The MicroRNA 2000 Transformer: Quantum Computing and Artificial Intelligence for Health [2 ed.] 9819931649, 9789819931644

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Yoichi Robertus Fujii

The MicroRNA 2000 Transformer Quantum Computing and Artificial Intelligence for Health Second Edition

The MicroRNA 2000 Transformer

Yoichi Robertus Fujii

The MicroRNA 2000 Transformer Quantum Computing and Artificial Intelligence for Health Second Edition

Yoichi Robertus Fujii Atsuta-Ku Kawada-Cho, 106-6 Nagoya, Japan

ISBN 978-981-99-3164-4 ISBN 978-981-99-3165-1 https://doi.org/10.1007/978-981-99-3165-1

(eBook)

1st edition: © Author 2017 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Th e M i c r o RN A 2 0 0 0 Tra ns fo r m er Q u

a n

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u m

a n d A rt i f i c i a l f or

C o m p u t i n g

I n t e l l i g e n c e H E A L T H

About the Second Edition

In recent years, microRNA (miRNA) deep sequencing and detecting techniques have become outsourced to be quick and smart very much. Therefore, lots of physicians have easily performed to investigate circulating miRNAs with human diseases for the precious medicine. The panel of plasma or serum miRNAs is a biomarker of diseases, such as cancers, Alzheimer’s disease (AD), infectious diseases, and immune diseases. 1. Now, miRNA is vital in bedside. When artificial intelligence (AI) is integrated into the smart clinic devices with miRNA panels, AI doctor would be realized on our ordinary lives. As we expected in the first edition, we can predict disease and elucidate the etiology of human diseases with the miRNA memory package (MMP) and miRNA entangling target sorting (METS). In this progression, we need more information on miRNA quantum language and artificial intelligence (MIRAI) for further future innovation of human health. 2. We obtained MicroRNA Quantum Code Book. We use analogous AI algorithm of transformer in METS/MIRAI. Transformer in deep learning AI, such as ChatGPT and BERT, is built on multi-head attention algorithm and appears as scaled dot-product attention. The inner product of the vector indicated the quantum energetic similarity between two quantum words, such as miRNA. Using The MicroRNA Quantum Code Book, which can achieve therapeutic targets in approximately 45 min, analysis of major depressive disorder from exosome miRNA in plasma, traumatic brain injury and AD (Chap. 5), analysis of brain glioma from miRNA of cerebrospinal fluid (Chap. 7), analysis of liver cancer (Chap. 10), influence of BCG treatment (Chap. 10), analysis of tumorigenesis of iPS cells (Chap. 9), etc. have been newly added. Further, Chaps. 1 and 12 were added to write about quantum computing and AI, and analysis of inflammatory bowel disease (IBD) and rheumatoid arthritis (RA), respectively. So, 5 years later, the prophecies of the first edition The MicroRNA 2000: from HIV-1 to Healthcare have all come true. Experience it with the second edition. Then, think about how it can be applied to the healthcare issues you are facing. The next vii

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About the Second Edition

5 years will be delivered to period during which AI-doctor METS/MIRAI™ will be delivered to everyone. Learn what problems can be solved and how they can be solved in the second edition, The MicroRNA 2000 Transformer: Quantum Computing and Artificial Intelligence for Healthcare. Go ahead and find therapeutic target with The MicroRNA Quantum Code Book. Godspeed! January 2023

Yoichi Robertus Fujii

Preference 2017

MicroRNAs (miRNAs), like language, is full of regulators for our lives. The functions of miRNAs are simple to tell–for instance, post-translational suppression; however, the mechanisms are tricky and the explanation of the role of miRNAs in evolution poses difficulties for graduates and even leading researchers. How is miRNA information used? What is the difference between CeRNA theory and RNA Wave 2000? What are the real rules for disease induction and cure? When would you use miRNA expression? How would you say miRNAs are information, not epigenetic? Where are miRNA locations? When are miRNAs transmitted? and so on. This book is a practical reference guide to this kind of problem. But there is no point whether it is false or true. This book is therefore intended not only for intermediate and advanced scientists or students, but also science enthusiasts. References include information from a wide variety of journals, from review articles to highly unique articles. Finally, research on the miRNA concept continues daily with more than 10,000 references per year as of 2016, so it’s up to the reader to decide if it’s false or true, if it’s close to the point. In 1928, the first report of RNA silencing might have been published by Wingard, suggesting that when the upper leaves of tobacco plants were initially infected with tobacco ringspot virus, the infected plants acquired some immunity to the virus, and then consequently the plants became asymptomatic and resistant to secondary infection. Later, Jacob and Monod at 1961 showed in their operon concept that the repressor inhibited lytic infection of phage to prokaryotes; therefore, the infected host had become resistant to lytic infection. In 1985, Coleman et al. showed in their miRNA immune system concept that RNA repressor of anti-sense RNA blocked viral RNA translation and miRNA had acquired immunity against viruses. In the late 1990s, Fire and Mello found the phenomenon of RNA silencing by double-stranded RNA suppressors in C. elegans; however, their discovery is distinct from viral infections at all. The human immunodeficiency virus type 1 (HIV-1) RNA genome encodes at least 12 proteins. The structural proteins, Gag, Env, and the enzymes, Pol, and the regulatory proteins, Tat, Rev, and the accessory proteins, Nef, Vpr, Vpu, and Vif. ix

x

Preference 2017

The Nef gene is located in the 3′ long terminal repeat (3′LRT) non-coding region of the HIV-1 RNA genome. It is known that high expression of the nef region overlapped with the 3′LTR has an evil role for AIDS; however, why and how? The nef region expression has induced plenty of activities, such as downregulation of CD4 and MHC I, alteration of signal transduction, the relationship to apoptosis, modulation of lipogenesis, upregulation of viral production, viral suppression, and rapid progression of disease. A better scenario has not shown these multiple characters of the non-coding 3′LTR including nef region. No one makes image if the 3′ LTR encodes miRNA genes except for me. When compared with artificially prepared exogenous RNA suppressors, endogenous miRNAs were encoded in the genome and controlled transcription and translation. Since miRNA did not execute target messenger RNA (mRNA) by cleavage, the miRNA machinery is different from previous RNA suppressors. Therefore, the HIV-1 RNA genome is also not cleaved by viral miRNAs. In 2000, we found hiv1-miR-N367 from provirus DNA of HIV-1, so, its miRNA is implicated in HIV-1 infection but it is not immune system, such as interferon. And hiv1miR-N-367 would target multiple mRNAs, which would render multiple functions of the nef/LTR region, suggesting that the nef/3′LTR region expression would show evil phenotype in AIDS. This book is a historical with respect, a reference, a systematic and new insightful about miRNA. Further, the book gives explanations of current topic of human diseases and medical care. Usually, it is a point distinct from the common sense of ready-made sciences. Do not hesitate to read through this book and please listen to me as much as possible. God bless you! February 2017

Yoichi Robertus Fujii

Preference 2014

We finished writing this book first edition The MicroRNA 2000: from HIV-1 to Healthcare in 2014, but when we sent a sample to bookstores, none of them published it. So, self-published in 2017.

xi

Contents

1

Quantum Computing and Artificial Intelligence for MicroRNAs . . . 1.1 Quantum Computing of Life . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Artificial Intelligence Workhorse . . . . . . . . . . . . . . . . . . . . . . . 1.3 First Take of microRNA Quantum Computing . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 2 3 6

2

Oxford miRNA Gardener . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 1987 Oxford . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 1983 Mobile RNA Element . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 1998 Fire and Ambros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Style Built up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 HIV-1 miRNA Sniper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 At 2000 RNA Wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 At 2012 miRNA in Food . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7 8 9 11 12 14 16 20 22

3

Mobile MicroRNAs: Potential for MicroRNA Biogenesis . . . . . . . . 3.1 Tribute to RNA Wave 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Biogenesis Map of miRNAs . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 A Large Pool of Circulating miRNA Versus a Small Pool of miRNA with the miRNA-Induced Silencing Complex (miRISC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Circulating Torus miRNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 miRNA–miRNA Programming . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Seed Theory Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 G Synergism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 G-Quantum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25 26 28

Food XenomiRNA Arise: MicroRNA Storm and Space . . . . . . . . . . 4.1 miRNA Storm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 XenomiRNA in Food-Derived Microvesicles . . . . . . . . . . . . . .

49 50 53

4

32 34 36 37 39 40 41

xiii

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Contents

4.3 miRNA as a Magic Bullet? . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 miRNA Genotype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 SIRT1 Connection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Neurodegenerative Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Cardiac Metabolism Information Nexus . . . . . . . . . . . . . . . . . . 4.8 miR-33 Pivot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9 Inflammation-Associated NF-κB . . . . . . . . . . . . . . . . . . . . . . . 4.10 New Food Information Processing . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

58 59 61 64 65 67 68 70 71

5

Exosomal MicroRNAs as Brain Memory Devices . . . . . . . . . . . . . . 83 5.1 miRNA Memory: Therapeutic Targets of Traumatic Brain Injury and Major Depressive Disorder . . . . . . . . . . . . . . . . . . . . . . . . 84 5.2 Role of miRNAs in Synapses . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.3 Memory Type and Location . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.4 Synaptic Plasticity by miRNA . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.5 Neuroplasticity by Secretion of Synaptosomal miRNAs . . . . . . . 92 5.6 Mobile Retroelements for Plasticity . . . . . . . . . . . . . . . . . . . . . 94 5.7 Uptake of Prions and Food miRNAs . . . . . . . . . . . . . . . . . . . . 97 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

6

Programmed Evolution by miRNA Memory . . . . . . . . . . . . . . . . . . 6.1 Programmed Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Acquired Phenotype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Quantum Theory of RNA Wave . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Quantum Tuning of miRNA Information . . . . . . . . . . . . . . . . . 6.5 MiR-AI: Vital Obesity miRNA Quantum Code for Type 2 Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

107 108 109 112 113

Communication in miRNAs Between Inflammation and Cancer . . . 7.1 Noncoding Proto-oncogene: Exogeneous miRNA Contamination in Cell Culture . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Burkitt’s Lymphoma Information by miR-155 . . . . . . . . . . . . . 7.3 Anti-inflammation/oncogenesis by miR-34? . . . . . . . . . . . . . . . 7.4 miR-21 and NOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Is the miR-17-92 Family Linked to Carcinogenesis? Brain Cancer Etiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Unknown Mdm2 Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

129

7

8

The Concept of “Virus” Is the Same as Exosomal miRNA Gene . . . 8.1 Colander for miRNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Gammaherpesvirus: Transfer and Ribosomal RNA-Derived miRNA Quantum Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Human Immunodeficiency Virus Type 1 . . . . . . . . . . . . . . . . . .

117 125

130 133 137 139 141 143 145 153 153 155 158

Contents

9

10

11

12

xv

8.4 Influenza Virus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Tropic Viruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Symphony of AIDS 2020 . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

161 163 164 166

ES Cells or iPS Cells, that Is the Question . . . . . . . . . . . . . . . . . . . . 9.1 Stem Cell Love Letter in the Bottle . . . . . . . . . . . . . . . . . . . . . 9.2 Direct Reprogramming of miRNAs: Carcinogenicity of iPS Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 GVHD in Stem Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Escape from High Wall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

171 172

“MIRAI” Healthcare: “Future” in Japanese . . . . . . . . . . . . . . . . . . 10.1 miRNA Quantum Language for Clinical Use . . . . . . . . . . . . . . 10.2 Sample Source of Circulating miRNA . . . . . . . . . . . . . . . . . . . 10.3 Chaos and Fuzzy: Flow of Information from Diagnosis to Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 A Universal MMP for miRNAs in Human Disease? . . . . . . . . . 10.5 MiR-34 Surveillance? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 MIRAI System for Healthcare: Hepatocellular Carcinoma . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

187 188 192

Deep Learning of miRNAs for Therapeutic Applications . . . . . . . . 11.1 Edible miRNAs Regulate Mendelian and Darwinism: COVID-19 Infection and Rice miRNAs . . . . . . . . . . . . . . . . . . 11.2 The Next Stage of Specific Delivery . . . . . . . . . . . . . . . . . . . . . 11.3 hiv1-miR-N367 Ortholog . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 HIV Edible Vaccine: Entangling Viral miRNAs and Host miRNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

207

215 219

Inflammatory Bowel Disease and Rheumatoid Arthritis . . . . . . . . 12.1 miRNA Biomarkers for Inflammatory Bowel Disease and Rheumatoid Arthritis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Inflammatory Bowel Disease Pathogenesis . . . . . . . . . . . . . . . 12.3 Rheumatoid Arthritis Pathogenesis . . . . . . . . . . . . . . . . . . . . . 12.4 Anticancer Condition of Autoimmune Diseases . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

223 224 225 226 227

173 177 180 182

193 197 199 201 202

208 211 214

. 223 . . . . .

Afterword: Finis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Fortuna Adversa Virummagnae Sapientiae Non Terret . . . . . . . . . . . . . 229 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231

Chapter 1

Quantum Computing and Artificial Intelligence for MicroRNAs MicroRNA Quantum Code with AI

You must keep sending work out: you must never let a manuscript do nothing but eat its head off in a drawer. You send that work out again and again, while you are working out another one. If you have talent, you will receive some measure of success-but only you persist. Asimov I

Overview “The MicroRNA Quantum Code Book” was published in 2023. This proved the following words. “Understanding humans with quantum” = “Humans live with quantum computing.” What helped me with it was the artificial intelligence (AI) that opened the door to the use of microRNAs (miRNAs) for human disease as diagnostic and predictive tools. In 2022, many reports have shown that AI is useful for medical applications. Therefore, based on this principle, miRNA entangling target sorting (METS)/miRNA quantum language and AI (MIRAI) may be able to accurately diagnose cancer, infectious diseases, and metabolic diseases at once using a panel of microRNAs (miRNAs). This is because the combination of METS/MIRAI and circulating miRNA biomarkers could clarify human disease differentiation and therapeutic targets. Quantum computers are being considered for studying protein structures and designing drugs, but these technologies only increase the speed of calculations already performed by supercomputers. Thus, it is not a theory that connects biology and quantum. However, METS/MIRAI creates a theory that combines miRNA, a biological information factor, with quantum algorithms and is far beyond existing thinking. Therefore, METS/MIRAI represents our lives themselves.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1_1

1

2

1.1

1

Quantum Computing and Artificial Intelligence for MicroRNAs

Quantum Computing of Life

“RNA controls RNA.” We know that microRNAs (miRNAs) are RNA genes and are inherited (Fujii 2023). MiRNAs are information. To understand how the information contained in miRNAs obeys the quantum laws of physics, we need to understand the relationship between miRNA quantum codes and organisms, especially human diseases. In general, quantum computing is thought to study protein structures and drug design; however, these applications of quantum computers are related to computing speed, and these algorithms for calculation speed have readily been established (Outeiral et al. 2020). It is information technology. What we are presenting here is a clear answer to the question Schröedinger could not explain: what is a living organism? It is METS/MIRAI (Fig. 1.1).

1.2

Artificial Intelligence Workhorse

Artificial intelligence (AI) has been applied to protein structure studies and drug discovery (Ferruz et al. 2023; Young et al. 2022; Kotsyfakis et al. 2022). These studies are the same as the research items for quantum computers. Aiming at

Fig. 1.1 MiRNA quantum computing of life

1.3

First Take of microRNA Quantum Computing

3

Fig. 1.2 Data of AI medical research in google scholar in 2010–2020

research speed and accuracy, computational technology is expected to advance sooner or later. Furthermore, the application of AI serves to clinical oncology, including histopathological analysis, prognostication, predicting responses to therapies, imaging, and precision medicine with genomic traits (Ling et al. 2022; Reel et al. 2022). Data volume with AI is quickly increasing year by year (Fig. 1.2). For METS/MIRAI, the AI and miRNA qubit algorithms were well synchronized with each other and helped outcome hub miRNA and therapeutic target validation data (Fujii 2023).

1.3

First Take of microRNA Quantum Computing

In the nearly 35 years since we created the link between small RNA and quantum theory in Oxford, AI, and quantum computing algorithms have become freely available to everyone. In “The MicroRNA Quantum Code Book,” we have explained miRNA entangling target sorting (METS)/miRNA quantum language and artificial intelligence (MIRAI), a disease therapeutic target search method based on quantum algorithms. However, there was no explanation for how it expanded biologically. Therefore, we updated “The MicroRNA 2000: from HIV-1 to Healthcare” first edition and explained the biological aspects of miRNA quantum

4

1 Quantum Computing and Artificial Intelligence for MicroRNAs

computing as the second edition, “The MicroRNA 2000: Quantum Computing and Artificial Intelligence for Healthcare.” The basic concept is “The RNA Wave.” First, we explain RNA Wave and prove that small RNA is transmitted as information (Chap. 2). We showed that small RNAs are biosynthesized as miRNAs and that RNAs exist as a circular torus (Chap. 3). It was shown that the cause of extinction of dinosaurs is obesity due to a storm caused by miRNA information, “RNA Storm” such as COVID-19 (Chap. 4). “Memory” was considered proof that neurodegenerative diseases are caused by miRNA information (Chap. 5). We discussed the robustness of the protein gene and miRNA gene of the human genome from an overview of miRNA quantum computing. In turn, this proves that miRNA is a gene (Chap. 6). The relationship between cancer and inflammation from miRNA clusters in the integration sites of retroelements was described (Chap. 7). MiRNAs made from viruses have a huge effect on human viral infections. In particular, the etiology of retrovirus human immunodeficiency virus type 1 (HIV-1) and coronavirus disease 2019 (COVID-19) (Chap. 8). The relationship between stem cell pluripotency and carcinogenesis was elucidated from the miRNA clusters (Chap. 9). We showed that miRNA, as a quantum substance, has both particle and wave properties. This makes it possible to superpose the outcome data (Chap. 10). Therefore, environmental miRNA data can cohere with host miRNA data. Therefore, dietary miRNA information has a profound impact on our health. From there, the conventional theory of evolution and genetics must change from the ground up (Chap. 11). Inflammationinduced carcinogenesis, such as hepatitis B virus (HBV)-induced liver cancer, is not necessarily true, and in the case of autoimmune diseases, miRNA-encoded programs that suppress inflammation and prevent malignant transformation are at work (Chap. 12). Overall, we have shown that humans live with quantum computing, METS/MIRAI. The unique idea is that small RNAs form a torus in quantum theory at Oxford (Fig. 1.3). However, quantum theory should be integrated into a topological space called a torus. When Schröedinger’s wave equation contains a magnetic field (B): Hψ =

1 2

- iħ

∂ ∂x

2

þ - iħ

∂ - Bx ∂y

2

ψ = Eψ

The torus space is T2. By unitary transformation, the wave function holds when the following pseudoperiodic conditions are satisfied: iBy

ψ ðx þ 1, yÞ = e ħ ψ ðx, yÞ, ψ ðx, y þ 1Þ = ψ ðx, yÞ Under this condition, H is a self-adjoint factor. Next, as a magnetic translation group (Brown 1964), to make the x-direction and y-direction conditions compatible: ψ ðx þ 1, y þ 1Þ = e

iBðyþ1Þ ħ

iB iBy

ψ ðx, y þ 1Þ = e ħ e ħ ψ ðx, yÞ

ð1:1Þ

1.3

First Take of microRNA Quantum Computing

5

Fig. 1.3 Torus in Oxford iBy

ψ ðx þ 1, y þ 1Þ = ψ ðx þ 1, yÞ = e ħ ψ ðx, yÞ

ð1:2Þ

Forms (1.1) and (1.2), iB iBy

iBy

e ħ e ħ ψ ðx, yÞ = e ħ ψ ðx, yÞ Therefore, eiB=ħ = 1 B = 2πħq q is integer (torus magnetic number). Magnetic flux can be quantized under discrete translational symmetry in B. Thus, small RNA can be expressed as quantum energy in the torus. The quantum computing algorithm was separately described later by Shor (1994). For miRNA qubits, fragment molecular orbital methods (FMO) were established in 1999 (Kitahara et al. 1999). We further calculated the approximate electronic state of the nucleic acid base according to the quantum computation algorithm (see Chaps. 6 and 10) (Fujii 2023). However, we had another debate in store, and it is the presence of endogenous miRNA.

6

1

Quantum Computing and Artificial Intelligence for MicroRNAs

References Brown E (1964) Bloch electrons in a uniform magnetic field. Phys Rev 133:A1038–A1044. https:// doi.org/10.1103/PhysRev.133.A1038 Ferruz N, Heinzinger M, Akdel M, Goncearenco A, Naef L et al (2023) From sequence to function through structure: deep learning for protein design. Comput Struct Biotechnol J 21:238–250. https://doi.org/10.1016/j.csbj.2022.11.014 Fujii YR (2023) The microRNA quantum code book. Springer Nature, Singapore Kitahara K, Sawai T, Asada T, Nakano T, Uebayashi M (1999) Fragment molecular orbital method: an approximate computational method for large molecules. Chem Phys Lett 312:319–324. https://doi.org/10.1016/S0009-2614(99)00874-X Kotsyfakis S, Iliak-Giannakoudaki E, Anagnostopoulos A, Papadokostaki E, Giannakoudakis K et al (2022) The application of machine learning to imaging in hematological oncology: a scoping review. Front Oncol 12:1080988. https://doi.org/10.3389/fonc.2022.1080988 Ling L, Aldoghachi AF, Chong ZX, Ho WY, Yeap SK et al (2022) Addressing the clinical feasibility of adopting circulating miRNA for breast cancer detection, monitoring and management with artificial intelligence and machine learning platforms. Int J Mol Sci 23:15382. https:// doi.org/10.3390/ijms232315382 Reel PS, Reel S, van Kralingen JC, Langton K, Lang K et al (2022) Machine learning for classification of hypertension subtypes using multi-omics: a multi-centre, retrospective, datadriven study. EBioMedicine 84:104276. https://doi.org/10.1016/j.ebiom.2022.104276 Shor PW (1994) Algorithms for quantum computation. The proceedings of the 35th annual symposium on foundations of computer science (FOCS): 124–134. Santa Fe Young RJ, Flitsch SL, Grigalunas M, Leeson PD, Quinn RJ et al (2022) The time and place for nature in drug discovery. JACS Au 2:2400–2416. https://doi.org/10.1021/jacsau.2c00415 Outeiral C, Strahm M, Shi J, Morris GM, Benjamin SC et al (2020) The prospects of quantum computing in computational molecular biology. WIREs Comput Mol Sci 11:e1481. https://doi. org/10.1002/wcms.1481

Chapter 2

Oxford miRNA Gardener MicroRNA Blossoms

The problem of self-delusion arises simply because science, like art, is an interpretative activity. Webster, S. Thinking About Biology

Overview Genetic traits are a curious question for biological research to elucidate the etiology of pathogenesis and are transmitted from cell to cell, individual to individual, and species to species. It is known that the transmission of genetic traits in humans is only due to inheritance by DNA from mother to child, but the conceptual scheme has been broken. First, human immunodeficiency virus (HIV)-transmitted human genome contains HIV-1 retroelement (RE) DNA in the genome. Approximately 50% of the human genome consists of REs. MicroRNA (miRNA) genes are thought to have evolved from REs, and the presence of HIV-1 miRNAs makes this a true story. Furthermore, miRNA genes within REs can be transferred reciprocally. Microvesicle-enveloped miRNAs themselves can be transferred through blood, cerebrospinal fluid, urine, feces, tears, saliva, ascites, sweat, semen, and milk without enzymatic degradation. Maternal genetic traits could be inherited by offspring via microvesicle-enveloped miRNAs, and then vertically acquired traits should be homed to offspring along with miRNA genes. Although the actual situation was unknown in old evolution theory, it used to be called the environmental factor of Darwinism, like the dark matter of the universe. Here, one of the environmental factors is miRNA genes in food, and the miRNAs in microvesicles (exosomes) are simultaneously responsible for nutrients as well as human inherited factors in food. Protein-coding and noncoding miRNA genes are associated with the 3′ untranslated region (3′UTR) of messenger RNA (mRNA). The 3′UTR is the target of miRNAs. Therefore, miRNA gene alteration plays an important role in human environmental adaptation. Thus, the information is not only DNA but also miRNA, the so-called “RNA information gene (Rig).” Most diseases are caused by the aberrant expression of Rigs. In addition, Rig in microvesicles could travel interstellarly from another planet to Earth and vice versa. Since human diseases are caused by RNA gene sets, various evolutions may occur between species depending on the Rig memory sets, so-called “programmed evolution.” RNA © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1_2

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waves are the very central tenets of life and are making an epoch in the history of social revolutions that are changing our lifestyles. In this chapter, we discuss curious miRNA information genes. Most of you probably already know that we live with miRNAs in our lives. We will try to explain the relationship between RNA Wave 2000 and human healthcare.

2.1

1987 Oxford

“Curiosity is a genetic trait shared by many species,” says Dr. Cookson (1994) in his book Gene Hunter. Human immunodeficiency virus (HIV) has a genetic trait in which HIV-1 particles carry two single-stranded RNAs (Chermann et al. 1985). If it makes sense to me as a standard researcher of RNA, it would be reasonable to assume that single-stranded RNA has no function and is a template of DNA to make proteins, because single-stranded RNA is susceptible to degradation by nucleases. However, I am a virologist. Early on, Drs. Cheng-Mayer and Niederman et al. (1989) reported that the negative factor of HIV-1 replication was named nef because the nef/ long terminal repeat (LTR) can suppress viral transcription from the LTR. However, with Nef protein, the mentioned nef activity yields different results, and Nef protein function is controversial as a positive factor. Furthermore, the mechanism of HIV-1 downregulation of transactivation is not completely clear. We thought why the nef/ LTR region occasionally repressed the transcription of the LTR-CAT reporter gene, but most experiments failed in the same assay. This is because there may be several factors that are not proteins, lipids, sugar chains, or DNA. When I was 31 years old and employed as a scientific officer at the Medical Research Council (MRC) in Cambridge, working at the Institute of Virology, Oxford, England, since 1987, we had the idea that small RNA from the nef/LTR region might affect viral replication in a mild tone. Therefore, we studied ambisense RNA function in insect viruses. The RNA Wave 2000 model was created from these data (Figs. 2.1 and 2.4). HIV gene recombinant baculovirus-infected insect cells produced large amounts of 32P-labeled small RNA fragments in the gel. However, all researchers believed that most of the low molecular weight bands were free 32P and that the lower position of the developed X-ray film was trimmed and could be thrown into the trash. Later, it was reported that the arbovirus RNA genome can make miRNA-like small RNAs (Asgari 2014). At this point, therefore, we first had to provide concrete evidence that the Nef protein is not implicated in promoter interference and that the Nef protein alone could not explain the translational suppressor phenomenon. Proteins are the basis of biological research. HIV-1 RNA is reverse transcribed and can be integrated into the human genome. Human papillomavirus has a double-stranded, circular DNA genome. Papillomavirus and HIV-1 have different nucleic acid genomes. Papillomaviruses are involved in the development of epithelial malignancies, especially cancer of the uterine cervix. Papillomavirus (HPV) E6 and E7 proteins inhibit the tumor suppressor p53 and Rb proteins. This is a well-known explanation for carcinogenesis due to

2.2

1983 Mobile RNA Element

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Fig. 2.1 A curiosity about the micro and nano aspects of AIDS research. Human immunodeficiency virus type 1 (HIV-1) has two single-stranded plus RNA genomes within the virion, but they do not form helical structures and are resistant to RNases. Furthermore, the HIV-1 RNA genome has noncoding regions called long noncoding regions (LTRs). The LTR nef exhibited positive or negative activity on HIV-1 proliferation, but it was unclear which nef functioned and how

papillomavirus infection. However, the E6 and E7 proteins are conserved in HPV, but their function differs between low- and high-risk types of HPV (Klingelhutz and Roman 2012). This evidence suggests that oncogenic activity may originate from small RNAs, namely, miRNAs. Quite recently, we found that cervical cancer is initiated by hpv16-miR-h3 HPV viral miRNA (Fujii 2022a). Although the chemical cores of both genomes are different, HIV-1, on the other hand, had no prominent carcinogenic proteins. Each event was inspired by the words of Dr. Zur Hausen (2008), who noted that “Research on infectious causes of human cancers has a great potential for future surprises,” and was used to help us to understand carcinogenesis. It suggested what is new. We created RNA Wave 2000, thinking that there are some ubiquitous substances in RNA and DNA viruses, which are small RNAs transcribed from viral genomes and host genomes (Fig. 2.2).

2.2

1983 Mobile RNA Element

Dr. McClintock (1953, 1983) showed that genetic elements can jump from one chromosomal position to another. She told me in my dream that if a small RNA jumps from the HIV-1 nef/LTR transcript in the human genome, the small RNA is

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Fig. 2.2 A fantasy from Montagnier and Barré-Sinoussi to zur Hausen. HIV-1 may not be a “virus.” It is just RNA information. HIV-1 rarely induces tumors such as human papillomavirus (HPV), a DNA virus, but HIV-1 does not have carcinogenic proteins such as HPV E6 and E7

responsible for HIV-1 latency, the negative regulation of HIV-1 replication, and the host’s homeostasis. Host small RNA can inhibit HIV-1 replication. Moreover, these small RNAs can travel everywhere and induce carcinogenesis. We woke up and found myself elegant. We realized that we needed to add new genes such as microRNAs and nano-RNAs to current biology and escape from the dogmatic world of DNA into the world of imaginary RNA. This is RNA Wave 2000 (Fig. 2.3). Additionally, we thought that a small RNA should act according to physicochemical laws, such as quantum theory. Mobile genetic elements, miRNAs, induce transcriptional and posttranscriptional regulation of genes. miRNAs are transported between and within inter- and intracellular, inter- and intraorgan, or interspecies and intraspecies in the environment. Small RNAs can self-proliferate. For example, Ebola virus produces viral miRNA (Liang et al. 2014), and favipiravir can block Ebola RNA-directed RNA polymerase (RdRp). In contrast, cell development is controlled by favipiravir, as favipiravir is not specific for Ebola prevention, and one of its side effects is the induction of teratogenesis in animals. Put another way, because mammalian development is controlled by cellular miRNAs and viral pathogenicity is also dependent on viral miRNAs, cellular and viral reverse transcriptase and telomerase have RdRp activity, so favipiravir can affect viral and cellular miRNA self-replication. There are two

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1998 Fire and Ambros

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Fig. 2.3 McClintock’s thoughts and HIV-1 infection. Jumping of miRNA elements, which are RNA information from HIV-1 RNA, alters the genotype and phenotype of infected cells, affecting not only neighboring cells but also distant cells. Of course, HIV-1 is vertically transmitted through breast milk, foods of infants, and is thought to carry RNA information

types of miRNA genes: resident miRNAs and genomic miRNAs. We will reach out to you, and in honor of Dr. McClintock, try replacing “miRNA” with “retroelement” (Fig. 2.4).

2.3

1998 Fire and Ambros

In 1998, C. elegans RNA silencing was discovered by Drs. Fire and Mellow. However, endogenous small RNAs were not identified in the RNA silencing discovery paper. Therefore, it is the gold standard for C. elegans, and miRNA silencing in mammals is, and still is, a difficult road. The problem is that no one believed my miRNA story and everyone ignored it. In particular, C. elegans endogenous small antisense, reported by Dr. Ambros’s laboratory (Lee et al. 1993), was inconsistent with artificial RNA interference by Dr. Fire et al. (1998). However, the RNA interference and Dr. Ambros lin-4 mechanism are exactly the same. However, our RNA Wave 2000 model consisted only of endogenous miRNAs, and further, the posttranscriptional and transcriptional regulation of miRNAs was greatly different from that of Drs. Ambros and Fire stories. On the other hand, it has already been reported by Pomerantz et al. (1990) that integrated HIV-1 provirus into the human genome produces small multiply spliced transcripts, and Cheng-Mayer et al. (1989) and Niederman et al. (1989) have shown that HIV-1 nef is a negative factor of HIV-1

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Fig. 2.4 A new idea for RNA Wave 2000. The problem is that no one believes my four criteria

transcription as described above but nobody described about small RNAs strongly labeled 32P as described above. However, we believe that the miRNA-mediated gene silencing neo-mechanism exists in human cells because of its potential and integrated retrotransposon-based expression system. “No, that would be the interferon (IFN) effect!!” There have been many criticisms of my concept many times. Eventually, all the big journals rejected my manuscript for lack of space. Later, it was found that the human genome contains approximately 50% of retroelements as noncoding regions (Lander et al. 2001). Therefore, human genomic RNA transcripts should have small RNAs from retroelements, similar to the integrated HIV-1 retrotransposon. Yes! Curiosity was born out of the British dream of “imagine,” and what started as a curiosity quickly became science (Fig. 2.5). However, in the end, research funds, research equipment, and research data were all thrown away, and we lost all our research resources.

2.4

Style Built up

Immanuel Kant (1724–1804) wished for world peace and said that” ‘Recognition (Erkennen in Germany)’ is derived from 50% of ‘Intuition (Anschauung)’ and 50% of ‘Concept (Begriff)’.” Therefore, we perceive phenomena in the natural sciences with intuition and concept. There were two concepts: promoter interference and latency. The former is that the retroviral promoter region is involved in transcriptional suppression (Cullen et al. 1984). In HIV-1 infection, CD4 levels return to normal after acute illness, and most patients remain clinically well for years (Fig. 2.6). Most retrovirologists intuit the similarity of these phenomena. We created

2.4

Style Built up

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Fig. 2.5 The human HIV-1 genome. HIV-1 makes multiply spliced RNA fragments from proviral DNA in the human genome. At times, HIV-1 nef transcripts make up 70% of all HIV-1 RNA transcripts. Therefore, nef might be involved in viral miRNA production. Solar eclipse

a miRNA gene concept for Northern blotting analysis, cloning of miRNAs from HIV-1-infected malignant T lymphoma cells, and vector STYLE available in vivo. STYLE was made from one of the retroelements, apathogenic feline spumavirus, cloned by our group. Subsequently, we experimentally demonstrated that miRNA hiv1-N-367, derived from the noncoding region LTR of proviral DNA in the human genome, regulates HIV-1 transcription. The nef/LTR miRNA was confirmed by computing methods by Bennasser et al. (2006); later, the hiv1-N-367 silencer activity was reproduced as artificial short interfering RNA (siRNA) by Yamamoto and French group (2009) in HIV-1-infected individual peripheral blood mononuclear cells (PBMCs). The hiv1-miR-N367 gene is a functional ortholog of hsa-miR192 (You et al. 2012). This concept of RNA gene silencers also provided intuition for applying them to the development of clinical applications, and currently, the retroelement of the human genome is the major source of small RNAs. We spoke with hepatitis C virus (HCV) researchers at the university at that time, but no one believed the concept and had no intuition (Fig. 2.6).

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Fig. 2.6 Latency of slow viral diseases. Most US virologists did not believe that the genome of an RNA virus contained the RNA information gene (Rig), but now maybe not be the case. The discovered “promoter interference” resembles the phenomenon of clinical HIV-1 latency. HIV-1 Rig has potential biological and clinical etiologies. Night moon

2.5

HIV-1 miRNA Sniper

First, cellular miRNAs hsa-miR-98/let-7, hsa-miR-181a-2, hsa-miR-624, hsa-miR29a, hsa-miR-29b, and hiv1-miR-N367 target the noncoding region of HIV-1, at least by computer research. Next, the concept of RNA silencers in HIV-1 infection had to be substantiated to prove the analogy of promoter interference and latency (Fig. 2.7a). Triboulet et al. (2007) reported that Drosha and Dicer KO T cells produce HIV-1 without latency. This report demonstrated, at least in part, the similarities between promoter interference and latency. Therefore, miRNA-mediated silencing may be involved in the latent state of HIV-1. An insight into the silencing of HIV-1 transcription and translation processes is that HIV-1 binds to CD4 and T-cell second receptors, and the particles are absorbed into the cell. The viral RNA genome is reverse-transcribed in the cytoplasm, and viral DNA fragments are integrated into the host genome. For chemotherapy, two processes in the viral life cycle are inhibited by reverse-transcriptase inhibitors and integrase inhibitors. Furthermore, proviral DNA is transcribed into mRNA and viral genomic RNA, and mRNA is translated into viral proteins such as Gag, Env, and Pol. In contrast,

2.5 HIV-1 miRNA Sniper

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Fig. 2.7 miRNA genes against HIV-1 genomic RNA. After infection, the virus tries to proliferate explosively. Therefore, the biological activity of HIV-1 promoter interference and clinical latency should depend on strong regulatory mechanisms because HIV-1 viral production is suppressed for long periods (latency). Cellular miRNAs (a) and hiv1-miR-N367 (b) target HIV-1 RNA

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although the viral replication process is controlled by both viral and host miRNAs, there are no drugs that inhibit this pathway of HIV proliferation. The viral proteins and genome are then assembled into viral particles, which bud with an envelope and mature with viral protease. This maturation process is blocked by protease inhibitors. Later, it was reported that HIV-1 proliferation is targeted by human cellular miRNAs and that HIV-1 positive factors, P300/CBP-associated factor (PCAF), Cyclin T1, and purine-rich element binding protein A (PURA, Pur-α) are suppressed by human cellular miRNAs. Furthermore, viral miRNA hiv1-miR-N367 targets HIV-1 genomic RNA, which could be associated with latency (Fig. 2.7b). The truth, whole truth, and nothing but the truth. Viral and cellular miRNAs are located here (see Chap. 8).

2.6

At 2000 RNA Wave

Now back to RNA Wave 2000. miRNA genes, as nonselfish RNA information, are contained in inserted intervening repeats of plant or meat diets, which are ingested, amplified, and then miRNA-induced RNA silencing in acceptor cells via endothelial cells and the bloodstream. Resident miRNAs were converted to DNA by reverse transcription and integrated into heterologous RNA information as genomic miRNAs with selfish genes, following Neo-Darwinism with retroelements. This is miRNA homing (Fig. 2.8). When medical applications for miRNA agents are

Fig. 2.8 Mobile miRNA homing with microvesicles

2.6

At 2000 RNA Wave

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Fig. 2.9 Engine of programmed evolution. In fact, exogenous miRNA (exomiRNA) is transferred from mother to child via the placenta, breast milk, and foods. ExomiRNAs are genes in the human genome; therefore, the maternal genotype is inherited in maternal programming. This is “programmed evolution.” The panel was modified from MicroRNA Protocols (Fujii 2013)

discussed, drug delivery is always first on the list. miRNAs are information, and miRNA information, along with retroelement RNA genetic information, is transferred from one cell to another cell. Therefore, we reasoned that mobile miRNA genes should be home from one genome to another genome via cross-kingdom. RNA viruses such as the HIV-1 retroelement are also RNA information genes, but it is easy for the RNA information itself to control RNA information and delivery with exogenous microvesicles (exosomes). RNA controls RNA. Since resident and genomic miRNA information must be inheritable, miRNA information is transferable, and acquired phenotypes can be inherited via miRNA information through programmed evolution. In turn, miRNAs are information, not a structural system. In our mammals, some of the acquired traits may be somatically inherited by motherto-child RNA information (Fig. 2.9). In fact, exogenous miRNA (exomiRNA) is transferred from mother to child via the placenta, breast milk, and foods (Maligianni et al. 2022; Chutipongtanate et al. 2022). ExomiRNAs are genes in the human genome; therefore, the maternal genotype is inherited in maternal programming (Bai et al. 2022). This is “programmed evolution.” The panel was modified from MicroRNA Protocols (see Fig. 2.9) (Fujii 2013). Moreover, paralogs and orthologs of miRNA genes in the DNA genome are evidence that miRNAs are self-proliferated according to the environment and programmed evolution. Furthermore, we will proceed with the idea of RNA Wave 2000. Alteration in miRNA profiling, through translation, transcription, splicing, epigenome,

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Fig. 2.10 Life regulator of RNA Wave 2000. In Buddhism, deva kings are twin statues (Wind God and Thunder God, or Aun) that guard humans against enemies. Genomic and resident miRNAs respond to damage

recombination, and mutation, can become coherent with heritable phenotypes, and information alterations can induce human disease. Therefore, the robustness of the gene regulatory network will partly rely on miRNAs conserved in resident miRNA pools under RNA Wave 2000 (Fig. 2.10). miRNAs have IDs that correspond to the RNA quantum language. Thus, environmental factors such as RNA information, stress, and quantum energy can influence the host miRNA quantum state and alter the miRNA qubit. Alteration of miRNA profiles can induce human diseases through environmental changes, including at the spatial level (Fig. 2.11). Here is our description of RNA Wave 2000 for the year 2000 and my answer as Rig’s disease to an inquiry from Dr. Zur Hausen, “What is new to understand human diseases.” Starting with the analogy of promoter interference and latency, we can learn that miRNAs are carcinogenic in various tumor phenotypes, such as virus- and environmental factor-induced tumors. miRNA information provides novel biomarkers and tools for the prevention and treatment of human disease (Fig. 2.12) (Ai et al. 2020; Fichtlscherer et al. 2010; Hanke et al. 2010; Hanson et al. 2009; Wang et al. 2010; Zampetaki et al. 2010). Based on four concepts of the RNA Wave, overexpression of oncogenic miRNAs may trigger tumorigenesis although aberrant gene tuning, and lack of tumor suppressor miRNA expression induces the main cause of aberrant gene expression. However, not only miRNAs that function as protein oncogenes or tumor suppressors but also aberrant miRNA gene expression combinations cause tumors. We found

2.6

At 2000 RNA Wave

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Fig. 2.11 Environmental factors of miRNAs. Crosstalk by miRNAs is complex because environmental factors can influence miRNA gene expression, and the miRNA genes themselves are also environmental factors. Therefore, Rigs regulate our lives in the environment and evolution as well. How to tune it? We wondered if the quantum RNA language (QRL) controls human health and diseases as RNA information gene diseases

that aberrant miRNA panels are involved in the etiology of human cancers such as breast, lung, colorectal, pancreatic, gastric, esophageal, hepatocellular, brain, bladder, thyroid, cervical and ovarian cancer, lymphoma, and leukemia (Fujii 2018, 2019a, b, 2020a, 2022a, b, c, 2023). Therefore, incompletely reprogrammed human induced pluripotent stem (iPS) cells can transform into tumor stem cells. Although exogenous miRNA (exomiRNA) genes are mobile, viral miRNAs insert into integration loci, and the insertion alters the expression profile of the Rigs, causing aberrant expression of transposable element (TE) transcripts and miRNAs. Integrated miRNAs can impose epigenetic regulation that can also induce tumorigenesis in host cells. A new category of genes called circulating Rigs (exomiRNA) was born. Circulating Rigs are involved in the concept of the RNA Wave. Therefore, circulating oncogenic and tumor suppressor miRNAs function when their aberrant expression induces tumorigenesis. Furthermore, in the seed and soil hypothesis of metastasis, primary tumor cells disseminate by intravasation into the blood or lymphatic system. This process requires a change in the primary tumor from an epithelial–mesenchymal transition (EMT) to a mesenchymal–epithelial transition (MET). EMT and MET are controlled by miRNA expression (Khanbabaei et al.

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Fig. 2.12 miRNA cancer biomarker

2022). On the other hand, exomiRNAs are mobile genes. We hypothesize that extracellular oncogenic miRNAs, such as circulating miRNAs, can readily be transported into the blood and the lymphatic system, and in distant tissues, oncogenic miRNAs incorporated into healthy cells alter the profile of miRNA expression. Aberrant incorporation of exomiRNAs induces tumors at metastasis sites. Thus, if circulating oncogenic miRNAs are present, miRNAs are also involved in oncogenesis and the EMT to MET in metastasis. It could be applied to the diagnosis, therapy, and prognosis of human cancers. In doing so, it is necessary to investigate whether the function of cellular miRNAs differs from that of circulating miRNAs.

2.7

At 2012 miRNA in Food

We leapfrogged my intuition and applied my leaps and bounds to the concept. In RNA Wave 2000, miRNA information acts not only as intraspecies genes but also as interspecies genes through food and may be useful in the treatment of fatal diseases, suggesting that food is medicine by Hippocrates (Fig. 2.13). Therefore, it is believed that plant miRNA information can be useful for human health management, such as the prevention and treatment of HIV-1 infection. At the very least, transgenic miRNA-informed fruits are possible for such applications. Recently, rice miRNA information was resistant to cooking and digestion and even entered human serum

2.7

At 2012 miRNA in Food

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Fig. 2.13 Edible miRNA agents for diseases. Food is the most important environmental factor. All miRNA criteria-intensive subjects are food. We thought that foods’ miRNAs could be the origin of humans on the globe (Fujii 2009), in “Regulation of Gene Expression by Small RNAs”

and tissues by Zhang et al. in the Research Center of MicroRNA Biology (2012). It has been shown that plant miRNA information may be involved in intervention in human gene expression. Although the production of escape viral mutants by antiHIV-1 chemotherapy treatment is a problematic issue, treatment with miRNA information can overcome escape virus production. If Darwinism is based on RNA Wave 2000, food culture could change our lives, so in the near future, therapeutic miRNA-based fruits will eradicate HIV-1 infection. Although we have had a severe respiratory syndrome human coronavirus 2 (SARS-CoV-2) pandemic worldwide, rice miRNA MIR2097-5p is responsible for the low degree of coronavirus disease 2019 (COVID-19) epidemics in Asian countries and Japan (Fujii 2020b, 2023). Furthermore, some people in Asia persistently infected with Helicobacter pylori (H. pylori) do not suffer from high incident rates of gastric cancer due to the availability of fresh fruits and vegetables (Mahachai et al. 2018). We found in METS/MIRAI simulation that xenomiRNAs, such as watermelon MIR156a, may cure H. pylori-infected people from the gastric precarcinogenic state (Fujii 2019b). Plant (see Chap. 11) diet culture serves COVID-19 infection in Asia. Therefore, at the same time, food safety and grade will be assessed by RNA information data in the diet.

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Fig. 2.14 Thanks a lot Oxford and Cambridge. Dear Your Majesty, we love the UK. In addition, thanks to Researchers Sans Frontiers for joining me in the world. Thank you again for your help in investigating miRNA data bioscience

Thanks to Cambridge and Oxford from a Japanese miRNA gardener (Fig. 2.14). The night of Alice’s Tea Party with us on Alice’s Adventures in Wonderland, Oxford, Alice realized “Time.” Even if “Time” stops, the tea ceremony continues forever. The quantum language of miRNAs may be implicated in “Time.” Certainly, “Time” is also related to meals. However, leaving the “Protein Card” behind, miRNA’s “Time” now begins to move together with “Quantum Rabbit.”

References Ai J, Zhang R, Li Y, Pu J, Lu Y et al (2010) Circulating microRNA-1 as a potential novel biomarker for acute myocardial infarction. Biochem Biophys Res Commun 391:73077. https://doi.org/10. 1016/j.bbrc.2009.11.005 Asgari S (2014) Role of microRNAs in arbovirus/vector interactions. Viruses 6:3514–3534. https:// doi.org/10.3390/v6093514 Bai K, Lee CL, Liu X, Li J, Cao D et al (2022) Human placental exosomes induce maternal systemic immune tolerance by reprogramming circulating monocytes. J Nanobiotechnology 20:86. https://doi.org/10.1186/s12951-022-01283-2 Bennasser Y, Le SY, Yeung LM, Jeang KT (2006) MicroRNAs in human immunodeficiency virus1 infection. In: Ying S-Y (ed) MicroRNA protocols. Humana Press, Totowa, pp 241–253

References

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Cheng-Mayer C, Iannello P, Shaw K, Luciw PA et al (1989) Differential effects of nef on HIV replication: implications for viral pathogenesis in the host. Science 246(4937):1629–1632 Chermann JC, Barré-Sinoussi F, Montagnier L (1985) A new human retorovirus associate with acquired immunodeficiency syndrome (AIDS) or AIDS-related complex. Prog Clin Biol Res 182:329–342 Chutipongtanate S, Morrow AL, Newburg D (2022) Human milk extracellular vesicles: a biological system with clinical implications. Cell 11:2345. https://doi.org/10.3390/cells111523245 Cookson W (1994) The gene hunters. Aurum Press Ltd, London Cullen BR, Lomedico PT, Ju G (1984) Transcriptional interference in avian retroviruses: implications for the promoter insertion model of leukemogenesis. Nature 307:241–245 Fichtlscherer S, De Rosa S, Fox H, Schwietz T, Fischer A et al (2010) Circulating microRNAs in patients with coronary artery disease. Circ Res 107:677–684. https://doi.org/10.1161/ CIRCRESAHA.109.215566 Fire A, Xu S, Montgomery MK, Kostas SA, Driver SE, Mello CC (1998) Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 391:806–811. https://doi.org/10.1038/35888 Fujii YR (2009) Oncoviruses and pathogenic microRNAs in humans. Open Virol J 3:37–51. https:// doi.org/10.2174/1874357901004010063 Fujii YR (2013) The RNA gene information: retroelement-microRNA entangling as the RNA quantum code. Meth Mol Biol 936:47–67. https://doi.org/10.1007/978-1-62703-083-0_4 Fujii YR (2018) The quantum language of the microRNA gene and anticancer: with a dynamic computer simulation of human breast cancer drug resistance. Integr Mol Med 5:1–13. https:// doi.org/10.15761/IMM.1000346 Fujii YR (2019a) Cancer simulation from stage minus one by quantum microRNA language: lung, colorectal and pancreatic cancers. Med One 4:e190023. https://doi.org/10.20900/mo.20190023 Fujii YR (2019b) Quantum microRNA network analysis in gastric and esophageal cancers: xenotropic plant microRNAs cure from cancerous paradox via Helicobacter pylori infection. Gastroenterol Hepatol Endosc 4:1–18. https://doi.org/10.15761/GHE.1000187 Fujii YR (2020a) The quantum microRNA immunity in human virus-associated diseases: virtual reality of HBV, HCV and HIV-1 infection, and hepatocellular carcinogenesis with AI machine learning. Arch Clin Biomed Res 4:089–129. https://doi.org/10.26502/acbr.50170092 Fujii YR (2020b) In silico study by quantum microRNA language for the development of antiCOVID-19 agents: COVID-19 is prevented by rice MIR2097-5p through suppression of SARSCov-2 viral microRNAs plus HIPK2 target proteins. Virol Immunol J 4:000256. https://doi.org/ 10.23880/vij.16000256 Fujii YR (2022a) In: Rezaei N (ed) Quantum microRNA surveillance against cancer: parallel dimensional analysis of integrated networks by quantum microRNA language in female genital neoplasms. Interdisciplinary Cancer Research Springer Nature, New York, pp 1–24. https://doi. org/10.1007/16833_2022_4 Fujii YR (2022b) In: Rezaei N (ed) Quantum microRNA surveillance against bladder cancer: quantum miRNA language/artificial intelligence (MIRAI) etiology analysis from serum/plasma or urine miRNA data. Interdisciplinary Cancer Research Springer Nature, New York. https:// doi.org/10.1007/16833_2022_5 Fujii YR (2022c) In: Rezaei N (ed) Quantum microRNA immunity and hematopoietic malignancies: etiological analysis of leukemia and lymphoma by quantum microRNA language with artificial intelligence (MIRAI). Interdisciplinary Cancer Research Springer Nature, New York. https://doi.org/10.1007/16833_2022_11 Fujii YR (2023) The microRNA quantum code book. Springer Nature, Singapore Hanke M, Hoefig K, Merz H, Feller AC, Kausch I et al (2010) A robust methodology to study urine microRNA as tumor marker: microRNA-126 and microRNA-182 are related to urinary bladder cancer. Urol Oncol 28:655–661. https://doi.org/10.1016/j.urolonc.2009.01.027

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Hanson EK, Lubenow H, Ballantyne J (2009) Identification of forensically relevant bodyb fluids using a panel of differentially expressed micrRNAs. Anal Biochem 387:303–314. https://doi. org/10.1016/j.ab.2009.01.037 Khanbabaei H, Ebrahimi S, García-Rodríguez JL, Ghasemi Z, Pourghadamyari H et al (2022) Noncoding RNAs and epithelial mesenchymal transition in cancer: molecular mechanisms and clinical implications. J Exp Clin Cancer Res 41:278. https://doi.org/10.1186/s13046-02202488-x Klingelhutz AJ, Roman A (2012) Cellular transformation by human papillomaviruses: lessons learned by comparing high- and low-risk viruses. Virology 424:77–98. https://doi.org/10.1016/ j.virol.2011.12.018 Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921. https://doi.org/10.1038/35057062 Lee RC, Feinbaum RL, Ambros V (1993) The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75:843–854. https://doi.org/10.1016/ 0092-8674(93)90529-y Liang H, Zhou Z, Yang S, Zen K, Chen X et al (2014) Identification of Ebola virus microRNAs and their putative pathological function. Sci China Life Sci 57:973–981. https://doi.org/10.1007/ s11427-014-4759-2 Mahachai V, Vilaichone RK, Pittayanon R, Rojbrowonwitaya J, Leelakusolvong S et al (2018) Helicobacter pylori management in ASIAN: the Bangkok consensus report. J Gastroentreol Hepatol 33:37–56. https://doi.org/10.1111/jgh.13911 Maligianni I, Yapijakis C, Nousia K, Bacopoulou F, Chrousos G (2022) Exosomes and exosomal noncoding RNAs through human gestation. Exp Ther Med 24:582. https://doi.org/10.3892/etm. 2022.11518 McClintock B (1953) Induction of instability at selected loci in maize. Genetics 38:579599. https:// doi.org/10.1093/genetics/38.6.579 McClintock B (1983) Nobel lecture. https://www.nobelprize.org/ Niederman TM, Thielan BJ, Ratner L (1989) Human immunodeficiency virus type 1 negative factor is a transcriptional silencer. Proc Natl Acad Sci USA 86:1128–1132 Pomerantz RJ, Trono D, Feinberg MB, Baltimore D (1990) Cells nonproductively infected with HIV-1 exhibit an aberrant pattern of viral RNA expression: a molecular model for latency. Cell 61:1271–1276. https://doi.org/10.1016/0092-8674(90)90691-7 Triboulet R, Mari B, Lin YL, Chable-Bessia C, Bennasser Y et al (2007) Suppression of microRNA-silencing pathway by HIV-1 during virus replication. Science 315:1579–1582. https://doi.org/10.1126/science.1136319 Wang F, Zheng Z, Guo J, Ding X (2010) Correlation and quantitation of microRNA aberrant expression in tissues and sera from patients with breast tumor. Gynecol Oncol 119:586–593. https://doi.org/10.1016/J.ygyno.2010.07.021 Yamamoto T, Samri A, Marcelin AG, Mitsuki YY, Vincent C et al (2009) Effect of lentivirus encoding HIV-1 Nef-U3 shRNA on the function of HIV-specific memory CD4(+) T cells in patients with chronic HIV-1 infection. AIDS 23:2265–2275. https://doi.org/10.1079/QAD. 0b013e328332817c You X, Zhang Z, Fan J, Cui Z, Zhang XE (2012) Funactionally orthologous viral and cellular microRNAs studied by a novel dual-fluorescent reporter system. PLoS One 7:e36157. https:// doi.org/10.1371/journal.pone.0036157 Zampetaki A, Kiechl S, Drozdov I, Willeit P, Mayr U et al (2010) Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circ Res 107:810– 817. https://doi.org/10.1161/CIRCRESAHA.110.226357 Zhang L, Hou D, Chen X, Li D, Zhu L et al (2012) Exogenous plant MIR168a specifically targets mammalian LDLRAP1: evidence of cross-kingdom regulation by microRNA. Cell Res 22:107– 126. https://doi.org/10.1038/cr.2011.158 Zur Hausen H (2008) Nobel lecture. German Cancer Research Center, Heidelberg. https://www. nobelprize.org/

Chapter 3

Mobile MicroRNAs: Potential for MicroRNA Biogenesis

The old but often overlooked principles were simple. Garrett, L. The Coming Plague

Overview As shown in Chap. 2, the short interfering RNA (siRNA) system was thought to be the same as the microRNA (miRNA) system. The miRNA genes are information because they are protein-noncoding RNAs. RNA informational genes (Rigs) are usually located in approximately 98% of the noncoding regions on the human genome. In contrast, miRNAs are deeply connected to the protein system. Rig is the master regulator of Central Dogma. Fine-tuning of protein expression by Rigs has been implicated in infection and cancer disease control. Traditional biogenesis of miRNAs is now a common pathway, but after biosynthesis, linear single-stranded miRNAs target linear single-stranded messenger RNA (mRNA), and miRNA seed regions target the 3′ untranslated region (3′ UTR) of mRNAs with a helix-like shape. The mature miRNA duplexes with mRNA in the Argonaute (Ago) protein. However, many human loop RNAs (loRNAs) have recently been reported, and the miRNA loop (lomiRNA) can be associated with Ago. Therefore, it is unknown whether helix recognition structures are common between miRNAs and mRNAs. Its helix geometry is unlikely to serve circular mRNA–miRNA interactions, and miRNA biogenesis or function has many exceptions, as does RNA interference. Thus, the miRNA machinery in circularity differs from RNA interference by Fire and Mellow. Due to the complexity of miRNA biogenesis and function, the biological relevance of circular mRNAs may be superimposed on circular torus miRNAs in RNA Wave 2000, suggesting that the coherence of torus miRNAs and torus mRNAs has implications for human health life. Beyond helices, the biosynthetic potential of tori could be a keystone for demonstrating this idea according to the quantum RNA language. In reality, circular RNA (circRNA) has been detected. Additionally, the miRNA quantum code was created from circular miRNA/miRNA relationships. Therefore, miRNA differs from RNA interference.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1_3

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Tribute to RNA Wave 2000

MicroRNA (miRNA) genes function according to the RNA Wave 2000 Sin Central Dogma: (1) miRNAs are mobile genetic elements and induce transcriptional as well as posttranscriptional gene regulation by networking architecture, (2) miRNAs expand into the environmental cycle of life, (3) miRNAs can self-proliferate, and (4) miRNAs carry two types of information, resident and genomic miRNA genes (Fujii 2008a). The RNA Wave 2000 criteria were created from the image in properties of retroviruses. When I was in Oxford to work on human immunodeficiency virus type 1 (HIV-1) nef in 1987, I had an image that nef function may not be protein but probably small RNA and it is junk RNA. The human genome currently contains more than 6000 annotated miRNA genes (miRBase 8.0), and some of these miRNAs circulate in the human blood. However, before 2000, there was no evidence other than viruses to understand that miRNAs are mobile genes. Furthermore, approximately 98% of the noncoding DNA in the human genome is known as junk. First, the RNA Wave 2000 concept was confirmed from studies of HIV-1 nef/3′ long terminal repeat (3′LTR). Prior to that, a tool of an original retroviral vector, not purchased ones, had been prepared (Omoto and Fujii 2005) because no one believed the existence of miRNAs at all, and the involvement of the nef/3′LTRderived miRNAs was suspected. To demonstrate the omnipresent nature of miRNAs, we used the retrovirus HIV-1 and our particular spuma retrovector (Fig. 3.1) (Yamamoto et al. 2002).

Fig. 3.1 Spuma retrovirus vector. The HIV-1 viral miRNA hiv1-miR-N367 was inserted into the spumavirus vector. The vector is independent of interferon (IFN) in vivo

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Fig. 3.2 MiRNA hypothesis. lin-4 antisense RNA was inhibited in translation. hiv1-miR-N364 suppressed translation and transcription. The latter model is RNA Wave 2000

Retroelements, including retrotransposons, LINEs and SINEs (human Alu), transposable elements (TEs), and satellites, make up approximately 45% of human genomic DNA. If the retrotransposon HIV-1 in the human genome could have been located as a noncoding retrotransposon in the noncoding DNA regions, the miRNA genes in HIV-1 RNA were also located in the human genome. The miRNA gene in the HIV-1 genome was shown to be evidence of both the rational existence of mobile RNA genes and their biological relevance. Our work on pharmaceutical and therapeutic challenges against acquired immunodeficiency syndrome (AIDS) was shared worldwide by Cambridge (Fujii 2008b) (Fig. 3.2). Meanwhile, in 2006, RNA interference won an award, showing that small RNAs can suppress messenger RNA (mRNA) translation in Caenorhabditis elegans (C. elegans). However, endogenous small RNAs have not yet been recognized in mammalian cells due to the nonspecific interferon (IFN) response of human cells to exogenous RNAs. Furthermore, in 1993, lin-4 antisense small RNA targeted mRNA via the same RNAinduced silencing complex (RISC) mechanisms as RNA interference in the nematode (Lee et al. 1993). However, the specificity of the RNA Wave was the regulation of transcription and posttranscription, which was different from the nonspecific IFN activity that the authorities thought. Although Drosha is now thought to be an IFN-independent antiviral factor (Shapiro et al. 2014), until the discovery of mammalian oncogenic miRNAs, the presence of miRNAs in humans was completely disbelieved. My HIV TE/miRNA and nanoRNA research for miRNA-based therapy,

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diagnosis, and prognosis has always been difficult. After approximately 25 years, the elucidation of miRNA biomedical functions was completed with the invention of miRNA entangling target sorting (METS)/quantum miRNA language and artificial intelligence (MIRAI) analysis protocols for human diseases (Fujii 2023).

3.2

Biogenesis Map of miRNAs

RNA silencing was observed in plants before the entire human genome was sequenced in the 1990s. An exogenous gene in the plasmid represses the expression of the endogenous cognate protein-coding gene in petunia flowers, suggesting RNA silencing of the homologous genes in the partial methylation of the chromosome (van der Krol et al. 1990). Exogenous genes were expressed in plasmids, and transcripts were processed into small double-stranded RNAs (Napoli et al. 1990). From 1991 to 1993, a small endogenous antisense RNA silenced the lin-4 gene in C. elegans (Wightman et al. 1991; Lee et al. 1993). Therefore, antisense research was all the rage at that time. Furthermore, exogenous and artificial small doublestranded RNA (dsRNA) cognate green fluorescent protein (GFP) or β-galactosidase genes specifically suppressed each cognate gene in nematodes (Fire et al. 1998). Transfected long dsRNAs were digested by RNase Dicer, and the processed dsRNAs were incorporated into the RISC. Argonaute (Ago) proteins in RISC bind short interfering RNA (siRNA), and Ago-siRNA complexes are also recruited to target messenger RNAs (mRNAs). mRNA is degraded by the P-body exoribonuclease Xrn1 (Newbury and Woollard 2004; Fillman and Lykke-Andersen 2005), and mRNA translation is ultimately repressed in C. elegans. In 2000, from the results of HIV-1 miRNA isolation, no one believed that endogenous miRNAs or nanoRNAs, which we named ourselves, would appear in humans. Fire’s RNA interference antisense reactions, such as Ambros’s or Ruvkun’s experiments, are just about RNA interference. Only 1.5–2.0% of the DNA genes that encode human protein are known. Proteincoding DNA genes are transcribed into proteins according to the central dogma of the Watson–Crick hypothesis. On the other hand, miRNA genetic information is derived from noncoding DNA regions of proteins in approximately 98% of the human genome. There is evidence that protein-coding transcripts account for approximately 1.5%, noncoding transcripts account for approximately 70–80% of the total transcripts (Li and Ramchandran 2010), and approximately 10,000 long noncoding RNA genes are annotated in GENCODE v7 (Derrien et al. 2012). Therefore, the precise central dogma of biology is the biogenesis and information of noncoding RNA (98%) rather than Watson and Crick’s central dogma information ( C MIR133A2 variant in the atrium in humans altered mRNA processing and the isomiRs changed target mRNAs (Kovalchuk et al. 2012). Problematic issues emerge from the variety of genotypes of genomic miRNAs from stem cells and differentiated cells. Since miRNA is a mobile gene, xenomiRNAs could make a variety of resident miRNA genes, and extracellular miRNA information from cardiac cells in heart failure or metabolic diseases would control the epigenetics of distant kidney and liver cells, in which the profiles of miRNAs could be altered by both the cardiac heart and metabolic diseases. Furthermore, environmental factors, such as temperature, air pollution, environmental chemicals, radiation, carcinogens, circadian rhythm, physical exercise, and foods, could affect miRNA expression profiles (Wilmink et al. 2010; Jardim 2011; Liu et al. 2011; Elamin et al. 2011; Shende et al. 2011; Fernandes-Silva et al. 2012; Hou et al. 2012). The alteration of the miRNA genotype could induce cardiac, metabolic, cancer, and immune diseases at the same time, suggesting that the core information of the RNA gene sets might be controlled by each other, not in a protein-consisting system, but with miRNA– miRNA interactions in exosomes between human cells and foods (Xu et al. 2010a; Wu et al. 2013; Guo et al. 2014) (see Chaps. 9 and 10).

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SIRT1 Connection

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SIRT1 Connection

Host cellular factors, histone deacetylases (HDACs) and histone acetylases (HATs), can alter chromatin structure and control protein-coding gene expression in the nucleus. SIRT1, a class III HDAC, can deacetylate the lysine residues (K) of transcription factors, such as position 50 (K-50) of the HIV-1 Tat protein (D’Orso and Frankel 2009). The SIRT1 heterodimer is a monomer with a molecular mass of 34.8 kDa in solution (Davenport et al. 2014). The HIV-1 Tat protein with a molecular mass of 16 kDa is a nuclear protein and is acetylated at K-50 and K-51 by CREB binding protein (CREBBP)/p300 and GNC5, and the K-acetylated Tat protein dissociates from a Tat-responsive (TAR) element of HIV-1 provirus DNA. Short transcripts of human retroelement DNA are transcriptionally elongated by Pol II from immediately downstream of the transcriptional initiation site at the 5′-long terminal repeat (5′-LTR). SIRT1 can inhibit transcriptional activation of the retroelement via K-deacetylation of the transactivating protein. Furthermore, SIRT1 is NAD+-dependent and plays an important role in metabolic homeostasis involved in controlling the accumulation of cholesterol, bile acids, triglycerides, or glucose. SIRT1 deacetylase catalyzes NAD+ and acetylated Tat protein to produce nicotinamide, O-acetyl-ADP-ribose, and the deacetylate Tat protein, respectively. Thus, normal metabolite intake can activate SIRT1 deacetylase and induce NAD+ after fasting and exercise to extend life span (Haigis and Sinclair 2010). First, SIRT1 is regulated by miR-34a, miR-449a, miR-22, miR-200a, miR-143/145, miR-217, miR-195, miR-199a, miR-132, miR-181c, miR-9, miR-93, circRNAs, and lncRNAs in a wide variety of pathways in humans (Yamakuchi 2012; Ashrafizadeh et al. 2022). The pathways highlighted here address only one aspect of miRNA regulation. Without METS/MIRAI, we cannot find true miRNA–target relationships in human diseases. Because Tat protein potency depends on redox modulation of NADH to NAD+ via SIRT1 (Zhang et al. 2009), electron shifts are involved in HIV-1 replication-associated metabolism and stress responses (Yang and Sauve 2006). In fact, inhibition of HIV-1 replication by expression of HIV-1 nef/3’LTR, including hiv1-miR-N367, affects lipid metabolism in human T cells (Otake et al. 2004). However, driving the SIRT1 electronic field raises one question. What is the substance that transmits stress? Environmental quantum energy, for example. There is striking evidence that SIRT1 is targeted by miR-34a (Zhang et al. 2009), and miR-34a suppresses both Tat and NFkB deacetylation and amplifies HIV-1 reactivation. Moreover, miR-34a is a circulating RNA gene (Li et al. 2011b; Shi et al. 2013). Thus, while environmental factors affect circulating RNA genes, questionable stress inducers may affect the miR-34a gene (Jacobs et al. 2013), suggesting that upregulation of miR-34a may be implicated in SIRT1 inhibition and may be involved in wasting and metabolic disease by HIV-1 infection (Katabira and Colebunders 1996; Grunfeld 2008). SIRT1 is a general transcriptional coregulator in metabolic pathways regulated by miRNA genes, aging, circadian clock, cancer, heart disease, and neurodegeneration, which suggests that Rigs control age-onset metabolism through SIRT1 tuning. Rigs

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have the responsibility for a specific role in transactivation proteins. Furthermore, SIRT1 also localizes to the nucleus and cytoplasm (Khudayberdiev et al. 2013). For instance, miR-93 suppresses oxidative stress via repression of SIRT1, which is involved in oxidative defense regulation by hepatic NAD+ in an aging-dependent manner (Li et al. 2011a), and miR-93 accumulates in the nucleus. miR-93 could directly regulate the transcription of the SIRT1 gene. Similarly, miR-320 targets SIRT1 and plays an active role in ischemia/reperfusion injury (Salloum et al. 2010). miR-320 is internalized into the nucleus and can directly regulate transcription. Although small amounts of miR-22 are localized to the nucleus, miR-22 is upregulated in human senescent fibroblasts, and SIRT1 is a direct target of miR-22 (Xu et al. 2011), suggesting that not only posttranscriptional but also transcriptional regulation may be involved in the regulation of miR-92 or miR-22. As previously mentioned, SIRT1 is downregulated by miR-34a (Yamakuchi et al. 2008). miR-34a is one of the regulators of SIRT1 and can target the 3′UTR of SIRT1 mRNA with imperfect pairing so that SIRT1 mRNA is not degraded. In fatty liver, miR-34a expression is high, but SIRT1 protein levels are low (Lee et al. 2010). An inverse combination of expression between miR-34a and SIRT1 has been implicated in nonalcoholic fatty liver disease and has been suggested to regulate hepatic fat metabolism. The miR-34a gene suppresses neuroblastoma, prostate cancer, hepatocellular carcinoma, colon cancer, ovarian cancer, and leukemia (Corney et al. 2010; Tivnan et al. 2011; Yamamura et al. 2012; Roy et al. 2012; Dang et al. 2013; Boysen et al. 2014) because p53 is induced by miR-34a (Bommer et al. 2007) and miR-34a decreases SIRT1; thus, p53 is acetylated and activated. Therefore, acetylated p53 regulates the cell cycle via p21WAP/CIF activation and induces apoptosis of tumor cells. (Fujii 2009). The p53 and miR-34a loops are functional decoherent feedforward cycles mediated by the transcription factor SIRT1, distinct from the incoherent model of feedforward cycles (Tsang et al. 2007; Osella et al. 2011; Eduati et al. 2012) (Fig. 4.6) because miR-34a is tuned to SIRT1. The p53/miR-34a/SIRT1 loop participates in another FXR/SIRT1 positive feedback loop (Lee and Kemper 2010). SIRT1 deacetylates the nuclear bile acid receptor farnesoid X receptor (FXR), and deacetylated FXR and RXR transactivate the metabolic repressor small heterodimer partner (SHP). SHP is recruited to the miR-34a promoter and downregulates miR-34a transcription (Lee and Kemper 2010). FXR and SIRT1 have been shown to regulate metabolic genes and be associated with metabolic diseases such as fatty liver and T2DM (Ma et al. 2006; Sinal et al. 2000). Again, P53 is a tumor suppressor and miR-34a is an obesity-related informative gene (see Sect. 4.2). Reduction of miR-34a increases the peroxisome proliferatoractivated receptor gamma (PPAR-γ) in adipose cells and promotes obesity. P53 is a transcription factor for miR-34. Acetylation is required for the activation of P53. Thus, P53 is inactivated after deacetylation of acetylated P53 by SIRT1. On the other hand, miR-34a targets SIRT1 mRNA. miR-34a blocks SIRT1 deacetylation activity and maintains P53 acetylation. Activated P53 further induces miR-34a expression. As shown in Fig. 4.5, the P53/miR-34a/SIRT1 loop constructs a feedforward cycle. miR-34a activated P53. In other words, a simple scheme holds that people who are not obese are less likely to develop cancer. However, since exosomal miR-34a is a

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Fig. 4.6 The miR-34a/SIRT1/P53 feedforward loop and other SIRT1 targeting miRNAs

circulating miRNA, feedforward information can spread from cell to cell and organ to organ. Noncoding RNAs (ncRNAs: miRNAs, lncRNAs, and circRNAs) and their orthologous genes in diet may also regulate nutrient feedforward loops (Ashrafizadeh et al. 2022). Of course, there are many types of ncRNAs that regulate P53, so it is not a simple analysis. Decoding the miRNA code by METS/MIRAI solved the dilemma of this research (Fujii 2023). In addition, nutrients themselves and chemical agents also influence miRNA profiles. The relationship between miR-34a and SIRT1 has been shown for cholesterol synthesis by Tabuchi et al. (2012). Statin treatment in patients with coronary artery disease caused upregulation of SIRT1 via downregulation of miR-34a. In subjects with nonalcoholic liver disease, miR-34a increased and inhibited SIRT1 with dephosphorylation of AMP kinase and HMG-CoA reductase in their inactive state (Min et al. 2012). These data suggest that statins can impair cholesterol synthesis to inhibit NADP+-dependent HMG-CoA reductase activity and subsequently suppress endothelial cell senescence via the SIRT1/miR-34a interaction. Furthermore, nicotinamide treatment inhibits leukemia cell proliferation through the p53/miR-34a/SIRT1 pathway (Boysen et al. 2014). Nicotinamide is a derivative of vitamin B3 niacin. Niacin is nutritionally equivalent to nicotinamide. Its derivatives NAD and NADP are coenzymes of SIRT1 and HMG-CoA reductase. The nutrient niacin has been thought to suppress free fatty acids in serum by inhibiting lipolysis through intracellular lipoprotein lipase hydrolysis of dietary triglycerides (Nelson et al. 2012). However, Lauring et al. (2014) reported that niacin efficacy was

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independent of niacin receptor and free fatty acid suppression, so beneficial suppression of blood-free fatty acids via niacin remains to be elucidated. It was reported that treatment of obese Zucker rats with niacin affected miRNA expression profiles in skeletal muscle (Couturier et al. 2014). miR-34a-5p, miR-29b-3p, miR-145-5p, and miR-24-2-5p were mainly upregulated, and miR-21-3p, miR-665, miR-466b-23p, and miR-466d were downregulated. Furthermore, niacin-induced miR-502-3p upregulation, and overexpression of miR-502-3p reduced insulin-dependent glucose uptake in human adipocytes (Montastier et al. 2019). These results suggest that the effects of niacin may involve not only the p53/miR-34a/SIRT1 loop but also other pathways. Furthermore, folic acid and vitamin B9 downregulate miR-34a (Tryndyak et al. 2009), and miR-34a controls SIRT1 in various pathways, including metabolism, inflammation, hypoxic responses, circadian rhythm, senescence, and longevity (Yamakuchi 2012). Therefore, niacin may not directly affect SIRT1 enzymatic activity through uptake into hepatocytes, but the RNA storm that responds to circulating niacin, i.e., niacin treatment, suppresses miR-34a expression. It may involve the p53/miR-34a/SIRT1 feedforward system during lipolysis (Okada et al. 2014). Feeding SIRT1-deficient mice a high-fat diet altered fatty acid metabolism and promoted fatty liver development (Xu et al. 2010b). Circulating miR-122 and miR-34a cooperate to balance normal lipid metabolism and alcoholic steatohepatitis or nonalcoholic fatty liver disease (Min et al. 2012; McDaniel et al. 2013). Thus, the impact of nutrients as environmental factors may alter miRNA profiles in the human circulatory system. Nutrients trigger RNA storms, and obesity should also be driven by mobile RNA genes.

4.6

Neurodegenerative Obesity

Since food nutrients directly affect weight gain, their regulation involves the central nervous system and hypothalamus and plays an important role in food intake signals (Dallman et al. 1992; Jeong et al. 2013). Arcuate nucleus (ARC) neurons in the hypothalamus have two distinctive circuits that control feeding behavior. One is the orexigenic neuropeptide Y/agouti-related protein (NPY/AGRP), and the other is the anorexigenic pro-opiomelanocortin/cocaine-and-amphetamine-regulated-transcript (POMC/CART). POMC is a complex propeptide that includes adrenocorticotrophin (ACTH), β-lipotropin, α-melanocyte-stimulating hormone, β-melanotropin, and β-endorphin and is primarily regulated by corticotropin-releasing factor (CRF) (Morton et al. 2006). Lack of POMC-derived peptide and POMC-null mutations in humans increase the risk of hyperphagia and obesity (Cone 2005; Speliotes et al. 2010). Dicer-deficient mice in POMC neurons exhibited increased epididymal fat mass and induced classical neurodegeneration (Schneeberger et al. 2012), suggesting that miRNAs were associated with hypothalamic neurodegeneration patients with obesity homozygous for POMC-null mutation (Farooqi et al. 2006). In POMC neurons, SIRT1 is expressed (Ramadori et al. 2008), and SIRT1 regulates

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Cardiac Metabolism Information Nexus

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POMC via FOXO1 acetylation (Çakir et al. 2009). Furthermore, in keratinocytes, UV irradiation induces p53 (Lu et al. 2009), and activation of p53 increases POMC transcription (Rizzato et al. 2011). miR-375 negatively regulates POMC expression by targeting MAP 3 K8 (Zhang et al. 2012b). POMC is targeted by miR-383, miR-384-3p, and miR-488, and the POMC activator leptin inhibits miRNA expression through the Janus kinase 2 (JAK2)-STAT3 and PI3K-AKT serine/threonine kinase (Akt) pathways (Derghal et al. 2019). Thus, POMC-dependent neurodegeneration with obesity is related to miRNA alteration. What genetic factors can receive information on subsequent environmental factors and transmit obesity-specific information from the brain to peripheral tissues through the circulatory system and vice versa, leading to changes in miRNAs? Since serum and central nervous system miR-34 is similarly altered during aging (Bhatnagar et al. 2014), changes in circulating plasma miRNAs may contribute to neurodegeneration in the brain through blood–brain barrier (BBB) (Cheng et al. 2013a). Abnormally high levels of the miR-34c gene were expressed in the hippocampus of Alzheimer’s disease patients (Zovoilis et al. 2011), and miR-34c was increased in the circulating plasma component of Alzheimer’s disease (Bhatnagar et al. 2014). Furthermore, the miR-34 family targets the mRNA of the microtubuleassociated tau protein involved in neurofibrillary tangles in Alzheimer’s disease known as tauopathy (Dickson et al. 2013). It has been reported that SIRT1 inhibitors cannot block tau deacetylation (Cook et al. 2014). Therefore, it is not certain whether tau acetylation is catalyzed by SIRT1. In any case, a decrease in miR-34a in aged mice was observed in calorie-restricted mice, and miR-34a downregulation reduced neuronal apoptosis by suppressing BCL2 apoptosis regulator (BCL2) associated X (BAX) and decreased downstream caspase9 by increasing BCL2 apoptosis regulator (Bcl-2) (Khanna et al. 2011). Therefore, neurodegenerative obesity is in part closely linked to miR-34 and its associated loops. In addition, diet-induced obesity increased colon tumorigenesis, and miR-34c was downregulated in mouse colon mucosa (Olivo-Marston et al. 2013). Taken together, cellular homeostasis to maintain normal cell proliferation during aging may increase the circulating miR-34 family to probably prevent oncogenesis and subsequent neurodegeneration, such as Alzheimer’s disease (Liu et al. 2012). Excessive upregulation of miR-34 can lead to fatty liver. Our METS/MIRAI results also show that blood levels of miR-34a-5p are elevated in Alzheimer’s disease and inhibit Notch receptor 2 (NOTCH2) (AUC: 0.87) (Fujii 2021b). NOTCH2 is highly expressed in the hippocampus and cerebral cortex and is associated with brain long-term memory in rodents (Gruden’ et al. 2017; Guo et al. 2017).

4.7

Cardiac Metabolism Information Nexus

Circulating miRNA genes contained within exosomes are stable after boiling and exposure to high or low pH solutions. Circulating miRNA remained stable after incubation at 25 °C and freeze–thaw cycles. Furthermore, circulating endogenous

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miRNAs are resistant to RNases (Tsui et al. 2002). The circulating miR-133 gene is one of the cardiometabolic nexuses. Coronary artery disease (CAD), including acute myocardial infarction (AMI) and acute ST-segment elevation myocardial infarction (STEMI), is closely associated with obesity and atherosclerosis, leading causes of death. miR-133, miR-208, and miR-499 are circulating and cardiac-specific or enriched miRNAs. miR-208 is expressed in the human heart and miR-208a is undetectable in the plasma of healthy individuals. Since the miR-499 gene is encoded by the myosin heavy chain gene, both miR-208a and miR-499 are elevated in the plasma of CAD patients. On the other hand, miR-133 is expressed in healthy subjects, differentially expressed between muscle progenitor cells, Myf5+ hemangioblasts, and brown adipocytes (BAC), and targets the 3′ UTR of Prdm16 transcription factor mRNA (Yin et al. 2013). Additionally, miR-133 circulates in plasma. Prdm16 is an activator of BAC adipogenesis because Prdm16 is a PPAR-γ2 cofactor that induces BAC genes, whereas miR-133 is its negative regulator (Trajkovski et al. 2012) (Fig. 4.5). Downregulation of Prdm16 in the BAC caused a fate-switch to myoblasts, and with upregulation of Prdm16, myoblasts or fibroblasts differentiate into BACs (Seale et al. 2008). Therefore, circulating miR-133 may also have partially determined cell fate selection between myocytes and BACs. Liu et al. (2013) showed that miR-133a knockout (KO) mice had more constant thermogenesis and lipolysis at 4 °C in subcutaneous BAC in vivo. Although these papers did not provide deep insight into which factors are involved between environmental factors and cellular miRNAs, the data clearly suggest that miR-133 KO mice are insensitive to lower glucose levels and increased insulin sensitivity against low temperature because miR-133 KO mice are defective in circulating miR-133 as a responder to the environment in plasma. Thus, circulating miRNAs simultaneously transmit cardiac and metabolic information from cell to cell and organ to organ, probably through RNA-to-RNA cross talk. In addition, dietary miR-133 may be implicated in heart and metabolic diseases. Later, CAD-related miRNA panels in serum/plasma contained miR-133b, miR-499a-5p, and miR-208a-3p (Fujii 2021a). Downregulation of miR-133b increased hyperpolarization-activated cyclic nucleotide-gated potassium channel 4 (HCN4) and potassium voltage-gated channel subfamily H member 2 (KCNH2). Therefore, miR-133b reduction is related to pacemaker dysregulation. Upregulation of miR-208a-3p suppressed CDKN1A, and suppression of CDKN1A is involved in the oxLDL-macrophage increase in the atherosclerotic lesion. Upregulation of miR-499a-5p decreased LIN28B. High plasma miR-499a-5p levels significantly enhanced the sensitivity of traditional risk factors to identify subjects with STEMI (Hoekstra 2016); however, its function in STEMI patients has not yet been clarified.

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miR-33 Pivot

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miR-33 Pivot

hsa-miR-33a and hsa-miR-33b are localized to introns of the SREBF2 and SREBF1 genes, which encode SREBP-2 and SREBP-1c proteins, respectively (Rayner et al. 2010; Rottiers et al. 2011) (Fig. 4.6). miR-33a is activated by low cholesterol, and miR-33b is activated by LXR ligands/insulin and cotranscribed with SREBBP-2 and SREBP-1c. As premiR-33 biogenesis is terminated by the microprocessor, microprocessor-recruited premiR-33 simultaneously shuts down the transcription of SREBP-1 and SREBP-2 mRNAs (Goedeke et al. 2013). Additionally, the production of miR-33 indirectly controls HMG-CoA reductase through the transcription factor SREBP. The basic helix-loop-helix-zipper (bHLH-Zip) domain of the SREBP transcription factor is released from SREBP at the endoplasmic reticulum (ER) membrane and locates to the nucleus, where HLH binds to the SRE and the bound DNA activates HMG-CoA reductase gene expression. Therefore, cholesterol biosynthesis is upregulated. At the same time, intronic miR-33a/b may also be produced, repressing the adenosine triphosphate-binding cassette transporters (ABCA1/ABCG1). ABCA1 mediates cholesterol efflux from atherogenic macrophages/foam cells and is critical for HDL biogenesis. Since inhibition of ABCA1 by miR-33a/b causes metabolic diseases and atherosclerosis, anti-miR-33 LNAs are potential therapeutic agents. Rottiers et al. (2013) showed preclinical data that 8-mer LNA anti-miR-33 seed treatment reduced HDL in obese and insulin-resistant monkeys. Furthermore, transduction of arterial endothelial cells (ECs) with an exosomereleasing vector containing anti-miR-33a-5p increased ABCA1 (Stamatikos et al. 2020). Overexpression of ABCA1 increased cholesterol efflux and decreased inflammatory gene expression. Therefore, treatment with anti-miR-33a-releasing EC transduction might reduce atherosclerosis (Saenz-Pipaon and Dichek 2022). miR-33 is an atherogenic information gene. miR-33a and miR-33b are intronic miRNAs that are expressed from the intron of SRBF genes, the SRBF gene-encoding SRBP controlling cholesterol synthesis. Therefore, miR-33 can regulate cholesterol production through regulation of SRBF expression. Furthermore, miR-33a/b targets ABCA1 mRNA, and miR-33b targets fatty acid synthase (FASN), stearoyl-CoA desaturase (SCD), and acetyl-CoA carboxylase (ACC) mRNA. Since miR-33 is expressed in cattle, meat miR-33 in exosomes can induce atherosclerosis via the bloodstream (Fig. 4.7). Plasma HDL concentrations were increased more with the 8-mer LNA anti-miR-33a and anti-miR-33b mixture than with anti-miR-33a or antimiR-33b alone. Although less than 10-mer nanoRNAs of miR-34 family agents have been reported to be effective in heart failure (Bernardo et al. 2012), subcutaneously (s.c.) delivered 8-mer LNA anti-miR-34 family mixtures effectively attenuated myocardial infarction-induced dysfunction and improved cardiac function compared to treatment with miR-34a alone. miR-33a and miR-33b can target IRS2, SIRT6, and AMP-activated protein kinase (AMPK) in carbohydrate metabolism; ABCA1, ABCG1, NPC intracellular cholesterol transporter 1 (NPC1), and AMPK in cholesterol metabolism; and carnitine O-octanoyltransferase (CROT), carnitine palmitoyltransferase 1A (CPT1A), NADHB, and AMPK in fatty acid metabolism,

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Fig. 4.7 miR-33 information and atherosclerosis

whereas plasma miR-33a and miR-33b levels were inversely related to decreased plasma HDL levels in patients treated with Xuezhikang, a Chinese red-yeast rice dietary supplement (Cao et al. 2014). Although tube feeding causes obesity, soft pellets fed to rodents increased lipogenic factors such as insulin resistance and increased circulating miR-33 (Bae et al. 2014). Furthermore, miR-33 was expressed in bovine intestinal tissue (Liang et al. 2014). Thus, as an example, it is strongly suggested that miR-33 through food intake is incorporated into the circulating sysytem, and dietary miRNA information diffuses to cells and organs. ExomiRNAs are related to atherosclerosis.

4.9

Inflammation-Associated NF-κB

Nuclear transcription factor-κB (NF-κB) protein can be deacetylated by SIRT1 and NF-κB is inactivated. Therefore, as mentioned above, the SIRT1 feedforward loop is relevant for NF-κB tuning. Furthermore, NF-κB is a key factor in controlling intracellular inflammation, as many miRNAs regulate NF-κB activity. miRNAs are increasingly being understood as interfaces for transmitting inflammatory information between organs through human circulation. NF-κB activation is due not only to acetylation but also to degradation of IκB induced by phosphorylation of IκB by IκB kinase (IKK), a blocker of IκB protein. IKK is an activator of NF-κB. miR-21

4.9

Inflammation-Associated NF-κB

69

indirectly induces NF-κB through AKT, and miR-517a/c directly induces NF-κB activation (Wang et al. 2016; Olarerin-George et al. 2013). In addition, miR-125a and miR-125b target TNF-α-induced protein 3 (TNFAIP3, A20) and activate the NF-κB pathway (Liu et al. 2016). miR-146b-5p has been reported to be decreased in monocytes of obese individuals (Hulsmans et al. 2012), and both miR-146a and miR-146b are abundant in endothelial cells in vivo and suppress endothelial cell inflammation by dampening activation in the NF-κB, AP-1, and MAPK/EGR pathways (Cheng et al. 2013b). Moreover, miR-146a and miR-146b target HUR RNA-binding proteins and suppress endothelial nitric oxide (eNO) production, thereby suppressing endothelial activation. Since miR-146b-5p can inhibit NF-κB, miR-146b-5p also suppresses NF-κB-mediated inflammation in monocytes, and downregulation of miR-146b-5p is associated with increased monocyte mitochondrial reactive oxygen synthetase (ROS) in obesity (Hulsmans et al. 2012). Thus, a reduction in miR-146 can lead to atherosclerosis. Chronic inflammation and oxidative stress can be implicated in the initiation, propagation, and development of obesity, insulin resistance, type 2 diabetes, and atherosclerosis (Ramalingam et al. 2017), suggesting that miRNAs may regulate the pathogenesis of these metabolic diseases (Kolodziej et al. 2022). In WAC, miR-132 also activates NF-κB and induces IL-8 and MCP-1 transcription. Circulating miR-132 was detected in obese individuals (Heneghan et al. 2011), but miR-132 was also detected in bovine muscle. Thus, bovine meat contains miR-132 (Miretti et al. 2013), and dietary miR-132 is transferred into the circulation of obese individuals, affecting NF-κB activation in obesity. Mu et al. (2014) and Xiao et al. (2018) isolated miRNAs containing exosome-like nanoparticles from edible plants, ginger, grapefruit, and carrot, and showed that these miRNAs were taken up by mouse intestinal cells. Plant-derived nanoparticles can deliver several chemotherapeutic agents, including siRNA, in vivo (Cieslik et al. 2022; Chen et al. 2022). These results suggest that plant and meat miRNAs are taken up by exosomes as food and taken up by intestinal cells, that bona fide Rigs are incorporated into mucosal cells and are also taken up by intestinal cells and are incorporated into the host via the bloodstream and/or lymph according to host conditions. Circulating miR-181b can reduce NF-κB activity (Sun et al. 2012). Although miR-181b does not target NF-κB, miR-181b can repress nuclear translocation of NF-κB by targeting importin-α3. Treatment with miR-181b inhibited NF-κB activation and atherosclerosis in apolipoprotein E (ApoE)-deficient mice fed a high-fat diet in vivo (Sun et al. 2014). Plasma levels of miR-181b are low in human patients with CAD and ApoE-deficient mice fed a high-fat diet and treated with miR-181b mimics and decreased NF-κB nuclear translocation. It was observed in vascular endothelium lesions. Thus, treatment with miR-181b affected vascular endothelial cells. NF-κB is an inflammatory molecule, and miRNAs regulate NF-κB expression. miRNAs may be deeply implicated in obesity, diabetes, and atherosclerosis, and if so, age-related diseases (ARD) may be inflammation-based diseases (Rakib et al. 2022). Many miRNAs within exosomes contribute to NF-κB regulation, and these miRNAs circulate not only in the bloodstream but also across kingdoms. Therefore,

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xenomiRNAs may be involved in the regulation of inflammation, diabetes, atherosclerosis, etc., via NF-κB. In METS/MIRAI, T2DM, and CAD were not involved in NF-κB, whereas pre-T2DM obese subjects were implicated in NFKB subunit 1 (NFKB1), and NFKB1 was augmented by hub miR-9-5p downregulation along with branch miR-155-5p, miR-508-3p, and miR-146a-5p (Fujii 2021a, 2023). Obesity may, therefore, be associated with NF-κB-dependent inflammation, which is linked to T2DM.

4.10

New Food Information Processing

Circulating miRNAs participate in lipid metabolism involved in atherosclerosis (Jebari-Benslaiman et al. 2022). Experimental and clinical data suggest that even small amounts of daily dietary miRNAs can regulate intracellular signaling-induced inflammation and inflammation-derived metabolic diseases. Furthermore, maternally acquired metabolic miRNA genotypes can be passed on to offspring (Hromadnikova et al. 2020). Thus, species evolution is programmed by RNA, and acquired metabolic miRNA genotypes are inherited from mother to child. The increasing obesity population may be caused by the metabolic miRNA storm. If so, the emergence and disappearance of the origin of species on Earth might be programmed by food miRNAs (programmed evolution). Different cosmic Rigs, such as Noah’s Ark, affect our genotypes and phenotypes. This evolution is completely different from Darwin’s phylogenetic tree. This can be easily argued because disease, i.e., traits are determined by miRNAs (Fig. 4.8). As a new type of food information processing, miRNAs contained in meat and plant exosomes are taken up by intestinal cells along with nutrients and transferred to the circulatory system. The sum of the effects of daily given miRNA information suggests that even small amounts of food miRNA can partially ameliorate metabolic diseases such as impaired glucose and lipid metabolism, adipocyte and myocyte mal-differentiation, neurodegeneration, obesity, insulin resistance, and diabetes. This is evidence that the human phenotype can be altered from lean to obese by food and/or dietary miRNA genes as environmental information, even in the absence of mutation or recombination. Furthermore, this genetic information can be transmitted from one person to another via RNA storms, such as COVID-19. Therefore, evolution may not only involve phylogenetic trees. For example, obesity-related miRNAs may have wiped out dinosaurs, and humans may be endangered due to obesity miRNAs. These are not related to the phylogenetic tree, although they are genetically informed. Programmed evolution by dietary miRNAs, such as Noah’s Ark, plays an important role in the evolution of species distinct from Darwinism. We need all life on Earth as a pool of miRNA genes and genomic DNA. Therefore, endangered species had to be maintained as ncRNA resources, not just DNA.

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Fig. 4.8 Relation between human diseases and programmed evolution

References Ashrafizadeh M, Zarrabi A, Mostafavi E, Aref AR, Sethi G et al (2022) Noncoding RNA-base regulation of inflammation. Semin Immunol 59:101606. https://doi.org/10.1016/j.smim.2022. 101606 Bader AG (2012) miR-34-a microRNA replacement therapy is headed to the clinic. Front Genet 3: 120. https://doi.org/10.3389/fgene.2012.00120 Bae CR, Hasegawa K, Akieda-Asai S, Kawasaki Y, Senba K et al (2014) Possible involvement of food texture in insulin resistance and energy metabolism in male rats. J Endocrinol 222:61–72. https://doi.org/10.1530/JOE-13-0553 Bernardo BC, Gao XM, Winbanks CE, Boey EJ, Tham YK et al (2012) Therapeutic inhibition of the miR-34 family attenuates pathological cardiac remodeling and improves heart function. Proc Natl Acad Sci U S A 109:17615–17620. https://doi.org/10.1073/pnas.1206432109 Bhatnagar S, Chertkow H, Schipper HM, Yuan Z, Shetty V et al (2014) Increased microRNA-34c abundance in Alzheimer’s disease circulating blood plasma. Front Mol Neurosci 7:2. https://doi. org/10.3389/fnmol.2014.00002 Bommer GT, Gerin I, Feng Y, Kaczorowski AJ, Kuick R et al (2007) p53-mediated activation of miRNA34 candidate tumor-suppressor genes. Curr Biol 17:1298–1307. https://doi.org/10.1016/ j.cub.2007.06.068 Boysen J, Sinha S, Price-Troska S, Warner SL, Bearss DJ et al (2014) The tumor suppressor axis p53/miR-34a regulates Ax1 expression in B-cell chronic lymphocytic leukemia: implications for therapy in p53-defective CLL patient. Leukemia 28:451–455. https://doi.org/10.1038/leu. 2013.298

72

4

Food XenomiRNA Arise: MicroRNA Storm and Space

Çakir I, Perello M, Lansari O, Messier NJ, Vaslet CA et al (2009) Hypothalamic Sirt1 regulates food intake in a rodent model system. PLoS One 4:e8322. https://doi.org/10.1371/journal.pone. 0008322 Cao L, Lin EJ, Cahill MC, Liu X, During MJ (2009) Molecular therapy of obesity and diabetes by a physiological autoregulatory approach. Nat Med 15:447–454. https://doi.org/10.1038/nm.1933 Cao R, Bai Y, Sun L, Zheng J, Zu M et al (2014) Xuezhikang therapy increases miR-33 expression in patients with low HDL-C levels. Dis Markers 2014:781780. https://doi.org/10.1155/2014/ 781780 Capozzi F, Bordoni A (2013) Foodomics: a new comprehensive approach to food and neutrition. Genes Nutr 8:1–4. https://doi.org/10.1007/s12263-012-0310-x Castro FO, Sharbati S, Rodriguez-Alvarez LL, Cox JF, Hultschig C et al (2010) MicroRNA expression profiling of elongated cloned and in vitro-fertilized bovine embryos. Theriogenology 73:71–85. https://doi.org/10.1016/j.theriogenology.2009.08.003 Chandrasekar V, Dreyer JL (2011) Regulation of MiR-124, let-7d, and MiR-181a in the accumbens affects the expression, extinction, and reinstatement of cocaine-induced conditioned place preference. Neuropsychopharmacology 36:1149–1164. https://doi.org/10.1038/npp.2010.250 Chartoumpekis DV, Zaravinos A, Ziros PG, Iskrenova RP, Psyrogiannis AI et al (2012) Differential expression of microRNAs in adipose tissue after long-term high-fat diet-induced obesity in mice. PLoS One 7:e34872. https://doi.org/10.1371/journal.pone.0034872 Chen JF, Mandel EM, Thomson JM, Wu Q, Callis TE et al (2006) The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Nat Genet 38:228–233. https://doi.org/10.1038/ng1725 Chen N, Sun J, Zhu Z, Cribbs AP, Xiao B (2022) Edible plant-derived nanotherapeutics and nanocarriers: recent progress and future directions. Expert Opin Drug Deliv 19:409–419. https://doi.org/10.1080/17425247.2022.2053673 Cheng L, Quek CY, Sun X, Belingham SA, Hill AF (2013a) The detection of microRNA associated with Alzheimer’s disease in biological fluids using next-generation sequencing technologies. Front Genet 4:150. https://doi.org/10.3389/fgene.2013.00150 Cheng HS, Sivachandran N, Lau A, Boudreau E, Zhao JL et al (2013b) MicroRNA-146 represses endothelial activation by inhibiting pro-inflammatory pathways. EMBO Mol Med 5:1017– 1034. https://doi.org/10.1002/emmm.201202318 Choi SE, Fu T, Seok S, Kim DH, Yu E et al (2013) Elevated microRNA-34a in obesity reduces NAD+ levels and SIRT1 activity by directly targeting NAMPT. Aging Cell 12:1062–1072. https://doi.org/10.1111/acel.12135 Chutipongtanate S, Morrow AL, Newburg D (2022) Human milk extracellular vesicles: a biological system with clinical implications. Cell 11:2345. https://doi.org/10.3390/cells11152345 Cianflone E, Scalise M, Marino F, Salerno L, Salerno N et al (2022) The negative regulation of gene expression by microRNAs as key driver of inducers and repressors of cardiomyocyte differentiation. Clin Sci 136:1179–1203. https://doi.org/10.1042/CS20220391 Cieslik M, Nazimek K, Bryniarski K (2022) Extracellular vesicles-oral therapeutics of the future. Int J Mol Sci 23:7554. https://doi.org/10.3390/ijms23147554 Cone RD (2005) Anatomy and regulation of the central melanocortin system. Nat Neurosci 8:571– 578. https://doi.org/10.1038/nn1455 Cook C, Carlomango Y, Gendron TF, Dunmore J, Scheffel K et al (2014) Acetylation of the KXGS motifs in tau is a critical determinant in modulation of tau aggregation and clearance. Human Mol Genet 23:104–116. https://doi.org/10.1093/hmg/ddt402 Corney DC, Hwang C–II, Matoso A, Vogt M, Flesken-Nikitin A et al (2010) Frequent downregulation of miR-34 family in human ovarian cancers. Clin Cancer Res 16:1119–1128. https://doi.org/10.1158/1078-0432.CCR-09-2642 Couturier A, Keller J, Most J, Ringseis R, Eder K (2014) Niacin in pharmacological doses alters microRNA expression in skeletal muscle of obese Zucker rats. PLoS One 9:e98313. https://doi. org/10.1371/journal.pone.0098313

References

73

D’Orso I, Frankel AD (2009) Tat acetylation modulates assembly of a viral-host RNA–protein transcription complex. Proc Natl Acad Sci U S A 106:3101–3106. https://doi.org/10.1016/j. molcel.2008.07.016 Dallman MF, Akana SF, Scribner KA, Bradbury MJ, Walker CD et al (1992) Stress, feedback and facilitation in hypothalamo-pituitary-adrenal axis. J Neuroendocrinol 4:517–526. https://doi. org/10.1111/j.1365-2826.1992.tb00200.x Dang Y, Luo D, Rong M, Chen G (2013) Underexpression of miR-34a in hepatocellular carcinoma and its contribution toward enhancement of proliferating inhibitory effects of agents targeting cMET. PLoS One 8:e61054. https://doi.org/10.1371/journal.pone.0061054 Davenport AM, Huber FM, Hoelz A (2014) Structural and functional analysis of human SIRT1. J Mol Biol 426:526–541. https://doi.org/10.1016/j.jmb.2013.10.009 de Barros Sene L, Mesquita FF, de Moraes N, Santos DC, Carvalho R et al (2013) Involvement of renal corpuscle microRNA expression on epithelial-to-mesenchymal transition in maternal low protein diet in adult programmed rats. PLoS One 8:e71310. https://doi.org/10.1371/journal. pone.0071310 Del Re B, Giorgi G (2013) Cell-host, LINE and environment. Mobile genet. Elements 3:e2404. https://doi.org/10.4161/mge.24040 Derghal A, Astier J, Sicard F, Couturier C, Landrier JF et al (2019) Leptin modulates the expression of miRNAs-targeting POMC mRNA by the JAK2-STAT3 and PI3K-AKt pathways. J Clin Med 8:2213. https://doi.org/10.3390/jcm8122213 Diabetes Genetics Initiative of Borad Institute of Harvard and MIT, Lund University, and Novartis Institute of BioMedical Research, Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PIW et al (2007) Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316:1331–1336. https://doi.org/10.1126/science.1142358 Dickson JR, Kruse C, Montagna DR, Finsen B, Wolfe MS (2013) Alternative polyadenylation and miR-34 family members regulate tau expression. J Neurochem 127:739–749. https://doi.org/10. 1111/jnc.12437 Dorn GW II (2013) MicroRNAs and the butterfly effect. Cell Cycle 12:5. https://doi.org/10.4161/ cc.23858 Eduati F, di Camillo B, Kaebiener M, Scheideler M, Cora D et al (2012) Dynamic modeling of miRNA-mediated feed-forward loops. J Comput Biol 19:188–199. https://doi.org/10.1089/cmb. 2011.0274 Elamin BK, Calleqari E, Gramantieri L, Sabbioni S, Negrini M (2011) MicroRNA response to environmental mutagens in liver. Mutat Res 717:67–76. https://doi.org/10.1016/j.mrfmmm. 2011.03.015 Farooqi IS, Drop S, Clements A, Keogh JM, Biernacka J et al (2006) Heterozygosity for a POMCnull mutation and increased obesity risk in humans. Diabetes 55:2549–2553. https://doi.org/10. 2337/db06-0214 Fernandes-Silva MM, Carvalho VO, Guimarães GV, Bacal F, Bocchi EA (2012) Physical exercise and microRNAs: new frontiers in heart failure. Arq Bras Cardiol 98:459–466. https://doi.org/10. 1590/S0066-782X2012000500012 Fu T, Choi SE, Kim DH, Seok S, Suino-Powell KM et al (2012) Aberrantly elevated microRNA34a in obesity attenuates hepatic responses to FGF19 by targeting a membrane coreceptor β-Klotho. Proc Natl Acad Sci U S A 109:16137–16142. https://doi.org/10.1073/pnas. 1205951109 Fujii YR (2008) Formulation of new argorithmics for miRNAs. Open Virol J 2:37–43. https://doi. org/10.2174/1874357900802010037 Fujii YR (2009) Oncoviruses and pathogenic microRNAs in humans. Open Virol J 3:37–51. https:// doi.org/10.2174/1874357901004010063 Fujii YR (2014) RNA information gene diseases: nano-RNA-based medical devices with corporate chemotherapy and gene therapy. In: Wang B (ed) RNA nanotechnology. CRC Press Taylor & Francis Group, Boca Raton, pp 385–434

74

4

Food XenomiRNA Arise: MicroRNA Storm and Space

Fujii YR (2021a) Analysis of coronary artery disease using serum/plasma data in a quantum microRNA language with artificial intelligence (MIRAI). SunText Rev Med Clin Res 2:143. https://doi.org/10.51737/2766-4813.2021.043 Fujii YR (2021b) Analysis for brain tumors and Alzheimer’s disease using quantum microRNA language with artificial intelligence (MIRAI). SunText Rev Med Clin Res 2:142. https://doi.org/ 10.51737/2766-4813.2021.043 Fujii YR (2023) The microRNA quantum code book. Springer Nature, Singapore Gao J, Wang WY, Mao YW, Gräff J, Guan JS et al (2010) A novel pathway regulates memory and plasticity via SIRT1 and miR-134. Nature 466:1105–1109. https://doi.org/10.1038/nature09271 García-Segura L, Pérez-Andrade M, Miranda-Ríos J (2013) The emerging role of microRNAs in the regulation of gene expression by nutrients. J Nutrigenet Nutrigenomics 6:16–31. https://doi.org/ 10.1159/000345826 Gharanei S, Shabir K, Brown JE, Weickert MO, Barber TM et al (2020) Regulatory microRNAs in brown, brite and white adipose tissue. Cell 9:2489. https://doi.org/10.3390/cells9112489 Gilbert W (1986) Origin of life: the RNA world. Nature 425:285–288 Glinsky GV (2008) Disease phenocode analysis identifies SNP-guided microRNA maps (MirMaps) associated with human “master” disease genes. Cell Cycle 7:3680–3694. https://doi.org/10. 4161/cc.7.23.7153 Goedeke L, Vales-Lara FM, Fenstermaker M, Cirera-Salinas D, Chamorro-Jorganes A et al (2013) A regulatory role for microRNA 33* in controlling lipid metabolism gene expression. Mol Cell Biol 33:2339–2352. https://doi.org/10.1128/MCB.01714-12 Gondret F, Guitton N, Gullerm-Regost C, Louveau I (2008) Regional differences in porcine adipocytes isolated from skeletal muscle and adipose tissues as identified by a proteomic approach. J Animal Sci 86:2115–2125. https://doi.org/10.2527/jas.2007-0750 Gong W, Xiao D, Ming G, Yin J, Zhou H et al (2014) Type 2 diabetes mellitus-related genetic polymorphism in microRNA and microRNA target sites. J Diabetes 6:279–289. https://doi.org/ 10.1111/1753-0407.12143 Gruden’ MA, Storozheva ZI, Ratmirov AM, Sherstnev VV (2017) Pattern of Notch2, numb, and Cas8 gene expression in relevant structures of the rat brain during formation of spatial memory. Bull Exp Biol Med 163:785–788. https://doi.org/10.1007/s10517-017-3903-y Grunfeld C (2008) Insulin resistance in HIV-1 infection: drug, host responses, or restoration to health? Top HIV Med 16:89–93 Guo Y, Mo D, Zhang Y, Zhang Y, Cong P et al (2012) MicroRNAome comparison between intramuscular and subcutaneous vascular stem cell adipogenesis. PLoS One 7:e45410. https:// doi.org/10.1371/journal.pone.0045410 Guo L, Zhao Y, Yang S, Zhang H, Chen F (2014) Integrative analysis of miRNA–mRNA and miRNA–miRNA interactions. Biomed Res Int 2014:907420. https://doi.org/10.1155/2014/ 907420 Guo J, Cheng X, Zhang L, Wang L, Mao Y et al (2017) Exploration of the Brn4-regulated genes enhancing adult hippocampal neurogenesis by RNA sequencing. J Neurosci Res 95:2071–2079. https://doi.org/10.1002/jnr.24043 Haigis MC, Sinclair DA (2010) Mammalian sirtuins: biological insights and disease relevance. Annu Rev Pathol 5:253–295. https://doi.org/10.1146/annurev.pathol.4.110807.092250 He B, Pan Y, Cho WC, Xu Y, Gu L et al (2012) The association between four genetic variants in microRNAs (rs11614913, rs2910164, rs3746444, rs22922832) and cancer risc: evidence from published studies. PLoS One 7:e49032. https://doi.org/10.1371/journal.pone.0049032 Heirich EM, Dimmeler S (2012) MicroRNAs and stem cells: control of pluripotency, reprogramming, and lineage commitment. Circ Res 110:1014–1022. https://doi.org/10.1161/ CIRCRESAHA.111.243394 Heneghan HM, Miller N, McAnena OJ, O’Brien T, Kerin MJ (2011) Differential miRNA expression in omental adipose tissue and in the circulation of obese patients identifies novel metabolic biomarkers. J Clin Endocrionol Metab 96:E846–E850. https://doi.org/10.1210/jc.2010-2701

References

75

Hoekstra M (2016) MicroRNA-499a-5p: a therapeutic target in the context of cardiovascular disease. Ann Transl Med 4:539. https://doi.org/10.21037/atm.2016.11.61 Hong DS, Kang YK, Borad M, Sachdev J, Ejadi S et al (2020) Phase 1 study of MRX34, a liposomal miR-34a mimic, in patients with advanced solid tumors. Br J Cancer 122:1630–1634. https://doi.org/10.1038/s41416-020-0802-1 Hooper E (1999) The river. Little, Brown and Company, London Hou L, Zhang X, Wang D, Baccarelli A (2012) Environmental chemical exposures and human epigenetics. Int J Epidemiol 41:79–105. https://doi.org/10.1093/ije/dyr154 Hromadnikova I, Kotlabova K, Krofta L, Siec J (2020) Association analysis in children born from normal and complicated pregnancies-cardiovascular disease associated microRNAs and the incidence of prehypertension/hypertension, overweight/obesity, valve problems and heart defects. Int J Mol Sci 21:8413. https://doi.org/10.3390/ijms21218413 Hudgens MG, Taha TE, Omer SB, Jamieson DJ, Lee H et al (2013) Pooled individual data analysis of 5 randomized trials of infant nevirapine prophylaxis to prevent breast-milk HIV-1 transmission. Clin Infect Dis 56:131–139. https://doi.org/10.1093/cid/cis808 Hulsmans M, Van Dooren E, Mathieu C, Holvoet P (2012) Decrease of miF-146b-5p in monocytes during obesity is associated with loss of the anti-inflammatory but not insulin signaling action of adiponectin. PLoS One 7:e32794. https://doi.org/10.1371/journal.pone.0032794 Jacobs LA, Bewick-Copley F, Poolman MG, Pink RC, Mulcahy LA et al (2013) Meta-analysis using a novel database, miRStress, reveals miRNAs that are frequently associated with radiation and hypoxia stress responses. PLoS One 8:e80844. https://doi.org/10.1371/journal.pone. 0080844 Jardim MJ (2011) microRNAs: implications for air pollution research. Mutat Res 717:38–45. https://doi.org/10.1016/j.mrfmmm.2011.03.014 Jebari-Benslaiman S, Galicia-Garcia U, Larrea-Sebal A, Olaetxea JR, Aliloza I et al (2022) Pathophysiology of atherosclerosis. Int J Mol Sci 23:3346. https://doi.org/10.3390/ ijms23063346 Jeong JY, Lee DH, Kang SS (2013) Effects of chronic resistant stress on body weight, food intake, and hypothalamic gene expressions in mice. Endo Metab 28:288–296. https://doi.org/10.3803/ EnM.2013.28.4.288 Jiang R, Lönnerdal B (2022) Milk-derived miR-22-3p promotes proliferation of human intestinal epithelial cells (HIECs) by regulating gene expression. Nutrients 14:4901. https://doi.org/10. 3390/nu14224901 Katabira ET, Colebunders R (1996) HIV infection in Africa. In: Friedman-Kien AE, Cockerell CJ (eds) Color atlas of AIDS, pp 203–219 Khanna A, Muthusamy S, Liang R, Sarojini H, Wang E (2011) Gain of survival signaling by downregulation of three key miRNAs in brain of calorie-restricted mice. Aging 3:223–235. https://doi.org/10.18632/aging.100276 Khudayberdiev SA, Zampa F, Rajman M, Schratt G (2013) A comprehensive characterization of the nuclear microRNA repertoire of postmitotic neurons. Front Mol Neurosci 6:43. https://doi. org/10.3389/fnmol.2013.00043 Kim S, Park JW, Lee MG, Nam KH, Park JH et al (2019) Reversine promotes browning of white adipocytes by suppressing miR-133a. J Cell Physiol 234:3800–3813. https://doi.org/10.1002/ jcp.27148 Klöting N, Berthold S, Kovacs P, Schön MR, Fasshauer M et al (2009) MicroRNA expression in human omental and subcutaneous adipose tissue. PLoS One 4:e4699. https://doi.org/10.1371/ journal.pone.0004699 Kochegarov A, Lemanski LF (2016) New trends in heart regeneration: a review. J stem Cells Regen Med 12:61–68. https://doi.org/10.46582/jsrm.1202010 Kolodziej F, McDonagh B, Burns N, Goljanek-Whysall K (2022) McroRNAs as the sentinels of redox and hypertrophic signaling. Int J Mol Sci 23:14716. https://doi.org/10.3390/ ijms232314716

76

4

Food XenomiRNA Arise: MicroRNA Storm and Space

Kovalchuk A, Lowings M, Rodriguez-Juarez R, Muhammad A, Ilnytskyy S et al (2012) Epigenetic bystander-like effects of stroke in somatic organs. Aging 4:224–234. https://doi.org/10.18632/ aging.100447 Kuehnen P, Mischke M, Wiegand S, Sers C, Horsthemke B et al (2012) An Alu element-associated hypermethylation variant of the POMC gene is associated with childhood obesity. PLoS Genet 8:e1002543. https://doi.org/10.1371/journal.pgen.1002543 Lauring B, Taggart AK, Tata JR, Dunbar R, Caro L et al (2014) Niacin lipid efficacy is independent of both the niacin receptor GPR109A and free fatty acid suppression. Sci Transl Med 4:148– 115. https://doi.org/10.1126/scitranslmed.3003877 Lee J, Kemper JK (2010) Controlling SIRT1 expression by microRNAs in health and metabolic disease. Aging 2:527–534. https://doi.org/10.18632/aging.100184 Lee J, Padhye A, Sharma A, Song G, Miao J et al (2010) A pathway involving farnesoid X receptor and small heterodimer partner positively regulates hepatic sirtuin 1 levels via microRNA-34a inhibition. J Biol Chem 285:12604–12611. https://doi.org/10.1074/jbc.M109.094524 Lefterova ML, Zhang Y, Steger DJ, Schupp M, Schug J et al (2008) PPARgamma and C/EBP factors orchestrate adipocyte biology via adjacent binding on a genome-wide scale. Genes Dev 22:2941–2952. https://doi.org/10.1101/gad.1709008 Li N, Muthusamy S, Liang R, Sarojini H, Wang E (2011a) Increased expression of miR-34a and miR-93 in rat liver during aging, and their impact on the expression of Mgst1 and Sirt1. Mech Aging Dev 132:75–85. https://doi.org/10.1016/j.mad.2010.12.004 Li X, Khanna A, Li N, Wang E (2011b) Circulating miR-34a as an RNA-based, noninvasive biomarker for brain aging. Aging 3:985–1002. https://doi.org/10.18632/aging.100371 Li WJ, Wang Y, Liu R, Kasinski AL, Shen H et al (2021) MicroRNA-34a: potent tumor suppressor, cancer stem cell inhibitor, and potential anticancer therapeutic. Front Cell Dev Biol 8:9. https:// doi.org/10.3389/fcell.2021.640587 Liang G, Malmuthuge N, McFadden TB, Bao H, Griebel PJ et al (2014) Potential regulatory role of microRNAs in the development of bovine gastrointestinal tract during early life. PLoS One 9: e92592. https://doi.org/10.1371/journal.pone.0092592 Liang Y, Liu H, Zhu J, Song N, Lu Z et al (2021) Inhibition of p53/miR-34a/SIRT1 axis ameliorates podocyte injury in diabetic nephropathy. Biochem Biophys Res Commun 559:48–55. https:// doi.org/10.1016/j.bbrc.2021.04.025 Lin SL, Ying SY (2013) Mechanism and method for generating tumor-free iPS cells using intronic microRNA miR-302 induction. Methods Mol Biol 936:295–312. https://doi.org/10.1007/978-162703-083-0_23 Liu C, Li B, Cheng Y, Lin J, Hao J et al (2011) MiR-21 plays an important role in radiation induced carcinogenesis in BALB/c mice by directly targeting the tumor suppressor gene big-h3. Int J Biol Sci 7:347–363. https://doi.org/10.7150/ijbs.7.347 Liu N, Landreh M, Cao K, Abe M, Hendriks GJ et al (2012) The microRNA miR-34 modulates aging and neurodegeneration in drosophila. Nature 482:519–523. https://doi.org/10.1038/ nature10810 Liu W, Bi P, Shan T, Yang X, Yin H et al (2013) miR-133a regulates adipocyte browning in vivo. PLoS Genet 9:e1003626. https://doi.org/10.1371/journal.pgen.1003626 Liu N, Wang L, Sun C, Yang L, Sun W et al (2016) MicroRNA-125b-5p suppresses Brucella abortus intracellular survival via control of A20 expression. BMC Microbiol 16:171. https://doi. org/10.1186/s12866-016-0788-2 Lu Z, Fisher TW, Hasse S, Sugawara K, Kamenisch Y et al (2009) Profiling the response of human hair follicles to ultraviolet radiation. J Invest Dermatol 129:1790–1804. https://doi.org/10.1038/ jid.2008.418 Lukasik A, Zielenkiewicz P (2014) In silico identification of plant miRNAs in mammalian breast milk exosomes-a small step forward? PLoS One 9:e99963. https://doi.org/10.1371/journal. pone.0099963 Ma K, Saha PK, Chan L, Moore DD (2006) Farnesoid X receptor is essential for normal glucose homeostasis. J Clin Invest 116:1102–1109. https://doi.org/10.1172/JCI25604

References

77

MacDougald OA, Lane MD (1995) Transcriptional regulation of gene expression during adipocyte differentiation. Annu Rev Biochem 64:345–373. https://doi.org/10.1146/annurev.bi.070195. 002021 Maligianni I, Yapijakis C, Nousia K, Bacopoulou F, Chrousos G (2022) Exosomes and exosomal noncoding RNAs through human gestation. Exp Ther Med 24:582. https://doi.org/10.3892/etm. 2022.11518 Malkani S, Chin CR, Cekanaviciute E, Mortreux M, Okinula H et al (2022) Circulating miRNA spaceflight signature reveals targets for countermeasure development. Cell Rep 33:108448. https://doi.org/10.1016/j.celrep.2020.108448 Maloyan A, Muralimanoharan S, Huffman S, Cox LA, Nathanielsz PW et al (2013) Identification and comparative analysis of myocardial miRNAs involved in the fetal response to maternal obesity. Physiol Genomics 45:889–900. https://doi.org/10.1152/physiolgenomics.00050.2103 Martino F, Lorenzen J, Schmidt J, Schmidt M, Broll M et al (2012) Circulating microRNAs are not eliminated by hemodialysis. PLoS One 7:e38269. https://doi.org/10.1371/journal.pone. 0038269 Mateo Leach IM, van der Harst P, de Boer RA (2010) Pharmacoepigenetics in heart failure. Curr Heart Fail Rep 7:83–90. https://doi.org/10.1007/s11897-010-0011-y McCormick JB, Fisher-Hoch S (1996) Level4. Turner Publishing, Inc., Atlanta McDaniel K, Herrera L, Zhou T, Francis H, Han Y et al (2013) The functional role of microRNAs in alcoholic liver injury. J Cell Mol Med 18:197–207. https://doi.org/10.1111/jcmm.12223 Melnik BC, John SM, Schmitz G (2013) Milk is not just food but most likely a genetic transfection system activating mTORC1 signaling for postnatal growth. Nut J 12:103. https://doi.org/10. 1186/1475-2891-12-103 Min HK, Kapoor A, Fachs M, Mirshahi F, Zhou H et al (2012) Increased hepatic synthesis and dysregulation of cholesterol metabolism is associated with the severity of nonalcoholic fatty liver disease. Cell Metab 15:665–674. https://doi.org/10.1016/j.cmet.2012.04.004 Miretti S, Martignani E, Accornero P, Baratta M (2013) Functional effect of mir-27b on myostatin expression: a relationship in Piedmontese cattle with double-muscled phenotype. BMC Genomics 14:194. https://doi.org/10.1186/1471-2164-14-194 Montag J, Hitt R, Opitz L, Schulz-Schaeffer WJ, Hunsmann G et al (2009) Upregulation of miRNA hsa-miR-342-3p in experimental and idiopathic prion disease. Mol Neurodegener 4:36. https:// doi.org/10.1186/1750-1326-4-36 Montastier E, Beuzelin D, Martins F, Mir L, Marqués MA et al (2019) Niacin induces miR-502-3p expression which impairs insulin sensitivity in human adipocytes. Int J Obes 43:1485–1490. https://doi.org/10.1038/s41366-018-0260-5 Morton GJ, Cummings DE, Baskin DG, Schwartz MW (2006) Central nervous system control of food intake and body weight. Nature 443:289–298. https://doi.org/10.1038/nature05026 Mu J, Zhuang X, Wang Q, Jiang H, Deng ZB et al (2014) Interspecies communication between plant and mouse gut host cells through edible plant derived exosome-like nanoparticles. Mol Nutr Food Res 58:1561–1573. https://doi.org/10.1002/mnfr.201300729 Nelson RH, Vlazny D, Smailovic A, Miles M (2012) Intravenous niacin acutely improves the efficiency of dietary fat storage in lean and obese humans. Diabetes 61:3172–3175. https://doi. org/10.2737/db12-0236/-/DCI Niculescu LS, Simionescu N, Sanda G, Carnuta MG, Stancu CS et al (2015) MiR-486 and miR-92a identified in circulating HDL discriminate between stable and vulnerable coronary artery disease patients. PLoS One 10:e0140958. https://doi.org/10.1371/journal.pone.0140958 Oba Y, Takano Y, Furukawa Y, Koga T, Glavin DP et al (2022) Identifying the wide diversity of extraterrestrial purine and pyrimidine nucleobases in carbonaceous meteorites. Nat Commun 13:2008. https://doi.org/10.1038/s41467-022-29612-x Okada N, Lin CP, Ribeiro MC, Biton A, Lai G et al (2014) A positive feedback between p53 and miR-34 miRNAs mediates tumor suppression. Genes Dev 28:438–450. https://doi.org/10.1101/ gad.233585.113

78

4

Food XenomiRNA Arise: MicroRNA Storm and Space

Olarerin-George AO, Anton L, Hwang YC, Elovitz MA, Hogenesch JB (2013) A functional genomics screen for microRNA regulates of NF-kappa B signaling. BMC Biol 11:19. https:// doi.org/10.1186/1741-7007-11-19 Olivo-Marston SE, Hursting SD, Perkins SN, Schetter A, Khan M et al (2013) Effects of calorie restriction and diet-induced obesity on murine colon carcinogenesis, growth and inflammatory factors, and microRNA expression. PLoS One 9:e94765. https://doi.org/10.1371/journal.pone. 0094765 Ortega FJ, Mercader JM, Catalán V, Moreno-Navarrete JM, Pueyo N et al (2013) Targeting the circulating microRNA signature of obesity. Clin Chem 59:781–792. https://doi.org/10.1373/ clinchem.2012.195776 Osella M, Bosia C, Cora D, Caselle M (2011) The role of incoherent microRNA-mediated feedforward loops in noise buffering. PLoS Comput Biol 7:e1001101. https://doi.org/10. 1371/journal.pcbi.1001101 Osone T, Yoshikawa T, Fujii YR (2015) microRNA memory II. A novel scoring integration model for prediction of human disease by microRNA/microRNA quantum multi-interaction. J Adv Med Phar Sci 5:1–18. https://doi.org/10.9734/JAMPS/2016/22095 Otake K, Omoto S, Yamamoto T, Okuyama H, Okada H et al (2004) HIV-1 Nef in the nucleus influences adipogenesis as well as viral transcription through the peroxisome proliferatoractivated receptors. AIDS 18:189–198. https://doi.org/10.1097/01.aids.0000104408.99588.65 Parsons XH (2012) MicroRNA profiling reveals distinct mechanisms governing cardiac and neural lineage-specification of pluripotent human embryonic stem cells. J Stem Cell Res Ther 2:124 Petrick JS, Brower-Toland B, Jackson AL, Kier LD (2013) Safety assessment of food and feed from biotechnology-derived crops employing RNA-mediated gene regulation to achieve desired traits: a scientific review. Regul Toxicol Pharmacol 66:167–176. https://doi.org/10.1016/j. yrtph.2013.03.008 Rajasethupathy P, Fiumara F, Sheridan R, Betel D, Puthanveettil S et al (2009) Characterization of small RNAs in Aplysia reveals a role for miR-124 in constraining synaptic plasticity through CREB. Neuron 63:803–817. https://doi.org/10.1016/j.neuron.2009.05.029 Rakib A, Kiran S, Mandai M, Singh U (2022) MicroRNAs: a crossroad that connects obesity to immunity and aging. Immun Aging 19:64. https://doi.org/10.1186/s12979-022-00320-w Ramadori G, Lee CE, Bookout AL, Lee S, Williams KW et al (2008) Brain SIRT1: anatomical distribution and regulation by energy availability. J Neurosci 28:9989–9996. https://doi.org/10. 1523/JNEUROSI.3257-08.2008 Ramalingam L, Menikdiwela K, LeMieux M, Dufour JM, Kaur G et al (2017) The renin angiotensin system, oxidative stress and mitochondrial function in obesity and insulin resistance. Biochem Biophys Acta Mol Basis Dis 1863:1106–1114. https://doi.org/10.1016/j.bbadis.2016. 07.019 Rayner KJ, Suárez Y, Dávalos A, Parathath S, Fitzgerald ML et al (2010) MiR-33 contributes to regulation of cholesterol homeostasis. Science 328:1570–1573. https://doi.org/10.1126/science. 1189862 Rizzato C, Scherer D, Rudnai P, Gurzau E, Koppova K et al (2011) POMC and TP53 genetic variability and risk of basal cell carcinoma of skin: interaction between hos and genetic factors. J Dermatol Sci 63:47–54. https://doi.org/10.1016/j.dermatol.2011.03.006 Rottiers V, Näär AM (2012) MicroRNAs in metabolism and metabolic disorders. Nat Rev Mol Cell Biol 13:239–250. https://doi.org/10.1038/nrm3328 Rottiers V, Hajafi-Shoushtari SH, Kristo F, Gurumurthy S, Zhong L et al (2011) MicroRNAs in metabolism and metabolic diseases. Cold Spring Harb Symp Quant Biol 2011:76. https://doi. org/10.1101/sqb.2011.76.011049 Rottiers V, Obad S, Petri A, McGarrah R, Lindholm MW et al (2013) Pharmacological inhibition of a microRNA family in nonhuman primates by a seed-targeting 8-mer antimiR. Sci Transl Med 5:212ra162. https://doi.org/10.1126/scitranslmed.3006840

References

79

Roy S, Levi E, Majumdar APN, Sarkar FH (2012) Expression of miR-34 is lost in colon cancer which can be re-expressed by a novel agent CDF. J Hematol Oncol 5:58. https://doi.org/10. 1186/1756-8722-5-58 Saba R, Goodman CD, Huzarewich RL, Robertson C, Booth SA (2008) A miRNA signature of prion induced neurodegeneration. PLoS One 3:e3652. https://doi.org/10.1371/journal.pone. 0003652 Saenz-Pipaon G, Dichek DA (2022) Targeting and delivery of microRNA-targeting antisense oligonucleotides in cardiovascular diseases. Atherosclerosis 697. https://doi.org/10.1016/j. atherosclerosis.2022.12.003 Salloum FN, Yin C, Kukreja RC (2010) Role of microRNAs in cardiac preconditioning. J Cardiovasc Pharmacol 56(6):581–588. https://doi.org/10.1097/FJC.0b013e3181f581ba Samad AA, Kamaroddin MF, Sajad M (2021) Cross-kingdom regulation by plant microRNAs provides novel insight into gene regulation. Adv Nutr 12:197–211. https://doi.org/10.1093/ advances/nmaa095 Sanchita TR, Asif MH, Trivedi PK (2018) Dietary plant miRNAs as an augmented therapy: crosskingdom gene regulation. RNA boil 15:1433–1439. https://doi.org/10.1080/15476286.2018. 1551693 Schneeberger M, Altirriba J, García A, Esteban Y, Castaño C et al (2012) Deletion of miRNA processing enzyme dicer in POMC-expressing cells leads to pituitary dysfunction, neurodegeneration and development of obesity. Mol Metab 2:74–85. https://doi.org/10.1016/j. molmet.2012.10.001 Seale P, Bjork B, Yang W, Kajimura S, Chin S et al (2008) PRDM16 controls a brown fat/skeletal muscle switch. Nature 454:961–968. https://doi.org/10.1038/nature07182 Shende VR, Goldrick MM, Ramani S, Eamest DJ (2011) Expression and rhythmic modulation of circulating microRNAs targeting the clock gene Bmal1 in mice. PLoS One 6:e22586. https:// doi.org/10.1371/journal.pone.0022586 Shereen MA, Khan S, Kazmi A, Bashir N, Siddique R (2020) COVID-19 infection: origin, transmission, and characteristics of human coronaviruses. J Adv Res 24:91–98. https://doi. org/10.1016/j.jare.2020.03.005 Shi S, Han L, Gong T, Zhang Z, Sun X (2013) Systemic delivery of micoRNA-34a for cancer stem cell therapy. Angew Chem Int Ed 52:3901–3905. https://doi.org/10.1002/anie.201208077 Simionescu N, Niculescu LS, Sanda GM, Margina D, Sima AV (2014) Analysis of circulating microRNAs that are specifically increased in hyperlipidemic and/or hyperglycemic sera. Mol Biol Rep 41:5765–5773. https://doi.org/10.1007/s11033-014-3449-2 Sinal C, Tohkin M, Miyata M, Ward J, Lambert G et al (2000) Targeted disruption of the nuclear receptor FXR/BAR impairs bile acid and lipid homeostasis. Cell 102:731–744. https://doi.org/ 10.1016/S0092-8674(00)000062-3 Sluijter JP, van Mil A, van Vliet P, Metz CH, Liu J et al (2010) MicroRNA-1 and -499 regulate differentiation and proliferation in human-derived cardiomyocyte progenitor cells. Arterioscler Thromb Vasc Biol 30:859–868. https://doi.org/10.1161/ATVBAHA.109.197434v1 Smith SB, Lin KC, Wilson JJ, Lunt DK, Cross HR (1998) Starvation depresses acylglycerol biosynthesis in bovine subcutaneous but not intramuscular adipose tissue homogenates. Comp Biochem Physiol B Biochem Mol Biol 120:165–174 Snow JW, Hale AE, Isaacs SK, Baggish AL, Chan SY (2013) Ineffective delivery of diet-derived microRNAs to recipient animal organisms. RNA Biol 10:1107–1116. https://doi.org/10.4161/ rna.24909 Speliotes EK, Willer CJ, Berndt S, Monda KL, Throleifsson G et al (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42:937– 948. https://doi.org/10.1038/ng.686 Stamatikos A, Knight E, Vojtech L, Bi L, Wacker BK et al (2020) Exosome-mediated transfer of anti-miR-33a-5p from transduced endothelial cells enhances macrophage and vascular smooth muscle cell cholesterol efflux. Hum Gene Ther 31:219–232. https://doi.org/10.1089/hum. 2019.245

80

4

Food XenomiRNA Arise: MicroRNA Storm and Space

Stoks PG, Schwartz AW (1979) Uracil in carbonaceous meteorites. Nature 282:709–710. https:// doi.org/10.1038/282709a0 Stoks PG, Schwartz AW (1981) Nitogen-heterocyclic compounds in meteorites: significance and mechanisms of formation. Geochim Cosmochim Acta 45:563–569. https://doi.org/10.1016/ 0016-7037(81)90189-7 Sun L, Xie H, Mori MA, Alexander R, Yuan B et al (2011) Mir193b-365 is essential for brown fat differentiation. Nat Mol Biol 13:958–965. https://doi.org/10.1038/ncb2286 Sun X, Icli B, Wara AK, Belkin N, He S et al (2012) MicroRNA-181b regulates NF-kB-mediated vascular inflammation. J Clin Invest 122:1973–1990. https://doi.org/10.1172/JCI61495 Sun X, He S, Wara AKM, Icli B, Shvartz E et al (2014) Systemic delivery of microRNA-181b inhibits nuclear factor-kB activation, vascular inflammation, and atherosclerosis in apolipoprotein E-deficient mice. Circ Res 114:32–40. https://doi.org/10.1161/CIRCRESAHA.113.302089 Tabuchi T, Sato M, Itoh T, Nakamura M (2012) MicroRNA-34a regulates the longevity-associated protein SIRT1 in coronary artery disease: effect of statins on SIRT1 and microRNA-34a expression. Clin Sci 123:161–171. https://doi.org/10.1042/CS20110563 Tian T, Zhun YL, Zhou YY, Liang GF, Wang YY et al (2014) Exosome uptake through clathrinmediated endocytosis and micropinocytosis and mediating mR-21 dilivery. J Biol Chem 289: 22258–22267. https://doi.org/10.1074/jbc.M114.588046 Tivnan A, Tracey L, Buckley PG, Alcock LC, Davidoff AM et al (2011) MicroRNA-34a is a potent tumor suppressor molecule in vivo in neuroblastoma. BMC Cancer 11:33. https://doi.org/10. 1186/1471-2407-11-33 Trajkovski M, Ahmed K, Esau CC, Stoffel M (2012) MyomiR-133 regulates brown fat differentiation through Prdm16. Nat Cell Biol 14:1330–1337. https://doi.org/10.1038/ncb2612 Tryndyak VP, Ross SA, Beland FA, Pogribny IP (2009) Downregulation of the microRNAs miR-34a, miR-127, and miR-200b in rat liver during hepatocarcinogenesis induced by a methy-deficient diet. Mol Carcinog 48:479–487. https://doi.org/10.1002/mc.20484 Tsang J, Zhu J, van Oudenaarden A (2007) MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. Mol Cell 26:753–767. https://doi.org/10.1016/j. molcel.2007.05.018 Tsui NB, Ng EK, Lo YM (2002) Stability of endogenous and added RNA in blood specimens, serum, and plasma. Clin Chem 48:1647–1653 Vienberg S, Geiger J, Madsen S, Dalgaard LT (2017) MicroRNAs in metabolism. Acta Physiol 219:346–361. https://doi.org/10.1111/apha.12681 Wang Y, Long L, Li T, Zhou Y, Jiang L et al (2014) Polymorphisms of microRNA-binding sites in integrin genes are associated with oral squamous cell carcinoma susceptibility and progression. Tohoku J Exp Med 233:33–41. https://doi.org/10.1620/tjem.233.33 Wang X, Wang C, Zhang X, Hua R, Gan L et al (2016) Bmi-1 regulates stem cell-like properties of gastric cancer cells by modulating miRNAs. J Hematol Oncol 9:90. https://doi.org/10.1186/ s13045-016-0323-9 Williams MD, Mitchell GM (2012) MicroRNAs in insulin resistance and obesity. Exp Diabetes Res 2012:484696. https://doi.org/10.1155/2012/484696 Wilmink GJ, Roth CL, Ibey RB, Ketchum N, Bernhard J et al (2010) Identification of microRNAs associated with hyperthermia-induced cellular stress response. Cell Stress Chap 15:1027–1038. https://doi.org/10.1007/s12192-010-0189-7 Winter KW, Hirschi KD (2014) Transfer and functional consequences of dietary microRNAs in vertebrates: concepts in search of corroboration: negative results challenge the hypothesis that dietary xenomiRs cross the gut and regulate genes in ingesting vertebrates, but important questions persist. bioessays 36:394–406. https://doi.org/10.1002/bies.201300150 Winter KW, McAlexander MA, Queen SE, Adams RJ (2013) Real-time quantitative PCR and droplet digital PCR for plant miRNAs in mammalian blood provide little evidence for the general uptake of delivery miRNAs. RNA Biol 10:1080–1086. https://doi.org/10.4161/rna. 25246

References

81

Wu B, Li C, Zhang P, Yao Q, Wu J et al (2013) Dissection of miRNA–miRNA interaction in esophageal squamous cell carcinoma. PLoS One 8:e73191. https://doi.org/10.1371/journal. pone.0073191 Xiao J, Feng S, Wang X, Long K, Luo Y et al (2018) Identification of exosome-like nanoparticlederived microRNAs from 11 edible fruits and vegetables. Peer J 6:e5186. https://doi.org/10. 7717/peerj.5186 Xu J, Li CX, Li YS, Lv JY, Ma Y et al (2010a) MiRNA-miRNA synergistic network: construction via coregulating functional modules and disease miRNA topological features. Nucleic Acids Res 39:825–836. https://doi.org/10.1093/nar/gkq832 Xu F, Gao Z, Zhang J, Rivera CA, Yin J et al (2010b) Lack of SIRT1 (mammalian sirtuin 1) activity leads to liver steatosis in the SIRT1+/- mice: a role of lipid mobilization and inflammation. Endocrinology 151:2504–2514. https://doi.org/10.1210/en.2009-1013 Xu D, Takeshita F, Hino Y, Fukunaga S, Kudo Y et al (2011) miR-22 represses cancer progression by inducing cellular senescence. J Cell Biol 193:409–424. https://doi.org/10.1083/jcb. 2010101000 Yamakuchi M (2012) MicroRNA regulation of SIRT1. Front Phys 3:68. https://doi.org/10.3389/ fphys.2012.00068 Yamakuchi M, Ferlito M, Lowenstein CJ (2008) miR-34a repression of SIRT1 regulates apoptosis. Proc Natl Acad Sci 105:13421–13426. https://doi.org/10.1073/pnas.0801613105 Yamamura S, Saini S, Majid S, Hirata H, Ueno K et al (2012) MicroRNA-34a modulates c-Myc transcriptional complexes to suppress malignancy in human prostate cancer cells. PLoS One 7: e29722. https://doi.org/10.1371/journal.pone.0029722 Yan Y, Zhang K, Zhou G, Hu W (2020) MicroRNAs responding to space radiation. Int J Mol Sci 21:6603. https://doi.org/10.3390/ijms21186603 Yang T, Sauve A (2006) NAD metabolism and sirtuins: metabolic regulation of protein deacetylation in stress and toxicity. AAPS J 8:E632–E643 Yin H, Pasut A, Soleimani VD, Florian C, Bentzinger CF et al (2013) MicroRNA-133 controls brown adipose determination in skeletal muscle satellite cells by targeting Prdm16. Cell Metab 17:210–224. https://doi.org/10.1016/j.cmet.2013.01.004 Zarkesh M, Tabaei K, Akbarzadeh M, Daneshafrooz A, Zadeh-Vakili A (2022) Association of miR-34a and miR-143 levels with PPARγ gene expression in adipose tissues of nondiabetic adults. J Physiol Anthropol 41:13. https://doi.org/10.1186/s40101-022-00286-0 Zhang HS, Zhou Y, Wu MR, Zhou HS, Xu F (2009) Resveratrol inhibited tat-induced HIV-1 LTR transactivation via NAD+-dependent SIRT1 activity. Life Sci 85:484–489. https://doi.org/10. 1016/j.lfs.2009.07.014 Zhang HS, Chen XY, Wu TC, Sang WW, Ruan Z (2012a) MiR-34a is involved in tat-induced HIV-1 long terminal repeat (LTR) transactivation by downregulation of SIRT1/NF-kB pathway. FEBS let 586:4203–4207. https://doi.org/10.1016/j.febslet.2012.10.023 Zhang L, Hou D, Chen X, Li D, Zhu L et al (2012b) Exogenous plant MIR168a specifically targets mammalian LDLRAP1: evidence of cross-kingdom regulation by microRNA. Cell Res 22:107– 126. https://doi.org/10.1038/cr.2011.158 Zhang L, Wu Y, Li Y, Xu C, Li X et al (2012c) Tanshinone IIA improves miR-133 expression through MAPK ERK1/2 pathway in hypoxic cardiac myocytes. Cell Physiol Biochem 30:843– 852. https://doi.org/10.1159/000341462 Zhao J, Lammers P, Torrance CJ, Bader AG (2013) TP53-independent function of miR-34a via HDAC1 and p21CIP1/WAF1. Mol Ther 21(9):1678–1686. https://doi.org/10.1038/mt.2013.148 Zovoilis A, Agbemenyah HY, Agis-Balboa RC, Stilling RM, Edbauer D et al (2011) microRNA34c is a novel target to treat dementias. EMBO J 30:4299–4308. https://doi.org/10.1038/emboj. 2011.327

Chapter 5

Exosomal MicroRNAs as Brain Memory Devices Synaptic miRNAs for memory

If you want to make a simulation of nature, you’d better make it quantum mechanical. Feynman, R.

Overview MicroRNAs (miRNAs) have been implicated in brain memory mechanisms. miR-9c, miR-31a, miR-305, miR-974, and miR-980 were involved in memory formation and retention in the Drosophila melanogaster central nervous system. Long-term memory was thought to exist as the pattern and strength of synaptic connections and new synaptic maintenance in neural circuits, but that circuit is distinct from memory. Of course, storage devices cannot work alone; they require circuits, compilers, and processors. Memory contains source code, such as computer binary representation code. Functional and active brain regions play important roles in CPU-like higher brain processing. According to brain function, specific profiles of miRNAs were exhibited in the hippocampus and the cortex, which are involved in synaptic plasticity and memory formation. However, the hippocampus is not the memory itself; it is just the location of the memory and part of the brain. Learning and memory processes require flexibility and stability of synaptic circuits because memory is involved in the storage of environmental inputs, and high-order human behaviors such as learning depend on memory storage. However, it is far from storing memory source code. While large numbers of neurons are orchestrated in the brain and connected as with target neurons where new neuron cells reside, miRNA genes in both neurons and glial cells become pools of memory code. Several molecules, such as cAMP/protein kinase A (PKA), mitogen-activated protein kinase (ERK/MAPK), mechanistic target of rapamycin kinase (mTOR) signaling proteins, alpha-amino-3-hydroxyl-5-methyl-4-isoxazole propionate (NMDA/AMPA) receptors, calcium/calmodulin-dependent protein kinase II (CaMKII), cAMP response element binding protein (CREB), tyrosine kinase, protein kinase C (PKC), and phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3-K), are implicated in neural circuits. However, these translated proteins failed to retain and store highly defined regulatory information upon their own synthesis to control high-dimensional behavior. Given that cell signaling cascades underpin the integration of memory and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1_5

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learning, the hypothesis of memory and learning through nonspecific signaling is too impractical for mathematical algorithms because cell signaling is not information. In addition, the environment has immeasurable effects on brain signaling and neurotransmission. In contrast, disease-specific miRNAs can affect most biological pathways. This strongly suggests that the source code of the memory within neurons in the brain may also be maintained by several groups of the miRNA quantum code in response to environmental factors. Thus, human exogenous miRNA (exomiRNA) memory codes are contained in extracellular body fluids and food-derived xenotropic miRNAs (xenomiRNA) also influence memory and consciousness as an environment. In this chapter, we discovered new therapeutic targets for traumatic brain injury and major depressive disorder by miRNA entangling target sorting (METS)/ miRNA quantum language and artificial intelligence (MIRAI). Atrophin 1 (DRPLA, ATN1) was enhanced by downregulation of the miR-92a-1-5p hub in traumatic brain injury. Neuregulin 1 (NRG1) was suppressed by upregulation of the miR-1587 hub in major depressive disorder.

5.1

miRNA Memory: Therapeutic Targets of Traumatic Brain Injury and Major Depressive Disorder

In “The biology of memory: A 40-Years Perspective” (Kandel 2009), five unsolved problems have been reported: (1) synaptic plasticity, (2) hippocampus memory, (3) memory retrieval and storage, (4) molecular nature of amnesia, and (5) prefrontal cortex memory. These issues are likely to discuss where potential memory resides and considerations of all the computational circuitry around the wires in a computer, such as synaptic connections. Leading to debates that link to memory devices, never before has it been a possibility. It is well known that memory consists of long- and short-term memory in humans. It corresponds to a computer’s hard disk (HD) and DRAM memory, respectively. The “brain” is relevant to the CPU, and in both, memories are stored in an “electronic” state. Here, we introduced the hypothesis that “quantum” in humans is used instead of “electron” in computers. Blood microRNA (miRNA) profiles have been found to change during trauma. If we can collect enough statistical data, we may be able to determine what is happening to “memory” and “consciousness” using the miRNA entangling target sorting (METS)/miRNA quantum language and artificial intelligence (MIRAI) analysis method. Therefore, traumatic brain injury alters sensory, motor, and cognitive functions and is a major cause of death and disability among young adults in the United States of America (USA). MiRNA panel data (miR-16, miR-92a, and miR-765) in plasma have statistically been shown to be useful for the diagnosis of traumatic brain injury (AUC: >0.8, 100% sensitivity and specificity) (Redell et al. 2010). For METS/MIRAI using this miRNA panel, two therapeutic targets were the outcome (Fig. 5.1).

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miRNA Memory: Therapeutic Targets of Traumatic Brain Injury and. . .

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Fig. 5.1 The etiological analysis of traumatic brain injury by METS/MIRAI

1. Dentatorubral-Pallidoluysian Atrophy protein, Atrophin 1 (DRPLA, ATN1) was enhanced by downregulation of miR-92a-1-5p along with human-specific miR-375, miR-6870-5p, miR-5698, miR-7111-5p, and miR-4723-5p. 2. Upregulation of miR-765 in combination with miR-7-5p suppressed calcium voltage-gated channel auxiliary subunit gamma 7 (CACNG7). DRPLA is a rare autosomal dominant progressive neurodegenerative disease caused by a mutation in the ATN1 gene (Bidollari et al. 2019). ATN1 is a polyglutamine-coding (CAG)n repeat expansion in seven different genes that causes spinocerebellar ataxias (du Montcel et al. 2014). CACNG7 is a type II transmembrane alpha-amino-3-hydroxyl-5-methyl-4-isoxazole propionate (AMPA) receptor regulatory protein (TARP) and regulates both trafficking and channel gating of AMPA receptors (Park et al. 2008). Furthermore, a systematic review and meta-analysis have shown that miRNA panels from blood and saliva data are potential diagnostic markers in trauma patients (Zhou et al. 2021; Hiskens et al. 2022). While depression is a psychiatric disorder and changes neuroplasticity and neurogenesis, eight circulatory biomarker miRNA

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Fig. 5.2 The etiological analysis of major depressive disorder by METS/MIRAI

candidates were also systematically reviewed in depressive disorder (Rasheed et al. 2022). For METS/MIRAI, the etiology of major depressive disorder was simulated in silico using circulating miRNA panels (Cuomo-Haymour et al. 2022). Eight miRNAs, upregulated miR-1587, miR-1207-5p, miR-6887-3p, and miR-4433b-5p and downregulated miR-196a-3p, miR-134-5p, miR-4433a-5p, and miR-6735-3p, have been reported. The highest diagnostic power 2 miRNAs (AUC: 0.78) were selected for analysis (Fig. 5.2). Four neuron-associated targets were the outcome:

1. Neuregulin 1 (NRG1) was suppressed by upregulation of miR-1587 along with miR-455-3p. 2. Neuronal calcium sensor 1 (NCS1) was inhibited by upregulation of miR-1587 along with miR-140-5p. 3. Activating transcription factor 7 (ATF7) was enhanced by downregulation of miR-196a-3p along with miR-29b-1-5p. 4. CD1B was reduced by upregulation of miR-1587 in combination with miR-36205p, miR-8085, miR-6731-5p, miR-6774-5p, miR-4658, miR-8075, and miR-635. miR-1587 is a human- and chicken-specific miRNA. The NRG1-Erb-B receptor tyrosine kinase (ERBB) pathway is associated with epilepsy, and lateral ventricle NRG1 treatment significantly reduced depression-like behaviors in a chronic social defeat stress (CSDS) mouse model (Wang et al. 2022). NCS1 has been implicated in psychiatric conditions such as autism, bipolar disorder and schizophrenia, and

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Fig. 5.3 METS/MIRAI analysis for Alzheimer’s disease (AD)

depressive-like behaviors were observed in NCS1 knockout (KO) mice (de Rezende et al. 2014). AFT7 is related to the SET domain bifurcated histone lysine methyltransferase 1 (SETDB1, ESET) complex and gene silencing by histone methylation, and ATF7 KO mice, therefore, exhibit abnormal behaviors (Maekawa et al. 2010). Thus, the pathogenesis of major depressive disorder is complex. Furthermore, myelin protective effects were observed as a blockade of inflammation by downregulation of CD1B (Khalili-Shirazi et al. 1998). It is “quantum miRNA surveillance,” and human-specific miRNAs are involved in the branch miRNAs. The etiology of Alzheimer’s disease (AD) has been simulated by METS/MIRAI (Fujii 2021). Conventional AD data of meta-analysis (AUC > 0.876) contain seven miRNAs, upregulated miR-34a/c-5p, and downregulated miR-7-5p, miR-191-5p, miR-15b-5p, miR-142-3p, and let-7b-5p (Fig. 5.3). 1. Aryl hydrocarbon receptor nuclear translocator like (ARNTL, BMAL1) was enhanced by downregulation of miR-142-3p along with miR-206. 2. Hyperpolarization-activated cyclic nucleotide-gated potassium channel 3 (HCN3) was reduced by upregulation of miR-34a/c-5p along with miR-449a and miR-449b-5p. 3. Vesicle-associated membrane protein 2 (VAMP2) was suppressed by upregulation of miR-34a/c-5p along with miR-373-3p, miR-372-3p, and miR-520d-3p.

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Fig. 5.4 MiRNA quantum code and brain memory

Therefore, AD therapeutic targets were circadian rhythm-related ARNTL, αrhythm during awake-related HCN3, and synaptic membrane docking in presynaptic membrane-related VAMP2 (AUC: 0.87). Amyloid β-related targets do not have outcome. Together, major depressive disorder, traumatic brain injury, and AD differ in their pathogenesis. Therefore, miRNAs regulate memory, neuroplasticity, and the pathogenesis of trauma and depression (Dotta et al. 2013; Atif and Hicks 2019; Gao et al. 2022; Hassan et al. 2022). We showed in silico that long noncoding RNA (lncRNA) and circular RNA (cricRNA) are related to human cancer and that miRNA is a key factor in lncRNA and circRNA function (Fujii 2018). LncRNAs and circRNAs are involved in various neurological disorders, such as traumatic brain injury and cognitive disorders, including depression (Chen et al. 2021; He et al. 2022). Altogether, brain memory is suggested to participate in miRNA coding (Fujii 2023) (Fig. 5.4). MiRNA memory packages (MMPs) construct short-term memory by miRNA quantum code. LncRNAs and circRNAs containing MMPs construct long-term memory by miRNA quantum code. As MMPs in exosomes are also exomiRNA memory in the circulatory system, both memory systems can transfer cell-to-cell, organ-to-organ, person-to-person, and planet-to-planet. The ability to measure previously unmeasurable mental states with the miRNA code will have a significant impact on psychiatry and psychology. In other words, the development of

5.2

Role of miRNAs in Synapses

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tools for diagnosis and differentiation makes it possible to treat anxiety and mental health. MiRNA genes play important roles in regulating neuronal functions, such as neuronal development, synaptogenesis, synaptic transmission, and neuronal plasticity. Environmental stresses such as X-ray radiation increase the expression of miR-34c and miR-488* in the hippocampus but not in the cortex and cerebellum of female and male mice (Koturbash et al. 2011). Microtubule quantum computation has been reported to be similar to synaptic neuronal connectivity and synchronization (Hameroff 2012, 2022), but the consciousness of control has been difficult to explain scientifically; therefore, a new idea for consciousness and cognition is needed. Microtubule entanglement among many neurons is a system, not information. Quantum computing systems can be applied to miRNA code (Fujii 2013, 2023), so the problem of memory must be computed using miRNA quantum code. The 2014 open-ended issue has been updated, but its content is still limited to synaptic connections (Kandel et al. 2014) and is a long way from elucidating human memory and consciousness. Three systems were included: system biology of synapses, systems neuroscience of memory, and system problems of brain disorders. On the other hand, miR-124 inhibits cAMP response element binding protein 1 (CREB-1) in Aplysia, and serotonin (5HT) inhibits miR-124, thereby enabling long-term memory. Furthermore, the relationship between miR-124 upregulation and AMPA receptor downregulation was observed in hippocampal memory and social behavior (Dotta et al. 2013; Gascon et al. 2014). There is a paper on neurotransmitter control mechanisms, and of course, there is no mention in the report that miRNA gene is information coded.

5.2

Role of miRNAs in Synapses

Long-term potentiation (LTP) is induced by frequent stimulation of presynaptic neurons, resulting in increased AMPA receptor expression in postsynaptic neurons. This implies that LTP increases synaptic signaling and is related to long-term memory in the brain. miR-134 controls synaptogenesis in hippocampal and cortical neurons. miR-134 inhibits spine and dendrite formation through LIM domain kinase 1 (LIMK1) and PUMILLO2 (Schratt et al. 2006; Fiore et al. 2009; Christensen et al. 2010). Therefore, miR-134 impairs synaptic plasticity. The mRNA of the voltagegated potassium channel Kv1.1 is targeted by miR-129 (Sosanya et al. 2013). The RNA-binding proteins HuD and miR-129 reversibly bind to Kv1.1 mRNA when mTORC1 kinase is inactive and active, respectively. Thus, HuD promotes Kv1.1 translation and miR-129 inhibits it, and miR-129/HuD is an action potential switching mechanism. Kv1.1 controls its potential frequency, and its mutations are associated with episodic ataxia in humans (Zerr et al. 1998). Several miRNAs, miR-9-5p, miR-204-5p, miR-128-3p, miR-26a-3p, miR-218, miR-22-3p, and miR-124-3p, etc. are involved in synaptic plasticity, which participates in major depressive disorder (MDD) (Rahmani et al. 2022).

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Three miRNAs, miR-129-5p, miR-101, and miR-221, can specifically target Fragile X mental retardation gene 1, FMR1 mRNA. Because the FMR1 product FMRP is an RNA-binding protein that modulates the expression of hundreds of mRNAs with synapse function, miR-129 is also a fine-tuner of synaptic function through FMR1 expression (Zongaro et al. 2013). miR-219 inhibits calcium/calmodulin-dependent protein kinase II gamma, CaMKII, a component of the NMDA glutamine receptor signaling pathway (Kocerha et al. 2009; Pan et al. 2014). miR-219 is associated with Alzheimer’s disease, schizophrenia, and circadian clock-related major depression (Kocerha et al. 2009; Saus et al. 2010; Zhao et al. 2015). Postsynaptically, miR-125a regulates synaptic plasticity through inhibition of postsynaptic density 95 (PSD-95) mRNA translation (Lugli et al. 2012; Stefanovic et al. 2014). The FMRP RGG box has an affinity for the G-quartet structure of the 3′ UTR of PSD-95 mRNA, and the miR-125 target site is also localized to the same 3′ UTR; thus, the membrane-associated guanylate kinase PSD-95, which is essential for glutamate receptor localization in the postsynapses, is controlled by both FMRP and miR-125 in an equivalent manner. miR-138 inhibits the Lypla1 depalmitoylating enzyme lysophospholipase 1 (Banerjee et al. 2009; Shi et al. 2010). Palmitoylation is implicated in synaptic plasticity as a regulator of the cytoskeletal regulator Gα. Not only inhibition but also activation of synaptic plasticity has been observed by miR-132 (Remenyi et al. 2010). For the expression of glutamine receptors, such as AMPA receptors, activity-regulatory cytoskeletal-associated proteins (Arcs) are inhibited by miR-34a, miR-193a, miR-326, and miR-19a (Joilin et al. 2014; Wibrand et al. 2012). Arc proteins are essential for the consolidation of LTP and plasticity. FMRP binds to the Arc mRNA 3′UTR. Therefore, Arc is also controlled by both FMRP and miRNAs, and ectopic miR-34a, miR-326, and miR-193a all enhanced BDNF-evoked Arc protein expression, suggesting that LTP plasticity, memory, and other adaptive change-related Arc protein expressions may be modulated with inversely related RNA-binding proteins by the coherence of miRNAs. If so, miRNA information genes might be specific memory devices because RNA-binding proteins are not memory specific. Furthermore, glutamate ionotropic receptor AMPA type subunit 2 (GluA2) is regulated by miR-124, miR-138, miR-485, and miR-181a (Ho et al. 2014). As calcium/calmodulindependent protein kinase II (CaMKII) activates GluA1 and miR-219 inhibits CaMKII, miR-219 is an indirect inhibitor of GluA1 (Kocerha et al. 2009; Pan et al. 2014). miR-125b suppresses the expression of the N-methyl-D-aspartate (NMDA) receptor GluN2A, whereas miR-132 activates GluA1, GluN2B, and GluN2A (Edbauer et al. 2010). Presynaptically, miR-485 inhibits the expression of the synaptic vesicle protein synaptic vesicle glycoprotein 2A (SV2A) in hippocampal neurons (Cohen et al. 2011). miR-485 suppresses dendritic spine density, postsynaptic density 95 (PSD-95) clustering, and surface expression of GluR2. Synaptotagmin 1 (SYT1), autophagy-related 9a (ATG9a), p73, and syntaxin 1A (STX1A) are targets of miR-34a (Morgado et al. 2015; Agostini et al. 2011), and subsequently, sacral endoplasmic reticulum Ca++ ATPase (SERCA2) expression is suppressed by miR-25 or miR-185 (Earls et al. 2012). 22q11 deletion syndrome (22q11DS)

5.3

Memory Type and Location

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constitutes a genetic risk factor for schizophrenia, with deletion of miR-25 and miR-185 in mouse models. Since SERCA2 is a target of both miRNAs, 22q11DS increases LTP and synaptic plasticity in response to increased Ca++ loading on the ER, leading to elevated neurotransmitter release and the LTP response. Therefore, these miRNAs are involved in the presynaptic release of neurotransmitters. Although we are not certain whether increased or decreased LTP causes neurodegeneration, this evidence indicates that miRNA genes regulate synaptogenesis, stabilization, and plasticity, both pre- and postsynaptically. Altogether, miRNAs are important keystones of neural memory as well as the circadian clock. Recently, whole-brain imaging with magnetic resonance imaging (MRI) was combined with the expression patterns of miRNAs, and its techniques could reveal brain morphological changes caused by miRNA alterations in synaptogenesis (Tsujimura et al. 2022).

5.3

Memory Type and Location

The brain stores two types of memory: explicit and implicit memory. The former is stored in the hippocampus and the cortex. The latter are found in other parts of the brain. In fact, miRNA profiles in the hippocampus differ from those of the cortex and cerebellum (Juhila et al. 2011; Koturbash et al. 2011; Liu et al. 2015), and miR-9-3p inhibition impaired hippocampal LTP and hippocampus-dependent memory (Sim et al. 2016), suggesting that qualitative variability and complexes of the miRNA memory code are similar to those described above. Although invertebrate Aplysia has simple brain neurons, Aplysia has miRNA gene information (Hao and Yang 2021; Noyes et al. 2021). Therefore, Aplysia memory is present. Furthermore, circRNA circAmrad levels were age-independent and correlated with the honeybee (Apis mellifera) task, and increasing circRNA levels were associated with memoryassociated host genes (Thölken et al. 2019). Therefore, social behavior-related memory is implicated in circRNAs. For insight into brain function, dysregulation of miR-34c, miR-1188, miR-328a, and miR-331 is involved in memory impairment of temporal lobe epilepsy in rats, and their expression patterns are identical between blood and hippocampus (Liu et al. 2015). This implied that the memory codes constructed by miRNA information were identical in the blood and brain hippocampus at this time point, and memory was present in both the blood and brain. All body fluids contain miRNAs, suggesting that miRNA memory codes are commonly used in all life and that memory is influenced by environmental factors.

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Synaptic Plasticity by miRNA

LTP is a type of synaptic plasticity, and synaptic plasticity is implicated in the molecular mechanisms of memory. MiRNAs were shown to be regulators of LTP. Therefore, memory is controlled by miRNA genes. However, it is not understood how deeply small RNAs are intertwined within the synaptic compartment. A pri-miRNA was found in the synaptic fraction containing the complex of Drosha and DGCR8. This implies that the machinery of neuronal miRNA biogenesis may be localized to dendritic spines (Smalheiser 2014). Nevertheless, the relationship between miRNA and memory at that time was incredible. Still cannot believe it. For a long time, the memory of the brain has been separated from the highest degree of truth. This suggests that the brain is the realm of the gods and that experimental memory may also differ from human memory. However, when the Internet network was built, people began to think that the mechanism of the memory device might be similar to brain experiments and social networks. After memory acquisition and retrieval memory related to hippocampal neuron plasticity, upregulation of miR-212, miR-132, miR-410, small nucleolar RNA (snoRNA), Snord 14d, and Snord 14e was observed in the mouse hippocampus (Peixoto et al. 2015). LTP is controlled by these miRNAs because LTP is induced by activation and trafficking of AMPA and NMDA subtypes of glutamate receptors. LTP persistence is regulated by the transcription factors CREB and Egr1. CREB and Egr1 are regulated by miR-134 and miR-146a, respectively (Zhu et al. 2015; Contreras et al. 2015). Furthermore, miR-124 targeted ionotrophic AMPA2 and AMPA3, and miR-124 decreased glutamate receptors in hippocampal demyelinated multiple sclerosis and frontotemporal dementia model mice (Dotta et al. 2013; Gascon et al. 2014; Ho et al. 2014). The miR-138-5p single nucleotide polymorphism (SNP) (r9882688) is associated with memory (Schröder et al. 2014). The miR-34b/c-5p SNP (rs2187473) is a potential genetic risk factor for cognitive function in major depressive disorder (Sun et al. 2020). Thus, neuronal plasticity is thought to be dependent on miRNAtuning specific protein expression. Furthermore, our data and evidence of the miR-138 SNP from a miRNA-related genome wide association study (miGWAS) suggest that the miRNA quantum codes might be an architectonic memory as a specific mass of miRNAs (Yoshikawa et al. 2015; Osone et al. 2015) (see Chap. 9).

5.5

Neuroplasticity by Secretion of Synaptosomal miRNAs

Synaptic plasticity requires local translation in the synaptic compartment as well as mRNA transport particles containing pri-miRNAs from the soma to the synapse (Lugli et al. 2008; Lugli et al. 2012). miRNAs regulate local translation according to various extracellular signals within synapses (Risbud and Porter 2013). A group of miRNAs was released into synaptic vesicles from synaptosomes upon stimulation

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Neuroplasticity by Secretion of Synaptosomal miRNAs

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Fig. 5.5 Neuro-memory in RNA Wave 2000. The lighthouse around AD 1600 at the “Miya-NoWatashi” (Emperor departure), which is a port in Aichi, Japan. A lighthouse is a gadget that uses a universal quantum code

with Ca++ or NGF, and the secreted miRNAs were incorporated into the recipient synaptosome and functioned (Xu et al. 2013) (Fig. 5.5). Experiments with synaptosomes are a model for the following neuronal junction events. Neurotransmitters play an important role in signaling from one neuron to another to maintain neuronal circuits in the brain; however, electronic activation by neurotransmitters is essential for learning and behavior as a circuit. In RNA Wave, miRNAs are contained in synaptic vesicles and released from synaptosomes. Secreted exogenous miRNAs (exomiRNAs) integrate into recipient synaptosomes and function. Therefore, exomiRNAs can transmit specific information from one neuron to another and through circulation to other organs. miR-29a, miR-99a, and miR-125a were secreted into the supernatant of synaptosomes in culture. This suggests that miRNAs are direct mediators of synaptic transmission of memory. Neurotransmitters and receptors simply switch synaptic memory readings on or off. Thus, synapses that are switched on carry action potentials that are partially specific to memory. Additionally, since there are approximately 14 billion synapses in the brain, it corresponds to 14 G of computer memory, and if the synapses themselves were storage devices, in the case of an 8-bit computer, the total number of synapses in one brain is equivalent to 1.75 G, synapse utilization is 10%, 175 M. The first iPhone storage is 128 M DRAM (32-bit). In other words, if synapses are memories, their number is too small to form human memories due to the complex learning and behavior in the brain. From these results, we can explain the memory device by considering that the memory device is miRNA

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Fig. 5.6 miRNA memory device in neurons

quantum codes, the synaptic aggregates constitute the processor core, the miRNA is the core packaging memory, and the synapses are wires (Fig. 5.6). miRNAs can regulate the expression of several pre- and postsynaptic proteins. As demonstrated in RNA Wave, a panel of miRNAs (MMPs) becomes the smallest of quantum coding memory. Synapses can therefore be wires and switches such as diodes, thyristors, and transistors. We can detect electrical spikes when neurons are activated. Quantum miRNA memory is stored in lncRNAs and circRNAs. miRNA memory builds human memory and consciousness.

5.6

Mobile Retroelements for Plasticity

Since miRNAs are involved in neuronal memory mechanisms (Wang et al. 2015), miR-9c, miR-31a, miR-305, miR-974, and miR-980 are involved in memory formation and retention in the central nervous system (Busto et al. 2015). Brain cytoplasmic 1 (BC1) RNA is a rodent and human noncoding RNA. BC1 RNA controls local translation of local synaptodendritic domains that are implicated in neural function and plasticity. BC1 RNA is derived from retroposed tRNA and brain-specific abundant RNA. BC1 is transcribed by pol III and activated by environmental stress. BC1 specifically inhibited the activity of eukaryotic initiation

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Mobile Retroelements for Plasticity

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factors 4A (eIF4A) and 4B (eIF4B), which are required for the initiation of mRNAs with structured 5′UTRs associated with neuronal activity. Upon neuronal activation, eIF is dephosphorylated at serine 406 mediated by protein phosphatase 2A, inhibiting the binding between eIF and BC1 (Eom et al. 2014). Therefore, BC1 translational control is dependent on neuronal stimulation. However, how do environmental factors specifically activate neuronal phosphatase 2A? Altered miRNA expression induced by environmental factors may directly regulate translation in synaptic dendritic neurons, whereas protein modification is indirect and nonspecific, as glutamine and its receptor reaction trigger dephosphorylation of eIF4 by phosphatase 2A. Similarly, primate BC200 and G22 are derived from Alu retroelements (Martignetti and Brosius 1993; Khanam et al. 2006), and both have similar functions; however, the nucleic acid sequence of rodent BC1 is not conserved with that of human BC200 (Tiedge et al. 1993). BC200 (200 nt in length) is also involved in synaptic plasticity (Smalheiser 2014). Dicer can target Alu as well as tRNA (Kaneko et al. 2011; Wang et al. 2015). Although human BC200 was reduced by less than 60% among individuals aged 49–86 years, age-matched Alzheimer’s disease increased BC200 in the hippocampus (Mus et al. 2007). Furthermore, approximately 300 million uncharacterized and unmapped noncoding RNAs (ncRNAs) in the human reference genome have been identified by next-generation deep sequencing (Kazemian et al. 2015) compared with the chimpanzee/gorilla genome. Therefore, ncRNAs as miRNA storage are somatically inherited and self-proliferated in the cytoplasm of human cells, and human beings evolved by programming with resident and genomic miRNAs. This suggests that brain memory storage is derived from not only ncRNAs but also somatically programmed RNAs with resident RNAs in the cytoplasm. Memories might have been summarized from ancient primates and conserved in the environment. Thus, instinct-containing memories may be organized as miRNA memory codes and persist not only in neurons but also in the cytoplasm (see Fig. 5.4). Furthermore, long-term memory is associated with the pattern and strength of synaptic connections and the maintenance of new synapses in neural circuits (Wang et al. 2012). Brain circuitry showed specific profiles of miRNAs in the hippocampus and the cortex involved in synaptic plasticity and memory formation (Bak et al. 2008). Since memory is the storage of environmental inputs and subsequent highorder human behavior depends on the storage of memory, the process of learning and memory requires flexibility and stability of synaptic circuits (Follert et al. 2014). It is necessary to retain miRNA memory information. A synaptic circuit cannot create a layered architecture. In the miRNA layer storage insight, neuroblasts were directly generated from adult human astroglia cells by miR-302/367 without tumorigenesis, so both neural and glial miRNAs become a pool of memory codes (Ghasemi-Kasman et al. 2015). Although astroglia cannot construct circuits with synapses, mobile miRNAs are capable of crosstalk between synapses and glial cells. Given that cellular signaling cascades underpin the integration of memory and learning, our memory and learning through nonspecific signaling are too complex to create mathematics. Without miRNA code memory, it is not clear what molecular mechanisms and environmental factors influence brain signaling and

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Fig. 5.7 Mobile miRNA memory code. “Miya-No-Watashi” is a port that has existed since approximately 600 AD. This is the watchtower. The watchtower played the role of a compiler

neurotransmission. It was later explained that disease-specific miRNAs can architect disease memory as miRNA quantum codes, and miRNA genes can control most biological pathways (Fujii 2023). Reports of circulating miRNA biomarkers for prion diseases are emerging, although the data number is still small (López-Pérez et al. 2021; Norsworthy et al. 2020). This suggests that miRNA quantum code storage in both neurons and glial cells also maintains the source code of brain memory (Fig. 5.7). That is, the miRNA source sequences were converted to their binary notation. A panel of miRNAs consists of miRNA qubits as memory. When miRNA memory passes through miRISC as a compiler, miRNA information may be memorized and identified, and in many cases, it works directly as memory without going through the compiler. In other words, the memory storage of miRNA is the DRAM device, and the brain is the CPU of the computer. miRNA-containing lncRNAs or circRNAs are memory chips, and miRNA memory is involved in mental memory and behavioral memory. Since 2010, little progress has been made in research on brain memory. This is largely because the miRNA quantum code has not been publicly recognized.

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Uptake of Prions and Food miRNAs

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Uptake of Prions and Food miRNAs

The Dietary MiRNA Database (DMD) was upped into the Internet in 2015 (Chiang et al. 2015), and due to the research enthusiasm of exogenous miRNA (exomiRNA), various dietary sources such as rice, tomato, milk, and meats have been used as a resource. Since plant exomiRNAs were found in human breast milk (Melnik et al. 2015), dietary miRNAs are commonly taken up by humans (Hirschi et al. 2015) and may affect neuronal plasticity and LTP (Maccani et al. 2013; Bland 2019). Dietary miRNAs may be novel agents of neurodegenerative diseases in humans. Prion disease is a classic food-borne neurodegeneration, as variant Creutzfeldt-Jacob disease (vCJD) is caused by transmission of the prion protein from bovine spongiform encephalopathy (BSE). The image of the term “contagion” implies a direct or indirect route of transmission; however, prion protein transmission is generally caused by the BSE beef diet. The prion protein bound to the Argonaut protein, and the prion protein efficiently suppressed the action of miRNAs. Moreover, prion proteins are regulated by miRNAs, and both miRNAs and prion proteins are found in meat foods. Since normal prion protein is present in normal cells and miRNA genes regulate prion protein (Montag et al. 2012; Contiliani et al. 2021), both miRNA and prion protein in the diet can cause CJD. The progression of prion neurodegenerative disorder is accompanied by perturbation of cholesterol homeostasis in the brain. This is because prion proteins localize to lipid rafts, and inhibition of cholesterol biosynthesis suppresses prion unfolding in scrapie-infected mice (Taraboulos et al. 1995; Ventrugno et al. 2009; Gilch et al. 2009). As discussed in Chap. 3, cholesterol homeostasis is controlled by miRNAs. This strongly suggests that prion diseases or prion disease-like neurodegeneration may involve dietary miRNAs. BSE studies have revealed that prions are the cause of CJD. However, prion diseases are a phenotype of several diseases: Kuru, CJD, variant CJD, familial CJD, sporadic CJD, Gerstmann-Sträussler-Scheinker (GSS) disease in humans, scrapie in sheep, BSE in cattle, transmissible mink encephalopathy in mink, chronic wasting disease in elk, and feline spongiform encephalopathy. Prion disease involves genetic, biochemical, and structural evidence as its cause. Expression of vCJD in humans required phenotypic alteration of the BSE prion protein by methionine 129 from valine (Wadsworth et al. 2004). However, in 1997, 20 different mutations of prion proteins associated with disease phenotypes were found in humans, including the proline 102 to leucine mutation in GSS (Prusiner 1997). Therefore, it is not clear which genetic mutations are critical in the etiology of prion diseases. GWAS may point to loci associated with BSE, even though genetic mutations play an important role in the expression of vCJD (Murdoch et al. 2011). The BSE SNP is on chromosome 1. However, the disease-related candidate gene on the chromosome is not the prion protein gene. In contrast, the normal prion protein gene locus is universally associated with human prion diseases (Mead et al. 2012). These results suggest that the BSE-associated gene locus is distinct from the human CJD locus, and thus, bovine prion protein may be transmitted from cattle to humans via meat

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food. Furthermore, it has been hypothesized that conversion of the prion protein from its predominant alpha-helical to a highly beta-sheet secondary structure is required (Ronga et al. 2006). It is not possible to explain how the prion protein secondary structure, alpha and beta, is maintained during hard protease digestion after meat prion protein is digested in humans. miRNAs in exosomes are nucleaseresistant, and prion-infected neural cells release exosomes containing miRNAs (Bellingham et al. 2012), and these miRNAs can be transmitted through circulation to uninfected cells or through meat to uninfected individuals. In the case of food, prion-infected bovine miRNAs can be taken up by epithelial cells, induce prion disease, and regulate the expression of beta prions in hosts. For example, miR-146a is ubiquitous in both humans and cattle and is upregulated in CJD and GSS (Lukiw et al. 2011), suggesting that bovine miR-146a is associated with human prion disease without the bovine prion protein. Although scrapie in sheep is transmitted from one animal to another (Kinberlin 1990) and chronic wasting disease in deer also infects with a prevalence of 50% (Gilch et al. 2011), alpha-synuclein prion-like protein in Parkinson’s disease cannot be transmitted from one to another at all (Chauhan and Jeans 2015). However, Parkinson’s disease pathology can be propagated to neighboring cells by transplantation (Li et al. 2008). Additionally, in Alzheimer’s disease and Huntington’s disease, insoluble amyloid-beta and polyglutamine-containing proteins accumulate in neuronal cells, respectively, and these proteins are noninfectious but propagate to other cells (Frost and Diamond 2010; Guest et al. 2011), resulting in progressive deterioration (Castellani et al. 2004). Overall, unfolding proteins are a ubiquitous event in neurodegeneration, but the mechanisms of infection and propagation and their differences are still unclear. What is certain is that the bovine miRNA program is responsible for human prion diseases. As shown in METS/MIRAI, H. pylori infection and hepatitis B virus (HBV) infection cause inflammation by H. pylori and HBV themselves, but tumorigenicity is not observed. Carcinogenesis is driven by the miRNA program in humans (Fujii 2023). The Key Is the miRNA Quantum Code As mentioned above, miRNAs are transmissible, and the human prion protein binds to the Argonaute protein (Gibbings et al. 2012) and the AMPA receptor (Hamilton et al. 2015), suggesting that exogenous miRNAs (exomiRNAs)-Argonaute protein complex or exomiRNAs in exosomes easily bind to the neuronal surface prion protein as a receptor on the postsynaptic membrane. Taken together, it is clear that miRNA genes in food, as an environmental factor, influence the induction and progression of neurodegeneration, including prion diseases. However, it is certain that there are some analogies in prion-like proteins that are common to human CJD and neurodegenerative diseases, and those similarities are associated with miRNA genes as a common factor (See Chap. 9). Furthermore, these analogies are involved in food miRNAs, as BSE is transmitted through meat. In meat, miRNAs may be encapsulated in exosomes containing prion proteins, thereby preventing degradation and transport to intestinal endothelial cells. Even if the amount of miRNA incorporated at one time is too low to derive biological control from miRNAs in the

5.7

Uptake of Prions and Food miRNAs

99

Fig. 5.8 Proof of the concept for food miRNA intake

nanomolar, femtomolar, or even picomolar range, daily miRNAs in the host should allow minimal dietary miRNA accumulation. Of course, picomoles of miRNAs themselves within exosome cargo may contribute to the regulation of host genes (Fabbri et al. 2014; Bryniarski et al. 2015). Therefore, if miRNAs are memory devices, miRNA malfunction caused by dietary miRNAs may be relevant to the pathogenesis of neurodegeneration with amnesia (Fig. 5.8). Altogether, prion and prion-like diseases involve miRNAs. Food contains miRNAs, and food xenomiRNAs affect human health through exomiRNA and cellular miRNA, so if miRNA-containing dietary prion can be transmitted like BSE, xenomiRNAs play an important role in the etiology of neurodegeneration. In turn, it becomes clear that miRNAs act as memory codes for diseases. We have achieved it with METS/MIRAI in “The MicroRNA Quantum Code Book.” In Drosophila, some miRNAs regulate memory consumption (Busto et al. 2015). This suggests that exomiRNA is a memory device, while the memory mechanism of the brain can be analogous to computational memory systems such as quantum computers. Since the miRNA quantum code was subsequently discovered as an mRNA qubit memory using the miRNA memory package (MMP) (Fujii 2023), the following definition was created. “Understanding Humans with Quantum” = “Humans Live with Quantum Computing” Based on this principle, it is possible to accurately diagnose cancer, infectious diseases, and metabolic diseases at once using miRNA memory codes (Fujii 2023).

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References Agostini M, Tucci P, Steiner JR, Shalom-Feuerstein R, Rouleau M et al (2011) microRNA-34a regulates neurite outgrowth, spinal morphology, and function. Proc Natl Acad USA 108:21099– 21104. https://doi.org/10.1073/pnas.1112063108 Atif H, Hicks SD (2019) A review of microRNA biomarkers in traumatic brain injury. J Exp Neurosci 13:1–12. https://doi.org/10.1177/1179069519832286 Bak M, Silahtaroglu A, Moller M, Christensen M, Rath MF et al (2008) MicroRNA expression in the adult mouse central nervous system. RNA 14:432–444. https://doi.org/10.1261/rna.783108 Banerjee S, Neveu P, Kosik S (2009) A coordinated local translational control point at the synapse involving relief from silencing and MOV10 degradation. Neuron 64:871–884. https://doi.org/ 10.1016/j.neuron.2009.11.023 Bellingham SA, Coleman BM, Hill AF (2012) Small RNA deep sequencing reveals a distinct miRNA signature released in exosomes from prion-infected neural cells. Nucleic Acids Res 40: 10937–10949. https://doi.org/10.1093/nar/gks832 Bidollari E, Rotundo G, Altier F, Amicucci M, Wiquel D et al (2019) Geneation of induced pluripotent stem cell line CSSi008-A (4698) from a patient affected by advanced stage of Dentato-rubral-pllidoluysian atrophy (DRPLA). Stem Cell Res 40:101551. https://doi.org/10. 1016/j.scr.2019.101551 Bland JS (2019) The dark matter of nutrition: dietary signals beyond traditional nutrients. Integr Med 18:12–15 Bryniarski K, Ptak W, Martin E, Nazimek K, Szczepanik M et al (2015) Free extracellular miRNA functionally targets cells by transfecting exosomes from their companion cells. PLoS One 10: e0122991. https://doi.org/10.1371/journal.pone.0122991 Busto GU, Guven-Ozkan T, Fulga TA, van Vactor D, Davis RL (2015) microRNAs that promote or inhibit memory formation in Drosophila melanogaster. Genetics 200:569–580. https://doi.org/ 10.1534/genetics.114.169623 Castellani RJ, Perry G, Smith MA (2004) Prion diseases and Alzheimer’s disease: pathogenic overlap. Acta Neurobiol Exp 64:11–17 Chauhan A, Jeans AF (2015) Is Parkinson’s disease truly a prion-like disorder? An appraisal of current evidence. Neuro Res Int 2015:345285. https://doi.org/10.1155/2015/345285 Chen M, Lai X, Wang X, Ying J, Zheng L et al (2021) Long noncoding RNAs and circular RNAs: insights into microglia and astrocyte mediated neurological diseases. Front Mol Neurosci 14: 745066. https://doi.org/10.3389/fnmol.2021.745066 Chiang K, Shu J, Zempleni J, Cui J (2015) Dietary microRNA database (DMD): an archive database and analytic tool for food-borne microRNAs. PLoS One 10:e0128089. https://doi. org/10.1371/journal.pone.0128089 Christensen M, Larsen LA, Kauppinen S, Schratt G (2010) Recombinant adeno-associated virusmediated microRNA delivery into the postnatal mouse brain reveals a role for miR-134 in dendritogenesis in vivo. Front Neural Circuits 3:16. https://doi.org/10.3389/neuro.04.016.2009 Cohen JE, Lee PR, Chen S, Li W, Fields RD (2011) MicroRNA regulation of homeostatic synaptic plasticity. Proc Natl Acad Sci USA 108:11650–11655. https://doi.org/10.1073/pnas. 1017576108 Contiliani DF, Ribeiro YA, de Moraes VN, Pereira TC (2021) MicroRNAs in prion diseases-from molecular mechanisms to insights in translational medicine. Cell 10:1620. https://doi.org/10. 3390/cells10071620 Contreras JR, Palanichamy JK, Tran TM, Fernando TR, Rodriguez-Malave NI et al (2015) MicroRNA-146a modulates B-cell oncogenesis by regulating Egr1. Oncotarget 6:11023– 11037. https://doi.org/10.18632/oncotarget.3433 Cuomo-Haymour N, Kaiser S, Hartmann-Riemer M, Guetter K, Klaus F et al (2022) Differential expression of serum extracellular vesicle miRNAs and analysis of target–gene pathways in major depressive disorder. Biomark Neuropsy 6:100049. https://doi.org/10.1016/j.bionps.2022. 100049

References

101

De Rezende VB, Rosa DV, Comim CM, Magno LAV, Rodrigues ALS et al (2014) NCS-1 deficiency causes anxiety and depressive-like behavior with impaired nonaversive memory in mice. Physiol Bhav 130:91–98. https://doi.org/10.1016/j.physbeh.2014.03.005 Dotta R, Chomyk AM, Chang A, Ribaudo MV, Deckard SA et al (2013) Hippocampal demyelination and memory dysfunction are associated with increased levels of the neuronal microRNA miR-124 and reduced AMPA receptors. Ann Neurol 73:637–645. https://doi.org/10.1002/ana. 23860 du Montcel ST, Durr A, Bauer P, Figueroa KP, Ichikawa Y et al (2014) Modulation of the age at onset in spinocerebellar ataxia by CAG tracts in various genes. Brain 137:2444–2455. https:// doi.org/10.1093/brain/awu174 Earls LR, Fricke RG, Berry RB, Baldwin LT, Zakharenko SS (2012) Age-dependent microRNA control of synaptic plasticity in 22q11 deletion syndrome and schizophrenia. J Neurosci 32: 14132–14144. https://doi.org/10.1523/JNEUROSCI.1312-12.2012 Edbauer D, Neilson JR, Foster KA, Wang CF, Seeburg DP et al (2010) Regulation of synaptic structure and function by FMRP-associated microRNAs miR-125b and miR-132. Neuron 65: 373–384. https://doi.org/10.1016/j.neuron.2010.09.035 Eom T, Muslimov IA, Tsokas P, Berardi V, Zhong J et al (2014) Neural BC RNAs cooperate with eIF4B to mediate activity-dependent translational control. J Cell Biol 207:237–252. https://doi. org/10.1083/jcb.201401005 Fabbri M, Paone A, Calore F, Galli R, Gaudio E et al (2014) MicroRNAs bind to toll-like receptors to induce prometastatic inflammatory response. Proc Natl Acad Sci USA 109:E2110–E2116. https://doi.org/10.1073/pnas.1209414109 Fiore R, Khudayberdiev S, Christensen M, Siegel G, Flavell SW et al (2009) Mef2-mediated transcription of the miR379-410 cluster regulates activity-dependent dendritogenesis by finetuning pumilio2 protein levels. EMBO J 28:697–710. https://doi.org/10.1038/emboj.2009.10 Follert P, Cremer H, Béclin C (2014) MicroRNAs in brain development and function: a matter of flexibility and stability. Front Mol Neursci 7:5. https://doi.org/10.3389/fnmol.2014.00005 Frost B, Diamond MI (2010) Prion-like mechanisms in neurodegenerative diseases. Nat Rev Neurosci 11:155–159. https://doi.org/10.1038/nrn2786 Fujii YR (2013) The RNA gene information: retroelement-microRNA entangling as the RNA quantum code. Methods Mol Biol 936:47–67. https://doi.org/10.1007/978-1-62703-083-0_4 Fujii YR (2018) The quantum language of the microRNA gene and anticancer: with a dynamic computer simulation of human breast cancer drug resistance. Integr Mol Med 5:1–13. https:// doi.org/10.15761/IMM.1000346 Fujii YR (2021) Analysis for brain tumors and Alzheimer’s disease using quantum microRNA language with artificial intelligence (MIRAI). SunText Rev Med Clin Res 2:142. https://doi.org/ 10.51737/2766-4813.2021.043 Fujii YR (2023) The microRNA quantum code book. Springer Nature, Singapore Gao YN, Zhang YQ, Wang H, Deng YL, Li NM (2022) A new player in depression: miRNAs as modulators of altered synaptic plasticity. Int J Mol Sci 23:4555. https://doi.org/10.3390/ ijms23094555 Gascon E, Lynch K, Ruan H, Almeida S, Verheyden JM et al (2014) Alterations in microRNA-124 and AMPA receptors contribute to social behavioral deficits in frontotemporal dementia. Nat Med 20:1444–1451. https://doi.org/10.1038/nm.3717 Ghasemi-Kasman M, Hajikaram M, Baharvand H, Javan M (2015) MicroRNA-mediated in vitro and in vivo direct conversion of astrocytes to neuroblast. PLoS One 10:e0127878. https://doi. org/10.1371/journal.pone.0127878 Gibbings D, Leblanc P, Jay F, Pontier D, Michel F et al (2012) Human prion protein binds Argonaute and promotes accumulation of microRNA effector complexes. Nat Struct Mol Biol 19:517–525. https://doi.org/10.1038/nsmb.2273 Gilch S, Bach C, Lutzny G, Vorberg I, Schätzl HM (2009) Inhibition of cholesterol recycling impairs cellular PrP (Sc) propagation. Cell Mol Life Sci 66:3979–3991. https://doi.org/10.1007/ s00018-009-0158-4

102

5

Exosomal MicroRNAs as Brain Memory Devices

Gilch S, Chitoor N, Taguchi Y, Stuart M, Jewell JE et al (2011) Chronic wasting disease. Top Curr Chem 305:51–77 Guest WC, Silverman M, Pokrinshevsky E, O’Neil MA, Grad LI et al (2011) Generalixation of the prion hypothesis to other neurodegenerative diseases: an imperfect fit. J Toxicol Environ Health Part A 74:1433–1459. https://doi.org/10.1080/15287394.2011.618967 Hameroff S (2012) How quantum brain biology can rescue conscious free will. Front Int Neurosci 6:93. https://doi.org/10.3389/fnint.2012.00093 Hameroff S (2022) Consciousness, cognition and the neuronal cytoskeleton-a new paradigm needed in neuroscinec. Front Mol Neurosci 15:869935. https://doi.org/10.3389/fnmol.2022. 869935 Hamilton A, Zamponi GW, Ferguson SS (2015) Glutamate receptors function as scaffolds for the regulation of ß-amyloid and cellular prion protein signaling complexes. Mol Brain 8:18. https:// doi.org/10.1186/s13041-015-0107-0 Hao L, Yang Z (2021) Dynamical mechanisms for gene regulation mediated by two noncoding RNAs in long-term memory formation. Neural Plast 2021:6668389. https://doi.org/10.1155/ 2021/6668389 Hassan M, Amir A, Shahzadi S, Kloczkowski A (2022) Therapeutic implications of microRNAs in depressive disorders: a review. Int J Mol Sci 23:13530. https://doi.org/10.3390/ijms232113530 He L, Zhang F, Zhu Y, Lu M (2022) A crosstalk between circular RNA, microRNA, and messenger RNA in the development of various brain cognitive disorders. Front Mol Neurosci 15:960657. https://doi.org/10.3389/fnmol.2022.960657 Hirschi KD, Pruss GJ, Vance V (2015) Dietary delivery: a new avenue for microRNA therapeutics? Trends Biotechnol S0167:7799. https://doi.org/10.1016/j.tibtech.2015.06.003 Hiskens MI, Mengistu TS, Li KM, Fenning AS (2022) Systematic review of the diagnostic and clinical utility of salivary microRNAs in traumatic brain injury (TBI). Int J Mol Sci 23:13160. https://doi.org/10.3390/ijms232113160 Ho VM, Dallalzadeh LO, Karathanasis N, Keles MF, Vangala S et al (2014) GluA2 mRNA distribution and regulation by miR-124 in hippocampal neurons. Mol Cell Neurosci 61:1–12. https://doi.org/10.1016/j.mcn.2014.04.006 Joilin G, Guévremont D, Ryan B, Claudianos C, Cristino AS et al (2014) Rapid regulation of microRNA following induction of long-term potentiation in vivo. Front Mol Neurosci 7:98. https://doi.org/10.3389/fnmol.2014.00098 Juhila J, Sipilä T, Lcay K, Nicorici D, Ellonen P et al (2011) MicroRNA expression profiling reveals miRNA families regulating specific biological pathways in mouse frontal cortex and hippocampus. PLoS One 6:e21495. https://doi.org/10.1371/journal.pone.0021495 Kandel ER (2009) The biology of memory: a forty-year perspective. J Neurosci 29:12748–12756. https://doi.org/10.1523/JNEUROSCI.3958-09.2009 Kandel ER, Dudai Y, Mayford MR (2014) The molecular and systems biology of memory. Cell 157:167–186. https://doi.org/10.1016/j.cell.2014.03.001 Kaneko H, Dridi S, Tarallo V, Gelfand BD, Fowler BJ et al (2011) DICER deficient induces Alu RNA toxicity in age-related macular degenenration. Nature 471:325–330. https://doi.org/10. 1038/nature09830 Kazemian M, Ren M, Lin JX, Liao W, Spolski R et al (2015) Comprehensive assembly of novel transcripts from unmapped human RNA-Seq data and their association with cancer. Mol Syst Biol 11:826. https://doi.org/10.15252/msb.156172 Khalili-Shirazi A, Gregson NA, Londei M, Summers L, Hughes RA et al (1998) The distribution of CD1 molecules in inflammatory neuropathy. J Neurol Sci 158:154–163. https://doi.org/10. 1016/s0022-510x(98)00121-x Khanam T, Rozhdestvensky TS, Bundman M, Galiveti CR, Handel S et al (2006) Tow primatespecific small nonprotein-coding RNAs in transgenic mice: neural expression, subcellular localization and binding partners. Nucleic Acids Res 35:529–539. https://doi.org/10.1093/nar/ gkl1082 Kinberlin RH (1990) Transmissible encephalopathies in animals. Can J Vet Res 54:30–37

References

103

Kocerha J, Faghihi MA, Lopez-Toledano MA, Huang J, Ramsey AJ et al (2009) MicroRNA-219 modulates NMDA receptor-mediated neurobehavioral dysfunction. Proc Natl Acad Sci USA 106:3507–3512. https://doi.org/10.1073/pnas.0805854106 Koturbash I, Zemp F, Kolb B, Kovalchuk O (2011) Sex-specific radiation-induced microRNAome responses in the hippocampus, cerebellum and frontal cortex in a mouse model. Mut Res 722: 114–118. https://doi.org/10.1016/j.mrgentox.2010.05.007 Li JY, Englund E, Holton JI, Soulet D, Hagell P et al (2008) Lewly bodies ingrafted neurons in subjects with Parkinson’s disease suggest host-to-graft disease propagation. Nat Med 14:501– 503. https://doi.org/10.1038/nm1746 Liu X, Wu Y, Huang Q, Zou D, Qin W et al (2015) Grouping pentylenetetrazol-induced epileptic rats according to memory impairment and microRNA expression profiles in the hippocampus. PLoS One 10:e0126123. https://doi.org/10.1371/journal.pone.0126123 López-Pérez Ò, Sanz-Rubio D, Hernaiz A, Betancor M, Otero A et al (2021) Cerebrospinal fluid and plasma small extracellular vesicles and miRNAs as biomarkers for prion diseases. Int J Mol Sci 22:6822. https://doi.org/10.3390/ijms22136822 Lugli G, Torvik VI, Larson J, Smalheiser NR (2008) Expression of microRNAs and their precursors in synaptic fractions of adult mouse forebrain. J Neurochem 106:650–661. https://doi.org/10. 1111/j.1471-4159.2008.05413.x Lugli G, Larson J, Demars MP, Smalheiser NR (2012) Primary microRNA precursor transcripts are localized at postsynaptic densities in adult mouse forebrain. J Neurochem 123:459–566. https:// doi.org/10.1111/j.1471-4159.2012.07921.x Lukiw WJ, Dua P, Pogue AI, Hill JM (2011) Upregulation of microRNA-146a (miR-146a), a marker for inflammatory neurodegeneration, in sporadic Creutzfeldt–Jakob disease (sCJD) and Gerstmann-Straussler-Scheinker (GSS) syndrome. J Toxcol Environ Health A 74:1460–1468. https://doi.org/10.1080/15287394.2011.618973 Maccani M, Padbury JF, Lester BM, Knopik VS, Marsit CJ (2013) Placental miRNA expression profiles associated with measures of infant neurobehavioral outcomes. Pediatr Res 74:272–278. https://doi.org/10.1038/pr.2013.102 Maekawa T, Kim S, Nakai D, Makino C, Takagi T et al (2010) Social isolation stress induces ATF-7 phosphorylation and impairs silencing of the 5-HT 5B receptor gene. EMBO J 29:184– 195. https://doi.org/10.1038/emboj.2009.318 Martignetti JA, Brosius J (1993) BC200 RNA: a neural RNA polymerase III product encoded by a monomeric Alu element. Proc Natl Acad Sci USA 90:11563–11567 Mead S, Uphill J, Beck J, Poulter M, Campbell T et al (2012) Genome-wide association study in multiple human prion diseases suggests genetic risk factors additional to PRNP. Human Mol Genet 21:1897–1906. https://doi.org/10.1093/hmg/ddr607 Melnik B, John SM, Schmitz G (2015) Milk consumption during pregnancy increases birth weight, a risk factor for the development of diseases of civilization. J Transl Med 13:13. https://doi.org/ 10.1186/s12967-014-0377-9 Montag J, Brameier M, Schmädicke AC, Gilch S, Schätzl HM (2012) A genome-wide survey for prion-regulated miRNAs associated with cholesterol homeostasis. BMC Genom 13:486. https:// doi.org/10.1186/1471-2164-13-486 Morgado AL, Xavier JM, Dionísio PA, Ribeiro MF, Dias RB et al (2015) MicroRNA-34a modulates neural stem cell differentiation by regulating expression of synaptic and autophagic proteins. Mol Neurobiol 51:1168–1183. https://doi.org/10.1007/s12035-014-8794-6 Murdoch BM, Murdoch GK, Settles M, McKay S, Williams JL et al (2011) Genome-wide scan identifies loci associated with classical BSE occurrence. PLoS One 6:e26819. https://doi.org/10. 1371/journal.pone.0026819 Mus E, Hof PR, Tiedge H (2007) Dendritic BC200 RNA in aging and in Alzheimer’s disease. Proc Natl Acad Sci USA 104:10679–10684. https://doi.org/10.1073/pnas.0701532104 Norsworthy PJ, Thompson AGB, Mok TH, Guntoro F, Dabin LC et al (2020) A blood miRNA signature associates with sporadic Creutzfeldt–Jakob disease diagnosis. Nat Commun 11:3960. https://doi.org/10.1038/s41467-020-17655-x

104

5

Exosomal MicroRNAs as Brain Memory Devices

Noyes NC, Phan A, Davis RL (2021) Memory suppressor genes: modulating acquisition, consolidation, and forgetting. Neuron 109:3211–3227. https://doi.org/10.1016/j.neuron.2021.08.001 Osone T, Yoshikawa M, Fujii YR (2015) microRNA memory II: a novel scoring integration model for prediction of human disease by microRNA/microRNA quantum multi-interaction. J Adv Med Phar Sci 5:1–18. https://doi.org/10.9734/JAMPS/2016/22095 Pan Z, Zhu LJ, Li YQ, Hao LY, Yin C et al (2014) Epigeneitc modification of spinal miR-219 expression regulates chronic inflammation pain by targeting CaMKIIγ. J Neurosci 34:9476– 9483. https://doi.org/10.1523/JNEUROSCI.5346-13.2014 Park E, Bell JD, Baker AJ (2008) Traumatic brain injury: can the consequences be stopped? CMAJ 178:1163–1170 Peixoto LL, Wimmer ME, Poplawski SG, Tudor JC, Kenworthy CA et al (2015) Memory acquisition and retrieval impact different epigenetic processes that regulate gene expression. BMC Genom 16(Suppl 5):S5. https://doi.org/10.1186/1471-2164-16-S5-S5 Prusiner SB (1997) Prion diseases and the BSE crisis. Science 278:245–251. https://doi.org/10. 1126/science.278.5336.245 Rahmani S, Kadkhoda S, Ghafouri-Fard S (2022) Synaptic plasticity and depression: the role of miRNAs dysregulation. Mol Biol Rep 49:9759–9765. https://doi.org/10.1007/s11033-02207461-7 Rasheed M, Asghar R, Firdoos S, Ahmad N, Nazir A et al (2022) A systematic review of circulatory microRNAs in major depressive disorder: potential biomarkers for disease progress. Int J Mol Sci 23:1294. https://doi.org/10.3390/ijms23031294 Redell JB, Moore AN, Ward NH 3rd, Hergenroeder GW, Dash PK (2010) Human traumatic brain injury alters plasma microRNA levels. J Neurotrauma 27:2147–2156. https://doi.org/10.1089/ neu.2010.1481 Remenyi J, Hunter CJ, Cole C, Ando H, Impey S et al (2010) Regulation of the miR-212/132 locus by MSK1 and CREB in response to neurotrophins. Biochem J 428:281–291. https://doi.org/10. 1042/BJ20100024 Risbud RM, Porter BE (2013) Changes in microRNA expression in the whole hippocampus and hippocampal synaptosome fraction following pilocarpine induced status epilepticus. PLoS One 8:e53464. https://doi.org/10.1371/journal.pone.0053464 Ronga L, Tizzano B, Palladino P, Ragone R, Urso E et al (2006) The prion protein: structural features and related toxic peptides. Chem Biol Drug Des 68:139–147. https://doi.org/10.1111/j. 1747-0285.2006.00427.x Saus E, Soria V, Escaramis G, Vivarelli F, Crespo JM et al (2010) Genetic variants and abnormal processing of premiR-128, a circadian clock modulator, in major depression patients with late insomnia. Human Mol Genet 19:4017–4025. https://doi.org/10.1093/hmg/ddq316 Schratt GM, Tuebing F, Nigh EA, Kane CG, Sabatini ME et al (2006) A brain-specific microRNA regulates dendritic spine development. Nature 439:283–289. https://doi.org/10.1038/ nature04367 Schröder J, Ansaloni S, Schilling M, Liu T, Radke J et al (2014) MicroRNA-138 is a potential regulator of memory performance in humans. Front Hum Neurosci 8:501. https://doi.org/10. 3389/fnhum.2014.00501 Shi Y, Zhao X, Hsieh J, Wichterle H, Lmpey S et al (2010) MicroRNA regulation of neural stem cells and neurogenesis. J Neurosci 30:14931–14936. https://doi.org/10.1523/JNEUROSCI. 4280-10.2010 Sim SE, Lim CS, Kim JI, Seo D, Chun H et al (2016) The brain-enriched microRNA miR-9-3p regulates synaptic plasticity and memory. J Neurosci 36:8641–8652. https://doi.org/10.1523/ JNEUROSCI.0630-16.2016 Smalheiser NR (2014) The RNA-centered view of the synapse: noncoding RNAs and synaptic plasticity. Phil Trans R Soc B 369:20130504. https://doi.org/10.1098/rstb.2013.0504 Sosanya NM, Huang PC, Cacheaux LP, Chen CJ, Nguyen K et al (2013) Degradation of high affinity HuD targets releases Kv1.1 mRNA from miR-129 repression by mTORC1. J Cell Biol 202:53–69. https://doi.org/10.1083/jcb.201212089

References

105

Stefanovic S, Bassell GJ, Mihailescu MR (2014) G quadruplex RNA structures in PSD-95 mRNA: potential regulators of miR-125a seed binding site accessibility. RNA 21:48–60. https://doi.org/ 10.1261/rna.046722.114 Sun N, Yang C, He X, Liu Z, Li X et al (2020) Impact of expression and genetic variation of micriRNA-34b/c on cognitive dysfunction in patients with major depressive disorder. Neuropsychiatr Dis Treat 16:1543–1554. https://doi.org/10.2147/NDT.S247787 Taraboulos A, Scott M, Semenov A, Avrahami D, Laszlo I et al (1995) Choresterol depletion and modification of COOH-terminal targeting sequence of the prion protein inhibit formation of the scrapie isoform. J Cell Biol 129:121–132 Thölken C, Thamm M, Erbacher C, Lechner M (2019) Sequence and structural properties of circular RNAs in the brain of nurse and forager honeybees (Apis mellifera). BMC Genom 20: 88. https://doi.org/10.1186/s12864-018-5402-6 Tiedge H, Chen W, Brosius J (1993) Primary structure, neural-specific expression, and dendritic location of human BC200 RNA. J Neurosci 13:2382–2390 Tsujimura K, Shiohama T, Takahashi E (2022) microRNA biology on brain development and neuroimaging approach. Brain Sci 12:1366. https://doi.org/10.3390/brainsci12101366 Ventrugno V, di Bari MA, Nonno R, Puopolo M, D’Agostino C et al (2009) Oral pravastatin prolongs survival time of scrapie-infected mice. J Gen Virol 90:1775–1178. https://doi.org/10. 1099/vir.0.009936-0 Wadsworth JD, Asante EA, Desbruslais M, Linehan JM, Joiner S et al (2004) Human prion protein with valine 129 prevents expression of variant CJD phenotype. Science 306:1793–1796. https:// doi.org/10.1126/science.1103932 Wang W, Kwon EJ, Tsai LH (2012) MicroRNAs in learning, memory, and neurological diseases. Learn Mem 19:359–368. https://doi.org/10.1101/lm.026492.112 Wang WP, Ho PY, Chen QX, Addepalli B, Limbach PA et al (2015) Bioengineering novel chimeric microRNA-34a for prodrug cancer therapy: high-yield expression and purification, and structural and functional characterization. J Pharmacol Exp Ther 354:131–141. https://doi.org/10. 1124/jpet.115.225631 Wang WW, Qiao Y, Qu H, Zhu L, Mu L et al (2022) The protective role of neuregulin1-ErbB4 signaling in a chronic social defeat stress model. Neuroreport 31:678–685. https://doi.org/10. 1097/WNR.0000000000001464 Wibrand K, Pai B, Siripornmongcolchai T, Bittins M, Berentsen B et al (2012) MicroRNA regulation of the synaptic plasticity-related gene arc. PLoS One 7:e41688. https://doi.org/10. 1371/journal.pone.0041688 Xu J, Chen Q, Zen K, Zhang C, Zhang Q (2013) Synaptosomes secrete and uptake functionally active microRNAs via exocytosis and endocytosis pathways. J Neurochem 124:15–25. https:// doi.org/10.1111/jnc.12057 Yoshikawa M, Osone T, Fujii YR (2015) microRNA memory I: the positive correlation between synergistic effects of microRNAs in cancer and a novel quantum scoring system. J Adv Med Phar Sci 5:1–16. https://doi.org/10.9734/JAMPS/2016/22134 Zerr P, Adelman JP, Maylie J (1998) Episodic ataxia mutations in Kv1.11 alter potassium channel function by dominant negative effects or haploinsufficiency. J Neurosci 18:2842–2848 Zhao Y, Bhattacharjee S, Dua P, Alexandrov PN, Lukiw WJ (2015) microRNA-based biomarkers and the diagnosis of Alzheimer’s disease. Front Neurol 6:162. https://doi.org/10.3389/fneur. 2015.00162 Zhou Q, Yin J, Wang Y, Zhuang X, He Z et al (2021) MicroRNAs as potential biomarkers for the diagnosis of traumatic brain injury: a systematic review and meta-analysis. Int J Med Sci 18: 128–136. https://doi.org/10.7150/ijms.48214 Zhu Y, Li CS, Wang YY, Zhou SN (2015) Change of microRNA-134, CREB and p-CREB expression in epileptic rat. Asi Paci J Trop Med 8:292–298. https://doi.org/10.1016/S19957645(14)60333-3 Zongaro S, Hukema R, D’Antoni S, Davidovic L, Barbry P et al (2013) The 3’UTR of FMR1 mRNA is a target of miR-101, miR-129-5p and miR-221: implications for the molecular pathology of FXTAS at the synapse. Human Mol Genet 22:1971–1982. https://doi.org/10. 1093/hmg/ddt044

Chapter 6

Programmed Evolution by miRNA Memory miRNA Memory Processing Software

I shall only say that the justification lies in the fact that human memory is necessarily limited. Turing, A. M.

Overview Diseases are deeply implicated in the aberrant expression of microRNA (miRNA) genes. The RNA Wave 2000 dogma consists of four criteria, as first described in Chap. 2. Again, (1) miRNA genes induce transcriptional and posttranscriptional silencing through a networking architecture; (2) RNA information supplied by miRNA genes as mobile genetic elements expand to intra- and intercellularly, intra- and interorganically, and intra- and interspecies under the circulation of life to the terrestrial environment; (3) mobile miRNAs can self-proliferate; and (4) cells contain two types of information as a resident and genomic miRNA genes. Given these criteria, diseases are programmed by miRNA genetic information. Abnormal miRNA information induces system errors. In Darwinism, spontaneous mutations and recombination of genomic DNA can cause diseases. Transferable miRNA information in exosomes is passed from mother to child via breast milk, placenta, etc. Although genomic miRNA genes in the DNA genome obey Mendel’s laws, movable miRNA genes are absent from both Mendelian and Darwinian rules. Therefore, the acquired phenotype is inheritable, and the phenotype of offspring is easily reprogrammed. Beyond Darwinism and Mendelian, reprogrammed evolution as a new age is directed by the programming of the miRNA gene language. To apply the miRNA gene information algorithm to the properties of RNA Wave, the a priori miRNA gene information was converted into binary qubits as physicochemical characters, and mathematically, the electron spins of miRNAs were measured and computed in a matrix. Bit-to-bit coherence of miRNAs was recorded as the static (single) nexus score (SNS) or dynamic (double) nexus score (DNS). Since alterations in miRNA expression were both upregulation and downregulation, the binary qubits of coherence miRNA expression changes were further calculated as SNS + change (SNSC) and DNS + change (DNSC). Subsequently, DNSC has been correlated with human disease. Human disease phenotypes will be simulated by DNSC with miRNA language and artificial intelligence (AI) computing © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1_6

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algorithm (MIRAI) in the future. The MIRAI can reduce overall healthcare cost containment.

6.1

Programmed Evolution

Unlike biological pathway searches, miRNA targeting database searches cannot treat miRNAs as cellular markers (Giri and Carthew 2014). Moreover, the principles of microRNA (miRNAs) 2012 do not include miRNA/miRNA information crosstalk mechanisms and exogenous miRNAs as an environment (Ebert and Sharp 2012). Therefore, the principles of miRNAs 2012 differ greatly from those of RNA Wave. Furthermore, the competing endogenous miRNA (ceRNA) seed theory is based solely on the search for targeting as a system (Salmena et al. 2011). RNA Wave 2000 contains seed theory and is built on the miRNA quantum language as information. Robust systems or the robustness of biological processes was a major theme of the rationale for miRNAs 2012 or ceRNA theory. What is Robustness? It is the ability of the system to maintain its function in spite of internal and external perturbation (Ebert and Sharp 2012). However, age-related human genome instability causes many human diseases, including cancer and progeria syndromes (Vijg and Suh 2013). In other words, miRNAs may be less robust and more variable when disease is associated with miRNAs. Here, we consider the relationship between disease prediction and miRNA quantum language by mutation of the target protein 3′ untranslated region (3′UTR). What is the Robustness? Unless the miRNA gene information is not systemic. The miRNA information itself is of internal and external origin. RNA Wave relates to mobile miRNA gene information. Therefore, miRNA information is not robust because many miRNAs are derived from retrotransposons, and the human genome containing miRNA genes is a retrotransposon zygote. There is also the idea that miRNAs act as buffers for gene expression (Ebert and Sharp 2012), but miRNAs are an informational, not a buffering mechanism. Therefore, miRNA profiles are aberrantly expressed in cancer. Thus, miRNA gene information is also not buffer. MiRNAs have been identified by many scientists as junk, next noise, and then buffering. However, we do not know that miRNA genetic information could serve as valuable information. In orchestration, a conductor needs all parts to play harmony, and when a soloist joins the orchestration, each part of the orchestra changes in response to the soloist, making the performance change permanent. Orchestration is not robust. Similar to this relationship between soloists and orchestra, human cells incorporate and change exogenous miRNA (exomiRNA) genes. A concerto cannot be performed hard copy human genomic DNA score alone (Mu et al. 2014; Lukasik and Zielenkiewicz 2014). The orchestration of life is organized by miRNAs. Mobile miRNA genes can create musical harmony. Multiple miRNA gene information plays notes, semibreve, minim, crotchet, quaver, semiquaver, demisemiquaver, hemidemi-semiquaver, etc., as a quantum language more flexibly. The miRNA language alters the effects of miRNAs through

6.2

Acquired Phenotype

109

Fig. 6.1 Fluctuation of the miRNA quantum language, like music

single nucleotide polymorphisms (SNPs) in the 3′UTR of target messenger RNAs (mRNAs), thus altering the phenotype of life without mutation of protein-coding regions. Furthermore, adenosine-to-inosine (A-to-I) RNA editing of miRNA (I is equivalent to G) changes miRNA gene function from cancer metastasis to tumor suppression in melanoma (Shoshan et al. 2015). Mendelian genetic rules are dependent on chromosomal alleles. Alleles are at a single locus. Approximately 6000 Mendelian characters in humans are governed by multiple loci. Since different variants of proteins follow the Mendelian alphabet, DNA that encodes the protein also participates in the Mendelian alphabet. In fact, coding regions are also targeted by miRNA (Liu et al. 2015). Thus, functional constraints are due to miRNA regulation, suggesting that protein sequence-based molecular clocks are not accurate in evolutionary models. Humans are fragile. Therefore, the meaning of mobile miRNA information is also flexible and likely to be flexibly adjusted by information between miRNAs. Ultimately, under the deep insight of miRNA information, there is no robustness to maintain the human genome. Conversely, the random phase approximation as a property of mRNAs is equivalent to the human genome. We used the miRNA quantum code to discover miRNA quantum fluctuations in disease (Osone et al. 2015). The miRNA quantum language fluctuated by mutations of the target protein messenger RNA (mRNA) 3′UTR in bladder cancer (Fig. 6.1). Therefore, again, miRNA information is not robust.

6.2

Acquired Phenotype

Mendelian is the rule of inheritance in genetics. Darwinism involves further rules of phylogenetic inheritance, natural selection, mutation, and survival fittest in the acquired genetic traits. Maternal protein restriction has been reported to affect the

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Fig. 6.2 Vertically inheritable transmission of miRNA gene information

reduction in nephron number and arterial hypertension in offspring and has been implicated in the repression of glomerular miR-200a, miR-141, and miR-429 (Sene et al. 2013). As previously mentioned, this strongly suggests that maternal environmental factors are involved in offspring phenotypes, and inherited and acquired traits were associated with miRNA regulation (Fig. 6.2). As described earlier, Darwinism involves the influence of environmental factors on genetic traits. However, there was no explanation as to “what mechanism?”. Preweaning growth hormone treatment in adult males caused let-7-related hypertension and cardiac hypertrophy in offspring (Gray et al. 2014). Paternal obesity affects the diet and/or exercise of maternal and paternal offspring that are regulated by miRNAs in sperm. Offspring programming phenotypes are expressed by paternal resident mRNAs that tune the cell cycle and apoptosis (McPherson et al. 2015). MiRNAs are mobile genetic elements. Therefore, the genomic miRNA genetic information in human genomic DNA is Mendelian, whereas maternal and paternal resident exogenous miRNA (exomiRNA) genetic information is transmitted to offspring through the oocyte, sperm, breast milk, and placenta. An acquired phenotype will depend on resident miRNA genetic inheritance. A neutral theory of molecular evolution in genetic drift was postulated by Japanese geneticist Kimura (1983). A neutral theory is based on both the DNA protein-coding region and a large part of the noncoding region of the human genome. In the former case, less than 2% of protein-coding DNA sequence evolution in the human genome is presented by a phylogenetic tree of each protein or part of a protein based on the central dogma of Watson-Crick base pairing. However,

6.2

Acquired Phenotype

111

protein-based phylogenies and DNA sequence-based phylogenies can differ even for the same protein without mutations. The discrepancy is sometimes explained by the fact that the third base of the codon triplet is not important in defining an amino acid and that the third base is drift, but the protein-coding genes are adequately conserved in species. Furthermore, AUG is the initiation codon as well as CUG, suggesting that the first base of the triplet on the codon also drifts (Starck et al. 2012). Why Drift? It completely contradicts robustness. Therefore, the triplet code is important because it determines the amino acid, but the third base is not. No one knows whether the bias in codon drift is due to mutation or natural selection (Yang and Nielsen 2008). Environmental factors, whether mutations or natural selection, may be key. However, what are the environmental factors? Furthermore, “junk,” where more than 98% of the human genome is noncoding regions, is evidence of drift and is also explained by Dr. Kimura as a big drift-based neutral theory. However, if the noncoding region encodes genes, the neutral theory and then molecular drift, as well as the phylogenic tree of molecular clock, are wrong (Fujii 2013a). In fact, thousands of miRNA genes are located in noncoding DNA regions, which means that noncoding regions have a strong purpose. Furthermore, evidence of variability among transfer RNA (tRNA) pools in different species remains to be elucidated as to why the most abundant codons in cells are recognized by the most abundant tRNAs (Tuller et al. 2010). Guzman et al. (2015) reported that miRNA-720 and miR-1274b were produced from tRNA and that these miRNAs were released from breast cancer cells through exosomes. This suggests that abundant tRNAs are miRNA pools. Although I do not know why transfer RNA-derived RNA fragments (tRFs) are not miRNAs, tRFs are a part of miRNAs, and many functional tRFs have been uploaded to The Cancer Genome Atlas (TCGA) of the National Cancer Institute (NCI) (cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) (Sun et al. 2020). Ribosomal RNAs (rRNAs) have also been demonstrated to produce miRNA genes (Yoshikawa and Fujii 2016). If evolutionarily the codon usage violates the protein synthesis canons, then either the mechanism of protein synthesis is controlled by tRNA-miRNAs (Haussecker et al. 2010), or protein synthesis itself is carried out by tRNA anticodon-loop miRNAs but may not have been done by tRNA. Therefore, it is possible that miRNA genes altered protein production and altered protein structure to innovate lifestyles. MiRNA genes go beyond proteins. Because, from RNA Wave, miRNA genes can vona fed drift between species. Simultaneously, miRNA genes exist in the environment, and miRNA gene information becomes an automaton for the entire life cycle on Earth (Fujii 2008). It is certain that the RNA Wave can further revolutionize the origin of species from the Darwinian narrative.

112

6.3

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Programmed Evolution by miRNA Memory

Quantum Theory of RNA Wave

Given the small number of identified miRNAs, bioinformatics available for small ncRNAs may simply follow Watson-Crick DNA base pairing. The Watson-Crick model is currently used for miRNA–target pairing, but a broader set of rules may benefit small RNAs from bioinformatics approaches. The origin of the code was hypothesized long ago, but archaic code (G, C) expands toward perfect variants (C, G, A, U) in the early evolution of mRNAs (Hartman 1975; Rodin and Ohno 1997). The global effect of miRNAs coordinates gene regulation according to imperfect Watson-Crick pairs. Therefore, miRNAs can target multiple genes, and a single gene can be regulated by multiple miRNAs. In addition, A, G, and C have no free amino side chain, but T has a free amino side chain. From the ionic electrical charge, A, G, and C have a positive charge, but T does not. Alongside the positive charge, the specific secondary structure of miRNAs is constituted by the arrangement of G:U and C:U base pairs. We deduced the trivial formula that the codes are represented by G, C, and U and not by A. In that case, the U:G wobble and the unassembled C:U pairs compromise the early seed sequence, such as 2–8 nucleotides (nts) from the 5′-end of miRNAs. Furthermore, the original position of the seed in the pre-miRNA may not be fixed in the sequence of the pre-miRNA. This formula was established far away from pre-miRNAs in Watson-Crick paired DNA genes. We checked the overlap of G, C and U, and U-G and C-U in pre-miRNAs, calculated the frequency of U-G and C-U repeats from the sequences of pre-miRNAs at window n = 7, and calculated the repeat diversity according to Kaiser’s algorithm. MiRNAs of the hiv1-miR-N367 family, the herpesvirus family, and the immune and oncogene family showed multiple wings when the calculated frequencies were represented by circles that were assumed to be Möbius strips (Fujii and Saksena 2008) (Fig. 6.3). Wings can be divided into four categories according to their vector functions: Seraphim, Cherubim, Thrones, and Dominations. In this vector function, the G hotspot was distinct from the seed region of each miRNA. As mentioned in Chap. 1, we considered in 1987 at Oxford whether the positive ionic charge of G and G-specific vector functions could be integrated into the electrostatic potential. Furthermore, these vectors can be represented as a wave equation, and the wave entanglement can be transformed as a scalar of vectors. Physicochemical energy is generally expressed as E = P2/2 m + V, adding a wave equation ψ, ψE = ψP2/ 2 m + ψV (Tesche 2000). Therefore, the energy quantization of pre-miRNA itself may be calculated mathematically and applied to Schrödinger’s variational method. Thus, the energy equation as an “RNA Wave” is different from the thermodynamic secondary structure of pre-miRNA by Watson-Crick pairs with only electronic charges. The full deciphering of the miRNA code begins with the superposition of in silico and in vivo systems, and the intrinsic role of miRNAs in creating life in the RNA world may be elucidated by the miRNA/miRNA coefficients. The miRNA quantum code may facilitate pharmaceutical and medical applications in RNA Wave 2000. The quantum potentials of G, C, A, and U were calculated by the fragment

6.4

Quantum Tuning of miRNA Information

113

Fig. 6.3 A Möbius strip of miRNAs

molecular orbital method (FMO) according to Schrödinger’s variational method. MOPAC software was used for the computation. It measures the quantum potential, not the polarity, and performs a binary notation conversion. Since G is only positive, G is yes (1) and C, A, and U are no (0) as miRNA qubits (Fujii 2013a, 2023). An FMO radar chart for miRNAs was created. Every miRNA has a unique shape, and FMO-based binary transformation is available for miRNA quantum analyses as described in MicroRNA Protocols (Fujii 2013a).

6.4

Quantum Tuning of miRNA Information

A miRNA/miRNA superposition in higher-dimensional quantum energies was applied to miRNA gene coding (Fujii 2013a). Quantum systems were first integrated into atomic states within RNA molecules. j ψ> =

n-1 i=0

j ψi > ωi

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Fig. 6.4 Hilbert space for miRNA qubits

For 1-methyl-uracil (U), 9-methyl-guanine (G), 9-methyl-adenine (A), and 1-methyl-cytosine (C), the quantum energies of the FMO values were -0.1, +0.4, -0.1 and -0.1, respectively. FMO values were transformed into binary states 1 and 0. j g> = j 1> j a> = j u> = j c> = j 0>: Furthermore, quantum miRNA language (QRL) bits, miRNA qubits such as miR-21 and hiv1-miR-N367 were shown as: X21: | 0010000000100010010010 > XN367: | 00010000001101000000 > A miRNA/miRNA superposition was applied to the matrix (Fujii 2013b) and shown as amplitude vectors of intersections between miRNA qubits (Figs. 6.4 and 6.5). One inner product space A and another B were constructed as the tensor product. Even when the computation of mRNA qubit superposition required high dimensionality, it could be expressed as a scalar in Hilbert space. The intersecting points were computed, and the total value number was presented as the single nexus score (SNS) (Yoshikawa et al. 2015): SNS =

n-1

Gi i=0

i = 0, 1, 2, . . ., n-1.

6.4

Quantum Tuning of miRNA Information

115

Fig. 6.5 The matrix of miRNA qubits

We hypothesized that miRNAs make the torus. The miRNA information was calculated in the state of the torus (Fig. 6.6) (Fujii 2013a). As A-G mismatch interactions have been observed (Villescas-Diaz and Zacharias 2003), the intersections cycled one by one, corresponding to the rotation of the double miRNA torus (Figs. 6.4 and 6.5). The rotation of the two miRNA qubits was represented by a matrix. The matrix corresponded to scalar space. Each vector of decreasing diagonal intersections of the matrix was summed up as SNS. Then, the total value number is presented as the dynamic nexus score (DNS) as:

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Fig. 6.6 The miRNA/miRNA torus

DNSN =

n-1

DNS a½i b½j = N i, j = 0

[i]{i = 0. 1, 2, . . . , n-1}, [j] {i = 0. 1, 2, . . ., n-1}, N: windows. Calculated DNS state from SNS. To clarify the state differences between qubits, DNS change (DNSC) was calculated as follows (Osone et al. 2015): n-1

DNSCx =

n-1 i, j = 0

a½i b½j -

i, j = 0

a½i b½j

a½i b½j

x = 0, 1, 2, . . . , m-1, i = 0, 1, 2, . . ., n-1. How important is a diagnosis by miRNAs? Genome informed therapy is the key to personalized and precious medicine. MiRNAs have been found in various human fluids (Fig. 6.7). Therefore, miRNAs have become noninvasive biomarkers. For protein biomarkers, multiple samples/person are required for disease diagnosis, whereas for miRNA, one sample/person is sufficient for disease diagnosis. MiRNA biomarkers have significant cost-effectiveness and noninvasive advantages, giving them an advantage over financial and physical stresses.

6.5

MiR-AI: Vital Obesity miRNA Quantum Code for Type 2 Diabetes

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Fig. 6.7 Diagnosis by miRNA biomarker

6.5

MiR-AI: Vital Obesity miRNA Quantum Code for Type 2 Diabetes

To study predictive tools of human diseases, we considered how to apply miRNA DNS to pathogenesis. There are multiple human diseases, as they are linked by genetic and environmental factors. Genome wide-associated studies (GWAS) are used to study human genetic diseases. GWASs survey the human genome for SNPs that are implicated in human diseases. Until now, GWAS has been aimed at detecting loci susceptible to complex human diseases. SNPs in either the miRNA seed or target mRNA 3′UTR influence noncomplementary binding between miRNAs and targets. Because miRNA genes can regulate the expression of almost any protein gene through transcription and translation processes, miRNA-associated polymorphisms are associated with many human diseases, such as cancers, diabetes, and neurodegeneration, including prion diseases (see Chaps. 4 and 5) (Goulart et al. 2015; Gong et al. 2015). For example, the C allele of miR-146a rs2910164 was positively associated with increased susceptibility to metabolic syndrome (Mehanna et al. 2015). miR-137 variants rs1625579 and rs1198588 were related to impaired neurocognitive function and worsening psychotic symptoms in Chinese patients with schizophrenia (Kuswanto et al. 2015). miR-27a rs895819 was susceptible to colorectal cancer (Wang et al. 2014). The indel polymorphism 602_606insCTCCC in the 3′UTR of the prion protein (PrP)-encoding gene was positively correlated with

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Fig. 6.8 Online disease prediction tools for miRNAs

scrapie in the central nervous system (Peletto et al. 2012). Several databases for miRNA target prediction are online, and PolymiRTS database 3.0 is a complete resource for linking polymorphic miRNA targeting to GWAS (Fig. 6.8). Seed-based prediction tools are available online for miRNA target prediction, and wobble pairing has been added to these tools. Most of the tools have been integrated, and even more tools are now available that combine PolymiRTS for disease-related miRNA targets and SNPs. Most human diseases were associated with synergism of miRNAs computed as SNS and DNS, suggesting that a priori miRNA genetic information is involved in pathogenesis. Human health status could be predicted by an algorithm using the DNS platform MIRAI, which could enable the containment of overall healthcare costs. We investigated the human disease correlation between seed/target scores of CSCs and DNSCs in the PloyMiRTS database (Fig. 6.9). PolymiRTS includes TargetScan context + score changes (CSC) associated with allelic variants of 3′UTR mRNA/miRNA based on site type, 3′ pairing, local AU, and position contribution (Fig. 6.10). Complete and accurate miRNA/mRNA interactions were provided by CLASH experiments (Fig. 6.9). The CSC of miRNAs is calculated at mutations in the 3′UTR of mRNA targets relative to the context + score (CS) of miRNAs in the original allele of the protein gene, and human disease association is combined with the miRNA-related GWAS (miRGWAS) disease phenotype (Ziebarth et al. 2012). In general, multiple mapping in GWAS indicates disease trait loci, but in the case of disease, many loci are detected. Of course, since protein genes are implicated in disease, miRNAs are also relevant to GWAS (Wang et al. 2014). As above, we

6.5

MiR-AI: Vital Obesity miRNA Quantum Code for Type 2 Diabetes

Fig. 6.9 PolymiRTS database

Fig. 6.10 Relation between miRNA and CSC in PolymiRTS

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Fig. 6.11 Correlation between CSC and DNSC

integrated the quantum potential as its interaction using DNSC. The coherent states of DNSC are computed according to quantum computing algorithms. DNSC was strongly correlated with CSC (Fig. 6.11). Therefore, human disease phenotypes could be distinguished by DNS through functioning magnetic field calculations of miRNAs such as the electric field tangent score (EFTS) (Osone et al. 2015). In this way, the DNS was cohesive, and diseases could be represented by mathematical formulas as specific shapes. This is different from the general GWAS multiple mapping analysis (Prabhu et al. 2021; Song et al. 2022). The EFTS formula was as follows: 50

50

→

E

EFTS =

k = - 50 l = - 50 50 50 →

F

k = - 50 l = - 50

6.5

MiR-AI: Vital Obesity miRNA Quantum Code for Type 2 Diabetes

! Ex = k

Mi

2

i=0 j=0

! Fy = k

Mi

2

i=0 j=0

Aijk x - X ijk x - X ijk

2

þ y - Y ijk

→ 2

3 2

2

3 2

Aijk y - Y ijk x - X ijk

2

þ y - Y ijk

121

x

→

y

A is the miRNA group, and each element of A is Ai {i = 0, 1, 2, ------, N-1}. Each sequence of Ai is Aij {j = 0, 1, 2, ------, Mi}, and the charge of Aij is Aijk {j = 0, 1, 2}. The coordinate of Aijk is shown as (Xijk, Yijk). EFTS was computed by the above vector space that is restricted by the optional units as described before (Osone et al. 2015). DNSC by miRNA/miRNA interaction differences are calculated corresponding to CSC from 50 miRNAs and mRNA target SNPs or mutations. On the other hand, DNS is calculated as the quantum energy between miRNAs due to miRNA/miRNA interactions. Since DNS is scalar, DNS (1, ------- N) can be coherent. Later, in Chap. 10, EFTS is useful as a weighting for DNS (Fujii 2023). We have shown the formula for EFTS calculation, but multiple miRNA/mRNA interactions as miRNA memory, that miRNA memory package (MMP) manages states of human diseases or health, and in miGWAS, it helps predict trait of human diseases (Figs. 6.12 and 6.13); however, it must be taken into consideration that the status is altered by miRNAs in dairy foods. Genome-specific miRNA-targeted mutations in bladder cancer patients, TP63tv3, TP63tv5, SLC14A1tv1, FGFR3tv3, CCNE1, PSCAtv1, TMEM129tv3, and APOBEC3Atv1 were targeted by 50 miRNAs (Figs. 6.1 and 6.12). EFTS-weighted DNS was calculated, and the results were expressed as MMP. MMP is derived from a scalar formula. Therefore, each MMR is coherent and can indicate the MMR of bladder cancer. Using the same procedure as for bladder cancer, bladder cancer, gastric cancer, hepatocellular carcinoma, and thyroid cancer can be stitched together to display the cancer MMP. Cardiac disease, immune disease, and infectious disease in MMP were represented. Allergic rhinitis, leishmaniasis, leprosy, malaria, AIDS, corneal astigmatism, glaucoma, myopia, age-related macular degeneration (ARMD), and aging could be displayed as MMRs (Fig. 6.13). If miRNAs control brain functions, MMP may be involved in the brain. Since miRNA memory in the brain can also be altered while being fed dietary miRNAs, brain memory must be preserved by the conservation of lncRNAs or circRNAs along with MMPs (Fujii 2018). Given that diseases are programmed by MMPs, it is possible that species evolution was programmed by MMPs as “programmed evolution.” Furthermore, brain memory may involve MMPs as miRNA quantum codes (Fig. 6.14). As shown in Chap. 4, food miRNAs may play an important role in sorting color memory by miRNA quantum codes for memory devices such as lncRNAs or circRNAs. Additionally, memories may be stored as exomiRNAs in

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miRNA/diseases quantum coherence Bladder cancer protein-related miRNAs

coherence

coherence

Diseases Cancers

Fig. 6.12 MicroRNA memory package in miGWAS

body fluids. In turn, the brain acts as the computer CPU. In that case, the miRNA memory of MMPs could be important for evolution (Fujii 2014) (see Chap. 5). If artificial intelligence (AI) technologies could be applied to miRNA quantum memory, it would further reduce healthcare costs with MIRAI, the Japanese term for “future” (see Chap. 11). MIRAI was eventually integrated into the miRNA entangling target sorting (METS) algorithm, and METS/MIRAI was used for pathogenesis analysis of human disease using vital circulating miRNA panels (Fujii 2023). As an example of human disease caused by programmed evolution by MMP, we show an etiological analysis of obesity to type 2 diabetes mellitus (T2DM) by METS/MIRAI (Fujii 2023). This is a case that followed the passage of 5 years from obesity (Fig. 6.15). From the beginning of obesity, insulin resistance-related adipogenesis occurs, forkhead box O1 (FOXO1) upregulation leads to a decrease in insulin levels, and one cut homeobox 1 (ONECUT1) and nuclear factor kappa B subunit 1 (NFKB1) increases lead to inflammation in the pancreas and a decrease in β cells. After 5 years, the reduction in KLF transcription factor 11 (KLF11) further suppresses insulin production. At the beginning of obesity, the decrease in blood insulin levels

6.5

MiR-AI: Vital Obesity miRNA Quantum Code for Type 2 Diabetes

Fig. 6.13 Genomic MMPs of diseases in miGWAS for prediction

Fig. 6.14 Brain memory related to MMR

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Fig. 6.15 Time-dependent wave equation from obesity to type 2 diabetes

is not clinically recognized, but insulin resistance and β cell loss silently progress under obesity. Changes in miRNAs are clear, and miRNA memory changes from miR-9-5p downregulation to miR-138-5p upregulation. Examining the translation of miRNA qubits over a 5-year period, the quantum energy DNS approximately tripled from obesity to T2DM (Fig. 6.16). In addition, the quantum energy in the seed region also increases (red arrows). That is, an MMP is formed in which G in the excited state increases. Therefore, linguistically, there is a clear transition from obesity to T2DM, from a stable language to a violently unstable miRNA quantum code. Furthermore, the miR-138-5p hub does not have a fill-safety function as a panel because it does not have branch miRNAs. Therefore, it becomes more unstable. If an RNA storm such as the COVID-19 pandemic occurs there, individuals who develop T2DM from obesity are likely to be lethal (Abu-Farha et al. 2020). In other words, not only dinosaurs but also humans will likely die out of obesity (see Chap. 4). This is “the programmed evolution” by MMP.

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Fig. 6.16 Five years in miRNA qubit of obesity to T2DM

References Abu-Farha M, Al-Mulla F, Thanaral TA, Kavalakatt S, Ali H et al (2020) Impact of diabetes in patients diagnosed with COVID-19. Front Immunol 11:576818. https://doi.org/10.3389/fimmu. 2020.576818 Ebert MS, Sharp PA (2012) Roles for miRNAs in conferring robustness to biological process. Cell 149:515–524. https://doi.org/10.1016/j.cell.2012.04.005 Fujii YR (2008) Formulation of new algorithmics for miRNAs. Open Virol J 2:37–43. https://doi. org/10.2174/1874357900802010037 Fujii YR (2013a) The RNA gene information: retroelement-microRNA entangling as the RNA quantum code. Methods Mol Biol 936:47–67. https://doi.org/10.1007/978-1-62703-083-0_4 Fujii YR (2013b) RNA wave for the HIV therapy: foods, stem cells and the RNA information gene. World J AIDS 3:131–146. https://doi.org/10.4236/wja.2013.32018 Fujii YR (2014) RNA information gene diseases: nano-RNA-based medical devices with corporate chemotherapy and gene therapy. In: Wang B (ed) RNA nanotechnology. CRC Press Taylor & Francis Group, Boca Raton, pp 385–434 Fujii YR (2018) The quantum language of the microRNA gene and anticancer: with a dynamic computer simulation of human breast cancer drug resistance. Integr Mol Med 5:1–13. https:// doi.org/10.15761/IMM.1000346 Fujii YR (2023) The microRNA quantum code book. Springer Nature, Singapore Fujii YR, Saksena NK (2008) Viral infection-related microRNAs in viral and host genomic evolution. In: Morris KV (ed) RNA and the regulation of gene expression. Horizon Scientific Press, London, pp 91–107 Giri R, Carthew RW (2014) microRNAs suppress cellular phenotypic heterogeneity. Cell Cycle 13: 1517–1518. https://doi.org/10.4161/cc.29013

126

6

Programmed Evolution by miRNA Memory

Gong J, Liu C, Liu W, Wu Y, Ma Z et al (2015) An update of miRNA SNP database for better SNP selection by GWAS data, miRNA expression and online tools. Database 2015:bav029. https:// doi.org/10.1093/database/bav029 Goulart LF, Bettella F, Sønderby JE, Schork AJ, Thompson WK et al (2015) MicroRNA enrichment in GWAS of complex human phenotypes. BMC Gen 16:304. https://doi.org/10.1186/ s12864-015-1513-5 Gray C, Li M, Reynolds CM, Vickers MH (2014) Let-7 miRNA profiles are associated with the reversal of left ventricular hypertrophy and hypertension in adult male offspring from mothers undernourished during pregnancy following preweaning growth hormone treatment. Enocrinology 155:4808–4817. https://doi.org/10.1210/en.2014-1567 Guzman N, Agarwal K, Asthagiri D, Saji M, Ringel MD et al (2015) Breast cancer-specific miR signature unique to extracellular vesicles includes “microRNA-like” tRNA fragments. Mol Cancer Res 13:891–901. https://doi.org/10.1158/1541-7786.MCR-14-0533 Hartman H (1975) Speculations on the evolution of the genetic code. Orig Life 6:423–427 Haussecker D, Huang Y, Lau A, Parameswaran P, Fire AZ et al (2010) Human tRNA-derived small RNAs in the global regulation of RNA silencing. RNA 16:673–695. https://doi.org/10.1261/rna. 2000810 Kimura M (1983) Neutral theory of molecular evolution. Cambridge University Press, Cambridge Kuswanto CN, Sum MY, Qiu A, Sitoh YY, Liu J et al (2015) The impact of genome wide supported microRNA-137 (MIR137) risk variants on frontal and striatal white matter integrity, neurocognitive functioning, and negative symptoms in schizophrenia. Am J Med Genet B Neuropsychiatr Genet 168B:317–326. https://doi.org/10.1002/ajmg.b.32314 Liu G, Zhang R, Xu J, Wu C, Lu X (2015) Functional conservation of both CDS- and 3’-UTR-located microRNA binding sites between species. Mol Biol Evol 32:623–628. https:// doi.org/10.1093/molbev/msu323 Lukasik A, Zielenkiewicz P (2014) In silico identification of plant miRNAs in mammalian breast milk exosomes-a small step forward? PLoS One 9:e99963. https://doi.org/10.1371/journal. pone.0099963 McPherson NO, Owens JA, Fullston T, Lane M (2015) Preconception diet or exercise interventions in obese fathers normalizes sperm microRNA profile and metabolic syndrome in female offspring. Am J Physiol Endcrinol Metab 308:E805–E821. https://doi.org/10.1152/ajpendo. 00013.2015 Mehanna ET, Ghattas MH, Mesbah NM, Saleh SM, Abo-Elmatty DM (2015) Association of microRNA-146a rs2910164 gene polymorphism with metabolic syndrome. Folia Biol 61:43–48 Mu J, Zhuang X, Wang Q, Jiang H, Deng ZB et al (2014) Interspecies communication between plant and mouse gut host cells through edible plant derived exosome-like nanoparticles. Mol Nutr Food Res 58:1561–1573. https://doi.org/10.1002/mnfr.201300729 Osone T, Yoshikawa T, Fujii YR (2015) microRNA memory II. A novel scoring integration model for prediction of human disease by microRNA/microRNA quantum multi-interaction. J Adv Med Phar Sci 5:1–18. https://doi.org/10.9734/JAMPS/2016/22095 Peletto S, Bertolini S, Maniaci MG, Colussi S, Modesto P et al (2012) Association of an indel polymorphism in the 3’ UTR of the caprine SPRN gene with scrapie positivity in the central nervous system. J Gen Virol 93:1620–1623. https://doi.org/10.1099/vir.0.041400-0 Prabhu BN, Kanchamreddy SH, Sharma AR, Bhat SK, Bhat PV et al (2021) Conceptualization of functional single nucleotide polymorphisms of polycystic ovarian syndrome genes: an in silico approach. J Endocrinol Investig 44:1783–1793. https://doi.org/10.1007/s40618-021-01498-4 Rodin S, Ohno S (1997) Four primordial modes of tRNA-synthetase recognition, determined by the (G, C) operational code. Proc Natl Acad Sci USA 94:5187–5188 Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP (2011) A ceRNA hypothesis: the rosetta stone of a hidden RNA language? Cell 146:353–358. https://doi.org/10.1016/j.cell.2011.07.014 Sene LB, Mesquita FF, de Moraes LN, Santos DC, Carvalho R et al (2013) Involvement of renal corpuscle microRNA expression on epithelial-to-mesenchymal transition in maternal low

References

127

protein diet in adult programmed rats. PLoS One 8:e71310. https://doi.org/10.1371/journal. pone.0071310 Shoshan E, Mobley AK, Braeuer RR, Kamiya T, Huang L et al (2015) Reduced adenosine-toinosine miR-455-5p editing promotes melanoma growth and metastasis. Nat Cell Biol 17:311– 321. https://doi.org/10.1038/ncb3110 Song M, Yu J, Li B, Dong J, Gao J et al (2022) Identification of functionally important miRNA targeted genes associated with child obesity trait in genome-wide association studies. BMC Genomics 23(Suppl 4):360. https://doi.org/10.1186/s12864-022-08576-8 Starck SR, Jiang V, Pavon-Eternod M, Prasad S, McCarthy B et al (2012) Leucine-tRNA initiates at CUG start codons for protein synthesis and presentation by MHC class I. Science 336:1719– 1723. https://doi.org/10.1126/science.1220270 Sun X, Yang J, Yu M, Yao D, Zhou L et al (2020) Global identification and characterization of tRNA-derived RNA fragment landscapes across human cancer. NAR cancer 2:zcaa031. https:// doi.org/10.1093/narcan/zcaa031 Tesche C (2000) Quantum mechanics: enhanced: Schrodinger’s cat is out of the hat. Science 290: 720–721. https://doi.org/10.1126/science.290.5492.720 Tuller T, Carmi A, Vestsigian K, Navon S, Dorfan Y et al (2010) An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell 141:344–354. https://doi. org/10.1016/j.cell.2010.03.031 Vijg J, Suh Y (2013) Genome instability and aging. An Rev Physiol 75:645–668. https://doi.org/10. 1146/annurev-physiol-030212-183715 Villescas-Diaz G, Zacharias M (2003) Sequence context dependence to tandem guanine: adenine mismatch conformations in RNA: a continuum solvent analysis. Biophys J 85:416–425. https:// doi.org/10.1016/S0006-3495(03)74486-5 Wang Z, Sun X, Wang Y, Liu X, Xuan Y et al (2014) Association between miR-27a genetic variants and susceptibility to colorectal cancer. Diagn Path 9:146. https://doi.org/10.1186/17461596-9-146 Yang Z, Nielsen R (2008) Mutation-selection models of codon substitution and their use to estimate selective strengths on codon usage. Mol Biol Evol 25:568–579. https://doi.org/10.1093/molbev/ msm284 Yoshikawa M, Fujii YR (2016) Human ribosomal RNA-derived resident microRNAs as the transmitter of information upon the cytoplasmic cancer stress. Biomed Res Int 2016:7562085. https://doi.org/10.1155/2016/7562085 Yoshikawa M, Osone T, Fujii YR (2015) microRNA memory I: the positive correlation between synergistic effects of microRNAs in cancer and a novel quantum scoring system. J Adv Med Phar Sci 5:1–16. https://doi.org/10.9734/JAMPS/2016/22134 Ziebarth JD, Bhattacharya A, Chen A, Cui Y (2012) PolymiRTS database 2.0: linking polymorphisms in microRNA target sites with human diseases and complex traits. Nucleic Acids Res 40: D216–D221. https://doi.org/10.1093/nar/gkr1026

Chapter 7

Communication in miRNAs Between Inflammation and Cancer miRNA Connection Between Inflammation and Cancer

Mehr Licht! von Goethe, J. W.

Overview Inflammatory and immune responses are controlled by microRNAs (miRNAs), and cancer is also regulated by miRNAs. Computing analysis and evidence from experimental and clinical investigations show that miRNAs can regulate most protein-coding genes. In contrast, cancer is implicated in proliferation, development, differentiation, apoptosis, inflammation, and the immune response, all of which miRNAs can modulate. In 2000, cancer and inflammation were assessed as separate issues. However, 10 years after RNA Wave 2000, it was reported that the tumor suppressive miR-34a suppressed the expression of the natural killer cell immunoreceptor NKG2D ligand (NKG2DL) ULBP2, suggesting that miR-34a may play a role in tumorigenesis and innate immune surveillance. Furthermore, oncogenic miR-155 contributes to the expression of IL-8 in pulmonary cystic fibrosis, suggesting that miRNA genes serve pleiotropic functions between cancer and inflammation. In November 2021, Google Scholar found 1574 miR-155 issues related to cancer and 895 issues related to immunity for a total hit count of 3727. miR-34a and miR-155 are biomarkers of colorectal cancer development and progression, and both miRNAs are secreted into the circulation; therefore, rectal cancer development and immune modulation are regulated by mobile miRNA genes. A major source of circulating exogeneous miRNAs (exomiRNAs) may come from foods. Thus, food xenotropic miRNAs (xenomiRNAs) may become communication tools, after which the environment may control both tumorigenesis and the immune system according to a new central dogma, the RNA Wave. Quantum miRNA immunity in 2020 and quantum miRNA surveillance were discovered in 2022 by miRNA entangling target sorting (METS)/miRNA quantum language and artificial intelligence (MIRAI).

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1_7

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Noncoding Proto-oncogene: Exogeneous miRNA Contamination in Cell Culture

Mutations and recombination of microRNA (miRNA) genes and altered profiles can cause human diseases, as shown in Chap. 5. In turn, non-Mendelian inheritance depends on resident miRNA gene profiles. Thus, Mendelian mutations and recombination in genomic protein-coding genes were influenced by resident miRNAs. Additionally, noncoding genes are reprogrammed and restored by resident miRNA genes in the cytoplasm and in the environment (Fig. 7.1). Tumorigenesis and inflammation are controlled by miRNAs. Both genomic and resident miRNA genes are implicated in cell transformation. Trait changes induced by environmental miRNAs such as food miRNAs are inherited in a non-Mendelian manner. Cancer and inflammation are more complex than the off-the-shelf concepts of carcinogenesis and inflammation. This can also be confirmed by experiments on the bench. Cultured cells are used for in vitro experiments on immunity, cancer, and inflammation. Fetal bovine serum (FBS) was added to the culture medium. FBS contains exogenous miRNA (exomiRNA), and when it is taken up by the cells used in the experiment, it affects cell function (Tosar et al. 2017; Mannerström et al. 2019). As a result, in vitro experimental results would have artifacts and discrepancies with in vivo experimental results (Fig. 7.2) (Alexander et al. 2015). Avian leukosis virus (ALV)-induced B-cell lymphoma is associated with c-myb, c-myc, and bic (Clurman and Hayward 1989; Tam et al. 1997). The former two genes

Fig. 7.1 Cancer in programmed evolution

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Fig. 7.2 Environmental miRNA contamination in vitro

are protein-coding proto-oncogenes, whereas the latter bic gene is a B-cell integration cluster and a noncoding proto-oncogene. The former c-myc gene, which is located at the telomeres of chromosome 8, contains the long noncoding RNA (lncRNA) plasmacytoma translocation 1 (PVT1). Aberrant expression of PVT1 has been found in various cancers, and PVT1 can sponge many miRNAs (Li et al. 2022). The bic region of the genome is also involved in retroviral integration sites in infected cells and tumorigenesis. The bic proto-oncogene is approximately 78% conserved among mice, chickens, and humans, but in humans, BIC is located on chromosome 21q21 and has three exons in 13 kb. BIC is a pri-miRNA, and exon 3 contains the miR-155 maturation gene (Tam 2001). O’Connell et al. (2007) reported that miR-155 may be implicated in inflammation and cancer. Poly (I:C) and interferon beta (IFN-β)-induced miR-155 expression in macrophages in vitro. On the other hand, HIV-1 R5 gp120 suppressed miR-155 expression compared with LPS stimulation in dendritic cells (Masotti et al. 2015). The miR-155 gene has been detected in acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and chronic lymphocytic leukemia (CLL) (Marcucci et al. 2013; Cui et al. 2014; Jurkovicova et al. 2014). These results indicate that miR-155 is a proto-oncogenic miRNA and that its expression is dependent on environmental factors. Therefore, miR-155 expression is influenced by the environment. For miRNA entangling target sorting (METS)/miRNA quantum language and artificial intelligence (MIRAI) analysis of AML and CLL, using circulating miRNA biomarker panels, upregulation of hub miR-155-5p decreased tumor protein P53 inducible nuclear protein

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1 (TP53INP1) (Fujii 2022a). TP53INP1 is a tumor suppressor, so decreasing TP53INP1 induces carcinogenesis (Saadi et al. 2015). Altogether, miR-155 may be an oncogenic factor. mR-155 is one of the microRNA memory package (MMP) data. Friend retrovirus, a murine leukemia virus (MLV), contains no protein protooncogene and induces erythroleukemia. The Friend virus insertion site in the mouse genome at 13q31-q32 is the Fli-3 gene. Fli-3 is the murine homolog of human C13orf25. Fli-3 is transcribed as a noncoding RNA and encodes the miR-17-92 cluster containing six miRNAs: miR-17, miR-20a, miR-18a, miR-19a, miR-19b, and miR-92a (Cui et al. 2007). In these miRNA genes, except for miR-18a, aberrant expression of the miR-17-92 cluster is associated with several cancers (Olive et al. 2009; van Haaften and Agami 2010), Burkitt’s B-cell lymphoma and T-cell lymphomagenesis (Beck-Engeser et al. 2008), and hepatocellular carcinoma due to hepatitis B virus (HBV) (Yan et al. 2015). On the other hand, miR-17/20a also functions as a tumor suppressor (Yu et al. 2010). In METS/MIRAI using vital miRNA biomarkers, miR-92a-1-5p was downregulated in patients with diffuse large B-cell lymphoma (DLBCL) and non-Hodgkin’s lymphoma (NHL), and downregulation of miR-92a-1-5p enhanced forkhead box C2 (FOXC2) transcription factor expression (Fujii 2022a). FOXC2 acts as an angiogenic factor and promotes cell proliferation, and FOXC2 is involved in tumor metastasis (Recouvreux et al. 2022). In contrast, upregulation of miR-92a-1-5p suppressed tumor suppressor, phosphatase and tensin homolog (PTEN) in gastric cancer (Fujii 2019a). miR-92a1-5p is one of the MMP data. Kis2 is another noncoding oncogene. Kis2 is located on chromosome X, and radiation murine leukemia virus (RadLV) commonly integrates into the Kis2 site. Kis2 noncoding RNA is a pri-miRNA for the miR-106-363 cluster (Landais et al. 2007). Mouse tumors overexpress miR-106a, miR-19b-2, miR-92a-2, and miR-20b, making these miRNAs oncogenic. In human T-cell leukemia, 46% of tumor samples were positive for pri-miR-106-363. Fli-3 and Kis2 contain paralogs and orthologs of miR-18, miR-19, miR-20, and miR-92, both of which are retroviral integration sites in human and murine chromosomes. Retroviruses encode miRNAs (Fujii 2009; Kincaid et al. 2012), and homologous sequences for human cell miRNAs have been found in V1, V2, V4, and V5 of the HIV-1 env gene (Holland et al. 2013) (see Chap. 11). We found cellular miRNA sequences that are completely identical to the HIV-1 sequence. This evidence suggests that not only miRNAs in exosomes but also retroelements, including retroviruses, are able to move between cross-kingdoms and across chromosomes, and they can be home. This is because retroelements of humans and plants contain a large number of miRNAs (Li et al. 2011; Barber et al. 2012; Qin et al. 2015). For retroviruses, retroelement miRNAs are derived from the environment. Moving miRNAs in noncoding proto-oncogenes are closely associated with environmentally influenced cancer. Retroelements such as retroviruses can transmit miRNAs throughout the kingdom. In this case, noncoding proto-oncogenes, including miRNAs, are horizontally transported between and within species (Fig. 7.3). In METS/MIRAI using circulating miRNA biomarkers, miR-106a/b-5p

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Fig. 7.3 miRNA transfer and homing

upregulation suppressed PTEN in patients with gastric cancer (Fujii 2019a) and liver cancer (Fujii 2020a). Upregulation of miR-20b-5p suppressed cyclin-dependent kinase inhibitor 1A (CDKN1A) in esophageal cancer (Fujii 2019a). miR-106a/b5p and miR-20b-5p contained in the MMP data. Thus, human BIC, the miR-17-92 cluster, and the miR-106-363 cluster, which are associated with retroviral integration sites, are clinically related to human cancer.

7.2

Burkitt’s Lymphoma Information by miR-155

As mentioned above, miR-155 has been found to be downregulated in certain tumor tissues, but Burkitt’s lymphoma B cells express high levels of miR-155. Burkitt’s lymphoma is caused by Epstein–Barr virus (EBV) infection, as 50–85% of Burkitt’s lymphoma patients in Africa are EBV positive. However, the relationship between EBV and transformation is not clear. Since miR-155 is also associated with the development of B-cell leukemia and even breast cancer, lung and gastric cancers, miR155 is believed to be a cancer-related miRNA. Although 90% of endemic

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Burkitt lymphomas carry EBV and 20% of EBV-associated sporadic Burkitt lymphomas, EBV produces viral miRNAs. In sporadic lymphoma without viral miRNAs, EBV infection itself affects the cellular miRNA profile. EBV BART63p regulates the IL-6 receptor, PTEN, and WT1 transcription factor (WT1) on infected B lymphocytes. EBV miRNA BHRF1 did not directly enhance virusassociated tumors in vivo because the miR-155 seed sequence is not an ortholog of viral miRNA (Wahl et al. 2013). On the other hand, the Kaposi’s sarcomaassociated herpesvirus (KSHV) miR-K11 seed sequence is a homolog of the miR-155 seed (Gottwein et al. 2007), and the Marek’s disease virus (MDV) miR-M4-5p is an ortholog of miR-155 (Zhao et al. 2009). Both miR-K11 and miR-M4-5p may be tumorigenic because miR-155 target mRNA is downregulated. miR-M4-5p indirectly activates the c-Myc oncogene through suppression of the TGF-β signaling pathway (Chi et al. 2014), and the virulent viral strain pRB-1B5 with a deleted miR-M4-5p gene abolished oncogenicity (Zhao et al. 2011). There is no clear evidence that alpha- and gamma-herpesvirus miRNAs directly induce tumors (Vojtechova and Tachezy 2018). Thus, we suggest that environmental viral miRNAs directly induce inflammation and/or that modified cellular miRNAs such as miR-155 may indirectly enhance oncogenesis. In METS/MIRAI analysis, miR-155 upregulation was associated with lymphoma and leukemia, as mentioned above (Fujii 2020a). Therefore, miR-155 is a carcinogenic factor. The hsa-miR-155 gene induces inflammation (Bhattacharyya et al. 2011). This means that prolonged inflammation can lead to cancer. Human T-lymphotropic virus type 1 (HTLV-1) is an oncogenic virus, and acute HTLV-1 infection induces CD4and CD8-dependent inflammatory HTLV-1-associated myelopathy (HAM) (Waclawik et al. 1996). HTLV-1 infection upregulates miR-155 in CD4+ T cells (Pichler et al. 2008). Finally, cell transformation of CD4+ or CD8+ T cells is the first step in HTLV-1 infection, and cell immortalization is independent of interleukin 2 (IL-2). HTLV-1-associated miR-155 is involved in tumorigenesis-related targets, including INPP5D inositol polyphosphate-5-phosphatase D (SHIP) and CCAAT enhancer binding protein beta (CEBPB), TP53INP1, forkhead box O3 (FOXO3), ras homolog family member A (RHOA), MSH1/2/6, suppressor of cytokine signaling 1 (SOCS1), meis homeobox 1 (Meis1), c-MAF BZIP transcription factor (c-MAF), activation-induced cytidine deaminase (AID), IL-1, inhibitor of nuclear factor kappa B kinase subunit epsilon (IKKE), telomere elongation protein 1 (ETS-1), and BTB domain and CNC homolog 1 (BACH1). The proto-oncogene B-cell integration cluster (BIC) is activated by retroviral insertion in human B-cell lymphomas, whereas miR-155 is located in the region of BIC, and the miR-155 gene is responsible for activation of the BIC oncogene. Since miR-155 regulates oncogenic processes, cell proliferation signals, resistance to cell death, immortality, angiogenesis, and metastasis, miR-155 is called an “oncomir” (Wang et al. 2012). For the first time, oncomir was discovered in the region of the plasmacytoma variant translocation gene (PVT1) in a multiple myeloma case, and murine leukemia retrovirus (MLV) could integrate into the murine Pvt1 region to induce T-cell lymphoma. Therefore, miR-155 is related to oncoviruses, and miR-155 seed sequences are similar to simian spumavirus miRNA S4 (Kincaid et al. 2014) and

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KSHV miRNA K12-11 seed (Skalsky et al. 2007). It is suggested that miR-155 may be transmitted from orthologous cellular genes to retroviruses and vice versa (Fig. 7.3). This may be similar to the relationship between oncoprotein genes and retroviruses. However, today, miRNAs work in the panel, so “oncomirs” are questionable, but the miR-155 hub along with branch miRNAs is a carcinogenic factor. It is very different from protein. The pathway of BIC/miR-155 oncogenic function is dependent on interferon regulatory factor 4 (IRF4) in macrophages (Wang et al. 2011). IRF4 and BIC are highly expressed in tumor cells and EBV-transformed B lymphocytes, whereas miR-155 is critical for the maturation of dendritic cells and dendritic cell-derived macrophages (Napuri et al. 2013). Activation of macrophages by Toll-like receptors (TLRs) regulates miRNA expression. On the other hand, miR-155 selectively increased TLR3 and TLR4 expressions (Swaminathan et al. 2012). This evidence suggests that miR-155 may inhibit HIV-1 infection of macrophages. However, in patients, miR-155 abundance differed among HIV-1 elite long-term nonprogressors, naive patients, and multiple-exposure uninfected patients (Bignami et al. 2014). Therefore, the anti-HIV effects of miR-155 are actually complex in naive viral HIV-1 infection. We developed quantum miRNA immunity as a defense program against HTLV-1 infection (Fujii 2022a). It was discovered using METS/MIRAI, but miR-155 was not involved (Fujii 2020a, 2022a, 2023). In addition, regarding adult T-cell leukemia/lymphoma (ATLL) by HTLV-1, it was found that HTLV-1 viral miRNA, htlv1-miR-3, targets telomerase reverse transcriptase (TERT) and promotes tumorigenesis of T lymphocytes (Fujii 2022a). In HIV-1 infection, upregulation of miR-16-5p suppresses BCL2 apoptosis regulator (BCL2), which inhibits infection by cell cycle arrest and apoptosis of T cells. This has been found to be the case (Fujii 2020a). miR-155 is a potential diagnostic and prognostic marker because it is associated with oncoviruses, tumorigenesis, invasion, and angiogenesis (Higgs and Slack 2013). Cancer biomarkers for miR-155 can be detected in esophageal cancer, lymphoma, colorectal cancer, leukemia, breast cancer, and lung cancer (He et al. 2013). However, the antitumor effects of miR-155 are controversial, based on one study on the antioncogenic and proimmunological effects of miR-155 (Huffaker et al. 2012). Because miR-155 knockout (KO) in mice results in loss of the germinal center of hematopoietic lymphocyte development (Thai et al. 2007), the oncogenic activity of miR-155 has been linked to the integral balance between its direct oncogenic role and indirect immune surveillance control. For instance, BRCA1 is a tumor suppressor and has been related to breast cancer. BRCA1 can repress miR-155 expression through HDAC2 binding, so high levels of oncogenic miR-155 may induce carcinogenesis in breast cells when BRCA1 is mutated (Chang et al. 2011). Moreover, synergy between miR-155 and other miRNAs likely determines its oncogenic activity (see Chap. 9). Although 3′UTR mutations of BRCA1 or 2 mRNA are involved in breast cancer, as mentioned in the PolymiRTS database, miR-9-5p, miR-4659-3p, miR-4484, and miR-548 binding sites in the 3′UTR of BRCA1 are altered, which explains the increased breast cancer risk (Sánchez-Chaparro et al.

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Fig. 7.4 Cell-to-cell recognition via miRNAs between the immune system

2020). Thus, the synergy between miRNAs may provide insight into breast cancer initiation in miRNA-related genome wide-associated studies (miGWAS). In this synergistic case, diet containing miRNAs as a miRNA pool also plays an important role together with endogenous miR-155 to induce oncogenesis in normal cells. In fact, restoration of miR-155 represses the proliferation of non-small cell lung carcinoma (NSCLC) cells with EGF receptor mutations (Lynch et al. 2004). Milk can transmit miR-155 gene information, which corresponds to the developing immune system, from mother to child by exosomes and is involved in FOXP3 expression, IL-4 signaling, immunoglobulin class switching to IgE and FcɛRI expression (Melnik et al. 2014). Furthermore, miR-155 in endogenous exosomes is released from dendritic cells and subsequently incorporated into recipient cells (Alexander et al. 2015). After incorporation, exogenous miR-155 regulates mRNA expression and controls inflammation in vitro and in vivo (Fig. 7.4). For autoimmune diseases, miR-155 has been implicated in rheumatoid arthritis and multiple sclerosis, and miR-155 plays an important role in systemic lupus erythematosus and inflammatory bowel disease (Xu et al. 2022). miR-155 is part of a potential biomarker for autoimmune diseases (Qu et al. 2014). Thus, circulating miRNAs are the environment for cells, and since miRNAs are genetic information, we suggest that the control of autoimmune diseases derives from a pool of controlling miRNA quantum codes. Signal transduction through cytokines, antibodies, and T-cell receptors (TcRs) is regulated by miRNAs (Fujii 2023). For example, a macrophage’s MHC plus antigen can bind to the T-cell receptor (TcR) on a T cell. In signaling models, binding is specific, but subsequent signal transduction is partially specific, and pathways are always devoid of genetic feedback mechanisms. In the RNA Wave 2000, miRNAs within exosomes always crosstalk between antigen-presenting cells and T cells following binding of T-cell recognition. miRNAs are genes. Therefore,

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miRNA genetic information is specific. This RNA Wave information network is ubiquitous in the immune system (Vignard et al. 2020). Watson and Crick’s central dogma is being overwritten by the RNA Wave’s central dogma. miR-155 partially controls both tumorigenesis and the immune system, and diseases are regulated by miRNA synergy (Fujii 2023). Similarly, the miRNA pool in the cytoplasm and nucleus and the circulatory system and diet may play important roles in miRNA surveillance that suppresses oncogenesis and inflammation. Quantum miRNA surveillance, a miRNA quantum code program, was subsequently discovered and proven to be an antitumor mechanism in humans (Fujii 2023). Additionally, some environmental and viral miRNAs are orthologs of endogenous miRNAs. This implies that environmental and viral miRNAs are involved in endogenous miRNAs. These miRNAs circulate in the bloodstream. Since viral miRNAs from viral infection are one of the environmental factors as non-Mendelian genes, it is possible that environmental miRNA information from diet influences carcinogenesis and inflammation and is inherited from mother to child. We found that a time-dependent decrease in severe respiratory syndrome human coronavirus 2 (SARS-CoV-2) infection is observed during an increase in airborne plant pollen levels, such as rice (Oryza sativa japonica), in Japan. Furthermore, coronavirus disease 2019 (COVID-19) infection and its death in Western countries and the USA are inconsistent with the amount of rice consumed. In addition, we found that rice osa-MIR2097-5p can target the genome of SARSCoV-2 (Fujii 2020b, 2023). In conclusion, environmental and endogenous miRNAs are related to inflammation (Fig. 7.5).

7.3

Anti-inflammation/oncogenesis by miR-34?

Although miR-34 may be a tumor suppressor gene (Heinemann et al. 2011), miR-34 inhibits inflammation by sirtuin 1 (SIRT1) inhibition through activation of PPAR-α and liver X receptor (LXR) and by inhibition of SREBP and nuclear factor-kB (NF-kB) (see Chap. 3) (Cantó and Auwerx 2012). In METS/MIRAI, no biomarker panel pattern involving miR-34 was observed in human cancers (Fujii 2023). Furthermore, miR-34 silencing in obese mice repressed NAD+ levels, which alleviated hepatic steatosis and inflammation (Choi et al. 2013). Natural killer (NK) cells are an innate immune surveillance system, and their receptor NKG2D is involved in the detection of abnormal self, such as malignantly transformed cells. NKG2D ligand (NKG2DL) is classified as MHC-I and belongs to the MHC class I chainrelated gene A and B (MICA and MICB) and UL16 binding protein 1–6 (ULBP1–6) families. NKG2DL is expressed on the cell surface of tumors, and NK cells and cytotoxic T cells recognize NKG2DL, but tumor cells shed NKG2DL and escape immune surveillance of NK cells by overexpression of NKG2DL. miR-34a and miR-34c target NKG2DL ULBP mRNA and may act as suppressors of cancer growth via p53 (Heinemann et al. 2011). Blood miR-34 and miR-122 are upregulated in human hepatic steatosis, fibrosis, and inflammation (Hermeking

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Fig. 7.5 Non-Mendelian commune between cancer and inflammation

2010; Yamada et al. 2013; Miyaaki et al. 2014). Therefore, miR-34 is an antiinflammatory miRNA, suggesting that inflammation is deeply implicated in oncogenesis and that cancer and inflammation may be one phenomenon. Kasinski and Slack (2012) showed that although treatment-resistant KrasLSL-G12D lung cancer model mice had elevated levels of miR-21 and miR-155 and induced lung tumorigenesis and severe lung inflammation, in vivo treatment with miR-34a mimics prevented tumorigenesis and inflammation. Human and mouse miRNA quantum codes are different from each other (Yoshikawa et al. 2015; Fujii 2023). Therefore, the results of lung tumorigenesis in mice may be subject to mouse bias. As described in Chap. 3, the miR-34 gene positively regulates tumor protein P53 (TP53) through SIRT1. p53 loss and mutation induce genetic changes in cancer. Cell cycling arrest, DNA damage, and apoptosis are triggered. As TP53 plays a key role in this process, miR-34 also regulates the apoptotic pathway in DNA damaged cells. Reduced TP53 expression has been implicated in tumorigenesis, and miR-34 level lowering occurs prior to TP53 mutation or loss. miR-34 can target cyclin-dependent kinase 4 (CDK4), cyclin E2 (CCNE2), cyclin D1 (CCND1), and cyclin-dependent kinase 6 (CDK6) in the cell cycle and BCL2, SIRT1, YY1 transcription factor (YY1), and baculoviral IAP repeat containing 5 (BIRC5) in apoptosis. The tumor suppressor gene loss genotype is one of Mendelian inheritance. Its inheritance, therefore, depends on allelic heterogeneity, whereas changes in miR-34 in the circulating miRNA profile are non-Mendelian and therefore allele-independent. Mendel’s laws were built on the protein-coding genes of approximately 2% but

7.4

miR-21 and NOS

139

until today the environmental factor was dark matter. As explained previously (Fujii 2009, 2023), miRNAs themselves are environmental factors that act as mobile genetic elements that influence apoptosis and cell cycle arrest. Knudson’s two-hit hypothesis of retinoblastoma (RB) with a mutation in the RB tumor suppressor gene as allelic heterogeneity has been confirmed in carcinogenesis, but not only inheritable alleles but also inheritable miRNA are also of great importance in cancer characterization. For instance, some protein-coding genes lose function in one allele, while genes retained in the second allele appear to be fully functional. Alternatively, tumors formed although both protein-coding genes were functional. The single nucleotide polymorphism (SNP) rs78378222 is located in the p53 3′UTR noncoding region and is associated with multiple cancers (Zhang et al. 2021). In this case, both p53 genes are normal, but mutations occurring in the germ line increase the risk of basal cell carcinoma of the skin, prostate cancer, brain cancer, colorectal adenoma, and esophageal squamous cell carcinoma. This is a unique feature of Li-Fraumeni syndrome, which lacks the p53 allele. In the latter case, loss of the p53 activator miR-34a was observed in neuroblastoma in 1p36 (Welch et al. 2007), and low miR-34a expression was observed in approximately 73% of pancreatic cancers (Chang et al. 2007) and over 90% of non-small cell lung cancer (NSCLC) (Boomer et al. 2007). Changes in miRNA gene expression are also “mutations” as well as RNA SNPs, presumably because the environmental influences changes in miR-34 expression. Given RNA genes according to RNA Wave, these mutations through environmental changes in mobile miRNAs are inherited from mother to child. Thus, Dr. Knudson’s theory of RB oncogenesis, as well as Drs. Fire and Mello’s RNA interference, Dr. Ambros’s antisense theory, Dr. Cullen’s theory of herpes DNA virus miRNAs, Darwinism-based protein gene phylogenic tree, or DNA protein encoding is different from RNA Wave by programmed evolution (Fujii 2013, 2014). The RNA Wave fully involves Mendelian and Darwin’s law (Fig. 7.6). This is a new era of evolutionary concepts.

7.4

miR-21 and NOS

miR-21 is important for immunoregulation, tumor initiation, and cancer progression. Other miRNAs, such as miR-125b, miR-155, miR-196, and miR-210, are also implicated in inflammation and cancer. In METS/MIRAI, miR-21-5p upregulation suppressed PTEN in colorectal cancer (CRC) (Fujii 2019b). miR-21-5p upregulation inhibited antiapoptotic BCL2 in hepatitis B virus (HBV) infection, so miR-21-5p is a key factor in liver injury and induces inflammation (Fujii 2020a). Therefore, the relationship between miRNAs, inflammation, and cancer consists of unique pathways that drive tumorigenesis. Epidemiological studies support that 25% of all cancers may result from inflammatory conditions (Hussain and Harris 2007; Mantovani et al. 2008). Reactive oxygen and nitrogen species, for example, OH, NO-, (O), and OONO-, not only cause inflammation but also damage due to tumor suppressor protein mutations such as DNA damage. However, it is not clear whether

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Fig. 7.6 RNA wave 2000 in evolution

these active free radicals can affect DNA in the nucleus (Lettieri-Barbato et al. 2022). Inflammation and NO stresses modulated lymphoma development in p53 KO mice, and inflammation also altered miRNA profiles such as miR-21 and miR-34 in p53 KO mice or NO synthase 2 (NOS2) KO mice, suggesting that inflammation and nitrosative stress may modulate cancer through the aberrant expression of specific miRNAs without DNA damage (Mathé et al. 2012). On the other hand, NOS2 KO mice in a KRASG12D-driven mouse lung cancer model showed reduced miR-21 expression and decreased lung tumor growth (Okayama et al. 2013). Furthermore, a Toll-like receptor (TLR), human TLR8, can bind to tumor-secreted miR-21 and miR-21 genes as agonists, leading to TLR-mediated NF-κB activation in inflammatory immune cells, which induces metastatic cancer from lung cancer stem cells (Fabbri et al. 2012). This is because miR-21 can suppress anticancer Rb1 (Shen et al. 2014). Furthermore, since miR-21-containing exosomes regulate atherosclerotic inflammation, miR-21 plays an important role in inflammation along with the let-7 family, miR-29, miR-17/92 family, miR-126, miR-133, miR-146, and miR-155 (Hulsmans and Holvoet 2013). These results demonstrate that miR-21 orthologs or chemical isomers have the same effects as exosomal miRNAs and that environmental factors influence the miRNA profile, indicating that miRNAs and environmental factors crosstalk with each other. This suggests that humans become diseased by programming by miRNA quantum codes (Fujii 2023). Therefore, the common denominator is quantum energy. The pathogenesis of human diseases programmed by MMPs is a central dogma equivalent to programmed evolution (Fujii 2014). Therefore, environmental factors, including circulating miRNAs, represent a Noah’s Ark for human disease induction (see Chap. 4).

7.5

Is the miR-17-92 Family Linked to Carcinogenesis? Brain Cancer Etiology

141

Designer baby creation based on genomic modification of DNA is wrong. Goodquality genetic enhancements do not make much sense. A person’s personality is determined by programmed environmental miRNAs, which depend on Noah’s Ark. Therefore, it can be said that humans, designed by environmental miRNAs on Earth and in space, are already many designer babies. In other words, even if the miRNA quantum code is known, the design of babies is absolutely impossible with human technology. Conversely, no one can escape miRNA information disease by MMP in the environment. However, elucidating the miRNA quantum code may have therapeutic and human health applications (Fujii 2023).

7.5

Is the miR-17-92 Family Linked to Carcinogenesis? Brain Cancer Etiology

The MYC oncogene promotes miR-17-92 cluster (miR-18a/b, miR-20a/b, miR-93, and miR-106a/b) expression at 13q31.3, 91,350,605–91,351,391. miR-17-5p and miR-20 in the cluster family can target the E2F mRNA 3′UTR and suppress the expression of E2F (Fujii 2009). The MYC-dependent transcriptional activation of E2F is directed from G1 to S and drives inducible transcription. miR-18a in the miR-17-92 cluster is highly expressed in prostate cancer, and miR-18a KO mice have reduced tumor growth (Hsu et al. 2014). Expression of the miR-17-92 cluster contributed to retinoblastoma (RB) oncogenesis and RB cell proliferation and invasion (Kandalam et al. 2012). miR-17-92 leads to inhibition of apoptosis and it is carcinogenic. In addition, miR-17-5p can feedback MYC transcriptional activity on E2F, and cyclin D1 may be a target of miR-17-5p. After cyclin D1 expression is repressed, cells arrest in G1 and undergo apoptosis (Deshpande et al. 2009). High levels of miR-17/20 in the miR-17-92 cluster serve as tumor suppressors, leading to recognition of natural killer (NK) cells (van Haaften and Agami 2010; Jiang et al. 2014). The miR-17-92 cluster inhibits cancer proliferation and invasion in breast, liver, and gastrointestinal cancers (Yu et al. 2010; Lin et al. 2013; Gits et al. 2013). Furthermore, the miR-17-92 cluster was deleted in 16.5% of ovarian cancers, 21.9% of breast cancers, and 20.0% of melanoma (Lin et al. 1999; Shao et al. 2002). This suggests that the miR-17-92 family may be bifunctional and that its function depends on synergy with combinations of other miRNAs and may be essential for miRNA gene information. For METS/MIRAI, miR-18a-5p upregulation suppressed PTEN, and miR-20b-5p upregulation reduced CDKN1A in esophageal cancer (Fujii 2019a). Upregulation of miR-106b-5p blocked PTEN in gastric and liver cancer (Fujii 2019a, 2020a). Furthermore, miR-93-5p and miR-92-1-5p upregulation inhibited PTEN and FOXC2 in AML and diffuse large B-cell lymphoma (BDBCL), respectively (Fujii 2022a). Therefore, the miR-17-92 cluster is a carcinogenic factor (see Chap. 8). miR-17-92 promotes Th1 cell survival and proliferation and upregulates IFN-γ, which is decreased in glioblastoma (Xiao et al. 2008; Sasaki et al. 2010), and

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Fig. 7.7 Glioma METS/MIRAI data from cerebrospinal fluid (CSF)

miR-17-92-expressing and EGFRvIII antigen-specific T cells improve the effects of adoptive transfer therapy against glioblastoma (Ohno et al. 2013). In this case, miR-17-92 overexpressing T cells released exosomal miR-17-92, which affected adjacent cells that miR-17-92 took up. This suggests that environmental miRNAs are associated with glioblastoma proliferation in situ. Therefore, not only circulating but also in situ miRNA information surveillance may be relevant for carcinogenesis. In 2022, we discovered the “quantum miRNA surveillance” program in cervical cancer using METS/MIRAI (Fujii 2022b), and the miR-17-92 cluster was associated with papillary thyroid carcinoma (PTC) (Fujii 2023). The tumor suppressor PTEN was inhibited by upregulation of the miR-17-92 cluster hub, and transforming growth factor beta receptor 2 (TGFBR2), a tumor suppressor, was also suppressed by upregulation of miR-17-5p in the miR-17-92 cluster in PTC. Therefore, miR-1792 is carcinogenic in PTC. Simultaneously, upregulation of the miR-17-92 cluster reduced hypoxia-inducible factor 1 subunit alpha (HIF1A). As HIF1A downregulation induces thyroid cancer cell apoptosis (Ding et al. 2016), the miR-17-92 cluster is related to quantum miRNA surveillance in PTC. The miR-17-92 cluster has pleiotropic functions. Although the data volume was not sufficient, the etiology of human glioma was simulated with cerebrospinal fluid (CSF) data (Kopkova et al. 2018) using METS/ MIRAI (Fig. 7.7). miR-21-5p hub upregulation suppressed the PTEN tumor suppressor but also reduced antiapoptotic BLC2. Therefore, miR-21-5p is biphasic.

7.6

Unknown Mdm2 Control

143

Furthermore, cyclin D1 (CCND1) was blocked by upregulation of the let-7b-5p hub, and HMGA2 metastatic factor was inhibited by let-7b-5p hub upregulation. Altogether, the cell cycle was arrested, tumor metastasis was decreased, and apoptosis was induced. These are “quantum miRNA surveillance.” In addition, it was easily found that the miR-17-92 cluster in the branches is associated with both carcinogenesis and “quantum miRNA surveillance” (Fig. 7.7). Altogether, cancer pathogenesis appears to cause a balance between the oncogenic miRNA quantum program and miRNA quantum surveillance. Therefore, it is not an event caused by the targeting of miRNA alone.

7.6

Unknown Mdm2 Control

In 2014, Reed et al. (2014) discussed the current poor understanding of normal physiological or pathological signals that regulate the expression of both NIAM and Mdm2 and the factors that require their function. However, it is known that Mdm2 is a target of miRNAs. Mdm2 may be an oncogene because Mdm2 is amplified in human cancers. It is expressed in many cancer types, while other oncogenes are highly overexpressed in transforming cells. MDM2 proto-oncogene (Mdm2) and Mdm4 are regulators of the p53 tumor suppressor. Mdm has a structural p53-binding domain and can physically block the p53 activation site, inhibiting its transactivation ability. Furthermore, Mdm2 can also be an E3 ubiquitin ligase (Honda et al. 1997) and degrade p53 within the proteasome (Hu et al. 2012). Ultraviolet (UV) or ionizing radiation (IR) stress activates ATM or ATR protein kinase, which phosphorylates Mdm2 and inactivates Mdm2. Therefore, UV and IR stresses can induce p53. However, it is not known how protein kinases are activated by the environmental energy of electromagnetic waves. miRNAs can be altered in expression by environmental stress. The miR-1942/192 cluster at 11q13.1 and the miR-215/194-1 cluster at 1q41.1 have the same seed sequence, and the miR-192, miR-194, and miR-215 genes are linked to the Mdm2 mRNA 3′UTR (Pichiorri et al. 2010). Three miRNAs function as tumor suppressors in the downregulation of metastatic tumors (Khella et al. 2013). miR-143, miR-145, and miR-605 within the same cluster target Mdm2 (Zhang et al. 2013; Xiao et al. 2011). Furthermore, Mdm2 direct silencers, miR-25, miR-35, miR-17-3p, and miR-193, have also been reported (Suh et al. 2012; Li and Yang 2012; Li et al. 2013). miR-18b targeted the Mdm2 mRNA 3′UTR in melanoma and suppressed melanoma migration and invasion in vitro and in vivo (Dar et al. 2013). In laryngeal carcinoma, miR-30 enhanced p53 antitumor activity in vivo (Li and Wang 2014), which was related to decreased Mdm2 expression. Although miR-221 is overexpressed in hepatocellular carcinoma (HCC) and miR-221 directly targets Mdm2, tumor cell growth is not inhibited by increased p53 through Mdm2, which decreases miR-221 gene function (Fornari et al. 2013). These data strongly suggested that miRNA function depends on miRNA–miRNA crosstalk. Furthermore, miR-339-5p is suppressed in several different cancers (Zhou et al. 2013), and

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low miR-339-5p expression is associated with cancer metastasis and poor prognosis (Li et al. 2014). Because miR-399-5p targets Mdm2 (Jansson et al. 2015), its anticancer effect is the same as that of other antitumor miRNAs. However, miR-339-5p showed p53-independent effects on both tumor migration/invasion and tumor growth (Zhang et al. 2014). miR-509, miR-660, and miR-661 also target the Mdm2 mRNA 3′UTR (Ren et al. 2014; Hoffman et al. 2014a; Fortunato et al. 2014), and miR-34a targets Mdm4 exon 11 (Mandke et al. 2012). Subsequently, the numbers of Mdm2 and Mdm4 may increase with further investigation. Appropriate miRNA/miRNA combinations may therefore contribute to inflammation and carcinogenesis influenced by environmental stress and environmental miRNAs such as dietary miRNAs. Environmental stresses may alter Mdm2 expression beyond miRNA expression. However, miRNA genes can now be calculated as scalars (Yoshikawa et al. 2015; Osone et al. 2015), so environmental energy can transform one miRNA vector into another. This means that Mdm2 expression is modulated by the miRNA quantum code (Fujii 2010, 2023), which is programmed evolution. Thus, it is clear that the kinase theory of oncogenesis and inflammation through signaling pathways is questionable. Quantum information in miRNAs is sufficient as the initial event. The Mdm2 mRNA 3′UTR has three putative miR-661 seed targeting sites, and these target sequences are contained in the Alu retrotransposon (Hoffman et al. 2014b). This suggests that the miR-194-2-miR-194 cluster, the miR-215-194-1 cluster, miR-25, miR-35, miR-17-3p, miR-193, etc., could target Alu retrotransposons. This means that these miRNA genes control Alu retroposition. Hoffman et al. (2014a, b) described that retrotransposons such as Alu contain multiple target occurrences of miRNAs that were already predicted for HIV-1 retrotransposons targeted by hiv1-miR-N367 (Fujii and Saksena 2008; Fujii 2010). Since retrotransposons are RNA genes of environmental origin (Kincaid et al. 2012; Qin et al. 2015), the p53-related Mdm2/4 3′UTRs and introns are also derived from retrotransposons and p53-associated inflammation and tumors; thus, evolution may also occur from the space of the miRNA information world as some acquired feature (see Chap. 4). The diversity and multifunction of Mdm targeting may depend on miRNA crosstalk, and evolutionary determinations by the miRNA quantum code provide evidence for programmed evolution (Fig. 7.8). miRNAs are programmed by miRNA/miRNA quantum codes to regulate communication with proteins. Programmed evolution is directed by miRNA/miRNA qubits in MMP. Therefore, MMPs have been implicated in the pathogenesis of human cancer and inflammation. Unraveling miRNA/miRNA language is the same as the traveling salesman problem algorithm, so we need a quantum processor.

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Fig. 7.8 Multiple miRNA/miRNA crosstalk in RNA wave as the miRNA quantum code

References Alexander M, Hu R, Runtsch MC, Kagele DA, Mosbruger TL et al (2015) Exosome-delivered microRNAs modulate the inflammatory response to endotoxin. Nat Commun 6:7321. https:// doi.org/10.1038/ncomms8321 Barber WT, Zhang W, Win H, Varala KK, Dorweiler JE et al (2012) Repeat associated small RNAs vary among parents and following hybridization in maize. Proc Natl Acad Sci U S A 109: 10444–10449. https://doi.org/10.1073/pnas.1202073109 Beck-Engeser GB, Lum AM, Huppi K, Caplen NJ, Wang BB et al (2008) Pvt 1-encoded microRNAs in oncogenesis. Retrovirology 5:4. https://doi.org/10.1186/1742-4690-5-4 Bhattacharyya S, Balakathiresan NS, Dalgard C, Gutti U, Armistead D et al (2011) Elevated miR-155 promotes inflammation in cystic fibrosis by driving hyperexpression of interleukin-8. J Biol Chem 286:11604–11615. https://doi.org/10.1074/jbc.M110.198390 Bignami F, Pilotti E, Bertoncelli L, Ronzi P, Gulli M et al (2014) Stable changes in CD4+ T lymphocyte miRNA expression after exposure to HIV-1. Blood 119:6259–6267. https://doi.org/ 10.1128/blood-2011-09-379503 Boomer GT, Gerin I, Feng Y, Kaczorowski AJ, Kuick R et al (2007) P53-mediated activation of miRNA34 candidate tumor-suppressor genes. Curr Biol 17:1298–1307. https://doi.org/10.1016/ j.cub.2007.06.068 Cantó C, Auwerx J (2012) Targeting sirtuin 1 to improve metabolism: all you need is NAD(+)? Pharmacol Rev 64:166–187. https://doi.org/10.1124/pr.110.003905 Chang TC, Wentzel EA, Kent OA, Ramachandran K, Mullendore M et al (2007) Transactivation of miR-34a by p53 broadly influences gene expression and promotes apoptosis. Mol Cell 26:745– 752. https://doi.org/10.1016/j.molcel.2007.05.010 Chang S, Wang RH, Akagi K, Kim KA, Martin BK et al (2011) Tumor suppressor BRCA1 epigenetically controls oncogenic microRNA-155. Nat Med 17:1275–1282. https://doi.org/10. 1038/nm.2459 Chi JQ, Teng M, Yu ZH, Xu H, Su JW et al (2014) Marek’s disease virus-encoded analog of microRNA-155 activates the oncogene c-Myc by targeting LTBP1 and suppressing the TGF-β signaling pathway. Virology 476:72–84. https://doi.org/10.1016/j.virol.2014.11.027

146

7

Communication in miRNAs Between Inflammation and Cancer

Choi SE, Fu T, Seok S, Kim DH, Yu E et al (2013) Aberrantly elevated microRNA-34a in obesity attenuates hepatic responses to FGF19 by targeting a membrane coreceptor β-Klotho. Proc Natl Acad Sci U S A 109:16137–16142. https://doi.org/10.1073/pnas.1205951109 Clurman BE, Hayward WS (1989) Multiple proto-oncogene activations in avian leucosis virusinduced lymphomas: evidence for stage-specific events. Mol Cell Biol 9:2657–2664 Cui JW, Li YJ, Sarkar A, Brown J, Tan YH et al (2007) Retroviral insertional activation of the Fli-3 locus in erythroleukemias encoding a cluster of microRNAs that convert Epo-induced differentiation to proliferation. Blood 10:2631–2640. https://doi.org/10.1182/blood-2006-10-053850 Cui B, Chen I, Zhang S, Mraz M, Fecteau JF et al (2014) MicroRNA-155 influences B-cell receptor signaling and associates with aggressive disease in chronic lymphocytic leukemia. Blood 124: 546–554. https://doi.org/10.1182/blood-2014-03-559690 Dar AA, Majid S, Rittsteuer C, de Semir D, Bezrookove V et al (2013) The role of miR-18b in MDM2-p53 pathway signaling and melanoma progression. J Nat Cancer Inst 105:433–442. https://doi.org/10.1093/jnci/djt003 Deshpande A, Pastore A, Deshpande AJ, Zimmermann Y, Hutter G et al (2009) 3’UTR mediated regulation of the cyclin D1 proto-oncogene. Cell Cycle 8:3592–3600. https://doi.org/10.4161/ cc.8.21.9993 Ding ZY, Huang YJ, Tang JD, Li G, Jiang PQ et al (2016) Silencing of hypoxia-inducible factor-1α promotes thyroid cancer cell apoptosis and inhibits invasion by downregulating WWP2, WWp9, VEGF and VEGFR2. Exp Ther Med 12:3735–3741. https://doi.org/10.3892/etm. 2016.3826 Fabbri M, Paone A, Calore F, Galli R, Gaudio E et al (2012) MicroRNAs bind to toll-like receptors to induce prometastatic inflammatory response. Proc Natl Acad Sci U S A 109:E2110–E2116. https://doi.org/10.1073/pans.1209414109 Fornari F, Milazzo M, Galassi M, Callegari E, Veronese A et al (2013) P53/mdm2 feedback loop sustains miR-221 expression and dictates the response to anticancer treatments in hepatocellular carcinoma. Mol Cancer Res 12:203–216. https://doi.org/10.1158/1541-7786.MCR-13-0312-T Fortunato O, Boeri M, Moro M, Verri C, Mensah M et al (2014) Mir-660 is downregulated in lung cancer patients and its replacement inhibits lung tumorigenesis by targeting MDM2-p53 interaction. Cell Death Dis 5:e1564. https://doi.org/10.1038/cddis.2014.507 Fujii YR (2009) Oncoviruses and pathogenic microRNAs in humans. Open Virol J 3:37–51. https:// doi.org/10.2174/1874357900903010037 Fujii YR (2010) RNA genes: retroelements and virally retroposable microRNAs in human embryonic stem cells. Open Virol J 4:63–75. https://doi.org/10.2174/1874357901004010063 Fujii YR (2013) The RNA gene information: retroelement-microRNA entangling as the quantum code. Methods Mol Biol 936:47–67. https://doi.org/10.1007/798-1-62703-083-0_4 Fujii YR (2014) RNA information gene diseases: nano-RNA-based medical devices with corporate chemotherapy and gene therapy. In: Wang B (ed) RNA nanotechnology. Pan Stanford Publishing Pte, Ltd, pp 385–434 Fujii YR (2019a) Quantum microRNA network analysis in gastric and esophageal cancers: xenotropic plant microRNAs cure from cancerous paradox via Helicobacter pylori infection. Gastroenterol Hepatol Endosc 4:1–18. https://doi.org/10.15761/GHE.1000187 Fujii YR (2019b) Cancer simulation from stage minus one by quantum microRNA language: lung, colorectal and pancreatic cancers. Med One 4:e190023. https://doi.org/10.20900/mo.20190023 Fujii YR (2020a) The quantum microRNA immunity in human virus-associated diseases: virtual reality of HBV, HCV and HIV-1 infection, and hepatocellular carcinogenesis with AI machine learning. Arch Clin Biomed Res 4:089–129. https://doi.org/10.26502/acbr.50170092 Fujii YR (2020b) In silico study by quantum microRNA language for the development of antiCOVID-19 agents: COVID-19 is prevented by rice MIR2097-5p through suppression of SARSCov-2 viral microRNAs plus HIPK2 target proteins. Virol Immunol J 4:000256. https://doi.org/ 10.23880/vij.16000256 Fujii YR (2022a) In: Rezaei N (ed) Quantum microRNA immunity and hematopoietic malignancies: etiological analysis of leukemia and lymphoma by quantum microRNA language with

References

147

artificial intelligence (MIRAI). Interdisciplinary Cancer Research Springer Nature, New York, pp 1–17. https://doi.org/10.1007/16833_2022_11 Fujii YR (2022b) In: Rezaei N (ed) Quantum microRNA surveillance against cancer: parallel dimensional analysis of integrated networks by quantum microRNA language in female genital neoplasms. Interdisciplinary Cancer Research Springer Nature, New York, pp 1–24. https://doi. org/10.1007/16833_2022_4 Fujii YR (2023) The microRNA quantum code book. Springer Nature, Singapore Fujii YR, Saksena NK (2008) Viral infection-related microRNAs in viral and host genomic evolution. In: Morris KV (ed) RNA and the regulation of gene expression. Horizon Scientific Press, London, pp 91–107 Gits CM, van Kuijk PF, Jonkers MB, Boersma AW, van ljcken WF et al (2013) MiR-17-92 and miR-221/222 cluster members target KIT and ETV1 in human gastrointestinal stromal tumors. Br J Cancer 109:1625–1635. https://doi.org/10.1038/bjc.2013.483 Gottwein E, Mukherjee N, Sachse C, Frenzel C, Majoros WH et al (2007) Viral microRNA functions as an ortholog of cellular miR-155. Nature 450:1096–1099. https://doi.org/10.1038/ nature05992 He J, Zhang F, Wu Y, Zhang W, Zhu X et al (2013) Prognostic role of microRNA-155 in various carcinomas: results from a meta-analysis. Dis Markers 34:379–386. https://doi.org/10.3233/ DMA-130984 Heinemann A, Zhao F, Pechlivanis S, Eberle J, Steinle A et al (2011) Tumor suppressive microRNAs miR-34a/c control cancer cell expression of ULBP2, a stress-induced ligand of the natural killer cell receptor NKG2D. Cancer Res 72:460–471. https://doi.org/10.1158/ 0008-5472.CAN-11-1977 Hermeking H (2010) The miR-34 family in cancer and apoptosis. Cell Death Differ 17:193–199. https://doi.org/10.1038/cdd.2009.56 Higgs G, Slack F (2013) The multiple roles of microRNA-155 in oncogenesis. J Clin Bioinf 3:17. https://doi.org/10.1186/2043-9113-3-17 Hoffman Y, Bublik DR, Pilpel Y, Oren M (2014a) miR-661 downregulates both Mdm2 and Mdm4 to activate p53. Cell Death Disease 21:302–309. https://doi.org/10.1038/cdd.2013.146 Hoffman Y, Pilpel Y, Oren M (2014b) microRNAs and Alu elements in the p53-Mdm2-Mdm4 regulatory network. J Mol Cell Biol 6:192–197. https://doi.org/10.1093/jmcb/mju020 Holland B, Wong J, Li M, Rasheed S (2013) Identification of human microRNA-like sequences embedded within the protein-encoding genes of the human immunodeficiency virus. PLoS One 8:e58586. https://doi.org/10.1371/journal.pone.0058586 Honda R, Tanaka H, Yasuda H (1997) Oncoprotein MDM2 is a ubiquitin ligase E3 for tumor suppressor p53. FEBS lett 420:25–27. https://doi.org/10.1016/S0014-5793(97)01480-4 Hsu TI, Hsu CH, Lee KH, Lin JT, Chen CS et al (2014) MicroRNA-18a is elevated in prostate cancer and promotes tumorigenesis through suppressing STK4 in vitro and in vivo. Oncogenesis 3:e99. https://doi.org/10.1038/oncsis.2014.12 Hu W, Feng Z, Levine AJ (2012) The regulation of multiple p53 stress responses is mediated through MDM2. Genes Cancer 3:199–208. https://doi.org/10.1177/1947601912454734 Huffaker TB, Hu R, Runtsch MC, Bake E, Chen X, Zhao J et al (2012) Epistasis between microRNAs 155 and 146a during T-cell-mediated antitumor immunity. Cell Rep 2:1697– 1709. https://doi.org/10.1016/j.celrep.2012.10.025 Hulsmans M, Holvoet P (2013) MicroRNA-containing microvesicles regulating inflammation in association with atherosclerotic disease. Cardiovasc Res 100:7–18. https://doi.org/10.1093/cvr/ cvt161 Hussain SP, Harris CC (2007) Inflammation and cancer: an ancient link with novel potentials. Int J Cancer 121:2373–2380. https://doi.org/10.1002/ijc.23173 Jansson MD, Damas ND, Lees M, Jacodsen A, Lund AH (2015) miR-339-5p regulates the p53 tumor-suppressor pathway by targeting MDM2. Oncogene 34:1908–1918. https://doi.org/10. 1038/onc.2014.130

148

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Jiang H, Wang P, Li X, Wang Q, Deng ZB et al (2014) Restoration of miR17/20a in solid tumor cells enhances the natural killer cell antitumor activity by targeting Mekk 2. Cancer Immunol Res 2:789–799. https://doi.org/10.1158/2326-6066 Jurkovicova D, Magyerkova M, Kulcsar L, Krivjanska M, Krivjansky V et al (2014) miR-155 as a diagnostic and prognostic marker in hematological and solid malignancies. Neoplasma 61:241– 251. https://doi.org/10.4149/neo_2014_032 Kandalam MM, Beta M, Maheswari UK, Swaminathan S, Krishnakumar S (2012) Oncogenic microRNA 17-92 cluster is regulated by epithelial cell adhesion molecule and could be a potential therapeutic target in retinoblastoma. Mol Vis 18:2279–2287 Kasinski AL, Slack FJ (2012) miRNA-34 prevents cancer initiation and progression in a therapeutically resistant K-ras and p53-induced mouse model of lung adenocarcinoma. Cancer Res 72: 5576–5587. https://doi.org/10.1158/0008-5472.CAN-12-2001 Khella HW, Bakhet M, Allo G, Jewett MA, Girgis AH et al (2013) miR-192, miR-194 and miR-215: a convergent microRNA network suppressing tumor progression in renal cell carcinoma. Carcinogenesis 34:2231–2239. https://doi.org/10.1093/carcin/bgt184 Kincaid RP, Burke JM, Sullivan CS (2012) RNA virus microRNA that mimics a B-cell oncomiR. Proc Natl Acad Sci U S A 109:3077–3082. https://doi.org/10.1073/pnas.1116107109 Kincaid RP, Chen Y, Cox JE, Rethwilm A, Sullivan CS (2014) Noncanonical microRNA (miRNA) biogenesis gives rise to retroviral mimics of lymphoproliferative and immunosuppressive host miRNAs. mBio 5:e00074–e00014. https://doi.org/10.1128/mBio.00074-14 Kopkova A, Sana J, Fadrus P, Slaby O (2018) Cerebrospinal fluid microRNAs as diagnostic biomarkers in brain tumors. Clin Chem Lab Med 56:869–879. https://doi.org/10.1515/cclm2017-0958 Landais S, Landry S, Legault P, Rassart E (2007) Oncogenic potential of the miR-106-363 cluster and its implication in human T-cell leukemia. Cancer Res 67:5699–5707. https://doi.org/10. 1158/008-5472.CAN-06-4478 Lettieri-Barbato D, Aquilano K, Punziano C, Minopoli G, Faraonio R (2022) MicroRNAs, long no-coding RNAs, and circular RNAs in the redox control of cell senescence. Antioxidants 11: 480. https://doi.org/10.3390/antiox11030480 Li L, Wang B (2014) Overexpression of microRNA-30b improves adenovirus-mediated p53 cancer gene therapy for laryngeal carcinoma. Int J Mol Sci 15:19729–19740. https://doi.org/10.3390/ ijms151119729 Li H, Yang BB (2012) Stress response of glioblastoma cells mediated by miR-17-5p targeting PTEN and the passenger strand miR-17-3p targeting MDM2. Oncotarget 3:1653–1668. https:// doi.org/10.18632/oncotarget.810 Li Y, Li C, Xia J, Jin Y (2011) Domestication of transposable elements into microRNA genes in plants. PLoS One 6:e19212. https://doi.org/10.1371/pone.0019212 Li Y, Gao L, Luo X, Wang L, Gao X et al (2013) Epigenetic silencing of microRNA-193a contributes to leukemogenesis in t(8; 21) acute myeloid leukemia by activating the PTEN/ PI3K signal pathway. Blood 121:499–509. https://doi.org/10.1182/blood-2012-07-444729 Li Y, Zhao W, Bao P, Li C, Ma XQ et al (2014) miR-339-5p inhibits cell migration and invasion and may be associated with the tumor-node-metastasis staging and lymph node metastasis of non-small cell lung cancer. Oncol Lett 8:719–725. https://doi.org/10.3892/ol.2014.2165 Li R, Wang X, Zhu C, Wang K (2022) LncRNA PVT1: a novel oncogene in multiple cancers. Cell Mol Biol Lett 27:84. https://doi.org/10.1186/s11658-022-00385-x Lin YW, Sheu JC, Liu LY, Chen CH, Lee HS et al (1999) Loss of heterozygosity at chromosome 13q in hepatocellular carcinoma: identification of three independent regions. Eur J Cancer 35: 1730–1734. https://doi.org/10.1016/S0959-8049(99)00205-1 Lin YH, Liao CJ, Huang YH, Wu MH, Chi HC et al (2013) The thyroid hormone receptor represses miR-17 expression to enhance tumor metastasis in human hepatoma cells. Oncogene 32:4509– 4518. https://doi.org/10.1038/onc.2013.309

References

149

Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA et al (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small cell lung cancer to gefitinib. N Engl J Med 350:2129–2139. https://doi.org/10.1056/NEJMoa040938 Mandke P, Wyatt N, Fraser J, Bates B, Berberich SJ et al (2012) MicroRNA-34a modulates MDM4 expression via a target site in the open reading frame. PLoS One 7:e42034. https://doi.org/10. 1371/journal.pone.0042034 Mannerström B, Paananen RO, Abu-Shahba AG, Moilanen J, Seppänen-Kaijansinkko R et al (2019) Extracellular small noncoding RNA contaminants in fetal bovine serum and serumfree media. Sci Rep 9:5538. https://doi.org/10.1038/s41598-019-41772-3 Mantovani A, Allavena P, Sica A, Balkwill F (2008) Cancer-related inflammation. Nature 454:436– 444. https://doi.org/10.1038/nature07205 Marcucci G, Maharry KS, Metzeler KH, Volinia S, Wu YZ et al (2013) Clinical role of microRNAs in cytogenetically normal acute myeloid leukemia: miR-155 upregulation independently identifies high-risk patients. J Clin Oncol 31:2086–2093. https://doi.org/10.1200/JCO.2012.45.6228 Masotti A, Donninelli G, Da Sacco L, Varano B, Del Cornò M et al (2015) HIV-1 gp120 influences the expression of microRNAs in human monocyte-derived dendritic cells via STAT3 activation. BMC Genomics 16:480. https://doi.org/10.1186/s12864-015-1673-3 Mathé E, Nguyen GH, Funamizu N, He P, Moake M et al (2012) Inflammation regulates microRNA expression in cooperation with p53 and nitric oxide. Int J Cancer 131:760–765. https://doi.org/ 10.1002/ijc.26403 Melnik B, John SM, Schmitz G (2014) Milk: an exosomal microRNA transmitter promoting thymic regulatory T-cell maturation preventing the development of atopy? J Transl Med 12:43. https:// doi.org/10.1186/1479-5876-12-43 Miyaaki H, Ichikawa T, Karno Y, Taura N, Honda T et al (2014) Significance of serum and hepatic microRNA-122 levels in patients with nonalcoholic fatty liver disease. Liver Int 34:e302–e307. https://doi.org/10.1111/liv.12429 Napuri J, Pilakka-Kanthikeel S, Raymond A, Agudelo M, Yndart-Aras A et al (2013) Cocaine enhances HIV-1 infectivity in monocyte derived dendritic cells by suppressing microRNA-155. PLoS One 8:e83682. https://doi.org/10.1371/journal.pone.0083682 O’Connell RM, Taganov KD, Boldin MP, Cheng G, Baltimore D (2007) MicroRNA-155 is induced during the macrophage inflammatory response. Proc Natl Acad Sci U S A 104:1604– 1609. https://doi.org/10.1073/pnas.0610731104 Ohno M, Ohkuri T, Kosaka A, Tanahashi K, June CH et al (2013) Expression of miR-17-92 enhances antitumor activity of T cells transduced with the anti-EGFRvIII chimeric antigen receptor in mice bearing human GBM xenografts. J Immunother Cancer 1:21. https://doi.org/10. 1186/2051-1426-1-21 Okayama H, Saito M, Oue N, Weiss JM, Stauffer J et al (2013) NOS2 enhances KRAS-induced lung carcinogenesis, inflammation and microRNA-21 expression. Int J Cancer 132:9–18. https://doi.org/10.1002/ijc.27644 Olive V, Bennett MJ, Walker JC, Ma C, Jiang I et al (2009) miR-19 is a key oncogenic component of mir-17-92. Genes Dev 23:2839–2849. https://doi.org/10.1101/gad.1861409 Osone T, Yoshikawa M, Fujii YR (2015) MicroRNA memory II: a novel scoring integration model for prediction of human disease by microRNA/microRNA quantum multi-interaction. J Adv Med Phar Sci 5:1–18. https://doi.org/10.9734/JAMPS/2016/22095 Pichiorri F, Suh SS, Rocci A, De Luca L, Taccioli C et al (2010) Downregulation of p53-inducible microRNAs 192, 194, 1nd 215 impairs the p53/MDM2 autoregulatory loop in multiple myeloma development. Cancer Cell 18:367–381. https://doi.org/10.1016/j.ccr.2010.09.005 Pichler K, Schneider G, Grassman R (2008) MicroRNA miR-146a and further oncogenesis-related cellular microRNAs are dysregulated in HTLV-1 transformed T lymphocytes. Retrovirology 5: 1742–1745. https://doi.org/10.1186/1742-4690-5-100 Qin S, Jin P, Zhou X, Chen L, Ma F (2015) The role of transposable elements in the origin and evolution of microRNAs in human. PLoS One 10:e0131365. https://doi.org/10.1371/journal. pone.0131365

150

7

Communication in miRNAs Between Inflammation and Cancer

Qu Z, Li W, Fu B (2014) MicroRNAs in autoimmune diseases. Biomed Res Int 2014:527895. https://doi.org/10.1155/2014/527895 Recouvreux MS, Miao J, Gozo MC, Wu J, Walts AE et al (2022) FOXC2 promotes vasculogenic mimicry in ovarian cancer. Cancers 14:4851. https://doi.org/10.3390/cancers14194851 Reed SM, Hagen J, Muniz VP, Rosean TR, Borcherding N et al (2014) NIAM-deficient mice are predisposed to the development of proliferative lesions including B-cell lymphomas. PLoS One 9:e112126. https://doi.org/10.1371/journal.pone.0112126 Ren ZJ, Nong XY, Lv YR, Sun HH, An PP et al (2014) Mir-509-5p joins the Mdm2/p53 feedback loop and regulates cancer cell growth. Cell Death Dis 5:e1387. https://doi.org/10.1038/cddis. 2014.327 Saadi H, Seillier M, Carrier A (2015) The stress protein TP53INP1 plays a tumor suppressive role by regulating metabolic homeostasis. Biochemie 118:44–50. https://doi.org/10.1016/j.biochi. 2015.07.024 Sánchez-Chaparro MM, Garza-Veloz I, Zayas-Villanueva OA, Martinez-Fierro ML, DelgadoEnciso I et al (2020) Genetic variants in the 3’UTR of BRCA1 and BRCA2 genes and their putative effects on the microRNA mechanism in hereditary breast and ovarian cancer. Diagnostics 10:298. https://doi.org/10.3390/diagnostics10050298 Sasaki K, Kohanbash G, Ueda R, McDonald HA, Reinhart TA et al (2010) miR-17-92 expression in differentiated T cells-implications for cancer immunotherapy. J Transl Med 8:17. https://doi. org/10.1186/1479-5876-8-17 Shao J, Li Y, Wu Q, Liang X, Yu X et al (2002) High frequency loss of heterozygosity on the long arms of chromosomes 13 and 14 in nasopharyngeal carcinoma in Southern China. Chin Med J 115:571–575 Shen F, Mo MH, Chen L, An S, Tan X et al (2014) MicroRNA-21 downregulates Rb1 expression by targeting PDCD4 in retinoblastoma. J Cancer 5:804–812. https://doi.org/10.7150/jca.10456 Skalsky RL, Samols MA, Plaisance KB, Boss IW, Riva A et al (2007) Kaposi’s sarcoma-associated herpesvirus encodes an ortholog of miR-155. J Virol 81:12836–12845. https://doi.org/10.1128/ jVI.01804-07 Suh SS, Yoo JY, Nuovo GJ, Jeon YJ, Kim S et al (2012) MicroRNAs/TP53 feedback circuity in glioblastoma multiforme. Proc Natl Acad Sci U S A 109:5316–5321. https://doi.org/10.1073/ pnas.1202465109 Swaminathan G, Rossi F, Sierra LJ, Gupta A, Navas-Martin S et al (2012) A role for microRNA155 modulation in the anti-HIV-1 effects of toll-like receptor 3 stimulation in macrophage. PLoS Pathog 8:e1002937. https://doi.org/10.1371/journal.ppat.1002937 Tam W (2001) Identification and characterization of human BIC, a gene on chromosome 21 that encodes a noncoding RNA. Gene 274:157–167. https://doi.org/10.1016/S0378-1119(01) 00612-6 Tam W, Ben-Yehunda D, Hayward WS (1997) Bic, a novel gene activated by proviral insertions in avian leucosis virus-induced lymphomas, is likely to function through its noncoding RNA. Mol Cell Biol 17:1490–1502 Thai TH, Calado DP, Casola S, Ansel KM, Xiao C et al (2007) Regulation of the germinal center response by microRNA-155. Science 316:604–608. https://doi.org/10.1126/science.1141229 Tosar JP, Cayota A, Eitan E, Halushka MK, Witwer KW (2017) Ribonucleic artifacts: are some extracellular RNA discoveries driven by cell culture medium components? J Extracell Vesicles 6:1272832. https://doi.org/10.1080/20013078.2016.1272832 van Haaften G, Agami R (2010) Tumorigenicity of the miR-17-92 cluster distilled. Genes Dev 24: 1–4. https://doi.org/10.1101/gad.1887110 Vignard V, Labbé M, Marec N, André-Grégoire G, Jouand N et al (2020) MicroRNAs in tumor exosomes drive immune escape in melanoma. Cancer Immunol Res 8:255–267. https://doi.org/ 10.1158/2326-6066.CIR-19-0522 Vojtechova Z, Tachezy R (2018) The role of miRNAs in virus-mediated oncogenesis. Int J Mol Sci 19:1217. https://doi.org/10.3390/ijms19041217

References

151

Waclawik AJ, Fadic R, Lotz BP, Beinlich BR, Lewandoski PJ et al (1996) CD8 and CD4 T-cellmediated polymyositis complicating the HTLV-1 associated myelopathy. Acat Neurol Scand 94:115–119 Wahl A, Linnstaedt SD, Esoda C, Krisko JF, Martinez-Torres F et al (2013) A cluster of virusencoded microRNAs accelerates acute systemic Epstein–Barr virus infection but does not significantly enhance virus-induced oncogenesis in vivo. J Virol 87:5437–5446. https://doi. org/10.1128/JVI.00281-13 Wang L, Toomey NL, Diaz LA, Walker G, Ramos JC et al (2011) Oncogenic IRFs provide a survival advantage for Epstein–Barr virus- or human T-cell leukemia virus type 1-transformed cells through induction of BIC expression. J Virol 85:8328–8337. https://doi.org/10.1128/JVI. 00570-11 Wang M, Zhang P, Li Y, Liu G, Zhou B et al (2012) The quantitative analysis by stem–loop realtime PCR revealed the microRNA-34a, microRNA-155 and microRNA-200c overexpression in human colorectal cancer. Med Oncol 29:3113–3118. https://doi.org/10.1007/s12032-0120241-9 Welch C, Chen Y, Stallings RL (2007) MicroRNA-34a functions as a potential tumor suppressor by inducing apoptosis in neuroblastoma. Oncogene 26:5017–5022. https://doi.org/10.1038/sj.onc. 1210293 Xiao C, Srinivasan L, Calado DP, Patterson HC, Zhang B et al (2008) Lymphoproliferative disease and autoimmunity in mice with increased miR-17-92 expression in lymphocytes. Nat Immunol 9:405–414. https://doi.org/10.1038/ni1575 Xiao J, Lin H, Luo X, Luo X, Wang Z (2011) miR-605 joins p53 network to form a p53: miR-605: Mdm2 positive feedback loop in response to stress. EMBO J 30:5021. https://doi.org/10.1038/ emboj.2011.463 Xu WD, Feng SY, Huang AF (2022) Role of mR-155 in inflammatory autoimmune diseases: a comprehensive review. Inflamm Res 71:1501–1517. https://doi.org/10.1007/s00011-02201643-6 Yamada H, Suzuki K, Ichino N, Ando Y, Sawada A et al (2013) Associations between circulating microRNAs (miR-21, miR-34a, miR-122 and miR-451) and nonalcoholic fatty liver. Clin Chim Acta 424:99–103. https://doi.org/10.1016/j.cca.2013.05.021 Yan H, Yang Y, Zhang L, Tang G, Wang Y et al (2015) Characterization of the genotype and integration patterns of hepatitis B virus in early- and late-onset hepatocellular carcinoma. Hepatology 61:1821–1831. https://doi.org/10.1002/hep.27722 Yoshikawa M, Osone T, Fujii YR (2015) MicroRNA memory I: the positive correlation between synergistic effects of microRNAs in cancer and a novel quantum scoring system. J Adv Med Phar Sci 5:1–16. https://doi.org/10.9734/JAMPS/2016/22134 Yu Z, Willmarth NE, Zhou J, Katiya S, Wang M et al (2010) microRNA 17/20 inhibits cellular invasion and tumor metastasis in breast cancer by heterotypic signaling. Proc Natl Acad Sci U S A 107:8231–8236. https://doi.org/10.1073/pnas.1002080107 Zhang J, Sun Q, Zhang Z, Ge S, Han ZG et al (2013) Loss of microRNA-143/145 disturbs cellular growth and apoptosis of human epithelial cancers by impairing the MDM2-p53 feedback loop. Oncogene 32:61–69. https://doi.org/10.1038/onc.2012.28 Zhang C, Liu J, Wang X, Wu R, Lin M et al (2014) MicroRNA-339-5p inhibits colorectal tumorigenesis through regulation of the MDM2/p53 signaling. Oncotarget 5:9106–9117. https://doi.org/10.18632/oncotarget.2379 Zhang P, Kitchen-Smith I, Xiong L, Stracquadanio G, Brown K et al (2021) Germline and somatic genetic variants in the p53 pathway interact to affect cancer risk, progression, and drug response. Cancer Res 81:1667–1780. https://doi.org/10.1158/0008-5472.CAN-20-0177 Zhao Y, Yao Y, Xu H, Lambeth L, Smith LP et al (2009) A functional microRNA-155 ortholog encoded by the oncogenic Marek’s disease virus. J Virol 83:489–492. https://doi.org/10.1128/ JVI.01166-08

152

7

Communication in miRNAs Between Inflammation and Cancer

Zhao Y, Xu H, Yao Y, Smith LP, Kgosana L et al (2011) Critical role of the virus-encoded microRNA-155 ortholog in the induction of Marek’s disease lymphomas. PLoS Pathog 7: e10001305. https://doi.org/10.1371/journal.ppat.1001305 Zhou C, Liu G, Wang L, Lu Y, Yuan L et al (2013) MiR-339-5p regulates the growth, colony formation and metastasis of colorectal cancer cells by targeting PRL-1. PLoS One 8:e63142. https://doi.org/10.1371/journal.pone.0063142

Chapter 8

The Concept of “Virus” Is the Same as Exosomal miRNA Gene RNA Information as a Virus

So much has happened in these two decades, so much has changed. Hooper, E. The River

Overview Virus is not the Latin word for “virus,” which means simply slimy liquid or poison. In the early twentieth century, viruses were referred to as infectious and filterable pathogens. However, that is just the RNA information within exosomes to be able to move in the twenty-first century. Even if the viral genome is DNA, a so-called DNA virus, viral transcript RNA is absolutely required for the production of viral proteins and the emergence of virulence. Viral RNA can be processed into viral microRNAs (miRNAs). Therefore, viruses are similar in their RNA genes in exosomes and are akin to miRNAs within exosomes in terms of RNA genetic information. In the future, as discussed in Chap. 8, the then-used term “virus” may disappear for carriers of miRNAs. In this chapter, because RNA viruses can produce miRNAs, “miRNAlike RNA” was not used. Again, viral miRNAs from RNA viruses are produced. Everyone knows that. miRNA entangling target sorting (METS)/miRNA quantum language and artificial intelligence (MIRAI) gave the answer.

8.1

Colander for miRNA

RNA viral infection is just transmission of RNA information. An RNA storm emerged, a pandemic of severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), which causes coronavirus disease 2019 (COVID-19), as described in Chap. 4. SARS-CoV-2 viral microRNAs (miRNAs) were discovered in silico (Fujii 2020a, b, c, 2021; Periwai et al. 2022) and bench (Neeb et al. 2022). These SARS-CoV-2 viral miRNAs regulate host protein genes and are associated with COVID-19 pathogenesis. Undoubtedly, RNA viruses produce miRNAs. Mosquitos are vectors of insect-borne viruses such as dengue virus, while mosquitos feed on the blood of their hosts. Mosquito miRNAs can affect mosquito viral © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1_8

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Fig. 8.1 miRNAs of dengue virus. The genome of dengue virus is RNA. Fact no.1

production (Zhou et al. 2014), and dengue viral miRNAs play an important role in its replication (Fig. 8.1) (Hussain and Asgari 2014). Therefore, not only mosquito miRNAs but also viral miRNAs can be transmitted from mosquitos to humans, and human circulating miRNAs in the blood can also be responsible for mosquito midgut cells (Bryant et al. 2010; Skalsky et al. 2010 “Cullen’s lab”; Hussain et al. 2013; Chen et al. 2014). This is proof of the transmission of RNA information through food. Subsequently, even though authoritative professors believed that RNA viruses never generated miRNAs, viral miRNAs are encoded by RNA viruses (Skalsky et al. 2014 “Cullen’s lab”). Milk miRNAs within exosomes (20–100 nm) are stable and have been delivered to infants or starving adults (Baier et al. 2014; Lukasik and Zielenkiewicz 2014; Melnik et al. 2014; Alsaweed et al. 2015; Shu et al. 2015). This phenomenon is similar to viral “infection.” Furthermore, incorporated milk miRNAs are implicated in host health (Melnik 2015), suggesting that miRNAs within exosomes can propagate to each other, and miRNAs are inheritable because miRNAs are noncoding genes. Since miRNAs are found in body fluids throughout the body, the opportunities and implications of miRNA infections may be expanded. The concept of “infection” was postulated by Jacob Helen as follows: (1) Pathogens are found in disease lesions. (2) Pathogens are isolated from pure cultures. (3) Pathogen transfer in pure cultures causes disease. (4) Pathogens contribute to disease transfer, and pathogens must separate from the host again. “Pathogens” can be replaced with “miRNA genes” in both infectious and noninfectious diseases. “Pandemic’ can be replaced with “RNA storm.” Therefore, food pathogens are equivalent to dietary miRNAs. Indeed, Frizzi et al. (2014) isolated miRNAs or siRNAs of plant and plant viruses from symptomatic tomatoes, watermelons, zucchini, and onions in grocery stores. RNAs smaller than these viral nucleic acids have been found to be highly complementary to human genes, miRNAs may be transferred with viruses and food-derived viruses, and these dietary miRNAs, including milk miRNAs, can regulate host genes (Baier et al. 2014).

8.2

Gammaherpesvirus: Transfer and Ribosomal RNA-Derived miRNA Quantum Code

155

Contrary to these factual insights, the rationale was that RNA viruses cannot make miRNAs because “miRNAs Degrade the Genome of RNA Viruses” (Cullen 2009, 2010, 2011, 2012; Whisnant et al. 2013). The rationale for this theory is as follows. (1) RNase can cleave RNA. (2) RNA interference also degrades target RNA. (3) At the same time, miRNAs are digested, so virus miRNAs cannot be taken up by humans. However, miRNAs cannot degrade messenger RNAs (mRNAs) because they are incompletely paired with the 3′ untranslated region (3′UTR) of target mRNAs (Fujii 2014). This is different from RNA interference. Since human saliva stably contains RNA viruses and miRNAs in exosomes (Pfeifer et al. 2015; Byun et al. 2015), miRNAs in exosomes and RNA virus particles are stable to RNase. Authority claims of RNA cleaving should be “noblesse oblige.” Because they found many; “miRNA-like small RNAs” from RNA viruses, Dengue virus, influenza virus, and retrovirus later. Thank you, Harwig, Das and Berkhout -Thank you, Harwig, Das and Berkhout-

8.2

Gammaherpesvirus: Transfer and Ribosomal RNA-Derived miRNA Quantum Code

DNA viral infection is the transmission of DNA plus RNA information. Kaposi’s sarcoma-associated herpesvirus (KSHV), also known as human herpesvirus 8, is a DNA virus of oncogenic human gammaherpesvirus. KSHV induces Kaposi’s sarcoma, primary effusion lymphoma, and multicentric Castleman’s disease (Cesarman et al. 1995a; Soulier et al. 1995). KSHV has been isolated from AIDS patients (Chang et al. 1994; Cesarman et al. 1995b). Human primary mesenchymal stem cells of different origins are reprogrammed to different phenotypes by KSHV infection (Lee et al. 2016). The KSHV genome encodes 12 viral pre-miRNAs (miR-K121~miR-K12-12) and 18 mature miRNAs (Fig. 8.2) (Cai et al. 2005; Pfeffer et al. 2005; Samols et al. 2005). As mentioned in Chap. 6, miR-K12-11, miR-K12-10a, and miR-K12-3 are linked to paralogs of miR-155, miR-142-3p, and miR-23 in the seeds, respectively. Furthermore, KSHV infection dysregulates approximately 15% of human cellular miRNAs (Viollet et al. 2015). Viollet et al. further showed that KSVH-derived miRNAs were expressed in approximately 9% of the total amount of miRNAs in infected cells. The dominantly expressed viral miRNA was miR-K12-

Fig. 8.2 miRNAs of Kaposi’s sarcoma-associated herpesvirus (KSHV)

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The Concept of “Virus” Is the Same as Exosomal miRNA Gene

Fig. 8.3 miRNAs of murine gammaherpesvirus 68 (MHV68). MHV68 produces viral tRNA (TMER), which is homologous to cellular tRNA. miRNAs are generated from tRNAs as tRNA fragments (tRFs), and there is fact no. 2

10a (40.5%), which is a paralog of cellular miR-142-3p, whereas the expression of cellular miR-142-3p was not altered by KSHV infection and ontology analysis. Viral miRNAs did not play a role as putative cellular miRNA competitors. miR-K10a overlaps the coding region of the Kaposin A/C protein, which has been shown to be an oncogenic protein of KSHV. However, miR-K10a alone but not Kaposin protein can have transformation activity because miR-K10a mimics cellular miR-142-3p, which is related to tumorigenesis (Forte et al. 2015). Furthermore, the HIV-1 Tat protein synergistically induced KS angiogenesis with the KSHV K1 oncoprotein by miR-891a-5p (Yao et al. 2015). Thus, the oncogenic activity of the protein should be re-evaluated as derived from noncoding RNA genes, suggesting that KSHV transcripts themselves, as environmental factors, may alter miRNA profiles and induce tumorigenesis in susceptible cells. Therefore, viruses are one type of RNA genetic information containing miRNAs in exosomes. Murine gammaherpesvirus 68 (γHV68; MHV68; MHV4) resembles human γHV, such as KSHV (Moody et al. 2013), but MHV68 induces B-cell tumors and chronic inflammatory diseases (Barton et al. 2011). Therefore, MHV68 is also an oncovirus and encodes miRNAs (Fig. 8.3). MHV68-infected cells express eight transfer RNA (tRNA)-like ncRNAs from the 6162 bp Hind III region, and their ncRNAs are called tRNA-miRNA-encoded RNAs (TMERs) because of their sequence similarity to eukaryotic tRNAs without aminoacylation (Bowden et al. 1997; Simas et al. 1998). However, the anticodon portion of these TMERs is not shared with mammalian tRNAs. Instead, TMERs encode at least 15 viral miRNAs, possibly 28 (Zhu et al. 2010; Reese et al. 2010). These are the cluster, miR-M1-1 ~

8.2

Gammaherpesvirus: Transfer and Ribosomal RNA-Derived miRNA Quantum Code

157

M1-6, miR-M1-7-5p and miR-M1-7-3p, miR-M1-8, miR-M1-9, miR-M1-10-5p and miR-M1-10-3p, miR-M1-11, miR-M1-12, miR-M1-13a-5p and miR-M1-13a-3p, miR-M1-13b-5p and miR-M1-13b-3p, and miR-M1-14-5p and miR-M1-14-3p. In addition, from MHV68-infected cells, two antisense viral miRNAs and three cellular miRNAs were detected by canonical generation from introns, and as noncanonical miRNAs, nine cellular miRNAs from snoRNAs, one cellular miRNA from tRNAIIeTA and tRNA-SerAGA, and 15 cellular miRNAs from introns were identified by deep sequencing (Xia and Zhang 2012). Feldman et al. (2014) showed that all TMER-deletion mutant MHV68 had decreased memory B cells and reduced pulmonary inflammation and lethal pneumonia compared with wild-type MHV68, although the infection was never changed during acute viral proliferation. It is suggested that MHV68 miRNAs are implicated in viral pathogenicity but not in the viral life cycle. In turn, viral miRNAs can control host cells and mice but not the virus itself. Therefore, there is intriguing evidence that viruses are not “viruses” but just information in cell-derived exosomes and that DNA viruses “yes, you can” make miRNAs. However, for MHV68, noncanonical miRNAs are produced by tRNAs. This means that RNA transcripts similar to the RNA virus genome can serve as a source of miRNAs, suggesting that RNA viruses can produce miRNAs. tRNA-derived small RNA (tRF) must be included in miRNAs because tRF has the same characteristics as RNA noncoding genes, such as approximately 20 nt in length (Martens-Uzunova et al. 2013), and targets the 3′UTR of protein gene mRNA (Zhou et al. 2017), primers of HTLV-1 (Ruggero et al. 2014), miR-1280 annotating tRNALeu (Huang et al. 2017), and breast cancer biomarkers (Wang et al. 2020). MiRNA quantum codes were different between ribosomal miRNAs and tRFs, but the distribution of DNSs in tRFs is involved in that in all human miRNAs (Fig. 8.4). A 3′tRF was shown to enhance translation initiation of the small ribosomal subunit protein RP28, and the tRF target site in the RP28 coding sequence is conserved in vertebrates (Kim et al. 2019). Therefore, tRFs regulate protein translation processes through ribosomes (George et al. 2022). EBV encodes 25 viral miRNAs (Fig. 8.5). As described before, EBV-derived miRNAs are involved in tumorigenesis and viral latency in infected cells. Japanese monkey Rhadinovirus (JMRV) also encodes 15 viral miRNAs (Fig. 8.6). Since these herpesviruses have double-stranded DNA genomes, the basic process of viral miRNA production from DNA viruses is the same as the canonical cellular biogenesis of miRNAs from the genome. DNA virus miRNAs have been suggested to represent the bona fed miRNA gene as the same RNA gene as the human doublestranded DNA genome. However, it resembles HIV-1 provirus DNA in the human genome.

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Fig. 8.4 MiRNA quantum codes of ribosomal miRNAs and tRFs. (a) DNSs of tRFs and ribosomal miRNAs (rmiRs) are represented in the upper panel. (b) DNSs of all human miRNAs (ALL) and ribosomal miRNAs (rmiR) are shown in the lower panel

8.3

Human Immunodeficiency Virus Type 1

Human immunodeficiency virus type 1 (HIV-1) encodes miRNAs as described in Chap. 1 (Fig. 8.7). As shown in Chap. 11, identical cellular miRNAs were found in the HIV-1 RNA sequences. Experimental and computational analyses have discovered HIV-1 miRNAs and endogenous short interference RNA (siRNAs) (Harwig et al. 2015). Seed homologous cellular miRNAs, miR-30d, miR-424, miR-374a, and miR-195 are also expressed. Furthermore, the HIV-1 Tat regulatory protein inhibits over 300 cellular miRNAs (Sardo et al. 2016). The transactivation response element (TAR) in HIV-1 RNA is a target of Tat, and HIV-1 transcription is activated by Tat binding together with the positive elongation complex (pTEFb), cyclin T, Cdk 9, TRBP, TFII H, RH116, and

8.3

Human Immunodeficiency Virus Type 1

Fig. 8.5 miRNAs of Epstein–Barr virus (EBV)

Fig. 8.6 miRNAs of Japanese monkey rhadinovirus

Fig. 8.7 HIV-1 miRNAs and seed homologous cellular miRNAs in the HIV-1 genome

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Fig. 8.8 Exosome transmitter of hiv1-miRN367 plus Nef protein. Exosomes are efficient vehicles of the hiv1-miRN367 gene and Nef protein from infected cells to noninfected cells

pol II. However, Eletto et al. (2008) reported that Tat upregulated miR-374, miR-128a, miR-128b, miR-100, miR-25, and miR-99a and downregulated let-7e, miR-298, let-7f, let-7c, let-7b, miR-320, and miR-214. Although HIV-1 can cross the blood–brain barrier in the central nervous system, HIV-1 does not infect neural cells. However, astrocyte infection can occur, leading to severe HIV-associated dementia called HIV-associated neurocognitive disorder (HAND) (Joseph et al. 2016). Conversely, the Nef protein can bind to the Ago2 protein and activate HIV1 replication (Aqil et al. 2013). hiv1-miR-N367 from the nef/3′ long terminal region (3′LTR) can block viral proliferation and maintain latency, whereas Ago2-bound Nef protein is a positive factor. Therefore, the nef/3′LTR self-regulates HIV-1 production via miRNA information. If exosomes contain both Nef protein and hiv1-miR-N367 (Fig. 8.8) and then exosomes are transported from latently infected cells to uninfected cells, Nef protein and hiv1-miR-N367 may change cell phenotypes. Evidence suggests that the Nef protein activates astrocytes and causes HIV-mediated dementia (Liu and Kumar 2015). In Nef-expressing monocytes, miRNA gene information altered phenotypes, and similar events in astrocytes may be induced by miRNAs (Aqil et al. 2015). Nef protein in exosomes may be transmitted to aberrant miRNA expression by Ago2. Furthermore, hiv1-miR-N367 is orthologous to hsa-miR-192 (You et al. 2012). miR-192 is primarily expressed in the liver and colon and can target farnesoid X receptor (FXR) mRNA in vitro and in vivo (Krattinger et al. 2015). This implies that hiv1-miR-N367 may also influence the pathogenesis of HIV-1 infection in hepatic lipogenesis. For instance, latent HIV-1 infection influences adipocyte generation (Otake et al. 2004). HIV-1 reduces the expression of FXR and PPAR-gamma (Renga et al. 2012). Although protein–protein interactions have been investigated in AIDS patients with and without HIV-associated dementia (Shityakov et al. 2015), heat shock protein and fibronectin were detected as differentially expressed gene products. These data suggest that without data integration of miRNA and gene expression, useful network data cannot be built on neurodegeneration due to HIV infection. Subsequently, these findings demonstrate that not only CD4 T-cell competence but also neural and

8.4

Influenza Virus

161

hepatic function in HIV-1-infected patients are perturbed by HIV-1 Tat and Nef, at least in part through alterations in miRNA expression.

8.4

Influenza Virus

Influenza viruses are RNA viruses. We have already discovered hiv1-miR-N367 in the genome of the RNA virus HIV-1, but a tyrannical professor did not believe it and punished my discovery despite the internet era. However, the best evidence for miRNA genes is that an RNA virus, HIV-1, encodes miRNAs, which are transferred from one cell to another via exosomes. RNA viruses make bona fed miRNAs (Varble et al. 2010). At the same time, Perez et al. (2010) in the laboratory of Dr. TenOver found miRNAs from influenza A virus H1N1 on February 17, 2010, and Umbach et al. 2010in Dr. Cullen’s laboratory also identified influenza virus miRNAs (Fig. 8.9). Some researchers have argued that miRNAs are epigenetic. It is the same logical reasoning that RNA viruses do not produce miRNAs. If so, RNA interference, mobile genetic elements, and RNA virus genomes, as US researchers have found, are all outside of genetic aria. Thus, they self-disrupt by epigenetic rationale strategies. miRNAs are genes, but they are not epigenetic. (1) Since miRNA is a nucleic acid on the viral genomic RNA (genomic miRNA gene), the protein encoded by the viral RNA genome is also a gene. If the miRNA is epigenetic, then the protein genes on the RNA virus genome are also epigenetic, (2) the genomic miRNA is heritable, and its single nucleotide polymorphisms (SNPs) are implicated in diseases by genome-wide association study (GWAS) analysis, (3) resident miRNAs are mobile

Fig. 8.9 miRNAs of influenza virus A. Fact no.3

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Fig. 8.10 miRNAs of retroviruses, bovine leukemia virus (BLV), bovine foamy virus (BFV), and simian foamy virus (SFV). Fact no.4

elements and, similar to RNA retroviruses, are transmitted from mother to newborn via the placenta and breast milk as the acquired character, (4) the resident miRNA genes commonly circulate in the environment across kingdoms, and (5) miRNA genes are related to programmed evolution. (6) Finally, an RNA virus encodes miRNAs in the RNA genome. Anyone please refutes the evidence as an antiepigenetic theory of miRNAs. If miRNA genes function as miRNA quantum codes involving Watson-Crick DNA base pairing, most scientific researchers can believe that they are not epigenetic (see Chap. 9). In other words, miRNA can only be described as genes that act at least “miRNAs regulate epigenetic.” Umbach et al. 2010 in the laboratory of Prof. Cullen in Duke on August 06, 2010, reproduced influenza viral miRNA. RNA viruses such as bovine leukemia virus (BLV), bovine foamy virus (BFV), and simian foamy virus (SFV) encode miRNA genes (Fig. 8.10).

8.5

Tropic Viruses

163

RNA Viruses Have Viral miRNAs All miRNAs are RNA genetic information near or within retroviral LTRs. RNA viruses, feline foamy virus (FFV), Rous sarcoma virus (RSV), and avian leukemia virus (ALV) also encode viral miRNAs (Aso et al. 2021; Paul et al. 2021; Yan et al. 2022). Furthermore, olive flounder endogenous retrovirus encodes miRNAs (Lee et al. 2019). In addition, regarding miRNA-based gene therapy, suppression of hsa-miR-664a3p has been reported to reduce viral replication of influenza A (H7N9) (Wolf et al. 2016). If miRNA is epigenetic, the concept of gene therapy using miRNA genes cannot be considered. Therefore, the discovery of hiv1-miR-N367 is evidence that miRNAs are not epigenetic. Influenza A virus infection inhibited host miRNA expression in infected cells (Jiang et al. 2015). In the acute phase of infection, host miRNA expression was suppressed, but in the recovery phase, host miRNA expression was restored. Therefore, host protein expression was altered from infection to recovery by miRNA regulation. Changes in miRNAs during infection were analyzed with a gene ontology (GO) search and Kyoto Encyclopedia of Genes and Genomes (KEGG) bioinformatics, but miRNA–target and protein–protein interactions based on bioinformatics analyses are too complex to reveal influenza etiology. Furthermore, Skalsky et al. (2016) found A→I editing of seeds in rhadinovirus miR-J6 with high frequency (approximately 75%) compared with cellular miR-22-3p (0.9%), miR-100 (0.5%), and miR-222-3p (0.8%). Since seed deamination events from A to G can change target mRNAs, viral infection is more complex to induce virulence. Influenza A virus vRNAs, as viral miRNAs, are composed of quasispecies; therefore, viral drifts and shifts in pandemics are not only due to hemagglutinin (HA) and neuraminidase (NA) but also to the heterogeneity of viral miRNAs in the seed sequences as noncoding genes.

8.5

Tropic Viruses

Ebola virus and dengue virus are RNA viruses. Ebola virus belongs to the family of Filoviridae and has a minus single-stranded RNA genome. Ebola virus encodes at least 11 viral miRNAs (Liang et al. 2014; Fu et al. 2015; Teng et al. 2015) (Fig. 8.11). Dengue virus belongs to the Flaviviridae family and has a plus single-stranded RNA genome. It is a cytoplasmic RNA virus. Dengue virus encodes 6 viral miRNAs (Hussain and Asgari 2014) and 4 viral PIWI-interacting RNAs (piRNAs) (Miesen et al. 2016). Virus-generated piRNAs are processed by host insect Ago3 and/or PIWI5 proteins, and these small RNAs can regulate biological pathways in host mosquito cells without pathogenicity. Since blood is the food of mosquitoes, dengue small RNAs are transmitted through the food, and miRNAs and piRNAs of dengue viruses are mobile genetic elements from vectors to human infected hosts.

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Fig. 8.11 miRNAs of Ebola Zair virus. Fact no. 5

Dengue infection causes fever, headache, in some cases hemorrhagic fever, and dengue shock syndrome, but the mechanism remains obscure. For symptomatic insight, Ebola infections such as Ebola-Z cause severe hemorrhage with fatality rates of 40–90% (Li and Chen 2014). Ebola’s etiology also remains ambiguous. Dengue virus infection upregulated serum miRNA expression, such as miR-1972, miR-518a-3p, miR-1538, miR-300, miR-21-5p, miR-589-5p, miR-1537-3p, miR-340-5p, miR-1207-5p, miR-632, miR-934, miR-1227-3p, miR-152-3p, miR-596, and miR-149-3p (Ouyang et al. 2016). Dengue virus belongs to the same genus as hepatitis C virus (HCV). Similar to miR-122 upregulation in HCV, one or two of these 15 miRNAs are therapeutic targets for dengue virus infection. A top-1 upregulator, miR-1972, was detected in the urine of patients with type 1 diabetes. However, miR-1972 has not yet appeared to have been detected in other urinary specimens from diabetic patients (Argyropoulos et al. 2015). Since miR-1972 is not a diabetes-specific biomarker, miR-1972 mimics may be candidates for anti-dengue drugs. Dengue virus miRNAs are encoded in the 3′ untranslated region (3′UTR) and 5′UTR as promoters of viral replication (Cahour et al. 1994; Alvarez et al. 2005a), and dengue-specific viral miRNAs may also be targets for the treatment of dengue infection. Furthermore, dengue cyclizes the RNA–RNA genome, and their 3′ and 5′UTR interactions may be necessary and sufficient for viral replication (Alvarez et al. 2005b). Evidence suggests that linear RNA can spontaneously loop in the absence of proteins, cyclized RNA–RNA interactions function, and viral miRNAs in the 3′ and 5′UTRs interact as sponges.

8.6

Symphony of AIDS 2020

Altogether, the evidence (facts no. 1–5 and more) is that (1) RNA viruses have miRNA genes, (2) viral particles are exosome-containing miRNAs, and (3) viral miRNAs are part of the environmental factors, indicating that viruses cause the RNA

8.6

Symphony of AIDS 2020

165

Fig. 8.12 Symphony of AIDS 2020

storm as a pandemic on Earth. This is because the COVID-19 pandemic has become a reality as the RNA storm and its viral miRNAs have been detected. In miRNA entangling target sorting (METS)/miRNA quantum language and artificial intelligence (MIRAI), hiv1-miR-N367 downregulated RB transcriptional repressor (RB1), which progresses the T-cell G1/S cell cycle and induces HIV-1 replication. In contrast, RB1 inhibition in HIV-1 R5 leads to an increase in apoptosis in infected monocytes; therefore, hiv1-miR-N367 induces immunodeficiency in the early stage of infection. For host mRNAs in HIV-1 infection, upregulation of miR-16-5p inhibited cyclin kinase 6 (CDK6), cyclin D1 (CCND1), cyclin D3 (CCND3), and cyclin E1 (CCNE1). CCND3, CCND1, and CDK6 form a complex in the cell cycle; therefore, HIV-1 cannot produce progeny virus after CD4+ T-cell infection. Simultaneously, miR-16-5p upregulation blocked antiapoptotic BCL2, so cell cycle arrest and apoptosis caused a decrease in the number of CD4+ T lymphocytes (Fujii 2020d). We have mentioned elucidating the miRNA code of HIV-1 Rosetta Stone, and METS/MIRAI was able to clarify the pathogenesis of acquired immunodeficiency syndrome (AIDS). This is truly an orchestration of an AIDS symphony (Fig. 8.12) (Fujii 2009, 2023). Gone are the days of debating whether RNA viruses produce miRNAs.

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References Alsaweed M, Hartmann PE, Geddes DT, Kakulas F (2015) MicroRNAs in breastmilk and the lactating breast: potential immunoprotectors and developmental regulators for the infant and the mother. Int J Environ Res Public Health 12:13981–14020. https://doi.org/10.3390/ ijerph121113981 Alvarez DE, de Lella Ezcurra AL, Fucito S, Gamarnik AV (2005a) Role of RNA structure present at the 3′UTR of dengue virus on translation, RNA synthesis, and viral replication. Virology 339: 200–212. https://doi.org/10.1016/j.virol.2005.06.009 Alvarez DE, Londeiro MF, Ludueña SJ, Pietrasanta LI, Gamarnik AV (2005b) Long-range RNA– RNA interactions circularize the dengue virus genome. J Virol 79:6631–6643. https://doi.org/ 10.1128/JVI.79.11.6631-6643.2005 Aqil M, Naqvi R, Bano AS, Jameel S (2013) The HIV-1 Nef protein binds Argonaute-2 and functions as a viral suppressor of RNA interference. PLoS One 8:e74472. https://doi.org/10. 1371/journal.pone.0074472 Aqil M, Mallik S, Bandyopadhyay S, Maulik U, Jameel S (2015) Transcriptome analysis of mRNAs in human monocytic cells expressing the HIV-1 Nef protein and their exosomes. Biomed Res Int 2015:492395. https://doi.org/10.1155/2015/492395 Argyropoulos C, Wang K, Bernardo J, Ellis D, Orchard T et al (2015) Urinary microRNA profiling predicts the development of microalbuminuria in patients with type 1 diabetes. J Clin Med 4: 1498–1517. https://doi.org/10.3390/jcm4071498 Aso S, Kitao K, Hashimoto-Gotoh A, Sakaguchi S, Miyazawa T (2021) Identification of feline foamy virus-derived microRNAs. Microbes Environ 36:ME21055. https://doi.org/10.1264/ jsme2.ME21055 Baier SR, Nguyen C, Xie F, Wood JR, Zempleni J (2014) MicroRNAs are absorbed in biological meaningful amounts from nutritionally relevant doses of cow milk and affect gene expression in peripheral blood mononuclear cells, HEK-293 kidney cell cultures, and mouse livers. J Nutr 144:1495–1500. https://doi.org/10.3945/jn.114.196436 Barton E, Mandal P, Speck SH (2011) Pathogenesis and host control of gammaherpesviruses: lessons from the mouse. Annu Rev Immunol 29:351–397. https://doi.org/10.1146/annurevimmunol-072710-081639 Bowden RJ, Simas JP, Davis AJ, Efstathiou S (1997) Murine gammaherpesvirus 68 encodes tRNAlike sequences which are expressed during latency. J Gen Virol 78:1675–1687. https://doi.org/ 10.1099/0022-1317-78-7-1675 Bryant B, Macdonald W, Raikhel AS (2010) microRNA miR-275 is indispensable for blood digestion and egg development in the mosquito Aedes aegypti. Proc Natl Acad Sci USA 107: 22391–22398. https://doi.org/10.1073/pnas.1016230107 Byun JS, Hong SH, Choi JK, Jung JK, Lee HJ (2015) Diagnostic profiling of salivary exosomal microRNAs in oral planus patients. Oral Dis 21:987–993. https://doi.org/10.1111/odi.12374 Cahour A, Pletnev A, Vazeille-Falcoz M, Rosen L, Lai CJ (1994) Growth-restricted dengue virus mutants containing deletions in the 5′ noncoding region of the RNA genome. Virology 207:68– 76. https://doi.org/10.1006/viro.1995.1052 Cai X, Lu S, Zhang Z, Gonzalez CM, Damania B et al (2005) Kaposi’s sarcoma-associated herpesvirus expresses an array of viral microRNAs in latently infected cells. Proc Natl Acad Sci USA 102:5570–5575. https://doi.org/10.1073/pnas.0408192102 Cesarman E, Moore PS, Rao PH, Inghirami G, Knowles DM et al (1995a) In vitro establishment and characterization of two acquired immunodeficiency syndrome-related lymphoma cell line (BC-1 and BC-2) containing Kaposi’s sarcoma-associated herpesvirus-like (KSHV) DNA sequences. Blood 86:2708–2714 Cesarman E, Chang Y, Moore PS, Said JW, Knowles DM (1995b) Kaposi’s sarcoma-associated herpesvirus-like DNA sequences in AIDS-related body-cavity-based lymphomas. N Engl J Med 332:1186–1191. https://doi.org/10.1056/NEJM199505043321802

References

167

Chang Y, Cesarman E, Pessin MS, Lee F, Culpepper J et al (1994) Identification of herpesvirus-like DNA sequences in AIDS-associated Kaposi’s sarcoma. Science 266:1865–1869. https://doi. org/10.1126/science.7997879 Chen RF, Yang KD, Lee IK, Liu JW, Huang CH et al (2014) Augmented miR-150 expression associated with depressed SOCS1 expression involved in dengue hemorrhagic fever. J Infect 69: 366–374. https://doi.org/10.1016/j.jinf.2014.05.013 Cullen BR (2009) Viral and cellular messenger RNA targets of viral microRNAs. Nature 457:421– 425. https://doi.org/10.1038/nature07757 Cullen BR (2010) Five questions about viruses and microRNAs. PLoS Pathog 6:e1000787. https:// doi.org/10.1371/journal.ppat.1000787 Cullen BR (2011) Viruses and microRNAs: RISC interactions with serious consequences. Genes Dev 25:188–194. https://doi.org/10.1101/gad.17352611 Cullen BR (2012) MicroRNA expression by an oncogenic retrovirus. Proc Natl Acad Sci USA 109: 2695–2696. https://doi.org/10.1073/pnas.1200328109 Eletto D, Russo G, Passiatore G, Del Valle L, Giordano A et al (2008) Inhibition of SNAP25 expression by HIV-1 Tat involves the activity of mir-128a. J Cell Physiol 216:764–770. https:// doi.org/10.1002/jcp.21452 Feldman ER, Kara M, Coleman CB, Grau KR, Oko LM et al (2014) Virus-encoded microRNAs facilitate gammaherpesvirus latency and pathogenesis in vivo. MBio 5:e00981–e00914. https:// doi.org/10.1128/mBio.00981-14 Forte E, Raja AN, Shamulailatpam P, Manzano M, Schipma MJ et al (2015) MicroRNA-mediated transformation by the Kaposi’s sarcoma-associated herpesvirus Kaposin locus. J Virol 89:2333– 2341. https://doi.org/10.1128/JVI.03317-14 Frizzi A, Zhang Y, Kao J, Hagen C, Huang S (2014) Small RNA profiles from virus-infected fresh market vegetables. J Agric Food Chem 62:12067–12074. https://doi.org/10.1021/jf503756v Fu Z, Liang HW, Zen K, Zhang CY, Chen X (2015) microRNAs and the Ebola virus. RNA Dis 2: e492. https://doi.org/10.14800/rd.492 Fujii YR (2009) Symphony of AIDS: a microRNA-based therapy. In: Gauer RK, Rossi JJ (eds) Regulation of gene expression by small RNAs. CRC Press, New York, pp 333–349 Fujii YR (2014) RNA information gene diseases: nano-RNA-based medical devices with corporate chemotherapy and gene therapy. In: Wang B (ed) RNA nanotechnology. PanSatnford Pub. Pte. Ltd, pp 385–434 Fujii YR (2020a) The etiology of COVID-19 in silico by SARS-Cov-2 infection with the quantum microRNA language-AI. Virol Immunol J 4:000243. https://doi.org/10.23880/vij-16000243 Fujii YR (2020b) The COVID-19 deadly risk assessment upon the updated etiologic computer simulation by quantum microRNA language in SARS-CoV-2 infection in eo. Int J Clin Case Stud Rep 3:142–154 Fujii YR (2020c) In silico study by quantum microRNA language for the development of antiCOVID-19 agents: COVID-19 is prevented by rice MIR2097-5p through suppression of SARSCov-2 viral microRNAs plus HIPK2 target proteins. Virol Immunol J 4:000256. https://doi.org/ 10.23880/vij-16000256 Fujii YR (2020d) The quantum microRNA immunity in human virus-associated diseases: virtual reality of HBV, HCV and HIV-1 infection, and hepatocellular carcinogenesis with AI machine learning. Arch Clin Biomed Res 4:089–129. https://doi.org/10.26502/acbr.50170092 Fujii YR (2021) Quantum microRNA assessment of COVID-19 RNA vaccine: hidden potency of BNT162b2 SARS-CoV-2 spike RNA as microRNA vaccine. Adv Case Stud 3:000552. https:// doi.org/10.31031/AICS.2021.03.000552 Fujii YR (2023) The microRNA quantum code book. Springer Nature, Singapore George S, Rafi M, Aldarmaki M, ElSiddig M, Al Nuaimi M et al (2022) tRNA derived small RNAssmall players with big roles. Front Genet 13:997780. https://doi.org/10.3389/fgene.2022. 997780

168

8

The Concept of “Virus” Is the Same as Exosomal miRNA Gene

Harwig A, Das AT, Berkhout B (2015) HIV-1 RNAs: sense and antisense, large mRNAs and small siRNAs and miRNAs. Curr Opin HIV AIDS 10:103–109. https://doi.org/10.1097/COH. 0000000000000135 Huang B, Yang H, Cheng X, Wang D, Fu S et al (2017) tRF/miR-1280 suppresses stem cell-like cells and metastasis in colorectal cancer. Cancer Res 77:3194–3206. https://doi.org/10.1158/ 0008-5472.CAN-16-3146 Hussain M, Asgari S (2014) MicroRNA-like viral small RNA from dengue virus 2 autoregulates its replication in mosquito cells. Proc Natl Acad Sci USA 111:2746–2751. https://doi.org/10.1073/ pnas.1320123111 Hussain M, Walker T, O’Neil SL, Asgari S (2013) Blood meal induced microRNA regulates development and immune associated genes in the dengue mosquito vector, Aedes aegypti. Insect Biochem Mol Biol 43:146–152. https://doi.org/10.1016/j.ibmb.2012.11.005 Jiang P, Zhou N, Chen X, Zhao X, Li D et al (2015) Integrative analysis of differentially expressed microRNAs of pulmonary alveolar macrophages from piglets during H1N1 swine influenza a virus infection. Sci Rep 5:8167. https://doi.org/10.1038/srep08167 Joseph J, Colosi DA, Rao VR (2016) HIV-1 induced CNS dysfunction: current overview and r es e a r ch p r i o r i t i es . C u r r HI V R e s 1 4 : 3 8 9– 3 9 9 . h tt p s : / / d o i . o r g / 1 0 . 2 1 7 4 / 1570162X14666160324124940 Kim HK, Xu J, Chu K, Park H, Jang H et al (2019) A tRNA-derived small RNA regulates ribosomal protein S28 protein levels after translation initiation in human and mice. Cell Rep 29:3816– 3824. https://doi.org/10.1016/j.celRep.2019.11.062 Krattinger R, Boström A, Schiöth HB, Thasler WE, Mwinyi J et al (2015) microRNA-192 suppresses the expression of the farnesoid X receptor. Am J Physiol Gastrointest Liver Physiol 310:G1044–G1051. https://doi.org/10.1152/ajpgi.00297.2015 Lee MS, Yuan H, Jeon H, Zhu Y, Yoo S et al (2016) Human mesenchymal stem cells of diverse origins support persistent infection with Kaposi’s sarcoma-associated herpesvirus and manifest distinct angiogenic, invasive, and transforming phenotypes. MBio 7:e02109–e02115. https:// doi.org/10.1128/mBio.02109-15 Lee HE, Jo A, Im J, Cha HJ, Kim WJ et al (2019) Terminal repeat of the endogenous retrovirusderived microRNAs in the olive flounder. Sci Rep 9:14007. https://doi.org/10.1038/s41598019-50492-7 Li YH, Chen SP (2014) Evolutionary history of Ebola virus. Epidemiol Infect 142:1138–1145. https://doi.org/10.1017/S0950268813002215 Liang HW, Zhou Z, Zhang SY, Zen K, Chen X et al (2014) Identification of Ebola virus microRNAs and their putative pathological function. Sci China-Life Sci 57:973–981. https:// doi.org/10.1007/s11427-014-4759-2 Liu X, Kumar A (2015) Differential signaling mechanism for HIV-1 Nef-mediated production of IL-6 and IL-8 in human astrocytes. Sci Rep 5:9867. https://doi.org/10.1038/srep09867 Lukasik A, Zielenkiewicz P (2014) In silico identification of plant miRNAs in mammalian breast milk exosomes-a small step forward? PLoS One 9:e99963. https://doi.org/10.1371/journal. pone.0099963 Martens-Uzunova ES, Olvedy M, Jenster G (2013) Beyond microRNA-novel RNAs derived from small noncoding RNA and their implication in cancer. Cancer Lett 340:201–211. https://doi.org/ 10.1016/j.canlet.2012.11.058 Melnik BC (2015) The pathogenic role of persistent milk signaling in mTORC1- and milkmicroRNA-driven type 2 diabetes mellitus. Curr Daibetes Rev 11:46–62. https://doi.org/10. 2174/1573399811666150114100653 Melnik BC, John SM, Schmitz G (2014) Milk: an exosomal microRNA transmitter promoting thymic regulatory T-cell maturation preventing the development of atopy? J Transl Med 12:43. https://doi.org/10.1186/1479-5876-12-43 Miesen P, Ivens A, Buck AH, van Riji RP (2016) Small RNA profiling in dengue virus 2-infected aedes mosquito cells reveals viral piRNAs and new host miRNAs. PLoS Negl Trop Dis 10: e0004452. https://doi.org/10.1371/journal.pntd.0004452

References

169

Moody R, Zhu Y, Huang Y, Cui X, Jones T et al (2013) KSHV microRNAs mediate cellular transformation and tumorigenesis by redundantly targeting cell growth and survival pathways. PLoS Pathog 9:e1003857. https://doi.org/10.1371/journal.ppat.1003857 Neeb ZT, Ritter AJ, Chauhan LV, Katzman S, Lipkin WI et al (2022) A potential role for SARSCoV-2 small viral RNAs in targeting host microRNAs and modulating gene expression. Sci Rep 12:21694. https://doi.org/10.1038/s41598-022-26135-9 Otake K, Omoto S, Yamamoto T, Okuyama H, Okada H et al (2004) HIV-1 Nef in the nucleus influences adipogenesis as well as viral transcription through the peroxisome proliferatoractivated receptors. AIDS 18:189–198. https://doi.org/10.1097/00002030-200401230-00007 Ouyang X, Jiang X, Gu D, Zhang Y, Kong SK et al (2016) Dysregulated serum miRNA profile and promising biomarkers in dengue-infected patients. Int J Med Sci 13:195–205. https://doi.org/10. 7150/ijms.13996 Paul S, Saikia M, Chakraborty S (2021) Identification of novel microRNAs in Rous sarcoma virus (RSV) and their target sites in tumor suppressor genes of chicken. Infect Genet Evol 96:105139. https://doi.org/10.1016/j.meegid.2021.105139 Perez JT, Varble A, Sachidanandam R, Zlatev I, Manoharan M et al (2010) Influenza a virusgenerated small RNAs regulate the switch from transcription to replication. Proc Natl Acad Sci USA 107:11525–11530. https://doi.org/10.1073/pnas.1001984107 Periwai N, Bhardwaj U, Sarrma S, Arora P, Sood V (2022) In silico analysis of SARS-CoV-2 genomes insights from SARS encoded noncoding RNAs. Front Cell Infect Microbiol 12: 966870. https://doi.org/10.3389/fcimb.2022.966870 Pfeffer S, Sewer A, Lagos-Quintana M, Sheridan R, Sander C et al (2005) Identification of microRNAs of the herpesvirus family. Nat Methods 2:269–276. https://doi.org/10.1038/ nmeth746 Pfeifer P, Werner N, Jansen F (2015) Role and function of microRNAs in extracellular vesicles in cardiovascular biology. Biomed Res Int 2015:161393. https://doi.org/10.1155/2015/161393 Reese TA, Xia J, Johnson LS, Zhou X, Zhang W et al (2010) Identification of novel microRNA-like molecules generated from herpesvirus and host tRNA transcripts. J Virol 84:10344–10353. https://doi.org/10.1128/JVI.00707-10 Renga B, Francisci D, D’Amore C, Schiaroli E, Carino A et al (2012) HIV-1 infection is associated with changes in nuclear receptor transcriptome, proinflammatory and lipid profile of monocytes. BMC Infect Dis 12:274. https://doi.org/10.1186/1471-2334-12-274 Ruggero K, Guffanti A, Corradin A, Sharma VK, de Bellis G et al (2014) Small noncoding RNAs in cells transformed by human T-cell leukemia virus type 1: a role for a tRNA fragment as a primer for reverse transcriptase. J Virol 88:3612–3622. https://doi.org/10.1128/JVI.02823-13 Samols MA, Hu AJ, Skalsky RL, Renne R (2005) Cloning and identification of a microRNA cluster within the latency-associated region of Kaposi’s sarcoma-associated herpesvirus. J Virol 79: 9301–9305. https://doi.org/10.1128/JVI.79.14.9301-9305.2005 Sardo L, Vakil PR, Elbezanti W, EI-Sayed A, Klase Z (2016) The inhibition of microRNAs by HIV-1 Tat suppresses beta catenin activity in astrocytes. Retrovirology 13:25. https://doi.org/ 10.1186/s12977-016-0256-y Shityakov S, Dandekar T, Förster C (2015) Gene expression profiles and protein–protein interaction network analysis in AIDS patients with HIV-associated encephalitis and dementia. HIV/AIDS Res Pall Care 7:265–276. https://doi.org/10.2147/HIV.S88438 Shu J, Chiang K, Zempleni J, Cui J (2015) Computational characterization of exogenous microRNAs that can be transferred into human circulation. PLoS One 10:e0140587. https:// doi.org/10.1371/journal.pone.0140587 Simas JP, Bowden RJ, Paige V, Efstathiou S (1998) Four tRNA-like sequences and a serpin homolog encoded by murine gammaherpesvirus 68 are dispensable for lytic replication in vitro and latency in vivo. J Gen Virol 79:149–153. https://doi.org/10.1099/0022-1317-791-149

170

8

The Concept of “Virus” Is the Same as Exosomal miRNA Gene

Skalsky RL, Vanlandingham DL, Scholle F, Higgs S, Cullen BR (2010) Identification of microRNAs expressed in two mosquito vectors, Aedes albopictus and Culex quinquefasciatus. BMC Genomics 11:119. https://doi.org/10.1186/1471-2164-11-119 Skalsky RL, Olson KE, Blair CD, Garcia-Blanco MA, Cullen BR (2014) A “microRNA-like” small RNA expressed by dengue virus? Proc Natl Acad Sci USA 111:E2359. https://doi.org/10.1073/ pnas.1406854111 Skalsky RL, Barr SA, Jeffery AJ, Blair T, Estep R et al (2016) Japanese macaque rhadinovirus encodes a viral microRNA mimic of the miR-17 family. J Virol 90:9350–9363. https://doi.org/ 10.1128/JVI.01123-16 Soulier J, Grollet L, Oksenhendler E, Cacoub P, Cazais-Hatem D et al (1995) Kaposi’s sarcomaassociated herpesvirus-like DNA sequences in multicentric Castleman’s disease. Blood 86: 1276–1280 Teng Y, Wang Y, Zhang X, Liu W, Fan H et al (2015) Systematic genome-wide screening and prediction of microRNAs in EBOV during the 2014 Ebolavirus outbreak. Sci Rep 5:09912. https://doi.org/10.1038/srep09912 Umbach JL, Yen HL, Poon LL, Cullen BR (2010) Influenza a virus expresses high levels of an unusual class of small viral leader RNAs in infected cells. MBio 1:e00204–e00210. https://doi. org/10.1128/mBio.00204-10 Varble A, Chua MA, Perez JT, Manicassamy B, García-Sastre A et al (2010) Engineered RNA viral synthesis of microRNA. Proc Natl Acad Sci USA 107:11519–11524. https://doi.org/10.1073/ pnas.1003115107 Viollet C, Davis DA, Reczko M, Ziegelbauer JM, Pezzella F et al (2015) Next-generation sequencing analysis reveals differential expression profiles of miRNA–mRNA target pairs in KSHV-infected cells. PLoS One 10:e0126439. https://doi.org/10.1371/journal.pone.0126439 Wang J, Ma G, Li M, Han X, Xu J et al (2020) Plasma tRNA fragments derived from 5′ ends as novel diagnostic biomarkers for early-stage breast cancer. Mol Ther 21:955–964. https://doi. org/10.1016/j.omtn.2020.07.026 Whisnant AW, Bogerd HP, Flores O, Ho P, Powers JG et al (2013) In-depth analysis of the interaction of HIV-1 with cellular microRNA biogenesis and effector mechanisms. MBio 4(2):e000193. https://doi.org/10.1128/mBio.00193-13 Wolf S, Wu W, Jones C, Perwitasari O, Mahalingam S et al (2016) MicroRNA regulation of human genes essential for influenza a (H7N9) replication. PLoS One 11:e0155104. https://doi.org/10. 1371/journal.pone.0155104 Xia J, Zhang W (2012) Noncanonical microRNAs and endogenous siRNAs in lytic infection of murine gammaherpesvirus. PLoS One 7:e47863. https://doi.org/10.1371/journal.pone.0047863 Yan Y, Chen S, Liao L, Gao S, Pang Y et al (2022) ALV-miRNA-p19-01 promotes viral replication by targeting dual specificity. Viruses 14:805. https://doi.org/10.3390/v14040805 Yao S, Hu M, Hao T, Li W, Xue X et al (2015) MiRNA-891a-5p mediates HIV-1 Tat and KSHV Orf-K1 synergistic induction of angiogenesis by activating NK-κB signaling. Nucleic Acids Res 43:9362–9378. https://doi.org/10.1093/nar/gkv988 You X, Zhang Z, Fan J, Cui Z, Zhang XE (2012) Functionally orthologous viral and cellular microRNAs studied by novel dual-fluorescent reporter system. PLoS One 7:e36157. https://doi. org/10.1371/journal.pone.0036157 Zhou Y, Liu Y, Yan H, Li Y, Zhang H et al (2014) miR-281, an abundant midgut-specific miRNA of the vector mosquito Aedes albopictus enhance dengue virus replication. Parasit Vectors 7: 488. https://doi.org/10.1186/s13071-014-0488-4 Zhou K, Diebel KW, Holy J, Skildum A, Odean E et al (2017) A tRNA fragment, tRF5-Glu, regulates BCAR3 expression and proliferation in ovarian cancer cells. Oncotarget 8:95377– 95391. https://doi.org/10.18632/oncotarget.20709 Zhu JY, Strehle M, Frohn A, Kremmer E, Höfig KP et al (2010) Identification and analysis of expression of novel microRNAs of murine gammaherpesvirus 68. J Virol 84:10266–10275. https://doi.org/10.1128/JVI.01119-10

Chapter 9

ES Cells or iPS Cells, that Is the Question Stem Cell Selection by Combined miRNAs

All I believe is true! I am able yet. All I want, to get. Browning, R. Mesmerism

Overview There is increasing evidence that microRNA (miRNA) genes regulate cell selfrenewal and pluripotency in stem cells. miR-302, miR-209, and miR-371 play important roles in stem cell regulation, and these miRNA clusters directly or indirectly control the ES cell-specific transcription factors Octamer-binding transcription factor 4 (Oct4), SRY-box transcription factor 2 (Sox2) and nanog homeobox (Nanog). Oct4, Sox2, KLF transcription factor 4 (Klf4), and Nanog plus MYC proto-oncogene (c-Myc) are OSKM Yamanaka factors for induced pluripotent stem (iPS) cells, and somatic cell reprogramming is driven by four protein DNA genes. Both iPS cells and embryonic stem (ES) cells are regulated by miRNAs. Therefore, direct reprogramming by miRNA genes may be safely applied to regenerative medicine without using oncogenic Myc. Furthermore, with miRNA, there is no need to force reprogramming directly from the iPS state, and no iPS-intermediated step is needed, allowing short-term therapeutic applications of reprogrammed cells. Humanization of animals by ES cells and iPS cells is an ethical problem in regenerative medicine, but cannibalization with humanized pigs and cows is a newly emerging problem. Thus, miRNA assessment is extremely helpful in determining the feasibility of all grafts. In this chapter, for miRNA entangling target sorting (METS)/miRNA quantum language and artificial intelligence (MIRAI), it is clarified that the carcinogenic property of OSKM iPS cells arises from the suppression of four tumor suppressors, phosphatase and tensin homolog (PTEN), cyclin-dependent kinase inhibitor 1A (CDNK1A), RB transcriptional corepressor 1 (RB1), and tumor protein P53 inducible nuclear protein 1 (TP53INP1).

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1_9

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Stem Cell Love Letter in the Bottle

To avoid graft-versus-host disease (GVHD), cells are not used (Fujii and Miura 2022). miRNAs in exosomes from stem cells may provide insights into safe therapeutics, wound healing, myocardial infarction, and neurodegeneration (Matsuzaka and Yashiro 2022). Mesenchymal stem cells secrete exosomes, which promote myogenesis and angiogenesis (Bian et al. 2019). Several miRNAs have been identified from mesenchymal stem cell exosomes, including myogenic miRNAs, miR-1, miR-133, miR-206, and miR-494 (Nakamura et al. 2015). These miRNAs are implicated in anti-apoptotic and antioxidant effects. In in vivo cardiomyocyte pretreatment, the myocardial infarction model may be improved by mesenchymal stem cell-derived exosomes containing miRNAs (Zhang et al. 2016). Administration of bone marrow-derived mesenchymal stem cells has been shown to accelerate recovery and repair from acute kidney injury in human trials (Fleig and Humphreys 2014) (Clinical trials.gov: NCT00733876, NCT00658073), and exosomal miRNAs produced by bone marrow-derived mesenchymal stem cells had proregenerative effects (Collino et al. 2015). It was concluded that exosomes are safer to use for myocardial infarction and acute kidney injury (AKI) than stem cell transplantation (Haider and Aramini 2020). Exosomes, including miRNAs from human neural stem cells, comply with Good Manufacturing Practice and in Clinical Trials for Stroke and Critical Limb Ischemia in the UK (Clinical Trials.gov: NCT01151124, NCT02117635, and NCT01916369) (Stevanato et al. 2016). Human circulating fibroblasts secreting exosomes may accelerate wound healing in diabetic model mice (Geiger et al. 2015). Exosomes from fibroblasts normalize proangiogenic miRNAs, miR-126, miR-130a, miR-132, anti-inflammatory miRNAs, miR-124a, miR-125b, and collagen deposition regulator miRNA, miR-21 included. Similar to direct reprogramming by miRNAs, mixtures of miR-125 and some mRNAs contained in PC12 cell exosomes can induce neuronal cell differentiation from human mesenchymal stem cells (Takeda and Xu 2015). Exosomal miR-653-5p derived from mesenchymal stem cells inhibits laryngeal papilloma progression (Clinical Trials: chictr-ior-17011021) (Hu et al. 2022). Important evidence is that mesenchymal stem cells do not differentiate into epithelia without additional information and that conditioned media enhanced survival in AKI model mice (Bi et al. 2007). However, the mechanism of this effect was controversial at that time. Thus, it is suggested that some factors derived from mesenchymal stem cells have information to improve renal injury. Since foodderived exosomes may now contain miRNAs, food also controls stem cell differentiation and proliferation, as well as pluripotency. Animal and plant stem cells secrete exosomes, food contains such exosomes, and these contain miRNAs; therefore, exosomes from foods may be a therapeutic resource.

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Direct Reprogramming of miRNAs: Carcinogenicity of iPS Cells

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Direct Reprogramming of miRNAs: Carcinogenicity of iPS Cells

Octamer-binding transcription factor 4 (OCT4), SRY-box transcription factor 2 (SOX2), KLF transcription factor 4 (KLF4), and MYC proto-oncogene (MYC) (OSKM) factors recombined into retrovectors were applied to genetically modify mouse embryonic fibroblasts to transduce fibroblasts into induced pluripotent stem (iPS) cells (Takahashi and Yamanaka 2006). Although mouse iPS cells have emerged as a promising model for regenerating therapy by Takahashi and Yamanaka (2006), this strategy carries the risk of tumorigenesis, the difficulty of selecting safe cells, and profound differences between inbred rodents and heterologous humans of different species. Two miRNAs, miR-93 and miR-106b, were highly induced during the early reprogramming stage by OSKM factors (Li et al. 2011). For METS/MIRAI (Fujii 2023), upregulation of miR-93-5p and miR-106b-5p inhibited phosphatase and tensin homolog (PTEN) and cyclin-dependent kinase inhibitor 1A (CDNK1A) tumor suppressors (Fig. 9.1). Furthermore, the RB transcriptional corepressor 1 (RB1) tumor suppressor was inhibited by upregulation of miR-106b-5p. Tumor protein P53 inducible nuclear protein 1 (TP53INP1) tumor suppressor was also reduced by upregulation of miR-93-5p. Therefore, four tumor suppressors were suppressed at once. It is strongly carcinogenic in this case (AUC: 1.00), but the data volume is still insufficient. PTEN reduction by upregulation of miR-106b-5p is also the key to tumorigenesis of hepatocytes (see Chap. 10). In addition, the miR-17-

Fig. 9.1 METS/MIRAI analysis of iPS cells

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Fig. 9.2 Big contradiction in iPS cell application

92 cluster (miR-20a-5p, miR-106b-5p, miR-93-5p) is related to glioma (see Chap. 7). From genome-wide association studies (GWAS), multiple disease-associated genes have been discovered in single nucleotide polymorphisms (SNPs) of protein alleles and noncoding regions (Reich and Lander 2001; Mehanna et al. 2015). In the case of autologous transplantation in humans, it is controversial whether the GWAS information in the disease-associated alleles can absolutely be robust; therefore, iPS-derived differential cells cannot be effectively and rationally applied for transplantation therapy for human diseases with safety. A DNA- and viral vector-free protocol was developed to make iPS cell derivation safe. However, no one has argued that the robustness of quantitative trail loci (QTL) of illness in the iPS genome is scientifically unpopular, thus justifying the applicability of iPS cells for safe transplantation and disease treatment experiments. However, it is quite reasonable that the same iPS cells from a patient cannot be safely used for both autologous transplantation and drug discovery with patient cells at the same time (Fig. 9.2). Conversely, iPS oncogenic organoids have recently been used for understanding carcinogenesis (Miura et al. 2021; Wang et al. 2022). Patient iPS cells cannot be used for transplantation and drug development at the same time. Since iPS cells collected from patients have disease trait loci in their genomic DNA, cells derived from patient iPS cells can be applied to drug development. However, iPS cells are not safe for autologous transplantation. Even

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xenografts using iPS cell banks require confirmation of the disease trait loci of the donor iPS cells along with HLA matching, which cannot eliminate the risk of future disease manifestation, such as cancer. Additionally, an assessment of miRNA genotype is needed. If the government covers this with national health insurance, the national finance will collapse. As such, providing regenerated iPS-derived cells from iPS cells to differentiated cells costs a single patient $1 million and a two-step procedure from intermediate to ready for accidental injury bedside use. Since it takes more than a month, cost and time are also sustainable development goal (SDG) issues (Fujii 2013). To overcome the concern of iPS cells, direct conversions have been developed to reprogram somatic cells such as mouse fibroblasts (Their et al. 2012; Han et al. 2012). Three specific cellular processes during reprogramming are (1) inhibition of apoptosis and increased proliferation, (2) mesenchymal-to-epithelial transition (MET), and (3) activation of pluripotency (El-Badawy and El-Badri 2015). Let-7, miR-21, miR-29, and miR-199 were able to repress the first process. In contrast, miR-138, the miR-93 family and the miR-302/367 cluster activate the proliferative phase. MET is upregulated by the miR-93 family, miR-200, miR-369, and miR-302/367. Pluripotency is also increased by miR-302/367. Let-7 blocks pluripotency. It has been suggested that miR-302/367 drives somatic cell reprogramming. In practice, miR-302/367 facilitates human iPS cells (Lin et al. 2008; Anokye-Danso et al. 2011). miR-302 can target lysine-specific histone demethylase 1A (AOF2), KDM1 and LSD1, histone H3K4 demethylase (AOF1), histone deacetylase complexrepressor component (MECP1-p66), and methyl-CpG binding domain protein 2 (MECP2) for pluripotent stages from differentiation (Lin et al. 2011) and cyclindependent kinase inhibitor 1 (CDKN1) and p21Cip1 for cell proliferation (Dolezalova et al. 2012). miR-367 is required for the efficiency of miR-302-dependent reprogramming for suppression of HDAC2 to maintain pluripotency (Zhang and Wu 2013). Therefore, somatic cells can be reprogrammed without transfection or transduction of OSKM mRNA by delivery as HIV Tat-conjugated recombinant OSKM proteins (Zhang et al. 2012). Because HIV-1 Tat is pathogenic for somatic cells, bedside applications of Tat fusion proteins are not possible. Therefore, iPS cells are tumorigenic and less efficient. Indeed, the tumor suppressor TP53 was blocked in iPS cells (Hong et al. 2009; Utikal et al. 2009; Marión et al. 2009; Li et al. 2009; Kawamura et al. 2009; Ayaz et al. 2022). The tumorigenic process is carried out by protein oncogene drivers, so the reported number of drivers is approximately 108, and their protein suppressors are 26. Therefore, 26 protective arms are required to protect against aberrant physiological processes by 108 oncogenes. For miRNAs, 23 are oncogenic miRNAs, 55 are tumor suppressive miRNAs, and 6 are biphasic miRNAs (Fig. 9.3). Comparing protein oncogenes and tumor suppressor genes, miRNAs suppress tumorigenicity. This means that conversion by protein gene expression is more dangerous than direct conversion by miRNA genes. For protein gene transformation, forced expression of OSKM may introduce less robustness than its somatic cells and may upregulate basal genomic expression levels (Fig. 9.4). miR-302/367 suppresses gene expression below baseline; therefore, relative OSKM expression is

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Fig. 9.3 Oncogenes and tumor suppressor genes

Fig. 9.4 Aberrancy of robustness by protein transformation

upregulated. Thus, direct conversion of miRNAs is not tumorigenic, but protein gene transformation results in tumors. The problem of tumorigenic iPS cells by the Yamanaka factor has not yet been resolved for the safe use of iPS cells. It has been suggested that miRNA genes play an important role in tumor suppression. Cancer stem cells are initiated by key oncogenes, and neoplastic

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mutations are promoted by increased expression of oncogenes. Furthermore, transactivation by transcriptional proteins may regulate networks of oncogenes (Willis 2016; Fernandes 2016). Therefore, overexpression of the transcriptional protein OSKM mimics the state of transformed iPS cells with that of cancer stem cells. miR-302/367 remains more robust to safe reprogramming. These speculations were strongly supported by evidence that miR-1, miR-133, miR-208, and miR-409 (miRNA panel) directly reprogrammed mouse noncardiac cells into their cardiomyocytes in vivo (Jayawardena et al. 2015). Reprogramming efficiency can be enhanced by 5- to 15-fold by selenium (Wang et al. 2016). Valproic acid (VPA) and miR-302/367 efficiently reprogram mouse astrocytes to neurons in vivo with no tumorigenesis observed at the 2-month follow-up (Ghasemi-Kasman et al. 2015). miR-9/9* and miR-124 promote direct conversion of human fibroblasts into neurons (Victor et al. 2014). Direct reprogramming by miRNA panels could be a powerful and safe tool for precious medical initiatives (Fig. 9.4). Human iPS cellcardiomyocytes released exosomal miRNAs, miR-363-3p, miR-355-3p, miR-1835p, miR-302-3p, and miR-200c-3p; however, the tumor suppressors PTEN and cyclin-dependent kinase inhibitor 1A (CDKN1A) were significantly inhibited in exosome-treated human umbilical vein endothelial cells (HUVECs) (Louro et al. 2022). The problem of tumorigenic iPS-derived exosomes emerged.

9.3

GVHD in Stem Cells

GVHD is implicated in the immunogenicity of stem cells on stem cell grafts. When iPS cells from autologous somatic cells or allogenic ES cells are rejected by the recipient immune system, the iPS cells, in comparison with ES cells, induce a T-celldependent immune response in syngeneic mouse grafts (Zhao et al. 2011). It fails and causes acute GVHD in recipients (Gao et al. 2016; Kawamura et al. 2016). GVHD remains a cause of death in the field of allogeneic hematopoietic cell transplantation, even when matched for class II antigens or HLA (Socié and Ritz 2014). Using nonhuman primates, both autologous grafts and allografts of iPS-derived neural cells in the brain induced immune responses and infiltration of T cells (Morizane et al. 2013). This suggests that Yamanaka factor-driven iPS cells are by no means reasonable to apply as autografts or allografts for treatment in terms of cost and time of anti-GVHD. It is expensive with normal resources for medical business. Severe acute GVHD occurs in approximately 50% of patients who undergo donor-recipient matching at the HLA locus, and six miRNAs are related to human renal allograft rejection in seven renal allograft biopsies classified as acute GVHD compared with normal biopsies (Anglicheau et al. 2009): let-7c, miR-1425p, miR-155, miR-223, miR-10b, and miR-30a-3p. Three miRNAs, miR-142-5p, miR-155, and miR-223, are available to predict acute GVHD in renal grafts with greater than 90% sensitivity and specificity. After allogeneic hematopoietic stem cell transplantation for lymphoma patients, miR-194

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and miR-518f are upregulated in blood samples from 14 patients with acute GVHD, which are detected before the onset of acute GVHD on donor–recipient matched 24 patients at HLA (Gimondi et al. 2016). The use of miRNA in acute GVHD in prediction, diagnosis, and prognosis remains sparse. However, the combination of CD146, soluble tumor necrosis factor receptor 1 (sTNFR1), and miR-100 or miR-194 strongly correlated with the onset of acute GVHD (AUC > 0.975) (Lia et al. 2022). Furthermore, tumorigenic iPS cells are not applicable to lymphoma patients. Human tumor cells highly express LIN28, as the LIN28 RNA-binding protein is a key protein regulating iPS and ES cell pluripotency (Viswanathan and Daley 2010; Albino et al. 2016). The miRNAs let-7, miR-9, miR-30, and miR-125 are known to suppress LIN28 in cancer cells and iPS cells (Zhong et al. 2010). In allogeneic hematopoietic transplantation, miR-423, miR-199a-3p, miR-93*, miR-377, miR-155, and miR-30a were upregulated in plasma samples from patients with acute GVHD (GVHD patients vs non-GVHD: 116 vs 52) (Xiao et al. 2013). For miR-423, miR-199a-3p, miR-93*, and miR-377, four miRNAs were not detected in the plasma of lung transplant patients or nontransplant sepsis patients. It is suggested that allogeneic transplantation has approximately 50% or more of the incidence of acute GVHD, and dysregulation of the miRNA profile is different among transplanted cells or organs. Therefore, iPS-derived cells in transplantation could also cause the same GVHD issues in the same manner, which would be costly and dangerous in aftercare. Furthermore, the miR-17-92 cluster in Fli-3 human homolog C13orf25 controls Th2 and increases Th1/Th17. Since acute GVHD is mediated by Th1/Th17 cells, miR-17/92 promotes acute GVHD. Lack of miR-17/92 or LNA treatment with anti-miR-17/92 can diminish acute GVHD (Wu et al. 2015). miR-17/ 92 and let-7 expression are low in pig iPS cells (Zhang et al. 2015). miR-155 is a key player in the differentiation back from iPS cells toward the original hematopoietic cells, and reprogrammed iPS cells are prone to differentiation back into the original lineage cells (Vitaloni et al. 2014). In clinical trials, miR-155 anti-LNA effectively mitigates GVHD (Clinical Trials.gov identifier #NCT01521039). As described in Chap. 6, miR-155 in the BIC proto-oncogene is implicated in pro-inflammatory events, but miR-155 deficiency may reduce acute GVHD through B cells or dendritic cells (Chen et al. 2015). Furthermore, miR-155 and miR-146a are biomarkers of GVHD in allogenic hematopoietic stem cell transplantation (Atarod et al. 2016). Thus, ES cells would be a much better transplant source than iPS cells because iPS cells have miRNA memory of donor cells and GVHD is easily induced by miR-155. The actual number of publications on ES cells with and without miRNAs was much higher than that on iPS cells in Google Scholar (Fig. 9.5). From the outset, the patient’s somatic cells have an aberrant profile of intracellular miRNA memory. Therefore, in HLA-matched graft cells, immunoreactive miRNAs may induce GVHD in autografts and even much more in allografts. Second, resident miRNA memory remains in the cytoplasm of somatic cells even when reprogrammed at the genomic DNA level. GVHD in iPS-derived cell transplantation presents GVHD as a patient model of disability, with a colossal defect that iPS-derived cells from patients cannot be safely and relevantly used for both

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Fig. 9.5 Data of iPS and ES cells with and without miRNA. In google scholar from 2001 to 2019, there were many more ES cell experiments than iPS cell experiments

allogenic transplantation and cell culture for drug development. GVHD is difficult to overcome. Even if you built an iPS cell bank, you will fall into the trap of GVHD. In other words, a trade-off, one is safe iPS transplantation and the other is GVHD caused by iPS cells. It also completely collapses logically in terms of the safe use of tissue transplantation (Fig. 9.2).

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Escape from High Wall

As mentioned above, tumorigenicity is one of the major obstacles to iPS cell transplantation treatment. Exosomes secreted from iPS cells showed similar recovery effects to acute myocardial ischemia/reperfusion, which causes cardiomyocyte loss through necrosis and apoptosis (Wang et al. 2015). iPS exosomes may be used without the risk of tumorigenesis. However, chronic lymphocytic leukemia (CLL)derived exosomes integrate into surrounding endothelial and mesenchymal stem cells, are induced to resemble cancer-associated cell phenotypes, and contribute to a tumor-supportive environment (Paggetti et al. 2015). As previously described in 8.2, human iPS exosomes suppress the antitumor protein PTEN (Louro et al. 2022). This report suggests that tumorigenic iPS exosomes lack absolute safety. As discussed in Chap. 6, the profile of miRNAs within exosomes of tumorigenic cells is either tumorigenic or tumor supportive. Thus, iPS cells are monster cells by the high wall because iPS cells or their derivatives permanently retain their tumorigenicity in miRNA memory (Fig. 9.6). In summary, iPS cells derived from Yamanaka factors have high antigenicity and tumorigenicity even in autologous transplantation. Its high antigenicity causes GVHD. These issues have not yet been resolved. miRNAs in exosomes from ES cells are being applied in bedside trials to cure GVHD. Direct conversion of miRNAs is safer than protein gene conversion. Tumorigenic cells are immunogenic because of the current bedside use of treatment with immunoreactive agents against cancer, such as nivolumab and

Fig. 9.6 Problematic issues of application with iPS cells induced by protein genes

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Fig. 9.7 Data of iPS and ES cell-derived exosomes with and without miRNA. In google scholar from 2010 to 2019, the number of published papers on ES cell exosomes increased much more than that on iPS cell exosomes

ipilimumab. The miRNA gene information indicates that the immunogenic and tumorigenic miRNA properties of iPS cells and derived cells differ significantly from those of ES cells. Therefore, miRNA gene information can be used not only for diagnostics but also for validation of cell preparation control in grafts and drug development. Furthermore, prognostic regulation has been involved in miRNA information for GVHD. Combined measurements of miRNAs, miR-15b, miR-103a, and miR-106a in stable grafts showed that grafts obtained from patients with T-cell-mediated rejection or urinary tract infection were discriminated at p < 0.001 or p < 0.001, respectively (Matz et al. 2016). In the future, pre- or posttransplantation assessments of food miRNAs will become available. Prior to clinical trials using iPS cells must be evaluated by METS/MIRAI analysis. As shown in Fig. 9.7, studies with ES cell-derived exosomes have gradually increased compared to those with iPS cell exosomes. miRNAs in ES cell exosomes are suitable for safe use during transplantation.

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ES Cells or iPS Cells, that Is the Question

Data on miRNA assessment of iPS cells and their differentiated cells have not yet been published. Could it be bad data in bedside to open?

References Albino D, Civenni G, Dallavalle C, Roos M, Jahns H et al (2016) Activation of the Lin28/let-7 axis by loss of ESE3/EHF promotes a tumorigenic and stem-like phenotype in prostate cancer. Cancer Res 76:3629–3643. https://doi.org/10.1158/0008-5472.CAN-1S-2665 Anglicheau D, Sharma VK, Ding R, Hummel A, Snopkowski C et al (2009) MicroRNA expression profiles predictive of human renal allograft status. Proc Natl Acad Sci U S A 106:5330–5335. https://doi.org/10.1073/pnas.0813121106 Anokye-Danso F, Trivedi CM, Juhr D, Gupta M, Cui Z et al (2011) Highly efficient miRNAmediated reprogramming of mouse and human somatic cells to pluripotency. Cell Stem Cell 8: 376–388. https://doi.org/10.1016/j.stem.2011.03.001 Atarod S, Ahmed MM, Lendrem C, Pearce KF, Cope W et al (2016) miR-146a and miR-155 expression levels in acute graft-versus-host disease incidence. Front Immunol 7:56. https://doi. org/10.3389/fimmu.2016.00056 Ayaz G, Yan H, Malik N, Huang J (2022) An updated view of the roles of p53 in embryonic stem cells. Stem cell 40:883–891. https://doi.org/10.1093/stmcls/sxac051 Bi B, Schmitt R, Israilova M, Nishino H, Cantley LG (2007) Stromal cells protect against acute tubular injury via an endocrine effect. J Am Soc Nephrol 18:2486–2496. https://doi.org/10. 1681/ASN.2007020140 Bian X, Ma K, Zhang C, Fu X (2019) Therapeutic angiogenesis using stem cell-derived extracellular vesicles: an emerging approach for treatment of ischemic diseases. Stem Cell Res Ther 30: 158. https://doi.org/10.1186/s13287-019-1276-z Chen S, Smith BA, Iype J, Prestipino A, Pfeifer D et al (2015) MicroRNA-155-deficient dendritic cells cause less severe GVHD through reduced migration and defective inflammasome activation. Blood 126:103–112. https://doi.org/10.1182/blood-2014-12-617258 Collino F, Bruno S, Incarnato D, Dettori D, Neri F et al (2015) AKI recovery induced by mesenchymal stem cell-derived extracellular vesicles carrying microRNAs. J Am Soc Nephrol 26:2349–2360. https://doi.org/10.1681/ASn.2914070710 Dolezalova D, Mraz M, Barta T, Plevova K, Vinarsky V et al (2012) MicroRNAs regulate p21 (Waf1/Cip1) protein expression and the DNA damage response in human embryonic stem cells. Stem Cells 30:1362–1372. https://doi.org/10.1002/stem.1108 El-Badawy A, El-Badri N (2015) Regulators of pluripotency and their implications in regenerative medicine. Stem Cells Clon Adv Appl 8:67–80. https://doi.org/10.2147/SCCAA.S80157 Fernandes J (2016) Oncogenes: the passport for viral oncolysis through PKR inhibition. Biomark Cancer 8:101–110. https://doi.org/10.4137/BIC.S33378 Fleig SV, Humphreys BD (2014) Rationale of mesenchymal stem cell therapy in kidney injury. Nephron Clin Pract 127:75–80. https://doi.org/10.1159/000363680 Fujii YR (2013) The xenotropic microRNA gene information for stem cell studies and clinical application. Stem Cell Dis 3(1):32–36. https://doi.org/10.4236/scd.2013.31005 Fujii YR (2023) The microRNA quantum code book. Springer Nature, Singapore Fujii S, Miura Y (2022) Immunomodulatory and regenerative effects of MSC-derived extracellular vesicles to treat acute GVHD. Stem Cells 40:977–990. https://doi.org/10.1093/stmcls/sxac057 Gao M, Yao H, Dong Q, Zhang H, Yang Z et al (2016) Tumorigenicity and immunogenicity of induced neural stem cell grafts versus induced pluripotent stem cell grafts in syngeneic mouse brain. Sci Rep 6:29955. https://doi.org/10.1038/srep29955

References

183

Geiger A, Walker A, Nissen E (2015) Human fibrocyte-derived exosomes accelerate wound healing in genetically diabetic mice. Biochem Biophys Res Comm 467:303–309. https://doi.org/10. 1016/j.bbrc.2015.09.166 Ghasemi-Kasman M, Hajikaram M, Baharvand H, Javan M (2015) MicroRNA-mediated in vitro and in vivo direct conversion of astrocytes to neuroblasts. PLoS One 10:e0127878. https://doi. org/10.1371/journal.pone.0127878 Gimondi S, Dugo M, Vendramin A, Bermema A, Biancon G et al (2016) A circulating miRNA panel for prediction of acute graft-versus-host disease in lymphoma patients undergoing matched unrelated hematopoietic stem cell transplantation. Exp Hemato 44:624–634. e1. https://doi.org/10.1016/j.exphem.2016.03.005 Haider KH, Aramini B (2020) Mircrining the injured heart with stem cell-derived exosomes: an emerging strategy of cell-free therapy. Stem Cell Res Ther 11:23. https://doi.org/10.1186/ s13287-019-1548-7 Han DW, Tapia N, Hermann A, Hemmer K, Höing S et al (2012) Direct reprogramming of fibroblasts into neural stem cells by defined factors. Cell Stem Cell 10:465–472. https://doi. org/10.1016/j.stem.2012.02.021 Hong H, Takahashi K, Ichisaka T, Aoi T, Kanagawa O et al (2009) Suppression of induced pluripotent stem cell generation by the p53-p21 pathway. Nature 460:1132–1135. https://doi. org/10.1038/nature08235 Hu B, Huang M, Tao L, Li Y, Kuang Y et al (2022) Mesenchymal stem cells-derived exosomal miR-653-5p suppresses laryngeal papilloma progression by inhibiting BZW2. Clinics 78: 100129. https://doi.org/10.1016/j.clinsp.2022.100129 Jayawardena TM, Finch EA, Zhang L, Zhang H, Hodgkinson CP et al (2015) MicroRNA induced cardiac reprogramming in vivo. Circ Res 116:418–424. https://doi.org/10.1161/CIRCRESAHA. 116.304510 Kawamura T, Suzuki J, Wang YV, Menendez S, Morera LB et al (2009) Linking the p53 tumor suppressor pathway to somatic cell reprogramming. Nature 460:1140–1144. https://doi.org/10. 1038/nature08311 Kawamura T, Miyagawa S, Fukushima S, Maeda A, Kashiyama N et al (2016) Cardiomyocytes derived from MHC-homozygous induced pluripotent stem cells exhibit reduced allogenic immunogenicity in MHC-matched nonhuman primates. Stem Cell Rep 6:312–320. https://doi. org/10.1016/j.stemcr.2016.01.012 Li H, Collado M, Villasante A, Strati K, Ortega S et al (2009) The lnk4/art locus is a barrier for iPS reprogramming. Nature 460:1136–1139. https://doi.org/10.1038/nature08290 Li Z, Yang CS, Nakashima K, Rana TM (2011) Small RNA-mediated regulation of iPS cell generation. EMBO J 30:823–834. https://doi.org/10.1038/emboj.2011.2 Lia G, Di Vito C, Bruno S, Tappoaro M, Brunello L et al (2022) Extracellular vesicles as biomarkers of acute graft-vs.-host disease after haploidentical stem cell transplantation and posttransplant cyclophosphamide. Front Immunol 12:816231. https://doi.org/10.3389/fimmu. 2021.816231 Lin SL, Chang DC, Chang-Lin S, Lin CH, Wu DT et al (2008) Mir-302 reprograms human skin cancer cells into a pluripotent ES-cell-like state. RNA 14:2115–2124. https://doi.org/10.1261/ rna.1162708 Lin SL, Chang DC, Lin CH, Ying SY, Leu D et al (2011) Regulation of somatic cell reprogramming through inducile mir-302 expression. Nucleic Acids Res 39:1054–1065. https://doi.org/10. 1093/nar/gkq850 Louro AF, Paiva MA, Oliveira MR, Kasper KA, Alves PM et al (2022) Bioactivity and miRNome profiling of native extracellular vesicles in human induced pluripotent stem cell-cardiomyocyte differentiation. Adv Sci 9:2104296. https://doi.org/10.1002/advs.202104296 Marión R, Strati K, Li H, Murga M, Blanco R et al (2009) A p53-mediated DNA damage response limits reprogramming to ensure iPS cell genomic integrity. Nature 460:1149–1153. https://doi. org/10.1038/nature08287

184

9

ES Cells or iPS Cells, that Is the Question

Matsuzaka Y, Yashiro R (2022) Advances in purification, modification, and application of extracellular vesicles for novel clinical treatments. Membranes 12:1244. https://doi.org/10.3390/ membranes12121244 Matz M, Lorkowski C, Fabritius K, Durek P, Wu K et al (2016) Free microRNA lecels in plasma distinguish T-cell mediated rejection from stable graft function after kidney transplantation. Transpl Immunol S0966-3274:30050–30058. https://doi.org/10.1016/j.trim.2016.09.001 Mehanna ET, Ghattas MH, Mesbach NM, Saleh SM, Abo-Elmaty DM (2015) Association of microRNA-146a rs2910164 gene polymorphism with metabolic syndrome. Folia Biol 61:43–48 Miura A, Yamada D, Nakamura M, Tomida S, Shimizu D et al (2021) Oncogenic potential of human pluripotent stem cell-derived lung organoids with HER2 overexpression. Int J Cancer 149:1593–1604. https://doi.org/10.1002/ijc.33713 Morizane A, Doi D, Kikuchi T, Okita K, Hotta A et al (2013) Direct comparison of autologous and allogenic transplantation of iPSC-derived neural cells in the brain of nonhuman primate. Stem Cell Rep 1:283–292. https://doi.org/10.1016/j.stemcr.2013.08.007 Nakamura Y, Miyaki S, Ishitobi H, Matsuyama S, Nakasa T et al (2015) Mesenchymal-stem-cellderived exosomes accelerate skeletal muscle regeneration. FEBS Let 589:1257–1265. https:// doi.org/10.1016/j.febslet.2015.03.031 Paggetti J, Haderk F, Seifert M, Janji B, Distler U et al (2015) Exosomes released by chronic lymphocytic leukemia cells induce the transition of stromal cells into cancer-associated fibroblasts. Blood 126:1106–1117. https://doi.org/10.1182/blood-2014-12-618025 Reich DE, Lander ES (2001) In the allelic spectrum of human disease. Trends Genet 17:502–510. https://doi.org/10.1016/S0168-9525(01)02410-6 Socié G, Ritz J (2014) Current issues in chronic graft-versus-host disease. Blood 124:374–384. https://doi.org/10.1182/blood-2014-01-514752 Stevanato L, Thanabalasundaram L, Vysokov N, Sinden JD (2016) Investigation of content, stoichiometry and transfer of miRNA from human neural stem cell line derived exosomes. PLoS One 11:e0146353. https://doi.org/10.1371/journal.pone.0146353 Takahashi K, Yamanaka S (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126:663–676. https://doi.org/10.1016/j.cell. 2006.07.024 Takeda YS, Xu Q (2015) Neuronal differentiation of human mesenchymal stem cells using exosomes derived from differentiating neuronal cells. PLoS One 10:e0135111. https://doi.org/ 10.1371/journal.pone.0135111 Their M, Wörsdörfer P, Lakes YB, Gorris R, Herms S et al (2012) Direct conversion of fibroblasts into stably expandable neural stem cells. Cell Stem Cell 10:473–479. https://doi.org/10.1016/j. stem.2012.03.003 Utikal J, Polo JM, Stadtfeld M, Maherali N, Kulalert W et al (2009) Immortalization eliminates a roadblock during cellular reprogramming into iPS cells. Nature 460:1145–1148. https://doi.org/ 10.1038/nature08285 Victor MB, Richner M, Hermanstyne TO, Ransdell JL, Sobieski C et al (2014) Generation of human striatal neurons by microRNA-dependent direct conversion of fibroblasts. Neuron 84: 311–323. https://doi.org/10.1016/j.neuron.2014.10.016 Viswanathan SR, Daley GQ (2010) Lin28: a microRNA regulator with a macro role. Cell 140:445– 449. https://doi.org/10.1016/j.cell.2010.02.007 Vitaloni M, Pulecio J, Bilic J, Kuebler B, Laricchia-Robbio L et al (2014) MicroRNAs contribute to induced pluripotent stem cell somatic donor memory. J Biol Chem 289:2084–2098. https://doi. org/10.1074/jbc.M113.538702 Wang Y, Zhang L, Li Y, Chen L, Wang X et al (2015) Exosomes/microvesicles from induced pluripotent stem cells deliver cardioprotective miRNAs and prevent cradiomyocyte apoptosis in the ischemic myocardium. Int J Cardiol 192:61–69. https://doi.org/10.1016/j.ijcard.2015.05.020 Wang X, Hodgkinson CP, Lu K, Payne AJ, Pratt RE et al (2016) Selenium augments microRNA directed reprogramming of fibroblasts to cardiomyocytes via Nanog. Sci Rep 6:23017. https:// doi.org/10.1038/srep23017

References

185

Wang Z, Zhao S, Lin X, Chen G, Kang J et al (2022) Application of organoids in carcinogenesis modeling and tumor vaccination. Front Oncol 12:855996. https://doi.org/10.3389/fonc.2022. 855996 Willis RE (2016) Targeted cancer therapy: vital oncogenes and a new molecular genetic paradigm for cancer initiation progression and treatment. Int J Mol Sci 17:1552. https://doi.org/10.3390/ ijms17091552 Wu Y, Heinrichs J, Bastian D, Fu J, Nguyen H et al (2015) MicroRNA-17-92 controls T-cell responses in graft-versus-host disease and leukemia relapse in mice. Blood 126:1314–1323. https://doi.org/10.1182/blood-2015-02-627356 Xiao B, Wang Y, Li W, Baker M, Guo J et al (2013) Plasma microRNA signature as a noninvasive biomarker for acute graft-versus-host disease. Blood 122:3365–3375. https://doi.org/10.1182/ blood-2013-06-510586 Zhang Z, Wu WS (2013) Sodium butylate promotes generation of human induced pluripotent stem cells through induction of the miR-302/367 cluster. Stem Cells Dev 22:2268–2277. https://doi. org/10.1089/scd.2012.0650 Zhang H, Ma Y, Gu J, Liao B, Li J et al (2012) Reprogramming of somatic cells via TAT-mediated protein transduction of recombinant factors. Biomaterials 33:5047–5055. https://doi.org/10. 1016/j.biomaterials.2012.03.061 Zhang S, Guo Y, Cui Y, Liu Y, Yu T et al (2015) Generation of intermediate porcine iPS cells under culture condition favorable for mesenchymal-to-epithelial transition. Stem Cell Rev 11:24–38. https://doi.org/10.1007/s12015-014-9552-x Zhang Z, Yang J, Yan W, Li Y, Shen Z et al (2016) Pretreatment of cardiac stem cells with exosomes derived from mesenchymal stem cells enhances myocardial repair. J Am Heart Assoc 5:e002856. https://doi.org/10.1161/JAHA.115.002856 Zhao T, Zhang ZN, Rong Z, Xu Y (2011) Immunogenicity of induced pluripotent stem cells. Nature 474:212–216. https://doi.org/10.1038/nature10135 Zhong X, Li N, Liang S, Huang Q, Coukos G et al (2010) Identification of microRNAs regulating reprogramming factor LIN28 in embryonic stem cells and cancer cells. J Biol Chem 285:41961– 41971. https://doi.org/10.1074/jbc.M110.169607

Chapter 10

“MIRAI” Healthcare: “Future” in Japanese MicroRNA Memory for Medicine

Each state of a dynamic system at a particular time corresponds to a ket vector. Dirac, P. A. M. The Principles of Quantum Mechanics

Overview Combining artificial intelligence (AI) and microRNA (miRNA) can provide precious medical devices. The Human Genome Project determined the complete genome sequences and protein gene functions, whereas genome-wide association studies (GWAS) failed to determine disease-associated alleles. At that time, it was speculated that the statistical accuracy of allelic heterogeneity at susceptibility loci played an important role in success or failure. However, most protein genes are controlled by miRNAs, as their fate is determined by interactions between the 3′ untranslated region (3’UTR) of messenger RNA (mRNA) and the seed of miRNAs. Therefore, miRNA expression profiles in noncoding regions are implicated in human diseases, such as common diseases, rather than single nucleotide polymorphisms (SNPs) in protein coding exons. Because one miRNA targets many mRNAs of protein genes and one mRNA target is regulated by many miRNAs, the miRNA expression package has been exploited as a precious medical biomarker for multiple diseases. Many doctors still believe that only protein structures and immune cells can cure human diseases, but miRNA genes are noncoding genes. MiRNAs are just information, and that information can cure human diseases. Protein structure is only 1–2% of genomic information’s dream, and the remaining 98% of genomic information must be awakened. Since human diseases are determined by the miRNA memory package (MMP), the feasibility of personalized medicine is completely dependent on miRNA combo (panel) information.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1_10

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miRNA Quantum Language for Clinical Use

MicroRNA (miRNA) is one of the RNA information genes (Rigs). Based on the quantum RNA information in Chap. 5, human blood or cell or tissue miRNA information has revealed implications between synergistic miRNA function as miRNA memory package (MMP) and human disease (Osone et al. 2015a). The correlation of double nexus score (DNS) fluctuation (DNSF) between cancer and normal tissues were expressed as a linear function with less than low p values. The DNSF fluctuation is calculated as follows: See Chap. 6 for the DNS formula. Each expression level of miRNA is defined as B0, . . ., BN-1. DNSF =

N -1

N

Xi

Bp ×

R

p=0

i=1 j=1

0 ≤ RND
0.715, sensitivity 0.6, specificity >0.5), serum (AUC >0.6, p < 0.0001), and cerebrospinal fluid ( p < 0.003) (Kumar and Reddy 2016; Burgos et al. 2014) (Fig. 10.6). These are also divergent among research groups. We collected clinical data and calculated the MMP of the miRNA profile for the diagnosis of AD (Fig. 10.7). However, more statistically significant bedside data for miRNA panels by meta-analysis are needed. Therefore, data mining is important for configurating MMP. Using MMPs, the profiles of sample miRNAs are distinct, but the radar charts of MMPs are somewhat similar to each other (Fig. 10.7). In addition, MMPs rely on unique miRNA profiles from sample sources, as shown in Fig. 10.6. In metabolic syndrome, miRNA profiles from different sample sources were analyzed in plasma (Zampetaki et al. 2010; Zhang et al. 2013; Wang et al. 2014)

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Chaos and Fuzzy: Flow of Information from Diagnosis to Treatment

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Plasma

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CSF continued

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Fig. 10.6 Sample sources of miRNA diagnosis for Alzheimer’s disease (AD). Profiles of miRNA are presented in plasma, serum, whole blood, and cerebrospinal fluid samples of Alzheimer’s disease (AD)

and serum (Karolina et al. 2012; Kong et al. 2011; Wang et al. 2016c) in type 2 diabetes mellitus (T2DM) and in obesity and T2DM (Pescador et al. 2013). The different MMPs also give different results (Fig. 10.8). Radar charts for three common metabolic disorders, atherosclerosis and T2DM from plasma data (Zhang et al. 2016) and obesity and T2DM, are clearly distinguishable from each other. It is known that obesity can induce T2DM (Villard et al. 2015) and that T2DM can induce atherosclerosis (Apro et al. 2016), but 5 MMPs are different (Fig. 10.8).

10.3

Chaos and Fuzzy: Flow of Information from Diagnosis to Treatment

Allelic heterogeneity at disease-prone loci revealed too many determinants to identify disease drivers, even within at least 1–2% of protein genomic information. However, given the success of MMP diagnosis, the identification of miRNAs susceptible to disease advances information processing for treatment by agents. In turn, a new algorithm will bring order to chaos, and the quantum properties of miRNA will lead to improved treatments with new drugs for the treatment of

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Fig. 10.7 The source map of MMP in Alzheimer’s disease. Blood (red), plasma (pale blue), serum (orange), and cerebrospinal fluid (light green) are represented as MMP of Alzheimer’s disease (AD)

Atherosclerosis Obesity

T2DM serum

T2DM plasma

T2DM Obesity

Fig. 10.8 MMP of metabolic syndrome. Type 2 diabetes mellitus (T2DM) (the bottom panels), obesity (right upper panel), and atherosclerosis (light upper panel) are presented as the MMP. Serum (left bottom panel) and plasma (center and right bottom panels) in diabetes are compared. Furthermore, the obesity plus diabetes case (right bottom panel) is also calculated

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Fig. 10.9 Common miRNAs among four neurodegenerative conditions Common miRNAs in Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FD), and Huntington’s disease (HD) were analyzed by Venn diagram

diseases that we have only imaged. Information flows are (1) to find core miRNAs related to similar diseases or complex syndromes, (2) to use experimentally validated data from miRTarBase for target selection, (3) to evaluate inferred data by TargetScan, and (4) to be powered by DNS. Searching for proven miRNA/miRNA interactions. We called this algorithm design miRNA entangling target sorting (METS) (Yoshikawa and Fujii 2016). Five panels with 100 miRNAs in AD, 44 miRNAs in PD, 28 miRNAs in FD, and 93 miRNAs in HD were collected, and common miRNAs were selected (Fig. 10.9). However, statistical processing of miRNA panel information is still insufficient. For linking with the STRING database (string-db.org, ver. 10.0), the protein/ protein interaction of STRING was tested to integrate into miRNA entangling target sorting (METS). We can understand the pathogenicity of human diseases through this architecture (Fig. 10.10) (Fujii 2023). miR-143 and miR-195-5p were selected in a Bacille Calmette-Guerin (BCG)-vaccination simulation (Tamgue et al. 2019; Sereshgi et al. 2020). THY1 was augmented by the downregulation of miR-1435p in combination with miR-103a-3p and miR-107. In mice, memory immunity to BCG vaccination is mediated by Thy1.2+ T-lymphocytes (Orme 1988). This METS/ miRNA quantum language and artificial intelligence (MIRAI) analysis allowed us to examine the etiology of severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) infection, excluding the association between coronavirus disease 2019 (COVID-19) and BCG treatment (Fujii 2020a, b, 2023). Universal miRNAs susceptible to neurodegeneration are investigated by the METS method. Six of the nine common miRNAs showed high DNSs, and miRTarBase selected their targets with high scores in CSC of above 0.1 for

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Fig. 10.10 Preparation of METS analysis. The quantum status of BCG-treated individuals is represented by preprocessing of METS/MIRAI

experimental evidence. Next, protein/protein interaction networks were also explored by STRING (Fig. 10.11). However, this METS analysis had no computer validation with a statistically integrated AI. Six selected miRNAs, miR-29a-3p, miR-29b-3p, miR-124-3p, miR-9-3p, miR-132-3p, and miR-138-5p, are associated with brain memory. The miR-29 family consists of miR-29a, miR-29b-1, miR-29b-2, and miR-29c, whereas METS programming miR-29a and miR-29b target BACE1 and BDNF, respectively. miR-29a is highly increased in the brain, and miR-29b expression is high in neurons (Jovicic et al. 2013). miR-124 is also a brain-enriched miRNA, but behavioral disorders cause a decrease in miR-124, which targets the AMPA receptor (Gascon et al. 2014). miR-124 targets beta-secretase 1 (BACE1) and brain-derived neurotrophic factor (BDNF). miR-9 can target the RE silencing transcription factor (REST) (Packer et al. 2008). In this analysis, miR-9 targeted BACE1, histone deacetylase 4 (HDAC4) and sirtuin 1 (SIRT1), and miR-124 targeted BACE1 and SIRT1. Therefore, the targets of miR-124 and miR-9 closely overlap. miR-132 is known to target methyl-CpG binding protein 2 (MeCP2), but miR-132 can target BDNF and SIRT1. The protein/protein interactions are complex, as both SIRT1 and HDAC4 are highly expressed as transcription factor regulators in the nucleus of

10.4

A Universal MMP for miRNAs in Human Disease?

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Fig. 10.11 MMP implication in neurodegenerative disorders. Six miRNA panels, miR-29a-3p, miR-29b-3p, miR-124-3p, miR-9-3p, miR-132-3p, and miR-138-5p, as MMPs are linked with four proteins, BACE1, BDNF, HDAC4, and SIRT1 (red square)

brain cells. MMPs can then control SIRT1 and HDAC4 and interacting transcription factors. However, the use of Venn diagram analysis contradicts the theory of multiple function miRNAs. Therefore, in the sense of etiological analysis of human diseases, searching for miRNA common denominators may not be relevant.

10.4

A Universal MMP for miRNAs in Human Disease?

To explore universal MMPs for therapeutic drug development, we compared neurodegenerative MMPs with a common miRNA 6 panel (Fig. 10.12). MMPs are calculated for nine neurodegenerative disorders and behavioral disorders. Bipolar disorder, fragile X syndrome, AD, PD, FD, HD, temporal lobe epilepsy, schizophrenia, and autism present a characteristic picture of MMPs. Universal MMPs from six miRNAs, miR-29a-3p, miR-29b-3p, miR-124-3p, miR-9-3p, miR-132-3p, and miR-138-5p, may be similar to bipolar disorder, AD, frontotemporal dementia, and schizophrenia MMP pictures, but these MMP pictures can be distinguished (Fig. 10.12). To further understand the relationship between universal MMPs and four key proteins in memory impairment disorders, pathways were investigated (Fig. 10.13). For depression, it was speculated that neurotrophic factors may be related to the induction of long-term potentiation (Aicardi et al. 2004). Memory defects in neurodegenerative disorders are related to miRNAs (Hernendez-Rapp et al. 2017). Cyclin

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bipolar_all autism_all fragile X_All

AD_Blood schizphrenia_all Brain Function

Temporal Lobe Epilepsy_HC

PD_full HD_full

FD_full

Fig. 10.12 Universal MMP for therapeutic agent development. Nine neurodegenerative disorders and behavioral diseases, bipolar disorder, fragile X syndrome, Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FD), Huntington’s disease (HD), temporal lobe epilepsy, schizophrenia, and autism, are represented as MMP (red), and universal MMP is centered (orange)

checkpoint abnormalities are associated with dementia (Katsel et al. 2013). Neurodegenerative disorders are complex. HDAC4 is involved in cognitive function in synaptic plasticity, neuronal survival and development, and ultimately behavior, and its functions involve the SUMOconjugated enzyme Ubc9 (Schwartz et al. 2016). Suppression of HDAC4 impairs synaptic plasticity, learning, and memory (Kim et al. 2012). Upregulation of HDAC4 has been observed in AD, frontotemporal lobar degeneration (dementia), autism spectrum disorders, and depression (Wu et al. 2016), possibly due to gap-stopping neuroprotection. HDAC is increased in neurodegenerative disorders. The universal MMPs miR-9 and miR-29b regulate HDAC4, but the etiological significance of HDAC4 and these miRNAs in neurodegenerative disorders remains to be clarified. As explained in Chap. 3, SIRT1 is intimately implicated in miR-34 and TP53. SIRT1 is linked to neuroprotection in AD, PD, and HD (Herskovits and Guarente 2013). This is the first report that the miR-9, miR-132, and miR-138 packages of universal MMPs control SIRT1 in neural memory. BDNF is also known to protect against amyloid-β peptide neurotoxicity in AD (Aicardi et al. 2004). BACE1 is a target for therapeutic strategies in AD; however, BACE knockout (KO) mice show neural dysfunction (Munro et al. 2016).

10.5

MiR-34 Surveillance?

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Fig. 10.13 Pathway analysis of miRNA comb. Six proteins, SIRT1, HDAC4, MeCP2, BDNF, BACE1, and AMPA, were connected in six miRNA panels, miR-9, miR-124, miR-132, miR-138, miR-29a, and miR-29b, in neuronal cells. Blue lines show suppression, red lines represent activation, and red dotted lines indicate secretion

Dysfunction includes hypermyelination, axon guidance errors in the olfactory bulb and hippocampus, and schizophrenia endophenotypes, ultimately reducing spine density and working memory deficits. BACE inhibitors in chemotherapy and monoclonal antibody treatment therefore have limited relevance for using adult brains, even if AD patients are less at risk than those with disabilities. In contrast, miRNA panel agents can be controlled to serve the brain in an integrated manner according to MMPs. Subsequently, METS/MIRAI with MMP showed the etiology of AD (Fujii 2021, 2023). Amyloid-β peptide-related target proteins were not output (Fujii 2021). Furthermore, therapeutic targets of major depressive disorders, traumatic brain injury, and AD are shown by METS/MIRAI in Chap. 5.

10.5 MiR-34 Surveillance? In Fig. 10.9, miR-34b/c is also included in the universal panel. In Chap. 3, miR-34 is involved in anticancer effects. Simultaneously, miR-34 is implicated in neurodegenerative diseases such as AD (Hernandez-Rapp et al. 2017). From METS analysis, miR-34a/c is associated with let-7, miR-206, miR-373, and miR-449 (Fig. 10.14). SIRT1, MYC, MTA2, MDM4, MET, AXL, and BCL2 are oncogenes or cancerrelated genes. On the other hand, SIRT1, MYC, MDM4, HDAC1, NOTCH1, and BCL2 are related to neurodegenerative disorders. Upregulation of the MDM4mouse doubled minute 4 homolog checkpoint protein is associated with progression of AD dementia via cell cycle events (Katsel et al. 2013). Although MDMx is a

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Fig. 10.14 MMP map of miR-34a/c. The five-miRNA panel controls the expression of ten proteins

suppressor of the p53 protein and miR-34 can target MDM4 exon 11, amyloid-β degraded MDM4 through caspase in AD model mice (Colacurcio et al. 2015). If so, miR-34 is useful for tumor suppression, but in neurodegenerative diseases, it is unclear whether MDM4 induces neural death or rescues neural death in AD or is due to species bias. Thus, in human tumor and neurodegenerative disorder cases, it is clear that miRNA function should be discussed in a miRNA panel, as shown in Fig. 10.14, but otherwise controversial. Increased NOCH1 expression levels have been observed in AD and are associated with gamma-secretase activation that releases amyloid-β peptide, as NOCH1 is a substrate for activating gamma-secretase (Woo et al. 2009). Subsequently, Hemandez-Rapp et al. (2017) confirmed that miR-132, miR-124, and miR-34 are associated with memory impairment. However, using circulating miRNA biomarkers, miR-34a/c-5p was not associated with AD in METS/MIRAI (Fujii 2021). These results remarkably demonstrate that disease etiology analysis by searching for universal miRNAs, symbolized by Venn diagrams, is misdirected. Furthermore, as described in Chap. 5, memory may be constructed by miRNA quantum codes and contained in not only neurons but also circulating systems as MMPs.

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MIRAI System for Healthcare: Hepatocellular Carcinoma

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MIRAI System for Healthcare: Hepatocellular Carcinoma

With DNS, miRNA/miRNA interactions become physical quantities, and physical values can be converted to binary values. Binary quantum information shows distinct expression among metabolic or neurodegenerative diseases. The miRNA quantum state of human diseases has been suggested to be useful for differential diagnosis and etiological analysis. Furthermore, MIRAI is involved in life management by miRNA quantum memory (Fujii 2023). MMPs are stored in gadgets, and personal MMP data are sent to the cloud database. A cloud database compares the MMP of disease data, while an AI algorithm explores the individual’s ailments. MIRAI feeds back healthcare information to mobile phones and mobile devices. It may be a good idea to use the Metaverse for AI doctor METS/MIRAI medical care. With the METS/MIRAI algorithm, miRNA panel agents could be delivered to individuals by drone as the delivery route (Fig. 10.15). The system is composed of three databases with an AI algorithm. One stores personal MMPs, and the other stores human disease MMP data. It is possible to check healthcare for disease prediction through mobile phones. The third database is for AI. AI doctor METS/MIRAITM diagnoses an individual’s illness and delivers agents for therapy. The system platform could be constructed on a quantum processor combined with personal super computers (PSCs). Need not human doctors. In 2023, the METS/MIRAI system using circulating miRNA panels elucidated the etiology of various human diseases through the miRNA quantum code (Fujii 2023).

Fig. 10.15 AI doctor METS/MIRAITM system for new healthcare in the cloud database

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Fig. 10.16 METS/MIRAI analysis of hepatocellular carcinoma for new healthcare. Network analysis by METS/MIRAI simulation was shown in human hepatocellular carcinoma (HCC) using circulating miRNA conventional panels in 2020. miRNA: red, upregulation. Proteins: blue, downregulation

Etiological analysis by conventional hepatocellular carcinoma (HCC) biomarkers was performed by METS/MIRAI analysis (stage I-II, 51.8%; III-IV, 28.0%) (Fig. 10.16) (Fujii 2020c). The AUC of this biomarker was over 0.95, and in a meta-analysis using 34 studies, the AUC for tumorigenesis was 0.92. The tumor suppressor protein PTEN was suppressed by upregulation of miR-106b-5p along with miR-20a-5p, miR-17-5p, and miR-214-3p. In addition, the tumor suppressor protein RB1 was also reduced by upregulation of miR-106a/b-5p. It is similar to the case of Yamanaka factor-induced pluripotent stem (iPS) cells. Although this is a sample of cancer etiologic analysis, METS/MIRAI is useful for the determination of cancer therapeutic targets.

References Aicardi G, Argilli E, Cappello S, Riccio M, Thoenen H et al (2004) Induction of long-term potentiation and depression is reflected by corresponding changes in secretion of endogenous brain-derived neurotrophic factor. Proc Natl Acad Sci U S A 101:15788–15792. https://doi.org/ 10.1073/pnas.0406960101

References

203

Apro J, Tietge UJ, Dikkers A, Parini P, Angelin B et al (2016) Impaired cholesterol efflux capacity of high-density lipoprotein isolated from interstitial fluid in type 2 diabetes mellitus. Arterioscler Thromb Vasc Biol 36:787–791. https://doi.org/10.1161/ATVBAHA.116.307385 Burgos K, Malenica I, Metpally R, Courtright A, Rakela B et al (2014) Profiles of extracellular miRNA in cerebrospinal fluid and serum from patients with Alzheimer’s and Parkinson’s diseases correlate with disease status and features of pathology. PLoS One 9:e94839. https:// doi.org/10.1371/journal.pone.0094839 Colacurcio DJ, Zyskind JW, Jordan-Sciutto KL, Espinoza CA (2015) Caspase-dependent degradation of MDMx/MDM4 cell cycle regulatory protein in amyloid ß-induced neural damage. Neurosci Lett 609:182–188. https://doi.org/10.1016/j.neulet.2015.10.031 Cui ZH, Shen SQ, Chen ZB, Hu C (2014) Growth inhibition of hepatocellular carcinoma tumor endothelial cells by miR-204-3p and underlying mechanism. World J Gastroenterol 20:5493– 5504. https://doi.org/10.3748/wjg.v20.i18.5493 Fix LN, Shah M, Efferth T, Farwell MA, Zhang B (2010) MicroRNA expression profile of MCF-7 human breast cancer cells and the effect of green tea polyphenon-60. Cancer Genomics Proteomics 7:261–277 Fujii YR (2020a) The etiology of COVID-19 in silico by SARS-Cov-2 infection with the quantum microRNA language-AI. Virol Immunol J 4:000243. https://doi.org/10.23880/vij-16000243 Fujii YR (2020b) The COVID-19 deadly risk assessment upon the updated etiologic computer simulation by quantum microRNA language in SARS-CoV-2 infection in eo. Int J Clin Case Stud Rep 3:142–154 Fujii YR (2020c) The quantum microRNA immunity in human virus-associated disease: virtual reality of HBV, HCV and HIV-1 infection, and hepatocellular carcinogenesis with AI machine learning. Arch Clin Biomed Res 4:89–129. https://doi.org/10.26502/acbr.50170092 Fujii YR (2021) Analysis for brain tumors and Alzheimer’s disease using quantum microRNA language with artificial intelligence (MIRAI). SunText Rev Med Clin Res 2:142. https://doi.org/ 10.51737/2766-4813.2021.043 Fujii YR (2023) The MicroRNA quantum code book. Springer Nature, Singapore Gao D, Zhang Y, Zhu M, Liu S, Wang X (2016) miRNA expression profiles of HPV-infected patients with cervical cancer in the Uyghur population in China. PLoS One 11:e0164701. https://doi.org/10.1371/journal.pone.0164701 Gascon E, Lynch K, Ruan H, Almeida S, Verheyden JM et al (2014) Alterations in microRNA-124 and AMPA receptors contribute to social behavioral deficits in frontotemporal dementia. Nat Med 20:1444–1451. https://doi.org/10.1038/nm.3717 Hernendez-Rapp J, Rainone S, Hébert SS (2017) MicroRNAs underlying memory deficits in neurodegenerative disorder. Prog Neuro-Psychopharmacol Biol Psychiatry 73:79–86. https:// doi.org/10.1016/j.pnpbp.2016.04.011 Herskovits AZ, Guarente L (2013) Sirtuin deacetylases in neurodegenerative diseases of aging. Cell Res 23:746–758. https://doi.org/10.1038/cr.2013.70 Jia W, Wu Y, Zhang Q, Gao GE, Zhang C et al (2015) Expression profile of circulating microRNAs as a promising fingerprint for cervical cancer diagnosis and monitoring. Mol Clin Oncol 3:851– 858. https://doi.org/10.3892/mco.2015.560 Jovicic A, Roshan R, Moisoi N, Pradervand S, Moser R et al (2013) Comprehensive expression analysis of neural cell-type-specific miRNAs identify new determinants of the specification and maintenance of neuronal phenotypes. J Neurosci 33:5127–5137. https://doi.org/10.1523/ JNEUROSCI.0600-12.2013 Karolina DS, Tavintharan S, Armugam A, Sepramaniam S, Pek SL et al (2012) Circulating miRNA profiles in patients with metabolic syndrome. J Clin Endocrinol Metab 97:E2271–E2276. https://doi.org/10.1210/jc.2012-1996 Katsel P, Tan W, Fam P, Purohit DP, Haroutunian V (2013) Cycle checkpoint abnormalities during dementia: a plausible association with the loss of protection against oxidative stress in Alzheimer’s disease. PLoS One 8:e68361. https://doi.org/10.1371/journal.pone.0068361

204

10

“MIRAI” Healthcare: “Future” in Japanese

Kim MS, Akhtar MW, Adachi M, Mahgoub M, Bassel-Duby R et al (2012) An essential role for histone deacetylase 4 in synaptic plasticity and memory formation. J Neurosci 32:10879–10886. https://doi.org/10.1523/JNEUROSCI.2089-12.2012 Kong L, Zhu J, Han W, Jiang X, Xu M et al (2011) Significance of serum microRNAs in prediabetes and newly diagnosed type 2 diabetes: a clinical study. Acta Diabetol 48:61–69. https://doi.org/10.1007/s00592-010-0226-0 Kumar S, Reddy PH (2016) Are circulating microRNAs peripheral biomarkers for Alzheimer’s disease? Bioinformatics Acta 1862:1617–1627. https://doi.org/10.1016/j.bbadis.2016.06.001 Ma L, Xu Z, Xu C, Jiang X (2016) MicroRNA-148a represents an independent prognostic marker in bladder cancer. Tumor Biol 37:7915–7920. https://doi.org/10.1007/s13277-015-4688-0 Matsumoto M, Nishimura T (1998) Mersenne twister: a 623-dementionally equidistributed uniform pseudorandom number generator. ACM Trans Model Comput Simul 8:3–30 Medimegh I, Omrane I, Privat M, Uhrhummer N, Ayari H et al (2014) MicroRNAs expression in triple negative vs nontriple negative breast cancer in Tunisia: interaction with clinical outcome. PLoS One 9:e111877. https://doi.org/10.1371/journal.pone.0111877 Munro KM, Nash A, Pigoni M, Lichtenthaler SF, Gunnersen JM (2016) Functions of the Alzheimer’s disease protease BACE1 at the synapse in the central nervous system. J Mol Neurosci 60:305–315. https://doi.org/10.1007/s12031-016-0800-1 Orme IM (1988) Evidence for a biphasic memory T-cell response to high dose BCG vaccination in mice. Tubercle 69:125–131. https://doi.org/10.1016/0041-3879(88)90075-x Osone T, Yoshikawa M, Fujii YR (2015a) Quantum fluctuations in biological functions: computational analysis of diseases with the human microRNA memory package. SAI Comp Conf (SAI) 2016:1206–1210. https://doi.org/10.1109/SAI.2016.7556131 Osone T, Yoshikawa M, Fujii YR (2015b) MicroRNA memory II: a novel scoring integration model for prediction of human disease by microRNA/microRNA quantum multi-interaction. J Adv Med Pharm Sci 5:1–18. https://doi.org/10.9734/JAMPS/2016/22095 Packer AN, Xing Y, Harper SQ, Jones L, Davidson BL (2008) The bifunctional microRNA miR-9/ miR-9* regulates REST and CoREST and is downregulated in Huntington’s disease. J Neurosci 28:14341–14346. https://doi.org/10.1523/JNEROSCI.2390-08.2008 Pasquier C, Gardes J (2016) Prediction of miRNA-disease associations with a vector space model. Sci Rep 6:27036. https://doi.org/10.1038/srep27036 Pescador N, Pérez-Barba M, Ibarra JM, Corbatón A, Martinez-Larrad MT et al (2013) Serum circulating microRNA profiling for identification of potential type 2 diabetes and obesity biomarkers. PLoS One 8:e77251. https://doi.org/10.1371/journal.pone.0077251 Schwartz S, Truglio M, Scott MJ, Fitzsimons HI (2016) Long-term memory in drosophila is influenced by the histone deacetylase HDAC4 interacting with SUMO-conjugating enzyme Ubc9. Genetics 203:1249–1264. https://doi.org/10.1538/genetics.115.183194 Sereshgi MMA, Majd AMM, Salimi S, Noorbazargan H (2020) Role of microRNAs in BCG therapy by induction of neutrophil extracellular traps in bladder cancer. Immunoregulation 3: 17–28. https://doi.org/10.32598/Immunoregulation.3.1.3 Song T, Xia W, Shao N, Zhang X, Wang C et al (2010) Differential miRNA expression profiles in bladder urothelial carcinoma. Asian Pac J Cancer Prev 11:905–911 Tamgue O, Gcanga L, Ozturk M, Whitehead L, Pillay S et al (2019) Differential targeting of c-Maf, Bach-1, and Elmo-1 by microRNA-143 and microRNA-365 promotes the intracellular growth of mycobacterium tuberculosis in alternatively IL-4/IL-13 activated macrophages. Front Immunol 10:421. https://doi.org/10.3389/fimmu.2019.00421 Tang S, Wu WK, Li X, Wong SH, Wong N et al (2016) Stratification of digestive cancers with different pathological features and survival outcomes by microRNA expression. Sci Rep 6: 24466. https://doi.org/10.1038/srep24466 Taskesen E, Huisman SM, Mahfouz A, Krijthe JH, de Ridder J et al (2016) Pancancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics. Sci Rep 6:24949. https://doi.org/10.1038/srep24949

References

205

Villard A, Marchand L, Thivolet C, Rome S (2015) Diagnostic value of cell-free circulating microRNAs for obesity and type 2 diabetes: a meta-analysis. J Mol Biomark Diagn 6:pii 251. https://doi.org/10.4172/2155-9929.1000251 Wang X, Sundquist J, Zöller B, Memon AA, Palmér K et al (2014) Determination of 14 circulating microRNAs in swedes and Iraqis with without diabetes mellitus type 2. PoLS One 9:e86792. https://doi.org/10.1371/journal.pone.0086792 Wang C, Hu J, Lu M, Gu H, Zhou X et al (2015) A panel of five serum miRNAs as a potential diagnostic tool for early-stage renal cell carcinoma. Sci Rep 5:7610. https://doi.org/10.1038/ srep07610 Wang F, Li B, Xie X (2016a) The roles and clinical significance of microRNAs in cervical cancer. Histol Histopathol 31:131–139 Wang S, Xiang J, Li Z, Lu S, Hu J et al (2016b) A plasma microRNA panel for early detection of colorectal cancer. Int J Cancer 136:152–161. https://doi.org/10.1002/ijc.28136 Wang C, Wan S, Yang T, Niu D, Zhang A et al (2016c) Increased serum microRNAs are closely associated with the presence of microvascular complications in type 2 diabetes mellitus. Sci Rep 6:20032. https://doi.org/10.1038/srep20032 Wong N, Khwaja SS, Baker CM, Gay HA, Thorstad WL et al (2016) Prognostic microRNA signatures derived from the cancer genome atlas for head and neck squamous cell carcinoma. Cancer Med 5:1619–1628. https://doi.org/10.1002/cam4.718 Woo HN, Park JS, Gwon AR, Arumugam TV, Jo DG (2009) Alzheimer’s disease and notch signaling. Biochem Biophys Res Commun 390:1093–1097. https://doi.org/10.1016/j.bbrc. 2009.10.093 Wu X, Weng L, Li X, Guo C, Pal SK et al (2012) Identification of a 4-microRNA signature for clear cell renal cell carcinoma metastasis and prognosis. PLoS One 7:e35661. https://doi.org/10.1371/ journal.pone.0035661 Wu Y, Hou F, Wang X, Kong Q, Han X et al (2016) Aberrant expression of histone deacetylases 4 in cognitive disorder: molecular mechanisms and a potential target. Front Mol Neurosci 9:114. https://doi.org/10.3389/fnmol.2016.00114 Yi R, Li Y, Wang FL, Miao G, Qi RM et al (2016) MicroRNAs as diagnostic biomarkers in colorectal cancer. World J Gastrointest Oncol 8:330–340. https://doi.org/10.4251/wjgo.v8. i4.330 Yoshikawa M, Fujii YR (2016) Human ribosomal RNA-derived resident microRNAs as the transmitter of information upon the cytoplasmic cancer stress. Biomed Res Int 2016:7562085. https://doi.org/10.1155/2016/7562085 Zampetaki A, Kiechl S, Drozdov I, Willeit P, Mayr U et al (2010) Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circ Res 107:810– 817. https://doi.org/10.1161/CIRCRESAHA.110.226357 Zekri AN, Youssef AS, El-Desouky ED, Ahmed OS, Lotfy MM et al (2016) Serum microRNA panels as potential biomarkers for early detection of hepatocellular carcinoma on top of HCV infection. Tumor Biol 37:12273–12286. https://doi.org/10.1007/s13277-016-5097-8 Zhang T, Lv C, Li L, Chen S, Liu S et al (2013) Plasma miR-126 is a potential biomarker for early prediction of type 2 diabetes mellitus in susceptible individuals. BioMed Res Inter 2013: 761617. https://doi.org/10.1155/2013/761617 Zhang JY, Gong YL, Li CJ, Qi Q, Zhang QM et al (2016) Circulating miRNA biomarkers serve as a fingerprint for diabetic atherosclerosis. Am J Transl Res 8:2650–2658 Zhu C, Ren C, Han J, Ding Y, Du J et al (2014) A five-microRNA panel in plasma was identified as potential biomarker for early detection gastric cancer. Br J Cancer 110:2291–2299. https://doi. org/10.1038/bjc.2014.119

Chapter 11

Deep Learning of miRNAs for Therapeutic Applications Cook PAD for Therapeutics

All things are poison and nothing is without poison. von Hohenheim, T. (Paracelsus)

Overview The RNA Precious Medical Initiative requires a safe, affordable, and sustainable device as an administration tool. Why do microRNA (miRNA) quantum language and artificial intelligence (MIRAI) heal us? Even if deep learning is done by a cool computer machine, the therapeutic tools are present like food, since plants are probably the origin of RNA species. While plant and meat miRNAs in exosomes may be transported daily from food to humans at dinner, edible RNA agents may be of interest in drug discovery upon MIRAI’s deep learning for therapeutic applications. Several plant expression systems are already in place, including edible vaccines, bananas, rice, alfalfa, mushrooms, potatoes, tomatoes, peanuts, and maize. Traditional Chinese herbal medicine has involved diet, acupuncture with burning Moxa to warm the skin, massages with botanical oils, and meditation under cooking incense, all of which are linked to miRNAs. The honeysuckle herb contains MIR2911, which is incorporated into the human circulation system via the intestine, and MIR2911 prevented H5N1 flue infection in mouse lungs and coronavirus disease 2019 (COVID-19) in severe respiratory syndrome human coronavirus 2 (SARS-CoV-2)-infected patients. MIR2097-5p in rice has been reported to be responsible for the low degree of COVID-19 epidemics in Asian countries, including Japan. Therefore, edible miRNA panel agents would solve the inconvenience of chemotherapy through complex information technology using quantum RNA language. Moreover, since maternal undernutrition inheritably influences heart failure in adult offspring via altered miRNAs in the mother’s uterus, these clinical reports suggest that environmental factors may be mediated by transferred miRNA comb genes, which can be passed from mother to child. Thus, Darwinism and Mendelian inheritance are controlled by miRNA gene information from food. MiRNA programmed evolutionary therapeutics may be developed by deep learning or AI. This chapter presents a program analysis diagram (PAD) for using miRNAs to cook therapeutics.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1_11

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Edible miRNAs Regulate Mendelian and Darwinism: COVID-19 Infection and Rice miRNAs

Honeysuckle MIR-2911 was incorporated into mice and humans to inhibit influenza and coronavirus disease 2019 (COVID-19) virus infection. The herb Honeysuckle is effective in preventing respiratory disease (Zhou et al. 2015, 2020a), and human SID transmembrane family member 1 (SIDT1) in the gastric pit cell membrane mediates dietary microRNA (miRNA) (including MIR-2911) uptake into cells (Zhou et al. 2020b). Exogenous miRNAs from food have been computationally characterized as identified in human circulation (Shu et al. 2015) and the database DMD (Chiang et al. 2015). However, altered cardiac function in offspring is involved in maternal undernutrition during pregnancy via miRNAs (Gray et al. 2014). Maternal obesity during pregnancy is associated with an increased risk of developing metabolic diseases in offspring via miRNAs (Fernandez-Twinn et al. 2014; Alfaradhi et al. 2016; Zheng et al. 2016). In Chap. 3, we introduced that miRNAs in mother’s breast milk can be transferred to infants (Alsaweed et al. 2015). Furthermore, artificial MRX34 in liposomes showed antitumor activity via intravenous injection in a phase I trial (Beg et al. 2016). For successful cancer immunotherapy, miR-148a inhibitors, including nanovaccines, increased immunosuppressive tumor-associated dendritic cells, resulting in robust tumor regression with prolonged survival (Liu et al. 2016). This evidence suggests that edible miRNA vaccines, including messenger RNA (mRNA) vaccines, may be developed for therapeutic approaches such as human immunodeficiency virus type 1 (HIV-1) infection and COVID-19 (Fujii 2013, 2023). To further understand the implications of edible miRNA agents, we simulate examples of miRNA incorporation by European and Asian diets (Fig. 11.1). Plant and meat miRNAs in exosomes are not degraded by cooking and digestion. Xenotropic miRNAs (xenomiRNAs) can enter human serum and tissues through the circulatory system. Incorporated miRNAs can regulate human protein genes and affect homeostasis. Furthermore, miRNA information is inherited from mother to child. Therefore, common disease phenotypes differ between rice- and beef-based people. In fact, METS/MIRAI showed that rice MIR2097-5p is responsible for the low degree of coronavirus disease 2019 (COVID-19) epidemics in Asian countries, including Japan (Fujii 2023) (Fig. 11.2). MIR2097-5p showed binding activity to severe respiratory syndrome human coronavirus 2 (SARS-CoV-2) viral miRNAs derived from the orf10 region and the 3′ untranslated region (3′UTR) of the viral genome. Inhibition of the hypoxia-related protein HIF1A was blocked by MIR20975p through downregulation of viral Cov-miR-5. Reduction of immune deficiencyrelated C1QA and the mitochondrial antiviral signaling protein (MAVS/VISA) was also blocked by MIR2097-5p through suppression of Cov-miR-4. Therefore, it is suggested that in Japan, both the number of patients and the number of deaths per million people are much lower than those in the USA and Europe (Fig. 11.2). Nutrients are the building blocks necessary to maintain healthy function and prevent illness in an individual. Until the twentieth century, three major nutrients— fat, sugar, and protein—provided humans with energy, but there was a significant

11.1

Edible miRNAs Regulate Mendelian and Darwinism: COVID-19 Infection and. . .

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Fig. 11.1 Inheritable food miRNAs and SARS-CoV-2 viral miRNAs

correlation between high fat intake and certain cancers, such as colon cancer. In contrast, a high-fiber diet is associated with cancer prevention. Both meat and rice have been suggested to contain specific information that regulates human health. This information is the starting point of the “Food and medicine cognate,” and food is life and the origin of species. However, no one can explain what food information is. It may also be an unsaturated fatty acid (Du et al. 2004), but this is controversial, as nutritional energy is not fully associated with colon cancer induction. Now, miRNA can be converted into quantum energy. European main dishes beef and Asian rice contain miRNA double nexus score (DNS). MiRNA DNSs are calculated according to the quantum coherence algorithm (Fig. 11.3a). Beef miRNA DNSs are superposed on human miRNA DNSs (Fig. 11.3b). The DNS distribution value of rice is different from the DNS distribution value of beef. Rice DNSs have a peak, but beef DNSs are distributed with a large peak and a small peak. The trend toward DNS distribution was retained after feeding on these miRNAs (Fig. 11.3b). This diagram represents both the quantum energy and the genetic information of each meal. In Chap. 6, we mentioned that Mendelian and Darwinism are involved in RNA Wave 2000. There is no doubt that dietary miRNA gene information drives the evolution of species. Therefore, food causes cancer because it contains evolutionary programming. In addition, the Food and Nutrition Board of the National Academy of Science has determined an estimated average requirement, which is based on estimated daily food intake levels, although miRNA information should also be estimated daily. As shown in Fig. 11.3b, differences are never too small to alter cells,

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Fig. 11.2 Therapeutic food miRNA for COVID-19. Rice MIR2097-5p blocked SARS-CoV-2 pathogenicity in METS/MIRAI analysis

organs, and individuals. Food chains and phylogenetic trees are linear networks of links. On the other hand, if miRNA uptake can induce mutations, the mutated time points are programmed by miRNA-mediated web cross-talk (Fig. 11.4). Darwinism included the underlying laws of dominant inheritance, genetically acquired characters, and survival of the fittest. A phylogenetic tree was built by these three backbones, and then its engine was inherited by mutation. However, if the genetic information of food xenomiRNA is used as a mutation engine, the number of mutation points will increase further, and the previous evolution theory thus far will become a ghost. Furthermore, phenotypes can be readily programmed with food miRNA information described using program analysis diagrams (PADs) called miRNA entangling target sorting (METS). Since protein enzymes are regulated by miRNAs, such as cancer in Chap. 6 and pluripotent cells in Chap. 8, miRNAs contained in retrotransposons can induce mutations and recombination. As the saying goes, “poison is medicine,” edible miRNA vaccines suggest that diagnosis with miRNA quantum language and artificial intelligence (MIRAI) as a smart gadget may aid in treatment. XenomiRNAs were later shown to be related to the etiology of human cancers and infectious diseases (Fujii 2023). A phylogenetic tree for protein genes (1–2% gene information) may be wrong. It is better to think of evolution based on the quantum code of miRNAs.

11.2

The Next Stage of Specific Delivery

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Fig. 11.3 DNSs of foods and humans. (a) DNSs of rice (osa; blue line) and beef (bta; orange line) are distributed as distinct line graphs. Rice has a peak (blue arrow), and beef has two peaks (orange arrows). (b) Both DNSs are compared with human DNSs. If rice miRNAs or beef miRNAs are incorporated into a human, it is clear that DNSs of humans could be affected by daily food DNSs of miRNAs. Arrows are in the same positions as in both panels

11.2

The Next Stage of Specific Delivery

Histograms of miRNA/miRNA interactions showed that quantum energy is preserved in food energy, which applies to the design and development of therapeutics. Incorporated food miRNAs control protein gene expression by targeting the 3′ untranslated regions (3′UTRs) of mRNA in incomplete pairing. Because miRNAs

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Fig. 11.4 The acquired characteristics in the miRNA gene information

can target multiple mRNAs, miRNA targeting does not degrade mRNAs but regulates them. However, miRISC can degrade the mRNA if the complementary sequences of the 3′UTR of the mRNA are a perfect match. Therefore, perfectly matched sequences of artificial miRNA target sites or combinatorial target sites can be designed in the 3′UTR of miRNA gene expression cassettes to control exogenous miRNA gene expression in a tissue-specific manner. However, it also has severe off-target effects. Next, we consider specific delivery strategies for miRNA panel agent information. The strategy improves the safety of therapeutic development for edible miRNA vaccines (Fig. 11.5) because treatment with miRNA targets optimized for mRNA did not alter the endogenous miRNA profile (Geisler et al. 2011; Xie et al. 2011). Therefore, sponge vectors have the potential to be used for adeno-associated vector (AAV) or lentivector applications. Geisler et al. (2011) showed by their excellent work that AAV9 containing three repeats of miRNA target sites against liverspecific miR-122 efficiently suppresses transducing gene expression in the liver and that the cytomegalovirus (CMV) enhancer plus a cardiac myosin light chain (MLC)-2v promoter allows transgene expression into the heart. Furthermore, they found that C/G mutations at the target site increased silencing efficacy (Geisler et al. 2013). Although they could not explain the enhancing effect of target site mutation from the thermodynamic stability or secondary structure, it is due to quantum effects by G substitution in the absence of G at the target site as described here. Qiao et al. (2011) constructed an AAV9 vector containing four copies of the hematopoietic lineage-specific miR-142-3p or miR-142-5p target site in the 3′UTR, whereas

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The Next Stage of Specific Delivery

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Fig. 11.5 miRNA delivery vector. (a) Target ON system for miRNA agent delivery. (b) The transducing cell could release miRNA agents in exosomes (green circles). Therefore, food cells transduced by miRNA agents can be used for edible miRNA vaccines

transgene expression declined in nonhematopoietic tissues in vivo. This causes AAV to not integrate into the host genome, and transduction by the AAV vector is transient. Therefore, host-integrating lentivectors or spumavectors are suitable for permanently transducing gene expression in gene therapy (Fujii 2014). Lentivectors bearing miR-124 targeting sites are effectively suppressed in neurons but not in astrocytes in vivo (Colin et al. 2009). The tTR protein can bind to the TetO7 DNA sequence and suppresses the expression of the transducing protein, but when

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tetracycline binds to the tTR protein, the tTR protein cannot bind to the TetO7 site. Although the TetO7-inserted lentivector can express miRNA, the lentivector plus spumavirus vector platform expressing miRNA can be controlled by the tTR suppressor and miRNA target site as a dual fail-safe mechanism (Fig. 11.5) (Amendola et al. 2009). Therefore, the miR-ON system can be constructed using lentivectors (Amendola et al. 2013), suggesting that in long-term culture, directly reprograming reproducing cells can safely and effectively be selected by the miR-ON system with a lentivector and spumavirus vector ex vivo because the miR-OFF system is available for pluripotent cell selection (Sachdeva et al. 2010; di Stefano et al. 2011). For the COVID-19 pandemic, the success of mRNA vaccines and the assessment of miRNAs favored by mRNA vaccines indicate that clinical application of ON- or OFF-mechanism drugs are possible (Costello et al. 2017; Fujii 2014, 2021).

11.3

hiv1-miR-N367 Ortholog

To elucidate the implications of viral miRNAs of HIV-1 and cellular miRNAs for the development of HIV edible miRNA agents, orthologous relationships between viral and cellular miRNAs were investigated. Sequence homologies were searched between HIV-1 and miRBase listed miRNAs. Three miRNA sequences were completely identified in those of HIV-1 in the HIV DataBase (hiv.lanl.gov/ content/sequence/NEWALIGN/align.html, GENOME). We found that hsa-miR6763-5p, bta-miR-2898, and efu-miR-9226 in the genome are orthologs in HIV-1; therefore, the HIV-1 transposon carries miRNAs, not only hiv1-miR-N367 but also orthologs. RNA viruses genetically have miRNAs because HIV-1 is an RNA virus, and HIV-1 would pass through the kingdom among bat, bovine, and human in class mammal. hiv1-miR-N367 (920 in 2397 of 2929; 38.3% in HIV-1), hiv1-miR-TAR5p (618; 25.8%), and miR-TAR-3p (861; 35.9%) sequences were completely conserved in quasispecies HIV-1 and SIV strains but not in HIV-2 strains. Therefore, miRNAs would also pass through among retroviral strains. Although hiv1-miRN367 and hsa-miR-192 are functional orthologs (You et al. 2012), as shown in Fig. 11.6, the seed sequence (1–8) is quite similar, and the miRNA qubit module in the seed is completely the same. The targets in the seed of hiv1-miR-N367 were investigated with TargetScan, and then GO annotations related to the genes of molecular and cellular function and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed (Fig. 11.7). The top three cellular components were the nucleus (30.2%), nucleolus (24.6) and plasma membrane (20.8). The top two molecular functions were related to protein binding (45.6) and zinc binding (19.0). The top two pathways in the KEGG analysis were pathways in cancer (23.2) and mitogen-activated protein kinase (MAPK) signaling (19.0). From these analyses, it is suggested that hiv1-miRN367 is a target protein related to cancer; therefore, hiv1-miR-N367 may be a tumor suppressor. In the case of functional orthologous miR-192, miR-192 can

11.4

HIV Edible Vaccine: Entangling Viral miRNAs and Host miRNAs

215

Fig. 11.6 Homology of hiv1-miR-N367 and miR-192. The seed match between hiv1-miR-N367 and miR-192 is represented in the sequence (thin blue square) and the miRNA qubit module (bold blue square). The miRNA qubit module of the seed is absolutely the same between two miRNAs

control the cell cycle via tumor suppressor p53, in addition to tumor suppressor miR-34a (Braun et al. 2008; Georges et al. 2008; Moore et al. 2015). Furthermore, miR-192 reduced tumor metastasis in human hepatocellular carcinoma by targeting the zinc transporter SLC39A6/SNAIL pathway (Lian et al. 2015). In contrast, miR-192 has recently been implicated in estrogen receptor-alpha-positive breast cancer by targeting estrogen receptor-alpha (Kim et al. 2016), in hepatocellular carcinoma by targeting the farnesoid X receptor (Krattinger et al. 2016), in pancreatic ductal adenocarcinoma against SERPINE1 (plasminogen activator inhibitor 1) (Botla et al. 2016), and in esophageal squamous cell carcinoma against Bim (BCL2 protein family) (Li et al. 2015). miR-192 is oncogenic, which is different from viral hiv-miR-N367. These data suggest that protein function analyses by KEGG were not enough to elucidate even single miRNA functions and that a miRNA function would be controlled by interactions among other miRNAs.

11.4

HIV Edible Vaccine: Entangling Viral miRNAs and Host miRNAs

In the gene ontology annotation, virus-related pathways were not found by the hiv1miR-N367 target search. Although HIV-1 is associated with cancers, Kaposi’s sarcoma, primary central nervous system lymphoma, systemic non-Hodgkin’s lymphoma, Hodgkin’s lymphoma, squamous cell carcinoma of the conjunctiva, oral cavity and anus, uterine cervix carcinoma, testicular carcinoma, and soft tissue carcinoma in children (Milliken 1998), HIV-1 does not have oncogenic protein. MiRNA target gene ontology revealed that hiv1-miR-N367 may play an important

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Fig. 11.7 hiv1-miR-N367 gene ontology. The seed of hiv1-miR-N367 was analyzed by its target gene ontology (GO) annotations. Cellular components (upper panel) and molecular function (middle panel) are represented. KEGG analysis (lower panel) is also depicted

11.4

HIV Edible Vaccine: Entangling Viral miRNAs and Host miRNAs

217

Fig. 11.8 Cook PAD by METS/MIRAI analysis of hiv1-miR-N367 function. hiv1-miR-N367 function is divergent. Eight HIV-1-positive factors are represented in beige, and two HIV-1negative factors are represented in sky blue. Each target is shown as a combination of miRNAs with hiv1-miR-N367

role in the above cancers. It is controversial. To further expand the computing investigation of hiv1-miR-N367 function, METS simulation of infection was performed, and Cook PAD by METS is shown in Fig. 11.8. This is the functional analogy assay between viral miRNA and human miRNA (Fujii 2020). Furthermore, DIANA TOOLS (diana.imis.athena-innovation.gr) and VIRmiRNA (crdd.osdd.net) can be used for viral miRNA extraction and viral miRNA target searches in human protein genes (Fujii 2022). Seventeen miRNAs are implicated in hiv1-miR-N367-mediated inhibition of HIV-1 transcription and proliferation and maintenance of HIV-1-infected cells. As previously mentioned (Fujii 2013), miR-20a/b-5p and miR-17-5p target P300/CBPassociated factor (PCAF), and miR-20a/b-5p and miR-108a control purine-rich element binding protein A (Pur-α, PURA). While both proteins are HIV-1 transcription activators, three miRNAs with hiv1-miR-N367 are suppressors of HIV-1 proliferation as a positive factor. Three miRNAs with miR-hiv1-miR-N367 also target RB transcription corepressor 1 (RB1) and Rac GTPase activating protein 1 (RACGAP1). RB1 is related to cell survival, and its expression supports antiapoptosis in HIV-1-exposed macrophages during latent infection (Gekonge et al. 2012). The RACGAP1 gene encodes a GTPase activating protein (GAP), and RACGAP1 binds the activating form of Rho GTPase, resulting in inhibition of the

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Rho pathway. Since inhibition of Rho by statins decreases HIV-1 production (del Real et al. 2004), RACGAP1 may be related to infected macrophages and can maintain the latent phase as the reservoir of HIV-1. hiv1-miR-N367, miR-20a/b-5p, miR-17-5p, miR-106a, miR-3666, miR-130b, let-7, miR-98, and miR-4458 inhibit latent infection (see Chap. 2). HIV-1 Vpr is associated with HIV-1-infected premature chromatid separation, and Vpr expression results in the displacement of MIS12, HP1-alpha/gamma-interacting protein; therefore, MIS12 suppression by miR-7-5p and hiv1-miR-N367 would block HIV-1 infection because the HP1/MIS12 interaction would have an important role as a hallmark of aneuploidy in HIV-infected individuals and for HIV-1 integration via p300 (Shimura et al. 2011). CUL5 binds to the Vif HIV-1 amplifier. Their binding is implicated in the assembly of an E3 ubiquitin ligase inducing anti-HIV-1 APOBEC3 ubiquitination and degradation (Wang et al. 2014). The inhibition of CUL5 expression by miR-7, miR-124, and miR-N367 may switch off the Vif effect by higher induction of APOBEC3, which may ultimately increase the ability of the anti-HIV-1 protein APOBEC3 to be incorporated into the particles of HIV-1. BCL2-forced expression allowed latent HIV-1 infection in T cells (Rawlings et al. 2015) suggesting that suppression of BCL2 by hiv1-miR-N367, miR-181b, and miR-34a/c would block latent viral infection. Activated leukocyte cell adhesion molecule (ALCAM) is associated with the entry of HIV-1 (Williams et al. 2013). Inhibition of ALCAM by hiv1-miR-N367, miR-124, and miR-449a/b would result in HIV-1 infection. CAV1, caveolin-1 inhibits HIV-1 through an NF-kB-dependent mechanism (Simmons Jr et al. 2012). Thus, hiv1-miR-N367 and miR-124 would accelerate HIV-1 production via inhibition of CAV-1 expression. miR-124 can suppress NF-kB and BCL2 apoptosis regulator (BCL2) (Jeong et al. 2015). This suggests that miR-124 may be implicated in latent infection and reactivation. MCM10 binds to Vpr, and its binding enhances proteasomal degradation of minichromosome maintenance 10 replication initiation factor (MCM10), which induces G2/M arrest (Romani et al. 2015). miR-124 and hiv1-miR-N367 target MCM10; therefore, miR-124 and hiv1-miR-N367 may also enhance HIV-1 production. Together, miRNA panel-containing hiv1-miR-N367, miR-20a/b-5p, miR-175p, and miR-106 mimics would be available for an edible HIV-1 vaccine through the cook PAD. Further investigation with AI suggested that hiv1-miR-N367 is statistically nononcogenic in human T cells and macrophages (carcinogenesis AUC: 40.00) (Fujii 2020). By METS/MIRAI analysis, hiv1-miR-N367 inhibited HIV-1 replication in trans, and hiv1-miR-N367 and host miRNAs activated viral production (Fujii 2020). Quantum network analysis is now going to be a useful tool to investigate the etiology of human diseases from circulating miRNA biomarker panels as the new vital. The dream in Oxford, 1987 comes true (Fujii 2023). Here, we have discussed the association of hiv1-miR-N367 viral miRNAs in the pathogenesis of AIDS, but we must also superimpose the association of host miRNAs in HIV-1 infection (Fig. 11.9). The results are shown in Chaps. 6 and 8. Now, go through the quantum gates, turn the corner at The Society of MiRNA, and listen to the evening concerts in the auditorium. Enjoy “The MiRNA Quantum Code

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Fig. 11.9 The Society of MicroRNA. YS ς is the equation for miRNA quantum energy in volume

Book” Orchestra’s Symphony of AIDS, and then compose and play to figure out the human disease yourself. “That’s right! You are the conductor”.

References Alfaradhi MZ, Kusinski LC, Fernandez-Twinn DS, Pantaleão LC, Carr SK et al (2016) Maternal obesity in pregnancy developmentally programs adipose tissue inflammation in young, lean male mice offspring. Endocrinology 157:4246–4256. https://doi.org/10.1210/en.2016-1314 Alsaweed M, Hartmann PE, Geddes DT, Kakulas F (2015) MicroRNAs in breastmilk and the lactating breast: potential immunoprotectors and developmental regulators for the infant and the mother. Int J Environ Res Public Health 12:13981–14020. https://doi.org/10.3390/ ijerph121113981 Amendola M, Passerini L, Pucci F, Gentner B, Bacchetta R et al (2009) Regulated and multiple miRNA and siRNA delivery into primary cells by a lentiviral platform. Mol Ther 17:1039– 1052. https://doi.org/10.1038/mt.2009.48 Amendola M, Giustacchini A, Gentner B, Naldini L (2013) A double-switch vector system positively regulated transgene expression by endogenous microRNA expression (miR-ON vector). Mol Ther 21:934–946. https://doi.org/10.1038/mt.2013.12 Beg MS, Brenner AJ, Sachdev J, Borad M, Kang YK et al (2016) Phase I study of MRX34, a liposomal miR-34a mimic, administered twice weekly in patients with advanced solid tumors. Investig New Drugs 35:180–188. https://doi.org/10.1007/s10637-016-0407-v

220

11

Deep Learning of miRNAs for Therapeutic Applications

Botla SK, Savant S, Jandaghi P, Bauer AS, Mücke O et al (2016) Early epigenetic downregulation of microRNA-192 expression promotes pancreatic cancer progression. Cancer Res 76:4149– 4159. https://doi.org/10.1158/0008-5472.CAN-15-0390 Braun CJ, Zhang X, Savelyeva I, Wolff S, Moll UM et al (2008) p53-responsive microRNA 192 and 215 are capable of inducing cell cycle arrest. Cancer Res 68:10094–10104. https:// doi.org/10.1158/0008-5472.CAN-08-1569 Chiang K, Shu J, Zempleni J, Cui J (2015) Dietary microRNA database (DMD): an archive database and analytic tool for food-borne microRNAs. PLoS One 10:e0128089. https://doi. org/10.1371/journal.pone.0128089 Colin A, Faideau M, Dufour N, Auregan G, Hassig R et al (2009) Engineered lentiviral vector targeting astrocytes in vivo. Glia 57:667–679. https://doi.org/10.1002/glia.20795 Costello A, Lao N, Clynes M, Barron N (2017) Conditional knockdown of endogenous microRNAs in CHO cells using TET-ON-SanDI sponge vectors. Mehods Mol Biol 1603:87–100. https://doi. org/10.1007/978-1-4939-6972-2_6 del Real G, Jiménez-Baranda S, Mira E, Lacalle RA, Lucas P et al (2004) Statins inhibit HIV-1 infection by downregulating rho activity. J Exp Med 200:541–547. https://doi.org/10.1084/jem. 20040061 di Stefano B, Martina S, Gentner B, Ungaro F, Schira G et al (2011) A microRNA-based system for selecting and maintain the pluripotent state in human induced pluripotent stem cells. Stem Cells 29:1684–1695. https://doi.org/10.1002/stem.726 Du C, Fujii YR, Ito M, Harada M, Morikawa E et al (2004) Dietary polyunsaturated fatty acids suppress acute hepatitis, alter gene expression and prolong survival of long-evans cinnamon rats, a model of Wilson disease. J Nutr Biochem 15:273–280 Fernandez-Twinn DS, Alfaradhi MZ, Martin-Gronert MS, Duque-Guimaraes DE, Piekarz A et al (2014) Downregulation of IRS-1 in adipose tissue of offspring of obese mice is programmed cell-autonomously through posttranscriptional mechanisms. Mol Metab 3:325–333. https://doi. org/10.1016/j.molmet.2014.01.007 Fujii YR (2013) RNA wave for HIV therapy: foods, stem cells and the RNA information gene. World J AIDS 3:131–146. https://doi.org/10.4236/wja.2013.32018 Fujii YR (2014) RNA information gene diseases: nano-RNA-based medical devices with corporate chemotherapy and gene therapy. In: Nanotechnology RNA (ed) . Wang B CRC Press Taylor & Francis Group, Boca Raton, pp 385–434 Fujii YR (2020) The quantum microRNA immunity in human virus-associated diseases: virtual reality of HBV, HCV and HIV-1 infection, and hepatocellular carcinogenesis with AI machine learning. Arch Clin Biomed Res 4:089–129. https://doi.org/10.26502/acbr.50170092 Fujii YR (2021) Quantum microRNA assessment of COVID-19 RNA vaccine: hidden potency of BNT162b2 SARS-CoV-2 spike RNA as microRNA vaccine. Adv Case Stud 3:000552. https:// doi.org/10.31031/AICS.2020.03.000552 Fujii YR (2022) In: Rezaei N (ed) Quantum microRNA surveillance against cancer: parallel dimensional analysis of integrated networks by quantum microRNA language in female genital neoplasms. Interdisciplinary Cancer Research Springer Nature, New York, pp 1–24. https://doi. org/10.1007/16833_2022_4 Fujii YR (2023) The microRNA quantum code book. Springer Nature, Singapore Geisler A, Jungmann A, Kurreck J, Poller W, Katus HA et al (2011) microRNA122-regulated transgene expression increases specificity of cardiac gene transfer upon intravenous delivery of AAV9 vectors. Gene Ther 18:199–209. https://doi.org/10.1038/ft.2010.141 Geisler A, Schön C, Größl T, Pinkert S, Stein EA et al (2013) Application of mutated miR-206 target sites enables skeletal muscle-specific silencing of transgene expression of cardiotropic AAV9 vectors. Mol Ther 21:924–933. https://doi.org/10.1038/mt.2012.276 Gekonge B, Raymond AD, Yin X, Kostman J, Mounzer K et al (2012) Retinoblastoma protein induction by HIV viremia or CCR5 in monocytes exposed to HIV-1 mediates protection from activation-induced apoptosis: ex vivo and in vitro study. J Leukoc Biol 92:397–405. https://doi. org/10.1189/jlb.1111552

References

221

Georges SA, Biery MC, Kim S, Schelter JM, Guo J et al (2008) Coordinated regulation of cell cycle transcripts by p53-inducible microRNAs, miR-192 and miR-215. Cancer Res 68:10105–10112. https://doi.org/10.1158/0008-5472.CAN-08-1846 Gray C, Li M, Reynolds CM, Vickers MH (2014) Let-7 miRNA profiles are associated with the reversal of left ventricular hypertrophy and hypertension in adult male offspring from mothers undernourished during pregnancy following preweaning growth hormone treatment. Endocrinology 155:4808–4817. https://doi.org/10.1210/en.2014-1567 Jeong D, Kim J, Nam J, Sun H, Lee YH et al (2015) MicroRNA-124 links p53 to the NF-kB pathway in B-cell lymphoma. Leukemia 29:1868–1874. https://doi.org/10.1038/leu.2015.101 Kim YS, Park SJ, Lee YS, Kong HK, Park JH (2016) miRNAs involved in LY6K and estrogen receptor α contribute to tamoxifen-susceptibility in breast cancer. Oncotarget 7:42261–42273. https://doi.org/10.18632/oncotarget.9950 Krattinger R, Boström A, Schiöth HB, Thasler WE, Mwinyi J et al (2016) microRNA-192 suppresses the expression of the farnesoid X receptor. Am J Physiol Gastrointest Liver Physiol 310:G1044–G1051. https://doi.org/10.1152/ajpgi.00297.2015 Li S, Li F, Niu R, Zhang H, Cui A et al (2015) Mir-192 suppresses apoptosis and promote proliferation in esophageal squamous cell carcinoma by targeting Bim. Int J Clin Exp Pathol 8:8048–8056 Lian J, Jing Y, Dong Q, Huan L, Chen D et al (2015) miR-192, a prognostic indicator, targets the SLC39A6/SNAIL pathway to reduce tumor metastasis in human hepatocellular carcinoma. Oncotarget 7:2672–2683. https://doi.org/10.18632/oncotarget.6603 Liu L, Yi H, Wang C, He H, Li P et al (2016) Integrated nanovaccine with microRNA-148a inhibition reprograms tumor-associated dendritic cells by modulating miR-148a/DNMT1/ SOCS1 axis. J Immunol 197:1231–1241. https://doi.org/10.4049/jimmunol.1600182 Milliken S (1998) HIV and cancer. In: Saksena N (ed) Human immunodeficiency viruses biology, immunology and molecular biology. Medical Syatems S.p.A., Genoa, pp 475–499 Moore R, Ooi HK, Kang T, Bleris L, Ma L (2015) MiR-192-mediated positive feedback loop controls the robustness of stress-induced p53 oscillations in breast cancer cells. PLoS Comput Biol 11:e1004653. https://doi.org/10.1371/journal.pcbi.1004653 Qiao C, Yuan Z, Li J, He B, Zheng H et al (2011) Liver-specific microRNA-122 target sequences incorporated in AAV vectors efficiently inhibits transgene expression in the liver. Gene Ther 18: 403–410. https://doi.org/10.1038/gt.2010.157 Rawlings SA, Ill FA, Kozhaya L, Torres VJ, Unutmaz D (2015) Elimination of HIV-1-infected primary T-cell reservoirs in an in vitro model of latency. PLoS One 10:e0126917. https://doi. org/10.1371/journal.pone.0126917 Romani B, Baygloo NS, Aghasadeghi MR, Allahbakhshi E (2015) HIV-1 Vpr enhances proteasomal degradation of MCM10 DNA replication factor through the Cil4-DDB1[VprBP] E3 ubiquitin ligase to induce G2/M cell cycle. J Biol Chem 290:17380–17389. https://doi.org/ 10.1074/jbc.M115.641522 Sachdeva R, Jönsson ME, Nelander J, Krikeby A, Guibentif C et al (2010) Tracking differentiating neural progenitors in pluripotent cultures using microRNA-regulated lentiviral vectors. Pro Natl Acad Sci USA 107:11602–11607. https://doi.org/10.1073/pnas.1006568107 Shimura M, Toyoda Y, Iijima K, Kinomoto M, Tokunaga K et al (2011) Epigenetic displacement of HP1 from heterochromatin by HIV-1 Vpr causes premature sister chromatid separation. J Cell Biol 194:721–735. https://doi.org/10.1083/jcb.201010118 Shu J, Chiang K, Zempleni J, Cui J (2015) Computational characterization of exogenous microRNAs that can be transferred into human circulation. PLoS One 10:e0140587. https:// doi.org/10.1371/journal.pone.0140587 Simmons GE Jr, Taylor HE, Hildreth JE (2012) Caveolin-1 suppresses human immunodeficiency virus-1 replication by inhibiting acetylation of NF-kB. Virology 432:110–119. https://doi.org/ 10.1016/j.virol.2012.05.016

222

11

Deep Learning of miRNAs for Therapeutic Applications

Wang H, Liu B, Liu X, Li Z, Yu XF et al (2014) Identification of HIV-1 vif regions required for CBF-β interaction and APOBEC3 suppression. PLoS One 9:e95738. https://doi.org/10.1371/ journal.pone.000095738 Williams DW, Calderon TM, Lopez L, Carvallo-Torres L, Gaskill PJ et al (2013) Mechanisms of HIV entry into the CNS: increased sensitivity of HIV infected CD14+CD16+ monocytes to CCL2 and key roles of CCR2, JAM-A, and ALCAM in diapedesis. PLoS One 8:e69270. https:// doi.org/10.1371/journal.pone.0069270 Xie J, Xie Q, Zhang H, Ameres SL, Hung JH et al (2011) MicroRNA-regulated, systemically delivered rAAV9: a step closer to CNS-restricted transgene expression. Mol Ther 19:526–535. https://doi.org/10.1038/mt.2010.279 You X, Zhang Z, Fan J, Cui Z, Zhang XE (2012) Functionally orthologous viral and cellular microRNAs studied by a novel dual-fluorescent reporter system. PLoS One 7:e36157. https:// doi.org/10.1371/journal.pone.0036157 Zheng J, Zhang Q, Mul JD, Yu J, Qi C et al (2016) Maternal high-calorie diet is associated with altered hepatic microRNA expression and impaired metabolic health in offspring at weaning age. Endocrine 54:70–80. https://doi.org/10.1007/s12020-016-0959-9 Zhou Z, Li X, Liu J, Dong L, Chen Q et al (2015) Honeysuckle-encoded atypical microRNA2911 directly targets influenza a viruses. Cell Res 25:39–49. https://doi.org/10.1038/cr.2014.130 Zhou LK, Zou Z, Jiang XM, Zheng Y, Chen X et al (2020a) Absorbed plant MIR2911 in honeysuckle decoction inhibits SARS-CoV-2 replication and accelerates the negative conversion of infected patients. Cell Discov 6:54. https://doi.org/10.1038/s41421-020-00197-3 Zhou Z, Zhou Y, Jiang XM, Wang Y, Chen X et al (2020b) Decreased HD-MIR2911 absorption in human subjects with the SIDT1 polymorphism fails to inhibit SARS-CoV-2 replication. Cell Discov 6:63. https://doi.org/10.1038/s41421-020-00206-5

Chapter 12

Inflammatory Bowel Disease and Rheumatoid Arthritis Autoimmune Diseases of MicroRNA Quantum Code

Part of the inhumanity of the computer is that once it is competently programmed and working smoothly, it is completely honest. Asimov I

Overview Chapter 7 mentioned that there is a deep relationship between inflammation and cancer, which are regulated by microRNAs (miRNAs). Although inflammation is also implicated in the immune response, both of which can be controlled by miRNAs, autoimmune diseases such as inflammatory bowel disease (IBD) and rheumatoid arthritis (RA) do not usually induce tumorigenesis. Ulcerative colitis (UC) and Crohn’s disease (CD) are commonly included in IBD. UC causes chronic inflammation of the colonic mucosa, and CD features transmural inflammation that occurs in many portions of the gut from the mouth to the anus. RA is also characterized by chronic inflammation as a consequence of sustained synovitis and progressive and irreversible joint damage. Therefore, patients with RA have pain, swelling, and stiffness in multiple joints bilaterally. To further understand the relationship between inflammation and cancer, the pathogenesis of IBD and RA was simulated by miRNA entangling target sorting (METS)/miRNA quantum language and artificial intelligence (MIRAI) using circulating miRNA biomarkers.

12.1

miRNA Biomarkers for Inflammatory Bowel Disease and Rheumatoid Arthritis

Inflammatory bowel disease (IBD) is related to chronic inflammation of the gastrointestinal tract as an autoimmune disease. IBD includes ulcerative colitis (UC) and Crohn’s disease (CD). Indeterminate colitis or IBD unclassified (IBDU) is an IBD with characteristics overlapping ulcerative colitis and Crohn’s disease (Podolsky 2002). Another autoimmune disease is rheumatoid arthritis (RA). RA is characterized by chronic inflammation of the synovial tissues and leads to irreversible joint © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1_12

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destruction and lifelong disability (Aletaha et al. 2010). Clinical data have shown that circulating miRNAs play a role as biomarkers of IBD and RA (Ahmed Hassan et al. 2020; Rodriguez-Muguruza et al. 2021). Therefore, the etiology of IBD and RA was investigated in silico by microRNA (miRNA) entangling target sorting (METS)/miRNA quantum language and artificial intelligence (MIRAI) (Fujii 2023).

12.2

Inflammatory Bowel Disease Pathogenesis

Three miRNAs were extracted as miRNA memory packages (MMP) of IBD from circulating biomarker profiles in plasma (Yan et al. 2020; Ahmed Hassan et al. 2020; Sun et al. 2022). miR-16-5p, miR-21-5p, and miR-92-1-5p (AUC in 3 miRNAs: 0,89) were upregulated. For METS/MIRAI, the etiology of IBD was the outcome (Fig. 12.1). 1. Transforming growth factor beta receptor 2 (TGFBR2) was suppressed by upregulation of the miR-21-5p hub along with miR-93-5p. 2. Tumorigenic high mobility group AT-hook 1 (HMGA1) was reduced by upregulation of the miR-16-5p hub along with let-7b-5p, let-7a-5p, miR-96a3p, and miR-124-3p.

Fig. 12.1 METS/MIRAI of IBD

12.3

Rheumatoid Arthritis Pathogenesis

225

3. BCL2 apoptosis regulator (BCL2), vascular endothelial growth factor A (VEGFA), and β amyloid precursor protein (APP) were decreased by upregulation of the miR-16-5p hub in combination with miR-34a/c-5p, miR-378a-3p, and miR-20a-5p, respectively. Some patients with Loeys–Dietz syndrome, an autosomal dominant disorder caused by heterozygous mutations of the genes encoding TGFBR1 or TGFBR2, develop early-onset IBD (Loeys et al. 2006; Naviglio et al. 2014). Further, miR-215p was found in feces of IBD (Zhou et al. 2021). Therefore, inhibition of TGFBR2 by miR-21-5p upregulation is a major risk factor for IBD. The dysregulation of apoptosis in the immune system results in autoimmune diseases such as IBD (Mudter and Neurath 2007). Therefore, BCL2 expression was increased in IBD. The levels of VEGF were increased in the serum of patients with IBD (Griga et al. 1998). Furthermore, in APP knockout (KO) mice, the immune environment of the intestine was activated (Puig et al. 2012). In addition, HMGA1 suppression inhibited carcinogenesis. Altogether, miR-16-5p hub upregulation prevented the progression of IBD. Therefore, these are quantum miRNA surveillances.

12.3

Rheumatoid Arthritis Pathogenesis

Three miRNAs were extracted as MMPs of RA from circulating biomarker profiles in plasma (Safari et al. 2021; Rodriguez-Muguruza et al. 2021). miR-146a-5p, miR-24-3p, and miR-125a-5p were upregulated, and the AUC of each miRNA was 0.8, 0.7, and 0.71, respectively. Profiles of patients with AR are female 70%, RF+: 70%, anti-CCP+: 56%, ESR: 34.2 m/h, high disease activity: 32%, medium disease activity: 42%, low disease activity: 8%. miR-125a-5p did not result in higher score data than the cutoff value of the double nexus score (DNS). In METS/MIRAI, the etiology of RA was the outcome (Fig. 12.2). 1. Hepatocyte nuclear factor 4 alpha (HNF4A) was inhibited by upregulation of the miR-24-3p hub along with miR-34a-5p and miR-629-5p. 2. Tumorigenic MYC proto-oncogene (MYC) was suppressed by upregulation of the miR-24-3p hub along with miR-34a/b/c-5p. 3. Dihydrofolate reductase (DHFR) was blocked by upregulation of the miR-24-3p hub. Aurora kinase B (AURKB) and tumor necrosis factor receptor-associated factor 6 (TRAF6) were decreased by upregulation of the miR-24-3p hub and miR-146a-5p in combination with let-7b/e-5p and miR-124-3p, respectively. In RA model mice, low expression of HNF4A was observed (Chen et al. 2021). Therefore, HNF4A inhibition by upregulation of the miR-24-3p hub may be a risk factor for progression of RA. Since AURKB was increased in patients with RA (Glant et al. 2013), suppression of AURKB reduced AR progression. The anti-RA drug methotrexate (MTX) competitively inhibits DHFR (Milic et al. 2012), and DHFR inhibition, therefore, suppresses AR. TRAF6 promotes the migration of RA

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Fig. 12.2 METS/MIRAI of RA

fibroblast-like synoviocytes (FLSs) (Wang et al. 2015). Thus, decreasing TRAF6 inhibits RA. Furthermore, tumorigenic MYC was inhibited, indicating quantum miRNA surveillance.

12.4 Anticancer Condition of Autoimmune Diseases METS/MIRAI showed the properties of cancer and autoimmune diseases. The difference between cancer induced by inflammation and autoimmune disease has been explained based on the disease condition stage, Treg function, and the presence of active oxygen (Elkoshi 2022). However, there was no clear answer. Compared with the METS/MIRAI analysis of induced pluripotent stem (iPS) cells (see Chap. 9), it is clear that in autoimmune diseases, IBD and RA, a powerful antitumor program works as miRNA quantum surveillance (Fujii 2023). Furthermore, disease progression is also suppressed by the miRNA-encoded program. In contrast, tumor suppressors were strongly suppressed by transduction of Yamanaka factor in iPS cells, but there was no antitumor program. Quantum programs are at work that try to maintain homeostasis even during the progression of autoimmune diseases. In turn, inflammation-induced carcinogenesis, such as hepatitis B virus (HBV)-induced liver carcinoma, is not necessarily true. In the case of autoimmune diseases, miRNA

References

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quantum programs suppress inflammation and prevent tumorigenesis. “Understanding humans with quantum” = “Humans live with quantum computing.” The presence of METS/MIRAI, which can understand these words, is truly a spectacle theater.

References Ahmed Hassan E, Abd El-Rehim AS, Mohammed Kholef EF, Abd-Elgwad Elsewify W (2020) Potential role of plasma miR-21 and miR-92a in distinguishing between irritable bowel syndrome, ulcerative colitis, and colorectal cancer. Gastroentrol Hepatol Bed Bench 3:147–154 Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT et al (2010) 2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European league against rheumatism collaborative initiative. Arthritis Rheum 62:2569–2581. https://doi.org/10.1002/art.27584 Chen M, Li MH, Zhang N, Sun WW, Wang H et al (2021) Pro-angiogenic effect of exosomal micropRNA-103a in mice with rheumatoid arthritis via the downregulation of hepatocyte nuclear factor 4 alpha and activation of the JAK/STAT3 signaling pathway. J Biol Regul Homeost Agents 35:629–640. https://doi.org/10.23812/20-537-a Elkoshi Z (2022) Cancer and autoimmune diseases: a tale of two immunological opposites? Front Immunol 13:821598. https://doi.org/10.3389/fimmu.2022.821598 Fujii YR (2023) The microRNA quantum code book. Springer Nature, Singapore Glant TT, Besenyei T, Kádár A, Kurkó J, Tryniszewske B et al (2013) Differential expressed epigenome modifiers, including aurora kinase A and B, in immune cells of rheumatoid arthritis. Arthritis Rheum 65:1725–1735. https://doi.org/10.1002/art.37986 Griga T, Tromm A, Spranger J, May B (1998) Increased serum levels of vascular endothelial growth factor in patients with inflammatory bowel disease. Scand J Gastroenterol 33:504–508. https://doi.org/10.1080/00365529850172070 Loeys BL, Schwartze U, Holm T, Callewaert BL, Thomas GH et al (2006) Aneurysm syndromes caused by mutations in the TGF-beta receptor. N Engl J Med 355:788–798. https://doi.org/10. 1056/NEJMoa055695 Milic V, Jekic B, Lukovic L, Bunjevacki V, Milasin J et al (2012) Association of dihydrofolate reductase (DHFR) -317AA genotype with poor response to methotrexate in patients with rheumatoid arthritis. Clin Exp Rheumatol 30:178–183 Mudter J, Neurath MF (2007) Apoptosis of T cells and the control of inflammatory bowel disease: therapeutic implications. Gut 56:293–303. https://doi.org/10.1136/gut.2005.090464 Naviglio S, Arrigo S, Martelossi S, Villanacci V, Tommasini A et al (2014) Severe inflammatory bowel disease associated with congenital alteration of transforming growth factor beta signaling. J Crohns Colitis 8:770–774. https://doi.org/10.1016/j.crohns.2014.01.013 Puig KL, Swigost AJ, Zhou X, Sens MA, Combs CK (2012) Amyloid precursor protein expression modulates intestine immune phenotype. J Neuroimmune Pharmacol 7:215–230. https://doi.org/ 10.1007/s11481-011-9327-y Podolsky D (2002) Inflammatory bowel disease. N Engl J Med 347:417–429. https://doi.org/10. 1056/NEJMra020831 Rodriguez-Muguruza S, Altuna-Coy A, Castro-Oreiro S, Poveda-Elices MJ, Fontova-Garrofé R et al (2021) A serum biomarker panel of exomiR-451a, exomiR-25-3p and soluble TWEAK for early diagnosis of rheumatoid arthritis. Front Immunol 12:790880. https://doi.org/10.3398/ fimmu.2021.790880 Safari F, Damavandi E, Rostamian AR, Movassaghi S, Imani-Saber Z et al (2021) Plasma levels of microRNA-146a-5p, microRNA-24-3p, and microRNA-125a-5p as potential diagnostic biomarkers for rheumatoid arthritis. Iran J Allergy Asthma Immunol 20:326–337. https://doi.org/ 10.18502/ijaai.v20i3.6334

228

12

Inflammatory Bowel Disease and Rheumatoid Arthritis

Sun L, Han Y, Wang H, Liu H, Liu S et al (2022) MicroRNAs as potential biomarkers for the diagnosis of inflammatory bowel disease: a systematic review and meta-analysis. J Int Med Res 50:1–18. https://doi.org/10.1177/03000605221089503 Yan H, Zhang X, Xu Y (2020) Aberrant expression of miR-21 in patients with inflammatory bowel disease: a protocol for systematic review and meta analysis. Medicine 99(17):e19693. https:// doi.org/10.1097/MD.0000000000019693 Zhou R, Qiu P, Wang H, Yang H, Yang X et al (2021) Identification of microRNA-15-5p and microRNA-21-5p in feces as potential noninvasive biomarkers for inflammatory bowel disease. Aging 13:4634–4646. https://doi.org/10.18632/aging.202428 Wang H, Chen W, Wang L, Li F, Zhang C et al (2015) Tumor necrosis factor-associated factor 6 promotes migration of rheumatoid arthritis fibroblast-like synoviocytes. Mol Med Rep 11: 2761–2766. https://doi.org/10.3892/mmr.2014.3104

Afterword: Finis

Fortuna Adversa Virummagnae Sapientiae Non Terret “Understanding humans with quantum” = “Humans live with quantum computing.” Based on this principle, it may be possible to accurately diagnose cancer, infectious diseases, and metabolic diseases at once using a panel of microRNAs (miRNAs). And the results of research by medical researchers and clinicians using a panel of “miRNA” will be the next amount of data, and the diagnosis will be wider and more accurate. Diagnosis must not only be possible to differentiate but pathological etiology must be identified as the basis for differentiation. The miRNA entangling target sorting (METS) analysis method may make this possible. Artificial intelligence (AI) doctors that use narration (interviews) on mobile devices have already been put to practical use, but humans lie, so narration would have limitations in diagnosis. Diagnosis with miRNA is vital, so if this is added to that, AI doctors may be responsible for doctors’ diagnosis and initial treatment. MiRNA therapeutics are already under clinical trials. If the METS/quantum miRNA language and AI (MIRAI) analysis method advances, we could identify methods for identifying therapeutic targets, which will lead to the development of new miRNA therapeutics. Human memory consists of long- and short-term memory. This corresponds to a computer’s hard disk (HD) and DRAM memory, respectively. The “brain” corresponds to the CPU, and in both memories are stored in an “electronic” state. In this book, I introduced the hypothesis that “quantum” in humans is used instead of “electron” in computing. Blood miRNA profiles have been found to change during trauma and trauma. If we can collect enough statistical data, we may be able to find out what is happening to “memory” and “consciousness” using the METS/MIRAI analysis method. Further accumulation of human miRNA profile data is required.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1

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Afterword: Finis

I would like to express my sincere gratitude to Ms. S. Kasai Executive Editor of Springer Nature Tokyo, who discovered my own book from a self-publishing forest and contributed to the publication of the second edition of “The microRNA 2000 Transformer: Quantum Computing and Artificial Intelligence for Health.” 2023, January

Index

A Adeno-associated vector (AAV), 212, 213 Alzheimer’s disease (AD), 87, 88, 93, 96, 190–195, 197–200 Antisense, 11, 27, 28, 38, 139, 157 Argonaute (Ago), 25, 28, 32, 98 Avian leukosis virus (ALV), 130, 163

B B-cell integration cluster (BIC), 131, 133–135, 178 Bovine leukemia virus (BLV), 31, 162 Bovine spongiform encephalopathy (BSE), 53, 54, 97–99 Brain memory, 83, 88, 95, 96, 121, 123, 196 Burkitt’s lymphoma, 133–137

C Central dogma, 25, 26, 28, 29, 110, 129, 137, 140 Coronavirus disease-2019 (COVID-19), 21, 52, 70, 137, 153, 165, 195, 207–210, 214 Creutzfeldt–Jakob disease (CJD), 97, 98 CRISPR, 32

D Darwinism, 7, 21, 70, 107, 109, 110, 207–210 Dengue virus, 153–155, 163, 164 Diabetes, 35, 49, 53, 55, 56, 69, 70, 117, 122, 124, 164, 193, 194

Dicer, 14, 28, 29, 31–33, 58, 95 Direct reprogramming, 171, 172, 177 Double nexus score (DNS), 107, 115–118, 120, 121, 124, 188–190, 195, 201, 209, 225

E Ebola virus, 10, 163 Edible vaccine, 207 Electric field tangent score (EFTS), 120, 121, 189–191 Embryonic stem (ES) cell, 53, 59, 60, 171, 177, 178, 180, 181 Epstein–Barr virus (EBV), 133, 134, 157, 159 Exogenous miRNA (exomiRNA), 17, 19, 53, 84, 88, 97, 99, 108, 110, 130, 212

F Feline foamy virus (FFV), 163 Fli, 132, 178

G Gene ontology (GO), 36, 163, 214–216 Genetically modified organisms (GMOs), 53 Genome-wide association studies (GWAS), 35, 97, 117, 118, 120, 161, 174, 187 Graft-versus-host disease (GVHD), 172, 177–181

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. R. Fujii, The MicroRNA 2000 Transformer, https://doi.org/10.1007/978-981-99-3165-1

231

232 H Hepatitis B virus (HBV), 98, 132, 139 Hepatitis C virus (HCV), 13, 38, 39, 164 Herpesvirus, 40, 112, 155 Histone deacetylases (HDAC), 60, 61, 198 HMG CoA, 63, 67 Human immunodeficiency virus type 1 (HIV-1), 4, 7–17, 20, 21, 26–28, 35, 39, 40, 51, 52, 61, 131, 132, 135, 144, 156–161, 165, 175, 208, 214, 215, 217, 218 Human T-lymphotropic virus type 1 (HTLV-1), 134, 135 Huntington’s disease (HD), 84, 190, 191, 195, 197, 198, 229

I Induced pluripotent stem (iPS) cell, 19, 53, 202, 226 Influenza, 155, 161–163, 208

K Kaposi’s sarcoma-associated herpesvirus (KSHV), 134, 155, 156 Kyoto Encyclopedia of Genes and Genomes (KEGG), 36, 163, 214–216

L Long term potentiation (LTP), 89–92, 97, 197

M Marek’s disease virus (MDV), 134 Mendelian, 107, 109, 110, 130, 138, 139, 207–210 Metabolic diseases, 1, 50, 59–62, 66, 67, 69, 70, 99, 229 Milk, 7, 11, 17, 38, 52, 54, 97, 107, 110, 136, 154, 162, 208 Miravirsen, 35 MiRNA entangling target sorting (METS), 1–4, 28, 35, 37, 40, 53, 55, 57, 59, 61, 63, 65, 70, 84–87, 98, 99, 122, 129, 131, 132, 134, 135, 137, 139, 141, 142, 153, 165, 171, 173, 181, 195, 196, 199–202, 208, 210, 217, 218, 223–227, 229 MiRNA memory package (MMP), 40, 99, 121, 122, 124, 132, 133, 141, 144, 187–194, 197–201, 224

Index miRNA quantum language and artificial intelligence (MIRAI), 1–4, 21, 28, 35, 37, 40, 41, 53, 55, 57, 59, 61, 63, 65, 70, 84–87, 98, 99, 108, 118, 122, 129, 131, 132, 134, 135, 137, 139, 141, 142, 153, 165, 171, 173, 181, 190, 195, 196, 199–202, 207, 208, 210, 217, 218, 223–227, 229 MiRNA-related GWAS (miRGWAS), 118 MRX34, 208 Murine leukemia virus (MLV), 132, 134

N Natural killer (NK), 129, 137, 141 Nef, 8, 9, 11, 13, 26, 61, 160, 161 N367, 13–16, 26, 39, 61, 112, 114, 144, 160, 161, 163, 165, 214–218 Nuclear factor-kB (NF-kB), 61, 137, 218

O Obesity, 4, 49, 51–58, 62, 64–66, 68–70, 110, 122, 124, 125, 193, 194, 208 Oncogene, 18, 112, 132, 134, 141, 143, 175–177

P Parkinson’s disease (PD), 190, 191, 195, 197, 198 Plasticity, 83, 84, 89–92, 94, 95, 97, 198 Prion, 53, 96–99, 117 Program analysis diagram (PAD), 207, 217, 218 Programmed evolution, 7, 17, 50, 70, 71, 121, 122, 124, 130, 139, 140, 144, 162

R Retroelement (RE), 7, 11, 13, 17, 52, 58, 61, 132, 196 Rhadinovirus, 157, 159, 163 RNA-induced silencing complex (RISC), 27–29 RNA Wave 2000, 8–12, 16–18, 20, 21, 26–29, 35, 39, 93, 108, 112, 129, 136, 140

S Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 21, 52, 137, 153, 195, 207–210 Simian foamy virus (SFV), 162

Index Single nexus score (SNS), 107, 114–116, 118 Single nucleotide polymorphism (SNP), 35, 57, 92, 97, 108, 117, 139, 161, 174, 187, 190

T Toll-like receptor (TLR), 135, 140

233 Tumor suppressor, 8, 18, 19, 62, 132, 135, 137–139, 141–143, 171, 173, 175–177, 202, 214, 215

X Xenotropic miRNA (xenomiRNA), 38, 49, 51–60, 69, 84, 129, 208