Roles of Skeletal Muscle in Organ Development: Prenatal Interdependence among Cells, Tissues, and Organs (Advances in Anatomy, Embryology and Cell Biology) 3031382145, 9783031382147

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Advances in Anatomy, Embryology and Cell Biology

Boris Kablar   Editor

Roles of Skeletal Muscle in Organ Development Prenatal Interdependence among Cells, Tissues, and Organs

Advances in Anatomy, Embryology and Cell Biology Editor-in-Chief Peter Sutovsky, Division of Animal Sciences and Department of Obstetrics, Gynecology and Women’s Health, University of Missouri, Columbia, MO, USA Series Editors Z. Kmiec, Department of Histology and Immunology, Medical University of Gdansk, Gdansk, Poland Michael J. Schmeisser, Institute of Microscopic Anatomy and Neurobiology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany Jean-Pierre Timmermans, Laboratory of Cell Biology and Histology/Core Facility Biomedical Microscopic Imaging, Department of Veterinary Sciences, University of Antwerp, Wilrijk, Belgium Sven Schumann, Inst, f. Mikroskop. Anatomie u. Neurobio, Johannes Gutenberg University of Mainz, Mainz, Rheinland-Pfalz, Germany

Advances in Anatomy, Embryology and Cell Biology publishes critical reviews and state-of-the-art research in the areas of anatomy, developmental and cellular biology. Founded in 1891, this book series has a long standing tradition of publishing focused and condensed information on a given topic with a special emphasis on biomedical and translational aspects. The series is open to both contributed volumes (each collecting 7 to 15 focused reviews written by leading experts) and single-authored or multi-authored monographs (providing a comprehensive overview of their topic of research). Advances in Anatomy, Embryology and Cell Biology is indexed in BIOSIS, Medline, SCImago, SCOPUS.

Boris Kablar Editor

Roles of Skeletal Muscle in Organ Development Prenatal Interdependence among Cells, Tissues, and Organs

Editor Boris Kablar Department of Medical Neuroscience, Anatomy and Pathology, Faculty of Medicine Dalhousie University Halifax, NS, Canada

ISSN 0301-5556 ISSN 2192-7065 (electronic) Advances in Anatomy, Embryology and Cell Biology ISBN 978-3-031-38214-7 ISBN 978-3-031-38215-4 (eBook) https://doi.org/10.1007/978-3-031-38215-4 © 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

Foreword

Levels of Interactions Postnatal Functional Interactions If asked “which organ systems in our bodies interact with one another,” we might answer “muscles interact with the skeleton to allow locomotion” or “nerves interact with muscles to allow muscles to function,” and that probably would be it. These are postnatal functional interactions. Deficiencies in postnatal functional interactions can result in minor morphological anomalies, behavioral defects, or deficiencies in social interactions. After a little more thought, we might add “the heart supplies blood to all organs,” but this is a different type of interaction than those between muscles, nerves, and the skeleton. Or we might add, “glands interact with other organs by producing hormones required to initiate their development,” yet another type of interaction. Most reviews/syntheses on interactions between organ systems concentrate on muscle-skeleton and/or nerve-muscle interactions using the approaches of functional morphology, functional anatomy, physiology, neuroscience, and physical anthropology. But, as this book points out to great effect, using skeletal muscle as the signaling organ, many organ systems interact with one another from embryonic origination, through development to adult function.

Embryonic Functional Interactions A second level (type?) of interaction occurs in the latter stages of embryonic development when tissues are developing, and organ systems initiated. As one example, some elements of the skeleton will not develop or grow until muscles are functioning in utero. This is elegantly demonstrated by knocking out the musclespecific genes Myf5-/- and Myod-/- in mouse embryos (Rot-Nikcevic et al. 2006), one v

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of the important series of experiments discussed in six of the seven chapters in this book. As a second example, reproductive organs require functioning glands and secretion of hormones before they can develop and mature. Such interactions affect morphogenesis, developmental physiology, maturation, growth, and ontogeny and are studied by developmental/cell biologists, reproductive biologists, endocrinologists, and neuroscientists. No general term applies to these interactions. I suggest embryonic functional interactions. Deficiencies in embryonic functional interactions often result in significant problems with organ morphology/function or behavioral delays.

Embryonic (Developmental) Inductions A third level (type?) of interaction occurs in the earliest stages of embryonic development when cells differentiate, tissues form, and the primordia of organs are laid down. We know these as inductive interactions or embryonic(developmental) inductions that are based on cellular signaling centers (Hall and Miyake 2000; Kavanagh 2018, Schneider 2018; and see Chaps. 3 and 6). Most occur between epithelial and mesenchymal cells where chemical signals from one cell type are required to change the fate of the second cell type. Some involve a single signal which when transmitted turns on the differentiation of cell type X, but most are a sequence(s) of interactions—often involving different signaling molecules and reciprocal interactions between epithelial and mesenchymal cells—that regulate progressive stages of cell differentiation and morphogenesis, osteoblast to osteocyte, myoblast to myocyte, for example. Blocking the signaling at any stage prevents cells from transiting to the next stage (Balic 2019; Hall and Miyake 2000; Thesleff 2000, and see Chaps. 3 and 6). Deficiencies in embryonic inductions can result in major defects with organ formation or morphology/function and are the bases of many syndromes in which multiple organs are affected.

A Continuum of Epigenetic Interactions Embryonic and developmental interactions are a continuum often with shared molecular and cellular control. A wonderful example is the sequence of hierarchical embryonic and functional interactions initiated by signaling of Sonic hedgehog (Shh) and Bone morphogenetic protein (BMP), which, through regulated gene expression in the craniofacial processes, initiates development and directional growth of the face (Hu et al. 2015). Altered signaling of growth factors and their receptors also modulates morphological integration (Martínez-Abadías et al. 2013). “Titration” of growth factors (over- or under-expression) therefore provides a basis for normal development, for variation around the norm, and for deviations from normal (Thesleff 2000; Reeves et al. 2001; Hu et al. 2015).

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Although these three levels and types of interactions occur at different stages of ontogeny, and although their mechanistic bases can vary, the three can be subsumed under the label epigenetics, or epigenetic interactions, in the sense introduced by C.H. Waddington (1942), who defined epigenetics as “the branch of biology which studies the causal interactions between genes and their products which bring the phenotype into being;” see Chap. 1 and Hallgrímsson and Hall (2011). Nowadays, epigenetics also is used to embrace molecular epigenetics and epigenomics—alterations in gene expression not caused by changes in DNA (Bagert and Muir 2022, and see Chaps. 2 and 4–6). Waddingtonian epigenetic models have been proposed that link embryonic inductions and embryonic functional interactions through analysis of the dynamics of cellular signaling centers and quantitative genetic analysis (Atchley and Hall 1991; Swiderski and Zelditch 2022). Such centers are modules (Abouhief 1997; Bolker 2000; De Frisco and Wagner 2022) that can be analyzed quantitatively through individual variation in ontogeny (Klingenberg 1996). A hierarchical analysis of such interactions provides the basis for current approaches to establishing links between genotype and phenotype in both development and evolution (De Frisco and Wagner 2022; Snell-Rood and Ehlman 2023, and see Chaps. 1, 3, and 6).

This Book This book concentrates on the role of one organ system—skeletal muscle—at the three levels of epigenetic interactions in regulating the development of other cells, tissues, and organs. Significantly, “muscles are not muscles are not muscles” as evident by the existence of skeletal, smooth and cardiac muscles, and as demonstrated when comparing the origination and subsequent development of head and trunk skeletal muscles at the cellular level and with respect to gene regulatory pathways/networks, as is done for eye development in Chap. 3. This approach of teasing out the interactions mediated by muscle—one of the four tissue types in the body, the other three being connective, epithelial and neural, (but see Neumann and Neumann 2021 for the need to broaden this four-type tissue classification)—and by one organ system (skeletal muscle)—is both novel and successful. For not only does skeletal muscle interact with the skeleton pre- and postnatally (introduced above and see Kablar 2011), muscles interact with a surprising number and range of tissues and organs with divergent functions. As is appropriate for understanding such interactions, these analyses are informed by information from molecular genetics and patterns of gene expression; gene knockout; phenogenomics (development as revealed through analysis of mechanisms controlled by genes); bioinformatics; systems biology; neuroscience, and from the more traditional sciences of embryology, anatomy, and cell biology. Because deviations in these interactions can be explained by mechanisms controlling normal development (ibid), chapters draw upon approaches from clinical specialists/ researchers, pathologists, endocrinologists, and reproductive physiologists.

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Experimental evidence for the role of muscles in cell, tissue, and organ development obtained by knocking-out muscle-specific genes in mouse embryos, has been the focus of the Kablar Lab in Halifax. These important experiments and data sets, which provide the major of our understanding of muscle action in embryos, are summarized and analyzed in Chap. 1, discussed in Chaps. 2 and 4 in the context of the plasticity of skeletal development and dependence of chondrogenesis and osteogenesis on muscle action, and in Chap. 5 in the context of skeletal muscles, motor neurons, and amyotrophic lateral sclerosis (Lou Gehrig’s Disease). Chapter 2 applies and proposes genomic approaches and network analysis to further understand these Myf5-/-:Myod-/- embryos. These include patterns of gene expression and analysis of gene pathways; cDNA microarrays (revealing some 40 potential candidate genes involved in muscle-based interactions/regulation); microRNA analysis (11 candidates revealed); G-protein signaling pathways (20 genes); DNA and single-cell sequencing; and RNA interference and CRISPR to silence genes and characterize knockout phenotypes. Molecular and mechanical regulation of lung development is the topic of Chap. 6. In addition to muscle-skeleton interactions introduced above, analysis of respiratory muscle-lung interactions, lung mechanobiology and the role of connective tissue growth factor (Ctgf), reveals that in the absence of respiratory muscle function, type I pneumocytes fail to form, type II pneumocytes fail to complete their differentiation, lungs fail to grow (and so are hypoplastic), and the organism fails to survive resulting in death (Chaps. 1 and 6). Furthermore, muscles supply neurotrophic factors to neurons in the central nervous system (CNS) required for neuron survival and maintenance, documenting trophic relationships between skeletal muscle and motor neurons (Chap. 5). In the absence of muscle action and trophic cell and hormonal interactions, motor neurons are progressively lost. At a different level, mechanical and functional interactions based in the activity of ocular and middle ear muscles are required for normal development/function of the eyes, inner, middle, and outer ears (Chaps. 3 and 7). Disruption or failure of such interactions usually has clinical consequences (Chap. 7). As you can see, the range of actions exerted by skeletal muscles is enormous, both prenatally and in adult life. Specialists of various types will enjoy and profit from this book when pondering how genes, development, morphology, and function are integrated, and what happens when those integrations go awry. Department of Biology, Dalhousie University, Halifax, NS, Canada

Brian K. Hall

References Abouhief E (1997) Developmental genetics and homology: a hierarchical approach. Trends Ecol Evol 12:405–408

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Atchley WR and Hall BK (1991) A model for development and evolution of complex morphological structures. Biol Rev Camb Philos Soc 66:101–157 Bagert JD and Muir TW (2022) Molecular epigenetics: chemical biology tools come of age. Annu Rev Biochem 90:287–320 Balic A (2019) Concise review: cellular and molecular mechanisms regulation of tooth initiation. Stem Cells 37:26–32 Bolker JA (2000) Modularity in development and why it matters to Evo-Devo. Am Zool 40:770–776 De Frisco J and Wagner GP (2022) Body plan identity: a mechanistic model. Evol Biol 49:123–141 https://doi.org/10.1007/s11692-022-09567-z Hall BK and Miyake T (2000) All for one and one for all: condensations and the initiation of skeletal development. BioEssays 22:138–147 Hallgrímsson B and Hall BK (eds) (2011) Epigenetics: linking genotype and phenotype in development and evolution. University of California Press, Berkeley, CA Hu D, Young NM, Xu Y, Hallgrímsson B and Marcucio RS (2015) A dynamic Shh expression pattern, regulated by SHH and BMP signaling, coordinates fusion of primordia in the amniote face. Development 142:567–574 Kablar B (2011) Role of skeletal musculature in the epigenetic shaping of organs, tissues and cell fate choices. In: Hallgrimsson B, Hall BK (eds) Epigenetics, linking genotype and phenotype in development and evolution, 1st ed. University of California Press, Berkeley, Los Angeles, pp. 256–268 Kavanagh KD (2018) Cellular signaling centers and the maintenance and evolution of morphological patterns in vertebrates. In: Hall BK and Moody SA (eds) Evolutionary cell biology: translating genotypes into phenotypes—past, present, future. CRC Press, Boca Raton Klingenberg CP (1996) Individual variation of ontogenies: a longitudinal study of growth and timing. Evolution 50:2412–2428 Martinez-Abadias N, Motch SM, Pankratz TL, et al. (2013) Tissue-specific responses to aberrant FGF signaling in complex head phenotypes. Dev Dyn 242:80–94 Neumann PE and Neumann EE (2021) General histological woes: definition and classification of tissues. Clin Anat 34:794–801 Reeves RH, Baxter LL and Richtsmeier JT (2001) Too much of a good thing— understanding effects of gene dosage in Down Syndrome. Trends Genet 17:79– 84 Rot-Nikcevic I, Reddy T, Downing KJ, et al. (2006) Myf5-/-:Myod-/- amyogenic fetuses reveal importance of early contraction and static loading by striated muscle in mouse skeletogenesis. Dev Genes Evol 216:1–9 Schneider RA (2018) Cellular control of time, size, and shape in development and evolution. In: Hall BK and Moody SA (eds). Cells in evolutionary biology: translating genotypes into phenotypes—past, present, future. CRC Press, Boca Raton, pp. 167–212

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Snell-Rood EC and Ehlman SM (2023) Developing the genotype-to-phenotype relationship in evolutionary theory: a primer of developmental features. Evol Dev. https://doi.org/10.1111/ede.12434 Swiderski DL and Zelditch ML (2022) Complex adaptive landscape for a “Simple” structure: The role of trade-offs in the evolutionary dynamics of mandibular shape in ground squirrels. Evolution 76:946–965 Thesleff I (2000) Genetic basis of tooth development and dental defects. Acta Odontol Scand 58:191–194 Waddington CH (1942) The epigenotype. Endeavour. 1:18–20

Preface

This book outlines the role of skeletal muscle in development of lung, central nervous system (CNS), including eye and ear, and skeleton. The book is relevant to developmental biologists and neuroscientists, tissue engineers, and health professionals. The book stresses the need to think about the developing body and its organs in terms of their mutual interdependence, and to think about diseases, such as pulmonary hypoplasia, amyotrophic lateral sclerosis, or cleft palate, in terms of that interdependence. Consequently, scientists, tissue engineers, and health professionals who read this book will be exposed to the ideas of interorgan communication and interdependence in homeostasis and disease. The book is a result of 25 years of research employing engineered mouse fetuses with no skeletal muscle. One of only four basic tissue types that make the body, muscle is the only tissue that can be completely ablated while allowing fetal survival. This experimental model system provides a unique opportunity holistically to study body development. A systematic anatomical analysis of such fetuses has been performed and several anatomical locations are found to be affected by the absence of the skeletal muscle. The book contains a summarized description of affected anatomical locations, such as the alveolar lung epithelium, motor neurons and giant pyramidal cells in the CNS, cholinergic amacrine cells of the retina, and type I hair cells of the crista ampullaris, followed by several, specific bioinformatics, and systems biology interventions (Chaps. 1 and 2). The book also provides an update on skeletal muscle development (Chap. 3), musculoskeletal developmental interactions (Chap. 4), trophic relationships between the skeletal muscle and the motor neurons (Chap. 5), mechanics of lung development (Chap. 6), and, finally, functional development of the ear (Chap. 7). Halifax, NS, Canada

Boris Kablar

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Contents

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3

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Skeletal Muscle’s Role in Prenatal Inter-organ Communication: A Phenogenomic Study with Qualitative Citation Analysis . . . . . . . . Boris Kablar Roles of Skeletal Muscle in Development: A Bioinformatics and Systems Biology Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jean-Sebastien Milanese, Richard Marcotte, Willard J. Costain, Boris Kablar, and Simon Drouin

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Overview of Head Muscles with Special Emphasis on Extraocular Muscle Development . . . . . . . . . . . . . . . . . . . . . . . . . Janine M. Ziermann

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Building a Co-ordinated Musculoskeletal System: The Plasticity of the Developing Skeleton in Response to Muscle Contractions . . . . Paula Murphy and Rebecca A. Rolfe

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Upper and Lower Motor Neurons and the Skeletal Muscle: Implication for Amyotrophic Lateral Sclerosis (ALS) . . . . . . . . . . . . 111 Fiorella Colasuonno, Rachel Price, and Sandra Moreno

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Mechanics of Lung Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Mark Baguma-Nibasheka and Boris Kablar

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Angular and Linear Accelerations, Ear, and the Skeletal Muscle . . . 151 You Sung Nam and Paul Hong

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About the Editor

Boris Kablar is a tenured (2005) full professor (since 2012) in the Departments of Medical Neuroscience, Anatomy and Pathology, Faculty of Medicine, Dalhousie University, Halifax, Canada, with an expertise in Human Histology, Embryology, Developmental Biology and Mouse Pathology and Phenogenomics. He has an MD (1987, Zagreb; 1995, Trieste) and a PhD (1998, Zagreb and Pisa) from Universities of Zagreb (Croatia), Trieste and Pisa (Italy). He was a postdoctoral fellow and a research associate (1995–2000; McMaster University, Canada) in the laboratory of Michael A. Rudnicki, working on skeletal muscle development. Since 2000, he has been a principal investigator working on the role of skeletal muscle in the development of many cells, tissues, and organs, such as motor neurons, lungs, skeleton, and special senses. This book, Roles of Skeletal Muscle in Organ Development: Prenatal Interdependence among Cells, Tissues, and Organs, is a result of 25 years of his research. He has produced more than 60 publications and his work has been cited more than 4000 times.

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Chapter 1

Skeletal Muscle’s Role in Prenatal Inter-organ Communication: A Phenogenomic Study with Qualitative Citation Analysis Boris Kablar

Abstract Gene targeting in mice allows for a complete elimination of skeletal (striated or voluntary) musculature in the body, from the beginning of its development, resulting in our ability to study the consequences of this ablation on other organs. Here I focus on the relationship between the muscle and lung, motor neurons, skeleton, and special senses. Since the inception of my independent laboratory, in 2000, with my team, we published more than 30 papers (and a book chapter), nearly 400 pages of data, on these specific relationships. Here I trace, using Web of Science, nearly 600 citations of this work, to understand its impact. The current report contains a summary of our work and its impact, NCBI’s Gene Expression Omnibus accession numbers of all our microarray data, and three clear future directions doable by anyone using our publicly available data. Together, this effort furthers our understanding of inter-organ communication during prenatal development. Keywords Skeletal muscle · Embryonic development · Cell interactions · Tissue interactions

1.1

Introduction

Anatomically (i.e., histologically) speaking, the mouse body consists of four basic types of tissue: epithelial, connective (including cartilage and bone), nervous and muscular (Mescher 2016). Muscular tissue types are further subdivided into three: skeletal (also called striated, voluntary muscle), smooth (mostly located in the wall

B. Kablar (✉) Department of Medical Neuroscience, Anatomy and Pathology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Kablar (ed.), Roles of Skeletal Muscle in Organ Development, Advances in Anatomy, Embryology and Cell Biology 236, https://doi.org/10.1007/978-3-031-38215-4_1

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of blood vessels, the gut tube, and several other organ systems), and cardiac (located in the heart). Embryonic and fetal development of the three muscle tissue types is very different and unrelated to each other, both anatomically and molecularly. While skeletal muscle is derived from the paraxial mesoderm, smooth muscle is derived from visceral layer of the lateral plate mesoderm around the gut tube (for the muscles of the gut tube and derivatives) and ectoderm (for the muscles of the pupil, mammary and sweat glands), and cardiac muscle is derived from visceral layer of the lateral plate mesoderm around the heart tube (Sadler 2012). Each of the three muscle tissue types develops under the influence of different molecules and molecular pathways, but some initially involved molecules are redeployed. Skeletal muscle is a derivative of the paraxial mesoderm, which expresses a set of genes that transform this mesoderm into presomitic mesoderm which then segments into epithelial somites. Somitic cells activate the myogenic program that results in formation of dermomyotome with Pax3-expressing myogenic precursor cells. Activation of Myf5, Mrf4, MyoD and Myog results in formation of mononucleated, postmitotic myocytes of the myotome. Myogenic precursor cells for the limb and girdle muscles migrate to their final locations to activate the myogenic program in those locations. Mononucleated myocytes eventually fuse into myotubes, mature into myofibers and become the prenatal and postnatal skeletal musculature (HernandezHernandez et al. 2017; Chal et al. 2018). Gene targeting techniques were employed to eliminate Myf5, Mrf4 and MyoD and to create mouse embryos and fetuses that entirely and specifically lack skeletal myoblasts and skeletal musculature, while allowing the embryos and fetuses to remain prenatally viable (Rudnicki et al. 1993; Kassar-Duchossoy et al. 2004; Comai et al. 2014). Considering that Myf5, Mrf4 and MyoD are skeletal musclespecific, their absence in the cells of the rest of the embryonic body should have no consequences, but the absence of skeletal muscle should. With this in mind, we performed detailed analysis of embryos and fetuses lacking Myf5 and MyoD (and Mrf4) (Rudnicki et al. 1993) to study the consequences that skeletal muscle absence has on organ systems (Kablar 2011). This is how we discovered several functions that skeletal muscle performs in the development of various other organ systems. The approach employed is unique, because to date no other basic tissue type has been so precisely and specifically eliminated, while leaving the embryo viable. In the context of embryonic development where everything starts from a fertilized egg, and the rest of the embryo (and placenta) is produced by a series of reciprocal inductive interactions (i.e., cell/tissue interdependence) and all kinds of levels of regulation (see below), specific and complete elimination of one basic tissue type from the very beginning, while leaving the embryo viable, has been a fascinating, invaluable and unique “model system” to study. Even though we did not design the studies to investigate the mechanisms by which the skeletal muscle’s absence affects other organ systems, because these studies are impossible to design and perform in vivo, for didactic reasons, I organized our findings into several “etiological” categories. But, before I list those categories, I would like to mention that, when discussing the organ and organismal

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Skeletal Muscle’s Role in Prenatal Inter-organ Communication:. . .

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form, the genotype-phenotype relationship, randomness, modularity, mechanical forces, cell and tissue modeling, several levels of embryonic induction become relevant. For example, tertiary embryonic induction (primary being of the brain by the notochord) is described by Scott F. Gilbert and Stuart Newman as an “instructive” (not “permissive”) induction of the environment to the genome (Müller and Newman 2003). Physical cues, which in many instances directly shape tissues during ontogenesis, are also defining animal main symmetries, both radial and bilateral, from the organs to the body plan level patterns, where molecular and physical forces act in harmony to create symmetrical structures, but physical forces direct this process (Hollo 2017). In fact, morphogenesis has been studied and discussed in the context of geometric and physical analogies for more than hundred years (Thompson 1917). For example, hydraulic control of mammalian embryo size, cell fate and cell proliferation has been recently described (Chan et al. 2019), while a series of papers have been published on mechanical regulation of bone, lung, neural, etc., development (Lecuit and Mahadevan 2017). Therefore, here are the four categories in which I organized our findings. First, skeletal muscle executes fetal breathing-like movements as respiratory musculature. We therefore studied lung development in the absence of respiratory musculature (Baguma-Nibasheka et al. 2012). This relationship between skeletal muscle and developing lung could be described as primarily “mechanical.” Here we propose that it is important to consider the insights into the mechanical control of lung organogenesis (i.e., developmental morphodynamics and mechanobiology) within the field of developmental biology, because it is significantly complementing our understanding of developmental control of organ formation (Mammoto et al. 2013). In other words, our understanding and descriptions of lung organogenesis, using growth and transcription factors, needs to be complemented by the inclusion of lung mechanobiology (Warburton et al. 2010). Second, skeletal muscle is innervated by motor neurons in the spinal cord and the brainstem. During prenatal development, skeletal muscle participates in the process of motor neuron number regulation (i.e., motor neuron survival and maintenance) by supplying combinations of neurotrophic factors to motor neurons in different central nervous system (CNS) locations, in relationship to the states of muscle differentiation (Baguma-Nibasheka et al. 2016). We studied motor neuron development in the spinal cord and brain in the complete absence of skeletal muscle, as well as in the presence of muscle at different differentiation steps. This relationship between skeletal muscle and developing motor neurons could be described primarily as cell–cell interactions of “direct contact” type or/and of “paracrine” and “autocrine” type or/and of “synaptic signaling” type. During prenatal development, this relationship changes from initial “direct contact” (or “juxtacrine signaling”) to more complex cell–cell interactions as the development proceeds and as the complexity of the structures involved increases (e.g., neuromuscular junction or motor end plate) (Mescher 2016). Third, skeletal muscle develops in proximity to the skeleton, and muscle and bone eventually function as one musculoskeletal system. We studied bone development in the absence of muscle (Rot and Kablar 2013; Rot et al. 2014). This relationship

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between skeletal muscle and developing bone could be described initially as primarily cell–cell interactions of “direct contact” type or/and of “paracrine” and “autocrine” type (i.e., “juxtacrine signaling”), and later in development also as “mechanical.” Fourth, skeletal muscle participates in the function of special senses, such as the eye and the ear. We studied eye and ear development in the absence of ocular and middle ear muscles, respectively (Baguma-Nibasheka and Kablar 2009a, 2009b; Rot et al. 2017). This relationship between skeletal muscle and developing eye or ear could be described as mostly “functional,” but essentially “mechanical.” During eye development the extraocular muscle executes fetal ocular movements (potentially related to the development of motion vision), while in the case of ear development the skeletal muscle is involved in transmission of acoustic waves and head positioning for the perception of angular acceleration or rotational movements (crista ampullaris), linear acceleration and static position (macula sacculi and utriculi) (Mescher 2016). Findings and ideas organized in these four categories are all examples of Waddingtonian epigenetics, i.e., the study of the sum of genetic and non-genetic factors that control gene expression and produce increasing phenotypic complexity during development. The importance of “inter-organ communication in physiology and disease” is also visible in the fact that EMBO/EMBL (European Molecular Biology Organization/European Molecular Biology Laboratory) has organized a symposium on this topic (N.B., due to Covid-19 this event has been postponed from 2020 to March 20–23, 2022). In fact, the basic thinking of Waddingtonian epigenetics is anticipated in the work of earlier natural scientists and philosophers. For example, Goethe and Schelling influenced Humboldt to write (Wulf 2015): “Whereas a clock consisted of parts that could be dismantled and then assembled again, an animal couldn’t - nature was a unified whole, an organism in which the parts only worked in relation to each other.” Even in general culture (Knausgaard 2012), one can read: “life is not a mathematical quantity, it has no theory, only practice, and though it is tempting to understand a generation’s radical rethink of society as being based on its view of the relationship between heredity and environment, this temptation is literary and consists more in the pleasure of speculating, that is of weaving one’s thoughts through the most desperate areas of human activity, than in the pleasure of proclaiming the truth.”

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Mechanical Role of Muscle in Lung Development

During experimentally produced prenatal absence of respiratory musculature, the lungs do not grow adequately, and the alveolar epithelial cells, type I and II pneumocytes, do not differentiate properly, resulting in pulmonary hypoplasia and death. While type I pneumocytes do not differentiate and are absent, type II pneumocytes survive but have difficulty assembling, storing and secreting the surfactant (Inanlou and Kablar 2005).

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After establishing the criteria for mouse pulmonary hypoplasia (Inanlou and Kablar 2005), we generated, employing the cDNA microarray approach, a profile of genes specific to type I and II pneumocytes (Baguma-Nibasheka et al. 2007, 2012), and deposited it in NCBI’s Gene Expression Omnibus (Edgar et al. 2002) accessible through GEO Series accession number GSE109783 (https://www.ncbi. nlm.nih.gov/geo/query/acc.cgi?acc=GSE109783). In this profile, there are many genes potentially related to different aspects of lung development, but one of them is particularly interesting as an example to follow. This gene is connective tissue growth factor (Ctgf). We established for the first time the role of Ctgf in alveolarization during lung maturation (Baguma-Nibasheka and Kablar 2008; Hall-Glenn and Lyons 2011) and that Ctgf null mutants have pulmonary hypoplasia (Baguma-Nibasheka and Kablar 2008; Holbourn et al. 2008), introducing Ctgf into the field of lung development and disease. In fact, an emerging role of CTGF (and CCN family of proteins) has now been revealed in tumorigenesis and carcinoma metastasis (Li et al. 2015), and specifically in regulation of metastatic potential in non-small-cell lung carcinoma (Kato et al. 2016). Finally, CTGF and CCN proteins are also emerging diagnostic and therapeutic targets for several diseases with an underlying pathogenesis of chronic inflammation or tissue injury (Jun and Lau 2011). Another gene, special AT-rich sequence-binding protein-1 (Satb1), identified in the profile we produced, is involved in prenatal number regulation of type I and type II pneumocytes (Baguma-Nibasheka et al. 2012). Recently, Satb1 was also found to be implicated in lung tumorigenesis, particularly of the non-small-cell lung carcinoma (Glatzel-Plucinska et al. 2018; Xu et al. 2019). In addition, a strong association with “chronic mucus hypersecretion” (characterized by an increased frequency of respiratory infections, an excessive lung function decline and increased hospitalization and mortality rates) was observed in association with SATB1 (on chromosome 3) employing a genome wide association study (Dijkstra et al. 2014). Finally, SATB1 presence is lost in early pre-invasive squamous cell carcinoma lesions, probably epigenetically silenced through histone modifications, and loss of SATB1 is associated with poor prognosis in lung squamous cell carcinomas (Selinger et al. 2011). Prenatally, in addition to biochemical signals, mechanical signals are essential for lung cells maturation (Inanlou et al. 2005; Ornitz and Yin 2012), and these signals have been carefully considered while developing strategies for whole lung tissue engineering (Calle et al. 2014). In utero, fetal breathing-like movements drive the amniotic fluid flow and expose the developing lung to mechanical forces which promote lung growth and lung cell differentiation leading to the lung’s fully functional maturation (Inanlou et al. 2005; Mammoto et al. 2013). There are many types of mechanical forces involved in development, such as spring forces, osmotic pressure, surface tension, traction, shear stress, etc., and various organ malformations are associated with abnormal mechanical microenvironments (Inanlou and Kablar 2003; Mammoto and Ingber 2010; Piccolo 2013). Recent technological advancements allow scientists to visualize vascular, respiratory, and other mouse in utero movements, employing Magnetic Resonance

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Imaging (MRI), for example (Zhang et al. 2018). There are also several ways to study and measure forces in mechanical biology employing various types of microscopies and other techniques (Sugimura et al. 2016; Mammoto and Ingber 2010). Moreover, failure to identify prenatal congenital inabilities to produce adequate embryonic and fetal movements (e.g., contractures and fetal akinesia), may have serious consequences. As a result of that, new standards and guidelines for prenatal diagnosis have been proposed (Filges and Hall 2013), which are also based on some of our discoveries (Baguma-Nibasheka et al. 2012). Two well-known lung diseases, cystic fibrosis (may include other organs) and asthma, also have physical stimuli (e.g., stretch-induced differentiation, amniotic fluid volume and flow, fetal breathing-like movements) embedded in the aspects of their mechanochemically regulated etiology and pathogenesis (Inanlou et al. 2005; Cohen and Larson 2008). In addition, pneumonia-associated genes have been previously reported using our microarray data (Baguma-Nibasheka et al. 2007; Cheng et al. 2013). Consequently, a new algorithm named “Cross-Time-point Gene Regulation Sequential pattern” (CTGR-Span) was developed to mine the CTGR-SPs employing datasets larger than previously possible and allowing for the elucidation of novel gene regulation mechanisms of biological and clinically relevant phenomena (Cheng et al. 2013). Physical stimuli are also important for tissue regeneration, for the growth of engineered tissues, and therefore need to be included into the strategies for lung regeneration (Inanlou et al. 2005; Petersen et al. 2011, 2012). In fact, the rate of lung development is controlled by the transmural pressure, as revealed by microfluidic chest cavities (Nelson et al. 2017). Finally, in the case of neuromuscular disorders, the noninvasive ventilation allows some patients to have almost normal life expectancy, whereas patients with other diagnoses have different degrees of improvements. The most effective time to introduce the noninvasive ventilation is when symptomatic sleep-disordered breathing develops (Fauroux and Lofaso 2005; Simonds 2006). Recent advances in respiratory care for neuromuscular disorders have benefited from the insights provided by our studies with mdx:MyoD, Myf5 and Myf5:MyoD null fetuses (Inanlou and Kablar 2003, 2005; Inanlou et al. 2005; Fauroux and Lofaso 2005; Simonds 2006). Employing the mouse as a mechanobiological “model” for pulmonary hypoplasia (Inanlou and Kablar 2005) allows us to link mechanical and biochemical signaling pathways, by utilizing mouse genetic engineering technologies and, simultaneously, by consulting several databases, such as: MGI (Mouse Genome Informatics), IMPC (International Mouse Phenotyping Consortium), and GXD (The Gene Expression Database for Mouse Development). In fact, our work was used as an example of the employment of gene ontology (GO), whose primary purpose is to describe the roles of gene products in an organism, to study the prenatal mouse development (BagumaNibasheka et al. 2007; Hill et al. 2010). Another example is the launch of GXD when our work was featured in the first figure of the publication as well (Kablar 2003; Smith et al. 2014).

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Significant advancements have been made in explaining the molecular basis of some aspects of the lung phenotype described in Myf5:MyoD null fetuses (Inanlou and Kablar 2005). In particular, the lack of pneumocytes type I, that we noticed employing transmission electron microscopy in the lungs from fetuses without any skeletal musculature, has now been explained at the molecular level. It appears that mechanical forces, in conjunction with the local growth factors, govern alveolar epithelial cell differentiation in the following manner: to flatten (e.g., become squamous), pneumocyte type 1 alveolar cells require mechanical forces created by the amniotic fluid inhalation. This phenotypic change allows pneumocytes type 1 to become a part of the air-blood barrier in the lungs, essential for the gas exchange. We made similar conclusions about the pneumocytes type 1 in 2005 using a different approach (Inanlou and Kablar 2005). On the other hand, pneumocyte type 2 alveolar cells require FGF10-mediated ERK1/2 signaling to produce cellular structural protrusions which in turn protect these cells from the flattening-inducing mechanical forces (Li et al. 2018). Complexity of lung development and of the role and differentiation states of 58 individual lung molecular cell types has been systematically and increasingly revealed by the Human Cell Atlas (HCA) (https://www.humancellatlas.org/), as evidenced in the HCA Developmental Lung Seminar Series on June 17, 2020.

1.3

Role of Muscle in Motor Neuron Development

One of the most striking discoveries while analyzing Myf5:MyoD null embryos and fetuses lacking all skeletal muscles (Rudnicki et al. 1993) was the gradual and essentially complete ablation of motor neurons from the spinal cord motor columns, the brainstem motor nuclei to the motor cortex of the brain (Kablar and Rudnicki 1999). This relationship between the skeletal muscle and the developing motor neurons was subsequently used to determine new regulators of the motor neuron number in the CNS. In particular, employing the cDNA microarray approach, we generated gene expression profiles specific to the muscles innervated by either the lateral motor column (LMC) motor neurons or the medial motor column (MMC) motor neurons (Baguma-Nibasheka et al. 2016), and deposited it in NCBI’s Gene Expression Omnibus (Edgar et al. 2002) accessible through GEO Series accession number GSE109784 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109784). We extensively analyzed the data and described the findings in our publication (Baguma-Nibasheka et al. 2016). Even though we found that in embryonic mice certain neurotrophic factors (e.g., BDNF, NT3, GDNF, and their combinations) rescue motor neurons in accordance to neurons’ anatomical location and muscle presence or absence (Kablar and Belliveau 2005; Geddes et al. 2006; Angka and Kablar 2007; Angka et al. 2008; Angka and Kablar 2009), the ability of several neurotrophic factors (e.g., VEGF, GDNF, CNTF,

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IGF1) to protect motor neurons from undergoing the programmed cell death in human clinical trials was essentially imperceptible (Tovar-y-Romo et al. 2014). Many aspects of the neurotrophic theory of Rita Levi-Montalcini and Santiago Ramon y Cajal have been proven at the molecular level, but the exact mechanisms of identifying the optimal surviving neurons is still unclear (Moreno and Rhiner 2014), and consequently various approaches to therapy of motor neuron diseases have been attempted. For example, in a review article it was elaborated that combining growth factors with stem cell therapy could be a way to treat motor neuron diseases (Suzuki and Svendsen 2008), considering that muscle plays an important role in motor neuron development and in providing trophic support to maintain motor neurons and their function (Kablar and Belliveau 2005; Suzuki and Svendsen 2008). Furthermore, the suggestion of non-neuronal cellular replacement in motor neuron diseases (Nayak et al. 2006) was also supported by the discoveries that muscle plays an important role in motor neuron trophic support during development and adulthood (Kablar and Belliveau 2005; Nayak et al. 2006). Our subtractive and comparative microarray analysis of the muscle groups innervated by either LMC or MMC motor neurons (Baguma-Nibasheka et al. 2016) has recently been cited in the context of hypogravity motor syndrome (HMS) (Kuznetsov et al. 2019a, 2019b). HMS is a sever microgravity effect, a consequence of weightlessness, on spinal cord motor neurons and the musculoskeletal system during human orbital space missions. Employing bioinformatics analysis of transcriptome changes, gene ontology and human phenotype ontology databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), it appears that molecular changes developing in space orbit and during the postflight recovery are like the molecular changes in “terrestrial” neuromuscular disorders (Kuznetsov et al. 2019a, 2019b). Importantly, during reprogramming and differentiation, the neural lineage cells seem to have the mechanical phenotype (Urbanska et al. 2017). This “intrinsic” mechanical phenotype is present regardless of, or in addition to, the influences outside of the neural lineage cells. The mechanical phenotype of neurons has not been considered within the muscle-motor neuron relationship hypothesized for the purpose of our current work. However, the potential presence of a mechanical phenotype in motor neurons will have consequences in the interpretation of our findings.

1.4

Role of Muscle in Development of the Skeleton

Employing mouse fetuses without any skeletal myoblasts and musculature we were able, for the first time, to examine development of the entire skeleton not only in the complete absence of skeletal muscle activity but also in the complete absence of the skeletal muscle tissue itself, which mostly eliminated: (a) the static loading from the muscle (different from the paralyzed muscle experiments), (b) the need for surgical intervention, and (c) the molecular signaling from skeletal myoblasts that

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might be influencing the bone development (Rot-Nikcevic et al. 2006; Kablar 2011). We subsequently focused on the events of bone fusion during development of the palate and the sternum, and we also focused on development of the secondary cartilage in the mandible and the clavicles (Rot-Nikcevic et al. 2006, 2007). Employing the cDNA microarray approach, we generated gene expression profiles specific to the cleft palate (Rot and Kablar 2013) and to the mandibular hypoplasia and its secondary cartilage (Rot et al. 2014) and deposited it in NCBI’s Gene Expression Omnibus (Edgar et al. 2002) accessible through GEO Series accession numbers. GEO Series accession number for the palate is GSE109780 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109780). GEO Series accession number for the mandible is GSE109779 (https://www.ncbi.nlm.nih.gov/ geo/query/acc.cgi?acc=GSE109779). We extensively analyzed the data and described the findings in our publications (Rot and Kablar 2013; Rot et al. 2014). In an editorial dedicated to the studies of morphogenesis (Lecuit and Mahadevan 2017), over a hundred-year period after the publication of Thompson’s influential book On Growth and Form (Thompson 1917), one review summarized the events of mechanical regulation between the skeletal muscle and the skeleton that lead to the establishment of the final form and function of the musculoskeletal system (Felsenthal and Zelzer 2017). Indeed, on several occasions throughout this review article, our work (Rot-Nikcevic et al. 2006) was cited to indicate various instances in which the dependence of skeletal development on the muscle is revealed, as was done elsewhere (Maurel et al. 2017). However, at the same time, it is striking to realize how much of skeletal development happens completely independently of the presence of the muscle, or any input whatsoever from the skeletal myoblasts, since almost the entire fetal skeleton was present in compound mouse mutants lacking the skeletal muscle tissue and the myoblasts (Rot-Nikcevic et al. 2006). It is possible that forces originating outside of the embryo contribute to skeletal development when muscle is absent, as recently documented (Nowlan et al. 2012). Nevertheless, the final form and function of the bones, such as their shape, size, density, morphology, fusions, joints, etc., (Rot-Nikcevic et al. 2006; Gomez et al. 2007; Kozhemyakina et al. 2015), and the integration within the musculoskeletal system, depends on the tight and complex connections between the bones and the muscles (Felsenthal and Zelzer 2017). Palate, mandible and clavicle development should be approached as a “complex integrated system” in which the final shape and function of each of these bones arise from multiple developmental modules with various integrating mechanisms, including epigenetic effects of muscles on bones (Rot-Nikcevic et al. 2006, 2007; Zelditch et al. 2008; Rolfe et al. 2013; Pollard et al. 2017). To identify the relevant genes and the molecular mechanisms that link mechanical stimuli to transcriptional control of cell differentiation, we contributed our microarray findings for the palate and the mandible (Rot and Kablar 2013; Rot et al. 2014) and these data are being employed by others. For example, pathogenic genes of syndromic micrognathia, including some from our list, were organized in potential functional groups (Chen et al. 2019). Other examples include: the functional matrix hypothesis, known and employed in the fields of orthodontics and dentofacial orthopedics (Moss 1997), and the vertical

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malocclusion developmental condition, present in long-face morphology (Chen et al. 2020). Evolutionary developmental biology also employs our findings to explain the evolution of form and function of the jaw (Woronowicz and Schneider 2019) and furcula (Pollard et al. 2017), as well as in search for an operational theory based on Darwin’s theory of evolution to explain the organization and behavior of organisms (Longo et al. 2015). In terms of data relevant to the palate development and cleft palate, the critical role played by members of the TGFβ superfamily in craniofacial development, such as growth differentiation factor 11 (Gdf11), was broadened by our recent discoveries (Rot and Kablar 2013; Mukhopadhyay et al. 2017). In addition, in Pierre Robin sequence, palatal shelf elevation failure seems to be caused by a specific developmental defect that leads to functional changes in tongue movements, rather than the physical obstruction by the tongue (Rot-Nikcevic et al. 2006; Kouskoura et al. 2016). Further generation and integration of data, the identification of precise molecular players and pathways employed by mechanical stimuli, the integration of mechanical and molecular signaling, etc., (Rolfe et al. 2013), will lead to new ideas about tissue engineering, tissue regeneration and other therapies suitable to treat the diseases of the musculoskeletal system, such as the cleft palate and micrognathia.

1.5

Role of Muscle in Development of the Special Senses

Employing mouse fetuses without any skeletal myoblasts and musculature we were able to analyze the developing eye and ear in the absence of extraocular muscles and middle ear muscles, respectively. In the paragraphs that follow I will first discuss the findings related to the eye and then I will describe our discoveries about the ear development, and their impact. When eye develops in the absence of extraocular muscles, the neural retina of mutant term fetuses does not contain any cholinergic amacrine cells (CACs), potentially involved in motion vision and directional selectivity (Kablar 2003 and references therein). We used this opportunity to perform cDNA microarray analysis (Baguma-Nibasheka et al. 2006) and discovered two molecules whose absence in mouse mutants also results in the absence of CACs: beta-transducin repeat containing (Btrc) and adaptor-related protein complex 3 delta 1 (Ap3d1) (Baguma-Nibasheka and Kablar 2009a, 2009b). This microarray analysis was not performed employing the Affymetrix Gene Chips and is therefore not deposited in NCBI’s Gene Expression Omnibus (Edgar et al. 2002). An analysis of AChE knockout mice reveals impaired development of the inner retina and a consequent, major photoreceptor degeneration (Bytyqi et al. 2004). Unfortunately, since our discoveries, for more than a decade, Btrc and Mocha mice (Ap3d1 nulls) have not been employed to study the role of CACs in motion vision and directional selectivity or other aspects of retinal development, structure, or function. In fact, development

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of CACs and motion vision seems to be visual activity-dependent only postnatally in mice (Zhang et al. 2005), even though new evidence is emerging that this may not be the case in all species, and especially not in humans (Zhang et al. 2019). We now know that during development extraocular muscles have various biochemical roles in the periocular microenvironment and are also involved in reciprocal interactions with the eye for its retinal differentiation, for secondary myotube formation and possibly for other roles (Kablar 2003; Noden and Francis-West 2006). We also know that MyoD has a role in morphogenesis of the lens and optic cup (Gerhart et al. 2009). It is my hope that, in the light of these new discoveries about the connections between myogenic regulatory factors, skeletal myogenesis and eye development, there will be more research attempting to fully understand the relationship between muscle and eye. One of the clinical disciplines that would benefit from this understanding is orthoptics. Orthoptics is concerned with disorders such as: nystagmus, binocular vision disorders (including amblyopia), and issues with extraocular muscle balance (e.g., version and vergence eye movements, refractive errors, accommodation imbalance, etc.). When the ear develops in the absence of the middle ear muscles and other head muscles, there are changes in all three parts of the ear: the outer, the middle and the inner ear (Rot and Kablar 2010; Hong et al. 2015). The inner ear areas most affected in mutant fetuses are the vestibular cristae ampullares, sensitive to angular acceleration, and in particular the type I hair cells, absent in the mutant cristae (Rot and Kablar 2010). Employing the cDNA microarray approach, we generated gene expression profiles specific to the lacking type I hair cells (Rot et al. 2017) and deposited it in NCBI’s Gene Expression Omnibus (Edgar et al. 2002) accessible through GEO Series accession numbers GSE109781 (https://www.ncbi.nlm.nih.gov/geo/query/ acc.cgi?acc=GSE109781). We extensively analyzed the data and described the findings in our recent publication (Rot et al. 2017). Our findings have not been utilized yet by the researchers who study the type I hair cells’ development, morphology and function in the crista ampullaris.

1.6

Future Directions

There are at least three directions one could pursue in the future. The first one is an approach based in bioinformatics and systems biology to define the next best experiment and to search for a common denominator between all seven microarray data sets (N.B., the seventh microarray data set is on the esophagus and is not discussed here because it was previously deposited in Baguma-Nibasheka et al. 2019). The second one is an extensive literature search (e.g., OMIM, Online Mendelian Inheritance in Man, and GARD, Genetic and Rare Diseases Information Center, databases) with a list of questions for human fetal pathologists to explore various ways in which our findings based in our engineered mouse modelling system correlates to the human embryo and fetus development with fetal akinesia. The third

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approach is to explore, via various database searches, all the possibilities in which skeletal muscle may serve as an endocrine organ during in utero development. The bioinformatics and systems biology approaches and tools, such as that of StarNet, BioBricks, Registry of Biological Parts, Database of Interacting Proteins (DIP), Protein Data Bank (PDB), ProtFun, STRING, EmbryoNet and BioCreAtIvE, can be employed to evaluate the additional meaning of each of the seven microarray data sets. The resulting data will help in defining the next best experiment, which will be explained and then performed in the future. Another way of utilizing bioinformatics could be to look for the common denominator that connects all seven microarray data sets deposited in NCBI’s Gene Expression Omnibus (Edgar et al. 2002). In fact, we have some preliminary findings in this regard. The employment of Cytoscape, to map the expression data onto WikiPathways, yielded some meaningful results on the following regulatory pathways: striated muscle contraction, muscle cell differentiation, G-protein signaling, translation factors (e.g., the most consistent gene was Eif2s3y, with its roles in initiation of translation), MAPK (Mitogen-Activated Protein Kinase) signaling (2 pathways) and ID (Inhibitor of DNA binding) signaling. The second future direction is an exploration of human in utero development affected by fetal akinesia (i.e., a group of conditions described as fetal inability to move, due to a variety of reasons, such as amyoplasia or skeletal muscle hypoplasia, arthrogryposis, oligohydramnios, CNS and peripheral nervous system anomalies, etc.) in relationship to our data on mouse in utero development in the absence of voluntary musculature. In the early nineties research on human amyoplasia and myopathies was mostly descriptive (Sarnat 1994), while the field of muscle developmental biology employing genetically engineered mice was revealing molecular mechanisms of muscle development (Rudnicki et al. 1993). Consequently, analyses of muscle’s role in development of other organs and tissues employing genetically engineered mice have been much more advanced (Kablar 2011) in comparison to the comparable studies in humans (De Vries and Fong 2007). Moreover, most of the attention in the “human” studies has been dedicated to findings that deal with the importance of fetal motility for the development of the musculoskeletal system (Nowlan 2015; Shea et al. 2015). For example, in another recent study (FeingoldZadok et al. 2017) the authors show that NEB mutations cause nemaline myopathy which in turn leads to FADS/AMC (fetal akinesia deformation sequence/ arthrogryposis multiplex congenita); therefore, they focus on the musculoskeletal system, but are interested in a collaboration (Prof. Orit Reish, The Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel, personal communication on June 25, 2020). Indeed, I would like to develop a comprehensive approach and further our understanding of muscle’s role in human intrauterine development by collecting information from public and private databases and by producing new data, alone and in collaboration, while paying attention to the following specific statements based on our previously explained discoveries in mice: (a) lung is hypoplastic, pneumocytes type I are absent and pneumocytes type II do not differentiate properly; (b) spinal cord motor neurons and motor cortex giant pyramidal cells undergo apoptosis and are virtually absent by term; (c) palate is cleft, mandible is hypoplastic and

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temporomandibular joint (TMJ) does not develop properly, sternum is cleft, clavicle is hypoplastic and acromioclavicular joint does not develop properly, cervical vertebra are enlarged and fused, some long bones are truncated and fused, and scapula is hypoplastic; (d) inner ear’s hair cells type I of the crista ampullaris are not present, supporting cells of the macula statica utriculi and sacculi are decreased in number; and (e) eye’s cholinergic amacrine cells of the retina are absent. In other words, it is time to design new hypotheses based on the existing new knowledge generated by various analyses in mice (this includes the vast amount of the microarray data mentioned earlier) and to transfer that knowledge to similar aspects of human intrauterine development. In addition, the aforementioned anatomical and histological analyses could be rendered more refined and informative if a novel approach of cleared tissue analysis with deep learning pipeline is applied, as explained in the HCA Developmental Spatial Cell Mapping Seminar Series on July 24, 2020 by Ali Erturk from Maximillian University in Munich (https://www. youtube.com/watch?v=vw8jtu---NA). Unfortunately, most of the cases of fetal akinesia would not come to full term and, because of the intrauterine fetal demise, the histological preservation of tissues would be very poor due to fast degeneration in utero (Dr. David Gaskin, retired from IWK Health Center Division of Anatomical Pathology, Halifax, Canada, personal communication on June 24, 2020). Finally, using the organoids, including the human ones, would increase our ability to understand the underlying mechanisms of the observed phenotypes, as explained in the HCA Developmental Stem Cell, Organoid Models, and Regenerative Biology Seminar Series on August 27, 2020 by Barbara Treutlein from ETH Zurich (Basel) and Karl Koehler from Harvard Medical School (https://www.youtube.com/watch? v=gg5k0c8tSgA). The third future direction is an exploration of skeletal muscle’s ability to function prenatally as an endocrine organ. I did not study or address this role of muscle previously in any of our studies. However, it has become clear that adult skeletal muscle is an endocrine organ that secretes proteins (myokines) and other metabolites mediating crosstalk between organs and tissues, such as brain, bone, gut, liver, pancreas, adipose tissue, skin, heart, and muscle itself (Iizuka et al. 2014; Karstoft and Pedersen 2016; Maurel et al. 2017; Barlow and Solomon 2018; Severinsen and Pedersen 2020). It is therefore important to examine the “endocrine” prenatal role of skeletal muscle as well. To that end it is necessary to first identify all the molecules and organs, described in the literature, that are secreted or influenced by the skeletal muscle, respectively. For the purposes of our studies, I would of course focus on the muscle-CNS (e.g., motor cortex, spinal cord, retina), muscle-inner ear and musclebone (e.g., palate, mandible, etc.) crosstalk (Karsenty and Olson 2016). Next, an examination of our microarray (Affymetrix) datasets from Myf5-/back muscle (“specified” myoblasts), MyoD-/- limb muscle (“committed” myoblasts) and Myf5-/-:MyoD-/- esophagus (“differentiated” myoblasts, separately considered in Baguma-Nibasheka et al. 2019), as compared to the control, and as compared to each other, would allow us to find out if any of the known molecules from the literature can be also detected in the microarray data. While endocrine control of muscle development has been studied, the knowledge of the prenatal

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endocrine role of muscle on other organs is mostly lacking (Iizuka et al. 2014; Severinsen and Pedersen 2020). Therefore, the current proposition would be the first comprehensive attempt of the kind, especially for the topic of muscle-lung endocrine crosstalk, that has been entirely neglected. Subsequently, our “lung,” “palate,” “mandible,” and “inner ear” microarray (Affymetrix) datasets (Baguma-Nibasheka et al. 2007; Rot and Kablar 2013; Rot et al. 2014, 2017) would be reinterpreted using new knowledge of the dependence of other organs on the endocrine role of muscle during intrauterine development (e.g., lung-muscle, palate-muscle, mandible-muscle, and inner ear-muscle endocrine crosstalk). Finally, the literature search on each of the relevant relationships, some of those previously found in our research, but attributed to other mechanisms, such as motor cortex giant pyramidal cells-muscle (Kablar and Rudnicki 1999) and retinamuscle (Baguma-Nibasheka et al. 2006) relationships, would be re-examined and reinterpreted in the light of the newly discovered muscle’s prenatal endocrine role. Compliance with Ethical Standards The author declares that he has no conflict of interest. This chapter is a review of previously published accounts, as such, no animal or human studies were performed. Acknowledgements This book chapter represents a summary of the work performed since the inception of my independent laboratory in July 2000, and specifically of the work performed in the last decade. I would like to thank some exceptional individuals who performed various parts of this work since the publication of the “Epigenetics” book (Kablar 2011): Mark Baguma-Nibasheka, Willard J. Costain, Paul Hong, Irena Rot and the members of Sandra Moreno and Mirna SaragaBabic laboratories. This work was funded (2000-2012) by operating and/or infrastructure grants from: the Banting Foundation, Hospital for Sick Children Foundation (HSCF), National Science and Engineering Research Council of Canada (NSERC), Canadian Institutes of Health Research (CIHR), Lung Association of Nova Scotia (LANS), Nova Scotia Health Research Foundation (NSHRF), Canada Foundation for Innovation (CFI), and Dalhousie Medical Research Foundation (DMRF). Finally, material transfer agreements with Regeneron and Amgen, Inc., helped aspects of this research. I thank Drs. Brian K. Hall, Sally A. Moody and Bruce R. Greenfield for critical reading of the manuscript, and the Department of Medical Neuroscience (Dalhousie University, Halifax) members for their support.

References Angka HE, Kablar B (2007) Differential responses to the application of exogenous NT-3 are observed for subpopulations of motor and sensory neurons depending on the presence of skeletal muscle. Dev Dyn 236:1193–1202 Angka HE, Kablar B (2009) Role of skeletal muscle in the epigenetic shaping of motor neuron fate choices. Histol Histopathol 24:1579–1592 Angka HE, Geddes AJ, Kablar B (2008) Differential survival response of neurons to exogenous GDNF depends on the presence of skeletal muscle. Dev Dyn 237:3169–3178 Baguma-Nibasheka M, Kablar B (2008) Pulmonary hypoplasia in the connective tissue growth factor (Ctgf) null mouse. Dev Dyn 237:485–493 Baguma-Nibasheka M, Kablar B (2009a) Abnormal retinal development in the Btrc null mouse. Dev Dyn 238:2680–2687

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Baguma-Nibasheka M, Kablar B (2009b) Altered retinal cell differentiation in the AP-3 delta mutant (Mocha) mouse. Int J Dev Neurosci 27:701–708 Baguma-Nibasheka M, Reddy T, Abbas-Butt A et al (2006) Fetal ocular movements and retinal cell differentiation: analysis employing DNA microarrays. Histol Histopathol 21:1331–1337 Baguma-Nibasheka M, Angka HE, Inanlou MR et al (2007) Microarray analysis of Myf5-/-: MyoD-/- hypoplastic mouse lungs reveals a profile of genes involved in pneumocyte differentiation. Histol Histopathol 22:483–495 Baguma-Nibasheka M, Gugic D, Saraga-Babic M et al (2012) Role of skeletal muscle in lung development. Histol Histopathol 27:817–826 Baguma-Nibasheka M, Fracassi A, Costain WJ et al (2016) Role of skeletal muscle in motor neuron development. Histol Histopathol 31:699–719 Baguma-Nibasheka M, Fracassi A, Costain WJ et al (2019) Striated-for-smooth muscle replacement in the developing mouse esophagus. Histol Histopathol 34:457–467 Barlow JP, Solomon TP (2018) Do skeletal muscle-secreted factors influence the function of pancreatic β-cells? Am J Physiol Endocrinol Metab 314:E297–E307 Bytyqi AH, Lockridge O, Duysen E et al (2004) Impaired formation of the inner retina in an AChE knockout mouse results in degeneration of all photoreceptors. Eur J Neurosci 20:2953–2962 Calle EA, Ghaedi M, Sundaram S et al (2014) Strategies for whole lung tissue engineering. IEEE Trans Biomed Eng 61:1482–1496 Chal J, Al Tanoury Z, Oginuma M et al (2018) Recapitulating early development of mouse musculoskeletal precursors of the paraxial mesoderm in vitro. Development. https://doi.org/ 10.1242/dev.157339 Chan CJ, Costanzo M, Ruiz-Herrero T et al (2019) Hydraulic control of mammalian embryo size and cell fate. Nature 571:112–116 Chen Q, Zhao Y, Qian Y et al (2019) A genetic-phenotypic classification for syndromic micrognathia. J Hum Genet 64:875–883 Chen T, Liu Z, Xue C et al (2020) Association of dysplastic coronoid process with long-face morphology. J Dent Res 99:339–348 Cheng CP, Liu YC, Tsai YL et al (2013) An efficient method for mining cross-timepoint gene regulation sequential patterns from time course gene expression datasets. BMC Bioinform. https://doi.org/10.1186/1471-2105-14-S12-S3 Cohen JC, Larson JE (2008) The Peter Pan paradigm. Theor Biol Med Model. https://doi.org/10. 1186/1742-4682-5-1 Comai G, Sambasivan R, Gopalakrishnan S et al (2014) Variations in the efficiency of lineage marking and ablation confound distinctions between myogenic cell populations. Dev Cell 31: 654–667 De Vries JIP, Fong BF (2007) Changes in fetal motility as a result of congenital disorders: an overview. Ultrasound Obstet Gynecol 29:590–599 Dijkstra AE, Smolonska J, van den Berge M et al (2014) Susceptibility to chronic mucus hypersecretion, a genome wide association study. PLoS One. https://doi.org/10.1371/journal. pone.0091621 Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30:207–210 Fauroux B, Lofaso F (2005) Non-invasive mechanical ventilation: when to start for what benefit? Thorax 60:979–980 Feingold-Zadok M, Chitayat D, Chong K et al (2017) Mutations in the NEB gene cause fetal akinesia/arthrogryposis multiplex congenital. Prenat Diagn 37:144–150 Felsenthal N, Zelzer E (2017) Mechanical regulation of musculoskeletal system development. Development 144:4271–4283 Filges I, Hall JG (2013) Failure to identify antenatal multiple congenital contractures and fetal akinesia--proposal of guidelines to improve diagnosis. Prenat Diagn 33:61–74

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Chapter 2

Roles of Skeletal Muscle in Development: A Bioinformatics and Systems Biology Overview Jean-Sebastien Milanese, Richard Marcotte, Willard J. Costain, Boris Kablar, and Simon Drouin

Abstract The ability to assess various cellular events consequent to perturbations, such as genetic mutations, disease states and therapies, has been recently revolutionized by technological advances in multiple “omics” fields. The resulting deluge of information has enabled and necessitated the development of tools required to both process and interpret the data. While of tremendous value to basic researchers, the amount and complexity of the data has made it extremely difficult to manually draw inference and identify factors key to the study objectives. The challenges of data reduction and interpretation are being met by the development of increasingly complex tools that integrate disparate knowledge bases and synthesize coherent models based on current biological understanding. This chapter presents an example of how genomics data can be integrated with biological network analyses to gain further insight into the developmental consequences of genetic perturbations. State of the art methods for conducting similar studies are discussed along with modern methods used to analyze and interpret the data. Keywords Genomics · Microarray · Sequencing · Network analysis · Bioinformatics · Systems biology

J.-S. Milanese · R. Marcotte · S. Drouin (✉) Human Health Therapeutics, National Research Council of Canada , Montreal, QC, Canada e-mail: [email protected] W. J. Costain Human Health Therapeutics, National Research Council of Canada, Ottawa, ON, Canada B. Kablar Department of Medical Neuroscience, Anatomy and Pathology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Kablar (ed.), Roles of Skeletal Muscle in Organ Development, Advances in Anatomy, Embryology and Cell Biology 236, https://doi.org/10.1007/978-3-031-38215-4_2

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Introduction

The genomics revolution continues to enable developmental biology studies that provide an ever-increasing level of insight into underlying developmental processes. Detailed understanding of tissue development under the normal and diseased states will inform the establishment of modern preventative and interventionist treatments. In the context of recent advances in cell and gene therapies, the prospect of developing therapeutic strategies for congenital disorders and aging are bright. Moreover, a thorough understanding of developmental signaling pathways is prerequisite for designing synthetic biology strategies for directing the differentiation and maturation of induced pluripotent stems cells and tissues that are suitable for functional tissue replacement. Thus, we focus on our efforts to use gene expression analysis and pathway analyses to understand the role of skeletal muscle in development. We also look ahead at recent advances in multi-omics and where anatomical analyses can benefit.

2.2

Muscle Genomics

As previously explained (Kablar 2011), we performed detailed analyses of mouse embryos and fetuses lacking Myf5 and Myod (and Mrf4) (Rudnicki et al. 1993; Kassar-Duchossoy et al. 2004), to study the consequences that skeletal muscle absence has on organ systems during prenatal development. Our discoveries can be summarized as follows: (a) lung is hypoplastic, pneumocytes type I are absent and pneumocytes type II do not differentiate properly (Inanlou and Kablar 2005b); (b) spinal cord motor neurons and motor cortex giant pyramidal cells undergo apoptosis and are virtually absent by term (Kablar and Rudnicki 1999); (c) palate is cleft, mandible is hypoplastic and temporomandibular joint (TMJ) does not develop properly, sternum is cleft, clavicle is hypoplastic and acromioclavicular joint does not develop properly, cervical vertebra are enlarged and fused, some long bones are truncated and fused, and scapula is hypoplastic (Rot-Nikcevic et al. 2006); (d) inner ear’s hair cells type I of the crista ampullaris are not present, supporting cells of the macula statica utriculi and sacculi are decreased in number (Rot and Kablar 2010); and (e) eye’s cholinergic amacrine cells of the retina are absent (Kablar 2003). Our next step was to obtain, employing the cDNA microarray approach, a profile of genes specific to the following: (a) type I and II pneumocytes (Baguma-Nibasheka et al. 2007, 2012); (b) the muscles innervated by either the lateral motor column (LMC) motor neurons or the medial motor column (MMC) motor neurons (BagumaNibasheka et al. 2016); (c) the cleft palate (Rot and Kablar 2013) and to the mandibular hypoplasia and its secondary cartilage (Rot et al. 2014); and (d) the lacking type I hair cells of the crista ampullaris (Rot et al. 2017). The “eye”

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microarray analysis was not performed employing the Affymetrix Gene Chips and therefore it is not listed here. We deposited our data in NCBI’s Gene Expression Omnibus (Edgar et al. 2002) accessible through GEO Series accession numbers, as follows: (a) for the lung (pneumocytes I and II) (Baguma-Nibasheka et al. 2007, 2012), GSE109783 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109783); (b) for the limb and back muscle (innervated by LMC and MMC motor neurons, respectively; two sets of data) (Baguma-Nibasheka et al. 2016), GSE109784 (https://www.ncbi. nlm.nih.gov/geo/query/acc.cgi?acc=GSE109784); (c) for the cleft palate (Rot and Kablar 2013), GSE109780 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE109780), and for the hypoplastic mandible (Rot et al. 2014), GSE109779 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109779); and (d) for the ear’s type I hair cells in the crista ampullaris (Rot et al. 2017), GSE109781 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109781). The “eye” microarray analysis was not performed employing the Affymetrix Gene Chips, as stated before, and therefore it is not deposited in NCBI’s Gene Expression Omnibus. The seventh deposited microarray data set is on the esophagus muscle development and is not listed here because it was previously described and deposited (BagumaNibasheka et al. 2019). There are various tools, databases (Sect. 2.8.1) and algorithms (Sect. 2.8.2) that can be employed to analyze these seven data sets, and here we describe the employment of Cytoscape and WikiPathways (http://apps.cytoscape.org/apps/ wikipathways).

2.2.1

Combined Analysis of Affymetrix Microarray Data

To identify a common denominator in all the relationships with the muscle, i.e., commonalities among datasets and specifically common expression and functional patterns, we combined the data and did several kinds of analyses. The original microarray data, as they appear in NCBI’s Gene Expression Omnibus (Edgar et al. 2002), were generated using different versions of the Affymetrix arrays: lung (MOE430A, MOE430B); palate, esophagus, limb muscle, back muscle (Mouse Genome 430 2.0); ear, mandible (Mouse Gene 1.0 ST). The gene annotations were updated for each dataset. The data were then combined using the gene IDs. Where multiple probe-sets in each array corresponded to a given gene, the average expression values were used. Genes that were not expressed (i.e., absent call in Affymetrix analysis) were excluded. This resulted in a list of 25,628 genes that were expressed in at least one of the arrays (some of these were internal control genes for the array). This list was reduced by excluding those genes that were not differentially expressed. Using an arbitrary two-fold cut-off, a list of 3928 genes was obtained. To identify commonalities, a list of 859 genes that were expressed in all tissues and differentially expressed in one or more tissues was generated.

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A variety of consolidation and clustering methods were employed to further analyze the data (e.g., MeV), but only one of the approaches undertaken, Cytoscape, produced meaningful results. There are two main issues in doing this evaluation. The first is that each dataset is derived from a different tissue, with tissue-specific expression patterns dominating the effects. For example, if 8 hybridizations (4 control and 4 experimental) using the Mouse Genome 430 2.0 arrays are considered, the samples cluster according to the tissue (palate, esophagus, limb muscle, back muscle). The other major issue is that there are differences related to the arrays used to perform the measurements. For example, array-specific factors make it difficult to compare data from ear, lung, and palate. Thus, when the data is combined, the clustering is obviously skewed by differences in the arrays used to collect the data. However, as mentioned above, the employment of Cytoscape to map the expression data onto WikiPathways (Kelder et al. 2012; Kutmon et al. 2016; Slenter et al. 2018; Martens et al. 2021) yielded meaningful results on several pathways. The first pathway of interest is the striated muscle contraction pathway. Figure 2.1 shows the muscle contraction pathway with the relative expression levels (cyan boxes) of the genes determined in the tissues examined overlaid on the network (boxplots of the expression data from lung, palate, esophagus, limb, back, ear, mandible; upregulated in green and downregulated in red). However, for the purpose and scope of this publication, it is sufficient to list some potentially relevant gene candidates. Myosin heavy chains are actin-based motor proteins, ubiquitously present in eukaryotic cells, responsible for the mechanical force and motile process within the cell, such as vesicular transport and cellular locomotion (Weiss et al. 1999). Consequently, several genes (38 in total) in the myosin heavy chain group of genes (Myh1, Myh3, Myh4, Myh6, Myh7, and Myh8) and in the myosin binding group of genes (Mybpc1 and Mybpc2) are downregulated in most of the seven tissues examined. The same is true for the genes encoding other structural regulatory proteins (Casq2, Myom1, Myom2, and Smpx), Z-disk specific genes (Actn2, Actn3, Des and somewhat Vim), and several other related genes (Tcap, Ttn, Dmd, Tmod1, and Neb). Within a model of triggering striated muscle, we find several groups of genes downregulated in most tissues: tropomyosin (Tpm1 and Tpm2), troponin-T (Tnnt1, Tnnt2, and Tnnt3), troponin-I (Tnni1, Tnni2, and Tnni3), troponin-C (Tnnc1 and Tnnc2), and actin (Acta1, Acta2, and Actc1). Within the contraction by Ca++, some genes belonging to the myosin light chain group of genes are downregulated: Myl1, Myl2, Myl3, and Myl4. The next relevant pathway is for microRNAs (miRNAs) involved in muscle cell differentiation (Fig. 2.2). This pathway shows Myod1 and Myf5 in the context of skeletal muscle differentiation. Within this pathway, the expression of serval genes (11 in total) is altered in the seven tissues examined. Most of the genes involved in muscle contraction are downregulated in most or at least some tissues (Pax7, Ezh2, Mef2c, Mir133b, PRKCQ, PRKACB). Occasional genes are upregulated in a few tissues (PRKCA, PRKAR1A, PRKAR2B). Changes were also observed in G-protein signaling pathways (Fig. 2.3), which provides some insight into how signaling is altered. Within this pathway, serval

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Fig. 2.1 Overlay of combined expression data on the striated muscle contraction pathway. The figure shows that most genes involved in muscle contraction were down regulated in tissues (lung, palate, esophagus, limb, back, ear, mandible) from Myf5-/-:Myod-/- embryos and fetuses (https://www.wikipathways.org/index.php/Pathway:WP216). Entities contained within an octagon are protein complexes

genes (20 in total) have altered expression in the 7 tissues examined. Most of the genes involved in G-protein signaling pathway are downregulated in most tissues (Akap10, Gnb3, Gngt1, Gng4, Adcy6, Rras, Gna14, Prkcq, Prkacb), while other genes are upregulated in most tissues (Akap4, Akap6, Akap12, Gnal, Kcnj3, Prkca, Pde1c, Pde4a, Prkar1a, Prkar2b). Adcy8 was upregulated in two tissues and similarly downregulated in two other tissues. According to the included information on this pathway, these genes are involved in transcription and cell responses. The most consistently upregulated gene was Eif2s3y, which encodes a protein that participates in translation. Its role in the initiation of translation is shown in the translation factors pathway (https://www.wikipathways.org/index.php/Pathway: WP307), along with 8 other genes that exhibited altered expression in Myf5-/-: Myod-/- nulls. The four MAP kinase signaling pathways, the classical MAP kinase pathway, the JNK and p38 MAP kinase pathways and the Erk5 pathway (https://

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Fig. 2.2 Overlay of combined expression data on the microRNAs (miRNAs) involved in muscle cell differentiation pathway (https://www.wikipathways.org/index.php/Pathway:WP2076). The figure shows the important role of miRNAs in mediating the effects of Myod1 and Myf5 on regulating skeletal muscle differentiation as well as how the observed alterations in gene expression of Pax7 are linked to Myod1 through two miRNAs

www.wikipathways.org/index.php/Pathway:WP3284) are visualized together and the expression of 38 genes in these signaling pathways were altered in Myf5-/-: Myod-/- nulls. Finally, the expression of 21 genes in the ID signaling pathway were found to be altered Myf5-/-:Myod-/- nulls (https://www.wikipathways.org/ index.php/Pathway:WP512). Unfortunately, analysis of the remaining pathways does not yield useful (interpretable) data at this point of time.

2.3 2.3.1

Modern Technologies for Generating Omics Data RNA Expression

Differential gene expression was initially assessed in a directed manner for individual genes using northern blotting and reverse transcription polymerase chain reaction (RT-PCR). The development of the differential display PCR (ddPCR) technique enabled the comparison of gene expression profiles in eukaryotic samples in a highthroughput manner (Liang and Pardee 1992; Chong et al. 2004). However, the complexity and other limitations of ddPCR resulted in the technique being displaced by high-throughput gene expression studies based on microarray analyses, a technology first invented in the 1980s. The key to microarray technology is DNA

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Fig. 2.3 Overlay of combined expression data on the G-protein signalling pathways. The figure shows the widespread effects of Myf5-/-:Myod-/- mutations on the expression of G-protein signalling genes in embryonic and fetal tissues (lung, palate, oesophagus, limb, back, ear, mandible) (https://www.wikipathways.org/index.php/Pathway:WP232)

hybridization, a process from which DNA will bind to a chip based on complementarity. Higher complementarity will result in a stronger interaction between the DNA molecule and the chip whereas lower complementarity will result in a weaker bond that is not resistant to being washed away. Microarray experimental design focuses on selecting two case studies (e.g., disease versus healthy). Messenger RNA (mRNA) is then isolated in both populations, from which RT-PCR is used to produce complementary or cDNA. Each group’s cDNA is labelled with a fluorescent dye (i.e., green; Cy3 and red; Cy5). Finally, cDNA is hybridized on the chip, enabling measurement in both groups for desired genes. Great advancements have been made using microarray analyses in several fields including cancer research (Solinas-Toldo et al. 1997; Golub et al. 1999; Alizadeh et al. 2000; Cooper 2001; Buckley et al. 2002; Weiss et al. 2003), stroke (Gilbert et al. 2003), gene function and discovery, pharmacogenomics (Eisen et al. 1998; Tamayo et al. 1999), antibiotic resistance (Marroki and Bousmaha-Marroki 2022), as well as in developmental biology (see Sect. 2.2) The data described in Sect. 2.2.1 are an example of how this approach is used to understand the consequences of gene mutations on prenatal development, and highlight improper differentiation in pneumocytes, absence of spinal cord motor neurons and motor cortex giant pyramidal cells and major deformities in the palate, sternum, clavicle, inner ear hair cells, and retina (Kablar 1999, 2003; Inanlou and Kablar 2005a; Rot-Nikcevic et al. 2006; BagumaNibasheka and Kablar 2007; Angka and Kablar 2009; Rot and Kablar 2010, 2013;

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Kablar and Rot 2011; Rot et al. 2014, 2017; Baguma-Nibasheka et al. 2016). However, since microarray technology suffered from several weaknesses, including lack of control over the population analyzed, low specificity and a limited number of probes produced from the manufacturers, it was ultimately superseded by sequencing-based methods. The first major breakthrough in DNA sequencing (DNA-seq) was discovered in 1977 where Frederick Sanger developed a new automated sequencing method, later named “Sanger sequencing” (or first-generation sequencing), which leveraged chain termination (Sanger et al. 1977). The major concept surrounding the chain termination method was the addition of dideoxyribonucleotides (ddNTPs), modified nucleotides with a fluorescent label, during polymerase chain reaction. Resulting DNA fragments would then be separated by size using gel electrophoresis. Finally, a laser excitation performed on each gel where the light emitted would be captured allowing for nucleotide detection. However, since all nucleotides are added one by one on the template strand followed by a ddNTP, Sanger sequencing always suffered in terms of scalability. Additionally, it was also found that accuracy in longer fragments (>900 base pair, bp) is much lower, leading to more complex experimental designs. Such limitations paved the way for second-generation sequencing, also known as next generation sequencing (NGS). In contrast to Sanger sequencing, NGS uses parallel reactions to sequence millions of DNA fragments simultaneously, thereby resulting in higher throughput, sensitivity and reduced cost. At first, a construct library of the sample DNA (or complementary DNA, cDNA) is processed into multiple fragments (100–800 bp). These fragments are denatured (single-stranded DNA) and ligated to an adaptor sequence, forming the library. Adaptors can also possess unique barcodes allowing for multiple samples to be recognized, a pooling technique called multiplexing. Finally, the entire library is sequenced simultaneously where each fragment goes through amplification using sequencing by synthesis (SBS). In short, SBS is a technique where chemically modified nucleotides are bound to the template strand by complementarity. Since every nucleotide contains a fluorescent tag (like Sanger sequencing), each nucleotide will be identified through a fluorescent signal. The cycle will repeat itself until the sequencing of the forward strand is complete, and the reads are subsequently washed away to allow for a new sequencing cycle to start on the reverse strand, a technique referred to as paired-end sequencing. In parallel to DNA-seq, RNA sequencing (RNA-seq) leverages the NGS and SBS principles to expand throughput and sensitivity. The primary difference is that RNA-seq is performed on RNA isolated from a sample, which is converted to cDNA prior to being subjected to NGS/SBS. Prior to NGS, as mentioned above, Sanger sequencing and microarrays were substantially more expensive resulting in more targeted approaches. Consequently, studies were highly focused on candidate genes usually combined with a small number of observations (patients, tumors, cells, etc.) (Kwon and Goate 2000). NGS’s throughput capabilities allowed scientists to increase their datasets combined with a much larger quantity of data (i.e., whole-exome versus targeted sequencing), which in turn, allowed for stronger and more accurate correlations. This generated a major shift in biology and medicine by providing the community the tools to explore

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multiple relationships using multiple data points such as cohort and population studies, genome-wide association studies (GWAS) (Hsu and Kiel 2012; Jones et al. 2021), quantitative trait loci (QTL) (Nezer et al. 1999; Masinde et al. 2002; Hernandez Cordero et al. 2018), transcriptomic profiling (Kho et al. 2006; de Oliveira and Louzada-Júnior 2014; Pillon et al. 2020), etc. This also gave rise to multiple new fields such as network biology, systems biology and artificial intelligence which we will briefly cover in Sect. 2.6.

2.3.2

Single-cell Sequencing

One of the biggest weaknesses of NGS technologies has always been the lack of precision when it comes to identifying heterogeneity (especially in small subpopulations). For example, in bulk transcriptomics, the data will always reflect the largest population expression profile contained within the dataset. As such, for entities that are very heterogeneous (tissues, tumors, cell populations, etc.), obtaining an accurate gene expression profile is impossible using traditional bulk NGS RNA-seq. Therefore, the main challenge when designing experiments was to either isolate the cell population of interest or select a population that was intrinsically homogeneous to counteract the weakness coming from the sequencing. Consequently, for concrete questions regarding specific conditions (i.e., cellular response under stress, microenvironment, regulation, differentiation, etc.), it became apparent that this process of cell selection was inefficient and inaccurate (contamination, bias, etc.). These limitations gave rise to single-cell sequencing. In essence, single-cell sequencing uses double barcoding to accurately identify cells and reads. Initially, cells are isolated using lysis and droplets (or beads) to capture mRNA from individual cells. Using reverse transcriptase, cDNA obtained from a single cell will be produced and associated with a unique molecular identifier (UMI). UMIs are usually a 10 bp sequence that serve as read barcodes (one unique UMI per RNA molecule). Cell barcodes (or feature barcodes) are usually a 16 bp unique random sequence inserted into each DNA fragment (typically cDNA). The resulting cDNA molecules will be individually marked with a cell barcode and a UMI (5′ tagging). Following PCR, the UMIs will allow for accurate identification of PCR duplicates. The cDNA library is then sequenced using NGS and the resulting data will contain UMI counts (i.e., genes, features, etc.) for each cell (single-cell resolution). Finally, bioinformatics, data analysis pipelines and clustering are used to identify cell subpopulations based on gene expression profiles (Haque et al. 2017; Olsen and Baryawno 2018). A typical workflow for a current single-cell sequencing pipeline is shown in Fig. 2.4. In summary, a cell population of interest undergoes NGS sequencing using the appropriate experimental strategy. The reads produced require data processing, like other omics data, prior to performing subsequent downstream analyses. Over the years, the technology has been refined to maximize throughput, efficiency and sequencing depth, as well as reducing costs to expand applications. Currently, the leaders in single-cell sequencing services are 10× Genomics,

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Fig. 2.4 The scRNAseq workflow begins at sample and tissue selection where a cell population of interest is isolated and extracted. Next, depending on the biological information desired, optimal (or multiple) experimental strategies are chosen. The cells then undergo NGS sequencing to produce raw counts. To optimize the reliability of the results, raw counts need to be pre-processed to remove potential biases (cell quality control or QC, cell cycle, etc.). Following data pre-processing, clustering is performed leading to the identification of potentially relevant cell subpopulations. Finally, downstream analyses are conducted based on the desired output. The images produced for this figure depict a 5k human peripheral blood mononuclear cells (PBMCs) data set from 10× Genomics. The figure was created with https://BioRender.com and images were created using the Seurat, Monocle3 and EnhancedVolcano R packages (Qiu et al. 2017; Cao et al. 2019; Hao et al. 2021; Blighe et al. 2022)

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Bio-Rad Laboratories Inc., Celsee, NanoString Technologies and Fluidigm Corporation. In addition, data analyses pipelines have also been improved and expanded via the scientific community. The most commonly used tools for single-cell analyses are Seurat and SingleCellExperiment, both of which are implemented in R (N.B., R is a statistical/computing language/environment) (Amezquita et al. 2020; Hao et al. 2021). As such, single-cell technologies can be leveraged for characterization of cells, tissues and heterogeneity, thereby providing the scientific community with references and atlases for many different datasets (Regev et al. 2017; Williams et al. 2022). For example, a recent study conducted human skeletal muscle single-cell transcriptomics (scRNAseq) and was able to characterize cellular heterogeneity by comparing gene expression profiles (i.e., endothelial cells, pericytes, satellites cells, PRG4+/LUM+ FAP cells, etc.) (Rubenstein et al. 2020). In mouse, scRNAseq on nuclei was used to accurately identify uninjured and regenerating muscle populations (Kim et al. 2020). Another application of scRNAseq using spinal cord cells highlighted a severe loss of cell-cell communication in mice affected with spinal muscular atrophy (Sun et al. 2022). Other investigations also illustrated bone marrow (BM) microenvironment heterogeneity under stress. In this particular review, the authors were able to dissect the molecular landscape of hematopoiesis in mouse by capturing the gene expression heterogeneity within the BM niche (Tikhonova et al. 2019). Additional applications of scRNAseq in dissecting cellular heterogeneity in diseases such as cardiomyopathy, Covid-19 muscle weakness and atherosclerosis, have also been reported (Chou et al. 2022; Soares et al. 2022; Chaffin et al. 2022). In developmental biology, leveraging single-cell technologies has enabled scientists to extract multiple cell states over time allowing for a reconstruction of developmental events. This concept has been applied to many biological processes including embryogenesis, stem cell differentiation and maturation, muscle regeneration and chronic inflammation (Luo et al. 2017; Lake et al. 2018; Cusanovich et al. 2018; Preissl et al. 2018; Buenrostro et al. 2018; De Micheli et al. 2020; Petrilli et al. 2020; Ziffra et al. 2021; Balboa et al. 2022). Use of single-cell sequencing has now expanded to multiple other applications such as immune and epigenomic profiling and cell surface protein profiling (see Fig. 2.4). Single-cell immune profiling harnesses variable, diversity, joining (VDJ) gene segments to characterize cell surface proteins, antigen specificity, T-Cell/BCell receptor clonotypes (paired VDJ profiling) and transcriptomic profiling. In contrast, single-cell epigenomics allows for a characterization of DNA methylation, histone modifications, chromatin accessibility and transcription factors at a cellular level. Single-cell surface protein phenotyping (also known as CITE-seq or REAPseq) combines scRNAseq and antibodies conjugated to oligos (Stoeckius et al. 2017; Peterson et al. 2017). This technology leverages immunophenotyping of cells while incorporating paired transcriptomic information. The barcodes can also be modified to any desired marker which translates into a limitless number of applications. A recent study showed a great example of leveraging a surface antibody combined with scRNAseq and mass cytometry in order to model muscle stem cell population heterogeneity in homeostasis and during regeneration in mice (Dell’Orso et al.

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2019). Additional information on single-cell databases, portal and atlases available to the community can be found in Sect. 2.8.

2.3.3

Other Resources for Drawing Inference from Genomics Data

Several resources are available to extract more meaning from genomics data. To determine if genes of interest have a mouse phenotype consistent with the tissue of interest, the International Mouse Phenotyping Consortium (IMPC) is an ideal database to examine. The IMPC is comprised of 21 research institutions with an aim to identify the function of every protein-coding gene in the mouse genome. The IMPC does not aggregate data from publications but instead generates its own data. The IMPC makes this dataset publicly available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Data or images generated by the IMPC are available for use in a publication, providing that the IMPC website (www. mousephenotype.org) and publication (Dickinson et al. 2016) are cited. The European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) is another valuable resource. EMBL-EBI is a research organization with over 20 member states and associates. EMBL-EBI’s data and tools are freely available (the exception is human genetic information). In addition to their genomics tools, EMBL-EBI has the following data resources and tools (according to the website: https://www.ebi.ac.uk/): AlphaFold database for protein structure predictions for numerous species; BioModels repository of peer-reviewed, published, computational models; ChEMBL open data resource of binding, functional and ADMET bioactivity data; Clustal Omega Multiple sequence alignment of DNA or protein sequences; HMMER for fast sensitive protein homology searches using profile hidden Markov models (HMMs) for querying against both sequence and HMM target databases; Annotation Platform consolidating text-mined and curated annotations. Furthermore, “A guide to molecular interactions,” an EMBL-EBI webinar, can be accessed here (May 26, 2021): https://www.ebi.ac.uk/training/ events/guide-molecular-interactions/ (based on Cytoscape).

2.4 2.4.1

Omics Strategies for Assessing Function RNA Interference

Genome editing was first invented in the 1990s, when scientists discovered engineered nucleases, enzymes that could break double-stranded DNA at specific positions, allowing for DNA modifications at a nucleotide (nt) resolution (insertion, deletion, etc.). Combined with repair DNA mechanisms, either non-homologous end

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joining (NHEJ) or homologous recombination (HR), DNA modifications could be introduced at the break point (also known as a double-strand DNA break, DSB or cleaving site). Initially, three different classes of nucleases were used for DSB: meganucleases, zinc finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs) (Woolf 1998). However, nuclease applications always suffered from lower throughput capabilities resulting from a laborious and complex design, which eventually paved the way for other gene silencing technologies such as RNA interference (RNAi). RNAi refers to a biological process which consists of RNA degradation by either translational or transcriptional repression, the most common defense mechanism against viruses in eukaryotes. Initially, double-stranded RNA (dsRNA) is transported into the cytoplasm and recognized by the ribonuclease (RNase) Dicer, which cleaves dsRNA into two single-stranded fragments (ssRNAs), also known as short interfering RNAs (or siRNAs). These siRNAs are called “passenger strand” (sense) and “guide strand” (antisense) and are usually 21–22 bp with a 2 nt 3′ overhang tag that allows for recognition by the RNA-induced silencing complex (RISC). The passenger strand will be degraded by a cleaving enzyme associated with the RISC (Argonaute2 or Ago2) whereas the guide strand will be recognized and integrated into the RISC to serve as a template for the complementary mRNA sequence, which is eventually recognized by the RISC and degraded by Ago2 leading to gene silencing. Since siRNAs reduce gene expression at the mRNA level, we refer to this mechanism as gene knockdown. Within the 21 bp span, siRNAs contain a 6 nt “seed sequence” (nt 2–8) that is responsible for perfect pairing with the targeted mRNA sequence (Sharp and Zamore 2000; Jackson et al. 2006). Thus, the major drawback of siRNAs is off-target binding related to the seed region matching thousands of mRNAs. Two major strategies have been developed to reduce off-target binding. The first approach uses chemical modifications at position 2 of the seed sequence which leads to a weaker interaction between the guide and the target, resulting in less off-target activity (Jackson et al. 2006). The second approach to reducing off-target effects relies on siRNA pooling. In siRNA pools, multiple siRNAs are designed to have the same target at different locations with each one having a different off-target signature, thus working in synergy to optimize on-target RNA matching (Jackson et al. 2006; Hannus et al. 2014). Because such techniques are scalable and flexible, siRNA pools (or screens) have allowed scientists to study functional effects at a genomewide level. For example, siRNA studies have uncovered new contributions and novel pathways in complex biological processes such cell differentiation (Kittler et al. 2005). In therapeutics, siRNA screens have been used to target, suppress and/or identify disease-associated genes in muscle growth, muscular dystrophy, cancer and bone marrow (Kim et al. 2016; Khan et al. 2016; Sugo et al. 2016).

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Clustered Regularly Interspaced Short Palindromic Repeats

Clustered regularly interspaced short palindromic repeats, or CRISPR, is the latest and most common gene silencing technique. It employs an endonuclease, CRISPRassociated protein 9 (or Cas9), capable of site-specific cleavage of DNA sequences enabling the creation or modification of DNA alterations (i.e., double-stranded breaks, insertions, deletions, etc.). As mentioned above, we refer to a gene knockout when gene silencing occurs at the DNA level (or in this context, CRISPR knockout). Cas9 sequence specificity is achieved by associating with a single guide RNA (sgRNA) composed of 17–20 nt, whose sequence is complementary to the genomic sequence to be targeted (Jinek et al. 2012). For proper binding, sgRNAs require a protospacer adjacent motif (PAM), which is an N-G-G trinucleotide sequence. As such, CRISPR target site availability is sometimes limited depending on PAM sequence locations. However, compared to TALENs or siRNAs, CRISPR/Cas9 is much easier and cheaper to design resulting in a higher throughput (Cong et al. 2013; Mali et al. 2013). Furthermore, CRISPR allows for the incorporation of a wide range of functionalities into the CRISPR/Cas9 machinery. As a result, CRISPR technology has mostly overshadowed siRNAs in functional studies. When a DNA modification is introduced via the CRISPR/Cas9 machinery, two repair mechanisms are generally used: NHEJ and HR. Cells will typically use NHEJ more frequently than HR. However, NHEJ is error-prone and will often result in nucleotide insertions or deletions. Consequently, if these alterations are in a coding region, frameshift mutations are created that often result in the loss of a functional allele. NHEJ is the preferred DNA repair pathway to create CRISPR knock-out and is often used in many different fields (drug discovery, functional genomics, etc.). Moreover, current cutting-edge applications of knockouts frequently employ multiple RNA guides to target multiple regions to improve the knock-out efficiency (similar siRNA pools). In contrast, HR is the preferred repair pathway when precise gene editing is required, as with CRISPR knock-in studies. Typically, gene knock-in experiments require a template DNA comprising the insertion sequence flanked by two homology arms that mediate insertion into the homologous flanking DNA regions. CRISPR knockins have been very successful in cell and gene therapy where longer sequences are often the cause of specific diseases (or phenotypes). However, since NHEJ is favored over HR, the general challenge with CRISPR knock-ins is improving the low efficiency (Guo et al. 2018). CRISPR can also be used to modify gene expression. This is achieved by mutating the enzymatic activity of Cas9, effectively creating a nuclease-dead Cas9 (dCas9) and fusing the resulting dCas9 to transcriptional regulators. The dCas9 transcriptional regulator maintains specificity by retaining the need for sgRNA binding to a complementary sequence in the promoter region of genes whose expression is being regulated. Integrating a transcriptional repressor to dCas9 enables inhibition of the expression of a target gene, commonly referred to as CRISPR interference (or CRISPRi). Conversely, CRISPR activation (CRISPRa)

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uses a dCas9 fused to transcriptional activator(s) that increase gene expression (Liu et al. 2019). Over the years, as the technology improved, scientists developed pooled CRISPR screening approaches to identify genes associated with specific pathogenesis (Kweon and Kim 2018). Pooled CRISPR screens consist of building a library including multiple sgRNAs to target multiple genes. Multiple screening enables more robust statistical assessment of knock-outs as well as introduction of replicates to minimize potential off-target outliers. Recent advances in CRISPR editing include base editing (BE) and prime editing (PE). Both techniques rely on dCas9 and do not generate DSBs. BE employs a dCas9 or a nickase Cas9 (nCas9), a modified Cas9 capable of inducing singlestranded break (SSB), coupled with a single-stranded DNA (ssDNA) deaminase and a base editor that will perform base transitions (C to T, T to C, A to G and G to A) (Komor et al. 2016; Gaudelli et al. 2017). PE instead is comprised of a fusion of nCas9 with reverse transcriptase and a template (pegRNA) that acts as the Cas9 sgRNA as well as the reverse transcriptase template for the desired edit of the genomic target site. PE is well suited for producing small insertions or deletions and twin prime editing systems have been developed for producing large deletions, insertions or replacements in the genome (Anzalone et al. 2022; Tao et al. 2023). PASTE editing further builds on the PE design by adding Bxb1 integrase to the nCas9-reverse transcriptase fusion protein. PASTE editing relies on two gRNAs, a Cas9 nicking guide and a modified pegRNA guide with attB sequences that are substrates for Bxb1 integrase. This system was shown to be capable of inserting large DNA constructs into the genome in a site-specific manner (Eid and Qi 2022; Yarnall et al. 2022).

2.4.3

CRISPR Applications for Phenotype Characterization

As discussed previously, many applications of genome editing with CRISPR involve knock-outs of specific biomarkers associated with a disease of interest. Like siRNAs screens, CRISPR screening is also a popular technique to study gain- or loss-offunction associated with a desired phenotype. For example, in musculoskeletal cancer research, different knock-outs of several genes including CD44, CD11K, PD-L1, TP53, ABCB1, etc. have been used in osteosarcoma treatment. These knockouts were shown to inhibit many biological processes in cancer cells such as cell migration, proliferation, invasion and drug resistance (Feng et al. 2015; Liu et al. 2016; Liao et al. 2017). Several studies have also reported the promise of gene correction in Duchenne muscular dystrophy (DMD). As stated above, DMD is caused by functional mutations in the dystrophin gene making it a prime candidate for CRISPR/Cas9 genome editing (Li et al. 2015; Chemello et al. 2020; Olson 2021). In Facioscapulohumeral muscular dystrophy (FSHD), a CRISPR/Cas9 therapeutic screening application was used to inhibit expression of DUX4 and hypoxia signalling, resulting in improvements to structural defects and muscle function (Lek et al. 2020). Various investigations have also combined genome engineering (CRISPR

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knock-outs and TALENs) with stem cells in order to further enhance comprehension of biology and diseases by driving cell differentiation (Hendriks et al. 2015; Karakikes et al. 2017). Muscle growth therapies, which involve the activity of the myostatin gene (MSTN), have also been explored in depth using genome editing and knock-outs (Yeh et al. 2017; Xu et al. 2020; Stadelmann et al. 2022). Taken altogether, these applications show the importance and versatility of genome editing in many different fields including gene therapy, drug design, and cancer (Qasim et al. 2017; Becker and Boch 2021). It is also important to mention that these large scale functional studies have fueled other fields such as bioinformatics and artificial intelligence. Since the quantity of information provided by such studies is massive, it has required the scientific community to build databases, algorithms and models to improve our understanding of several diseases, pathways and biological processes.

2.5

Spatial Biology

Similar to single-cell sequencing, spatial biology expanded rapidly due to the limitations of other technologies. Using scRNAseq, scientists can dissect cell subpopulation heterogeneity within a desired sample. However, neither NGS nor singlecell approaches account for cell positioning or location. In other words, when using NGS or single-cell sequencing, spatial relationships and cellular interactions are ignored. However, such dynamics can often be critical for numerous biological processes (e.g., microenvironment, selection pressure, stress response, cross-talk, etc.). In addition, the structure of proteins and cells can also have a major effect on function, emphasizing the need to study the spatial component. Thus, exploring the spatial context under specific conditions can be very insightful in many different fields. Spatial assays rely on immunohistology and NGS to assess spatial properties of cells (2D or 3D context). Currently, spatial assessments can be performed using transcriptomics, proteomics and epigenomics. The two major techniques used for spatial transcriptomics are in situ sequencing (ISS) and in situ hybridization (ISH; or FISH, fluorescence in situ hybridization). In both cases, a formalin-fixed, paraffin-embedded (FFPE) sample is used. FFPE has been shown to preserve RNA structure counteracting degradation following extraction (Zhang et al. 2017). The major difference between ISS and ISH is that the former can generate novel observations while the latter is a targeted method primarily used to explore/confirm hypotheses. In ISH technologies, probes with a fluorescent label are matched to the desired corresponding mRNA sequence. The probes are then visualized through microscopy to identify spectral profiles. Traditionally, FISH has suffered from low throughput and scaling due to the limited number of detected probes (one probe per gene). In the latest, improved FISH techniques, such as sequential FISH (seqFISH+) (Eng et al. 2019) and multiplexed error-robust FISH (MERFISH) (Chen et al. 2015), genes are barcoded and probes are hybridized over multiple rounds permitting sequence assessment. In addition, probes can be designed to match mRNA, DNA or proteins allowing for different types of spatial analyses

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(transcriptomics, proteomics, etc.). Several studies used ISH techniques to characterize brain cells spatial organization (Shah et al. 2017). Moreover, studies showing that MERFISH and seqFISH+ were capable of profiling histone modifications and the chromatin state in embryonic stem cells and brain cells have given rise to spatial epigenomics (Shah et al. 2018; Takei et al. 2021; Lu et al. 2022). With the ISS technique, mRNA of interest is reversed transcribed into cDNA that is recognized by a padlock probe that enables rolling-circle amplification (RCA). Each RCA cycle produces its own rolling-circle product (RCP). Depending on the padlock probe type (gap-filling or barcode), the targeted sequence will either be generated by DNA polymerization and ligation or by ligation alone. As with previous NGS approaches, each base has its own fluorescent dye, and each RCP will display the color corresponding to its matching base. The probe is then washed away and the iteration will continue until the desired number of reads has been produced (Ke et al. 2013). Finally, imaging analysis is required to assess base-calling by compiling fluorescence patterns. Great examples of emerging ISS-based technologies are fluorescent in situ sequencing (FISSEQ), Visium ST, Slide-seq and digital spatial profiling (DSP) (Lee et al. 2015; Ståhl et al. 2016; Rodriques et al. 2019; Merritt et al. 2020). In contrast to RCA in FISSEQ, DSP uses photo-cleavable oligos bound to RNA probes (or antibodies for spatial proteomics) on the region of interest (ROI). Following exposure of the ROI to UV light, cleaved oligos are released and collected in a plate for future reading. The process is repeated until all ROIs have been exposed to light. Finally, previously cleaved oligos undergo NGS sequencing where reads are counted and mapped back to the ROI using barcoding. The output result is a map of protein or transcript activity from the tissue structure. Leveraging ISS technologies has led to great discoveries in tissue morphology such as heart, intestine and liver (Asp et al. 2019; Hou et al. 2021; Fawkner-Corbett et al. 2021). Taken together, these techniques demonstrate the powerful future applications of spatial analyses in various fields. Similar to the single-cell field, spatial technologies are also evolving quite rapidly.

2.6 2.6.1

Introduction to Network Biology Biological Networks

Biological networks represent links and relationships between multiple variables or objects, such as genes, proteins, metabolites, phenotypes, etc. By definition, a network is comprised of vertices (or nodes) and edges. Vertices are generally objects of interest that are experimentally quantified (e.g., mRNA transcripts, proteins, metabolites, etc.), whereas edges are defined as specific interactions within the desired context (e.g., physical interactions, regulation, signalling, etc.) and can have directions and weight to represent their functional effects. For example, it is possible to model interactions (e.g., inhibition, self-regulation, etc.) that are unidirectional (or bidirectional) by including directions to edges (directed graph) as well

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as incorporating scores to these edges (e.g., gene expression, proteomics, methylation, etc.). While “omics” technologies can query whole systems through unbiased or targeted data collection, the relationship between individual variables and objects is not directly explored or quantified. Inferring higher-order biological function (s) from these large datasets requires the creation of networks incorporating relationship quantification or weight. A variety of networks have been used by the scientific community for many different applications. For example, in chondrocyte development (Nishimura et al. 2018), network mapping of known interactions led to new insights in the regulatory interplay between transcription factors such as NAFT, Sox9, RUNX2, Ihh and BBF2h7. In arthritis, roles and functions of novel microRNAs were identified by integration of a combination of protein-protein and regulatory interaction data (Ali et al. 2021). Single-cell transcriptomics (scRNAseq) studies have shown that gene regulatory networks (GRNs) are extremely valuable for identifying distinct changes in gene expression over a time period allowing for a reconstruction of a differentiation trajectory (Deshpande et al. 2022) (see Fig. 2.4). By combining scRNAseq and GRNs, an additional study identified WNT and MITF as hypertrophic agents during human induced pluripotent stem cell chondrogenesis (Wu et al. 2021). These studies clearly show that network biology, using the appropriate datasets, is an essential tool to identify higher-level interactions and functions, thus allowing for a better understanding of biological molecular mechanisms.

2.6.2

Network Theory, Analysis and Concepts

As presented in the previous sections, NGS brought high-throughput data availability to a new scale and paved the way for new fields such as network and systems biology. One of the major strengths of these fields is the ability to integrate multiple genes and their interactions within the same model. This concept enables the investigation of various genes in order to define, weigh and quantify their relationships in specific contexts (e.g., disease, chronic condition, risk factor, etc.). In other words, network biology relies on network analysis to uncover, define or strengthen associations and relationships leading to insights in higher-level biological functions. Within that scope, two major concepts are often used: centrality and modularity. The concept of centrality consists of quantifying the importance of a vertex based on neighboring vertices and edges. However, such centrality can be subjective and very specific to the network; it is therefore essential to understand these concepts before drawing conclusions. Four major indices are used to measure centrality: degree, closeness, betweenness and eigenvector (Bonacich 1987; Borgatti 2005). However, we will refrain from explaining in details how these indices are calculated as this section is simply an introduction to network theory. Several examples of biological networks applications include TGF-β role in skeletal muscle development (Chen et al. 2016), muscle contraction and excitation (Smith et al. 2013), drug

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repurposing in muscle atrophy (Manian et al. 2021) as well as stem cell differentiation (Cahan et al. 2014; Nisaa and Ben-Zvi 2022). In contrast to centrality, modularity focuses on the network topology and strongly associated modules within the network. Modules are often referred to as clusters, communities or groups of vertices highly interconnected. Modularity is calculated by subtracting the number of interactions within a given module with the expected number of edges if such edges were assigned randomly. In other words, modularity measures the connectivity of a specific community compared to random while preserving the network topology. For example, a previous study modeled interactions between the musculoskeletal system and the central nervous system by combining anatomical and functional connectivity between 36 muscles (Kerkman et al. 2018). In chronic obstructive pulmonary disease (COPD), a study identified several skeletal muscle dysfunctions such as creatinine metabolism, calcium homeostasis, oxidative stress and inflammatory response using network modules (Tényi et al. 2018). Other interesting network concepts include perturbation analyses, random-walks and diffusion. In biology, inducing perturbations can be very useful to understand how specific molecular changes affect network topology (del Sol et al. 2010; Santolini and Barabási 2018). For example, we could simulate the impact of mutations within a protein-protein interaction (PPI) network to study a disease of interest (Noh et al. 2018). Mutations will act as perturbations within the network and disrupt the initial network state. The network will then undergo various iterations until it reaches a new stable state with new values. These newly computed values will act as quantifiable measures for given perturbations (e.g., mutations, drugs, phenotypes, etc.). On the other hand, random-walks are a probabilistic approach to assess the probable path of a given variable. Consequently, perturbations combined with random-walks can allow the assessment of probabilities of any path within the desired network and even predict the most likely statistical outcome (or paths, modules, etc.). This concept is also referred to as network propagation. Network propagation leverages relationships between nodes in order to emphasize specific interactions (or paths) based on given perturbations. Thus, it becomes feasible to monitor, quantify and integrate data (and its associated impact) between nodes in a specific context. Several applications of network propagation have been used in the literature for different purposes (e.g., cancer, rare diseases, drug targets, drug combinations, etc.) (Ji et al. 2019; Cheng et al. 2019; Milanese et al. 2019, 2021). These studies highlight the power and versatility of network biology in modeling complex diseases, relationships, interactions and even biological processes and responses. See Sects. 2.3 and 2.4 for available tools and resources.

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Analytic Methods to Infer Biology from Data

As highlighted above, NGS technologies paved the way for high-throughput data production and analyses. As a result, many new fields and technologies have emerged over the years pushing, once again, the limit of science and our understanding of biology. In this section, we will briefly cover cutting-edge fields and technologies that have recently gained popularity.

2.7.1

Multi-omics

As NGS provided a cornerstone for high-throughput sequencing, data availability also grew exponentially over the years. Performance optimizations and cost reductions continue to yield datasets that are larger and richer than previously possible. Moreover, the drastic reduction in the cost of sequencing has caused NGS to be the go-to experiment for many new studies. This trend has enabled the characterizing of many new diseases and phenotypes (rare diseases, drug resistance, disorders, etc.) (Köser et al. 2014; Posey 2019; Turro et al. 2020; Garg et al. 2022). This massive quantity of information gave birth to multi-omics. Previously, scientists would focus on a specific question using one data type, such as using metabolomics to characterize the effect of a disease state on the levels of metabolites (Stojanovic et al. 2021). Today, the possibility and value of simultaneously exploring and correlating multiple data types (modalities) has become feasible and affordable. Generally, when combining modalities, the goal is to find subpopulations with shared characteristics (clusters) by extracting relevant signals coming from different sources. The objective is to identify and decipher traits (or features) that are not evident when analyzing a single biological process (data type). Thus, multi-omics extracts shared features (genes, proteins, etc.) across the dataset by incorporating information from different omics technologies (see Fig. 2.4). For example, by incorporating transcriptomics and proteomics of myotubes in human skeletal muscle, a study was able to further understand hyperammonemia, a critical cellular response to stress associated with several health conditions, and identify key regulatory pathways, such as senescence (Welch et al. 2021). Muscular dystrophy (MD), a disease encompassing over thirty distinct conditions (e.g., DMD, FSHD, Emery-Dreifuss, etc.) has also benefited from multi-omics studies on many different fronts. The prognosis and symptoms of MD can vary drastically from one disorder to another. In some cases, the progression can be very slow and manageable over a normal lifespan with physical therapy (FSHD, Becker MD). However, in the most severe disorders, individuals will progressively experience severe muscle weakness and loss of function such as cardiac failure and paralysis, which will result in a much lower life expectancy (DMD, EmeryDreifuss). As of today, there is still no cure for MD, emphasizing the focus of interest around a better understanding of the molecular pathogenesis. By using multi-omics, a great number of studies have identified biomarkers related to MD

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disorders for diagnosis, prognosis and future therapies (miRNAs, proteomics, transcriptomics, etc.) (Roberts et al. 2015; Heier et al. 2020; Mournetas et al. 2021). Since multi-omics technologies rely heavily on data analysis, it is critical to mention that data integration is crucial when analyzing multiple data types. Thus, when examining heterogeneous data and various modalities, it becomes essential to recognize the experimental context. As such, depending on the data’s provenance, the integration technique will vary (Ulfenborg 2019; Argelaguet et al. 2021; Santiago-Rodriguez and Hollister 2021). If both data types come from the same assays (experiments, data types, etc.), we refer to it as horizontal integration, which works under the assumption that the features provided from the data are similar since their origin is the same (or produced from the same experiment). As a result, when performing clustering, the communities will represent populations with shared features (genes, proteins, etc.). This technique is very powerful for comparing batch effects or creating benchmark analyses (e.g., comparing results between internal experiments and literature). A few examples of tools using horizontal data integration are: Canonical Correlation Analysis (CCA) (Hotelling 1992), Seurat v4 (Hao et al. 2021), Multi-Omics Factor Analysis v2 (MOFA+) (Argelaguet et al. 2020) and integrative non-negative matrix factorization (iNMF) (Gao et al. 2021). In contrast, vertical integration is suitable when comparing the same variables (cells, samples, patients, etc.) with different modalities (transcriptomics, proteomics, metabolomics, etc.). Under these conditions, the integration method will assume the entities are similar and the resulting clusters will provide key features based on a specific modality. As such, it is possible for one modality to provide more insight into certain subpopulations than others. A few examples of vertical integration tools are: Scanorama (Hie et al. 2019), scmap (Kiselev et al. 2018), single-cell variational inference (scVI) (Lopez et al. 2018) and linked inference of genomic experimental relationships (LIGER) (Liu et al. 2020). Finally, the multi-omics field has also deeply benefited from artificial intelligence (AI) concepts, which is briefly covered in the next subsection.

2.7.2

Artificial Intelligence

AI is a much older concept than most people realize. In fact, early descriptions of AI date back to the 1950s after Alan Turing built the first computer. At that time, scientists were debating if computers could be used for complex problem-solving, which was ultimately demonstrated by Turing’s cracking of the enigma code during World War II. As a result, many different fields such as mathematics and engineering started exploring the possibility of building electronic brains (or machines). Seventy years later, the AI field has grown substantially and has produced valuable applications in different fields (i.e., finance, banking, sports, science, etc.). Unsurprisingly, application of AI is also becoming quite popular in biology and genomics, specifically considering the increase in data generation resulting from NGS. It is important to distinguish that machine learning is a concept within the AI field, even though the

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two terms are often used synonymously. Machine learning (ML) uses algorithms to decipher layers of information and extract patterns to make predictions within the data. Great examples of ML include facial recognition, internet searching, suggestions based on history and/or profile, etc. In contrast, AI encompasses multiple concepts where the objectives are to mimic the human brain within machines. Great examples of AI applications are automated customer support, robots, virtual assistants, such Siri and Alexa, etc. Learning is divided into two major categories: supervised and unsupervised learning. The major difference between supervised and unsupervised learning is the prior knowledge provided to the algorithm, also known as labels. In supervised learning, users provide true criteria for the data used. As such, supervised learning will always approximate the best relationship between given variables and the desired output. The most common examples of supervised learning are regression and classification. In contrast, unsupervised learning infers labels and structure from the data without prior knowledge. Subsequently, different unsupervised techniques will often yield different structures for the same dataset. It is therefore very hard to compare performance between approaches when using unsupervised learning. Two great examples of unsupervised learning are clustering and dimensionality reduction. Over the years, as datasets grew in size, scientists turned to “deep learning” (DL) in an attempt to extract solutions from more complex structures. While being a subset of ML, DL relies on hidden layers of data input and output so that the machine can learn and adapt through each layer. Compared to standard ML, DL often requires much larger data sets (millions compared to hundreds); it also requires greater computing power and execution time, but once the model is trained, it requires less human input than ML. The best example of DL is neural networks (NN). In NNs, the data is divided into an input layer, one of multiple hidden layers, and an output layer with several interconnected nodes (like neural connections). Depending on the data structure and the neural network type, there can be multiple hidden layers within the algorithm in order to decipher the best features. Different learning techniques and applications relevant to developmental biology are described in Sect. 2.8.2.

2.8

Applications and Resources

As stated above, the scientific community is constantly refining methods and concepts to provide the most accurate representation. In general, databases, models and algorithms are open source and available to all. Thus, it is mandatory to overview resources publicly accessible for general scientific purposes. In this subsection, we will briefly cover some relevant applications as well as essential resources related to previously discussed fields (e.g., network biology, AI, NGS, etc.). It is important to emphasize that the scientific community shares an incredible amount of knowledge and is constantly refining methods. Therefore, this section should be seen as an introduction to relevant tools.

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Databases

STRING (https://string-db.org/) is one of the largest curated protein-protein interaction (PPI) online databases. STRING was originally designed to be a reference to predict protein-protein associations. However, over time, integration of multiple omics datasets as well as experimental methods led to a more complete catalogue. STRING is currently used to find known PPIs and predict new interactions across many different organisms. The STRING database contains information on 67.6 million proteins and over 20 billion interactions and is actively and regularly updated (von Mering 2004; Szklarczyk et al. 2017, 2021). INTACT is another molecular interaction database (https://www.ebi.ac.uk/ intact). It is hosted and maintained by the European Bioinformatics Institute. All interactions reported on the database are derived from literature curation or direct submission. Compared to STRING, INTACT also offers a catalogue of PPIs as well as RNA-protein interactions. It contains over one million interactions (Orchard et al. 2014). The Database of Interacting Proteins (https://dip.doe-mbi.ucla.edu), also known as DIP, is a PPI database hosted by UCLA university. Interactions are computationally and manually curated from 8200 publications to filter for the most reliable PPI encyclopedia. It integrates newly reported interactions from The International Molecular Exchange Consortium (IMEx) submitted by the scientific community (Salwinski 2004). The Protein Data Bank (PDB; http://www.rcsb.org/pdb/), is one of the largest structural databases freely available online. It stores three-dimensional data of biological macromolecules. The database offers structural visualization as well as many structural analyses (mapping, alignment, symmetry, etc.). Scientists can also deposit newly found structures which undergo validation so that the database publishes the most accurate data (Berman 2000). UniProtKB (or UniProt, https://www.uniprot.org/) is a database that focuses on high-quality protein sequences and functional annotation across different species. It is comprised of two different databases, Swiss-Prot which contains sequences that have been manually annotated and TrEMBL which contains records that are automatically annotated. The database also offers analysis tools such as mapping and alignment (The UniProt Consortium et al. 2021). The Registry of Standard Biology Parts (http://parts.igem.org) is synthetic biology collection that catalogues biological parts (promoters, coding sequences, terminators, etc.) that can be mixed or matched to build systems and devices. The database contains several catalogues specific to certain fields (e.g., drug delivery, CRISPR, reporter proteins, etc.) as well as assembly protocols. The database’s objective is to share, improve and report building blocks for biological systems to facilitate reproducibility. As with many other databases, users are also invited to submit and share their findings in order to improve the registry (Peccoud et al. 2008). Similarly, the BioBricks Foundation is a tremendous resource for molecular biologists that

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provides access to genetic material (free genes: The Stanford FreeGenes Project, https://stanford.freegenes.org/) under their OpenMTA (Kahl et al. 2018). BioCreAtIvE (Critical Assessment of Information Extraction in Biology, https:// biocreative.bioinformatics.udel.edu/) is a database containing ground truths for various datasets. The objective of the database is in fact to compare and monitor various approaches for performances as well as providing improvements on new methods (Hirschman et al. 2005). In the single-cell field, several databases are shared and available to the public. For example, the Single Cell Portal is a web-based application hosted by the Broad Institute. It regroups single-cell studies, raw and processed data, as well as visualization and analysis tools. Another great resource is The Human Cell Atlas (HCA, https://www.humancellatlas.org/) (Rozenblatt-Rosen et al. 2017; Regev et al. 2017), an atlas funded by The Wellcome Trust, where a multitude of single-cell datasets for different human cell types are shared. The mission of the HCA is to ultimately create complete reference maps for all human cells. The HCA has also started to incorporate spatial datasets into the database in order to provide more accurate mapping of cell types. For different organisms, users can refer to the Single Cell Expression Atlas (https://www.ebi.ac.uk/gxa/sc/home), a collection of studies for 12 different species hosted by EMBL-EBI (Papatheodorou et al. 2020).

2.8.2

Applications, Tools and Algorithms

Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (or STARNET, http://starnet.mssm.edu/) is a framework using biological networks applied for cardiovascular and coronary artery disease (CAD). STARNET uses gene expression (RNA-seq), single-nucleotide polymorphism array and tissue expression quantitative trait loci (eQTL) to estimate CAD heritability (or to measure CAD susceptibility). STARNET uses co-expression, GRNs and Eigengene combined with a Bayesian approach to identify key modules for different CADs (Koplev et al. 2022). Several ML and DL approaches have shown promise in classifying magnetic resonance imaging (MRI) of muscles to diagnose MD. Some approaches exhibit more than 90% accuracy when predicting if muscles are associated with dystrophy (Verdú-Díaz et al. 2020; Liao et al. 2021; Yang et al. 2021). As an example, ML has been used to design peptides for efficient delivery of antisense nucleotides for effecting DMD gene therapy (Schissel et al. 2021). In developmental biology, many AI approaches have been used to predict cell differentiation states as well as cell growth and division. A study showed that NNs can effectively identify birth and division of bacteria cells and yeast (Kamimura and Kobayashi 2021). By comparing DNA sequences, another study showed that NNs are capable of predicting immune cell differentiation using chromatin opening regions (cis-regulatory) (Maslova et al. 2020). A ML approach using Support Vector Machine (SVM) also showed it was accurately able to classify cells during the cell

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cycle using fluorescent images (Narotamo et al. 2021). SVM is a technique within supervised ML that allows for classification by leveraging linear regression. EmbryoNet is another great example of a DL algorithm that was implemented to predict phenotypic changes in embryonic development. The authors used 2D and 3D (image recognition) NNs with prior labelling to predict phenotypic diseases such as craniofacial malformations, autism, etc. (Naert et al. 2021). In single cell analysis, several tools are available to predict cell differentiation (also known as cell trajectory). For example, using scRNAseq, Monocle3 is able to reconstruct cell trajectories by comparing expression profiles of different cell populations and using reverse graph embeddings (Qiu et al. 2017). The same group also developed Cicero, a powerful tool able to predict cis-regulatory regions using single-cell chromatin accessibility data (or scATACseq) (Pliner et al. 2018). Briefly, this is achieved by using graphical regression (LASSO) and correlation matrices on subsets of cells sharing similar distal elements profiles. For spatial analyses, a few examples of the latest tools used for downstream analyses include STUtility, Giotto and STLearn. STUtility leverages two major vectors (initial gene expression and lag expression vector) combined with Pearson correlation to reconstruct 3D representation of the analyzed tissue (Bergenstråhle et al. 2020). Giotto leverages neighboring clustering along with cell expression and physical space to assess spatial patterns (Dries et al. 2021). Finally, the most recent tool, STLearn, incorporates deep-learning gene expression normalization based on spatial localization. From these normalized values, spatial trajectories, clustering and cell-to-cell interactions analyses can be performed allowing for a full integration of the data (Pham et al. 2020).

2.9

Conclusions

The advances in “omics” techniques along with the tools that can be employed in deriving insight from large and diverse datasets has ushered in an era where experimental biologists are able to reconsider what is experimentally feasible. Today’s researchers can ask questions that were previously unanswerable. Moreover, the accessibility of genomic engineering and synthetic biology has resulted in an environment where ingenuity may be the only limitation facing biological scientists. Compliance with Ethical Standards J-SM, RM, WJC, BK & SD declare that they have no conflicts of interest to disclose. This chapter is a review of previously published accounts, as such, no animal or human studies were performed.

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Chapter 3

Overview of Head Muscles with Special Emphasis on Extraocular Muscle Development Janine M. Ziermann

Abstract The head is often considered the most complex part of the vertebrate body as many different cell types contribute to a huge variation of structures in a very limited space. Most of these cell types also interact with each other to ensure the proper development of skull, brain, muscles, nerves, connective tissue, and blood vessels. While there are general mechanisms that are true for muscle development all over the body, the head and postcranial muscle development differ from each other. In the head, specific gene regulatory networks underlie the differentiation in subgroups, which include extraocular muscles, muscles of mastication, muscles of facial expression, laryngeal and pharyngeal muscles, as well as cranial nerve innervated neck muscles. Here, I provide an overview of the difference between head and trunk muscle development. This is followed by a short excursion to the cardiopharyngeal field which gives rise to heart and head musculature and a summary of pharyngeal arch muscle development, including interactions between neural crest cells, mesodermal cells, and endodermal signals. Lastly, a more detailed description of the eye development, tissue interactions, and involved genes is provided. Keywords Branchiomeric muscles · Pharyngeal arch · Head muscle

3.1

Background

The vertebrate head is the most complex part of the vertebrate body and consists of a mix of organs and tissues with hidden boarders and patterns. The head development starts early during embryogenesis and depends on cell-cell interactions, precisely working gene regulatory networks, and other factors. In general, muscles can be grouped by their location, histology, function, and / or developmental origin (Fig. 3.1). It is easy to distinguish smooth muscles, and heart and skeletal muscles based on their histology as the former has spindle shaped cells with single nuclei and J. M. Ziermann (✉) Howard University College of Medicine, Washington, DC, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Kablar (ed.), Roles of Skeletal Muscle in Organ Development, Advances in Anatomy, Embryology and Cell Biology 236, https://doi.org/10.1007/978-3-031-38215-4_3

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Fig. 3.1 Muscle grouping by different criteria. (a) Muscles are often distinguished based on their location. The easiest form is to separate cranial (head) and postcranial muscles. The neck is a transition zone where some muscles belong to cranial muscles and others to the postcranial muscles. The heart lies clearly in the thorax but is developmentally closer related to head muscles than to other muscles in the body as both develop from the cardiopharyngeal field. (b) A clearer way of grouping muscles is by their histology or (c) their innervation or developmental origin. The grouping by function has the disadvantage to exclude some muscles developmentally related to others. For example, the stylohyoid muscle is an elongated muscle in the neck that is innervated by cranial nerve (CN) VII and develops from pharyngeal arch (PA) 2, but since it is not in the face it does not belong to the muscles of facial expression, but it belongs to the hyoid muscles (See Table 3.1). Extraocular eye muscles are a functional group with members from different mesodermal origin and with different innervations. See text for more details

the latter have clear striations due to the presence of sarcomeres, which are the contractile units of these muscles, and have 1–2 (heart muscle) to 100 s (skeletal muscle) of nuclei. However, all other categories are harder to use for the distinction of specific muscle groups. For example, trying to group muscles in the head and the transitional region from head to the shoulder girdle have been proven to be difficult. Some neck muscles are innervated by cranial nerves, which makes them head muscles (e.g., the trapezius muscle is innervated by cranial nerve XI, CN XI, spinal accessory nerve). Other neck muscles are innervated by dorsal primary rami (DPR) of spinal nerves, which denotes them as true back muscles and therefore postcranial muscles (e.g., the splenius capitis is innervated by DPR C3 & C4, C = cervical = neck region). Yet, other muscles are innervated by ventral primary rami (VPR) of spinal nerves, which makes them also postcranial muscles (e.g., thyrohyoid muscle in the neck is innervated by the nerve to thyrohyoid that carries nerve fibers from VPR C1). Not only are these muscles innervated by different nerves, but they also originate from different mesodermal populations and have connective tissue derived from

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Table 3.1 Head muscles with their innervation by cranial nerves (CN) and mesodermal developmental origin from four different sources: prechordal mesoderm, pharyngeal arch (PA) mesoderm (paraxial mesoderm that migrated into pharyngeal arches), lateral plate occipital mesoderm, and occipital mesoderm Muscle group Extraocular muscles

Mandibular muscles Hyoid muscles Pharyngeal muscle Larynx and pharynx Superficial neck muscles Hypoglossal muscles

Innervation Oculomotor, CN III Trochlear, CN IV Abducens, CN VI Trigeminal, CN V

Developmental origin Prechordal mesoderm, PA 1 related mesoderm

Functional group Extraocular muscles

PA 1 mesoderm

Facial, CN VII

PA 2 mesoderm

Glossopharyngeal, CN IX Vagus, CN X

PA 3 mesoderm

Spinal accessory, CN XI Hypoglossal, CN XII

Lateral plate occipital mesoderm Occipital somite mesoderm

Muscles of mastication Muscles of facial expression Stylopharyngeus muscle Laryngeal & pharyngeal muscles Neck muscles

PA 4 & PA 6 mesoderm

Tongue muscles

different cell lineages. Postcranially the muscle connective tissue is mesodermal derived, while in the head the connective tissue is mainly (not all) neural crest derived. This brings us to the interaction of different cells that are essential for the proper organization of the head. There are three basic germ layers involved in the embryonic development (Tam et al. 1993). Ectoderm forms the outer layer, endoderm the inner layer, and mesoderm the intermediate layer. The endoderm develops into the inner lining of the tube-like structures in the vertebrate bodies, such as intestines, pharynx, and larynx. The ectoderm will give rise to skin and to a fourth germ layer, the neural crest. Neural crest cells (NCCs) produce a diversity of cell-types in the head (e.g., glial cells, melanocytes, odontoblasts, mesenchymal cells, chondroblasts and chondrocytes, osteoblasts, and osteocytes). Specifically, NCCs are indispensable for the head development as they form cranial ganglia (e.g., VII, IX, X), connective tissue of the head muscles, and most of the viscerocranium (i.e., the facial skeleton). The mesoderm gives rise to skeletal structures and muscles and, grows with different speed dependent on the time and location it is developing (McKinney et al. 2020). The mesoderm formed during gastrulation is highly regionalized by the expression of specific genes at specific times (Parameswaran and Tam 1995; Arnold and Robertson 2009). It gives rise to (1) chordamesoderm, (2) trunk paraxial mesoderm, (3) intermediate mesoderm, (4) lateral plate mesoderm, and (5) head mesoderm (prechordal and paraxial). The first three differentiate into the notochord, most postcranial muscles and axial skeleton, and the urogenital system, respectively. The latter two give rise to the limb skeleton, heart and vascular system, and to blood vessels, bones, and muscles of the head (Gilbert 2000). Mesoderm that

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will give rise to muscles is called myogenic mesoderm. The postcranial paraxial mesoderm is segmented (somites) while the cranial paraxial mesoderm is unsegmented. For the understanding of muscles development which are innervated by cranial nerves and therefore belonging to the head muscle group, four mesodermal groups are important (Table 3.1) (Noden and Francis-West 2006; Nathan et al. 2008). The prechordal mesoderm and mesoderm related to the first pharyngeal arch (PA1) form the extraocular eye muscles (EOMs); the pharyngeal arch (PA) mesoderm (unsegmented head paraxial mesoderm) will form PA muscles (also called branchiomeric muscles); the occipital somites (segmented trunk paraxial mesoderm) give rise to a cell stream that migrates back into the head to form tongue and intrinsic laryngeal muscles; and the lateral plate mesoderm in the occipital region will give rise to two large muscles in the neck that are also innervated by cranial nerves (Fig. 3.1c, Table 3.1) (Brand-Saberi and Christ 1999; Noden and Francis-West 2006; Grifone and Kelly 2007; Rios and Marcelle 2009; Sambasivan et al. 2011; Yusuf and Brand-Saberi 2012; Ziermann et al. 2018). The head muscle development is regulated by interactions between the four germ layer derivatives and distinct transcription factors and signaling molecules. Studies in chordates (Ciona intestinalis, mouse, chicken, zebrafish, and others) revealed the evolutionary conserved cardiopharyngeal field which enables the development of craniofacial and heart structures (Tirosh-Finkel et al. 2006; Grifone and Kelly 2007; Tzahor 2009; Tzahor and Evans 2011; Harel et al. 2012; Razy-Krajka et al. 2014; Diogo et al. 2015; Kaplan et al. 2015; Razy-Krajka et al. 2018; Nomaru et al. 2021). An also evolutionary conserved gene regulatory network was present before the development of a distinct head, which was essential for the diversification of myogenic cells in vertebrates (Stolfi et al. 2010; Wang et al. 2013; Stolfi et al. 2014; Tolkin and Christiaen 2016; Prummel et al. 2019). In this chapter, I first shortly highlight the different regulatory mechanism between head and trunk muscle development. This is followed by a summary of PA (branchial arch) muscle development, which was covered extensively, but, due to the description of the cardiopharyngeal field, gained new insights. Finally, the development of the extraocular eye muscles (EOMs) in relation to the orbit are described in more detail, as these are often ignored in head muscle focused texts.

3.2

Head Versus Trunk Muscle Development

Molecular markers of head muscle development differ from those of somitic (trunk and limb) muscles, in particular during early developmental steps but also during differentiation processes (Mootoosamy and Dietrich 2002; Tzahor et al. 2003; Nathan et al. 2008; Sambasivan et al. 2009; Buckingham and Rigby 2014). The mesodermal cells first need to commit to the myogenic fate and then another set of genes controls the differentiation of the myogenic cells into muscle cells (Fig. 3.2). The initiation of somitic myogenesis is regulated by Pax3 (Tajbakhsh et al. 1997;

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Fig. 3.2 Process of muscle development from specification of embryonic precursors to fusion of myoblasts to myotubes, and finally, to formation of myofibers. Myogenesis and the underlying gene regulatory network (GRN) differs between somite-derived muscles (Pax3+) (hypoglossal and trunk; a, b) and head mesoderm (Pitx2+, Tbx1+; a, c, d). (a) PAX3 and PAX7 are initially activated in embryonic mesodermal progenitors. Several progenitor cells commit to myogenesis after exposure to MRFs (Myf5, MyoD, Mrf4) and differentiate into muscle precursor cells, called myoblasts. Myoblasts then differentiate to myocytes, which start fusing with each other to form myotubes due to another wave of MRFs (MyoG, Mrf4, and less important MyoD). The assembly of myotubes to form myofibers is regulated by MRFs, MEF2 and SRF. Modified from Ju et al. (2015). (b, c) Myogenesis in hypobranchial/trunk muscles compared to head and heart musculature. (b) modified from Braun and Gautel (2011), (c) modified from Buckingham (2017). (d) Myogenesis in the mandibular arch. Modified from Harel et al. (2012)

Hacker and Guthrie 1998; Tremblay et al. 1998; Chang and Kioussi 2018). In the head this process is regulated by a set of transcription factors, such as Isl1 (Islet1), Tcf21 (Capsulin), Msc (MyoR), Tbx1 (T-box protein 1), Pitx2 (pituitary homeobox 2), and Lhx2 (LIM homeobox protein 2), which are all not required during somitic myogenesis (Hacker and Guthrie 1998; Mootoosamy and Dietrich 2002; Dong et al. 2006; Bothe et al. 2007; Dastjerdi et al. 2007; Grifone and Kelly 2007; Shih et al.

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2007; Bryson-Richardson and Currie 2008; Shih et al. 2008; Sambasivan et al. 2009; Tzahor 2009; Sambasivan et al. 2011; Tzahor and Evans 2011; Tzahor 2015). These transcriptional regulators are essential activators of the MyoD family of myogenic regulatory factors (MRFs), which control the differentiation into skeletal muscle. The members of the MyoD family are basic-helix-loop-helix (bHLH) transcription factors. They can initiate the myogenic program in non-myogenic cells if they are overexpressed by suppressing the other cell fate what will lead to the formation of differentiated muscle (Weintraub et al. 1991). Four MyoD family members have been identified so far: MyoD, Myf5, Mrf4, and MyoG (Myogenin). The first three are myogenic determination factors; in their absence no skeletal muscles form. The latter one is a differentiation factor that controls the differentiation of myoblasts into skeletal muscle fibers, a function that Mrf4 and MyoD can also perform (Moncaut et al. 2013). The activation of the myogenic determination genes during embryonal development has distinct spatiotemporal characteristics (Buckingham and Rigby 2014). All skeletal muscles of the head and neck have a common regulatory program which includes the activation of myogenic determination factor genes Myf5 and MyoD (Sambasivan et al. 2011). However, the upstream mechanisms that regulate the transcription of Myf5 and MyoD differ between extraocular muscles, branchiomeric muscles (cranial paraxial mesoderm derived), and hypobranchial muscles (occipital somite derived) (Tajbakhsh et al. 1997; Hacker and Guthrie 1998; Noden and Francis-West 2006; Sambasivan et al. 2011; Schubert et al. 2019). From here on I will use the term pharyngeal arch (PA) muscles to include all muscles derived from pharyngeal / branchial arches. Head muscle precursors show an opposite response to certain molecular signals as compared to the trunk myogenic progenitors. For example, the Wnt and Shh signaling stimulates trunk myogenesis but inhibits head myogenesis (Mootoosamy and Dietrich 2002; Tzahor et al. 2003). In mouse, head mesoderm is independent of the T/Tbx6 network, which is in this respect different from the myogenic trunk mesoderm (Nandkishore et al. 2018). Additionally, head mesoderm is defined by the inhibition of Wnt/β-catenin and Nodal, while trunk mesoderm is dependent on Wnt and FGF (Nandkishore et al. 2018). FGF-ERK and BMP block both head and trunk myogenic differentiation, but the Wnt/β-catenin signaling pathway leads to opposing results in head and trunk myogenesis (Tzahor et al. 2003; Michailovici et al. 2014; Michailovici et al. 2015). Wnt signals from the neural tube induce myogenesis in the trunk (Münsterberg et al. 1995; Stern et al. 1995; Tajbakhsh et al. 1998). However, in the head, myogenesis is locally repressed by BMP and Wnt signals but induced by antagonists of these pathways (Noggin, Gremlin, and Frzb, respectively). Noggin, Gremlin, and Frzb are expressed by adjacent tissues, such as for example cranial neural crest cells (NCCs) (Tzahor et al. 2003), which therefore can regulate the head myogenesis. Furthermore, connective tissue of head musculature originates from NCCs, while it derives from lateral plate mesoderm or forms locally in trunk musculature (Christ et al. 1974; Matsuoka et al. 2005; Noden and Trainor 2005; Noden and Francis-West 2006). In the neck, as a transition from head to trunk, a mixture of connective tissue origin is again observed (Heude et al. 2018; Adachi et al. 2020).

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Head myogenic territories are established in the early cranial mesoderm by dynamic signaling events (Bothe et al. 2011; Vyas et al. 2020). As of today, for each muscle group in the head a specific underlying gene regulatory network could be identified. PA muscles have distinct molecular features compared to other skeletal progenitor populations (Grifone and Kelly 2007; Bryson-Richardson and Currie 2008; Kelly 2010; Sambasivan et al. 2011; Buckingham 2017), as do the eye muscles and the neck muscles. This topic will be discussed in more detail in the following parts.

3.3 3.3.1

Cardiopharyngeal Mesoderm and Pharyngeal (Branchial) Arch Muscles Cardiopharyngeal Mesoderm

Several transcriptional regulators, including Isl1, Tbx1, and Pitx2, are shared between the second heart field (SHF) and pharyngeal arch (PA) muscle progenitor cells (Cai et al. 2003; Kelly et al. 2004; Dong et al. 2006; Shih et al. 2007; Kong et al. 2014). Furthermore, retrospective lineage analyses, single cell transcriptomics, as well as functional studies in mouse, zebrafish, and Ciona, among others, have shown that groups of PA muscles share a clonal origin with specific muscular regions of the heart (Lescroart et al. 2010, 2015, 2022; Diogo et al. 2015; Kaplan et al. 2015). These observations lead to the conclusion that head and heart muscle progenitors derive from a common cardiopharyngeal mesoderm (Bothe and Dietrich 2006; Tirosh-Finkel et al. 2006; Grifone and Kelly 2007). The cardiopharyngeal field is a mesodermal progenitor field and part of the lateral plate mesoderm that gives rise to the PA muscles, some eye muscles, and the first and second heart fields (FHF and SHF) (Fig. 3.3) (Diogo et al. 2015; Swedlund and Lescroart 2019; Ziermann 2020; Lescroart et al. 2022). The FHF forms a linear heart tube. The mesodermal cells of the SHF, which are closely related to the PA mesoderm, add mesodermal cells anterior and posterior to the heart tube. The regulation of skeletal and cardiac muscle differentiation is quite different, yet, their progenitor cells express common upstream factors and belong to the same cell lineages (Lescroart et al. 2010, 2012, 2014, 2015).Therefore, specific head muscle groups derived from PA mesoderm are closer related to specific myocardial regions due to their clonal relationship in the cardiopharyngeal field (Fig. 3.3). Not only mesodermal progenitors are shared during the head and heart development, but also parts of the cranial neural crest streams. Neural crest cells (NCCs) from more anterior PAs as well as those that migrate through posterior PAs contribute to the heart. The anterior ones give rise to smooth muscles in the right ventricle (Arima et al. 2012), mirroring the myocardial contribution of the anterior cardiopharyngeal mesoderm (Lescroart et al. 2022). However, here I focus on head muscles only, but provide for the interested reader a few examples of research

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Fig. 3.3 Correlation between head and heart muscle development. (a) ventral view of mouse embryo aged E7.5 with the cardiac fields color coded. Pink: first heart field; green & blue second heart field. Lighter colors signify anterior, darker colors the posterior part of the second heart field. (b) Head and heart muscles in a human adult with color codes as defined in left figure, with the addition of brown—muscles of mastication and right ventricle, yellow—extraocular muscles, and dark red—somite derived neck muscles. Coding shows which heart muscle regions are clonally closer related to which head muscle regions. Muscles of facial expression on the left side (light blue) of the head are partially removed to show deeper muscles. Figure modified from Ziermann (2020)

regarding the cardiopharyngeal field (Razy-Krajka et al. 2014, 2018; Stolfi et al. 2014; Kaplan et al. 2015; Prummel et al. 2019; Racioppi et al. 2019; Wang et al. 2019; Nomaru et al. 2021) as well as reviews (Diogo et al. 2015; Swedlund and Lescroart 2019; Lescroart et al. 2022).

3.3.2

Pharyngeal Arch Muscle Development

Mesodermal cells in the head region migrate into each of the pharyngeal arches (PAs), which are effectively lateroventral pockets at the cranial end of any vertebrate embryo. Lateral to the mesodermal cells, neural crest cells (NCCs) also migrate into the PAs (Lumsden et al. 1991; Kulesa and Fraser 2000; Cerny et al. 2004; Ziermann et al. 2018). This core tissue of loosely connected and not yet differentiated NCCs and mesodermal cells (paraxial and lateral plate mesoderm) is now called mesenchyme. Thus, each PA consists of an outer membrane (ectoderm), an inner membrane (endoderm), and a mesenchymal core (Noden 1988; Graham and Smith 2000). This mesenchymal chore gives rise to arch specific cartilages and bones, arteries, and muscles (Fig. 3.1c, Table 3.1) (Evans and Noden 2006; Tirosh-Finkel et al. 2006). Most commonly five bilateral PAs develop from anterior to posterior; the anteriorly located PAs 1 and 2 giving rise to most of the craniofacial structures, and the posteriorly located PAs 3, 4, and 6 giving rise to structures in the neck. PA5 regresses during embryonic development and does not give rise to adult structures.

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The mesoderm of the 1st PA will form muscles of the mandibular arch, including muscles of mastication; the 2nd PA mesoderm will form muscles of the hyoid arch, including muscles of facial expression; and the mesoderm of the posterior arches gives rise to laryngeal, pharyngeal, and esophageal muscles (Fig. 3.1c). Each arch is associated with a specific cranial nerve (CN), i.e., mandibular arch (1st PA, CN V), hyoid arch (2nd PA, CN VII), branchial arch (posterior PAs) muscles (3rd PA, CN IX; 4th & 6th PAs, CN X) (Table 3.1; Sambasivan et al. 2009, 2011; Ziermann et al. 2018; Sudiwala and Knox 2019). This highly conserved pattern is observed in all vertebrates and was therefore likely present in the last common ancestor of all extent vertebrates. Most cranial NCCs migrate first as a continuous sheet that moves dorsolaterally between the overlying ectoderm and the underlying ventral mesoderm before splitting into distinct streams (Noden 1975, Lumsden et al. 1991, Sechrist et al. 1993, Shigetani et al. 1995, Baker et al. 1997, reviewed by Ziermann et al. 2018). The splitting of these streams is influenced by the cranial placodes (Szabo-Rogers et al. 2009; Szabó and Mayor 2018; Szabó et al. 2019). The identity of each stream, from anterior to posterior (or rostral-caudal) along the embryo, is determined by molecular markers, such as Hox, ephrins, and ephrin-receptors (Eph) (Smith et al. 1997; Ruhin et al. 2003). The first emerging stream, which is also the most rostral cohort of NCCs, is commonly called mandibular crest, even though its cells migrate into the frontonasal prominence, and the maxillary and mandibular prominences. Only the latter two prominences are related to the mandibular arch (1st PA). NCCs from the posterior mesencephalon, rhombomere 1 (R1), and R2 migrate to fill the 1st PA. One of the mechanisms facilitating the migration of mesodermal cells and NCCs is chemotaxis. For example, the endoderm (light orange in Fig. 3.4) of the pharyngeal pouch expresses stromal cell-derived factor 1 (SDF1). Its receptor, Cxcr4a, is expressed by migrating NCCs (Miller et al. 2008; Yahya et al. 2020), which follow the increasing SDF1 gradient toward the endoderm into the PAs. In culture head mesodermal cells randomly move as individuals but move faster if NCCs are present (McKinney et al. 2020). In vivo, mesodermal cells migrate directionally while maintaining the relationships to neighboring cells and NCCs move through them at a faster pace. Inside the core, in terms of coordinating their respective activities during head development, mesodermal cells and NCCs are interdependent (Ziermann et al. 2018; McKinney et al. 2020) (Fig. 3.4). This interdependence is particularly evident during head muscle development, when cranial NCCs influence mesodermal cells to commit to myogenesis and formation of muscle precursor cells, i.e., myoblasts (Rosero Salazar et al. 2020). However, this interdependence is nuanced. For instance, upon arrival in the PAs, the cranial NCCs control the differentiation of the PA core mesoderm into PA muscles (Fig. 3.4), but the early myogenic program is initiated independent of cranial NCCs. Work in zebrafish shows that the proliferation of pharyngeal NCCs is regulated by tln1 (talin1), PDGFs, and PDGF-receptors (McCarthy et al. 2016). Tln1 is important for cell-matrix adhesion between cells; tln1 connects cytoplasmic tails of integrins (transmembrane proteins) to the actin cytoskeleton via vinculin during craniofacial morphogenesis (Ishii et al. 2018).

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Fig. 3.4 Process by which cranial neural crest cells (NCCs) contribute to muscle tissues. (a) NCCs (green) migrate into the pharyngeal arches in all vertebrate models studied thus far. One of the mechanisms facilitating this migration is chemotaxis: the endoderm (light orange) of the pharyngeal pouch expresses SDF1. Its receptor, Cxcr4a, is expressed by migrating NCCs. (b) Next, NCCs surround the mesodermal cells (dark orange) in the pharyngeal core and start their compaction. Expression of tln1 keeps NCCs around the mesodermal core proliferating which increases the density of these mesodermal cells in the core. (c) Further, NCCs and mesodermal cells express Pitx2, which initiates myogenic development by activating Tbx1. (d) Cranial NCCs then invade the developing muscles and express different transcription factors. For instance, Tcf4 and Osr1 are expressed in cranial NCCs forming dense irregular connective tissue (DICT) that surrounds and separates pharyngeal arch muscles, while Scx is expressed in cranial NCCs forming extraocular and pharyngeal arch muscle tendons. (e) To regulate the differentiation of myofibers, Scx + cranial NCCs produce FGF10, which induces myogenic regulatory factors (MRFs). In response, retinoic acid (RA) expressed from the mesodermal cells counters the activity of cranial NCCs by preventing the differentiation of mesodermal precursor in the pharyngeal arch core cells thereby maintaining their proliferation

Furthermore, tln1 maintains proliferation of the NCCs located around the mesodermal core (Fig. 3.4b), thereby increasing the density of mesodermal cells in the PA core (Ishii et al. 2018; Rosero Salazar et al. 2020). In mouse, both cranial NCCs and mesodermal cells express Pitx2 during embryonic development (Shih et al. 2007). PITX2 induces TBX1 to regulate the proliferation and concentration of mesodermal cells surrounded by cranial NCCs in the PA core (Grenier et al. 2009; Kong et al. 2014). TBX1 is essential for the early myogenesis of PA muscles because it activates myogenic regulatory factors (MRFs, e.g., Myf5, MyoD, MyoG; see earlier in this chapter) and Pax7 (Kong et al. 2014). The early myogenic program (expression of Tbx1, MRFs, Tcf21, and desmin) in PA muscle development and muscle differentiation (myosin expression) also occurs in the experimentally induced absence of cranial NCCs (Tzahor et al. 2003; von Scheven et al. 2006; Rinon et al. 2007). However, the expression domains are altered and the muscles themselves are reduced (Tzahor et al. 2003, von Scheven et al. 2006, Rinon et al. 2007). Therefore, early PA muscle development can start independently of NCCs but to proceed normally, interaction with NCCs is required (Trainor and

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Tam 1995; Grenier et al. 2009; Rosero Salazar et al. 2020). The later head muscle differentiation requires a delicate balancing of cranial NCC-derived activators and neural tube-derived inhibitors. The neural tube in the head skeletal muscle formation is repressed by BMP and canonical Wnt signaling molecules secreted by the dorsal neural tube (Rinon et al. 2007). The secretion of BMP inhibitors (Gremlin, Noggin) and Wnt inhibitors (Frzb) by cranial NCC induces the myogenic differentiation of the cardiopharyngeal mesoderm in vitro (Tzahor et al. 2003). Scleraxis-positive (SCX+) NCC precursors also regulate the differentiation of myofibers by producing FGF10, which induces MRFs (Fig. 3.4e). NCCs induce myofiber differentiation that is countered by retinoic acid (RA) (Fig. 3.4e). RA suppresses the activity of cranial NCCs and prevents the differentiation of mesodermal precursor cells, thereby maintaining their proliferation (El Haddad et al. 2017; McGurk et al. 2017). Degradation of RA is needed for normal muscle patterning and attachments. This degradation occurs via retinoic-acid-catabolizing enzyme Cyp26b1, leading to the formation of myofibers and tendons (McGurk et al. 2017). If this RA-induced suppression fails, then differentiation occurs prematurely, leading to decreased muscle mass. Since the endoderm-derived epithelium regulates NCC proliferation, and NCC interaction is relevant for mesoderm-derived muscle differentiation (Rinon et al. 2007), it is important to point out that the connective tissue and tendon development in the head also depends on NCC proliferation (Olsson et al. 2001). Specifically, some cells of the NCC-derived muscular connective tissue differentiate into muscle tendons. The transcription factors Tcf4 and Osr1 are expressed in cranial NCCs forming dense irregular connective tissue (DICT) that surrounds and separates PA muscles (Fig. 3.4d). The transcription factor Scleraxis (Scx) is expressed in cranial NCCs forming extraocular and PA muscle tendons (Grenier et al. 2009; Tokita and Schneider 2009; Han et al. 2014; Nassari et al. 2017) (Fig. 3.4d). The absence of Tbx1 does not seem to affect the Scx + NCCs and tendon attachments (Grifone et al. 2008) and muscles may be only required during tendon differentiation, but not during initiation of tendon development (Grenier et al. 2009). It was shown that these interactions of muscle and tendon during development are similar in craniofacial and limb development, despite the different embryological origin of the cell types, i.e., mesoderm-derived connective tissue and tendons in limbs and NCC-derived connective tissue in the PA muscles and eye muscles (Grenier et al. 2009).

3.4 3.4.1

Eye Muscle Development Overview Eye Development

The beginning of the gastrulation is also the time when the eye field is determined in the anterior neuroectoderm (Chow and Lang 2001). In humans, the start of eye development is visible at around 3 weeks post fertilization. Eye development is

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highly connected to the development of the diencephalon (Heisenberg et al. 2001). The contact of the diencephalon with the overlying epithelium leads to the establishment of the optic sulci and later optic pits. The eye development begins laterally on the head (Fig. 3.5a) and the eyes move antero-medially later. Neural crest cells (NCCs) from the first or mandibular stream do not only migrate into the mandibular arch (1st branchial arch, 1st PA) but also into the forehead area contributing to the facial skeleton including the orbit (Piest 2002). With respect to eye and orbit development, the NCCs contribute to most of the cranial bones surrounding the orbit, nerves, and sensory ganglia, as well as sclera, choroid, iris, and ciliary body (Langenberg et al. 2008). When the mesoderm migrates to the orbit it mixes with the NCCs and becomes mesenchyme. Mesenchyme is not derived from a specific embryonic layer, but it is a term to describe loose embryonic connective tissue. The optic pits become larger, round pocket like structures and are then called optic vesicles (Fig. 3.5b). The deep end of the optic vesicle is attached to the forebrain and via a constriction forms the optic stalk (Kuwabara 1975; Naumann et al. 1986; Evans and Gage 2005). NCCs from the mesencephalon and

Fig. 3.5 Eye development. (a) Ventrolateral view of a human embryo at 4 weeks post conception. The lens placode is clearly visible on the lateral side of the frontonasal process. (b) The eye development begins with a primordium, the optic vesicle, that induces the lens placode development on the overlying epithelium. The optic vesicle itself becomes the retina. Upon contact between lens placode and optic vesicle, the vesicle starts transformation into an optic cup. This cup finally becomes bilayered (far right). It stays in contact with the diencephalon via the optic stalk that contains the optic nerve. Simultaneously to this process develops the lens. The superficial lens placode invaginates and thickens and is finally detached from the ectoderm. The lens-overlying epithelium forms the cornea. Stages below figure indicate developmental times in human (d, days of gestation), mouse (E, embryonic days), and zebrafish (hpf, hours past fertilization) (Richardson et al. 2017). (c) Human orbit with the seven bones that formed it, colored

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diencephalon migrate to the optic vesicles, which guide the migration of these NCCs (O’Rahilly and Müller 2007; Langenberg et al. 2008; Williams and Bohnsack 2015). All seven bones that form the orbit (Fig. 3.5c) are NCC-derived: frontal bone, zygomatic bone, maxillary bone, lacrimal bone, ethmoid, sphenoid, and palatine bone (Shumway et al. 2018). In humans, the first signs of extraocular eye muscles (EOMs) can be observed in the sixth week post fertilization. Two mesodermal cell clusters can be distinguished. The first one derived from paraxial mesoderm related to the branchial arch one, gives rise to the lateral rectus and superior oblique muscles. The other portion is of prechordal mesoderm origin and gives rise to the other four EOMs (medial rectus, inferior rectus, superior rectus, and inferior oblique) (Gilbert 1957; Bohnsack et al. 2011). Fate mapping studies in chicken have demonstrated the migration of prechordal mesodermal cells to the paraxial mesoderm to form EOMs (Noden and Francis-West 2006). In the lamprey, the mesodermal origin of the EOMs is from premandibular (prechordal), mandibular (1st branchial arch), and hyoid (2nd branchial arch) mesoderm (Suzuki et al. 2016). Therefore, the origin of EOM mesoderm seems to be conserved in vertebrates. Relevant here is that the mesodermal cells that will give rise to the EOMs migrate independently of the NCCs into the region of differentiation (Sevel 1981; Bohnsack et al. 2011). This is in contrast to the branchial arch mesoderm, that migrates with the NCCs into the branchial arch core (Ziermann et al. 2018). The interaction between eye, EOMs, and NCCs is needed for the differentiation of myotubes and later the mature muscle fibers (Noden and Francis-West 2006; Bohnsack et al. 2011). While the PA muscles are derived from mesoderm of the same PA and their connective tissue is derived from the same PA and they are innervated by the cranial nerve associated with this PA, the EOMs development is a bit more chaotic. The EOMs are innervated by three different cranial nerves (CN IV—trochlear nerve— superior oblique muslce, CN VI—abducens nerve—lateral rectus muscle, CN III— oculomotor nerve—superior rectus, medial rectus, and inferior rectus, inferior oblique, and levator palpebrae superior muscles), the mesoderm is prechordal and related to 1st PA, and the connective tissue is NCC-derived. The muscle precursors of EOMs form tightly aggregated groups that cross the neural crest-mesoderm interface when entering the periocular territories (Evans and Noden 2006). When the cells reach their destination, they establish stable, permanent relations with their connective tissues. While most of the EOMs development seems to be evolutionary conserved, there are some differences. For example, in sharks there are mesodermal contributions to the tendons of EOMs described, which are NCC-derived in chicken and mouse (Kuroda et al. 2021). A mesodermal origin of the tendons in EOMs of humans (or mammals) was also favored by some (Sevel 1986). The lamprey has two EOMs innervated by the abducens nerve and three by the oculomotor nerve (the levator palpebrae superior muscle is not present in lampreys). The mesenchymal cells in the PAs migrate together, but the NCCs in the orbit are required to be present first to guide the migration of the mesodermal cells (Langenberg et al. 2008). Therefore, the optic vesicle appears to have the role of

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an organizer important for the facial and orbital development (Kish et al. 2011). After reaching the orbit, the mesodermal streams divide into two complexes at the apex of the orbit (Sevel 1981). During the following differentiation processes, individual muscle groups form (Noden and Francis-West 2006). The superior group forms the superior rectus, superior oblique, and the levator palpebrae superior muscles; the inferior group forms the inferior rectus and inferior oblique muscles; and the remaining two muscles, medial and lateral rectus, form from both groups (Sevel 1981). Early in development all EOMs can be distinguished, but their tendons only appear later (Sevel 1981; Sevel 1986; Williams and Bohnsack 2015). By week 17 of human fetal development, the EOM tendons are clearly distinguishable, however, their insertion sites seem not to be fully established until week 22 (Sevel 1986). In humans, the individual EOMs can early on be distinguished by their muscle bellies as they differentiate with their fascia and nerves. This process is tightly linked to the growth and ossification of orbital bones, except the ethmoid which ossifies later (Koornneef 1976). However, the orbital connective tissue septa develop only from the third month onwards (Koornneef 1976). At the fifth month of pregnancy the eye development represents the adult configuration. There is one other muscle that is important during the eye development. It is the so-called Müller orbital muscle that appears during the eighth week post-fertilization in humans. Its mesodermal origin is still controversially discussed (Vázquez et al. 1990; Osanai et al. 2011). Usually, it disappears during fetal development but, in some cases, it might be present as a vestigial element (De Battista et al. 2011; Cho et al. 2021). The orbital muscle separates the floor of the orbit from the pterygopalatine fossa until the floor ossifies and occupies more than 50% of the orbital floor by week 10 of the human post-fertilization development (Koornneef 1976; Rodríguez-Vázquez et al. 1999; Osanai et al. 2011). The maxillary nerve (maxillary division of trigeminal nerve, CN V2) that passes through the orbital muscle and the infraorbital nerve (branch of CN V2) are in close proximity to this muscle (Vázquez et al. 1990, Osanai et al. 2011). During week 16, osteoprogenitor cells start to move into the orbital muscle (Koornneef 1976). This is the beginning of the end of the orbital muscle and its tissue is consequently replaced by collagen fibers, seemingly initiating also the ossification of the inferior orbital wall (Osanai et al. 2011).The smooth muscle fibers of the orbital muscle are being replaced by connective tissue and by week 20 the muscle resembles more a membrane-like structure than an actual muscle (Osanai et al. 2011). It was hypothesized that the remaining tissue of the orbital muscle undergoes membranous ossification and therefore forming the floor of the orbit (Osanai et al. 2011). This would be the only muscle-to-bone transition throughout the human body. Based on the osteology of the bones around the inferior orbital fissure it is hard to see which part would correspond to the ossified orbital muscle remains and further studies are needed to verify or discard the hypothesis by Osanai et al. (2011).

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Gene Regulatory Mechanisms during Eye Development

There are several reviews on eye development and pathologies resulting from misregulation of essential genes (e.g., Graw 2010; Heavner and Pevny 2012; Sinn and Wittbrodt 2013). Therefore, I focus here on the main processes and highlight tissue interactions. The development of the eye vesicle depends on the contact with the diencephalon and on the repression of Wnt, which is mediated partially through the transcription factor Six3 (Loosli et al. 1999; Lagutin et al. 2003; Liu et al. 2010). The following morphogenesis of the optic cup and retina is regulated by retinal homeobox transcription factors (Rx), which are present in all vertebrates but in different numbers. For example, the mouse has one Rx orthologue (Rax) while fish have three paralogues (Rx1, Rx2, Rx3) (Mathers et al. 1997; Chuang et al. 1999). Pax6 is a highly conserved transcription factor (Czerny and Busslinger 1995) in vertebrates and invertebrates, and its misexpression can trigger ectopic eye development (Halder et al. 1995; Tang et al. 1998; Gehring and Ikeo 1999). Pax6 mutant mice and rats begin their eye development but cannot maintain it (Hill et al. 1991; Philips et al. 2005). Pax6 can activate Six3 expression, which is required for the retina development. The transcription factor Otx2 is also involved in the retinal development and initiates the eye field specific genetic network containing transcription factors Pax6, Six3, and Rx3 (Andreazzoli et al. 1999; Mathers and Jamrich 2000; Chow and Lang 2001; Lupo et al. 2004). When these genes are expressed, Otx2 is down regulated (Andreazzoli et al. 1999). The ectopic expression of Six3 and Rx3 restricts Otx2 expression (Andreazzoli et al. 1999), furthermore supporting this negative feedback mechanism. The overlapping expression of Pax6, Six3, and Rx3 establishes the retinal identity in the eye anlage, which is early in the development still a single, centrally positioned eye field. Into this field several factors related to the TGFß, FGF, and Shh families are secreted from the underlying axial mesoderm. This results in the separation of the single retina primordium into two retinal primordia. Then the hypothalamic cell stream migrates into the eye field and separates it into two eye primordia (Rebagliati et al. 1998; Varga et al. 1999; England et al. 2006). Simultaneously, Shh is expressed in the ventral midline in the underlying mesendoderm sensitizing the overlaying neuroectoderm to be responsive to FGF signals (Lupo et al. 2006). Shh is expressed as a gradient and high Shh concentrations result in differentiation of more proximal parts, such as the optic stalk, while low concentrations lead to distal structure formation. In response to Shh several other homeobox transcription factors are expressed, including Vax1 (Hallonet et al. 1999), Vax2 (Barbieri et al. 1999), and Pax2 (Dressler et al. 1990). Six3 continuous to be expressed as it is still required for cell proliferation and differentiation of the retinal progenitor cells. Geminin and Six3 antagonize each other during the formation of the eye field (Del Bene et al. 2004). Six3 inhibits Geminin, which allows cell proliferation in the eye field. Geminin, on the other hand, influences the activity of Six3 as transcription factor, which results in a positive control of transcriptional activation and neuronal differentiation. The developing

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optic vesical enlarges by the increase of migratory retinal progenitor cells (Rembold et al. 2006). The migration is regulated by cell adhesion and signaling molecules with their responding receptors. One of these molecules is the Ig-domain protein NLCAM, which is a downstream target of Rx3, and regulates the early cell migration during optic vesicle morphogenesis (Brown et al. 2010). Another mechanism is chemotaxis via the SDF-Cxcr4 axis, which is like the migration of pharyngeal arch mesenchymal cells (NCCs and mesodermal cells) (Miller et al. 2008; Yahya et al. 2020) described earlier in this chapter. The mesendoderm ventral to the eye field expresses SDF1 and Cxcr4 is expressed in the migrating cells (Bielen and Houart 2012). The optic vesicle evagination begins with the start of neurulation by a slow movement of Rx3+ retinal progenitor cells. These cells are so slow that by the closure of the neural tube other migrating cells have surrounded and overtaken them. The different speed at which cells arrive in the optic vesicle is important for the differentiation of the optic cup. In fish, three phases can be distinguished during the optic cup formation. First, the epithelial sheet of retinal progenitor cells bends from anterior to posterior; second, the dorso-ventral bending of that sheet occurs, and finally, third, the closure of the optic fissure occurs. The first two phases happen in parallel, while the third phase finalizes the cup formation (Martinez-Morales and Wittbrodt 2009). With the cup formed, the retina differentiation continues. This process is conserved in vertebrates and involves neural cell fates (Cepko et al. 1996; Livesey and Cepko 2001). First, retinal ganglion cells are established in the retina, which is followed by a surge in neurogenesis (Laessing and Stuermer 1996; Hu and Easter Jr 1999). Shh is one of the drivers during this surge (Neumann and NuessleinVolhard 2000) and Fgf coordinates retinal ganglion differentiation (MartinezMorales et al. 2005). With the optic cup formation and retina differentiation arrive also NCCs in the orbit, which will then guide the migration of the mesodermal cells (Langenberg et al. 2008). These mesodermal cells give rise to the extraocular muscles (EOMs). EOMs derive from prechordal mesoderm and paraxial mesoderm (see earlier in this chapter; Noden and Francis-West 2006; Lescroart et al. 2010). This subset of head muscles underlies different genetic regulation from that of PA (branchiomeric) muscles. EOMs and abdominal wall muscles are specified by Pitx2 and PA muscles are specified by Pitx2 and Tbx1 (Chang and Kioussi 2018). EOMs and 1st PA-derived muscles depend on Pitx2 (Dong et al. 2006). However, MyoR (Msc), Tbx1, and Tcf21 (Capsulin) seem not to be important for the EOM development (Lu et al. 2002; Kelly et al. 2004). Further, myogenic determination factors of the MyoD family in EOMs are different from PA muscles (Sambasivan et al. 2009). Pitx2 and RA regulate the expression of muscle specific transcription factors Myf5, MyoG, and MyoD1 (Diehl et al. 2006; Zacharias et al. 2011). In Myf5/Mrf4 double mouse mutants EOMs are absent, but PA1-derived muscles are unaffected and, in the trunk, Pax3 is compensating for the lack of Myf5 and Mrf4 (Sambasivan et al. 2009). EOMs formation is initiated by Myf5 or Mrf4 and PA muscles are dependent on Myf5, Mrf4, or MyoD. Therefore, in the absence of Myf5 and Mrf4 PA muscle development can be rescued by MyoD, but EOMs development cannot (Buckingham and Rigby 2014). This is an example of site-dependent regulation. In

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Pitx2 mouse mutants the EOMs and PA1-derived (mandibular) muscles were affected, but the PA2-derived (hyoid) muscles showed hardly any changes (Shih et al. 2007). However, in zebrafish Pitx2 mutants the jaw musculature (PA1-derived) is affected while the MyoD expression as well as the EOMs organization is not (Weigele and Bohnsack 2020). Capsulin/MyoR double mutant mice had similar abnormalities with PA1-derived muscles absent and PA2-derived muscles present (Lu et al. 2002). This clearly demonstrates that subsets of muscles in the head derived from a similar progenitor (PA mesoderm) have different gene regulatory networks; that is, for PA1 muscles Pitx2, Capsulin, and MyoR are unique gene regulators (Lu et al. 2002; Shih et al. 2007). PA-based Fgf8 (fibroblast growth factor 8) supports the development of PA1-derived muscles but represses the specification of EOMs (von Scheven et al. 2006). Periocular NCCs also express Foxl2 (Forkhead Box L2) which activates the expression of Acta2, which encodes for smooth muscle α-actinin in EOMs in humans and mice. Myf5, MyoG, and MyoD1 induce myoblast differentiation and Acta2 expression. However, so far, there has not been made a connection between Pitx2 and Foxl2 (Weigele and Bohnsack 2020). Yet, the disturbance of NCCs in the orbit leads to a multitude of anomalies, including EOMs malformations (Bohnsack et al. 2011, reviewed by Weigele and Bohnsack 2020). Since the eye development itself is regulating EOMs development, any disturbance that leads to partial or complete loss of the optic vesicle can lead to partial or complete loss of EOMs (Bohnsack et al. 2011). That is, the Pitx2 phenotype in mice and humans may be related to NCC signaling but it needs to be clarified if here is a direct or indirect influence. The transcription factors Tcf4 and Osr1 are expressed in cranial NCCs forming dense irregular connective tissue that surrounds and separates PA muscles, and the transcription factor Scx is expressed in cranial NCCs forming extraocular and PA muscle tendons (Fig. 3.4; Grenier et al. 2009; Tokita and Schneider 2009; Han et al. 2014; Nassari et al. 2017). Here seems to be another example of evolutionary differences as NCC derived EOMs tendons are described for chicken and mice but for sharks these tendons are reported to receive mesodermal contribution (Kuroda et al. 2021). Acknowledgment The author’s research is supported by NSF #2000005. Compliance with Ethical Standards The author declares no conflict of interest. This chapter is a review of previously published accounts, as such, no animal or human studies were performed.

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Chapter 4

Building a Co-ordinated Musculoskeletal System: The Plasticity of the Developing Skeleton in Response to Muscle Contractions Paula Murphy and Rebecca A. Rolfe

Abstract The skeletal musculature and the cartilage, bone and other connective tissues of the skeleton are intimately co-ordinated. The shape, size and structure of each bone in the body is sculpted through dynamic physical stimuli generated by muscle contraction, from early development, with onset of the first embryo movements, and through repair and remodelling in later life. The importance of muscle movement during development is shown by congenital abnormalities where infants that experience reduced movement in the uterus present a sequence of skeletal issues including temporary brittle bones and joint dysplasia. A variety of animal models, utilising different immobilisation scenarios, have demonstrated the precise timing and events that are dependent on mechanical stimulation from movement. This chapter lays out the evidence for skeletal system dependence on muscle movement, gleaned largely from mouse and chick immobilised embryos, showing the many aspects of skeletal development affected. Effects are seen in joint development, ossification, the size and shape of skeletal rudiments and tendons, including compromised mechanical function. The enormous plasticity of the skeletal system in response to muscle contraction is a key factor in building a responsive, functional system. Insights from this work have implications for our understanding of morphological evolution, particularly the challenging concept of emergence of new structures. It is also providing insight for the potential of physical therapy for infants suffering the effects of reduced uterine movement and is enhancing our understanding of the cellular and molecular mechanisms involved in skeletal tissue differentiation, with potential for informing regenerative therapies. Keywords Skeletal development · Mechanoregulation · Muscle immobilisation · Tendon · Joint

P. Murphy (✉) · R. A. Rolfe School of Natural Sciences, Trinity College Dublin, The University of Dublin, Dublin 2, Ireland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Kablar (ed.), Roles of Skeletal Muscle in Organ Development, Advances in Anatomy, Embryology and Cell Biology 236, https://doi.org/10.1007/978-3-031-38215-4_4

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Interdependence of the Musculature and the Skeleton: The Importance of Mechanical Regulation

The musculature and the skeleton are intimately linked throughout life, from development, through homeostasis and aging. In his seminal work in 1917, On Growth and Form, D’Arcy Thompson wrote that “between muscle and bone there can be no change in the one but it is correlated with changes in the other”(Thompson 1917). This relationship has long been recognised in the remodelling of bone in response to physical stimulation (e.g. increased bone density in the dominant limbs of athletes), reduced stimulation (e.g. space flight) and the loss of mechanoresponsiveness in degenerative skeletal diseases such as osteoarthritis and osteoporeosis (Vico and Hargens 2018; Ozcivici et al. 2010). In 1888, Wolff’s law identified bone strength as being proportional to physical loading and was incorporated by Frost in a “mechanostat” theory of bone as an adaptable material where biochemical changes within the tissue, that carry out bone remodelling, are responsive to forces exerted by the muscle (Frost 2001; Chen et al. 2010). Furthermore the interdependency works both ways with muscle pattern also responding to skeletal morphology (Tokita and Schneider 2009). Functional muscle is not only important for healthy maintenance of the skeletal system but muscle derived mechanical forces generated by embryo movements are required for development of a healthy skeleton, as evidenced by human congenital abnormalities in cases of reduced foetal movement (reviewed in (Shea et al. 2015). In 1983 Moessinger formalised a description of Foetal Akenesia Deformation Sequence (FADS) including multiple joint contractures, facial abnormalities, foetal growth retardation and short umbilical cords (also known as Pena and Shokeir phenotype) (Hall 2009; Moessinger 1983). Noting the similarity of effects seen in a rat model where a muscle relaxant was administered to pregnant females, he pointed out that despite varied aetiologies the common factor in theses congenital cases is reduced foetal movement. Some causes of reduced movement are intrinsic to the foetus, including neuromuscular disorders, while others are extrinsic, for example due to abnormal uterine structure or multiple foetuses leading to movement restrictions. With extrinsic causes there is generally a better chance of survival and recovery with postnatal physiotherapy (Niles et al. 2019). Cases of multiple joint contractures have also been referred to as arthrogryposis multiplex congenita (AMC) with 10% of such cases also showing hypomineralised and fragile long bones, prone to fracture at birth (Hall 2014; Niles et al. 2019; Rodríguez and Palacios 1991; Miller and Hangartner 1999). This temporary brittle bone disease is distinct from osteogenesis imperfecta (Forlino et al. 2011), permanent brittle bone disease, caused by mutations in collagen genes, and its regression, usually within the first year of life, shows the plasticity of the mechanical response of the developing skeleton. Joint dysplasia, in particular Developmental Dysplasia of the Hip (DDH), is more common in new born infants (reviewed in (Nowlan 2015) and has been associated with abnormal positioning of the femoral head in the acetabulum, due to abnormal limb position, uterine pressure or ligament laxity (Shefelbine and Carter 2004).

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As early as the 1930s animal models were used to show the effects of embryo immobilisation on the developing skeleton (e.g. (Murray and Selby 1930; Fell and Canti 1934) but a more concerted focus in recent years, using a variety of immobilisation approaches and different species (Table 4.1), has deepened appreciation for the role of muscle in directing appropriate skeletal formation, and is starting to provide important insights into the cellular and molecular mechanisms involved. Understanding how mechanical stimulation from muscle influences cell differentiation and tissue patterning during development of skeletal tissues is of particular interest as uncovering the mechanisms involved holds promise for biomedical application for improving tissue regeneration and successful tissue engineering. Interdependency between functional muscle and skeleton ensures the maintenance of a functional system for locomotion even if one or other tissue is altered, but the mechanical response during development of the skeletal system is also a striking example of developmental plasticity (West-Eberhard 2003). This contributes to our understanding of adaptive evolution through morphological integration (Hallgrímsson et al. 2002) providing an explanation for the emergence of new co-ordinated morphologies as seen in the huge variety of vertebrate limb structures from human arms and hands to bat wings and dolphin flippers. The musculoskeletal system is made up of interconnected muscles, bones and cartilages with other connective tissues surrounding the muscle, attaching it to bone (tendons) and interconnecting bones (ligaments). Different embryonic origins of cells form the skeletal elements in different parts of the embryo e.g. lateral plate mesoderm at the level of the limbs gives rise to the limb skeleton and migrating neural crest cells derived from the neural ectoderm contribute largely to the cranial skeleton. Bones can be formed by two entirely different processes: endochondral and intramembraneous ossification. Despite the diversity of processes involved there are examples of mechanoresponsiveness to muscle forces throughout the skeletal system. Here we present examples of the importance of biophysical stimuli from muscle contraction for multiple aspects of bone, joint and tendon formation, with particular focus on the limb skeleton, the secondary cartilages of the jaw and clavicle, and the formation of the spine. We further review current evidence that reveals aspects of the cellular and molecular mechanisms involved and the important gaps still to be filled.

4.2

Interdependence of Muscle and Skeletal Tissues in the Developing Vertebrate Limb: The Mechanical Response

Vertebrate limbs arise as buds of mesenchyme cells covered in ectoderm along the flank of the embryo. These buds elongate and subdivide into three territories along the future shoulder to digit tip (proximal to distal) axis, widening to a flattened paddle at the distal end to form the digital plate (autopod), while the more proximal regions give rise to the stylopod (where the humerus or femur will form) and the

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Table 4.1 Most frequently used embryo immobilisation techniques Mouse Genotype Pax3Spd/Spd (splotch delayed)

Effect Muscleless limbs

Pax3Sp/Sp(splotch) Myf5nlacZ/nlacZ:MyoD-/-

Muscleless limbs Muscleless

Myf5nlacZ/+:MyoD-/-

Reduced muscle

Muscular dysgenesis (mdg/mdg) Rat Treatment Removal of amniotic fluid Tubocurarine injection Chick Treatment Decamethonium bromide (or decamethonium iodide)1

Non-contractile muscle

Effect Induced oligohydramnios Paralysis

Example references (Palacios et al. 1992)

Effect Rigid paralysis

Example references (Drachman and Sokoloff 1966; Hall and Herring 1990; Mitrovic 1982; Persson 1983; Mikic et al. 2000; Hosseini and Hogg 1991a; Roddy et al. 2011b; Rolfe et al. 2021; Germiller and Goldstein 1997) (Peterson et al. 2021; Osborne et al. 2002; Pitsillides 2006) (Ruano-Gil et al. 1985)

Pancuronium bromide

Flaccid paralysis

Reserpine

Hypermotility (low doses); paralysis (high doses) Hypermobility

4-aminopyridine (4-AP) Spinal resection–surgical technique Chorioallantoic graft or coelomic graft of early limb bud Explant

Zebrafish Genotype/ treatment myod fh261 MS222 anaesthetisation

Example references (Vogan et al. 1993; Kahn et al. 2009; Rolfe et al. 2014; Shwartz et al. 2012; Shea and Murphy 2021) (Franz et al. 1993; Nowlan et al. 2010a) (Kablar et al. 2003; Rudnicki et al. 1993; Rot-Nikcevic et al. 2006; Nowlan et al. 2010a; Gomez et al. 2007; Sotiriou et al. 2019) (Rudnicki et al. 1993; Nowlan et al. 2010a; Sotiriou et al. 2019) (Pai 1965a; Kahn et al. 2009; Sharir et al. 2011)

Regional paralysis No intrinsic movement

(Rodríguez et al. 1992)

(Rolfe et al. 2021; Hammond et al. 2007; Pan et al. 2018) (Drachman and Sokoloff 1966; Wong et al. 1993; Edom-Vovard et al. 2002) (Mitrovic 1982; Murray and Selby 1930; Hall 1986; Edom-Vovard et al. 2002)

No intrinsic movement (can be externally stimulated

(Lelkes 1958; Khatib et al. 2023)

Effect Most craniofacial muscles absent Immobilisation

Example references (Hinits et al. 2011; Brunt et al. 2016) (Brunt et al. 2016) (continued)

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Table 4.1 (continued) Mouse Genotype myf5 and myod morphant Nic mutant (b107) 0.016% tricaine anaesthetisation

Effect Absence of head musculature Paralysis Immobilisation

Example references (Hinits et al. 2009; Shwartz et al. 2012) (Westerfield et al. 1990; Shwartz et al. 2012) (Shwartz et al. 2012)

Note 1, other agents used: botulinum toxin (Murray and Drachman 1969; Drachman and Sokoloff 1966); succinylcholine (Ruano-Gil et al. 1978); (Hall 1972) and tubocurarine (Hall 1972)

Fig. 4.1 Co-ordinated development of muscle, tendon and the cartilage template of future bones in the developing chick embryo limb. (a) chick hindlimb at Hamburger and Hamilton (HH) stage 32 (Hamburger and Hamilton 1992) showing developing tendons in green (α-Tenascin antibody) and developing muscle masses in red (α-myosin antibody); 3D representation of developing cartilage rudiments in blue. (Adapted from (Roddy et al. 2009). Th indicates thigh musculature, Sh shin; Ft foot. (b) Diagrammatic representation of the developing musculoskeletal system in the embryonic chick hindlimb including indication of the different embryonic origins of muscle (somite) and skeleton, tendons, ligaments and other connective tissues (lateral plate mesoderm) in the earlier embryo. Diagram on the right shows a cross section through the limb bud region of a HH15 embryo indicating the cellular origins of muscle and skeletal cell types, as indicated. Scale bar 1 mm

zeugopod (where the radius and ulna or tibia and fibula will form) (reviewed in (Zeller et al. 2009; McQueen and Towers 2020)). The component tissues of the limb have distinct embryonic origins: the cells that give rise to the skeletal rudiments, joints, tendons and the connective tissue surrounding the muscle originate from the lateral plate mesoderm at the site of the emerging limb bud while the future muscle cells migrate into the limb bud from more dorsally located somites (Christ et al. 1977; Chevallier et al. 1977) (Fig. 4.1). Despite different origins there is striking co-ordination from earliest stages in the differentiation and patterning of the future muscle, tendons and skeletal rudiments (Kardon 1998; Roddy et al. 2009) (Fig. 4.1).

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Cells condense at the core of the limb bud, switching on Sox9 expression and differentiating as cartilage to form a template for the future skeletal rudiments, first visible as a Y shaped anlage prefiguring the most proximal elements (e.g. humerus, radius and ulna), with condensation and differentiation extending and branching progressively distally into the digital region (Thorogood and Hinchliffe 1975). The close relationship between the emerging tissues, not only allows trophic interactions but also mechanical input, most obviously resulting from the movement generated by embryonic muscle contractions. Developing limbs experience a large range of visible movements compared to other regions of the embryo so it is not surprising that limbs provide some of the clearest examples of the importance of movement for appropriate skeletal development. Spontaneous muscle contractions begin early in the vertebrate embryo: In humans, first movements were recorded at 9 weeks (approximately Carnegie stage 18) (de Vries and Fong 2006); similarly in the mouse model of mammalian development, clear embryo movement also begins just after the first muscle twitches at embryonic day (E) 12.5/Theiler stage (TS)20 (Theiler 1989) (Suzue and Shinoda 1999). In the chick we have recently refined earlier observations to reveal body movements from E3/Hamburger and Hamilton (HH) stage 22 and the first limb displacements at HH23 (Rolfe et al. 2021; Hamburger and Balaban 1963) which reflects observed sensitivity to early immobilisations. The mouse and chick provide complimentary models for embryonic immobilisation, each with valuable advantages. Access to chick embryos developing in externally laid eggs allows easy visualisation and immobilisation via surgery or addition of pharmacological agents; most commonly decamethonium bromide to induce rigid paralysis, pancuronium bromide for flacid paralysis and 4-aminpyridine or reserpine for hypermobility (Table 4.1; previously reviewed (Nowlan et al. 2010b)). In the mouse, several different genetic lines harbour mutations that affect muscle development and permit analysis of limb skeletal development in the complete absence of muscle (Pax3Spd/Spd (Vogan et al. 1993) and Myf5nlacZ/nlacZ:MyoD-/double mutants (Kablar et al. 2003)), with reduced muscle in Myf5nlacZ/+: MyoD-/embryos (Rudnicki et al. 1993) and immobile muscle in muscular dysgenesis mouse embryos (mdg/mdg) (Pai 1965a) (Table 4.1). Figure 4.2 summarises the effects of immobilisation seen across mouse and chick models using different approaches to examine lack of movement.

4.2.1

Effects of Immobilisation on Joint Formation

A key determinate of species-specific shape and structure in the vertebrate limb is the placement of synovial (diarthrotic) joints that separate the skeletal elements and allow full, frictionless motion (reviewed in (Rux et al. 2019)). Synovial joints also occur elsewhere, for example in the jaw. It has been well established that embryonic movement is required for the definition of appropriate tissue arrangement in synovial joints, providing clear evidence for the basis of the close relationship between

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Fig. 4.2 Overview of the main effects of immobilisation on the developing skeletal system in chick (left) and mouse (right) embryo experimental models, as reviewed here. Major effects are listed in the centre, with example illustrations. Approximate locations of recordings of these effects in the mouse and chick embryo are indicated by coloured circles using the colour key represented by the borders on the illustrations e.g. yellow; secondary cartilages, green; spine etc. Secondary cartilage: reduction/absence in mouse and chick mandible and mouse clavicle. Example shown is outline drawings of mouse mandibles showing reduced coronoid (cr), condylar (cd) and angular (an) processes in absence of muscle (Rot-Nikcevic et al. 2006). Spine: cartilaginous fusions (Red arrows) observed between adjacent cervical vertebrae following immobilisation. Scale bars 500 μm (control) and 100 μm (immobile) (Rolfe et al. 2017). Ossification: delayed and abnormal ossification. The illustrated example shows sections of stage matched embryos with delayed expression of Ihh mRNA. Rudiment shape: skeletal elements are shorter, more circular in circumference and smoother, lacking characteristic features. Joints: joint lines are reduced with fusion of cartilage across joints and abnormally shaped skeletal rudiment termini. Illustration of abnormal domains of

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emerging muscle, tendon, cartilage and bone in the limb and its importance for the formation of an integrated functional structure. The first indication of the position of future joints is the appearance of a region of more flattened cells, the interzone, which goes on to form three distinct layers; the chondrogenic layers at the termini of the adjacent skeletal rudiments (future sites of articular cartilage), and an intermediate layer where the synovial cavity will form (reviewed in (Decker et al. 2014). Cell lineage studies show that interzone cells contribute to all joint tissues (Koyama et al. 2008), but the developmental process is dynamic with contribution of cells also from adjacent territories of transient cartilage and marginal cells (Ray et al. 2015; Shwartz et al. 2016). The emerging tissues go on to form the intricate structures that will facilitate smooth and stable movement; i.e. the complimentary shapes of the opposing articular cartilages, the stabilising structures including ligaments, joint capsule and lining surrounding the synovial cavity, filled with lubricating fluid. The effect of embryo immobilisation on joint formation was first shown in early chick experiments following explant of the limb, surgical denervation or pharmacological immobilisation (Drachman and Sokoloff 1966; Fell and Canti 1934; Murray and Selby 1930; Ruano-Gil et al. 1978; Mitrovic 1982; Persson 1983; Lelkes 1958; Mikic et al. 2000; Roddy et al. 2011b), the common effects being loss of cavitation, fusion of multiple joints (limb and jaw synovial joints), and loss of menisci and patella. On the other hand, when hyperactivity was induced in chick embryos, joint cavities enlarged (Ruano-Gil et al. 1985). The muscle dysgenesis mouse (mdg), which has a spontaneous mutation leading to lack of muscle contraction and paralysis, was described as having abnormal joint cavitation (Pai 1965a). More systematic developmental analysis from early stages (E12.5) showed ectopic development of transient cartilage across the presumptive joint region, characterised by downregulation of joint specific markers and ectopic expression of an early marker of transient cartilage, Col2a1, with complete cartilage fusion in extreme cases (Nowlan et al. 2010a; Kahn et al. 2009; Roddy et al. 2011b; Rolfe et al. 2013) (Fig. 4.2). While the earliest events of joint specification (interzone formation) were not affected, tissue patterning of emerging territories within the presumptive joint was lost. It was particularly notable that while joint fusions were described at multiple joints including extreme effects at the elbow, the knee joint did not fuse in the mouse muscle-less and immobile muscle embryos (Nowlan et al. 2010a; Kahn et al. 2009), although it did show loss of the patella (Eyal et al. 2015) and changes to the shape of

Fig. 4.2 (continued) Wnt and BMP signalling across an immobilised joint with cartilage fusion (Singh et al. 2018); outlines show contours of cross-sections through chick distal femoral condyles, control outlines in blue and immobilised outlines in red (Roddy et al. 2011b) (note in particular the flattened shape and reduced lateral condyle); example histology of hip joint (Rolfe et al. 2021). Tendons: late tendon development shows failure to mature with reduced size and spacing (Peterson et al. 2021). Effects not represented here include failure of sternal and palate fusion in immobilised embryos (Rot and Kablar 2013)

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the rudiment termini (Sotiriou et al. 2019). The apparent absence of effect on joint separation in the knee was puzzling since the knee joint is strongly affected in chick embryos (Roddy et al. 2011b). It was speculated that this may be due to the developing knee of muscle-less mouse embryos experiencing sufficient stimulation from passive movements from the uterine wall and littermates within the context of mammalian internal development (Nowlan et al. 2012) or, more recently, due to the acute flexion that occurs at the knee joint over time providing a threshold level of mechanical stimulation (Kim et al. 2022), rather than an intrinsic difference in the mechanosensitivity of mammalian knee joint developing tissues, which would be surprising. This has indeed been corroborated recently by mouse hindlimb explants being responsive to externally applied movement, forming more normal cavitation than unstimulated controls (Khatib et al. 2023). Insight into the molecular mechanisms disturbed during joint formation has been achieved with the demonstration of loss of correct spatial localisation of Wnt and BMP signalling pathway activities at the joint line (Singh et al. 2018; Rolfe et al. 2018). The importance of Canonical Wnt/β-catenin signalling for joint formation was already recognised through mouse mutants (Guo et al. 2004; Später et al. 2006; Kan and Tabin 2013), but notably, immobilisation resulted in more extreme joint patterning disturbance and joint fusions than inhibiting Wnt signalling, indicating that additional molecular mechanisms are disturbed in the immobilisation effect (Rolfe et al. 2018). Kahn et al. (Kahn et al. 2009) showed that Wnt signalling is disturbed in immobile mouse embryos and a prominent role for Wnt signalling was indicated by transcriptomic analysis comparing the humerus and associated joints of immobile embryos (Pax3Spd/Pax3Spd) and controls, which showed that Wnt was the most disturbed signalling pathway, with BMP signalling also highly disturbed (Rolfe et al. 2014). BMP signalling is normally active at a distance from the joint line, in territories of transient cartilage that will later be replaced by bone through endochondral ossification (Ray et al. 2015) while cartilage at the joint line will endure to form articular cartilage, so important for joint function. Therefore, taking advantage of different immobilisation approaches across mouse and chick, and the different advantages of the two models, we examined Wnt and BMP signalling activity at the joint lines of immobilised embryos compared to controls and showed that in immobile limbs, Wnt signalling activity is lost at the joint line while BMP signalling activity and Col2a1 expression spreads across the abnormal joint territory (Singh et al. 2018) indicating that joint fusions are caused by the loss of appropriate spatial restriction of cell signalling defining the territories of transient and permanent articular cartilage (Fig. 4.2). The smooth movement and stability of the joint is dependent on complimentary, interlocking shapes of the interfacing ends of the bones; a feature also disturbed by immobilisation. Roddy et al. (Roddy et al. 2011b) carried out shape analysis at the chick knee joint comparing immobilised and control embryos and showed the detailed changes leading to more “flattened” joint surfaces and loss of functional outgrowths; with strongest effects on the lateral femoral condyle where reduced cell proliferation coincides with loss of protrusion (Fig. 4.2). Similar shape analysis in mouse embryos with reduced or absent muscle also showed flattening of

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characteristic protrusions (Sotiriou et al. 2019), although less pronounced at later stages (Sotiriou et al. 2022). Such changes in shape, also noted in the immobilised zebrafish jaw (Brunt et al. 2016), would impact joint function and stability, potentially further reducing the patterns of normal stimuli generated by movement of the joint and providing an explanation for human developmental disorders associated with reduced foetal movement such as developmental dysplasia of the hip (Giorgi et al. 2015). As previously noted, Temporary Brittle Bone Disease suffered by human infants with reduced foetal movement can regress during the first year of life as bones strengthen due to postnatal movement (Miller and Hangartner 1999). This suggests a capacity for the system to recover, at least in some respects, if movement resumes, and opens the possibility of physical therapy treatments for such developmental problems (Niles et al. 2019). To investigate the capacity to recover we compared the effects of prolonged immobilisation to a period of immobilisation followed by normal recovery or followed by hyperactivity stimulation in chick embryos and showed that hyperactivity following a period of immobilisation led to reduced joint fusion and commencement of cavitation, particularly at the hip joint (Rolfe et al. 2021). Similarly, external manipulation of immobilised limbs led to improved hip joint morphogenesis and cavitation compared to unmanipulated limbs (Bridglal et al. 2021). The plasticity of the developing joint, contributed to by the response to movement, is also shown by mouse mutants and embryo manipulations where skeletal elements are missing, leading to joints forming within a completely different morphological context e.g. mutant mice that lack Hox11 paralogous genes are missing zeugopod elements (Koyama et al. 2010) and amphibian limb amputations (Tsutsumi et al. 2015). The “new” joints have altered morphology to produce functional interfaces between very different interfacing skeletal rudiments. These studies show the remarkable plasticity of the skeleton in response to its biophysical context and the mechanical loads it experiences.

4.2.2

Ossification

Embryo movement is required for normal initiation and progression of ossification and mineral deposition, demonstrated in animal models (Hosseini and Hogg 1991b; Nowlan et al. 2008a, 2010a) and reflected in the brittle bones of human infants resulting from reduced uterine mobility, leading in some cases to fracture of bones at birth (Rodríguez et al. 1988a, b; Rodríguez and Palacios 1991; Hall 2009) (Fig. 4.2). Nowlan et al. showed reduced and abnormally shaped ossification fronts at E14.5 in some skeletal rudiments (Nowlan et al. 2010a). Gomez et al. (Gomez et al. 2007) used micro-CT scanning to focus on mineralisation and showed shorter mineralisation zones and less mineralisation in the Myf5-/-; MyoD-/- muscle-less mouse embryo at E18.5. This has consequences for both bone morphology and bone strength. Increased ossification has been shown in cultured femurs under hydrostatic pressure (Henstock et al. 2013). Optimal bone mineral density is controlled through

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life by a balance between resorption by osteoclasts and deposition by osteoblasts which is known to respond to mechanical input (Frost 2001). Mineral deposition at the periosteum increases the width and influences the circumferential outline of the bone. Osteoblasts deposit mineral at the periosteal (outer) surface while osteoclasts resorb bone on the inner endosteal aspect. Different rates of mineral deposition in different positions leads to specific cross-sectional shapes and to bone curvature. Several studies have shown simpler, more cylindrical shapes and straighter bones in immobilised embryos (Gomez et al. 2007; Rodríguez et al. 1992; Sharir et al. 2011; Pai 1965b; Hall and Herring 1990; Rot-Nikcevic et al. 2006). Sharir et al. (Sharir et al. 2011) monitored structural and mineral changes over normal development in the appendicular bones and showed asymmetric mineral deposition and transient cortical thickening leading to anisotropic circumferential growth and bone curvature. They incorporated these observations into a model for optimal load bearing bone capacity and showed that in mdg mouse embryos, devoid of muscle load, the typical circumferential outline of each bone is lost and the bones are mechanically inferior. So mechanical input from movement builds stronger bones that are optimally shaped (circumferential and curvature) by modulating patterns of mineral deposition.

4.2.3

Size and Shape of Skeletal Elements

There is an enormous variety of shapes/morphologies among the more than 200 bones of the vertebrate skeleton; each is sculpted as it develops through patterns of localised growth, curvature, ossification, the formation of protrusions (tuberosities) for tendon and ligament attachment and the placement and morphogenesis of joints. Limb long bones in particular derive their individual and complex shapes, with tuberosities for muscle attachment, the need to accommodate articulation at joints and the formation of sesamoid bones that can redirect force and alter movement. The skeletal rudiments are not passive in this sculpting process as their emerging shape and form influences the magnitude and patterns of stimuli generated by muscle movement (Nowlan et al. 2008a). Immobilisation studies have shown that mechanical stimulation from movement influences multiple aspects of shape generation (Fig. 4.2). Impacts on joint formation, ossification, circumferential growth and curvature have already been covered in the previous sections so here the focus will be on longitudinal growth, the formation of tuberosities (entheses) for tendon attachment and sesamoid formation. Immobilisation in both chick and mouse models results in shorter skeletal rudiments with limb long bones and the scapula affected most (Drachman and Sokoloff 1966; Hall and Herring 1990; Nowlan et al. 2008b, 2010a; Pai 1965b; Pollard et al. 2017; Hosseini and Hogg 1991a, b; Rot-Nikcevic et al. 2006; Gomez et al. 2007). Differential effects on individual growth plates lead to changes in limb proportions (Pollard et al. 2017; Hall and Herring 1990) and is responsible for the

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abnormal protrusion of the lower beak seen in immobilised chick embryos since the upper jaw is reduced more than the lower (Hosseini and Hogg 1991a). The increase in rudiment length when eggs are incubated at higher temperatures are believed to be due to increased embryo motility (Hammond et al. 2007). Longitudinal growth is contributed to by chondrocyte proliferation in the proliferative zone, by the organisation of chondrocytes into elongated columns, by chondrocyte hypertrophy and extracellular matrix (ECM) deposition (Wilsman et al. 1996). Movement has been shown to affect proliferation, chondrocyte column elongation and matrix deposition. The size of the proliferative zone and chondrocyte proliferation are reduced in immobilised chick skeletal rudiments (Roddy et al. 2011a; Germiller and Goldstein 1997). Finite Element modelling based on realistic morphologies captured from the embryo showed that regions of elevated proliferation rates in the rudiment epiphyses correspond to regions under expansion and regions of high dynamic stimuli generated by muscle contractions (Roddy et al. 2011a). Chondrocyte proliferation and differentiation are controlled by a negative feedback loop between IHH and PTHrP signalling (Vortkamp et al. 1996) and expression of both Ihh and Pthrp genes were shown to be altered under immobilisation, Pthrp upregulated in chick femora (Roddy et al. 2011b) and Ihh downregulated in sutured mouse jaws (Jahan et al. 2014). Mechanical influence on chondrocyte proliferation has also been demonstrated in culture (Wu and Chen 2000). The organisation of chondrocytes into columns whereby daughter cells resulting from cell division intercalate and stack, makes an important contribution to longitudinal growth. Shwartz et al. (Shwartz et al. 2012) showed that chondrocyte stacking in zebrafish pharyngeal cartilage, in multiple immobilisation models, fails to occur, leading to abnormal skeletal morphology. Interestingly in muscle-less limb mouse embryos (Spd) the effect is not as severe since intercalation of chondrocytes and column formation process does occur, but results in shorter columns than normal. Entheses are sites of ligament and tendon attachment and are prominent shape features of long bones. Tendon attachment sites in particular provide the structurally important interface between tendon and bone, vital for tolerating the high forces of muscle contraction. This is achieved by a gradation of structures from tendon to fibrocartilage, to mineralised fibrocartilage, to bone (Schwartz et al. 2012; Felsenthal and Zelzer 2017). Entheses formation is a good example of the interdependence between muscle, tendon and bone involving different types of cross tissue regulation. Entheses are reduced in immobilised mice (Pai 1965b; Nowlan et al. 2010a; Rot-Nikcevic et al. 2006; Gomez et al. 2007; Blitz et al. 2009); while initiation is movement independent, immobilisation leads to arrest of chondrocyte proliferation and structure loss (Blitz et al. 2009). Reduced loading also leads to reduced mineral deposition and fibrocartilage (Thomopoulos et al. 2007; Tatara et al. 2014). The normal graded structure of entheses gives the required mechanical properties, preventing tearing and allowing the transmission of force and, as such, entheses serve as adaptors for the important mechanical interdependency of the tissues (reviewed in (Felsenthal and Zelzer 2017). Chick immobilisation experiments showed absence of characteristic sesamoids of the hind limb including the knee patella (Drachman and Sokoloff 1966; Hosseini and

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Hogg 1991a; Roddy et al. 2011b). Sesamoids are small bones positioned within tendons, providing leverage and facilitating distribution of forces and, due to their mechnosensitivity, were believed to form from tendon tissue in response to load. Eyal et al. (Eyal et al. 2015) however showed that the patella forms initially as part of the femoral cartilage template and subsequently becomes separated from the femur by a mechanosensitive joint formation process that fails to occur in muscle-less mouse embryos. So this feature of skeletal morphogenesis is due to mechanically sensitivity joint formation.

4.2.4

Tendon Maturation

Tendons are composed of fibrous connective tissue and connect muscle to bone, playing a key role in the integration of the musculoskeletal system and the transmission of loads due to muscle contraction. Tendon homeostasis and repair are known to require appropriate mechanical stimulation with TGFβ signalling playing a key role in the response, activating expression of Scleraxis (Scx) transcription factor and collagen target genes (Maeda et al. 2011). During limb development there are multiple examples of cross-regulation between muscle, tendon and cartilage rudiments ensuring the construction of a functional system (Edom-Vovard and Duprez 2004; Huang et al. 2015; Huang et al. 2013; Wang et al. 2010). While the earliest step in tendon development (specification) is independent of muscle contraction, later stages are affected by lack of muscle or immobilisation. Kardon (Kardon 1998) examined muscle and tendon development in chick limbs developing in the absence of either tissue and showed that tendon is required for correct muscle patterning and that in the absence of muscle, proximal and distal tendons are affected differently; while proximal tendons degenerate, distal tendons fail to mature. Immobilisation of embryos consistently shows reduced size and altered structure of tendons (Mikic et al. 2000; Beckham et al. 1977; Germiller et al. 1998; Peterson et al. 2021; EdomVovard et al. 2002; Pan et al. 2018) (Fig. 4.2). Havis et al. showed that TGFβ signalling and FGF signalling independently upregulate the tendon marker gene Scx and that both are regulated by muscle movement (Havis et al. 2016). Furthermore, the zinc finger transcription factor Egr1 has been shown to be mechanosensitive (Gaut et al. 2016) and is postulated to lie upstream of the TGFβ mechanoresponse. During development tendon precursor cells (tenocytes) align in the position of the future tendon, forming channels for collagen deposition (Richardson et al. 2007; Kalson et al. 2015). The ratio of cell to ECM reduces over time and at a critical point in later development there is a rapid increase in collagen fibril length coincident with a much stiffer and stronger tendon structure (McBride et al. 1988; Birk et al. 1995; Peterson et al. 2021). This transition is sensitive to embryo movement in the chick where immobilisations result in reduced mechanical modulus and failure to undergo normal developmental maturation (Pan et al. 2018; Peterson et al. 2021). The transition appears to take place later in mouse and is similarly dependent on mechanical stimulation (Theodossiou et al. 2021). This suggests that there is a

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Fig. 4.3 Tendons in the ankle region of control and immobilised embryos at E17 show reduced size and dramatic loss of spacing between tendons. The immobilisation regime used here was daily treatment with decamethonium bromide from E14 to E16. The most dorsal tendon is the Achilles tendon. dt distal tibiotarsus, t tarsometatarsus, AT Achilles Tendon. Scale bar as indicated

major reorganisation of the collagenous network during late-stage development that is dependent on movement. We recently showed that this maturation in mechanical properties in chick coincides with a step change in the size of the tendons and the spacing between tendons in the chick hindlimb ankle, as well as changes in the alignment of fibrils, and that in immobilised embryos structural maturation does not take place (Fig. 4.3) (Peterson et al. 2021). Currently our understanding of the molecular and cellular mechanisms involved in transducing the mechanical stimulation from movement that bring about structural and mechanical maturation of the tendon is lacking. At the same time tendon injuries and tendinopathies present major challenges for clinical treatment since repair is frequently limited or not possible (Schweitzer Jr. et al. 2018). Tissue engineering attempts have failed to produce a robust load bearing tissue, presumably because of our limited understanding of the regulatory mechanisms that need to be triggered (Bianchi et al. 2021). Furthering our knowledge of the mechanisms involved in tendon construction and maturation in the embryo/neonate holds the promise of recapitulating developmental steps to better effect (Glass et al. 2014).

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Secondary Cartilage Formation: Sculpting Functional Shape

In the embryo, bone can develop directly from condensation of mesenchymal cells (intramembranous ossification (or membrane bone)) or by replacing a pre-existing cartilage template (endochondral ossification). The long bones of the limbs that we have been considering in detail form by endochondral ossification. The cartilage template that precedes ossification is referred to as primary cartilage; secondary cartilage arises from the periosteum (or adjacent mesenchyme) and provides a means of growth and morphogenesis of membrane bones, adding functional features (Hinton 2014). In mammals, membrane bones form almost entirely in the craniofacial skeleton, the clavicle being the only membrane bone to form outside. It has been proposed that this predominance of endochondral ossification in the post-cranial skeleton may have evolved to better accommodate fast growth during embryogenesis since membrane bone can only grow from the surface (reviewed in (Hall 1987)). Membrane bones of the facial skeleton, in particular the lower jaw, have been studied extensively. Another important difference to note about membrane bones of the craniofacial region is that most of them are formed from migrating neural crest cells, in particular all of the facial skeleton, derived within the maxillary, mandibular, and hyoid arches. So, unlike the situation in the limb where the migratory cells give rise to muscle and the endogenous cells form the skeleton, in the facial region it is the opposite- the migratory neural crest form the skeleton while the muscle forms from resident cells. Also note that the appearance of secondary cartilage derived from the periosteum of membranous bone involves a switch from osteogenesis to chondrogenesis. Secondary cartilage develops on craniofacial membrane bones, including the mandibles, and also on the mammalian clavicle. Mandibles and clavicles are the first bones to ossify (Hall 1987) and demonstrate secondary cartilage formation that is particularly sensitive to mechanical stimulation from movement. The mouse mandible has three sites of secondary cartilage formation; the condylar, coronoid and angular processes of the lower jaw, while the clavicle has a single site. Chick embryo immobilisation showed that the clavicle is among the most strongly affected bones analysed, both in terms of growth reduction and absence of secondary cartilage initiation (Hall 1986; Hall and Herring 1990). In mdg mice both clavicles and mandibles are reduced and abnormally shaped, with indication that sites of secondary cartilage initiate but do not maintain (Pai 1965a; Herring and Lakars 1982). Rot-Nikcevic et al. carried out a detailed analysis of clavicles and mandibles in mouse embryos developing in the absence of muscle, recording alterations in the morphology, and asking if initiation and/or maintenance of secondary ossification sites are affected (Rot-Nikcevic et al. 2007). They showed that the effects differed according to rudiment and site going from absence of initiation of the angular process to dramatically reduced size of the condylar and coronoid processes (Fig. 4.2). A microarray comparison between transcripts in amyogenic and wildtype mandibles identified candidate genes involved in muscle dependent mandibular

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development (Rot et al. 2014). Sutured mouse embryonic jaws also showed reduced and deformed condylar processes (Jahan et al. 2014). The sites and details of secondary cartilage differ between mammals and birds. In birds, secondary cartilage sites are common where membrane bones form articulations and there is great variety in their precise shape and placement across birds with different feeding habits. This diversity can offer insights into the importance of mechanically directed developmental plasticity in sculpting functional morphologies (Solem et al. 2011). Ducks, who feed by levered straining, have a prominent bony process on the lateral surface of the lower jaw, at the point of muscle attachment. On the other hand, chickens and quails who feed by pecking have only a small bony ridge. Solem et al. showed that paralysis of duck embryos led to loss of the characteristic process resulting in a smoother mandible resembling that of the chick (Solem et al. 2011). A study of cichlid fish examined associations between facial bones and muscles using 3-dimensional morphometrics (Conith et al. 2019). They found that the shape of some bones appears to be more sensitive to muscle load than others. They also show strong associations that go beyond the points of muscle insertion implying that muscle load influences broader features of the craniofacial skeleton and that changes in the biophysical environment during development or through the life of the animal could alter morphology. Such changes could be due to heritable changes affecting development (e.g changing features of adjacent tissues) or due to changes in a novel environment. It is interesting in this context that zebrafish jaw shape was shown to be particularly sensitive to movement (Shwartz et al. 2012; Brunt et al. 2016). Jaw shape in mice has also been shown to be altered in response to mastication forces stimulating osteocyte differentiation (Inoue et al. 2019). The interdependence of muscle and skeleton in sculpting facial structures is also illustrated by neural crest cell interspecies transplantation experiments where quail neural crest are transplanted into a duck embryo leading to quail derived facial skeleton with quail shape, but also muscle pattern and attachment sites secondarily altered to the quail pattern (Tokita and Schneider 2009; Solem et al. 2011).

4.4

Effects of Reduced Movement on the Spine

Less is known about the influence of foetal movements on the spine although spinal deformities have been described in conditions and syndromes of reduced in utero foetal movement such as FADS/Pena Shokeir syndrome (Bisceglia et al. 1987; Gupta et al. 2011; Hall 2009; Ochi et al. 2001; Persutte et al. 1988; Tomai et al. 2017) and arthrogryposis (Hall 2014; Ma and Yu 2017; Nouraei et al. 2017; Yfantis et al. 2002). Spinal defects associated with foetal akinesia include curvature abnormalities (Ma and Yu 2017; Zhang et al. 2021; Hall 2014; Tomai et al. 2017; Persutte et al. 1988; Ochi et al. 2001; Nouraei et al. 2017) and failed development of vertebrae (Bisceglia et al. 1987; Crane and Heise 1981). The curvature abnormalities observed due to foetal akinesia include scoliosis (abnormal lateral curvature)

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(Nouraei et al. 2017; Yfantis et al. 2002; Zhang et al. 2021) and lordosis (abnormal ventral curvature) (Nouraei et al. 2017; Tomai et al. 2017) and range phenotypically from mild to severe, with suggestions that the observed variability in effects depends on the developmental window in which movement is interrupted (Filges et al. 2019). Kyphosis (abnormal dorsal curvature) is not associated. As with effects on forming joints, ossification and skeletal shape, embryonic immobilisation models also show abnormalities of the developing spine that reflect clinical observations. Spinal curvature abnormalities were described in early chick immobilisation studies (Murray and Drachman 1969; Sullivan 1966, 1974; Hosseini and Hogg 1991a) including ‘the lateral bending of the cervical spine, rotated about its own axis’(Murray and Drachman 1969) and ‘the buckling of the vertebral column into an S-shaped curvature’(Sullivan 1966). The structural and functional descriptions of paralysis associated with these abnormalities include: immobile neck-joints (Drachman and Coulombre 1962), absent articulations of the vertebral bodies and ill-defined articulation between the vertebral articular processes in the cervical spine (Murray and Drachman 1969), fusions and asymmetries of cervical vertebrae (Sullivan 1974; Hosseini and Hogg 1991a), extensive union between vertebrae, and absent articulation of the first cervical vertebra (Sullivan 1966) (Fig. 4.2). Similarly, altered foetal movement in mammalian embryonic environments further support in utero movement playing a critical role in spinal morphogenesis (Moessinger 1983; Panter et al. 1990). Induction of foetal akinesia in a rat model reported contractures in the spine and tail, with the neck in a permanent flexed position (Moessinger 1983). Spinal contractures including torticollis (wryneck), scoliosis and lordosis, especially in the cervical and lumbar regions due to the wedging of vertebrae were observed in an immobile embryonic goat model induced through the maternal consumption of conium seed and nicotiana glauca (Panter et al. 1990); similarly reported in calf and porcine development (Keeler 1974; Panter et al. 1985). The duration of reductions in foetal movements are an important factor in the severity and permeance of deformities observed, particularly in the spinal column, with short term foetal immobility resulting in modest to moderate phenotypes that resolved by 8–10 weeks postpartum (Panter et al. 1990). The murine embryonic models with absent or immobile muscle also display characteristic spine malformations of reduced foetal movement. Postural anomalies including enlarged and fused cervical vertebrae are present in embryos that have a complete absence of striated muscle (Myf5-/-:MyoD-/-)(Rot-Nikcevic et al. 2006), while immobile muscle in mdg embryos show defects in cervical, thoracic and lumbar vertebral segmentation including vertebral body fusion (Herring and Lakars 1982; Kahn et al. 2009; Pai 1965b) and abnormal development of intervertebral discs (Levillain et al. 2021). Alterations in the alignment of collagen fibres and mechanical properties of the annulus fibrosus are also observed in the cervical region of immobile mdg embryos at later stages (Levillain et al. 2021). It has been over 50 years since spinal defects were listed among the skeletal malformations resulting from chick embryo immobilisations but only recently have more detailed studies been performed, focusing on the specific effects of muscle forces on the spine (Rolfe et al. 2017, 2021; Levillain et al. 2019). Quantification of

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spinal curvature defects using three-dimensional imaging of paralysed spines, as an analogy for Cobb angle measurements performed in abnormal human curvatures, identified significant kyphotic and lordotic curves following paralysis (Rolfe et al. 2017; Levillain et al. 2019; Lonstein 1999). Defects were observed in the cervical, thoracic and lumbar regions, the cervical region being the most severely affected (Levillain et al. 2019; Rolfe et al. 2017, 2021). The onset and duration of paralysis impacted the severity of defects in the spine, including the extent of curvature abnormalities, number and site of vertebral fusions, extent of vertebral wedging and vertebral shape changes (Fig. 4.2) (Levillain et al. 2019; Rolfe et al. 2017). The most severe defects were observed with paralysis at early stages of spine patterning (Levillain et al. 2019; Rolfe et al. 2017, 2021), with later and shorter periods of paralysis resulting in milder to moderate phenotypes, further supporting that the timing of foetal motility is a critical influence on the normal development of the spine, aligning with the variability seen clinically following foetal akinesia (Filges et al. 2019; Rolfe et al. 2017). The ability of the spine to recover if movement resumes was investigated through induced movement following immobility, but did not result in any spinal improvements under the conditions investigated (Rolfe et al. 2021). However, given the variability in defects observed, dependent on the timing of initiation and duration of paralysis, the capacity to recover following resumption of movement at different time points needs to be more fully assessed. Combining clinical and experimental evidence, it is clear that foetal movement plays an important role in normal morphogenesis of the spine. Interruptions in foetal movement, at critical windows, leads to congenital spine defects. Much remains to be investigated, including the molecular mechanisms impacted by alterations in the mechanical environment in the spine. Another important consideration is the impact on supporting connective tissues, essential for physiological function, such as the stabilising vertebral ligaments. Investigation of the mechanisms involved could further our understating of the origins of idiopathic scoliosis, where it is possible that mild or undiagnosed congenital spinal deformities might contribute to the progressive nature of conditions presenting in adolesence.

4.5

The Mechanisms that Transduce the Effects of Muscle Movement in the Developing Skeleton

Mechanical loading can produce a variety of biophysical stimuli- such as compression, tension, hydrostatic pressure, osmotic pressure, and fluid shear stress. How these stimuli are sensed by the cell and transduced to a biological output has been challenging to address. As reviewed in previous sections, a number of candidate regulatory pathways within the responding cells have been shown to be fundamentally altered, in particular the precise localisation of BMP and Wnt signalling at the joint which is lost under multiple immobilisation scenarios across chick and mouse

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(Singh et al. 2018; Rolfe et al. 2018). This shows how cells at the joint line are misdirected by inappropriate mechanical cues, resulting in joint fusions. A transcriptomic study was designed to search for genes that are differentially regulated in the immobile humerus and associated joints of muscle-less mouse embryos, compared to wildtype littermates (Rolfe et al. 2014). As previously mentioned Wnt and BMP pathway genes were strongly impacted. Genes associated with the cytoskeletal architecture and with Hippo/YAP signalling were also altered. Genes encoding integrins, cadherins and other ECM associated proteins were down regulated in immobilised tissue (Rolfe et al. 2014). YAP signalling is also of particular interest as a candidate mechanism because of its association with sensing the biophysical environment in other biological contexts (Nishioka et al. 2009; Dupont et al. 2011; Halder et al. 2012). Looking closer at YAP signalling we showed differential localisation of active YAP and YAP target gene expression associated with regions of growth in the enlarging condyles, which is altered in the absence of movement (Shea et al. 2020). This could be a contributory mechanism to altered shape of rudiments at joint interfaces. Less is known about how mechanical cues alter these effector pathways; what mechanism(s) mediates the cues at the cell surface. Candidate mechanosensory mechanisms include integrin signalling (cell-ECM interactions), cadherin signalling (cell-cell interactions), and mechanosensitive ion channels that respond to physical changes in the cell membrane (stress activated) (reviewed in (Dieterle et al. 2021). A number of different stress activated ion channels could together cover a broad spectrum of stresses in skeletal cells: Piezo1 and Piezo2 respond to high levels of strain and cell deformation while TPRV4 can transduce more physiological levels of strain (Kanju et al. 2016; Lee et al. 2014). It is interesting that mutation in Piezo2 leads to arthrogryposis and Walker syndrome characterised by kyphoscioliosis and joint contractures (Chesler et al. 2016; Ma et al. 2023). Recently, involvement of the TRPV4 Ca2+ ion channel in sensing movement has been demonstrated in mouse limb explants in culture (Khatib et al. 2023). This study showed that TRPV4 is expressed in the developing rudiment where its accumulation is sensitive to movement and that addition of a TRPV4 antagonist could reduce the shape features that are responsive to movement. Another mechanism that could sense physical stimuli is the primary cilium which projects outward from the cell surface and senses the mechanical environment (reviewed in (Spasic and Jacobs 2017)). In multiple contexts, primary cilia act as foci for Hedgehog (Hh), Wnt, and calcium signalling, with pathway components localised to the cilium (reviewed in (Bangs and Anderson 2017; Elliott and Brugmann 2019)). The physical characteristics of the cilium indicate a role in mechanosensing with the axoneme projecting into the extra-cellular space where It can interact with components of the extra cellular matrix as well as ligands or other diffusible molecules. During development, the primary cilium senses fluid flow at the embryonic node, playing a role in left-right asymmetry, as well as calcium signalling during kidney development (reviewed in (Bisgrove and Yost 2006). In growth plates of chickens, ciliogenesis is induced by loading (Rais et al. 2015). But less is known about the primary cilium on the developing skeletal rudiments.

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Although no functional relationship has been shown to date, we have demonstrated localised patterns of primary cilia on the chondrocytes of the developing mouse skeletal rudiments at stages when development is impacted in muscle-less mutants (Shea and Murphy 2021).

4.6

Implications for Future Work

The final shape and size of each bone is sculpted by mechanical cues from the musculature. During development of the musculoskeletal system there is close co-ordination and interdependence between the emerging tissues with reciprocal cross regulation between the forming musculature and skeleton. Focus here has been on the environmental effects of mechanical stimulation from movement, i.e., the influence of contracting muscles on tissue patterning, shape, structure and mechanical properties of the component elements of the skeleton, with examples from the developing limb, jaw and spine. The remarkable plasticity of the skeletal system in response to mechanical load from the musculature that this cumulative body of work reveals, is important for building an integrated system that ensures structural and functional compatibility between the elements and allows co-ordinated evolution. Developmental plasticity builds a finely tuned functional system, ensuring the capacity to respond to changes and fluctuations (West-Eberhard 2003, 2005a). The skeletal system has such remarkable plasticity that behavioural changes, perhaps in response to a changed environment, could bring about morphological change. WestEberhard argues that morphological changes can take place in response to environmental change acting within the plasticity of the evolved system, and that such changes could then be subject to selection with the addition of further heritable change. She cites the report of Dutch anatomist Slijper of a goat with malformations of the upper limbs that adopted bipedal movement and upon autopsy showed extensive co-ordinated changes throughout the musculoskeletal system (WestEberhard 2003, 2005a). The focus of Neo-Darwinism on gradual adaptation of existing traits falls short in providing an explanation for the origin of novelty; developmental plasticity and skeletal response to mechanical cues can provide such an explanation where a new structure would not require independent genetic mutations affecting each of the component tissues: rather a change affecting one tissue, its structure or performance, could impact other elements, maintaining a functional, integrated system. For example the ectopic formation of cartilage or bone within tissues that have chondrogenic capacity could give rise to extra skeletal elements such as sesamoids and in the case of the giant panda, the radial sesamoid in the wrist was co-opted to form an extra digit (Wang et al. 2022). The morphological innovation giving the “apposable” digit 1 in birds, allowing the grasp required for perching, is achieved by twisting of the metatarsal during development and this is lost following paralysis of chick embryos (Francisco Botelho et al. 2015). Another example is the loss of

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muscles and associated joints that occurred during the evolution of cetacean fins (Cooper et al. 2007). Waddington coined the term “epigenetic landscape” to represent the network of regulatory interactions involved in cell fate and tissue patterning decisions (reviewed in (Siegal and Bergman 2002; Tronick and Hunter 2016). Such networks of gene interaction, including input from environmental stimuli, connect the genotype to the phenotype and, importantly, it is the phenotype that can provide a selective advantage. Skeletal developmental plasticity is a great example to explain and further explore such concepts (Davila-Velderrain et al. 2015). Congenital abnormalities resulting from reduced foetal movement are heterogeneous and present several difficulties in terms of prenatal diagnosis and management (Niles et al. 2019). The demonstrated plasticity of skeletal development and the capacity for recovery indicated in experimental models gives hope for possible remedial intervention, especially in the case of extrinsic causes and milder abnormalities. More detailed information on the capacity for recovery and the threshold levels of stimulation needed for normal development would be very valuable. The large number of aspects of skeletal development that can be impacted by reduced movement including, joint function, bone strength and spinal curvature makes intervention more complicated but the progressive nature of conditions such as joint dysplasia and scoliosis highlights the importance of early diagnosis and remedial intervention. Tissue engineering approaches to regenerate skeletal tissues including cartilage, bone and tendon, hold great promise in the treatment of widespread skeletal conditions caused by damage or disease. Progress has been made on some fronts but major barriers still exist, often related to our limited understanding of what is required to direct stable cell differentiation and functional tissue assembly. The approach not only needs to provide cells with the potential to form specific tissues but also the environment that encourages appropriate differentiation, trophic and mechanical. The appropriate treatment regimen is vital. We need to understand fully the biological properties of the cells and how they respond to all aspects of the environment to be able to recapitulate essential aspects of the system. While our knowledge of the effects of mechanical stimulation on many aspects of skeletal development has increased enormously in recent years and aspects of the molecular mechanisms involved have been uncovered, we need to have a fuller understanding of the key cellular responses involved. This needs to be a major focus for future work to realise the promise to regenerative therapies. Compliance with Ethical Standards Authors Paula Murphy and Rebecca Rolfe declare that they have no conflict of interest. This chapter is a review of previously published accounts, as such, no animal or human studies were performed. Acknowledgements The authors wish to thank former PhD students and other members of the research team, and all who contributed to the work reviewed here, especially Niamh C Nowlan, Karen Roddy and Claire Shea. They would also like to thank funders, in particular Science Foundation Ireland, Irish Research Council, the Wellcome Trust and the National Institute of Health.

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Chapter 5

Upper and Lower Motor Neurons and the Skeletal Muscle: Implication for Amyotrophic Lateral Sclerosis (ALS) Fiorella Colasuonno, Rachel Price, and Sandra Moreno

Abstract The relationships between motor neurons and the skeletal muscle during development and in pathologic contexts are addressed in this Chapter. We discuss the developmental interplay of muscle and nervous tissue, through neurotrophins and the activation of differentiation and survival pathways. After a brief overview on muscular regulatory factors, we focus on the contribution of muscle to early and late neurodevelopment. Such a role seems especially intriguing in relation to the epigenetic shaping of developing motor neuron fate choices. In this context, emphasis is attributed to factors regulating energy metabolism, which may concomitantly act in muscle and neural cells, being involved in common pathways. We then review the main features of motor neuron diseases, addressing the cellular processes underlying clinical symptoms. The involvement of different muscle-associated neurotrophic factors for survival of lateral motor column neurons, innervating MyoD-dependent limb muscles, and of medial motor column neurons, innervating Myf5-dependent back musculature is discussed. Among the pathogenic mechanisms, we focus on oxidative stress, that represents a common and early trait in several neurodegenerative disorders. The role of organelles primarily involved in reactive oxygen species scavenging and, more generally, in energy metabolism— namely mitochondria and peroxisomes—is discussed in the frame of motor neuron degeneration.

Fiorella Colasuonno and Rachel Price contributed equally with all other contributors. F. Colasuonno Department of Experimental Medicine , University of Rome “Tor Vergata”, Rome, Italy Department of Science, LIME, University Roma Tre, Rome, Italy R. Price · S. Moreno (*) Department of Science, LIME, University Roma Tre, Rome, Italy Laboratory of Neurodevelopmental Biology, Neurogenetics and Molecular Neurobiology, IRCCS Fondazione Santa Lucia, Rome, Italy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Kablar (ed.), Roles of Skeletal Muscle in Organ Development, Advances in Anatomy, Embryology and Cell Biology 236, https://doi.org/10.1007/978-3-031-38215-4_5

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We finally address muscular involvement in amyotrophic lateral sclerosis (ALS), a multifactorial degenerative disorder, hallmarked by severe weight loss, caused by imbalanced lipid metabolism. Even though multiple mechanisms have been recognized to play a role in the disease, current literature generally assumes that the primum movens is neuronal degeneration and that muscle atrophy is only a consequence of such pathogenic event. However, several lines of evidence point to the muscle as primarily involved in the disease, mainly through its role in energy homeostasis. Data from different ALS mouse models strongly argue for an early mitochondrial dysfunction in muscle tissue, possibly leading to motor neuron disturbances. Detailed understanding of skeletal muscle contribution to ALS pathogenesis will likely lead to the identification of novel therapeutic strategies. Keywords Motor neurons · Amyotrophic lateral sclerosis · Skeletal muscle · Neurotrophins · Development · Neurodegeneration · Oxidative stress · Lipid metabolism · PPAR · Mitochondria

Abbreviations ALS ANS ATXN2 C9orf72 CNS CNTF FF FGF FR FTLD FUS GDNF GRN HMC IGF iPSCs LMC MMC MN MND MRF NFH NMJ NT PGC-1α

amyotrophic lateral sclerosis autonomic nervous systems ataxin-2 gene chromosome 9 open reading frame 72 central nervous systems ciliary neurotrophic factor fast-twitch fatigable fibroblast growth factor fast-twitch fatigue-resistant frontotemporal lobar degeneration FUsed in sarcoma/translocated in liposarcoma glial cell line-derived neurotrophic factor progranulin hypaxial motor column insulin-like growth factor induced pluripotent stem cells lateral motor column medial motor column motor neuron motor neuron disease myogenic regulatory factor neurofilament heavy chain neuromuscular junction neurotrophin peroxisome proliferator-activated receptor γ coactivator-1α

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PPAR ROS S SCA2 SOD1 TARDBP

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peroxisome proliferator-activated receptor reactive oxygen species slow-twitch fatigue-resistant spinocerebellar ataxia type 2 Cu,Zn-superoxide dismutase TAR DNA-binding protein 43

Introduction

Motor neurons (MNs) are found in both the central and the autonomic nervous systems (CNS, ANS), controlling a variety of downstream targets. In the CNS, two main categories, namely upper MNs in the cerebral cortex and lower MNs, located in the brainstem and spinal cord, may be recognized (Stifani 2014). The upper and lower MNs together are responsible for the action and control of body movement, using different neurotransmitters—glutamate for upper MNs, and acetylcholine for lower MNs—and a wide range of neuromodulators. Besides, the connectivity is guaranteed by the achievement of proper cytoarchitecture and cell differentiation, which in turn strongly depend on neurotrophic support by the appropriate targets. Any dysfunction in the nervous motor system, at any level (tissue, cell, molecular) results in devasting disorders, affecting the overall functioning of the body and leading to premature death. Such dysfunctions may either occur during development or relate to later lesions to the mature nervous tissue. The same neurotrophic factors, promoting cell survival during CNS maturation, are also involved in the response to neuronal damage, due to either exogenous or endogenous insults, by activating a variety of protection/regeneration pathways. The objective of the present Chapter is to clarify the relationships between motor neurons and the skeletal muscle, during development and in the pathogenesis of amyotrophic lateral sclerosis and related motor neuron diseases.

5.2 5.2.1

Upper and Lower MN Development Upper MNs

Upper MNs are responsible for voluntary movement initiation, muscle tone maintenance and posture regulation, to provide a stable background upon which to initiate voluntary activity (de Lahunta et al. 2021). Pyramidal and extrapyramidal components of the upper MNs system largely overlap in their anatomy and are interconnected to cooperate. Neurons of the pyramidal system are mostly localized in motor area in the frontal lobe or the adjacent parietal lobe of the cerebral cortex, and project to the brainstem. Their terminal branches are found in the gray matter of the caudal brainstem and spinal cord. The latter is an uninterrupted monosynaptic corticospinal pathway from the

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cerebrum to the spinal cord by way of the pyramids of the medulla (de Lahunta et al. 2021). The extrapyramidal system was first described by Johann Prus in 1898, who proposed the existence of alternative pathways, called the “extrapyramidal tracts,” that “delivered epileptic activity” from the cerebral cortex to the spinal cord. Such a system is a multineuronal, multisynaptic corticospinal pathway, composed of neurons located in the cerebral cortex, including the motor area, and descending into the brainstem. Their axons extend caudally from specific brainstem nuclei to the spinal cord without crossing the pyramids of the medulla. Their targets are subcortical and brainstem neurons. The telodendron of the brainstem neuron is in the gray matter of the spinal cord (de Lahunta et al. 2021). The signal necessary to perform a movement begins in the primary motor cortex of the brain, which is in the precentral gyrus. Specifically, in layer V of the motor cortex are the cell bodies of the upper MNs, referred to as Betz cells which have long apical dendrites that branch up into layer I (Genc et al. 2019). Damage to upper MNs leads to a characteristic set of clinical symptoms known as the upper MN syndrome. Typical manifestations of upper MN lesions include uncontrolled movement, decreased sensitivity to superficial reflex stimulation and spasticity (Ivanhoe and Reistetter 2004; Stifani 2014). Other disturbances may include weakness, spasticity, clonus, and hyperreflexia. Upper MNs lesions have a wide differential diagnosis which ranges from cerebrovascular accidents, traumatic brain injury, malignancy, infections, inflammatory disorders, neurodegenerative disorders, and metabolic disorders (Emos and Agarwal 2022).

5.2.2

Lower MNs

The lower MN system allows the transmission of the signal from the upper MN to the effector muscle. This system includes three types of neurons: somatic MNs, directly innervating skeletal muscles, special visceral efferent (branchial) MNs, and general visceral MNs (Stifani 2014). Somatic MNs present their cell bodies in the motor nuclei of the cranial nerves of the brainstem and in the ventral horn of the spinal cord. Special visceral efferents refer to nerve fibers that innervate the voluntary striated muscles of the larynx and pharynx, as well as the muscles of facial expression and mastication. These efferents are present in the following nerves: trigeminal, facial, glossopharyngeal, vagus and accessory. The general visceral neurons are unipolar cells whose somata can be found somewhere along a cranial nerve, or in the dorsal root of the spinal cord. Although they share the same function and, possibly, development and degeneration mechanisms, MNs are not a homogeneous population. Indeed, to recruit different muscle fibers, establishing and controlling muscle contraction, their diversity is a functional necessity.

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Overall, the human body has more than 120,000 MNs located in the spinal cord innervating more than 300 bilateral pairs of muscles, in turn composed of more than 100 million muscle fibers. Most commonly, mature MNs are functionally classified according to the muscle fibers they innervate into α, β, and γ MNs: with α-MNs innervating extrafusal skeletal muscle and driving muscle contraction; β-MNs innervating both intra- and extrafusal fibers, and γ-MNs innervating intrafusal muscle fibers of the muscle spindle and playing complex roles in motor control. Based on the properties of motor units formed by α-MNs, the latter are organized into: fast-twitch fatigable (FF), fast-twitch fatigue-resistant (FR), and slow-twitch fatigue-resistant (S) (Burke et al. 1973; Kanning et al. 2010). Morphological features of α-MNs subtypes are consistent with these different functions. According to their targets and functions, α- and γ-MNs morphology is remarkably different. Indeed, γ-MNs display smaller size and axon diameter, making their conduction slower (Burke et al. 1977). Moreover, their dendritic branches are significantly less complex (Westbury 1982). Such variations prefigure different programs for α- and γ- MN specification. Early discrimination of MNs in the spinal cord can be made, based on the selective expression of the nuclear receptor Err3 in γ-MNs, at early postnatal stages (Friese et al. 2009). Also, the glial cell line-derived neurotrophic factor (GDNF) receptor subunit GFRα1 is highly expressed in γ-MNs (Shneider et al. 2009). At difference, expression of Neuronal Nuclei antigen (NeuN) is restricted to α-MNs (Friese et al. 2009; Shneider et al. 2009). Even among α-MNs subtypes, variations in shape, excitability, firing patterns, and conduction velocities, are detectable, though less prominent than between α- and γ-MNs. Type FF-MNs display larger size and axon diameter, accompanied by more numerous axonal branches and presynaptic neuromuscular terminals per MNs and a more complex dendritic tree (Cullheim et al. 1987). S-MNs show smaller somata and axons, while properties of FR-MNs are considered intermediate. According to the so-called “size principle”, smaller S-MNs have higher input resistance, thus requiring lower synaptic activation to initiate action potentials. This implies that during muscle contraction they are the first to reach the threshold, while large motor neurons are the last (Mendell 2005). Noteworthy, to coordinately function, MNs are spatially grouped to reflect both their ontogenesis and their mature function. In neurodevelopment, post-mitotic MNs are grouped into motor columns all stretching along the antero-posterior axis of the neural tube. These are: the medial motor column (MMC), innervating postural muscles; hypaxial motor column (HMC) innervating respiratory muscles, and lateral motor column (LMC) innervating limb muscles (reviewed by Dasen and Jessell 2009). In each column, the complex of MNs innervating a single skeletal muscle is defined as a motor pool, sharing molecular and morphological properties. Such similarities include distinct expression patterns of transcription factors (Dasen and Jessell 2009), in turn crucial for the expression of pool-specific membrane proteins, such as neurotransmitter receptors, axonal guidance receptors, and adhesion molecules (Rekling et al. 2000; Dalla Torre di Sanguinetto et al. 2008).

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Developmental Relationship Between Lower MNs and Skeletal Muscle

Skeletal muscle is a dynamic tissue of the human body representing 40% of total body weight, containing 50–75% of all body proteins, and accounting for 30–50% of whole-body protein turnover. Structural, contractile, and regulatory proteins contribute to multiple functions of the human body starting from the conversion of chemical into mechanical energy to generate force and power, and the production of movement (Frontera and Ochala 2015). As muscle plays a central role in wholebody protein metabolism, muscle mass depends on the balance between synthesis and degradation of proteins. In addition, skeletal muscle also represents a storage for important substrates such as carbohydrates or amino acids, that are needed by skin, brain, and heart for the synthesis of organ-specific proteins. Thus, it is not surprising that muscle dysmetabolism contributes to the genesis of many pathological conditions and/or chronic diseases (Wolfe 2006). In all vertebrates, the development of skeletal muscle occurs following a comparable pattern. Primitive segments named somites arise from the paraxial mesoderm in a cranial to caudal progression on either side of the notochord and neural tube, they then compartmentalize into a dorsal epithelial derma-myotome, and a medioventral mesenchymal sclerotome. A group of cells located in the medial region of the derma-myotome, are commonly believed to migrate laterally, thus leading to the formation of the myotome, the compartment of the somite responsible for originating the skeletal muscle (Kaehn et al. 1988; Kahane et al. 1998). Skeletal muscle is mainly composed of syncytial extrafusal muscle fibers embedded in a connective tissue network which is continuous with the tendon. Each myofiber contains many nuclei controlling a common cytoplasm filled with myofibrils composed of sarcomeres in series, the shortening of which generates force via interactions between actin and myosin (Zammit 2017). Myofibers are multinucleated and post-mitotic cells and each nucleus within a muscle fiber controls the protein types synthesized in that specific region of the cell. These regions, known as nuclear domains, have a highly regulated size. Protein expression in adjacent domains of a single fiber appears to be coordinated in such a way that the myosin produced is similar across the length of the fiber (Frontera and Ochala 2015). The regulation of skeletal muscle development is guaranteed by a group of transcription factors, named myogenic regulatory factors (MRFs), including MyoD, myogenin, Myf-5 and MRF4 (Kablar et al. 1997, 1998, 2003; Kablar and Rudnicki 2000; Zammit 2017). Specifically, in mature myofibers, MRF4 displays the strongest expression, related to its homeostatic role, in repressing genes associated to hypertrophy, possibly by direct interaction to inhibit MEF2 activity. Back (epaxial) musculature depends on Myf5 and is innervated by MMC MNs. Myf5 locus appears only active in muscle spindles and satellite cells (Zammit 2017). MyoD and myogenin are muscle regulatory factors involved in muscle development and differentiation (Megeney and Rudnicki 1995; Perry and Rudnick 2000). Limb musculature depends on MyoD expression and is innervated by LMC MNs. In adult

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muscle, MyoD is mainly expressed in adult fast muscle fibers and regulates fast muscle development (Maves et al. 2007). On the other hand, myogenin is predominantly expressed in slow muscle fibers and its expression increases oxidative metabolism in muscle. Another modulator of muscle phenotype is peroxisome proliferator-activated receptor (PPAR)γ coactivator-1α (PGC-1α), a metabolic regulator whose expression enhances slow oxidative muscle phenotype (Lin et al. 2002; Park et al. 2013; Baguma-Nibasheka et al. 2016). Interestingly, regulatory roles of PPARs in skeletal muscle fuel metabolism and inflammation have recently been emphasized, particularly in relation to the impact of their agonists on muscle chronic disease, contraction, and sepsis (Manickam et al. 2020; Crossland et al. 2021). Energy metabolism regulation by PPARs in muscle, as in other tissues, mainly affects peroxisomes, which closely cooperate with mitochondria in their lipid metabolism-related functions (Wanders et al. 1987; Faust et al. 2014; Fritzen et al. 2020). The nervous system exerts control over skeletal muscles by two mechanisms, namely neuromotor control, by which nerve impulses originating in the brain cortex or brainstem initiate muscle contraction; and neurotrophic control, exerted by the release of soluble factors from the nerve terminals of MNs at the neuromuscular junction (NMJ) (Cisterna et al. 2014). The importance of neural influences on skeletal muscle is apparent from the rapid and severe muscular atrophy that occurs whenever there is loss of neural continuity (Tomanek and Lund 1973; Zeman et al. 2009). On the other hand, developing muscles influence the development of MNs, through the action of muscle-derived neurotrophic factors on the CNS (Kablar and Belliveau 2005). In addition, skeletal muscle expresses several neurotrophin receptors, providing the basis for neurotrophin signaling within the muscle compartment (Chevrel et al. 2006; Sakuma and Yamaguchi 2011). Neurotrophins (NTs) are small multipurpose proteins fostering not only the development and function of different neuronal populations, but most importantly their survival. Some of these molecules can also induce the differentiation of progenitor cells to form neurons (Henderson et al. 1998). Neurotrophic factors include members of the classic neurotrophin family (nerve growth factor, NT-3, NT-4/5, brain-derived neurotrophic factor); the transforming growth factor-beta family members (GDNF, neurturin and persepin); the ciliary neurotrophic factor (CNTF) family of neurotrophic cytokines, and classic growth factors such as insulin-like growth factor-1/2 (IGF-1, IGF-2) and fibroblast growth factors (FGF-1, 2 and 5) (Lewin and Barde 1996). Some of these molecules are expressed in Schwann cells and they influence both motoneurons and skeletal muscle (Chevrel et al. 2006). The role of muscle in the epigenetic shaping of developing MN fate choices was studied employing an approach based on mouse mutagenesis and pathology. The developmental role of skeletal muscle was studied in Myf5 and MyoD knockouts (Rudnicki et al. 1993, Angka and Kablar 2009; Kablar 2011; Baguma-Nibasheka et al. 2016). In the latest article, cDNA microarray analysis, comparing developing MyoD / mouse limb musculature and Myf5 / back musculature with the control and with each other, at embryonic day 13.5 was performed. Such developmental

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stage was selected, as it coincides with MN cell death and inability of myogenesis to undergo its normal progression in the absence of Myf5 and MyoD. The lists of upand down-regulated genes in this study shed light onto the issue of how the myogenic program couples with the neurotrophic one. Moreover, different trophic requirements of LMC and MMC, potentially relevant to diseases preferentially affecting the lateral column, could be recognized. Genes including Kif5c, Stxbp1 and Polb, differentially expressed in the MyoD / limb muscle, and Ppargc1a, Glrb and Hoxd10, differentially expressed in the Myf5 / back muscle, proved to be actual regulators of MN numbers (Baguma-Nibasheka et al. 2016).

5.4

Motor Neuron Diseases

Motor neuron diseases (MNDs), such as amyotrophic lateral sclerosis (ALS), by definition, imply the dysfunction and death of both upper and lower MNs. Lower MNDs are clinically characterized by muscle atrophy, weakness and hyporeflexia, without sensory involvement. They may arise from pathogenic processes affecting anterior horn cells or motor axons and/or surrounding myelin. Neuromuscular junction pathology and muscle disorders may mimic a lower MN disorder and are taken into consideration for differential diagnosis (Zayia and Tadi 2022). These syndromes can be classified as hereditary, sporadic, and immunemediated. The latter are crucial to be identified, as effective treatments are available. Kennedy’s disease is the main hereditary lower MND, affecting adults and it is a late-onset spinal muscular atrophy and distal motor neuropathy. Other acquired syndromes, such as infectious, paraneoplastic, and radiation-induced neuropathies are well known and characterized, whilst focal lower MN syndrome is an amyotrophic lateral sclerosis (ALS)-mimicking state affecting young people (Verschueren 2017). The microenvironment around MNs, i.e., the surrounding CNS, Schwann cells along the axonal path, NMJ and muscle, can provide paracrine signals able to promote motor neurons’ survival, or trigger their programmed cell death. Being major effectors of neural impulses, and providers of neurotrophic factors, skeletal muscles play a critical role in the onset and progression of MNDs, and a defective production and/or function of neurotrophins is therefore a key factor in their pathogenesis. Interestingly, different muscle-associated neurotrophic factors are needed by LMC MNs, innervating MyoD-dependent limb muscles, or MMC MNs, innervating Myf5-dependent epaxial (back) musculature (Kablar and Belliveau 2005; Angka et al. 2008; Alaynick et al. 2011). Skeletal muscle-derived IGF-1, that exerts its anabolic functions inducing muscle hypertrophy in an autocrine/paracrine manner (Musarò and Rosenthal 2006), also acts on innervating MNs and neighboring Schwann cells, and even at a distance, on hippocampal neurons (Trejo et al. 2001). Indeed, it has been observed that localized expression of MLC/mIGF-1 transgene in the muscles of SOD1G93A ALS mice allowed muscle integrity and

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enhanced satellite cell activity, inducing calcineurin-mediated regenerative pathways. Moreover, it stabilized NMJs, reduced inflammation in the spinal cord, and enhanced MN survival, thus delaying the onset and progression of the disease (Dobrowolny et al. 2008). The precise correlation between neurons, neurotrophic factors, and trophic actions on myofibers in pathology is however still unclear (Cisterna et al. 2014). An interesting approach to define muscle-provided triggers of MN death relevant to MNDs consisted of knocking out MRFs, as Myf5 and MyoD (Rudnicki et al. 1993). The consequence is not only an embryo without any skeletal musculature, but even the complete absence of lower and upper MNs, which is the pathological definition of amyotrophic lateral sclerosis (Kablar and Rudnicki 2000). Interestingly, upregulation of Myod1 and Myf5 and other MRFs have been detected in the skeletal muscle of late stage SOD1G93A mice, suggesting an attempt of muscle regeneration. However, whether the enhanced levels of transcripts reflect accumulation of stabilized RNA molecules, or induced transcription, is not clear, as only a blunted response at protein level is observed (Manzano et al. 2011; Pansarasa et al. 2014). As to the pathogenic mechanisms shared by MNDs, oxidative stress has long been recognized as a primary culprit (Cookson and Shaw 1999; Carrí et al. 2003; Cunha-Oliveira et al. 2020). Indeed, cellular and molecular features of MNs, namely large size, long axons and high energy requirements, involve active oxidative metabolism, making them susceptible to oxidative stress and mitochondrial damage. Also, the abundance of biomolecules prone to oxidative damage, particularly lipids, make MNs especially vulnerable to redox status imbalance, caused by either overproduction of reactive oxygen species (ROS), or inadequate antioxidant response. Since skeletal muscle is a primary source of ROS, the microenvironment surrounding MNs may be critical in the maintenance of cellular homeostasis. It is known that cholinergic neurons contain abundant concentrations of scavenging enzymes, such as catalase and superoxide dismutases (Moreno et al. 1995, 1997; Kent et al. 1999), however, when conditions like physical exercise or pathological states increase ROS production, antioxidant response in MNs may be insufficient, generating oxidative stress. This is predicted to severely impact on mitochondrial function, thus perpetuating and exacerbating oxidative damage (Quijano et al. 2016). Interestingly, ALS has been linked to strenuous physical exercise (Daneshvar et al. 2021).

5.5

Involvement of Skeletal Muscle in ALS Pathogenesis

ALS is the most common adult MND, characterized by degenerative changes in both upper MNs in the neocortex, and lower MNs in the brainstem and spinal cord, associated with skeletal muscle atrophy and weakness (Baloh et al. 2007; Dobrowolny et al. 2008; Wong and Martin 2010; Masrori and Van Damme 2020). “Amyotrophic” comes from the Greek language and it means “no muscle nourishment,” whilst “lateral” identifies the portions of the spinal cord where the nerve cells,

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innervating the skeletal muscles, are located and undergo degeneration and pathological changes leading to scarring or hardening (“sclerosis”) in the region. ALS onset typically occurs in late middle life, with an incidence of 2 per 100.000 and prevalence of 5.4 per 100.000 individuals over the age of 15 years. Risk increases with age, with a peak around 75 years (Morgan and Orrell 2016; Loeffler et al. 2016). The initial clinical presentation of ALS is variable, most commonly starting with muscle weakness in the limbs. About 25%–30% of cases may instead present with dysarthria, dysphagia, and dysphonia, caused by a bulbar onset of the disease. Patients usually die from respiratory insufficiency. In most patients, ALS pathology is sporadic, but some individuals carry gene mutations, the most common of which are SOD1, C9orf72, TARDBP and FUS (Hardiman et al. 2017; Masrori and Van Damme 2020). The late onset suggests a multistep process in which genetic factors are penetrant only when combined with lifestyle or environmental factors, as also supported by incomplete heritability of known pathogenic mutations. Age and male gender are the only confirmed epidemiological risk factors associated with the development of ALS (Mead et al. 2023). The first genetic cause of ALS to be identified was a mutation to Cu,Znsuperoxide dismutase (SOD1) (Rosen 1993). SOD1 variants are present in around 20% of familial ALS, and 1% of the sporadic form. Over 170 different genetic alterations have been identified since then, and the possible pathogenic mechanism seems to be associated with protein misfolding and oxidative stress (Morgan and Orrell 2016). Besides SOD1, a hexanucleotide expansion in chromosome 9 open reading frame 72 (C9orf72) is the most common mutation causative of frontotemporal lobar degeneration (FTLD) and ALS. ALS patients usually have repeats of several hundreds to thousands, compared to a normal range of up to 20 repeats. The possible pathogenic mechanisms include toxicity induced by repeat containing RNAs, and loss of C9orf72 function due to epigenetic changes resulting in decreased C9ORF72 mRNA expression (Gendron et al. 2014). TAR DNA-binding protein 43 (TARDBP/TDP-43) mutations are found in around 5% of patients with familial ALS. TDP-43 is a versatile molecule participating in various cellular processes, markedly RNA metabolism (Butti and Patten 2019). It is primarily localized in the nucleus but also shuttles to the cytoplasm, where it is incorporated into stress and RNA granules (Mandrioli et al. 2020). Moreover, TDP-43 is involved in transcription, translation, mRNA transport and stabilization, microRNA production, and long non-coding RNA processing (Ratti and Buratti 2016). TDP-43 stress granule formation is caused by pathological conditions and cellular stress. Specifically, TDP-43 forms large inclusion bodies composed of aberrant proteins presenting different post-translational modifications such as hyper-phosphorylation, poly-ubiquitination, and proteolytic cleavage (Berning and Walker 2019). Loss of function may occur through depletion of TDP-43 in the nucleus of MNs, when TDP-43 is included in the cytoplasmic ubiquitinated protein inclusions induced by mutant TARDBP, resulting in dysregulation of nuclear RNA metabolism (Lattante et al. 2013).

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Fig. 5.1 Schematic representation of healthy vs. ALS affected spinal cord and skeletal muscle. The image compares normal shaped cells (on the left) with an atrophied muscle with an ALS-affected MN (on the right). For ALS neuron, a list of pathological features, including mitochondrial dysfunction and oxidative stress, is highlighted. A zoom of the NMJ shows NTs released from either MNs or skeletal muscle, suggesting the importance of reciprocal influence exerted by skeletal muscle and neurons

Finally, FUsed in sarcoma/translocated in liposarcoma (FUS) is found in around 5% of patients with familial ALS. FUS is a nucleoprotein regulating RNA and DNA binding, gene expression, and mRNA splicing. TARDBP and FUS share structure and functionality since both proteins play an important role in mRNA transport, axonal maintenance, and MN development (Morgan and Orrell 2016). Other genes have been identified as risk factors for ALS, e.g., neurofilament heavy chain (NFH) and progranulin (GRN). There is evidence of an intermediate length expansion of the CAG repeat in the ataxin-2 gene (ATXN2), with repeat numbers of 27–33 found in 5% of patients with ALS. Notably, the same gene with more than 34 CAG repeats causes spinocerebellar ataxia type 2 (SCA2), thus suggesting common pathogenic mechanisms of ALS with triplet diseases (Morgan and Orrell 2016). Pathogenic mechanisms of ALS, though incompletely understood, include oxidative stress, mitochondrial impairment, axonal and vesicular transport defects, glutamate mediated excitotoxicity, autophagic dysfunction, neuroinflammation and altered RNA metabolism (Fig. 5.1).

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Although originally defined as pure MND by Jean-Martin Charcot in 1869, ALS is now recognized as a multisystemic disease. Increased energy expenditure and lipid metabolism alterations manifest prior to the onset of the pathology before the loss of MNs. In fact, alterations in one or more cellular pathways, not only in MNs and neighboring astrocytes and microglia, but even in cells belonging to extra-neural organs/tissues, including muscle and brown adipose tissue, and possibly gut, have been considered fostering the onset and progression of the pathology (Pansarasa et al. 2014; Dupuis et al. 2018; Burberry et al. 2020; Appel et al. 2021). Being a key determinant of whole-body metabolic rate, skeletal muscle plays a critical role in ALS, contributing to defective energy metabolism and determining a derangement of basal metabolic rate, switching its metabolism toward an oxidative phenotype at the expense of a glycolytic one, and to the preferential use of fatty acids as fuel in ALS mouse models. Indeed, conditional Tardbp knockout mice show reduced expression levels of Tbc1d1, a gene known to mediate leanness and linked to obesity, which would explain the weight loss observed in such model and, possibly, in ALS patients with TDP-43 mutation (Chiang et al. 2010). Strong evidence argues for a primary and early role of skeletal muscle in ALS pathogenesis. Skeletal muscle fiber function clearly correlates with rate of disease progression in patients (Krivickas et al. 2002). Even in SOD1G93A mouse model, early involvement of muscle in MN degeneration has been described (Hegedus et al. 2008). In fact, skeletal muscle-restricted expression of human mutated SOD1, besides causing myofiber cell death and NMJ abnormalities, results in distal axonopathy and activation of a caspase-dependent neuronal apoptotic pathway (Wong and Martin 2010). This suggests a non-autonomous mechanism for MN degeneration. As to the pathomechanisms involved, mitochondrial dependent pathways were found to be perturbed in both muscle and neuronal cells. Emphasis on muscular early involvement in ALS is also given by studies reporting mitochondrial impairment in early pre-symptomatic SOD1G93A mice (Scaricamazza et al. 2020, 2021). Specifically, defective activity of the energy-transducing enzyme Complex I was detected in SOD1G93A skeletal muscle. Failure in such activity triggers the activation of the AMPK, which in turn promotes catabolic pathways, including lipid oxidation in skeletal muscle. This known action of AMPK is consistent with the increased lipid oxidation and inhibition of glucose utilization observed in early pre-symptomatic mice (Scaricamazza et al. 2020, 2021). Further and compelling evidence of muscle involvement in ALS pathogenesis, with special reference to lipid dysmetabolism, comes from a recent report addressing the role of FUS mutation in multiple models (Zhou et al. 2022). In this study, ALS patient muscle biopsies, K510Q-FUS myocyte cell line C2C12, and K510Q-FUS transgenic fly muscle were used to investigate cellular lipid metabolism. The authors observed lipid droplet accumulation and defective β-oxidation in all the examined samples, thus emphasizing the primary role played by muscle cells in the disease progression (Zhou et al. 2022). In the context of energy dysmetabolism, while the role of mitochondria has long been recognized and is presently thoroughly being investigated, possible involvement of peroxisomes remains largely unexplored. Notably, peroxisomal alterations

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have been associated with neurodegenerative diseases, involving energy dysmetabolism (Lizard et al. 2012; Fanelli et al. 2013). More specifically, participation of peroxisomes to pathogenic mechanisms, occurring in a rare genetic syndrome, involving motor neuron degeneration, caused by riboflavin transporter deficiency, have recently been shown (Colasuonno et al. 2022). Peroxisomes are ubiquitous cytoplasmic organelles, playing a key role in both production and scavenging of ROS, as well as in fatty acid metabolism (Islinger et al. 2018). They are also involved in a wide variety of anabolic and catabolic functions relevant to both muscular and nervous tissue. Previous reports on altered levels of lipids in the CNS and peripheral systems in ageing (López-González et al. 2015) and neurodegenerative disorders led to the hypothesis that impaired peroxisomal function might contribute to the progression of neurodegeneration in TDP-43 proteinopathies. Transcriptomic data analysis of genes involved in peroxisomal machinery and lipid metabolism, from frontal cortex area of post-mortem samples of such disorders, have revealed changes in their expression profiles. Particularly, those controlling peroxisomes biogenesis and β-oxidation, fatty acid metabolism and acylcarnitine biosynthesis were found to be altered in fronto-temporo-lateral dementia cases. It will certainly be worthwhile investigating these and other gene profiles in ALS in vivo models, in both muscle and nervous tissue.

5.6

Conclusions

The aim of this Chapter was to provide an overview of the interrelationships linking skeletal muscle and nervous tissue, focusing on their interplay in development and disease. Notwithstanding the great amount of data so far collected, emphasizing the role of muscle tissue in shaping differentiation, maturation, and survival of MNs, several open issues need to be addressed. Concerning development, it will be interesting to compare cDNA microarray analysis data from Myf5 and MyoD knockout mice with corresponding human conditions, to clarify precise dependance of the different MN subtypes from specific muscles. Moreover, since spinal MNs functions are finely regulated by several subtypes of interneurons, whose diversity is generated in a precise temporal sequence, it will be worthwhile investigating whether skeletal muscle exerts an effect on their differentiation (Deska-Gauthier and Zhang 2021). An even more complex picture emerges from studies on MNDs, due to their heterogeneity and multifactorial origin, involving genetic and environmental cues. However, shared pathogenic pathways seem to contribute to clinical, histopathological and molecular features. Among these, disturbed energy balance, including lipid and ROS metabolism seems a common and early trait. Noteworthy, over the years light has been shed on the tightly connected functions of mitochondria and peroxisomes. Both classes of organelles not only control lipid and ROS metabolism, but also share a redox-sensitive relationship, so that (dys)functions in either of the two determine changes in the function and biogenesis of the

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other. Peroxisomes are dynamic organelles able to respond to physiological and pathological environmental changes and extracellular stimuli, by altering their enzyme content, morphology, size and abundance. Interestingly, peroxisome proliferators proved neuroprotective in several in vivo and in vitro pathological models. Specifically, fenofibrate, a synthetic PPARα agonist, has been observed to reduce weight loss and motor dysfunction, therefore delaying progression of the pathology, in the ALS mouse model SOD1G93A (Esmaeili et al. 2016). The observed neuroprotective effects were accompanied by a preserved mitochondrial function. Besides in vivo models, the recently developed technology to reprogram patientspecific cells (i.e., fibroblasts or blood) is significantly contributing to the study of MNDs and to drug screening. Many laboratories are taking advantage of the use of induced pluripotent stem cells (iPSCs) to generate MNs from patients with sporadic disease or carrying specific mutations. Such patient-specific models—and organoids derived thereby—will certain be of great help in dissecting molecular mechanisms underlying specific pathological features and test potential drugs interfering with pathogenic pathways. Compliance with Ethical Standards The authors declare no conflict of interest. This chapter is a review of previously published accounts, as such, no animal or human studies were performed. Acknowledgements We wish to thank Dr. Martina Terricola and Dr. Chiara Marioli for their helpful assistance. This work was supported by Lazio Innova -Bandi per Gruppi di Ricerca 2020AMETISTA to S.M. (Prot. GeCoWEB n. A0375-2020- 36668; CUP: F85F21003700009) and by The Grant of Excellence Departments 2023-2027, MUR, Italy.

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Chapter 6

Mechanics of Lung Development Mark Baguma-Nibasheka and Boris Kablar

Abstract We summarize how skeletal muscle and lung developmental biology fields have been bridged to benefit from mouse genetic engineering technologies and to explore the role of fetal breathing-like movements (FBMs) in lung development, by using skeletal muscle-specific mutant mice. It has been known for a long time that FBMs are essential for the lung to develop properly. However, the cellular and molecular mechanisms transducing the mechanical forces of muscular activity into specific genetic programs that propel lung morphogenesis (development of the shape, form and size of the lung, its airways, and gas exchange surface) as well as its differentiation (acquisition of specialized cell structural and functional features from their progenitor cells) are only starting to be revealed. This chapter is a brief synopsis of the cumulative findings from that ongoing quest. An update on and the rationale for our recent International Mouse Phenotyping Consortium (IMPC) search is also provided. Keywords Fetal breathing-like movements · Lung development · Pulmonary hypoplasia · Alveolar epithelium

6.1

Introduction

Lung development is a complex process regulated by many transcription factors, hormones, cytokines, growth and morphogenic factors, expressed and present in the lung at different stages of development, and with their deletion leading to death in utero or soon after birth (Costa et al. 2001; Ornitz and Yin 2012). M. Baguma-Nibasheka (✉) Department of Pharmacology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada e-mail: [email protected] B. Kablar Department of Medical Neuroscience, Anatomy and Pathology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Kablar (ed.), Roles of Skeletal Muscle in Organ Development, Advances in Anatomy, Embryology and Cell Biology 236, https://doi.org/10.1007/978-3-031-38215-4_6

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During the very early stages of lung development, and therefore long before FBMs are involved or relevant, both mesoderm and endoderm express homeobox genes and contain homeobox transcription factors, retinoic acid and its receptors, various growth factors, all responsible for specification of the foregut, primary lung field and initial budding regulation. Hox genes are expressed along the anteriorposterior axis (endoderm and mesoderm) and may have a primary role in the patterning of the foregut (Lazzaro et al. 1991; Bogue et al. 1996; Ornitz and Yin 2012). Specifically, Hoxb5 and Nkx2.1 (TTF-1) are expressed in the primary lung field and Hoxb5 may regulate, from mesenchyme, the expression of secreted factors that pattern the endoderm (Krumlauf et al. 1987). Shh from the foregut endoderm regulates lung morphogenesis signaling through Gli genes expressed in the mesenchyme (Park et al. 2000; Kumar et al. 2005). Shh null mice exhibit fusion of the tracheoesophageal tube, loss of asymmetry of the lung (appears as one lobe) and diminished expansion of the alveolar region (Litingtung et al. 1998). Nkx2.1 expression in the foregut is itself regulated via the Wnt/beta-catenin signaling pathway (an evolutionarily conserved mechanism that plays an important role in maintaining cellular homeostasis), while inactivation of Wnt2a, Wnt2b, or beta-catenin is known to cause lung aplasia (Ornitz and Yin 2012). The lung bud interacts with the splanchnic mesoderm and in turn lung development is regulated by a series of transcription and growth factors, as reviewed previously (Costa et al. 2001). Mesenchymal-epithelial interactions and signaling during lung morphogenesis have been studied employing genetically engineered mice. Some of the relevant factors include: hepatocyte nuclear factor-3β (HNF-3β), sonic hedgehog (Shh), TTF-1 (Nkx2.1), HNF-3/forkhead homolog-8 (HFH-8), Gli (glioma-associated, GLI-Kruppel family members), GATA transcription factors (zinc finger DNA binding proteins that control the development by activating or repressing transcription), and retinoic acid receptor beta, RARβ (retinoids bind to nuclear retinoic acid receptors) (Whitsett 1998; Park et al. 2000; Kumar et al. 2005; Snyder et al. 2005). HNF-3β regulates Nkx2.1, which is important for development of distal pulmonary structures (alveolarization), and later for regulation of surfactant protein genes and, indirectly, alveolar stability (Kumar et al. 2005). Similarly, fibroblast growth factor (FGF)10 signaling, via the FGF2b receptor in the mesenchymal cells at the tips of the primary bronchial (or lung) buds, stimulates cell proliferation in the adjacent endoderm and its outgrowth (Volckaert and De Langhe 2015), whereas Shh in the ventral foregut endoderm stimulates proliferation of the adjacent mesenchyme, and thus the absence of either Shh or FGF10 prevents branching morphogenesis of the primary bronchial buds (Abler et al. 2009; Ornitz and Yin 2012). In fact, Fgf10-/- fetuses die after birth due to disruption of pulmonary branching morphogenesis (Peters et al. 1994). The same goes for TGF-β (transforming growth factor beta, a multifunctional cytokine belonging to the transforming growth factor superfamily), whose signaling pathway has several roles in embryonic and postnatal lung development and hemostasis (Bartram and Speer 2004). Its deletion disrupts branching morphogenesis and induces lung hypoplasia (Noe et al. 2019). Lack of Tgf-β1 is also associated with

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inhibition of FGF10 signaling pathway and abnormal development of the vascular system, but the lung epithelium is not affected. Finally, genetically engineered mouse studies in which the SPC gene regulatory region (pulmonary-associated surfactant protein C, SPC, an extremely hydrophobic surfactant protein essential for lung function and homeostasis after birth) was used to increase distal epithelial cell expression of either Shh (Bellusci et al. 1997) or keratinocyte growth factor, KGF (Simonet et al. 1995), resulted in increased proliferation of lung mesenchyme, leading to defects in branching morphogenesis (Peters et al. 1994). In summary, morphogenesis and differentiation of the lung are dependent upon precise signaling between epithelial cells (derived from the foregut endoderm) and cells from the mesenchyme (embryonic connective tissue, derived from several sources, such as mesoderm and neural crest). These interactions influence signaling networks that mediate cell proliferation, fate, migration and differentiation, because lung mesenchyme possesses growth factor receptors which respond to protein ligands secreted by the adjacent endoderm or epithelial cells (Whitsett 1998).

6.2

Stages of Lung Development

The lung consists of a series of branched tubules, including trachea (because of its developmental origin), bronchi and bronchioles, that conduct air (“conducting portion”) from the pharynx to the “respiratory portion,” a gas-exchange area in the respiratory bronchioles, alveolar ducts, sacs and alveoli. As previously mentioned, the lung develops from the foregut (Hogan 1999) and this process involves the evagination of endodermal lung buds ventrally into the neighboring mesenchyme (Cardoso 2000, 2001), subsequently undergoing four successive developmental stages: pseudoglandular, canalicular, saccular and alveolar, to the final formation of its respiratory portion, the respiratory bronchioles, alveolar ducts, sacs and alveoli (Ten Have-Opbroek 1991; Hogan and Zaret 2002). Each stage of lung development is marked by unique and well-described anatomical and histological changes. During the pseudoglandular stage, which in mice starts at mid-gestation at embryonic day (E)9.5–16.5, developmental events are primarily focused on and responsible for generation of the bronchial tree. The primitive lung appears like a gland (for example, a salivary or another exocrine gland) made up of acinar and tubular epithelial elements and a relatively undifferentiated distal endoderm (Ten Have-Opbroek 1981). The first specification of SRY-box transcription factor 9 (SOX9)-positive tip progenitors to either type I or type II pneumocytes of the alveolar epithelial cells is observed at E13.5 (reviewed in Eenjes et al. 2022). From E15.5 onward SOX9-positive progenitors are still involved in branching of distal tips, while cells in this recently branched epithelium now express HomeodomainOnly Protein homeobox (HOPX), as a marker of type I cells (reviewed in Eenjes et al. 2022).

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In the canalicular stage (E16.5–17.5) (N.B., as is ubiquitous during embryonic development, the cranial structures, including the part of the lung, develop faster than the caudal parts, resulting in partial overlap between stages), conducting airways, such as early forms of trachea, bronchi and bronchioles, become evident, and the distal endoderm begins to form vascularized respiratory portion, such as the branches of terminal bronchioles, known as respiratory bronchioles and alveolar ducts (Laudy and Wladimiroff 2000). The saccular stage (E17.5-postnatal, P5) is characterized by a thinning of the mesenchyme and an increase in the number of alveolar sacs, their extensive vascularization, and further differentiation of the endoderm into type I and type II pneumocytes, depicting the formation of large smooth-walled airspaces or saccules which will give rise to functioning alveoli after birth (Ten Have-Opbroek 1981). Toward the end of this stage, the fetal lung can support air exchange in prematurely born human neonates (Warburton et al. 2010). In the alveolar stage, P5–30, the alveolar sacs develop into mature alveolar ducts, sacs and alveoli. Alveolarization is the process by which most of the gas exchange surface is formed during this stage. The alveolar epithelium postnatally consists of cuboidal surfactant-producing alveolar type II pneumocytes and flattened gas-exchange performing alveolar type I pneumocytes. Genome-wide expression profiling has measured developing lung transcriptomes and the obtained data suggest that additional substages exist during lung development (Kho et al. 2009).

6.3

FBMs and Pulmonary Hypoplasia in Genetically Engineered Mice

Lung development is composed of growth (increase in size with structural specialization) as well as maturation (cellular and functional differentiation). Initially, it seemed that maturation mainly relied on hormonal factors, while lung growth appeared to depend largely upon physical factors and mechanical forces that affect cell cycle kinetics and cell differentiation (Liu and Post 2000; Inanlou et al. 2005). It has however become apparent that both growth and maturation may depend on mechanical forces (Inanlou and Kablar 2005b). As reviewed in Inanlou et al. 2005, FBMs produced by the respiratory muscles’ response to the neuronal activity of the brainstem’s respiratory center (Harding 1997) are detectable by E14.5 in the mouse (Abadie et al. 2000; Niblock et al. 2020), and at 10 weeks gestation in human embryos (de Vries et al. 1986). A lack of FBMs leads to pulmonary hypoplasia (small lungs in a small or normal thoracic cavity) (Liggins et al. 1981; Kitterman 1996; Harding 1997; Tseng et al. 2000; Inanlou and Kablar 2003, 2005a, b). Pulmonary hypoplasia is the most common autopsy diagnosis in the first week after birth (Nakamura et al. 1992) and is an immediate cause of death (Wigglesworth et al. 1981; Wigglesworth and Desai 1982).

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Hypoplastic lungs have fewer and smaller peripheral airspaces (alveolar ducts, sacs and alveoli), appearing to have been arrested at earlier stages of development (Porter 1998). In the mouse, the lungs will have a wet-lung to body weight ratio of less than 4% (Seegmiller et al. 1986). Our own studies on mouse embryos (Inanlou and Kablar 2003, 2005a, b; Inanlou et al. 2006) have indeed confirmed that the absence of FBMs may be associated with disturbed cell cycle kinetics. Those studies also revealed disturbances in the expression of several major mediators of lung organogenesis, including thyroid transcription factor-1 (TTF-1, also known as NKX2.1), which is one of the earliest markers of lung endoderm (Ornitz and Yin 2012), and insulin-like growth factor (IGF), as well as platelet-derived growth factor-B (PDGF-B) and its receptor, affecting lung maturation and the differentiation of both type I and type II pneumocytes. In summary, we found that in the Myf5: MyoD null embryos and fetuses, which entirely lack skeletal muscle and therefore have no FBMs, the lung weight was significantly decreased due to reduced cell proliferation and increased cell death in the lung tissue. The hypoplastic lung was arrested at the canalicular stage. TTF-1 lost its normal proximal-to-distal distribution gradient. In addition to the failure in growth, the lungs also exhibited failures in cell differentiation. Type II pneumocytes, responsible for the synthesis of surfactant, failed to assemble (did not utilize glycogen adequately), store (had irregular lamellar bodies, as revealed by transmission electron microscopy, TEM) and secrete (had irregular myelin figures, as revealed by TEM) the surfactant. Type I pneumocytes failed to differentiate from a cuboidal cell type into the squamous cell type (failed to flatten) to become a part of the blood-air barrier (Inanlou and Kablar 2005b). These results indicate that both the growth of the lung, and the differentiation of the alveolar epithelium (type I and II pneumocytes), depend on mechanical stimuli from the respiratory musculature. The normal morphology of the lung tissue in the muscleless fetuses, coupled with the very specific differentiation failures of type I and type II pneumocytes, was an opportunity to perform a cDNA microarray analysis to reveal a profile of genes involved in type I and type II pneumocyte differentiation (Kablar 2011). In other words, the differences in gene expression patterns between the control and the mutant (Myf5:MyoD) lung could be related to the described, specific differentiation failures of the alveolar epithelium. Nine named genes were found to be up-regulated and fifty-four down-regulated at least two-fold in the amyogenic embryos (BagumaNibasheka et al. 2007). Our analysis also revealed four molecules (transcription and growth factors) whose knockout mutants die at birth due to respiratory failure, indicating that these factors may be involved in the mechanochemical signal transduction pathways underlying FBM-related pulmonary hypoplasia: T-cell receptor ß, variable 13 (Tcrb-V13), connective tissue growth factor (Ctgf), special AT-rich sequence binding protein 1 (Satb1), and myeloblastosis oncogene (Myb), and immunostaining showed altered distribution patterns of the SATB1, C-MYB, and CTGF proteins (Baguma-Nibasheka et al. 2007). Tcrb-V13 (also known as lung Krüppel-like factor, LKLF) nulls had been analyzed by another group before our microarrays were performed, and, consistent with our microarray data, pulmonary hypoplasia was revealed in the mouse chimeras,

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though with apparently normal pneumocytes (Kuo et al. 1997). To examine later stages of lung development, the embryonic lethality due to germ line LKLF null mutation was circumvented, and some highly chimeric animals that died at birth were histologically examined, and abnormalities in their lung development was observed, confirming that LKLF plays an important role in normal lung development (Wani et al. 1999). Ctgf knockouts die at birth from respiratory failure due to skeletal dysplasia (Ivkovic et al. 2003), and Ctgf null lungs revealed all the criteria for mouse pulmonary hypoplasia, including the failure of type II pneumocytes to properly assemble and store the surfactant (Baguma-Nibasheka and Kablar 2008). However, despite the arrest at the canalicular stage of the Ctgf knockout lung development, Ctgf did not seem essential for the differentiation of type I pneumocytes, as indicated by the presence of normal podoplanin staining (a marker for those cells) and by TEM. This may possibly be because, despite their reduced thoracic volume (Ivkovic et al. 2003), these embryos may have had sufficient muscular activity, in the form of FBMs, to induce that stage of pneumocyte differentiation. Since the Ctgf gene is highly sensitive to mechanical stress, its encoded protein could serve as a downstream effector of mechanical cues affecting cell-matrix anchorage in morphogenesis (Chaqour and Goppelt-Struebe 2006; Zaykov and Chaqour 2021). CTGF is a mediator of activity of growth factors, such as PDGF, transforming growth factor-1 (TGF-1), vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF), and bone morphogenic proteins (BMPs), which induce its action (Brigstock 2003; Perbal 2004; Chaqour and Goppelt-Struebe 2006; Leask and Abraham 2006; Zaykov and Chaqour 2021). CTGF is also induced by cytokines, hormones, mitogens, oxygen deprivation, and mechanical stress (Kubota and Takigawa 2015). CTGF controls mutually exclusive cellular responses (proliferation versus differentiation, survival versus death), and this ability is dependent on the upstream signaling pathways, the cell type, and the presence of other growth factors and cytokines (Lau and Lam 1999; Leask and Abraham 2003; Desnoyers 2004; Wu et al. 2006; Ramazani et al. 2018). Satb1 codes for a global genome organizer protein, SATB1, which mediates chromatin looping for coordinated expression of cytokine genes (Kohwi-Shigematsu et al. 2012). Our examination of Satb1 null lungs (Alvarez et al. 2000) at P1 also revealed several criteria for mouse pulmonary hypoplasia, including a reduction of lung weight, disturbance of the TTF-1 gradient, and the ratio between type II and I pneumocytes was strikingly affected. We found a significant increase in the percentage of type II cells and a halving of type I cells within the pneumocyte population in the Satb1-/- lungs versus the controls (Baguma-Nibasheka et al. 2012). Similarly, Myb null lungs (Sumner et al. 2000) at E15.5 revealed several criteria for mouse pulmonary hypoplasia (reduced lung weight, and a decrease in the cell proliferation index, PI). The average PI in Myb-/- lungs was reduced to less than half in both the epithelium and the mesenchyme as compared to the controls (Baguma-Nibasheka et al. 2012). For comparison, we also examined the fetal lungs of mice lacking some genes which microarray analysis had shown to be downregulated in our amyogenic fetuses but whose knockout does not appear to affect

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viability (Baguma-Nibasheka et al. 2007), namely: Rag1 (recombination activating gene 1) null lungs (Mombaerts et al. 1992) and Rock2 (Rho-associated kinase 2) null lungs (Pelosi et al. 2007), both at E18.5, and they showed a normal lung phenotype (Baguma-Nibasheka et al. 2012). To follow up on the studies just summarized above (Baguma-Nibasheka et al. 2007, 2012; Baguma-Nibasheka and Kablar 2008), in 2023 a list of sixty-three named genes we found to be significantly up- or down-regulated in the amyogenic embryos (specifically Tables 2 and 3, in Baguma-Nibasheka et al. 2007) was submitted to the International Mouse Phenotyping Consortium (IMPC, http:// www.mousephenotype.org, an international organization independently attempting to identify the function of every protein-coding gene in the mouse genome) for ongoing updates. Such experiments will get us closer to identifying new molecular functions of the players from our list. Specifically, our list allows us to attribute functions in lung development and disease to new players while defining which features of the lung phenotype are the result of the absence of the gene alone and which are due to the absence of the mechanical forces of muscular activity. More about our own IMPC search will be described later in this chapter. Unrelated to our microarray data, and to investigate molecular mechanisms responsible for the development of primary lung hypoplasia, separate deletions of various genes such as Fgf9, Fgf10, Adam17, Nfib, Shh, and Dip2b have been associated with lung hypoplasia in mice (Pepicelli et al. 1998; Colvin et al. 2001; Zhao et al. 2001; Gründer et al. 2002; Yu et al. 2004; Noe et al. 2019; Sah et al. 2020). In addition, there are more examples of mutations in genetically engineered mice reported to have abnormal lung phenotype, with or without abnormal tracheal phenotype (Cardoso and Lu 2006; Warburton et al. 2010). However, these mutations and phenotypes are not necessarily linked to the FBMs-related etiology and pathogenesis of pulmonary hypoplasia. Finally, much information on the expression and activity of various factors has also been obtained from in vitro experiments employing normal, healthy tissue and on tissues explanted from mutants at specified gestational ages and subjected to various chemical and mechanical challenges (Liu et al. 1999), to identify which of the factors are triggered by mechanical forces and to define the transduction pathways that translate mechanical signals into meaningful gene instructions for the pulmonary cells and proper lung morphogenesis. However, we must focus on skeletal muscle-related mouse mutant modelling systems for the scope and purpose of this chapter.

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Differentiation of Pulmonary Epithelia and the Mechanical Forces

During very early stages of lung development, but especially during the saccular and alveolar stages of lung development, the alveolar epithelial progenitor cells in distal airways differentiate into epithelial type I and type II pneumocytes (Ten HaveOpbroek 1981, 1991; Hogan and Zaret 2002; reviewed in Eenjes et al. 2022). Type I alveolar epithelial cells are thin and flat, making up the majority of the alveolar surface area (98% in the mouse), and function mainly in gas exchange via their close contact with vascular endothelial cells, whereas the type II pneumocytes, in contrast, are cuboidal in shape, with two primary functions: they produce pulmonary surfactant, and are alveolar stem cells in adult lungs (Stone et al. 1992; Rawlins et al. 2009; Morrisey and Hogan 2010; Desai et al. 2014). Pulmonary surfactant is a complex mixture of phospholipids, neutral lipids, and proteins that is synthesized, packaged, and secreted by alveolar type II pneumocytes (Zuo et al. 2008). Spreading as a thin layer over the air-liquid barrier of alveoli, it is responsible for maintaining lung surface tension and preventing atelectasis (alveolar collapse) at the end of expiration (Batenburg 1992). Approximately 90% of surfactant is formed by phospholipids (phosphatidylcholine, phosphatidylglycerol, phosphatidylethanolamine) with the remaining 10% formed by proteins. These are primarily four surfactant proteins, A-D, each with its specific function. Surfactant proteins A and D (SP-A, SP-D) function in innate immunity, whereas surfactant proteins B and C (SP-B, SP-C) organize the surfactant protein into tubular myelin, which is essential in reducing surface tension (Batenburg 1992). Recent experiments have also affirmed that mechanical forces have an essential function in promoting the differentiation of both pneumocyte cell types I and II. For instance, one group demonstrated that type I pneumocyte differentiation occurs at the basal side of the distal airway epithelium through FGF10-mediated ERK1/2 signaling, and that mechanical forces from amniotic fluid inhalation and local growth factors synergistically control that process, whereas FGF10-mediated signaling induces a protrusive structure in some cells, protecting them from mechanical force-caused flattening to specify type II cell fate (Li et al. 2018). It is also suspected that some unknown intercellular interactions between blood vessels and epithelial cells may contribute to distal sac formation (Yang et al. 2016). In contrast to the alveoli, the conducting airways are lined by epithelial cells of a number of cell types, and, relevant to this story, specifically of these five types: club (previously Clara), ciliated, neuroendocrine, basal, and goblet cells (Ornitz and Yin 2012), with the neuroendocrine cells producing neuropeptides such as calcitonin, the goblet cells producing mucus, and the club cells (CC) producing secretoglobins such as CC secretory protein (CCSP). The CC may also give rise to both ciliated and neuroendocrine cells (Tsao et al. 2009). Interestingly, from the sixty-three named genes we found to be significantly up- or down-regulated in the amyogenic embryos (Tables 2 and 3 in Baguma-Nibasheka et al. 2007), which list we submitted to the IMPC for further investigation (last update in April 2023), eighteen more null mouse

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mutants have so far been created and, among these, the Secretoglobin Family 3A Member 1, Scgb3a1-/- mouse, whose respiratory system was studied in greater depth, appeared healthy and fertile, and did not show any lung or other abnormal phenotypes (Naizhen et al. 2019), although CC secretory function in producing secretoglobins is thought to play an important role in airway homeostasis and disease pathogenesis (Stripp et al. 2002). Unfortunately, the lungs of the remaining seventeen newly produced mouse mutants have not been studied yet by the IMPC (Dickinson et al. 2016). A follow-up PubMed (NIH/NCBI) search (last update in April 2023) using these seventeen genes, where the first four are upregulated and the remaining thirteen are downregulated, gathered partial functional information for these genes. So far it is known that these genes play a role in lung pathologies, especially tumorigenesis, which may be related to the epithelial phenotypes in mice considering that carcinomas have epithelial origin. However, data on the possible mechanochemical role for these genes in lung prenatal development is lacking, as do the respiratory systemrelated data from mouse mutagenesis and phenogenomics. Here is the summary of our findings. The four upregulated genes, in order of fold change (Table 2 in BagumaNibasheka et al. 2007) are: Chia (chitinase, acidic), with molecular function in metabolic catalytic and transport activity, is found to have a role in alveolar epithelial cell regeneration, airway remodeling, and adult lung tissue integrity (Hu et al. 2021). Car3 (carbonic anhydrase 3), with molecular function in metabolic catalytic and transport activity, has not been related to the lung phenotype so far. Sdc4 (syndecan 4), with molecular function in cytoskeletal organization and biogenesis, is found to inhibit the development of pulmonary fibrosis via TGF-β signaling (Tanino et al. 2019). Tnnt2 (Troponin T2, cardiac), also with molecular function in cytoskeletal organization and biogenesis, is found to be aberrantly expressed in lung cancer, but its prevalence increases with pathological severity (Tsuruda et al. 2022). The thirteen downregulated genes, in order of fold change (Table 3 in BagumaNibasheka et al. 2007) are: Uhrf1 (ubiquitin-like, containing PHD and RING finger domains, 1), a transcription factor with a function in lung tumorigenesis, was found relevant in predicting poor prognosis by triggering cell cycle in lung adenocarcinoma (Tu et al. 2020). Myl1 (myosin, light polypeptide 1), with molecular function in cytoskeletal organization and cell adhesion, was correlated to the postnatal lung development in the marsupials, together with several contractile protein candidate genes (Modepalli et al. 2018). Def6 (differentially expressed in FDCP 6), with molecular function in cytoskeletal organization and cell adhesion, was found to be related to tumorigenesis, and to be a marker for carcinoma staging and predicting survival, in the lung and other organs (Wang et al. 2021). Rac2 (RAS-related C3 botulinum substrate 2), with molecular function in cytoskeletal organization and cell adhesion, was found to be among key contributors to the onset of tumorigenesis in non-small cell lung carcinoma (Gupta et al. 2022). Lck (lymphocyte protein tyrosine kinase), with molecular function in tyrosine kinase pathway activity, was included within five-gene signature closely associated with relapse-free and survival of patients with non-small cell lung carcinoma (Chen et al. 2007). Itk (IL2-inducible

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T-cell kinase), with molecular function in tyrosine kinase pathway activity, was found to be a member of a series of prognosis-related genes associated with the lung adenocarcinomas (Xu et al. 2020). Lat (linker for activation of T cells), with molecular function in tyrosine kinase pathway activity, has not been related to the lung phenotype so far, and neither has Pcna (proliferating cell nuclear antigen), with molecular function in cell cycle regulation. Ccr9 (chemokine C-C motif receptor 9), was fond to suppress apoptosis of lung cancer cells (Li et al. 2015). Phgdh (3-phosphoglycerate dehydrogenase), with metabolic and housekeeping molecular functions, was found to define a metabolic subtype in lung adenocarcinomas with poor prognosis (Zhang et al. 2017). Dck (deoxycytidine kinase), with metabolic and housekeeping molecular functions, was found to have an important role in non-small cell lung cancer (Achiwa et al. 2004). Eif2s3x (eukaryotic translation initiation factor 2, subunit 3, structural gene X-linked), with metabolic and housekeeping molecular functions, was found within genome-wide expression profile differences between male and female pneumocytes type II, likely influencing adult lung physiology and pathophysiology (Sierra et al. 2023). Reg3g (regenerating isletderived 3 gamma), was found to be a candidate marker for CC precursors and CCs, supporting the idea that the diversification of the CCs occurs early in development (Guha et al. 2014). Independently from the IMPC, our PubMed search (last updated in May 2023) of the remaining genes (Tables 2 and 3 in Baguma-Nibasheka et al. 2007), revealed strikingly specific information on alveolar epithelial differentiation during lung development for the following three genes. In vivo genomic and genetic analyses show that Tcf7 (transcription factor 7, T-cell specific) promotes lung SOX9 epithelial progenitors, where type I pneumocyte-specific genes (Sema3a, Pmp22, and Igfbp7) were activated in mutant cells right after the trajectory origin, whereas type II pneumocyte-specific genes (Sftpb and Abca3) were activated at the origin, which is reminiscent of their spatial bias during normal alveolar differentiation (Gerner-Mauro et al. 2020). Ankrd1 (ankyrin repeat domain 1) has been identified recently as a marker of pneumocyte type I cells, in addition to type II pneumocytespecific markers (Sftpc, Lamp3), canonical pneumocyte type I markers (Ager, Cav1, Pdpn), mouse-specific pneumocyte type I markers (Hopx and Aqp5) and more recent human pneumocyte type I markers (Ankrd1, Clic5, Rtkn2) (Burgess et al. 2023). Furthermore, canonical YAP/TAZ target gene, Ankrd1, is in the topmost enriched transcripts in type I pneumocytes, which is consistent with findings that activated nuclear YAP/TAZ is important for the differentiation and maintenance of the mouse type I pneumocyte program, together with Ctgf (Burgess et al. 2023). Scap1 (src family associated phosphoprotein 1), as a part of SCAP/INSIG/SREBP signaling pathway, integrated with the Wnt/β-catenin and glucocorticoid receptor signaling pathways, influences epithelial development and cell cycle at E17.5, surfactant physiology, lipogenesis, and phospholipid transport during perinatal lung maturation, making this gene essential for the synthesis of pulmonary surfactant by type II pneumocytes, and therefore for the functional maturation of this cell type (Bridges et al. 2014).

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In conclusion, it is clear that the existence of mouse mutants generated by the IMPC is an opportunity to employ mouse pathology to reveal the roles each of these 17 genes may play in development of the respiratory system and, by combining this information with our published data (Baguma-Nibasheka et al. 2007, 2012), provide an insight into the role of these candidate genes in mechanochemical signal transduction pathways at play during FBM-related events crucial for lung morphogenesis and alveolar cell differentiation.

6.5

Effects of Mechanical Forces on Lung Growth and Cell Differentiation

Physical forces sometimes referred to as stretch or distension include a variety of different mechanical forces, such as: stress (force per unit surface area), strain (lengthening of a structure, considered in Wang et al. 2006 and Schmitt et al. 2012), shear stress (force of fluid flow on cell surface, considered in Li et al. 2018), spring force (returns the spring to its original length), surface tension (differences in intracellular adhesion and cytoskeletal contractility, considered in Yang et al. 2016), and pre-stress (isometric tension that balances intracellular and extracellular tensional pulling). Physical forces are generated inside and between cells, tissues and organs, and are as essential as genes and chemical signals for the control of embryonic development. In utero, FBMs drive the amniotic fluid flow and expose the developing lung to mechanical forces which promote lung growth and lung cell differentiation leading to the lung’s fully functional maturation (Joe et al. 1997; Wirtz and Dobbs 2000; Inanlou and Kablar 2003, 2005a, b; Mammoto and Ingber 2010; Piccolo 2013; Li et al. 2018). The type of physical force will likely employ different transduction pathways and molecules to translate the mechanical stimuli to meaningful information at the cellular level. Various surgical methods have been developed to investigate in vivo the role of physical forces in lung growth and alveolar epithelial differentiation. For example, surgically induced tracheal ligation, diaphragmatic hernia, and spinal cord or phrenic nerve transection were performed to cause the lack of different types of physical stimulation to the developing lung (Wirtz and Dobbs 2000). In summary, these different surgically induced approaches resulted in different and opposing effects on the lung. For example, under-distension (as it occurs in diaphragmatic hernia, lung liquid drainage, or abolition of FBMs) caused the lung to be too small and it favored type II pneumocyte phenotype at the expense of the type I pneumocyte phenotype, while over-distension (as it occurs in tracheal ligation) had the opposite effect (Liu and Post 2000; Wirtz and Dobbs 2000; Inanlou et al. 2005; Schmitt et al. 2012). The stretch-induced changes in gene expression could happen through the cyclic AMP (cAMP) pathway. cAMP is an important second messenger influencing various cellular pathways. A cAMP-PKA-dependent signaling pathway is shown

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to mediate phenotypic changes in type II pneumocytes in response to cyclic strain (Wang et al. 2006). Also, as indicated in Schmitt et al. 2012, stretch-induced type II cell maturation may involve epidermal growth factor receptor (EGFR), which binds to at least seven ligands, and whose activation is reported to mediate fetal type II pneumocyte differentiation (Sanchez-Esteban et al. 2004). Mechanical forces could become biochemical signals also by activation of stretch-activated ion channels, which alter intracellular concentrations of calcium, an important cofactor for the activation of signaling pathways. Blocking straininduced calcium channels, or chelating intracellular calcium stores, abolishes the proliferation of lung cells in culture, suggesting that calcium entry into cells, via stretch-activated ion channels, is vital for fetal lung growth (Liu et al. 1995). Blockade of voltage-dependent calcium channels also causes hypoplasia in lung explants (Roman 1995). Inhibiting calmodulin, a major calcium-signaling molecule, in type II pneumocytes of transgenic mice, disrupts lung development (Wang et al. 1996). These observations further highlight the importance of mechanical forces, including specifically the FBMs, in the activation of cellular pathways and the critical roles they play in promoting normal alveolar epithelial cell differentiation.

6.6

Role of Fetal Respiratory Musculature, the Diaphragm and the Intercostal Musculature

The development of all skeletal muscle of the trunk, including the muscles of respiration, the executors of FBMs, has been well described previously by us and other authors (Kablar and Rudnicki 2000; Merrell and Kardon 2013; Wood et al. 2020; Esteves de Lima and Relaix 2021). Basically, epithelial spheres known as somites, from the paraxial mesoderm on either side of the neural tube, form the myotomes whose myoblasts will develop and migrate laterally (to a different extent depending on the muscle) and, under the influence of the myogenic regulatory factors (MRFs) Myf5, Mrf4, MyoD and later Myog, give rise to skeletal muscle (Esteves de Lima and Relaix 2021). Other transcription factors such as Pax3, Pax7, Mef2 and c-Met play further roles in this context (Tajbakhsh and Buckingham 1994; Yang et al. 1996; Buckingham et al. 2003; Buckingham 2007). The major muscle of respiration is the diaphragm, a large dome-shaped structure that contracts rhythmically to expand the thoracic cavity. It consists of a thick crural region that attaches to the vertebrae and a thinner costal region that attaches to the ribs (Pickering and Jones 2002; Merrell and Kardon 2013). The diaphragm is innervated by the left and right phrenic nerves (Hammond et al. 1989; Allan and Greer 1997) and vascularized by the phrenic, internal thoracic, and intercostal arteries (Stuelsatz et al. 2012). According to Merrell and Kardon (2013), the diaphragm and its associated connective tissues develop from three sources: the septum transversum, the pleuroperitoneal folds and the somites. The septum (E9) is the initial barrier between

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the thoracic and the abdominal cavities (Perry et al. 2010). It gives rise to the central tendon and non-muscular components of the diaphragm (Iritani 1984). The pleuroperitoneal folds (E11–12.5) are two transient pyramidal structures on either side of the esophagus that eventually fuse with the septum (Iritani 1984; Allan and Greer 1997). The muscular part of the diaphragm, like other trunk skeletal muscles, is derived from the somites (Allan and Greer 1997; Babiuk et al. 2003). The migration of muscle progenitors starts at E11, and by E13.5 the phrenic nerve has branched and innervated the developing diaphragm (Greer et al. 1985; Uetani et al. 2006; Philippidou et al. 2012; Sefton et al. 2018). By E15.5, the three different embryonic sources have been coordinated to form a definitive diaphragm. Its further morphogenesis and the differentiation of its progenitor cells and myoblasts into multinucleate myofibers is, as in other skeletal muscles, determined by the MyoD family of MRFs (Inanlou et al. 2003). Many clinical case reports indicate that infants lacking FBMs in utero suffer from pulmonary hypoplasia and most of them do not survive the neonatal period (Blott et al. 1990; Roberts and Mitchell 1995). Our own analysis of mdx:MyoD-/- 9th mouse embryos (in which the muscle of the diaphragm is significantly thinned and not functional, causing diminished FBMs) confirms that the lack of mechanical stretch necessary for proper lung development leads to pulmonary hypoplasia by decreasing lung cell proliferation (Inanlou et al. 2003; Inanlou and Kablar 2003). Besides the diaphragm, the intercostal skeletal muscles may play some role in fetal respiratory activity. Myf5-/- mouse embryos, which lack the rib cage and functional intercostal muscles, also die at birth (Braun et al. 1992; Tajbakhsh and Buckingham 1994; Kablar et al. 1997). Analysis of their lungs showed that they were hypoplastic, due to a decrease in cell proliferation and increased apoptosis (Inanlou and Kablar 2005a). This may be related to a lack of sufficient pulmonary distention to induce the mechanochemical signals that trigger lung maturation, since the expression of TTF-1, PDGFs, and IGF in those lungs was found to be significantly perturbed (Inanlou and Kablar 2005a).

6.7

Conclusion

Lung development is a complex process involving multiple mechanical factors, transcription and growth factors, and other molecular players. Reports on the effect of factors known to be induced by mechanical forces further support the concept that mechanical signals from FBMs are vital to normal pulmonary development and maturation, as summarized in Fig. 6.1. However, a multitude of linkages are still being unraveled as our understanding of the whole process continues to grow.

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Fig. 6.1 Mechanochemical signaling in lung development: mechanical forces and transcription and growth factors. Summarized cascade of factors by which mechanical forces, mostly from fetal breathing-like movements (FBMs), lead to pulmonary organogenesis and its functional maturation. Mechanical forces (stretch or distension) include static stress, cyclic strain, shear stress, spring force, surface tension, isometric tension and more. Mechanical forces “translate” into biochemical signals via various and mostly unknown mechanisms but involve the following transcription and growth factors: TCRB-V13, CTGF, SATB1, MYB, FGFs, IGF, PDGFs, TTF-1, TGFs, EGF, VEGF, BMPs and more known and yet to be attributed factors. These mechanochemical interactions influence cell cycle kinetics, cell migration and other developmental events, leading to normal lung morphogenesis (organ growth and development of airways and gas exchange surface) and cell differentiation (development of specialized pneumocytes from progenitor cells). See text for a more detailed explanation (Abbreviations: TCRB-V13 T-cell receptor ß, variable 13, CTGF connective tissue growth factor, SATB1 special AT-rich sequence binding protein 1, MYB myeloblastosis oncogene, FGFs fibroblast growth factors, IGF insulin-like growth factor, PDGFs platelet-derived growth factors, TTF-1 thyroid transcription factor-1, TGFs transforming growth factors, EGF epidermal growth factor, VEGF vascular endothelial growth factor, BMPs bone morphogenetic proteins)

Compliance with Ethical Standards The authors declare no conflict of interest. This chapter is a review of previously published accounts and as such, no animal or human studies were performed.

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Chapter 7

Angular and Linear Accelerations, Ear, and the Skeletal Muscle You Sung Nam and Paul Hong

Abstract The ear serves two vital functions of hearing and maintaining balance. It achieves these roles within three major compartments: the outer, the middle, and the inner ear. Embryological development of the ear and its associated structures have been studied in some animal models. Yet, the role of skeletal muscle in ear development and its related structures is largely unknown. Research suggests the outer ear and parts of the inner ear may require skeletal muscle for normal embryogenesis. Here, we describe the role of skeletal muscle in the development of the ear and its associated structures. Moreover, we report the possible consequences of defect in the skeletal muscle of the ear and the clinical correlates of such consequences. Keywords Angular acceleration · Linear acceleration · Ear development · Skeletal muscle · Embryology · Otoliths · Vestibular function · Semicircular canal

7.1

Introduction

The ear serves two important roles associated with survival: hearing (auditory function) and maintaining balance (vestibular function). The ear is divided into three major compartments, the outer, the middle, and the inner ear. There are many associated structures within each compartment that work together to accomplish the two roles mentioned above. The auditory and the vestibular pathways are complex. However, here we describe the two pathways in simpler terms. For audition, the sound energy waves travel through the external acoustic meatus and canal of the outer ear to the tympanic membrane of the middle ear. When the sound

Y. S. Nam Faculty of Medicine, Dalhousie University, Halifax, NS, Canada P. Hong (✉) IWK Health Centre, Department of Surgery, Dalhousie University, Halifax, NS, Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Kablar (ed.), Roles of Skeletal Muscle in Organ Development, Advances in Anatomy, Embryology and Cell Biology 236, https://doi.org/10.1007/978-3-031-38215-4_7

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waves vibrates the tympanic membrane, the three ossicles of the middle ear (malleus, incus, and stapes) are put in motion to transfer the sound energy to the cochlea of the inner ear. Within the cochlea, the sound wave is converted into electrical impulses by the hair cells. The inner hair cells within the spiral Organ of Corti that resides in the scala media of the cochlea facilitates the transfer of the signal to the primary auditory cortex within the brain to allow audition (Peterson et al. 2022). The vestibular or balance system is mainly divided into two inner ear structures, the macula and the crista ampullaris, which detect linear and angular accelerations, respectively. The macula provides neural feedback to the utricle and the saccule about horizontal and vertical motions, respectively. Rotation of the head or the angular acceleration is detected by the three semicircular canals or ducts that contain the crista ampullaris (Casale et al. 2022). There are critical anatomical structures associated with attaining normal auditory and vestibular function. Thus, it is critical to understand the development process of the ear and its associated structures since an anomaly in one part can lead to an adverse outcome. As this report focuses on the role of skeletal muscle in development, the current chapter will discuss what possible outcome may result in ear development in the absence of skeletal muscle during embryogenesis. To illustrate the role of skeletal muscle in the development of various ear structures, a previous study used a compound-mutant mice model that completely lacked skeletal muscle (Myf5-/-:Myod-/-). Interestingly, the external auditory meatus and canal was absent in the compound-mutants. There was a malformation and a complete collapse of the distal ear canal, and the auricle was buried under the epithelium (refer to Figs. 1 and 2, Hong et al. 2015). Normally, there are two muscles within the middle ear space, the stapedius and the tensor tympani. As expected, these muscles were absent in the compoundmutants, which could make the chain of the three ossicles malfunction and malformed, and the transfer of sound energy not as efficient (Rot et al. 2017). The inner ear has six different sensory fields including three cristae ampullares in the three semicircular canals, two maculae within the saccule and the utricle, and the organ of Corti within the cochlea. As mentioned above, they serve roles in angular acceleration, linear acceleration, and hearing, respectively (Rot et al. 2017). In the compound-mutants lacking skeletal muscle, there was a complete absence of vestibular tenascin-positive type I hair cells in cristae ampullares, meaning there would be no sense of angular acceleration (Rot and Kablar 2010). Interestingly, the cochlear sensory field was not affected, and the macula was affected to a much lesser degree, indicating the hearing and detection of the linear acceleration may not be as altered (Rot and Kablar 2010). In addition to the defective crista ampullaris, the compound-mutants were not able to tilt their heads due to the fused cervical vertebrae in response to absence of skeletal muscle, making it harder or simply unable to detect any form of angular acceleration (Rot and Kablar 2010). Evidently, there may be many structures that are either malformed or absent in response to having no skeletal muscle during embryogenesis and subsequent developmental periods. In summary, when skeletal muscles are absent, the external ear, which is the first component the sound wave must pass through, is malformed.

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Functional consequence could be that the auditory function of the ear is abnormal (conductive hearing loss). Moreover, the ability to detect angular acceleration would be impacted due to absence of crista ampullaris, meaning that the sense of balance and its associated functions would be affected. Below, we describe the major clinical correlates that could manifest due to absence of the described structures.

7.2

Microtia/Aural Atresia and Other Congenital External Ear Anomalies

Microtia describes a spectrum of external ear disorder that is characterized by underdevelopment of the pinna/auricle to complete agenesis of the ear (anotia). The prevalence depends on the geographical region; however, it is known to range from 0.83 to 1.74 per 10,000 births. Microtia is known to be more prevalent in the Hispanic, Asian, Native American, and the Andean populations. More males tend to be affected than females, and unilateral cases are much more common than bilateral involvement (Luquetti et al. 2012). Embryologically, neural crest cells of the ectoderm and other embryonic cells (endothelial and cranial mesoderm) communicate with each other to drive the facial morphogenesis, including the external ear (Noden and Trainor 2005). Importantly, the outer ear development involves the mesenchyme of the first and second branchial arches (Luquetti et al. 2012). Several skeletal muscles are associated with the relevant branchial apparatus that give rise to the external ear. Thus, the absence of skeletal muscle could affect the branchial apparatus which may give rise to the abnormal morphology of microtia and other congenital external ear anomalies (Hong et al. 2015). Specific examples, other than microtia, may include cryptotia (helical rim cartilage is buried under skin) and prominent ears (ears that ‘stick out’). In addition to the cosmetic anomaly, children with microtia can often present with conductive hearing loss, where the sound wave do not enter the ear canal to be transmitted to the middle ear, cochlea and ultimately to the auditory cortex. The conductive hearing loss is caused by a condition called aural atresia where the external auditory canal fails to form (Andrews and Hohman 2022). Middle ear structures such as the ossicles or the tympanic membrane could also be malformed as both the external and middle ear structures are derived from first and second branchial arch, and first branchial pouch, groove, and membrane (Sadler 2012). Fortunately, there are various treatment options for microtia, which include observation, treating the hearing loss with devices such as bone-anchored hearing aids, implanting a prosthetic ear for cosmetic improvement, or reconstructing the pinna with alloplastic implantation or costal cartilage autografting (Andrews and Hohman 2022). Reconstruction of the pinna with autologous costal cartilage has been the most used method in the pediatric surgical setting (Bly et al. 2016).

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Benign Paroxysmal Positional Vertigo

Vertigo, in simple terms, is described as a perception of motion when the individual is stationary. There are two main types of vertigo, central and peripheral. Central vertigo is related to a problem in the central nervous system, whereas peripheral vertigo involves an abnormality in the inner ear (Thompson and Amedee 2009). Benign paroxysmal positional vertigo (BPPV) is the most common form of peripheral vertigo. The lifetime prevalence of BPPV in females, males, and overall was 3.2%, 1.6%, 2.4% respectively. Moreover, the one-year incidence was 0.6% and a German study found that 1.1 million adults suffered BPPV annually (von Brevern et al. 2007). Pathophysiology of BPPV is related to crista ampullaris as it is essential in regulating angular acceleration as mentioned above. To understand BPPV, it is important to know how the normal semicircular canals function to maintain balance and the specific role the crista ampullaris plays. Semicircular canal resides in the inner ear in three perpendicular or orthogonal planes. Each canal has a tubular arm called crura (singular, crus) and each crura has an ampullary end closer to the top or the front position. It is the ampullary end that the crista ampullaris sits on and each crista ampullaris contains the cupula that detects the flow of liquid in the semicircular canal in response to directional change. For example, if there is a movement towards left, the fluid in the left-sided horizonal canal lags which makes the cupula deflect rightwards. In response to the deflection, a neural signal is created to confirm the rotation of head to the left (Palmeri and Kumar 2022). In the case of BPPV, the cupula is not able to sense the motion and the head movement response is either absent or incorrect. This sensory discrepancy may lead to the sensation of vertigo and nausea in response to sudden head movements and change in posture such as standing up from lying down position (Hornibrook 2011). To diagnose BPPV, the supine lateral head test (Dix-Hallpike test) is used. While the patient lies supine and flexes the neck 30 degrees from horizontal, the clinician rotates the head to one side for one to two minutes and quickly rotates the head to the opposite direction. If the patient feels vertigo along with the onset of rotatory nystagmus (flickering eye movements), a diagnosis can be made (Palmeri and Kumar, 2022). Once a diagnosis is made, the treatment of BPPV is done by using the canalith repositioning (Epley) maneuver (Xiang-Dong 2011). It is important to note that BPPV is not treated pharmacologically, but rather using the Epley maneuver to treat the symptoms. The prognosis of BPPV is promising although recurrence can be seen in 40–50% of individuals within 5-years (Xiang-Dong 2011).

7.4

Conclusion

From an evolutionary perspective, human ears serve a vital role in survival by allowing us to hear and maintain balance. This chapter demonstrated the development of the ear from an embryological perspective and medical conditions that may

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develop when skeletal muscles are absent during embryogenesis using a well-known compound-mutant mouse model. Future studies to investigate the origin and the development of the ear in other models and its functions to further improve the clinical world of auditory and vestibular medicine is needed. Compliance with Ethical Standards Funding: Not applicable. Conflict of Interest: Authors declare no conflict of interest. Ethics Approval: This chapter is a review of previously published accounts, as such, no animal or human studies were performed.

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