of infection prior to the onset of ME/CFS, one hypothesis is that infectious agents such as herpesviruses elicit a chronic inflammatory response that lead to the disorder (Ruiz-Pablos et al. 2021; Ariza 2021; Komaroff and Cho 2011; Pedersen et al. 2019). To date there have been four published clinical trials that have evaluated the efficacy of anti-herpes medications for the treatment of ME/CFS (Montoya et al. 2013; Lerner et al. 2007; Straus et al. 1988; Kogelnik et al. 2006). In a double-blind, randomized crossover design, 27 individuals with persisting antibodies to EBV early antigens were randomized to intravenous acyclovir (500 mg per m2 of body-surface area) or saline administered every 8 h for 7 days followed by 800 mg acyclovir administered orally four times

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daily for 30 days or matching placebo (Straus et al. 1988). After a six-week “washout” phase, participants then received 5 weeks of the alternative treatment. Improvement in self-reported symptoms was observed in both treatment phases, indicating that acyclovir was not superior to placebo for the treatment of ME/CFS in this group of patients (Straus et al. 1988). Lerner and colleagues randomized 27 EBV-positive participants meeting Centers for Disease Control (CDC) criteria for ME/CFS to valacyclovir (1.0 g every 6 h, n ¼ 14) or placebo (n ¼ 13) for 3 months (Lerner et al. 2007). The main outcome was physical activity measured by Energy Index point scores which showed a 1.12 unit (122 kcal/day) increase at 6 months in the valacyclovir group and a 0.42-unit increase (65 kcal/day) in the placebo group. However, it was unclear if this difference was statistically significant. The two other clinical trials made use of valganciclovir as opposed to acyclovir or valacyclovir. In an open-label study, 12 EBV and HHV-6-positive individuals with long-standing symptoms of central nervous system (CNS) dysfunction and fatigue were treated with 6 months of valganciclovir (900 mg twice per day for 3 weeks followed by 900 mg once per day) (Kogelnik et al. 2006). Nine out of 12 participants experienced a remission of symptoms and showed a reduction in antiviral IgG titers. In a follow-up study, 30 participants were randomized 2:1 to valganciclovir (900 mg twice daily for 21 days followed by 900 mg once daily) or placebo for 6 months (Montoya et al. 2013). There was a greater improvement in the primary outcome, i.e. score on the Multidimensional Fatigue Inventory, in the valganciclovir versus placebo group but this difference was not statistically significant. There was, however, a significant group difference in some secondary outcomes, including an increase in Th1 cytokines (IL-2, IL-12, IFN-γ) over the course of the trial in the valganciclovir arm versus the placebo arm (Montoya et al. 2013). It is of note in these studies that valacyclovir and valganciclovir have relatively little efficacy against EBV so the development and application of improved methods for the prevention and treatment of EBV-related disorders would represent a step forward in this area.

2.5

Treatment of Psychiatric Disorders

To our knowledge, there are five published treatment studies of psychiatric disorders with anti-herpetic medications (DeLisi et al. 1987; Dickerson et al. 2003a, 2009a; Prasad et al. 2013; Breier et al. 2019). All five of these studies examined the efficacy of acyclovir or its prodrug, valacyclovir, in the context of schizophrenia. In the initial trial, eight patients with chronic schizophrenia or schizoaffective disorder and elevated EBV titers received 2 weeks of placebo treatment followed by 4 g/day of acyclovir for 6 weeks, 1 g/day of acyclovir for 2 weeks, and a further 4–6 weeks of placebo (DeLisi et al. 1987). There was no significant difference between symptoms measured with the Brief Psychiatric Rating Scale between periods of active treatment and placebo. Subsequently, Dickerson et al. conducted an open-label trial of the effects of valacyclovir on positive and negative symptoms measured with the

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Positive and Negative Syndrome Scale (PANSS) (Dickerson et al. 2003a). After a 2-week lead-in period in which 65 individuals with schizophrenia were administered placebo, treatment was initiated with adjunctive 1 g valacyclovir, administered twice a day for up to 16 weeks along with the participants’ standard medication. Interestingly, although valacyclovir has predominantly anti-HSV-1 properties, the subgroup of participants who were seropositive for CMV rather than HSV-1 showed a significant improvement in psychiatric symptoms (Dickerson et al. 2003a). In a follow-up study, 47 CMV seropositive outpatients with schizophrenia were randomly assigned to receive valacyclovir 1 g twice daily (n ¼ 24) or placebo (n ¼ 23) for 16 weeks after a 2-week placebo run-in (Dickerson et al. 2009a). There was no significant difference in the change in positive and negative symptoms (PANSS score) between the two treatment arms. Two more recent trials were built upon an extensive literature of cross-sectional associations between HSV-1 seropositivity and cognitive impairment in patients with schizophrenia (Thomas et al. 2013; Dickerson et al. 2003b, 2008; Shirts et al. 2008; Yolken et al. 2011; Watson et al. 2013; Prasad et al. 2012; Schretlen et al. 2010) as well as associations between HSV-1 seropositivity and reductions in gray matter volume in schizophrenia and healthy populations (Prasad et al. 2007, 2011; Schretlen et al. 2010; Pandurangi et al. 1994). In the initial proof-of-principle trial, 24 HSV-1-seropositive individuals with schizophrenia were randomized to either 1.5 g of valacyclovir (n ¼ 12) or placebo (n ¼ 12) twice daily for 18 weeks in addition to their regular medications (Prasad et al. 2013). Relative to the placebo arm, improvements in verbal memory, working memory, and visual object learning but not psychotic symptoms were observed in the valacyclovir arm. In the follow-up VISTA trial, 170 participants with schizophrenia (HSV-1 positive, N ¼ 70; HSV-1 negative, N ¼ 96) were randomized 1:1 to valacyclovir (1.5 g twice daily) or placebo for 16 weeks (Breier et al. 2019). The valacyclovir group did not differ significantly from the placebo group on the primary or secondary neurocognitive endpoints.

2.6

Summary

There is some evidence that valganciclovir treatment of CMV disease in the context of HIV and sepsis reduces markers of systemic inflammation and may protect against the development of CAD after organ transplantation. Nevertheless, these data should be interpreted with caution since the number of studies focusing on inflammatory outcomes is small. In the case of ME/CFS and schizophrenia the focus has been on EBV and HSV-1, respectively, and thus most trials have been conducted with acyclovir or valacyclovir. In general, open-label studies or small feasibility trials have produced promising results but larger randomized controlled trials have been disappointing. One of the difficulties with treating psychiatric populations is that all available anti-herpes medications target only replicating and not latent virus. Thus, one would expect these medications to be effective solely in the lytic phase of the infection (Prasad et al. 2013). However, in immunocompetent populations viral

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reactivations may be transient or sporadic and are not easily quantified since viremia is rare. One solution is a longer duration of treatment, but this is expensive, increases the risk of adverse side-effects, and is impractical for larger trials. Second, variation in sequence occurs naturally between strains of herpesviruses and has the potential to impact pathogenesis and response to medications (Griffiths and Reeves 2021). Third, studies of recipients who are seropositive often fail to differentiate between control of reactivation and reinfection with a different strain of the virus (Griffiths and Reeves 2021). Together, these factors may bias trials toward negative outcomes and thus it may be premature to conclude that anti-herpetic medications have no therapeutic benefits. Given the robust evidence implicating CMV in depression and some evidence for the anti-inflammatory effects of valganciclovir, a clinical trial to evaluate the anti-depressant effects of anti-CMV medications is indicated. Furthermore, the availability of vaccines offers the possibility of long-term effects from a single or small number of immunizations as evidenced by the efficacy of VZV vaccines to prevent the long-term effects of VZV infections. Such vaccines would make practical the large-scale adequately powered studies needed to evaluate effects on psychiatric outcomes. Such immunizations could also be ethically employed on individuals at high risk for the development of psychiatric disorders in an attempt to prevent viral infection/reactivation and subsequent conversion to an active psychiatric disorder. These studies and the eventual expansion to other populations including children might illustrate the principle that prevention of a viral infection is more effective than its treatment and might lead to practical means for reducing the psychiatric burden of infections in the general population.

3 Toxoplasma gondii T. gondii is a potentially neurotropic protozoan parasite that infects approximately one third of the world’s population (Kim and Weiss 2008). Its definitive host is the cat, but humans and other warm-blooded animals may become infected through ingestion or inhalation of oocysts (sporozoites) and serve as intermediate hosts (Torrey et al. 2007; Severance et al. 2016). After ingestion in humans from oocysts from cat feces present in the environment or tissue cysts from the undercooked meat from infected animals, T. gondii undergoes replication in the form of tachyzoites. However, the parasite is usually controlled by the immune system and is forced to enter a latent phase, forming semi-dormant intracellular cysts (bradyzoites) which can persist for an extended period of time in muscle, liver, heart, retina, and brain (Severance et al. 2016). In immunosuppressed populations, these cysts may rupture, and the proliferating parasites are thought to induce an inflammatory response that may lead to serious CNS disease (Brown et al. 2005). Neonates can also become infected from the transplacental passage of tachyzoites from their infected mothers. In addition to its neurotropism, T. gondii has attracted the attention of researchers in the psychiatric field because: (a) the Toxoplasma infection can result in altered levels of dopamine, probably generated as part of the immune response in the brain

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(Zhang et al. 2020; Alsaady et al. 2019), (b) preclinical studies have demonstrated that T. gondii infection can have behavioral sequelae and alter noradrenergic signaling in animal models (Berdoy et al. 2000; Laing et al. 2020; Johnson and Johnson 2021), and (c) medications like haloperidol and valproic acid that are commonly used to treat psychotic disorders have anti-Toxoplasma properties in vitro (JonesBrando et al. 2003). Finally, T. gondii cannot synthesize tryptophan which means that one of the body’s natural defense mechanisms against the parasite is to degrade tryptophan by upregulating the conversion of tryptophan to kynurenine (Xiao et al. 2018). Kynurenine is in turn metabolized into several neuroactive metabolites including the NMDA receptor antagonist, kynurenic acid which has been implicated in the pathophysiology of psychosis, and the NMDA receptor agonist, quinolinic acid, which likely plays a role in the pathophysiology of depression (Savitz 2017, 2020).

3.1

Toxoplasma gondii and Psychiatric Illness

Here, we briefly highlight the evidence for the link between T. gondii and psychiatric disorders. This summary is not intended to be comprehensive, and the reader is referred to numerous reviews (Yolken and Torrey 2008; Severance et al. 2016; Xiao et al. 2018; Del Grande et al. 2017; Postolache et al. 2021) and meta-analyses on the subject as well as the chapter by Dr. Xiao in this volume. Two initial meta-analyses, one published in 2007 (Torrey et al. 2007) and one in 2012 (Arias et al. 2012), reported that there was a significantly greater frequency of IgG seropositivity to T. gondii in individuals with schizophrenia than comparison controls with ORs of 2.73 and 2.70, respectively. The effect size appeared to be similar in first episode and chronically-ill patients (Torrey et al. 2007). Another more recent meta-analysis reported an OR of 1.81 which decreased to 1.43 after correction for possible publication bias (Sutterland et al. 2015). Similarly, a meta-analysis of 16 studies comprising 2,353 patients and 1,707 controls that measured IgM antibodies in participants with acute psychosis reported an OR of 1.68 with a stronger effect in patients with chronic illness (OR ¼ 2.54) than first episode patients (Monroe et al. 2015). The cross-sectional association between T. gondii infection and schizophrenia also receives support from several prospective studies. Brown and colleagues were able to quantify T. gondii IgG levels in maternal serum samples from 63 pregnancies where the offspring went on to develop schizophrenia-spectrum disorders and 123 matched control samples (Brown et al. 2005). Women with high T. gondii titers were 2.61 times more likely to have a child who would go on to develop schizophrenia (Brown et al. 2005). Mortensen and colleagues measured T. gondii IgG antibodies from neonatal heel stick blood samples stored on filter paper in a Danish population-based register (Mortensen et al. 2007). One hundred and eightysix cases of early-onset (