Exploring a Complex Phenomenon

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Aging Exploring a Complex Phenomenon



Aging Exploring a Complex Phenomenon

Edited by

Shamim I. Ahmad


CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2018 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Printed on acid-free paper International Standard Book Number-13: 978-1-138-19697-1 (Hardback) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http:// www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data Names: Ahmad, Shamim I., editor. Title: Aging : exploring a complex phenomenon / editor, Shamim I. Ahmad. Other titles: Aging (Ahmad) Description: Boca Raton : Taylor & Francis, 2018. | Includes bibliographical references. Identifiers: LCCN 2017028774 | ISBN 9781138196971 (hardback : alk. paper) Subjects: | MESH: Aging | Age Factors | Geriatrics--methods Classification: LCC QP86 | NLM WT 104 | DDC 612.6/7--dc23 LC record available at https://lccn.loc.gov/2017028774 Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com


For the aging book The editor dedicates this book to his late father, Abdul Nasir, and mother, Anjuman Ara, who played very important roles to bring him to this stage of academic achievements with their esteemed love, sound care, and sacrifice. Dedication also goes to his wife, Riasat Jan, for her patience and persistent encouragement to produce this book, as well as to his children, Farhin, Mahrin, Tamsin, Alisha, and Arsalan, especially the latter two for providing great pleasure with their innocent interruptions, leading to his energy revitalization. Finally, his best wishes go to the aged ailing patients for their remaining life to run smoothly.



Contents Preface ..............................................................................................................................................xi One World, One Humanity............................................................................................................... xv Acknowledgments...........................................................................................................................xvii Editor...............................................................................................................................................xix Contributors.....................................................................................................................................xxi

Section I Introduction to Aging Chapter 1 A Synopsis on Aging.....................................................................................................3 João Pinto da Costa Chapter 2 Understanding Aging after Darwin............................................................................ 23 Michael A. Singer

Section II Aging Hypothesis Chapter 3 Evolutionary Theories of Aging: A Systemic and Mechanistic Perspective.............. 43 Quentin Vanhaelen Chapter 4 The Indispensable Soma Hypothesis in Aging........................................................... 73 Marios Kyriazis Chapter 5 Programmed Aging Paradigm and Aging of Perennial Neurons............................... 91 Giacinto Libertini Chapter 6 The Genetic Program of Aging................................................................................. 117 Xiufang Wang, Huanling Zhang, Libo Su, and Zhanjun Lv Chapter 7 The Origins of Aging: Multicellularity, Speciation, and Ecosystems...................... 135 Michael A. Singer Chapter 8 Human Culture: Urbanization and Human Aging.................................................... 161 Michael A. Singer Chapter 9 Aging Epigenetics: Accumulation of Errors and More............................................. 175 Vasily V. Ashapkin, Lyudmila I. Kutueva, and Boris F. Vanyushin vii

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Section III Diseases Associated with Aging and Treatment Chapter 10 The Premature Aging Characteristics of RecQ Helicases........................................209 Christ Ordookhanian, Taylor N. Dennis and J. Jefferson P. Perry Chapter 11 Cockayne Syndrome and the Aging Process............................................................ 231 María de la Luz Arenas-Sordo Chapter 12 Cancer: The Price for Longevity............................................................................... 237 Karel Smetana Jr., Barbora Dvořánková, Lukáš Lacina, Pavol Szabo, Betr Brož, and Aleksi Šedo Chapter 13 Nodular Thyroid Disease with Aging....................................................................... 247 Enke Baldini, Salvatore Sorrenti, Antonio Catania, Francesco Tartaglia, Daniele Pironi, Massimo Vergine, Massimo Monti, Angelo Filippini, and Salvatore Ulisse Chapter 14 HIV and Aging: A Multifaceted Relationship.......................................................... 259 Edward J. Wing Chapter 15 Maturation, Barrier Function, Aging, and Breakdown of the Blood–Brain Barrier............................................................................................... 271 Elizabeth de Lange, Ágnes Bajza, Péter Imre, Attila Csorba, László Dénes, and Franciska Erdő Chapter 16 Senescent Cells as Drivers of Age-Related Diseases................................................ 305 Cielo Mae D. Marquez and Michael C. Velarde Chapter 17 Osteoimmunology in Aging...................................................................................... 335 Lia Ginaldi, Daniela Di Silvestre, Maria Maddalena Sirufo, and Massimo De Martinis Chapter 18 Manipulating Aging to Treat Age-Related Disease.................................................. 351 V. Mallikarjun and J. Swift Chapter 19 Therapeutic Options to Enhance Poststroke Recovery in Aged Humans................. 357 Aurel Popa-Wagner, Dumbrava Danut, Roxana Surugiu, Eugen Petcu, Daniela-Gabriela Glavan, Denissa-Greta Olaru, and Raluca Sandu Elena

Section IV Mechanisms of Aging Chapter 20 Increase in Mitochondrial DNA Fragments inside Nuclear DNA during the Lifetime of an Individual as a Mechanism of Aging................................................ 383 Gustavo Barja

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Chapter 21 Mitochondrial Oxidative Stress in Aging and Healthspan....................................... 395 Dao-Fu Dai Chapter 22 Immunology of Aging and Cancer Development..................................................... 423 T. Fulop, J. M. Witkowski, G. Dupuis, A. Le Page, A. Larbi, and G. Pawelec Chapter 23 Oxidation of Ion Channels in the Aging Process...................................................... 437 Federico Sesti Chapter 24 Lipid Raft Alteration and Functional Impairment in Aged Neuronal Membranes........................................................................................... 455 Julie Colin, Lynn Gregory-Pauron, Frances T. Yen, Thierry Oster, and Catherine Malaplate-Armand Chapter 25 Autophagy: The Way to Death or Immortality? Activators and Inhibitors of Autophagy as Possible Modulators of the Aging Process.................................... 475 Galina V. Morgunova, Alexander A. Klebanov, and Alexander N. Khokhlov

Section V Treatments in Aging Chapter 26 Aging: Grounds and Determents.............................................................................. 489 Sreeja Lakshmi and Preetham Elumalai Chapter 27 Skin Aging Clock and Its Resetting by Light-Emitting Diode Low-Level Light Therapy............................................................................................................ 499 R. Glen Calderhead

Section VI Healthy and Successful Aging Chapter 28 Social Structure and Healthy Aging: Case Studies................................................... 517 Jong In Kim and Gukbin Kim Chapter 29 Successful Aging: Role of Cognition, Personality, and Whole-Person Wellness.........525 Peter Martin, Leonard W. Poon, Kyuho Lee, Yousun Baek, and Jennifer A. Margrett Chapter 30 Physical Activity in Prevention of Glucocorticoid Myopathy and Sarcopenia in Aging........................................................................................... 543 Teet Seene and Priit Kaasik Chapter 31 Reductionism versus Systems Thinking in Aging Research.................................... 559 Marios Kyriazis

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Section VII Anti-Aging Drugs Chapter 32 Anti-Aging Drug Discovery in Experimental Gerontological Studies: From Organism to Cell and Back.............................................................................. 577 Alexander N. Khokhlov, Alexander A. Klebanov, and Galina V. Morgunova

Section VIII Aging in Caenorhabditis elegans Chapter 33 Caenorhabditis elegans Aging Is Associated with a Decline in Proteostasis.......... 599 Elise A. Kikis

Section IX Hibernation and Aging Chapter 34 Hibernation and Aging: Molecular Mechanisms of Mammalian Hypometabolism and Its Links to Longevity............................................................ 617 Cheng-Wei Wu and Kenneth B. Storey

Section X Mathematical Modeling of Aging Chapter 35 The Role of Mathematical Modeling in Understanding Aging................................ 637 Mark Mc Auley, Amy Morgan, and Kathleen Mooney Index............................................................................................................................................... 653


Preface AGING: AN UNEXPLAINED PHENOMENON Since the time mankind has developed a “thinking brain,” there has been an everlasting curiosity to know what aging is and whether it will ever be possible to increase the human life span or achieve immortality? The first book on this subject was written as early as 1582 by Mohammad Ibn Yusuf Al-Harawi titled Ainul Hayat. The book discussed the diet, lifestyle, and environmental factors influencing aging. According to PubMed, serious research on ageing and its first publication in scientific journals was recorded in 1925. Since then, 395,708 research and review articles have been published in various journals on this subject. From this, it is clear that the subject is not only extremely important but also very difficult to reach to the bottom of its molecular mechanisms and control, and hence remains an unexplained phenomenon. The mechanism of aging, in fact, appears to be very complex and, according to Chapter 6 of this book, over 300 theories alone on aging have been proposed. Among those, some are more acceptable than others, but none stands clear that can bring all the available research data on one platform. Among the more accepted theories on aging are the adaptive theory, cellular senescence theory, DNA damage theory, free radical and oxidative stress theory, genetic program theory, immunological theory, mitochondrial mutation theory, and the telomerase shortening theory. Less important are the antagonistic pleiotropy and stress theory, biogerontologic theory and psychosocial theories, chaos theory, codon restriction theory, continuity theory of normal aging, cross-linkage of macromolecule theory, developmental theory, ecological stress theory, evolutionary theory, gene regulation theory, general theory of aging, kinetic theory, melatonin theory, metabolic causes of aging, modern evolutionary mechanisms theories, neuroendocrine theory, parametabolic theory, participation theory, programmed and non-programmed theory, rate of metabolic theory, redundant message theory, somatic mutation theory, reliability theory of aging and longevity, error theory, thermodynamic theory, transcriptional event theory, unifying theory of aging, united theory of aging, and many more. The research publication existing in such a large number, especially covering the areas such as mechanisms of aging, age-related diseases, healthy aging and antiaging agents, and devices to control or slow down the aging process points toward a continued deep interest in this subject. It is almost impossible to detail all these aging theories and only a selected number of them have been presented in this book. Sections I and II in this book present the indispensable Soma hypothesis programmed aging of perennial neurons, the genetic program theory, aging epigenetics, and a couple of rare and novel approaches on aging affected by urbanization and associated culture as well on the role of multicellularity, speciation, and ecosystem on aging. Section III addresses a number of diseases and the malfunctioning of body organs and systems specifically associated with aging. Most important of these are premature aging in which a number of RecQ helicases play roles in maintaining the genome stability leading to progeria and some other diseases. Thus, a mutation in the WRN gene leads to Werner’s syndrome in which, besides rapid aging phototypes, the subjects are prone to cancer susceptibility. Another progeroid disease, known as Hutchinson–Gilford progeria, ironically could not be included in this book. Bloom syndrome (BLM gene mutation and another RecQ helicase) gives rise to a variety of cancers and multiple malignancies including several other physical deformities. Mutation in RecQ4 can lead to skin abnormalities and skeletal defects. These have been intricately highlighted in Chapter 10. The other children’s disease associated with aging is Cockayne syndrome, which is a disorder with premature aging and short life expectancy due to a mutation in one of the two genes, ERCC8 or ERCC6, known as CSA and CSB, respectively. It is a complex and progressive disease leading to developmental and xi

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cognitive delay apart from several other physiological and biochemical impairments. This has been comprehensively described in Chapter 11. The advancement of age and the probability of cancer susceptibility have been linked with the decreased gene repair activities and the accumulation of mutation contributing to multiple deleterious biological and molecular events; Chapter 12 highlights this issue. Other less important diseases linked with old age are osteoporosis due to immunological problems (Chapter 17), nodular thyroid diseases (Chapter 13), and breakdown of blood–brain barrier (Chapter 15). These are intricately presented to enrich the knowledge of our readers. Chapter 16 presents a generalized view of agerelated diseases, and Chapters 18 and 19 highlight the manipulation of aging to treat age-related diseases and the therapeutic options available to enhance the post-stroke recovery in aged humans. Section IV covers the mechanisms of aging as in Chapter 20 is described the significance of increased mitochondrial DNA fragments inside nuclear DNA, which seems to occur throughout life and plays a role in the aging process. These studies have mostly been carried out on yeast and in mice and implied to long living animals, which have low rates of mitochondrial reactive oxygen species production and thus less damage to their DNA. Chapters 21 and 23 address the important roles of oxidative stress and oxidation of ion channels in aging. The involvement of immune systems in aging and cancer development is described in Chapter 22 and the modulation of aging by autophagy is described in Chapter 25. The role of lipid raft alteration and impairment of age neural membrane have been addressed in Chapter 24. Section V focuses on the skin’s aging clock (Chapter 27) and on the grounds and determents of aging (Chapter 26). Section VI is equally valuable in making readers aware on the topics of healthy and successful aging. This issue has been addressed by different authors differently, such as in Chapter 28 the social structure and its influence on healthy aging is explained and in Chapter 29 the author describes that cognition, personality, and the whole person is responsible for determining the wellness and extension of age. Physical activities also have major impacts not only on age longevity but also can be helpful in maintaining good health and wellness. Section VII covers valuable chapters on finding anti-aging drugs. The fact is that the desire to live longer is fairly powerful and although ample funds are being spent on the aging research, a satisfactory outcome is still awaited. Certain vitamins (such as vitamin E) and biochemical agents, plant extracts, and so on have been listed to have antiaging ability but none so far has clearly and categorically been shown to have any significant effect on the human aging process. Interestingly, however, success has been achieved in producing mutant lower organisms, flies, and small animals through genetic manipulation that have a longer life than their non-mutant counterparts. Mutant mice living longer were reported by Migliaccio et al. (Nature, 402, 309–313, 1999) in which the mice had mutations in gene p66 shc protein and lived almost one third longer. Resistance to oxidative damage was considered to be the prime reason. Since then, a large number of studies have been carried out to find other reasons for the longevity in mutant mice, but a clear, full-length picture is still awaited. Research on long-lived Drosophila melanogaster revealed that evolutionary conserved function for the mitochondrial electron transport chain in the modulation of life span was responsible (Linford and Pletcher, Curr Biol, 19, 2009) and in another report on Caenorhabditis elegans age-1 (hx546) mutant was shown to have an increased mean life span averaging 40% and a maximal life span averaging 60% at 20°C (Friedman and Johnson, Genetics, 118, 75–86, 1988). These results are providing promising information to extend the work on finding the magic potion(s) for human beings to increase their aging life. Sections VIII and IX address aging in lower organisms and those that having longer life due to hibernating in adverse environmental conditions. The authors of Section X uniquely presents their own devised mathematical models of aging. From the materials presented in this book, it is evident that the interest in aging, especially discovering antiaging drugs and craving for a longer life, is not waning; hence, it is anticipated that the information presented will stimulate further research among both specialists and novices in the

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field with excellent overviews of the current status of research and pointers to future research goals. However, an interesting situation remains to imagine how the world is going to look when human beings will live for 150 years or more. Shamim I. Ahmad, BSc, MSc, PhD School of Science and Technology Nottingham Trent University Nottingham, United Kingdom



One World, One Humanity AN APPEAL TO THE UNITED NATIONS SECURITY COUNCIL AND THE HEAD OF THE STATES It is yet to be determined when the desire beings originated in humans to have eternal life or at least to extend it as much as possible. Whenever, the desire must be very strong and hence the thinking brain must have been extensively used to find the ways to achieve them. According to the research record of PubMed although 395,708 research papers (May 2017) on various aspects of aging have been published, reflecting the public demands and the tireless efforts of the scientists and the industries to achieve it, yet the goal remains fairly aloof. The only achievement we can see is the increased number of geriatrics and centenarians, which has been due to the massive development in medical science, and the knowledge gained through research about the better quality of life leading to age longevity. Recent developments in gene technology, however, have managed to prolong the life of certain microbes, flies, and small animals, and these have been addressed in this book. The next question therefore asked is whether this technology could ever be applied to humans to prolong their life span. It is an open-ended question and, in the editor’s opinion, it is highly unlikely for many years to come due to stringent ethical issues and controls. The alternative, therefore, is to discover the most active and safe antiaging agents, which can be tested on animals and later applied on humans beings. In the editor’s opinion, it may not be too long that such magic potion(s) will be found and, if safely used from the early age, it may slow down the aging process throughout the life and may culminate in the childhood age to stay say up to 20 years, the teen age may extend up to 60 years, youth up to 120 years, old age up to 150 years, and then the death at the ripe age 160 or over. It is purely assumptive but not impossible. Now, if we look at the other side of the coin, we can see that the present world, especially the developed and the developing countries, are extensively busy in developing more and more superpowered war weapons of various kinds, competing with each other, which can fairly rapidly kill the human species at massive scales. Also, it is apparent that most often innocent people, including children, women, and the fragile and old people, will become victims of such actions. There is no doubt that, with the passage of time, biological, chemical, and nuclear weapons are being manufactured and stored in increasing quantities in several countries; the sensible and crunchy question being asked is why. If there are weapons, there is use, and if there is use it has only one consequence: death of the human species and massive infrastructure destruction besides the collateral heartbreaking results. Here I must add that in this world there still exist very many kindhearted people and organizations who are risking their lives and their resources to reduce this destruction, and I salute them. I recall someone asking of the very famous and super intelligent Einstein his opinion about the Third World War; the answer he gave was, “I do not know about the third war but I know about the fourth that it will be fought by bows and arrows.” The editor sincerely hopes that the destruction of mankind should never reach that level, and that the human species does not deserve to see the Third World War causing their massive elimination and other destructions predicted by this ingenious scientist and great philosopher. Hence, I am appealing to the United Nations Security Council and the Head of every State in the world to think seriously about the prediction presented by this highly respected scientist, and put all their efforts possible to minimize the production, storage, and especially the use of the weapons of mass destruction. Minimizing weapons production and storage will have the ripple effect and the number of wars should reduce, thus saving this world and especially our human race. Thinks we humans are tied up with one slogan and one philosophy – the xv

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One World, One Humanity

Philosophy of Humanity and this should be applied with full strength – please adapt this slogan, One World, One Humanity. Shamim I. Ahmad, BSc, MSc, PhD School of Science and Technology Nottingham Trent University Nottingham, United Kingdom


Acknowledgments The editor cordially acknowledges the authors of this book for their contribution and in-depth knowledge, high skill, and professional presentation. Without their input, it would not have been possible to bring out this book on this topic. He would also like to acknowledge the staff, especially Ms. Hillary Lafoe, Ms. Jennifer Blaise, and Mr. Chuck Crumly of CRC Press, for their hard work, friendly approach, and patience and also CRC Press/Taylor & Francis Group for their efficient and highly professional handling of this project. Finally, he wishes to acknowledge his university for providing him this platform and especially the IT service staff members for helping him on every IT-associated problem with their superskills. Finally acknowledgement presented to Ms. Teena Lawrence, Mr. Todd Perry, and the staff of the printing division for their most professional input to this excellent production.

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Editor Shamim I. Ahmad, after obtaining his master’s in botany from Patna University, Bihar, India, and his PhD in molecular genetics from Leicester University, England, joined Nottingham Polytechnic as a grade 1 lecturer and was subsequently promoted as a senior lecturer. After serving for about 35 years in Nottingham Polytechnic (which subsequently became Nottingham Trent University), he volunteered for early retirement, although yet serving as a part-time senior lecturer. He is now involved with writing medical books. For more than three decades, he has researched on different areas of molecular biology/genetics including thymineless death in bacteria, genetic control of nucleotide catabolism, development of anti-AIDs drug, control of microbial infection of burns, phages of thermophilic bacteria, and microbial flora of Chernobyl after the accident at the nuclear power station. However, his main interest, which started about 30 years ago, is DNA damage and repair, specifically by near ultraviolet light, mostly through the photolysis of biological compounds, production of reactive oxygen species, and their implications on human health including skin cancer. He is also investigating near ultraviolet photolysis of nonbiological compounds such as 8-methoxypsoralen and mitomycin C and their importance in psoriasis treatment and in Fanconi anemia. Collaborating with the University of Osaka, Japan, in his latest research publication, he and his colleagues were able to show that a number of naturally occurring enzymes were able to scavenge the reactive oxygen species. In 2003 he received a prestigious “Asian Jewel Award” in Central Britain for Excellence in Education. His longtime ambition to produce medical books started in 2007 and since then has published Molecular Mechanisms of Fanconi Anemia; Molecular Mechanisms of Xeroderma Pigmentosum; Molecular Mechanisms of Cockayne Syndrome; Molecular Mechanisms of Ataxia Telangiectasia; Diseases of DNA Repair; Neurodegenerative Diseases; and Diabetes: An Old Disease a New Insight. As a co-author, he has also published Obesity: A Practical Guide; Thyroid: Basic Science and Clinical Practice; and Diabetes: A Comprehensive Treatise for Patients and Caregivers, published by Landes Bioscience/Springer. Recently CRC Press/Taylor & Francis Group has published his book on Reactive Oxygen Species in Biology and Human Health and a book on Ultraviolet Light in Human Health, Diseases and Environment will be out soon by Springer.

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Contributors María de la Luz Arenas-Sordo Servicio de Genética Instituto Nacional de Rehabilitación Secretaria de Salud México City, Mexico Vasily V. Ashapkin Belozersky Institute of Physico-Chemical Biology Lomonosov Moscow State University Moscow, Russia Yousun Baek Department of Human Development and Family Studies Iowa State University Ames, Iowa Ágnes Bajza Faculty of Information Technology and Bionics Pázmány Péter Catholic University Budapest, Hungary Enke Baldini Department of Surgical Sciences, “Sapienza” University of Rome Rome, Italy Gustavo Barja Department of Animal Physiology-II Faculty of Biological Sciences Complutense University of Madrid (UCM) Madrid, Spain Betr Brož Catholic Theology Faculty Charles University Prague, Czech Republic R. Glen Calderhead Clinique L Dermatology Goyang-si, Gyeonggi-do, South Korea Antonio Catania Department of Surgical Sciences “Sapienza” University of Rome Rome, Italy

Julie Colin UR AFPA (INRA USC 340, EA 3998) Équipe Biodisponibilité et Fonctionnalités des Lipides Alimentaires (BFLA) Université de Lorraine Vandœuvre-lès-Nancy, France Attila Csorba Department of Pharmacognosy Faculty of Pharmacy University of Szeged Szeged, Hungary Dao-Fu Dai Department of Pathology University of Iowa Carver College of Medicine Iowa City, Iowa Dumbrava Danut Department of Functional Sciences Center of Clinical and Experimental Medicine University of Medicine and Pharmacy of Craiova Craiova, Romania Elizabeth de Lange Translational Pharmacology Cluster of Systems Pharmacology Leiden Academic Center for Drug Research Leiden University Leiden, The Netherlands Massimo De Martinis Department of Life, Health and Environmental Sciences University of L’Aquila L’Aquila, Italy László Dénes Faculty of Information Technology and Bionics Pázmány Péter Catholic University Budapest, Hungary xxi

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Taylor N. Dennis Department of Biochemistry University of California Riverside, California Daniela Di Silvestre Department of Life, Health and Environmental Sciences University of L’Aquila L’Aquila, Italy G. Dupuis Department of Biochemistry Graduate Program in Immunology Faculty of Medicine and Health Sciences University of Sherbrooke Sherbrooke, Quebec, Canada Barbora Dvořánková First Faculty of Medicine Institute of Anatomy and BIOCEV Charles University Prague, Czech Republic

Contributors

Lia Ginaldi Department of Life, Health and Environmental Sciences University of L’Aquila L’Aquila, Italy Daniela-Gabriela Glavan 2nd Psychiatry Clinic Hospital University of Medicine and Pharmacy of Craiova Craiova, Romania Lynn Gregory-Pauron UR AFPA (INRA USC 340, EA 3998) Équipe Biodisponibilité et Fonctionnalités des Lipides Alimentaires (BFLA) Université de Lorraine Vandœuvre-lès-Nancy, France Péter Imre Faculty of Information Technology and Bionics Pázmány Péter Catholic University Budapest, Hungary

Raluca Sandu Elena Department of Functional Sciences, Center of Clinical and Experimental Medicine University of Medicine and Pharmacy of Craiova Craiova, Romania

Priit Kaasik Institute of Sport Sciences and Physiotherapy University of Tartu Tartu, Estonia

Preetham Elumalai Department of Processing Technology (Biochemistry) Kerala University of Fisheries and Ocean Studies Kochi, Kerala, India

Alexander N. Khokhlov Evolutionary Cytogerontology Sector School of Biology Lomonosov Moscow State University Moscow, Russia

Franciska Erdő Faculty of Information Technology and Bionics Pázmány Péter Catholic University Budapest, Hungary Angelo Filippini Department of Surgical Sciences “Sapienza” University of Rome Rome, Italy T. Fulop Research Center on Aging Graduate Program in Immunology Faculty of Medicine and Health Sciences University of Sherbrooke Sherbrooke, Quebec, Canada

Elise A. Kikis Biology Department The University of the South Sewanee, Tennessee Gukbin Kim Global Management of Natural Resources University College London (UCL) London, United Kingdom Jong In Kim Division of Social Welfare and Health Administration and Institute for Longevity Sciences Wonkwang University Iksan, Republic of Korea

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Contributors

Alexander A. Klebanov Evolutionary Cytogerontology Sector School of Biology Lomonosov Moscow State University Moscow, Russia

Zhanjun Lv Department of Genetics Hebei Medical University Hebei Key Lab of Laboratory Animal Shijiazhuang, Hebei Province, China

Lyudmila I. Kutueva Belozersky Institute of Physico-Chemical Biology Lomonosov Moscow State University Moscow, Russia

Catherine Malaplate-Armand UR AFPA (INRA USC 340, EA 3998) Équipe Biodisponibilité et Fonctionnalités des Lipides Alimentaires (BFLA) Université de Lorraine Vandœuvre-lès-Nancy, France

Marios Kyriazis ELPIs Foundation for Indefinite Lifespans London, United Kingdom Lukáš Lacina First Faculty of Medicine Institute of Dermatovenerology and BIOCEV Charles University Prague, Czech Republic Sreeja Lakshmi Department of Processing Technology (Biochemistry) Kerala University of Fisheries and Ocean Studies Kochi, Kerala, India A. Larbi Singapore Immunology Network (SIgN) Biopolis Agency for Science Technology and Research (A*STAR) Singapore Kyuho Lee Department of Human Development and Family Studies Iowa State University Ames, Iowa A. Le Page Research Center on Aging Graduate Program in Immunology Faculty of Medicine and Health Sciences University of Sherbrooke Sherbrooke, Quebec, Canada Giacinto Libertini Department of Translational Medical Sciences Federico II University Naples, Italy

V. Mallikarjun Wellcome Trust Centre for Cell-Matrix Research Division of Cell Matrix Biology and Regenerative Medicine School of Biological Sciences Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester, United Kingdom Jennifer A. Margrett Department of Human Development and Family Studies Iowa State University Ames, Iowa Cielo Mae D. Marquez Institute of Biology University of the Philippines Diliman Quezon City, Philippines Peter Martin Department of Human Development and Family Studies Iowa State University Ames, Iowa Mark Mc Auley Faculty of Science and Engineering University of Chester Chester, United Kingdom Massimo Monti Department of Surgical Sciences “Sapienza” University of Rome Rome, Italy

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Kathleen Mooney Faculty of Health and Social Care Edge Hill University Lancashire, United Kingdom Amy Morgan Faculty of Science and Engineering University of Chester Chester, United Kingdom Galina V. Morgunova Evolutionary Cytogerontology Sector School of Biology Lomonosov Moscow State University Moscow, Russia Denissa-Greta Olaru Department of Ophthalmology Medlife Clinic Craiova, Romania Christ Ordookhanian Department of Biochemistry University of California Riverside, California

Contributors

Eugen Petcu Griffith University School of Medicine Queensland, Australia and Queensland Eye Institute Brisbane, Queensland, Australia João Pinto da Costa CESAM and Department of Chemistry University of Aveiro Aveiro, Portugal Daniele Pironi Department of Surgical Sciences “Sapienza” University of Rome Rome, Italy Leonard W. Poon Institute of Gerontology University of Georgia Athens, Georgia

Thierry Oster UR AFPA (INRA USC 340, EA 3998) Équipe Biodisponibilité et Fonctionnalités des Lipides Alimentaires (BFLA) Université de Lorraine Vandœuvre-lès-Nancy, France

Aurel Popa-Wagner Griffith University School of Medicine Southport, Queensland, Australia

G. Pawelec Center for Medical Research Second Department of Internal Medicine University of Tübingen Tübingen, Germany

Queensland Eye Institute Brisbane, Queensland, Australia

J. Jefferson P. Perry Department of Biochemistry University of California Riverside, California and Amrita University Kerala, India and Universidad Francisco de Vitoria Madrid, Spain

and

and Department of Functional Sciences Center of Clinical and Experimental Medicine University of Medicine and Pharmacy of Craiova Craiova, Romania Aleksi Šedo First Faculty of Medicine Institute of Biochemistry and Experimental Oncology Charles University Prague, Czech Republic

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Contributors

Teet Seene Institute of Sport Sciences and Physiotherapy University of Tartu Tartu, Estonia Federico Sesti Department of Neuroscience and Cell Biology Robert Wood Johnson Medical School Rutgers University Piscataway, New Jersey Michael A. Singer Faculty of Health Sciences Queen’s University Kingston, Ontario, Canada Maria Maddalena Sirufo Department of Life Health and Environmental Sciences University of L’Aquila L’Aquila, Italy Karel Smetana Jr. First Faculty of Medicine Institute of Anatomy and BIOCEV Charles University Prague, Czech Republic Salvatore Sorrenti Department of Surgical Sciences “Sapienza” University of Rome Rome, Italy Kenneth B. Storey Department of Biology Carleton University Ottawa, Canada Libo Su Department of Genetics Hebei Medical University Hebei Key Lab of Laboratory Animal Shijiazhuang, Hebei Province, China Roxana Surugiu Department of Functional Sciences Center of Clinical and Experimental Medicine University of Medicine and Pharmacy of Craiova Craiova, Romania

J. Swift Wellcome Trust Centre for Cell-Matrix Research Division of Cell Matrix Biology and Regenerative Medicine School of Biological Sciences Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester, United Kingdom Pavol Szabo First Faculty of Medicine Institute of Anatomy and BIOCEV Charles University Prague, Czech Republic Francesco Tartaglia Department of Surgical Sciences “Sapienza” University of Rome Rome, Italy Salvatore Ulisse Department of Surgical Sciences “Sapienza” University of Rome Rome, Italy Quentin Vanhaelen Insilico Medicine Inc. Johns Hopkins University Baltimore, Maryland Boris F. Vanyushin Belozersky Institute of Physico-Chemical Biology Lomonosov Moscow State University Moscow, Russia Michael C. Velarde Institute of Biology, University of the Philippines Diliman, Quezon City, Philippines Massimo Vergine Department of Surgical Sciences “Sapienza” University of Rome Rome, Italy Xiufang Wang Department of Genetics Hebei Medical University Hebei Key Lab of Laboratory Animal Shijiazhuang, Hebei Province, China

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Edward J. Wing Department of Medicine Warren Alpert Medical School of Brown University Providence, Rhode Island J. M. Witkowski Department of Pathophysiology Medical University of Gdańsk Gdańsk, Poland Cheng-Wei Wu Department of Biology University of Florida Gainesville, Florida

Contributors

Frances T. Yen UR AFPA (INRA USC 340, EA 3998) Équipe Biodisponibilité et Fonctionnalités des Lipides Alimentaires (BFLA) Université de Lorraine Vandœuvre-lès-Nancy, France Huanling Zhang Department of Genetics Hebei Medical University Hebei Key Lab of Laboratory Animal Shijiazhuang, Hebei Province, China


3 A Systemic and Mechanistic

Evolutionary Theories of Aging Perspective Quentin Vanhaelen

CONTENTS Introduction....................................................................................................................................... 43 Phenotypic Fluctuations: An Evolutionary Mechanism to Improve Fitness..................................... 48 Definition and Measurement of Biological Aging............................................................................ 51 Relationships between ARGs, ARDs, and Aging............................................................................. 52 Aging as a Result of Dynamical Instabilities Caused by Mutation Accumulation........................... 55 Aging as an Evolutionary Mechanism to Adapt to Environmental Pressures.................................. 58 Aging as a Result of the Disruption of Cellular Dynamical Equilibriums.......................................60 Conclusion........................................................................................................................................64 References.........................................................................................................................................66

INTRODUCTION On our earth, there exist a tremendous number of different species. Since the seminal work of Darwin, this diversity is explained in part as being the result of a succession of adaptations occurring at various timescales as a response to environmental or internal changes. Understanding how and why these adaptations, called evolutionary changes, occur is one of the most fundamental questions in life science. Owing to the inherent complexity of the problematic and the accumulation of various experimental data demonstrating the variety of evolutionary patterns encountered within the tree of life, the initial version of the theory of evolution proposed by Darwin has continuously undergone modifications and improvements. Its most recent version, known as the Modern Synthesis, has been shaped upon the experimental discoveries and theoretical progress made in the different fields of life sciences and was formalized by Ronald Fisher, J.B.S. Haldane, Sewall Wright, and Julian Huxley [1,2]. Broadly speaking, Modern Synthesis is based on the mechanics of evolution and the formalism describing the effects of the driving forces of evolution. As its name suggests, the main achievement of this evolutionary theory is to provide a conceptual framework able to unify the point of view of geneticists, naturalists, and paleontologists about the mechanisms behind evolutionary changes. Experimental observations show that each species can be characterized by a set of specific treats. Nevertheless, the development of an individual of any species follows a common sequence, the life cycle, which contains two main stages. The first one is growth which includes birth, childhood, and maturation, and the second one is the reproduction stage which is followed by aging and death. Evolution is thought to act on the different steps of this sequence by progressively modifying specific traits through different types of mechanisms. Historically, natural selection was considered as the only driving force of evolution and for that reason studies in that field were essentially restricted at the level of the phenotypic traits. The development of population genetics and molecular genetics allowed the analysis of biological changes induced by evolution from a molecular and a cellular perspective and it is now admitted that other processes such as random genetic drifts act as a part of the 43

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Aging: Exploring a Complex Phenomenon

global driving force of the evolution [3–5]. These studies showed, for example, that modifications at the genomic level can arise through mutations affecting the expression of specific genes, which in turn affects one or several phenotypic traits. Modifications affecting some traits, called changing life history traits, have a bigger impact than modifications on other minor traits of the species. Furthermore, the modifications can have beneficial, neutral, or deleterious effects and modifications which are beneficial for the species on a long-term basis are not necessarily beneficial for a single individual on the timescale of a life span. Globally, it is assumed that evolution acts by eliminating detrimental mutations through natural selection while keeping neutral and beneficial ones unaffected. Currently, determining which process between natural selection, genetic drift, and other suggested mechanisms should be considered as the dominant term of the forces of evolution is still a matter of debate. For example, while theories based on the Selectionist Hypothesis still see selection as the main force of evolution [6,7], the neutral theory proposed by Kimura and based on the neutralist hypothesis supports that evolutionary forces are ruled by random genetic drift [8–10]. This debate should actually be considered from a broader perceptive. Indeed, several hypotheses behind the theoretical foundations of Modern Synthesis are under strong pressure [11] or were discarded [12]. This implies that although the current version of Modern Synthesis is still the main theoretical framework to analyze the various aspects of the evolution, one can expect that new conceptual developments could emerge [13,14]. Independently of the equilibrium between the various known or still unknown processes contributing to the forces of evolution, the purpose of the evolutionary mechanism is to ensure that, on a more or less long-term basis, a species is able to adapt to changes occurring in its environment, a necessary condition to guarantee the perpetuation of the species. Thus, a successful adaptation will result in an improved equilibrium between mortality and reproduction rates in order to allow each generation to produce a larger progeny. Indeed, more descendants imply that the beneficial traits are more easily carried on into future generations and can contribute to strengthening the species. The number of descendants of a species is associated with its fitness function. By modifying the mortality and reproduction rates, evolution works toward increasing the fitness and the fitness function is usually expressed in terms of reproductive and mortality rates. Thus, from an evolutionary perspective, the changing life history traits of a species are the ones whose adaptation induces the most significant difference in the level of fitness. Aging and its associated biological process of organismic decay called senescence is a treat shared by most, if not all, living species. Historically, it is Leonard Hayflick and Paul Moorhead who discovered that normal human fibroblasts have a finite proliferative capacity in culture [15]. They named this process cellular senescence, and assumed that it could be associated with the onset of aging. From a molecular point of view, cellular senescence can be defined as a process in which cells cease dividing and undergo distinctive phenotypic alterations, including profound chromatin changes and tumor-suppressor activation. Nevertheless, from an evolutionary perspective, the potential functions of senescence and aging with respect to survival, adaptability, or natural selection are still not well understood. During the past decades, many theories were suggested to explain from various perspectives the role of aging and its associated regulatory mechanisms. From an evolutionary perceptive, aging can be analyzed from different complementary points of view. First, as a trait, does aging, despite having obvious deleterious effects at the level of individuals, provide any significant advantages in terms of probability of survival for the kin? Is it possible that these advantages could depend on the context and environment faced by the species? To answer this question, most of the evolutionary theories of aging are based on the evolutionary mechanics and suggest mechanisms of aging using the interplay between the processes of mutation and selection. Nevertheless, these theories diverge on many key aspects. For example, they provide different answers when it comes to defining aging as a beneficial, neutral, or detrimental trait for individuals and/or kin. Furthermore, they often support different mechanisms as ultimate causes of aging [16–18]. These different mechanisms are most of the time complementary and represent incomplete descriptions of aging from

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different perspectives which should be unified in a more integrated framework. To that end, it has been recently suggested in Reference [19] to categorize the main theories of aging depending on how aging is supposed to affect the fitness function. The four main categories obtained can be summarized as follows. The first category is called maladaptive aging. An example is given by the mutation accumulation theory proposed by Medawar and Charlesworth which defines aging as a by-product of natural selection with an accumulation of genes with negative effects at old age from one generation to another. This accumulation of deleterious genetic material is caused by the lack of selection pressure in long-lived animals [20]. The second category is called secondary aging. The antagonistic pleiotropy theory belongs to this category. This theory proposes that some genes can have positive effects at young ages but become harmful when the organism gets older. Thus, both mutation accumulation and antagonistic pleiotropy predict that specific mutations in particular genes will cause senescence; however, antagonistic pleiotropy adds an adaptive aspect in the sense that mutations that are damaging for the organism later in life could actually be favored by natural selection if they are advantageous early in life, resulting in increased reproductive success. Another example of this category is the disposable soma theory. The disposable soma theory is actually an improved version of the antagonistic pleiotropy in the sense that it provides a more detailed explanation about how a gene could have both deleterious and positive effects. The hypothesis supported by the disposable soma theory is that organisms face a trade-off between dedicating energy to reproduction or investing it in the maintenance and growth of their somas. This ultimately results in disruption of repair mechanisms, leading to an accumulation of cellular damage. The third category is called assisted death. These theories consider aging as a beneficial trait for the kin. Finally, the category of senemorphic aging considers that aging can be beneficial or detrimental depending on the environmental conditions [21]. Another purpose of evolutionary theories of aging is to understand how aging patterns and mortality curves obtained from demographic data are shaped. Indeed, aging patterns strongly vary between species [22] and in order to explain these observations, it is necessary to identify what are the mechanisms whose effects can result in the shapes of survival curves. Currently, there are several of these causal models which are able to describe several important topological features of these mortality curves, but generally these evolutionary demographic models of aging do not provide us with an integrated framework for understanding the origin of the diversity of aging patterns observed through the tree of life. The difficulty comes from the fact that the mortality curves are the result of complex relationships between living style, effects of natural selection, environmental conditions, and fine-tuning of cellular mechanisms, all of these parameters being at various degrees specific to each species. Demographic models have been built to describe the effects of natural selection together with mechanisms such as accumulation of mutations. They usually also integrate other variables such as resources availability and reproduction rate and can take into account the effects of competition between species. However, a more challenging task is to include the effects of the fine-tuning of cellular mechanisms. Indeed, incorporating such complex information within these models requires having a detailed description of these mechanisms, that is, a description of how aging occurs and propagates with the living system. There are currently many recognized mechanisms of aging including inflammation, apoptosis, oxidative stress, accumulation of DNA damage, cell-cycle deregulation, mitochondrial dysfunction, and telomere shortening, just to name a few. Each of these mechanisms can be associated with specific disparate damages and pathologies, called aging-related diseases (ARDs), which commonly appear when individuals get older. For example, various types of cancers are identified as ARDs and their origin is assumed to be connected to genomic instability and decreased capacity for DNA repair, two characteristics of both cancer and aging [23]. Telomere length and telomerase activity are also involved in aging and diseases like Alzheimer’s dementia [24]. Mitochondrial dysfunction is another common property of aging and cardiovascular diseases [25,26]. Chronic inflammation also appears in old individuals and could also contribute to cardiovascular diseases [27] and neurodegenerative diseases [28]. Hence, during the past decades,



many of these mechanisms triggering ARDs have been used to elaborate specific theories of aging. There are currently an impressive number of theories trying to explain the onset and propagation of aging, usually from a restricted set of molecular mechanisms associated with ARDs. Nevertheless, according to Trindade et al. [19], these causality theories of aging can be classified into two classes. First, there are the entropy-based theories. These theories see death as the result of a relatively long period of degeneration. From a mechanistic point of view, aging is then described as a collection of cumulative changes to the molecular and cellular structure of the adult organism, which result from essential metabolic processes, but which also, once they progress far enough, increasingly disrupt metabolism, resulting in pathology and death [29,30]. Second, there are the sudden death theories. The main hypothesis behind these theories is that death follows either a relatively short period of degeneration or is an almost instantaneous process. Altogether, these theories provide different insights of aging and are helpful to develop adapted therapies to cure ARDs. Nevertheless, they remain based on one or a few mechanisms connected to a subset of disparate damages and ARDs, and consequently, they do not provide the comprehensive and unified mechanistic description required to fully integrate the systemic nature of aging. The origin of the systemic nature of aging is a consequence of the hierarchical organization of living systems such as the human body. Indeed, the human body is a multilevel complex system firstly constituted of billions of independent cells which form different types of tissues. These tissues are the main blocks used to assemble organs and these organs are themselves organized in different systems such as lymphatic, respiratory, digestive, urinary, and reproductive systems to achieve specific tasks. As a result, dysfunctions affecting even a restricted number of biological processes within the cells of one or several organs can propagate to all parts of the body. This explains why aging cannot be fully understood or controlled when monitoring only a restricted number of physiological processes. The study of this systemic organization of the living system, which has its equivalent inside any single cell, has been made possible by two main technological trends. First of all, there is the accumulation of various high-throughput data generated from different research areas such as proteomics, genomics, chemoproteomics, and phenomics which provides a better insight of the main components of the living cell [31–33]. Second, there is the progress made in computational and mathematical sciences [34]. These progresses, combined with the availability of increasingly powerful computational resources, allowed the development of software for retrospective analysis as well as the maintenance of web-based databases which are required for the gathering, classification, and efficient use of these experimental data. These trends have also been accompanied by a conceptual change within biology with the transition from a qualitative, structural, and most of the time static description of the cell to a more systemic description in terms of functional but also dynamical properties [35–37]. For example, in addition to the traditional approaches of biochemistry and biophysics, studies focused on the hierarchical organization of the cellular environment and, on the dynamics of its components, have identified dynamical motifs and cycles [38,39] as key elements involved in the regulation of the cellular behavior. Inside the cytoplasm, proteins interacting together are organized as structured modules such as the signaling pathways which are well known for being involved in the transmission of all signals received from the external environment to any concerned cellular components. Inside the nucleus, genes and transcription factors also form structured dynamical patterns called gene regulatory networks (GRN). GRN can be visualized in the form of a directed graph whose nodes represent the genes. An edge between two nodes represents an interaction which can be either an inhibition or activation. These structures are deeply involved in many regulatory processes including the regulation of genes expression. The regulation of gene expression is a complex process also involving mechanisms such as mRNA splicing [40,41], chromatin remodeling [42], and epigenetics modifications. Epigenetics refers to how transcription, DNA replication, and other aspects of genome function are regulated in a manner that is independent of DNA sequence.


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Epigenetic modifications are dynamical adaptations of the structure of the chromatin which contribute to the regulation of gene transcription. The structure of the chromatin can be modified at the level of the histones, the predominant protein components of chromatin, via different molecular processes [43–46]. It has been demonstrated that the histone modifications are regulated by conserved protein modules and follow a well-organized dynamical scheme known as the histone code [47,48]. The apparent complexity of these dynamical structures is progressively better understood with the establishment of relationships between their topology and their dynamical behavior [49]. For example, the complexity of the regulatory machinery can be characterized in terms of generic properties such as robustness [50], modularity, and evolvability [51–53]. Within this framework, it is intuitively easier to understand how components of the proteome or genome participate together in many different processes which occur in different cellular compartments. As a consequence, a dysfunction of a small set of molecules affecting a restricted number of defined epigenetic [54] and metabolic processes [55,56] may propagate to all parts of the cell, leading to a progressive disruption of the general homeostasis. Hence, the systemic description of the organism naturally leads to the definition of aging as a dynamical and systemic process whose external symptoms are the disparate damages and ARDs described above [57,58]. This chapter is organized as follows. We begin with a presentation of an example of an evolutionary experiment performed on mutated colonies of Escherichia coli. E. coli is a simple organism with a short life span which can be easily cultured in various experimental conditions. It can also be modified through genetic engineering to introduce mutation if necessary. This example illustrates how it is possible to follow the effects of selected mutations over generations by selecting species with appropriate traits. This type of experiment allows to observe in real time the effects of the evolutionary dynamics and to identify regulators of such mechanisms. In this experiment, one is interested in understanding how stochasticity in gene expression can play a role in the evolutionary process. Indeed, it has been shown that the magnitude of fluctuations in protein abundance among bacterial species such as E. coli is large and is reflected in the diversity of phenotypes that can be encountered. Fluctuations in gene expression have an important impact on the selection and robustness of biological functions and traits. We will discuss how phenotypic fluctuations increase the ability of a species to quickly adapt to various types of environments including severe conditions. Furthermore, to better understand the quantitative connections between phenotypic fluctuations, effects of mutations, robustness, selection, and fitness, we discuss several theoretical works which show how these relationships can be modeled and studied. These simple models show how systems eliciting stochastic fluctuations are shaped through evolution to be more robust with respect to noise and mutations during development. Another model illustrates how a combination of selection driven by fluctuations is accompanied by optimal growth rate selection to improve fitness and robustness. In the last example, the possibility that the growth rate could be an alternative mechanism for the adaptation to changes in environment is discussed. In the following section, we begin our journey through the evolutionary theories of aging by clarifying the fundamental difference between the common chronological age and its biological counterpart. Biological age is intrinsically connected to health status, which is itself dependent on the ability to maintain homeostasis, which decreases with age. The systemic nature of aging makes any measurement of biological age a technical and conceptual challenge. Modern computational techniques which offer great promises for addressing these issues are shortly described. In the next section, we discuss the relationships between aging and its most obvious symptoms, the disparate damages. It is well known that these disparate damages are a consequence of the onset of age-related diseases (ARDs). Moreover, the role played by specific genes which undergo change in expression with age, the so-called age-related genes (ARG), is also investigated. Using computational analysis, it is found that these ARGs are strongly connected to genes known to be related to ARDs. Furthermore, the results tend to demonstrate that aging could be the consequence of local



topological changes in the vicinity of ARGs which ultimately affect the stability of the complete biological system. In the following section, the parametric models and associated demographic models used to model the aging patterns obtained from life tables are introduced. We discuss how such functions can be helpful to identify generic properties of these aging patterns. The limitations of these simple models are emphasized and the possibilities offered by more complex models recently published are described. These models integrate explicit descriptions of key biological mechanisms such as senescence or mutations and, as a result, they are able to suggest mechanistic explanations of the aging patterns observed. Furthermore, by reinforcing the description of the connections between the apparition of mutations, the selection process, and the topological properties of GRN, these models are able to suggest mechanisms to explain several experimental findings. Interestingly, these models suggest that the various theories of aging, far from being mutually exclusive, could be unified within a single framework. The next section focuses on mathematical models based on population dynamics which includes environmental constraints. The results show how changes in environment can affect the behavior and the survival capacity of individuals. This confirms the fact that evolutionary changes are triggered in order to adapt to the characteristics of the environment. This can be seen as a continuous optimization of the fitness function. These models also clarify the role played by aging in terms of mechanism of evolution. In the last section, following a bottom-up approach, we develop a succinct mechanistic and systemic description of central cellular components involved in the regulation of cellular maintenance. Recent results regarding our understanding of senescence, telomere attrition, regulation of ROS, and epigenetic regulation demonstrate that aging should be considered as the consequence of the disruption of the cellular homeostasis. These findings illustrate that it is important to consider a living organism as a dynamical system made of connected components organized through different levels of organization with genomic and proteomic levels being interconnected [59–62]. This feature has a strong implication in the sense that any biochemical process, including the most complex ones, is always a component of a wider set of connected processes acting on each other in continuous interaction with the external environment of the living organism. We end up with a conclusion emphasizing that aging is essentially a systemic process that is a consequence of not only local malfunctions of specific components but also a result of a disruption of interactions occurring between different subsystems that are critical for the maintenance of the homeostasis within the body. Within this framework, cellular maintenance is mainly a continuous search for a dynamical homeostasis between the constraints inherent to the biochemical cellular processes, the evolutionary forces and the external environment. Thus, aging should be considered as a disruption of this dynamical homeostasis which could also serve as an evolutionary mechanism favoring adaptation to strong changes in living conditions.

HENOTYPIC FLUCTUATIONS: AN EVOLUTIONARY P MECHANISM TO IMPROVE FITNESS The behavior of living organisms is regulated by biochemical reactions. As biochemical reactions occur, concentrations of metabolites, proteins, and mRNA vary over time. It is well known that biochemical compounds, especially mRNA, can be present at very low levels. Low concentrations have strong implications on the temporal behavior of these compounds because it induces stochasticity, that is, rather than switching from one specific concentration corresponding to a given steady state to another fixed one, the concentration of these compounds continuously fluctuates around the equilibrium. These fluctuations are caused not only by the stochasticity in intracellular reaction processes but also by external noise. Strong fluctuations can affect the level of proteins; the various processes there are involved in and lead ultimately to different phenotypes. Since these fluctuations are a characteristic shared by all species, one may ask whether they serve a specific purpose from


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an evolutionary point of view. Are these fluctuations providing an advantage in terms of adaptation abilities, improved fitness, or robustness? The impact of these phenotypic fluctuations has been examined in relation to biological processes, differentiation, and also evolution. It has been suggested that these fluctuations prevent the maintenance of a state with higher function in the sense that, under fixed environmental conditions, fluctuations around an optimal state could reduce fitness. Thus, a decrease in fluctuation during the evolutionary process is advantageous as it reduces the fluctuation around the fittest state, which in turn contributes to maintenance of optimal traits and functions. Experiments have shown that the enhancement of a trait is accompanied by a decrease in both the fluctuation and rate of evolution, and other studies made additional findings by showing that the fitness improvement with generation is accompanied by a reduction of the phenotypic fluctuation. Other experiments have shown that the magnitude of phenotypic fluctuations actually increases with the rate of evolution under fixed environmental conditions. Another example of an evolutionary experiment is presented in Reference [63]. In this case, cycles of mutation and selection for higher GFP fluorescence were carried out in E. coli, cultured in a severe environment allowing a small number of individuals to survive, in order to better understand how natural selection could affect these phenotypic fluctuations and what effects these fluctuations could have on the fitness. Interestingly, when the selection from one generation to the next one is based on the average phenotype of each genotype, the fluctuation decreased gradually. On the other hand, when selection is based on the individual phenotype, the fluctuations tend to increase. These findings support the hypothesis that under severe selection happening at the individual levels, the increase in phenotypic fluctuation could be an evolutionary strategy as these fluctuations together with a genetic diversity allow maintaining a diverse phenotype which is necessary to adapt to a severe environment when it is encountered. From this point of view, the phenotypic fluctuations may be a mechanism maintained in the evolutionary history of facing severe environment. Modeling approaches can be helpful for formulating and testing hypotheses to explain the connections between the fitness function, the effect of natural selection, and dynamical characteristics of the biological system such as robustness and the presence of stochastic fluctuations. Such models can help understand why a large amount of phenotypic noise has been preserved through evolution and how it can intervene in the evolution of biological functions. An example of such a modeling approach is presented in Reference [64]. The mathematical model is based on a GRN which provides the genotype of the living system and hence different genotypes can be obtained by simply varying the topology of the GRN. The effects of natural selection and the fitness function associated with a given phenotype are computed as follows. First, a set of Boolean-based dynamical equations is established to simulate the change of the gene expression level over time. These equations form the developmental dynamical systems. A state variable is associated with each gene and is set to zero if the gene is inhibited or to one otherwise. The set of values associated with these variables defines the gene expression pattern. Second, starting from an initial condition, one computes the evolution of the dynamical system. The set of values of the state variables changes in time according to the dynamical equations and eventually reaches a stationary pattern which is the phenotype. Third, the fitness function is computed using these state variables. Concretely, the fitness is determined by setting a target gene expression pattern. The fitness is at its maximum if a given set of genes is activated after a transient time and at its minimum if all are inhibited. The fitness is set to zero, which is the optimal value, if all the target genes are on. Finally, during reproduction, mutations are introduced by modifying the topology of the network and, as a result, there is a redistribution of dynamical systems at each generation. Selection is applied after the introduction of mutation at each generation. Among the mutated networks, those with higher fitness values are selected. Since the model contains a noise term, the fitness can fluctuate at each run, which leads to a distribution of the fitness, even among individuals sharing the same network. For each individual network, the average fitness over a given number of runs is taken and networks with the highest fitness are selected from one generation to the next. If evolution serves to increase fitness, dynamical systems with a higher function should be shaped through this selection–mutation process.



Robustness is defined as the ability to function against changes in the parameters of a given system. In the case of the system considered here, one needs to consider changes of genetic and epigenetic origin. The former concerns structural robustness of the phenotype, that is, rigidity of the phenotype against the genetic changes produced by mutations which are represented by changes in the network topology. The latter concerns the robustness against the stochasticity that can appear as a result of fluctuations in initial states and stochasticity occurring during developmental dynamics or in the environment. In terms of dynamical systems, these two types of robustness are the stability of a state, also called attractor, to external noise and the structural stability of the state against changes in the underlying equations. The results obtained with this model are in agreement and complete the experimental observations discussed in Reference [63]. The average fitness computed using this model exhibits three distinct behaviors depending on the level of noise. For a very low level of noise, the average fitness stays lower than the fittest value. On the other hand, for very large values of noise, the distribution of the average fitness is sharp and concentrated around the top value. Nevertheless, the top fittest value is never achieved. However, for a middle range of noise level, the distribution is not only sharp but also concentrated at the fittest value. Even individuals with the lowest fitness approach the fittest value. This range of value, called the robust concentration region, is reduced as the mutation rate increases. Interestingly, individuals evolved at very low noise do not have robustness against mutation, whereas individuals evolved at higher noise do. In other words, the fitness landscape has an almost neutral region where it is insensitive to mutation, demonstrating the evolution of mutational robustness. To summarize, under high noise conditions, the selection process favors a developmental process that is robust against noise. This robustness to noise is then embedded into robustness against mutations. Hence, a dynamical system that is robust both to noise and to structural variation is shaped through evolution under noise. This establishes a correlation between developmental robustness to noise and genetic robustness to mutation with the former leading to the latter. Cells adapt to a variety of environmental conditions by changing the pattern of gene expression and metabolic flux distribution. As discussed in the introduction, adaptive responses can be explained by signal transduction mechanisms, where extracellular events are translated into intracellular events through regulatory molecules. However, considering that the huge number of various environmental conditions encountered by a living species such as E. coli is higher than the set of regulatory gene mechanisms available, one may ask whether an alternative adaptation process could exist in addition to adaptation through gene regulation by signal transduction mechanisms. Actually, it has been shown that species such as E. coli cells are able to select appropriate intracellular state according to environmental conditions without the help of signal transduction. To better explain this alternative mechanism of adaptation, a more elaborated model combining a GRN together with a metabolic network has been developed in Reference [65]. In this model, the regulation of gene expression affects the metabolic network which is also affected by changes in external conditions. The mathematical model takes into account the change in gene expression over time and includes effects linked to the synthesis of proteins, dilution of proteins by cell volume growth, and the molecular fluctuations arising from stochasticity in chemical reactions. In order to include the effects of the environment, temporal changes in concentrations of metabolic substrates are also considered. They are given by metabolic reactions and transportation of substrates from the outside of the cell and some nutrient substrates are supplied from the environment by diffusion though the cell membrane, to ensure the growth of the cell. The temporal evolution of both expression levels and growth rate are analyzed for various levels of noise and one notices that without noise or for a very small level of noise, the cellular state rapidly converges deterministically to an attractor and the final growth rates are broadly distributed. This broad distribution is explained by the fact that, in this model, the selection of the more adapted state is only allowed by the stochasticity of gene expression. Thus, in the absence of noise, selection cannot happen and cells simply remain trapped in the first attracting state they encounter. When the level of noise is very large, the cellular state continues to change without being able to settle into any attractor and a similar broad distribution of the growth


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rates is also obtained. On the other hand, for a range of average values of noise, one observes that the selected states are always associated with a significantly higher growth rate. From a dynamical point of view, these observations are explained by the fact that growth rate is the deterministic part of the protein expression dynamics. If the noise is very small, this deterministic part determines completely the selection of the state. As noise level increases, there is a competition between the deterministic and stochastic parts which result in the selection of an optimal growth rate according to the level of noise. It is worth mentioning that selecting a state with a high growth rate is also a way to increase the robustness of the chosen state and connected biological functions as a higher growth rate reduces the probability to switch to another state because of noise. It can also be noted that any perturbation of the environment leads to a perturbation of the state, but ultimately the system still chooses the available state with the optimal growth rate.

DEFINITION AND MEASUREMENT OF BIOLOGICAL AGING The age commonly attributed to individuals, called chronological age, measures how long a human has been alive. This measurement is based on arbitrary units from the calendar time and is disconnected from clinical measurements of the physiological status of the individuals. The actual physiological health of the individual is better represented by the biological age, also called the physiological age. The biological age can be broadly defined as a measure of how well the different organs, physiological processes, and regulatory systems of the body perform and at what extent they are being maintained. Thus, in theory, monitoring the biological age can provide a better estimate of the health status than the chronological one. Consequently, it could be a quantity of great interest for building theories of aging or demographic models and for testing their predictions using real data. However, aging is a systemic process which involves and affects many different physiological systems at the same time and, consequently, obtaining a correct estimate of the biological age is not straightforward. In practice, it is possible to measure separately any physiological process inside the body with clinical procedures based on the use of predefined biomarkers. A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes or pathogenic processes. Generally, biomarkers are developed with the purpose of measuring a very well-defined functionality within the body and, as such, they are not necessarily adapted for measuring the effects of a systemic process such as aging. So far, there have been several attempts to develop markers of aging. These methods monitor not only one but a restricted set of physiological functionalities whose disruptions are known to trigger the onset of specific diseases and malfunctions correlated with aging. Nevertheless, these biomarkers of aging consider a restricted number of cellular mechanisms involved in aging and, as a result, they are unable to represent the health state with enough accuracy. Furthermore, many of these biomarkers, such as biomarkers based on the measurement of epigenetic mechanisms, are not easily measured or targeted with already known clinical interventions. Thus, an accurate and practical measurement of the biological age requires a set of biomarkers preferably selected from standard clinical biomarkers. Taken together, these biomarkers should not only be an objective quantifiable and easily measurable characteristics of biological aging but should also be able to take into account that aging is not a single specific process, but rather a suite of changes that are felt across multiple physiological systems. From an experimental point of view, the development of biomarkers is a time-consuming and tedious multistep process which includes proof of concept, experimental validation, and analytical performance validation and more effective approaches can be helpful for developing the complex biomarkers required to measure biological age. As discussed above, the increase in throughput technologies has generated a massive accumulation of various types of -omics data which can be used in many ways for the development of adapted biomarkers. In practice, the use of these data can actually be complicated because they are highly variable, high-dimensional, and sourced from multiple often incompatible data platforms.



Nevertheless, several types of appropriate in silico methods can be applied to exploit this huge amount of information in order to identify potential biomarkers and contribute to accelerating the development process. For example, machine learning (ML) techniques are already routinely used in biomarker development. Among them, deep learning (DL) methods are the latest generation of ML techniques and they have already shown promising results in different applications such as predicting various physical and chemical properties, modeling drug–target interactions using structural data, or predicting toxicity issues [66–68]. Owing to this flexibility and adaptability of DL, these methods are now considered as interesting computational approaches for tackling many current biomedical related issues [69–71]. DL techniques are based on the use of deep architecture, called Deep Neural Networks (DNNs). DNNs are collections of units, also called neurons, connected in an acyclic graph. DNN-based models are often organized into distinct layers of units. For regular DNNs, the most common layer type is the fully connected layer in which units between two adjacent layers are fully pairwise connected, but units within a single layer share no connections. One of the main features of these DNNs is that units are controlled by nonlinear activation functions. This nonlinearity, combined with the deep architecture, makes it possible to take into account complex combinations of the input features leading ultimately to a wider understanding of the relationships between them. Once correctly trained and optimized, DNNs are capable of providing more reliable final output than standard ML approaches. Recently, Putin et al. [72] have published a study demonstrating the capacity of transcriptomic-based DNN methods to accurately predict biological age. They were able to identify a set of relevant biomarkers which can be used for tracking physiological processes related to aging. The features used as inputs, a set of 41 biomarkers for each sample, were extracted from tens of thousands of blood samples from patients undergoing routine physical examinations. Although being highly variable in nature, the blood biochemistry test has the advantage of being very simple to perform in practice. Furthermore, it is approved for clinical use and, as a consequence, commonly used by physicians. In the study, identified biomarkers and, as a consequence, the associated physiological processes were ordered according to their importance with respect to the aging process itself. The five most important biomarkers identified were albumin whose low level is associated with increased risk for heart failure in the elderly, glucose which is linked to metabolic health, alkaline phosphatase whose level in blood increases with age, erythrocytes which are known to be damaged by oxidative stress, and urea which is known to increase oxidative stress. These five biomarkers monitor the physiological status of renal, liver, and metabolic systems as well as respiratory function. This kind of computational approach demonstrates interesting performances and further improvements have already been suggested. For example, adding other sources of features including transcriptomic and metabolomics markers from blood, urine, individual organ biopsies, and even imaging data. Furthermore, one should take into account genetic determinants, environmental conditions, and living styles which can be very different between human communities and can affect the aging rates across countries. From a more practical point of view, specific care should be taken when collecting the samples because there might be specific biases coming from the methods used to collect and analyze clinical samples which differ substantially across health systems. Furthermore, in order to better represent the diversity of the aging patterns observed through the different populations, one should probably implement population-specific algorithms. Indeed, it is unlikely that a single algorithm could accurately predict biological age for all populations. The measurements of the biological age being more representative of the health of individuals, it could be interesting to analyze the difference between mortality curves obtained from measurement based on chronological or biological age measurements and for what conditions they strongly differ from each other.

RELATIONSHIPS BETWEEN ARGS, ARDS, AND AGING We have discussed in the introduction that several evolutionary theories explain the onset of aging by the fact that specific genes, called ARGs, which are beneficial at a young age can, for whatever


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reason, become harmful at older age. Also, we have mentioned that various sources support that it is indisputable that many diseases appear or have a higher probability to appear as individuals get older, a concrete example, well known in developed societies, being the dramatic increase in the onset of cancers within the population aged 65 years and older compared to the population aged between 20 and 44 years [73,74]. We have already mentioned that these ARDs could be interpreted as being the external symptoms of systemic disruptions of the homeostasis within the organism. However, to better define the connection between ARGs, ARDs, and aging (senescence), it is necessary to clearly identify what distinguishes ARGs from other genes and also whether they have specific functions or occupy a specific location within GRNs. Currently, the establishment of mechanistic relationships between the ARDs, ARGs, and the aging process itself is still a matter of intensive research but several studies have provided interesting results in recent years. To begin with, it is worth mentioning that attempts to identify ARGs and related ARDs using gene expression data alone have provided us with results revealing that from as few as 442 [75] to as many as 8277 [76] human genes can be related to aging. This significant variation can be explained by the fact that, in many studies, the interactions between genes of the associated network are taken into consideration with poorly designed topology measures and that ARGs are identified mainly using the fact that these genes demonstrate a significant change in expression with age. In order to overcome these limitations, more complex approaches have been developed. For example, in Reference [77], ARGs are identified by investigating how the topology of the corresponding protein–protein interactions (PPI) networks evolves with age. To perform this analysis, they firstly designed a set of dynamic age-specific PPI networks by selecting all proteins that correspond to actively expressed genes at different ages and all PPIs involving these active proteins. Hence, each age-specific network is the network that is active at a given age. These networks were then analyzed with various constraining measures to study how the topology can evolve at the global and local levels. The authors have formulated three interesting conclusions. First, global network topologies do not change with age and the overlap of age-specific networks is large. The age-specific networks share on average 92% of the nodes and 89% of the edges, depending on age, whereas every pair of the networks shares at least 82% of the nodes and 74% of the edges. Second, local topologies of PPIs show significant modifications with age. More precisely, local topologies around only a subset of proteins in the networks do change with age and aging-related information remains stored only locally within the networks. This feature can explain why other global network analyses were unable to uncover any aging-related information. Third, the nodes of the network which undergo major modifications in their interactions are related to the ARGs and ARDs. This conclusion is based on the observation of significant overlaps between predicted and well-known ARGs. Furthermore, there is an overlap between functions and diseases that are enriched in their aging-related predictions and those that are enriched in well-known aging-related data. In their study, the authors were also able to provide experimental evidences that diseases which are enriched in their aging-related predictions are also related to human aging. Other studies have been performed to analyze these specific relationships. These works, based on a top-bottom strategy, are also based on systemic approaches in the sense that they analyze the interactions between biological processes and related genes rather than considering them separately. A common characteristic of these strategies is that they use various kinds of rather large interaction networks (including PPIs, gene interactions, pathway maps, etc.) where, on the one hand, one identifies genes undergoing strong changes in their regulation with age (ARGs) and, on the other hand, one identifies genes that are included in the transcriptomic profiles associated with ARDs or nonARDs. The core of the statistical analysis which follows relies firstly on applying topology-based criteria to determine whether ARDs-related genes are closely connected (directly or indirectly) to ARGs. Second, one identifies overlapping sets of ARGs and ARD-genes that could clarify the mechanistic connections between them. By following this strategy, it has been shown that cellular senescence was interconnected with the aging process and ARDs, either by sharing common genes and regulators or common PPIs and signaling pathways [78]. In another work, on the basis of a



network of ARGs and disease genes only, it has been found that ARGs were topologically closer to disease genes than by random chance. Furthermore, the results also supported that diseases having their transcriptomic signatures significantly close to ARGs can be clearly distinguished from non-ARDs [79]. Recently, Yang et al. [80] presented the results of a similar study made on a larger scale. By using an elaborated combination of statistical and topology-based analysis of gene–gene interactions within modularized networks, they have been able to identify not only meaningful biological processes involved in the diseases and their connections to ARGs but also key genes and modules related to biological processes that potentially mediate disease-aging connections. This includes, for example, modules involved in cell-cycle regulation and modules controlling the response to decreased oxygen levels. These results are corroborated by the fact that decreased oxygen levels are usually associated with aging [81] and, more generally, hypoxia condition is known for being involved in cancers [82], tumor survival, and inflammatory response [83]. Furthermore, the complexity of the observed interactions suggests that connections between ARDs and ARGs are mediated by different numbers of sub-networks. They also conclude, in agreement with [79], that ARDs and non-ARDs have significantly different interaction patterns with ARGs in modularized networks. These important topological features confirm that the onset of ARDs is strongly connected to the aging process and, as a result, these features could be used to differentiate ARDs from nonARDs. Not surprisingly, various types of cancers including lung cancer, melanoma, bladder cancer, prostate cancer, leukemia, and breast cancer are found to be closely connected to ARGs. These findings are supported by previous studies emphasizing the significant increase in the onset of these diseases with age after maturity [74]. More interestingly, the functional modules associated with these ARDs include pathways well known for their involvement in various mechanisms of aging. Among them, one can cite the intrinsic apoptotic pathway activated in response to DNA damage and various regulatory pathways of apoptosis, the signal transduction by the p53 class mediator and several modules related to cell cycle. These findings demonstrate the role played by the evolution of the topology of the PPI network in the onset of aging. However, it remains to investigate what mechanisms drive these topological changes within the PPI networks during aging and how these changes occur. As will be discussed in the next section, the interplay between the different levels of organization of the regulatory systems and the interactions with the external environment plays a central role in this process. To conclude, one can describe similar findings of a computational data analysis published by Rodriguez et al. [84]. The aim of this study was to identify connections between ARDs, ARGs, and senescence. Interestingly, the analysis was performed by combining human genome-wide association studies (GWAS) and senescence data from various human databases. By making use of the relationship between senescence and ARDs, together with the abundance of information on the effects of genetic variants associated with complex disease, they were able to identify evidences supporting that specific mutations in particular genes cause senescence. These findings are in agreement with the main hypothesis of the mutation accumulation and the antagonistic pleiotropy theories. The authors ended up with three interesting conclusions. First, risk alleles associated with diseases which appear at a late age have higher frequencies than risk alleles associated with diseases that manifest themselves earlier in life. From these observations, one can deduce that natural selection allows late-onset genetic variants with large effects on disease risk to reach higher frequencies, another observation consistent with the mutation accumulation theory. Second, the analysis of the patterns of pleiotropy shows that, when age thresholds from 40 to 50 years are considered, there is a significant excess of antagonistic pleiotropies with a maximum at ages 46–50 years. Third, in all data-sets used, they observed a high level of association between senescence (ARGs) and pleiotropic genes. Furthermore, using molecular evolution techniques to screen for the signature of natural selection in genes and genomic regions involved in early–late antagonistic pleiotropies, they have identified several genes that increase survival or fertility at early ages but have deleterious effects at older ages.


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GING AS A RESULT OF DYNAMICAL INSTABILITIES A CAUSED BY MUTATION ACCUMULATION Demographic data (life tables) such as the ones available from the Human Mortality Database (http://www.mortality.org) are the primary source of information from which demographic trends and key parameters including mortality rates and fertility rates can be analyzed. Using life tables, it is possible to draw graphs, called survival curves, showing the number or proportion of individuals surviving to each age for a given species. According to Demetrius [85], the various patterns of survival curve can be classified into three types. Type-I survival curves which change at early and middle ages and then decline at late ages, as seen for humans. Type-II curves almost linearly decrease with age, as seen for short-lived birds. Type-III curves quickly decrease at early ages, as seen for most plants. On these curves, the onset of processes such as senescence, for example, can be identified as an increase in mortality and a decrease in fertility with age or by the sharpness or abruptness of the increase in mortality. Furthermore, when analyzing the patterns of these survival (or alternatively mortality) curves, one observes that they elicit specific topological features which provide meaningful information about the aging patterns of the species. More precisely, three important characteristics can be identified. First, trajectories of life and, as a consequence, mortality and fertility rates strongly vary from one species to another [22]. The reason for this variety is still a matter of debate and its understanding is one of the main motivations of current aging research. Nevertheless, a possible explanation is that the number of contributions to the mortality rate could be higher than initially expected. Indeed, various analyses have shown that the age-trajectory of mortality can be decomposed into three parts: a first part due to the accumulation of unfavorable mutations, a second part which is the result of the selection processes that optimize the trade-offs necessitated by resource limitations, and a last part attributed to unavoidable external risks of death. Second, as thoroughly discussed in Reference [86], human survival curves show a tendency to evolve toward the slowest aging rates. This dynamics is called rectangularization [87] and it formally refers to the tendency of survival curves of the human population of developed countries to evolve over time, over the past several decades, universally toward a rectangular shape. This observation could be explained by the fact that human mortality curves evolve toward the slowest aging rates, also a synonym of healthy aging. Third, although mortality curves show a clear increase in mortality with age, this trend is not applicable to very old age. Indeed, after growing exponentially, the risk of death saturates at a constant level at very old ages. The formation of this plateau is a phenomenon known as mortality deceleration. The reason explaining why mortality rate slows down for this specific period of life is not clearly understood. There are various parametric mathematical models which have been elaborated to represent the shape of the survival curves [88,89]. One widely used model is the Gompertz law [90] which represents the mortality curve as an exponential function of time. The model contains only two parameters: One to capture the initial level of adult mortality, also called initial mortality rate (IMR), and the Gompertz exponent slope which is inversely proportional to the mortality rate doubling time (MRDT). MRDT represents the rate of aging, that is, the relative change in mortality with a given age. In practice, these parameters can be inferred from life tables, although this operation is not necessarily trivial [91]. The Gompertz law is adapted to describe the survival curves of individuals living in a protected environment where external causes of death are rare. When this assumption is not valid, the Gompertz–Makeham law can be used. This model adds to the age-dependent component, represented by the standard Gompertz law, an age-independent component, called the Makeham term, which takes into account external factors affecting the mortality rate. This generalized law of mortality is able to describe the age dynamics of human mortality rather accurately in the age window from about 30 to 80 years of age. Another commonly used function to represent the survival curves is the Kohlrausch–Williams–Watts (KWW) function. This function is more complex but, interestingly, it is possible to model the three different kinds of survival curves cited above by using the same KWW function with various parameter values. For a specific range of ages, the



predictions made using these functions can be useful for the quantification of survival dynamics to scientists, such as demographers, biologists, and gerontologists. Nevertheless, since mortality is by far more complex than a two- or three-parameter process, these functions taken alone do not suffice and difficulties arise for explaining the mortality rate at a very old age, and alternative mathematical curves such as the Heligman–Pollard, the Kannisto, the quadratic, and the logistic models share similar issues [89,92]. The difficulties encountered by these parametric functions to accurately describe the survival curves are in part explained by the fact that they do not include a detailed description of the major processes that intervene in the survival rate of all species. Thus, more elaborated models are required for explaining the aging patterns and the topology of the survival curves in terms of the combined effects of accumulation of mutations, senescence, effects of natural selection, or any other parameter which is supposed to intervene in the onset and propagation of aging. Conceptually, there is a need for models able to provide formal representations of demographic processes which are coherent with the current evolutionary framework. From a historical perspective, Hamilton was the first to establish a mathematical description of the forces of natural selection [93,94]. His mathematical model expressed the survival rate in terms of the mortality rate and other parameters describing the demographic evolution of a population. The derivation of this model is based on the hypothesis that mutation accumulation is the main process behind the onset and progression of senescence through cellular tissues. In his work, Hamilton concluded that senescence was an inevitable outcome of the evolutionary process. However, other analysis performed with a similar model but with another choice of values for the parameters provided different results [95], and the extension of the mathematical formulation of this model to include nonlinear effects also challenged the traditional established scenario of aging [96]. An example of a more recent model is presented in Reference [97]. The authors have computed a mortality function to describe the survival and senescence processes. The idea is to investigate the biochemical mechanisms responsible for the shape of the Gompertz law of mortality as well as the effect of the temperature. The main hypothesis of the model is that there is a living energy which is used to protect the chemical substance that is critical for life from being impaired by damaging energy. This living energy is proportional to the quantity of a vital molecular unit and linearly decreases with time. Thus, changes in certain substances and inherent energy are involved in the aging process [98–101] and senescence is assumed to result from the imbalance between damaging energy and protecting energy for the critical chemical substance in the body. Here, telomere length is taken as the only vital molecular unit representing this living energy. The dynamical equation for the global survival function is obtained from a first-order kinetic equation for the evolution of a molecular component and contains a time-dependent coefficient rate. Furthermore, a term is added to take into account that the living energy can be reduced and enlarged by the onset of diseases or effects of medical treatments, respectively. The parameters of the function were estimated by fitting survival curves of several countries. The results show that the mortality function is similar to the Gompertz mortality function for young and middle-aged individuals. However, the rate of senescence increases when the protecting energy decreases in a temperature-dependent manner. In agreement with previous experimental findings, the analytical expression of the function predicts that reduced temperatures induce an increased life span. The model is able to explain the plateau observed on the mortality curves at late ages. This plateau is reached when the protecting energy decreases to its minimal levels. Interestingly, the curves are in agreement with the previously observed tendency of rectangularization [102,103] of the patterns of human survival curves. Regarding the rectangularization, it is worth mentioning that a new mathematical formulation of the survival curve based on the KWW function presented in Reference [86] provides a quantitative criterion for the rectangularization tendency. The authors hypothesized that evolution toward the slowest aging rates could be a species-independent, scale invariant, universal aspect in survival dynamics of living systems. Thus, common features of mortality curves such as the Gompertz law and the mortality plateau could


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spontaneously emerge from the age-dependent shaping exponents that dynamically evolve toward the slowest aging rates of living systems. From an evolutionary point of view, the slowest aging rates could be a consequence of the fact that living systems tend to evolve to optimize their capabilities and strategies for survival. Other models recently published combine an explicit mathematical formulation describing the effects of natural selection and the propagation of mutations together with several systemic properties such as the topology of GRNs associated with the onset of aging. For example, the study described in Reference [104] proposes a quantitative model for explaining aging as a consequence of the critical dynamical behavior elicited by GRNs. This specific behavior can in turn induce dynamical instabilities which are interpreted as a cause of aging. Interestingly, the presence of this instable behavior is the result of specific topological and connectivity properties of the GRNs. This is coherent with the experimental findings described in Reference [77] regarding the connections between the topology of networks associated with ARGs, ARDs, and the onset of aging itself. The mathematical model is built on the assumption that mutation accumulation is the main cause of aging and other potential causes and how they could affect each other are not taken into account. Nevertheless, this model is able to establish relationships between the mortality rate and the increasing number of regulatory errors. Furthermore, using experimental data, the model is also able to recover the main properties of the Gompertz law. Interestingly, several characteristic timescales such as MRDT and IMR were explicitly related to generic parameters of the network such as translation, gene repair, and protein turnover rates. Another interesting point is that the model suggests that the dynamics of aging could be a combination of a stochastic component, directly related to the accumulation of regulatory errors and a deterministic component, linked to some development program. This has at least two strong implications. First, it supports the hypothesis discussed earlier that current theories of aging (antagonistic pleiotropy and theories based on damage/error accumulation) describe only a restricted aspect of aging and that the driving mechanism suggested by these theories could be complementary. This illustrates that establishing connections between the standard formulation of the forces of selection and the mechanistic description of aging could lead to a merging of these theories. This situation is also encountered when comparing from a mechanistic point of view the description of aging proposed by the mutation accumulation and disposable soma theories. These mechanisms are not only complementary but also provide a conceptual link to the biochemical nature of aging. Second, the results obtained in Reference [104] show that the dynamics of these networks contain characteristics that can be interpreted from a non-programmed as well as programmed point of view [105–108]. Thus, this is a step to establish a bridge between two (apparently) opposed interpretations of aging. This does not only suggest that these theories could be compatible but also that programmed and non-programmed theories of aging could be two aspects of a deeper mechanistic process that requires a more detailed description. Finally, the work presented in Reference [109] investigates other quantitative aspects of the relationships between aging and stability of GRNs involved in aging which were developed in Reference [104]. The dynamical model is based on a set of linearized equations describing the temporal evolution of the improperly copied proteins and improperly expressed genes occurring as a result of mutation accumulation. Although simple, these equations include a coupling rate constant characterizing the regulation of gene expression by the proteins and a DNA repair rate for representing the action of the repair system which can prevent harmful effects arising from the accumulation of genetic mutations. Furthermore, a constant to measure the overall connectivity of the GRN and a generic constant to take into account the combined efficiency of proteolysis and heat shock response systems, mediating degradation, and refolding of misfolded proteins are also included. Using appropriate approximations, the authors were able to derive the corresponding effective equation for the age-dependent changes in the number of regulatory errors and, consequently, the mortality. The solution of this equation gives a time-dependent analytical expression of the mortality rate which includes all parameters described above. By performing a stability analysis of this solution, they identified a parameter characterizing the stability of the associated GRN. This



parameter is itself dependent on the propagation rate of gene-expression-level perturbations and it can be directly interpreted as the Gompertz coefficient. Depending on the value of this parameter, the mortality rate shows two very different behaviors. For one range of values, the system is instable and the number of errors in gene expression defects grows exponentially with mortality. In that case, one recovers the expression of the well-known Gompertz law. However, for another range of values, the network stability is conserved and mortality rate is shown to increase at a smaller rate. These analytical predictions were successfully tested using several experimental data. Interestingly, this model not only suggests quantitative and experimentally verifiable links between the life span of a species and the stability of its most vulnerable GRNs related to aging but also several approaches to slow down the aging process. Unsurprisingly, acting at the level of the repair and maintenance system appears to be the most straightforward solution to slow down aging but other suggestions are discussed. The description of aging provided by this model also emphasizes the role of the GRNs in the regulation of the levels of somatic maintenance and repair functions. The results obtained by the last two models are interesting in the sense that they do not only provide us with mechanistic explanations regarding the various patterns of mortality rates observed but are also able to connect the dynamics of GRNs, especially its instable character, to the process of aging itself and suggest testable hypotheses and mechanisms to explain the experimental data from which the relationships between aging, ARDs and the change in the topology of these networks were initially deduced. Of course, it must be stressed that these models are based on a simplified view of aging in the sense that the sole driving force generating instability in the network is the accumulation of transcription errors and mutations and this mechanistic description based on the regulation of genes is incomplete. Indeed, GRNs are controlled by various signaling pathways and other mechanisms taking place in the cytoplasm where proteins are involved in many processes which can in turn regulate the transcription in a positive or negative fashion. These details are important and other evolutionary demographic models which include an explicit description of the mutation accumulation and epistasis between genes, such as the one developed in Reference [110], could be improved with a more detailed description of the regulation of the mutated genes.

GING AS AN EVOLUTIONARY MECHANISM TO A ADAPT TO ENVIRONMENTAL PRESSURES Individuals are under the constant pressure of their direct environment. One can expect that the environment and, more precisely, characteristics such as resource availability and competition between individuals, which can result from resource scarcity, may affect the survival rate of a group of individuals. From this point of view, evolutionary changes can act as adaptation mechanisms for improving the survival rate. From an evolutionary perceptive, aging being also a major evolutionary trait, it is of interest to ask whether it provides an advantage to individuals facing environmental changes and competition to access vital resources. In what follows, we describe two different population models which incorporate various types of environmental changes. The model presented in Reference [111] simulates the temporal evolution of two populations of species which are in competition for supremacy in a spatial grid. These two populations are initially identical but one can die of senescence (the aging population), whereas the other can only die due to competition and incidents. Individuals of the population suffering the effects of senescence die at the same programmed age. The model takes into account the availability of the resources and allows competition between individuals. The interactions between individuals include only individual competition but a small viscosity term is also introduced to explore the effects of group selection. Members of each population can produce offspring and since the progeny is created near the parents’ locations, competition can happen between the parents and their progeny. Moreover, although the initial conditions of the simulations are set to provide enough resources for both populations, these optimal conditions are not stationary. Finally, the model includes the effects of mutations which can appear from one generation to another and are assumed to help each species to


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remain competitive. The survival ability of individuals is characterized by a fitness function whose value decreases by a small amount at each time step of the simulation. This simple implementation allows capturing the influence of the environment including the changing conditions. In practice, the fitness function of the parents and their progeny is not necessarily the same and can be modified by a small random value. This allows taking into account the effects of mutations that can occur from one generation to another. The results obtained can be classified into three distinct cases. First, in the absence of mutation and changing conditions, the senescent population is driven to extinction very quickly while the other survives and dominates. Second, when mutations are introduced, senescence causes the extinction of the aging population in most simulations but, nevertheless, this population manages to survive longer and it is even able to lead the non-aging population to extinction in some cases. Interestingly, this trend increases as the mutation rate increases. When analyzing the combined effects of both random mutations and environmental changes, one observes that the average fitness of a new generation is a little larger than that of the previous ones. It means that, on average, each generation is a little better adapted than the previous one. The evolution of several simulations shows that the average fitness of the senescent population becomes larger than that of the non-senescent ones. Interestingly, the age of death set for the senescent population plays an important role in its ability to survive. When this value is very high, it is unlikely that any individual could reach it and any difference that could exist between both groups becomes negligible but, on the other hand, if the aging population dies too soon, the price of senescence is far too large to be overcome by any fitness advantage. However, as soon as the age of death is large enough to overcome that cost, the aging population is able to dominate the non-aging one. Thus, when no mutations or environmental changes are present, this model suggests that death by senescence appears to have a too important evolutionary cost for a species that adopts it [112,113]. However, in the presence of mutations, the aging population has an improved survival rate because it can adapt faster to new conditions. Furthermore, when introducing changes in the environment, the extinction of the non-aging population becomes a systematic outcome and this result holds for a large range of parameter values. It is worth mentioning that these conclusions are in agreement with the hypothesis supporting the senemorphic aging theory mentioned in the introduction. The fact that the age of death or senescence rate plays a critical role in the ability of the aging population to survive is also worth mentioning. These results are in agreement with another evolutionary demographic model of aging which supports that “evolution favors sustenance over senescence if the sacrifice in reproduction to achieve sustenance is smaller than the sacrifice in life expectancy resulting from senescence” [114]. These results support that senescence seems to be a well-adapted answer to changes that can be adopted by evolutionary dynamics as aging produces a pruning effect on the populations, eliminating older, less adapted individuals who had managed to survive only by chance. In what fellows, another model based on the simulation of population dynamics is described. Its purpose is to analyze the relationships between aging, mutation accumulation, and effects of natural selection to understand how these interactions affect the fitness of individuals on a long-term basis [115]. In this model, each individual is represented by its own genome that defines the probabilities to reproduce and survive. The exact impact of this genome on survival and the reproduction rate at different life stages depends on how it evolves due to mutation and selection. The probability of survival through each step depends on three main factors: the status of the genome, the difference between the resources available and the size of population, and an extrinsic death crisis parameter. At the beginning, the population is composed of genetically heterogeneous individuals, whose genomes are represented by randomly generated bit arrays. Each individual from the initial population has a chronological age, and becomes one time-unit older at each step of the simulation. Under these conditions, individuals evolve through a sequence of discrete time intervals and one may analyze the evolution of this genetically heterogeneous population under various external constraints including the size of the initial population, the effects of limited resources (which are fixed at the beginning of the simulation), and whether reproduction is sexual or asexual.



The results obtained agree with several characteristics of real population aging. For example, the populations develop a longer reproductive life span and survival under stable environmental conditions. Faster aging appears in more unstable populations because they have large portions of the genome that accumulate mutations which have deleterious effects on the individuals. The model predicts an increased early survival and a rapid increase in the death rate and decrease in fertility after sexual maturation, a scenario which is coherent with the already known aging patterns. Interestingly, results also show that the time-dependent decrease in individual survival and reproduction is more pronounced in the sexual model than in the asexual model. Finally, the fitness effect of beneficial mutations acting in late life is negligible compared to mutations affecting survival and reproduction at earlier ages and one can observe an age-dependent increased genetic variance following sexual maturation. Together, the models discussed in this section provide another point of view describing the multifactorial nature of aging. It might not be surprising that the different mechanisms which intervene in the onset of aging can lead to different outcomes depending on whether they are considered together in a single model or taken into account separately. From a dynamical perspective, the evolutionary mechanisms, the environmental constraints, and the dynamical cellular equilibriums which must be maintained to sustain the living organism act on very different timescales and they have their own dynamics. Nevertheless, they are obviously also under the continuous influence of each other. This fact reinforces the importance of assembling demographic models of aging which are capable of including the various causes of aging altogether in one single framework. Furthermore, the development presented here also supports the hypothesis discussed earlier that the disruptions of the dynamical equilibriums described in the previous sections could also be a consequence of modifications in the allocation strategy of resources to maintenance and repair [114], a hypothesis supported by well-established evolutionary theories of aging such as the disposable soma theory. The rate, schedule, and nature of these modifications should be considered as the product of an evolutionary optimization constrained by the necessity to respond to individual variations and environmental changes. The continuously evolving constraints faced by any species could explain why, rather than creating a perfect organism, natural selection promotes survival by ensuring that an individual can maintain the efficiency of key biological functions while maintaining mechanisms allowing adaptation whenever necessary. The phenotypic fluctuations discussed at the beginning of this chapter were suggested to be an example of such mechanism. Exposure to severe environmental changes but also limited availability of resources can explain why natural selection prefers to act through a continuous search for an equilibrium using optimization (resulting in individuals performing all functional obligations reasonably well simultaneously but not perfectly in terms of each individual task) rather than maximization. Indeed, maximization reduces adaptability because it creates individuals which could perfectly maintain a set of functionalities in detriment to some others. From this point of view, aging could be considered as a consequence of the constant search for an equilibrium established at and between different levels of organization and defined as the optimal solution of an evolutionary process with respect to the constraints encountered by any species within their environment [12,116].

GING AS A RESULT OF THE DISRUPTION OF A CELLULAR DYNAMICAL EQUILIBRIUMS Self-renewal ability and pluripotency maintenance are two characteristics of pluripotent stem cells (PSCs) which attract much interest. At the genomic level, pluripotency maintenance is regulated by transcription factors [117–119] which act as master regulators of GRNs [120–122]. GRNs are organized following various dynamical motifs which act together and contribute to improving the adaptation abilities and robustness of the system [123]. From a dynamical point of view, transcription factors have been shown to continually attempt to specify differentiation to their own lineage. Consequently, direct external interventions, through activation or inhibition of one or several


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signaling pathways, are necessary to reinforce the pluripotency state or to control the differentiation to a specific lineage [124–126]. From this point of view, the pluripotency state could be considered as a metastable state whose maintenance depends on the properties of the external environment of the cell. Consequently, the most accurate approaches to model the dynamics of such open system exchanging continuously with its external environment do not only take into account the dynamics of the gene regulatory network but also include the network formed by the signaling pathways which are responsible for receiving and transmitting signals from and to the environment [127,128]. Major epigenetic events intervene during the transition from the pluripotency state to the differentiated state. Indeed, differentiation is also characterized by a large remodeling of the chromatin structure [129,130]. The chromatin remodeling occurring during early differentiation induces the silencing of hundreds of genes while others become available for expression [131–133]. hTERT is one of the genes which is inhibited during the early differentiation process [134]. It is well known for being a catalytic subunit with reverse transcriptase activity identified as part of the telomerase complex [135], although it is also involved in many other cellular functionalities [136,137]. Telomerase is a cellular enzyme which, through the de novo addition of TTAGGG repeats to the chromosome ends, is capable of compensating the progressive telomere attrition which occurs at each cell division [138]. Telomerase activity is also controlled by regulators of the apoptosis pathways [139] and when telomeres shorten down to a critical length, they are identified as DNA damage. As a result, a DNA damage signaling response mediated by p53, an important check point whose activation prevents the proliferation of cells when DNA has undergone damages, is activated and cells ultimately become senescent. Thus, inhibition of hTERT induces the decrease in telomerase activity and the loss of self-renewal capacities. These findings lead to the identification of telomerase regulation as a key regulator of aging [140] and a stem cell theory of telomere mediated aging has been formalized on this basis [141]. The fate of hTERT is an interesting illustration of how maintenance and disruption of homeostasis may affect key biological functionalities. Indeed, although silencing of hTERT occurs during stem cell differentiation, it is not clear whether this silencing is required for proper differentiation or if it is a collateral damage of the chromatin remodeling [142]. It should be noticed that many different genes coding for an identical protein are usually found in different chromosome locations. This guarantees that the requested promoter will be available for transcription when needed even after modifications of the chromatin structure. The absence of this feature in the case of hTERT could mean that, from an evolutionary point of view, maintaining a significant level of telomerase activity is not favored. On the other hand, the fact that hTERT may be expressed via exogenous activation means that there is no sustained pressure to continuously prevent hTERT expression. The fact that the activity of a pro-growth regulator such as hTERT requires the maintenance of a set of elaborated mechanisms to protect the organism against uncontrolled proliferation and cancer could be an explanation. Although PSCs are not present in mature organisms, other types of stem cells such as unipotent, bipotent, or multipotent stem cells exist in most adult tissues. They are involved in the maintenance of tissue homeostasis and also contribute to tissue repair and regeneration. Adult tissue stem cells generally reside within specific compartments called stem cell niches. The size of these specific stem cell populations depends on the balance between self-renewal and differentiation. As for PSCs, this balance is a function of the external environment of the cell and the role of the stem cell niches is to maintain the specific conditions required for the preservation of self-renewal capacities. Inside the niches, stem cells are organized according to their telomere length [143]. The longest telomeres are located in the most primitive adult stem cell compartments while the shortest telomeres belong to the more differentiated compartments. Similar to the case of PSCs, differentiation and lineage commitment of adult stem cells is determined by a complex epigenetic program which is itself controlled by several signaling mechanisms [144,145] and pathways such as Wnt [146], Notch [147], and Hedgehog [148] which react to the influence of environmental factors whose effects are transmitted to stem cells by their niches [149]. The niches themselves can undergo dynamical changes to



regulate stem cell function according to the external signals received. These changes can affect the stem cell fate in various ways and when the condition are not favorable, stem cells loss their ability to self-renew and in some circumstances the activation of senescence pathways can occur, resulting in the depletion of the stem cell pool [150]. Thus, as discussed in References [144,151], the external environment has an important effect with regard to stem cell aging. Moreover, owing to the p53 checkpoint discussed above, only those stem cells with sufficiently long telomeres will be allowed to regenerate tissues [152,153]. These observations support the hypothesis that the maintenance of a sufficient telomere length is a direct way to prevent tissue degeneration and aging. Moreover, the situation is more complex because even if decreased stem cell mobilization leads ultimately to tissue degeneration, it also provides the organism with a mechanism for cancer protection. Once again, the maintenance of a dynamical equilibrium between antagonistic processes is the key. Several important works performed on mice [154–157] have proven that telomerase activation may be used to slow down aging only if the extra proliferation afforded by lengthened telomeres is controlled by other mechanisms. The conclusion is that the optimal situation relies on the maintenance of a balance between lengthening and shortening activities which contributes to a positive maintenance of the telomere length. This systemic mechanism is called telomere homeostasis and is discussed in further detail in Reference [158] and more recently in Reference [159]. The different signals received from the environment by the signaling pathways are centralized by mTOR (mechanistic target of rapamycin) [160]. mTOR is a large serine/threonine protein kinase which regulates cellular and organismal homeostasis as well as various metabolic cues involved in the regulation of stem cell self-renewal [161]. Broadly speaking, mTOR is responsible for managing cell growth, proliferation, and self-renewal maintenance and, as such, it acts as a sensor which interprets the received signals and adapts the behavior of the cell accordingly [162,163]. Among the regulatory pathways connected to mTOR, one can cite the pathways regulating the level of reactive oxidative species (ROS), the glucose and amino acid metabolism or the LKB1–AMPK pathway which restricts cell growth under energetically unfavorable conditions such as increased cellular AMP/ATP ratios. It is worth mentioning that all of them are known for being involved in the onset of aging. From a mechanistic point of view, the mTOR pathway is composed of two distinct parts organized around the mTORC1 and mTORC2 complexes. The formation and initial activation of these complexes is dependent on the availability of essential amino acids and other nutrients such as glucose [160,164]. The concentration of fully activated mTORC1 is a function of the signals received from upstream pathways such as the LKB1–AMPK pathway [165] and several key growth receptors involved in the self-renewal maintenance and differentiation of stem cells [163]. One of the major downstream targets of mTORC1 is the set of processes controlling the formation of ribosomal proteins and translation of proteins [166]. The implication of mTORC1 in the regulation of mRNA translation also establishes the mechanistic connection between mTORC1 and the second dynamical complex mTORC2. Indeed, recent experimental findings have shown that the collaboration between activated ribosomes by mTORC1 and effectors of the PI3 K pathway is required for the activation of mTORC2 [167,168]. mTORC2 regulates pro and anti-apoptotic molecules such as p53, BAX, and BCL-XL [169,170]. The fact that p53 is a downstream target of mTORC2 may also explain why the mTORC2 signaling cascade could contribute to resistance or survival in the face of DNA damage in cancer cells [171]. From a dynamical point of view, the interaction between the regulators of apoptosis, mTORC2 and mTORC1, is an example of a negative feedback loop which increases the robustness of the system by allowing an accurate cellular response when changes occur at the genomic level or in the external environment of the cell. Considering the central implication of mTORC1 and mTORC2 in the regulation of key cellular processes, it has been emphasized that a comprehensive molecular description of aging requires an in-depth understanding of the dynamics of the mTOR pathway [172]. It is well known that nutrient and energy availability as well as their correct management by mTOR is critical for the development and maintenance of the organism. The various components


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involved in the mTOR pathway mediate complex mechanistic relationships between, on the one hand, the external environment and, on the other hand, cell proliferation and development as well as cellular and energy metabolism. Donohoe and Bultman [173] have extended these mechanistic connections by analyzing the metaboloepigenetics, that is, how energy metabolism and the epigenetic control of gene expression interact directly with each other. To that end, detailed evidences supporting that diet and energy metabolism can directly affect gene expression through the regulation of epigenetic mechanisms are provided. From a mechanistic point of view, it is emphasized that energy metabolites such as SAM, acetyl-CoA, or ATP can act as cofactors of epigenetic enzymes regulating DNA methylation, posttranslational histone modifications, etc. Since energy metabolites are themselves dependent on bioactive food components and nutrients, they play the role of a rheostat able to maintain the homeostasis by regulating the level of activity of epigenetic enzymes which work to control gene transcription. This mechanistic relationship between energy, nutrients availability, and epigenetic enzymes has many implications. For example, maternal diet can affect gene expression in a mother’s offspring in utero, and these changes could persist after the birth and for the entire life of the offspring. The regulation of the cell-cycle progression is also dependent on the nutrient availability and cofactor abundance. Indeed, if cells are in an external environment lacking enough energy to properly proceed to the next cell division, they are stopped at the G1 checkpoint and become quiescent in order to prevent genomic instability and onset of cancer. The DNA-response damage triggered by the failure to maintain a minimal telomere length is called replicative senescence. Replicative senescence is one of the different subtypes of DNAdamage induced senescence. Another one is the stress-induced senescence which is caused by high intracellular levels of ROS induced by the RAS–RAFMEK–ERK cascade which activates the p38 MAPK–p16 pathway leading to an increased transcriptional activity of p53. The increased level of ROS is often attributed to higher oxidative stress and, as such, it is considered as a factor of aging. The effects of increased level of ROS observed in older individuals are the subject of intensive research [174,175]. This increase has been shown to have strong consequences on the bioenergetics and metabolism which can be partially explained by the mechanistic connections established between ROS, stress-induced senescence, and p53 activation through mTOR signaling. However, the role of ROS and associated oxidative stress could be more complex as initially expected. For example, ROS appears to be an important physiological regulator of several intracellular signaling pathways [176] and oxidative stress plays an important role in the regulation of stem cell selfrenewal [161]. Indeed, although it was shown that an increase in ROS reduces the adult stem cells’ self-renewal abilities and induces stem cell senescence and tissue damage, data also suggest that a minimal level of intracellular ROS is essential to maintain quiescence of several types of stem cells such as hematopoietic stem cells. Thus, taken together, these evidences show that ROS is involved in various metabolism processes and that its effects are complex and probably depend on its concentration. In order to describe this dual role of ROS, a conceptual model called the ROS rheostat was presented in Reference [161]. The fundamental hypothesis behind this model is that the level of intracellular ROS monitors stem cell fate decisions. With this aspect taken into account, the regulation of the homeostasis within stem cell niches seems to be more dependent on the establishment of an optimal level of ROS rather than on a complete elimination of ROS inside the intracellular compartments. The elaboration of such a model contributes to the building of a more refined description of the mechanisms behind the maintenance of the quiescent state of long-term adult stem cells. Although senescence was historically defined and is still largely considered as an irreversible cellcycle arrest mechanism responsible for the onset and propagation of aging, recent findings suggest that senescence could actually have both negative and positive effects. This dual role has been recently formalized within a unified model of senescence called the senescence–clearance–regeneration model [184]. The model describes the mechanism which functions in the case of adult somatic damages. The purpose of this mechanism is to restore the damaged tissue by elimination of the damaged cells. In the ideal situation, that is, in a healthy organism, senescence initiates tissue repair by recruiting immune cells through the senescence-associated secretory phenotype. In that case, disposal of



senescent cells by immune-mediated clearance is efficient and under control: macrophages clear the senescent cells, and progenitor cells repopulate and regenerate the damaged tissue. However, this programmed sequence may be impaired upon persistent damage or aging. Indeed, during agingrelated senescence, the switch from temporal to persistent cell-cycle arrest appears unscheduled and the program of senescence–clearance–regeneration is not finalized. The disruption of this program could come from the fact that in aging organisms the immune system undergoes several changes in both the innate and adaptive immunity that culminate in age-associated immunodeficiency. As this evolution occurs, the immune system becomes unable to efficiently recruit macrophages. As a result, the efficiency of senescent cell clearance is strongly reduced and leads to an accumulation of senescent cells in tissues. Thus, the negative effects of senescence on tissues are essentially caused by a disruption of a collaborative program involving the immune system. These findings support the idea of an extended role of senescence that should be taken into account by the current demographic evolutionary aging theory in order to build a new conceptual framework to explain the diversity of patterns within the life table, the unexpected lack of association between the length of life and the degree of senescence, and why senescence has evolved in some species and not in others. Critical biological functionalities such as senescence, telomere attrition, or the regulation of ROS appear to be more complex than initially expected. These mechanistic models provide a more comprehensive overview of how a single cellular process can have both positive and negative effects. Taken together, these findings support the hypothesis that aging should be interpreted as the longterm result of the disruption of different dynamical equilibriums established between antagonistic processes rather than as the result of a sudden appearance of isolated molecular processes or components with intrinsic negative effects. Of course, considering the systemic organization of the cellular environment, it is obvious that this progressive disruption of dynamical equilibriums which was described here only at the proteomic level has also strong implications at the genomic level and could probably play a central role in the local topological changes characterizing the emergence of ARGs and related ARDs. The effects of the apparition and accumulation of mutations which appear randomly at the genomic level as a result, for example, of dysfunctions in the mechanisms regulating DNA duplication cannot be neglected, but they must be considered within a larger framework which should include the dynamical description of biological processes such as the ones described here. This will allow integrating the fact that the tuning of dynamical equilibriums established between different molecular processes is specific to each species or individual. The need for improvement was pointed out previously when the pace of life and the shape of mortality patterns have been related to the metabolic rate of an organism and to its capacity for repair, regeneration, and growth, respectively [177]. Interestingly, there is an increasing amount of information and number of dynamical models available for many of these fundamental processes. For example, computational modeling has already been used to study the mechanisms behind histones modifications and their effects on aging of stem cells [178]. One can also mention the successful elaboration of complete models of individual living organisms [179], the realistic descriptions of some dynamical subsystems of the human body such as the ones involved in metabolism [180,181], and the development of more efficient methods to simulate population dynamics [182,183]. On a long-term basis, advances made in the understanding of the systemic biochemical mechanisms involved directly or indirectly in the onset and progression of aging will be used to build more realistic models. For example, an in silico population whose members are described by a biochemical model similar to the one in Reference [179] could be implemented as part of a demographic model. In that case, the evolution of key parameters such as mortality rate and fertility could be directly obtained from the biochemical processes occurring inside the living organisms.

CONCLUSION In this chapter, we have emphasized that the understanding of aging from biological, molecular, and evolutionary perspectives can be greatly improved by taking into account the latest developments


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made in the field of cellular, systemic, and molecular biology. These approaches that mainly rely on building reduced mathematical models sometimes combined with transcriptomic data to capture key features of the aging dynamics have proven to provide in-lighting information. For example, we have seen that large-scale analysis of biological data using top-bottom approaches identified functional modules as responsible for the onset of various ARDs. ARGs were identified in sub-networks undergoing topological modifications. Interestingly, mechanistic models based on a bottom-up approach were able to suggest hypotheses to explain how these topological changes caused by mutation accumulation could make GRNs instable and trigger the onset and progression of aging. These connections established between the disparate damages of aging and dynamical and topological properties of the cellular environment illustrate with the other dynamical models described here that aging appears to be caused by disruptions of dynamical equilibriums established between cellular biochemical processes, although other external constraints can also intervene. These disruptions can affect the ability of various components to interact together. This can induce changes in the topology of core GRNs, which in turn could affect other key cellular functionalities. Interestingly, we have seen that the mechanisms behind the ROS rheostat model, the telomere homeostasis model, or the senescence–clearance–regeneration model are in agreement with the fundamental basis of several evolutionary theories of aging such as the antagonistic pleiotropy theory and the disposable soma theory. Thus, more detailed descriptions of molecular processes involved in aging do not only improve evolutionary theories of aging individually but also show that these theories simply describe different aspects of the same fundamental process. Obviously, it is important to underline that the models described here are always the result of a consensus between simplicity and complexity. Indeed, in any modeling process, if one wants to make possible the extraction of meaningful and interpretable results, simplifications are always necessary to reduce the system to a set of parameters and variables which can be more easily interpreted. One can expect that more detailed models will be elaborated in the near future. Other fields of sciences such as system biology will continue to contribute to building a more integrated and detailed description of aging. This might be achieved by implementing more detailed mathematical models for the forces of natural selection and senescence and for dynamics of mutation accumulation [184,185]. Hence, these models will continue to be integrated step by step in demographic models of aging. Furthermore, these models should take into account the fine-tuning of the molecular processes occurring among species as well as the environmental constraints experienced by the individuals. From a mechanistic perspective, the interplay between the environment and the evolutionary processes, including aging, has also been discussed. We have seen that stochastic fluctuations of gene concentrations, a pure mechanistic property, were thought to be a potential evolutionary mechanism. Phenotypic fluctuations could work to improve the adaptability of the species facing severe environmental conditions. Mechanistic models were also used to study the relationships between these fluctuations, the evolution of the fitness over generations, and another dynamical property called robustness. From an evolutionary point of view, models simulating the dynamics of populations suggest that aging could provide an advantage to adapt to environmental changes. Thus, whenever necessary, aging could act as a mechanism working to optimize the fitness function across the generations. However, this evolutionary mechanism might not be of primary importance for species evolving in a safe and controlled environment. This fact is illustrated by the rectangularization of the survival curves observed for the populations with an easy access to all necessary resources. Of course, when analyzing the effects of environment in developed human societies, other factors must be considered such as diet, quality, and access to health care systems or working and social conditions. These results obtained from different kind of methods also illustrate that multidisciplinary approaches provide new insights and contribute to improve our understanding of aging. Aging is one of the most complex biological processes to be addressed by the scientific community. The purpose of this work was obviously not to cover an extensive review of all aspects of the subject. The



general guideline was rather to emphasize several current methodological and conceptual trends which show promising perspectives. The conclusion is that aging research is in need of multidisciplinary and global approaches. We have illustrated this point with several interesting progresses made regarding the mechanistic description of aging. These results show that the contributions of scientists from various fields and domains of expertise can provide new insights and unexpected new directions of investigation. Aging is a phenomenon for which contributions from all fields of sciences will certainly continue to give promising results in the future.

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