Handbook of the Biology of Aging

By Peter Hornsby

Handbook of the Biology of Aging, Ninth Edition, provides a comprehensive synthesis and review of the latest and most important advances and themes in modern biogerontology. The book focuses on the trend of ‘big data’ approaches in the biological sciences, presenting new strategies to analyze, interpret and understand the enormous amounts of information being generated through DNA sequencing, transcriptomic, proteomic, and metabolomics methodologies applied to aging related problems. Sections cover longevity pathways and interventions that modulate aging, innovative tools that facilitate systems-level approaches to aging research, the mTOR pathway and its importance in age-related phenotypes, and much more.

• Assists researchers in keeping abreast of research and clinical findings outside their subdiscipline
• Helps medical, behavioral and social gerontologists understand what basic scientists and clinicians are discovering
• Includes new chapters on genetics, evolutionary biology, bone aging, and epigenetic control
• Examines the diverse research being conducted in the study of the biology of aging

• Language: English
• Publisher: Academic Press
• Release date: Jan 19, 2021
• ISBN: 9780128162835

Book preview

Handbook of the Biology of Aging

Ninth Edition

Nicolas Musi

Barshop Institute for Longevity and Aging Studies, South Texas Veterans Health Care System, University of Texas Health Science Center, San Antonio, TX, United States

Peter J. Hornsby

Barshop Institute for Longevity and Aging Studies and Department of Cellular and Integrative Physiology, University of Texas Health Science Center, San Antonio, TX, United States

Table of Contents

Cover image

Title Page

Copyright

Dedication

List of contributors

About the editors

Foreword

Preface

Part I: Basic mechanisms, underlying physiological changes, model organisms and interventions

Chapter 1. Longevity as a complex genetic trait

Abstract

Outline

Introduction

Defining the aging gene-space

Nongenetic sources of complexity

Emerging tools for studying complex genetic traits

Conclusions

References

Chapter 2. DNA damage and repair in aging

Abstract

Outline

Introduction

Overview of the DNA damage response

Linkage of DNA repair mechanisms to aging

DNA damage theory of aging

Aging disorders associated with DNA damage

Conclusions and future perspectives

Acknowledgments

References

Chapter 3. Mechanisms of cell senescence in aging

Abstract

Outline

What is senescence? Building blocks and feedback loops

The building blocks of the senescent phenotype

Terminology

Senescence during aging in vivo

Senolytics and senostatics as antiaging interventions

Conclusions

References

Chapter 4. The nature of aging and the geroscience hypothesis

Abstract

Outline

Aging and the geroscience hypothesis

What is aging and why does aging occur?

Immortal versus mortal systems and entities

The disposable soma concept

What are the factors that determine longevity?

The force of natural selection over the lifespan

Genes that exhibit antagonistic pleiotropy

The molecular mechanisms of aging and the geroscience hypothesis

Conclusions

References

Chapter 5. Sirtuins, healthspan, and longevity in mammals

Abstract

Outline

Introduction

Sirtuin-driven lifespan extension in invertebrates

Sirtuin enzymatic activity

Sirtuins and mammalian longevity

Genetic variants of human sirtuin genes

Sirtuins as modulators of responses to caloric restriction

Roles for sirtuins in diverse disease states

Cancer

Metabolic syndrome

Cardiovascular dysfunction

Inflammatory signaling

Neurodegenerative disease

Pharmacological modulation of sirtuin activity

Conclusions

Acknowledgments

References

Chapter 6. Integrative genomics of aging

Abstract

Outline

Introduction

Post-genome technologies and biogerontology

Challenges in data analysis

Data integration

Concluding remarks

Acknowledgments

References

Chapter 7. Thermogenesis and aging

Abstract

Outline

Introduction

Thermogenic adipose tissue

Thermogenesis, energy metabolism, and aging

Regulation of thermogenesis in brown adipose tissue

Impact of environmental temperature on laboratory mice

Concluding remarks

References

Chapter 8. Yeast as a model organism for aging research

Abstract

Outline

Introduction

Asymmetric cell division in yeast

Emergence of yeast as a model organism in aging research

Types of aging in yeast

Replicative lifespan

Alternative methods to measure replicative lifespan

Mother enrichment program

Microfluidics in the research into aging in yeast

Chronological lifespan

Replicative lifespan versus chronological lifespan

Hallmarks of aging and yeast

Epigenetic alterations in yeast aging

Calorie restriction in yeast

Adenosine monophosphate kinase

Loss of proteostasis

Systems biology and yeast aging

Relevance of yeast in human aging and the future

Acknowledgments

References

Chapter 9. Model organisms (invertebrates)

Abstract

Outline

Introduction

Caenorhabitis elegans

Drosophila melanogaster

Other invertebrate models in aging research

Rotifers

Flatworms

Bivalves

Sea urchins

Hydra

Turritopsis dohrnii—the immortal jellyfish

References

Chapter 10. NIA Interventions Testing Program: A collaborative approach for investigating interventions to promote healthy aging

Abstract

Outline

Introduction

Features of the Interventions Testing Program experimental design

Types of intervention proposals sought by the Interventions Testing Program

Challenges encountered implementing testing protocols

Summary of Interventions Testing Program findings

Studies nearing completion

Pathology of drug-treated mice

Collaborative Interactions Program

Tests of health outcomes

Interactions with the Mouse Phenome Database

The ITP at 15 years: synopsis and future goals

References

Further reading

Chapter 11. Aging in nonhuman primates

Abstract

Outline

Introduction

Why do primates live so long?

Aging by domain

Manipulation of aging

References

Part II: Organ systems in humans and other animals, human health and longevity

Chapter 12. Senotherapeutics: Experimental therapy of cellular senescence

Abstract

Outline

Premise for translational research and therapeutically targeting cellular senescence

Cellular senescence and the senescence-associated secretory phenotype

Cell senescence as a therapeutic target for age-related diseases

Biological markers of cellular senescence

Experimental therapy for cellular senescence

Senolytics: Foundation and discovery

Fisetin

Senotherapeutics: Broader definitions and deeper understanding

Other senotherapeutics

Senotherapeutics and the brain

Senomorphics

Senomorphic and senotherapeutic actions of geroscience-guided interventions

Clinical translation of senotherapeutics

Conclusions

References

Chapter 13. The role of neurosensory systems in the modulation of aging

Abstract

Outline

Introduction

Sensations that regulate animal lifespan

Signaling pathways involved in neurosensory modulation of aging

Opportunities and challenges in studying neurosensory modulation of aging

Concluding remarks

Acknowledgments

References

Chapter 14. Aging of the sensory systems: hearing and vision disorders

Abstract

Outline

Introduction

Aging of the auditory system

Aging of the visual systems

Conclusions

Abbreviations

References

Chapter 15. Cardiac aging

Abstract

Outline

Introduction

Cardiac aging in humans

Cardiac aging in the mouse model

Other models of cardiac aging

Molecular mechanisms of cardiac aging

Interventions for cardiac aging

Concluding remarks and future perspectives

References

Chapter 16. The aging immune system: Dysregulation, compensatory mechanisms, and prospects for intervention

Abstract

Outline

Introduction

Innate and adaptive immunity

Age and immunity

Effect of age on hematopoiesis

Effect of age on innate immunity

Natural killer cells

Dendritic cells

Effect of age on adaptive immunity

Impact of thymic involution and thymectomy on T cells

Immune cell function

T-cell function

B-cell function

Clinical consequences of immunosenescence

Effect of age on vaccination

Immune senescence and all-cause mortality

Interventions to restore appropriate immunity

Perspectives

References

Chapter 17. Microbiome changes in aging

Abstract

Outline

Introduction

Overview of the structure and function of the microbiome

Relationship between the microbiome and aging

Contribution of the microbiome to aging-related conditions

Microbiome-targeted therapies as potential antiaging interventions

Conclusions

References

Chapter 18. Lipidomics of aging

Abstract

Outline

Introduction

Lipids in biology of aging

Acknowledgments

References

Chapter 19. Trends in morbidity, healthy life expectancy, and the compression of morbidity

Abstract

Outline

Introduction

Dimensions of morbidity

The length of life cycles and population health

Trends in population prevalence of physiological dysregulation, diseases and conditions, functioning loss and disability, and life expectancy

Length of life and length of healthy life

Conclusions

References

Further reading

Author Index

Subject Index

Copyright

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Dedication

This volume is dedicated to the memory of Dr. Edward Masoro, editor of the fifth, sixth, and seventh editions of this handbook; his name will always be associated with this important series of volumes. Dr. Masoro passed away on July 11, 2020, at the age of 95.

Dr. Masoro obtained his A.B. degree at the University of California at Berkeley in 1947 and was then awarded the PhD degree in physiology in 1950. Following early faculty positions, he was appointed as the Founding Chair of the Department of Physiology at the newly established medical school of the University of Texas system in San Antonio (see Chen, 2003, and Critser, 2010, for accounts of his early career). He persuaded other faculty members of the department to join him in a new line of research that probed the mechanisms of aging in rodents, a line of research that began to put San Antonio on the map as a major center for studies in the basic biology of aging. He established the Aging Research and Education Center of the University of Texas Health Science Center San Antonio to catalyze research on the biology of aging across basic and clinical science disciplines. The culmination of this process was the founding of the Barshop Institute for Longevity and Aging Studies, one of the first centers in the nation dedicated to basic research in the biology of aging.

Dr. Masoro’s seminal work established the paradigm of extension of rodent lifespan by caloric restriction, which became a cornerstone of research into mechanisms of aging. His early research in the biology of aging focused on then-popular ideas of how calorie restriction in rodents extended lifespan. It was thought that calorie restriction affected the growth of young animals and that the smaller animals had an extended lifespan. However, he showed that calorie restriction worked in adult animals. His subsequent work tested many physiological hypotheses about the mechanisms of calorie restriction (Masoro, 2009). Investigations into this topic continue to this day, and work on the mechanisms of calorie restriction has stimulated new areas of research, such as pharmacological interventions that act as calorie restriction mimetics.

As members of the Barshop Institute, we will always be grateful to Dr. Masoro for his work on the basic research in the biology of aging at San Antonio, and we recognize that the current thriving enterprise in aging research here would never have been possible without his pioneering efforts.

References

Chen, 2003 Chen I. Hungry for science. Science of Aging Knowledge Environment. 2003;2003(1):NF1 8 January.

Critser, 2010 Critser G. Eternity soup: Inside the quest to end aging New York: Crown Publishing 2010.

Masoro, 2009 Masoro EJ. Caloric restriction-induced life extension of rats and mice: A critique of proposed mechanisms. Biochimica et Biophysica Acta. 2009;10:1040–1048.

List of contributors

Andrzej Bartke,     Southern Illinois University School of Medicine, Department of Internal Medicine, Springfield, IL, United States

Ying Ann Chiao,     Aging and Metabolism Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States

Eileen M. Crimmins,     Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States

Dao-Fu Dai,     Department of Pathology, Carver College of Medicine, University of Iowa, Iowa City, IA, United States

Justin Darcy,     Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA, United States

João Pedro de Magalhães,     Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, United Kingdom

Qunfeng Dong,     Department of Public Health Sciences, Loyola University Chicago, Chicago, IL, United States

Yimin Fang,     Southern Illinois University School of Medicine, Department of Neurology, Springfield, IL, United States

William Giblin,     Department of Pathology, University of Michigan, Ann Arbor, MI, United States

Xianlin Han

Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States

Division of Diabetes, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States

David E. Harrison,     The Jackson Laboratory, Bar Harbor, ME, United States

Erin Hascup,     Southern Illinois University School of Medicine, Department of Neurology, Springfield, IL, United States

Kevin Hascup,     Southern Illinois University School of Medicine, Department of Neurology, Springfield, IL, United States

Peter J. Hornsby,     University of Texas Health Science Center San Antonio, San Antonio, TX, United States

Akihiro Ikeda,     Department of Medical Genetics and McPherson Eye Research Institute, University of Wisconsin, Madison, WI, United States

Jamie N. Justice,     Sticht Center for Healthy Aging and Alzheimer’s Prevention, Internal Medicine – Gerontology and Geriatric Medicine, Wake Forest School of Medicine (WFSM), Winston-Salem, NC, United States

Ajinkya S. Kawale,     Department of Biochemistry and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States

Brian K. Kennedy

Department of Biochemistry and Physiology, Yong Loo School Lin School of Medicine, National University of Singapore, Singapore, Singapore

Centre for Healthy Longevity, National University Health System, Singapore, Singapore

Singapore Institute for Clinical Sciences, A*STAR, Singapore, Singapore

Jung Ki Kim,     Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States

Ron Korstanje,     The Jackson Laboratory, Bar Harbor, ME, United States

Anita Krisko,     Department of Experimental Neurodegeneration, University Medical Center Goettingen (UMG), Goettingen, Germany

Surinder Kumar,     Department of Pathology, University of Michigan, Ann Arbor, MI, United States

Cyril Lagger,     Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, United Kingdom

Morgan E. Levine,     Department of Pathology, Yale University School of Medicine, New Haven, CT, United States

David B. Lombard

Department of Pathology, University of Michigan, Ann Arbor, MI, United States

Institute of Gerontology, University of Michigan, Ann Arbor, MI, United States

Francesca Macchiarini,     Division of Aging Biology, National Institute on Aging, Bethesda, MD, United States

Samuel McFadden,     Southern Illinois University School of Medicine, Department of Neurology, Springfield, IL, United States

Richard A. Miller,     Department of Pathology and Geriatrics Center, University of Michigan, Ann Arbor, MI, United States

Ludmila Müller,     Department Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany

Erin Munkácsy

Barshop Institute for Longevity and Aging Studies, UT Health, San Antonio, TX, United States

Department of Molecular Medicine, UT Health, San Antonio, TX, United States

Laura J. Niedernhofer,     Institute on the Biology of Aging and Metabolism, Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, United States

Miranda E. Orr

Sticht Center for Healthy Aging and Alzheimer’s Prevention, Internal Medicine – Gerontology and Geriatric Medicine, Wake Forest School of Medicine (WFSM), Winston-Salem, NC, United States

W.G. Hefner Veterans Affairs Medical Center, Salisbury, NC, United States

Juan Pablo Palavicini

Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States

Division of Diabetes, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States

Graham Pawelec

Department of Immunology, University of Tübingen, Tübingen, Germany

Health Sciences North Research Institute, Sudbury, Ontario, Canada

Andrew M. Pickering

Barshop Institute for Longevity and Aging Studies, UT Health, San Antonio, TX, United States

Department of Molecular Medicine, UT Health, San Antonio, TX, United States

Center for Neurodegeneration and Experimental Therapeutics, Department of Neurology, The University of Alabama at Birmingham, Birmingham, AL, United States

Department of Neurobiology, The University of Alabama at Birmingham, Birmingham, AL, United States

Peter S. Rabinovitch,     Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, United States

Kelly R. Reveles

College of Pharmacy, The University of Texas at Austin, Austin, TX, United States

Pharmacotherapy Education & Research Center, UT Health San Antonio, San Antonio, TX, United States

Nadia Rosenthal,     The Jackson Laboratory, Bar Harbor, ME, United States

Corinna N. Ross

Population Health Program, Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, United States

Department of Life Sciences, Texas A&M University San Antonio, San Antonio, TX, United States

Yi Sheng,     Department of Aging and Geriatric Research, Institute on Aging, University of Florida, Gainesville, FL, United States

Shinichi Someya,     Department of Aging and Geriatric Research, University of Florida, Gainsville, FL, United States

Randy Strong,     Department of Pharmacology, The University of Texas Health Science Center at San Antonio, and the Geriatric Research, Education and Clinical Center (GRECC) and Research Service of the South Texas Veterans Health Care System, Texas, TX, United States

Patrick Sung,     Department of Biochemistry and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States

George L. Sutphin,     University of Arizona, Tucson, AZ, United States

Robi Tacutu,     Systems Biology of Aging Group, Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Bucharest, Romania

Suzette D. Tardif,     Population Health Program, Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, United States

Thomas von Zglinicki,     Newcastle University Biosciences Institute, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom

Robert J. Wessells,     Department of Physiology, Wayne State University School of Medicine, Detroit, MI, United States

Rui Xiao

Department of Aging and Geriatric Research, Institute on Aging, University of Florida, Gainesville, FL, United States

Department of Pharmacology and Therapeutics, College of Medicine, University of Florida, Gainesville, FL, United States

Center for Smell and Taste, University of Florida, Gainesville, FL, United States

Guang Yang,     Department of Aging and Geriatric Research, Institute on Aging, University of Florida, Gainesville, FL, United States

Eric H. Young

College of Pharmacy, The University of Texas at Austin, Austin, TX, United States

Pharmacotherapy Education & Research Center, UT Health San Antonio, San Antonio, TX, United States

Amina R.A.L. Zeidan

College of Pharmacy, The University of Texas at Austin, Austin, TX, United States

Pharmacotherapy Education & Research Center, UT Health San Antonio, San Antonio, TX, United States

Yuan S. Zhang,     Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

About the editors

Dr. Nicolas Musi is a tenured Professor of Medicine (Division of Geriatrics and Gerontology and Division of Diabetes) and Director of the Barshop Institute for Longevity and Aging Studies and the San Antonio Claude D. Pepper Older Americans Independence Center. He is also Associate Director for Research of the San Antonio Geriatric Research, Education and Clinical Center. He is an active educator and research mentor, and supervises clinical and research fellows, residents, and graduate students. In this role, he also functions as Director of a T32 Training Grant on the Biology of Aging.

Dr. Peter J. Hornsby obtained a PhD in Cell Biology at the Institute of Cancer Research of the University of London. He has held faculty positions at the University of California San Diego, the Medical College of Georgia, and Baylor College of Medicine. Currently he is Professor in the Department of Physiology and Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center, San Antonio.

Foreword

Thomas A. Rando and Laura L. Carstensen, Stanford Center on Longevity, Stanford University, Stanford, CA, United States

Since the inaugural publication of the Handbooks of Aging in 1976, the series has played a key role in promoting and guiding gerontological science. By preserving foundational knowledge and illuminating emerging areas, the series has served as a core resource for established researchers and an inspiration for students of gerontology. From its inception, gerontological science has been cross-disciplinary. The three-volume series has played a key role in maintaining cohesion in a science that spans dozens of disciplines.

The need to understand aging only increases in importance over time. The global population has now passed an important tipping point, moving from a world where children predominate to one in which there are more older people than youth. This reshaping of the age distribution in the population demands grand investments in the science of aging.

Thankfully, the science of aging is also growing faster than ever across social and biological sciences. Along with phenomenal advances in the understanding of the biology of aging as well as genetic influences on aging trajectories, and susceptibility to age-related diseases has come the awareness of the critical importance of the physical and social environments in which people age and the psychological factors that modulate and sometimes alter genetic predispositions.

The Handbooks of Aging series, comprised of the Handbook of the Biology of Aging, the Handbook of the Psychology of Aging, and the Handbook of Aging and the Social Sciences, is now in its ninth edition. The Handbook of Aging and the Social Sciences and the Handbook of the Psychology of Aging have long provided conceptual anchors and frameworks to the social and behavioral sciences while also addressing emerging topics that did not exist decades ago, such as the fluidity of race and gender, groundbreaking insights into the role of sleep in cognitive aging, and the ways that smartphones, robots, and social media can modify the experience of aging. The handbooks also provide cutting-edge updates to the understanding of genetics, built environments, and intergenerational commitments. The 9th edition of the Handbook of the Biology of Aging introduces geroscience, a discipline that did not exist 10 years ago and is now among the most vibrant in all of science. This edition also provides updates on the exciting advances in the genetics and integrative genomics of aging and longevity as well as the biology and therapeutic opportunities afforded by the studies of cellular senescence.

What has not changed over the editions is the superb synthesis of the field. The editors of the 9th edition extend a long tradition of giants in the field giving generously of their time and knowledge to produce consistently excellent volumes. Their thoughtful selection of topics and recruitment of deeply knowledgeable authors is reflected throughout the series. We are most grateful to Nicolas Musi and Peter J. Hornsby, editors of the Handbook of the Biology of Aging, Kenneth F. Ferraro and Deborah S. Carr, editors of the Handbook of Aging and the Social Sciences, and K. Warner Schaie and Sherry Lynn Willis, editors of the Handbook of the Psychology of Aging.

We also express our deep appreciation to our publishers at Elsevier, whose profound interest and dedication to the topic has facilitated the publication of the Handbooks through many editions. We remain eternally grateful to James Birren, for establishing the series and shepherding it through the first six editions that played a profound role in establishing the tradition of multidisciplinary science in the field of aging.

Preface

Nicolas Musi and Peter J. Hornsby

As this series of volumes enters its 9th edition, it may well be said that the field of the biology of aging has reached its maturity. There are several themes that are reflected in the contents of this volume.

1. In the past—now in reality the distant past—the biology of aging was a separate field of research, relatively unconnected to the mainstream of biological investigation. This has changed radically. Aging is now incorporated into a broad range of related fields, including cancer, diabetes and other metabolic diseases, immunology, and more. This has changed the way research in aging is viewed in many ways, and the benefits have been mutual on both sides. Research in aging has benefited from the input from these related areas, while those related areas have benefited from concepts developed in the biology of aging. Just one example is cellular senescence. Once considered a topic of narrow interest to a small group of investigators in technical aspects of cell culture, it has now been incorporated into multiple other areas, including cancer and metabolic diseases, as well as aging itself.

2. The second development reflected in the chapters in this volume is the degree to which the biology of aging has been incorporated into the basic biology of each organ system. For example, the basic biology of bone and the musculoskeletal system is incomplete without a consideration of the changes that take place in aging. Those include changes in the endocrine system, as well as stem cell biology. No account of the complete biology of any organ system is valid unless it incorporates the biology of aging as an integral part.

3. The third significant development is the merging of translational and clinical research with basic biology. As the field has matured, it has become possible to apply biological findings to human patients, either in clinical trials or clinical practice. Translational research has also assumed a much greater prominence. Basic biology has benefited from these clinical and translational investigations. As results have emerged from those studies, they have stimulated new avenues of investigation in basic biology. For example, studies on interventions that may increase rodent life span have already had a major impact on clinical and translational investigation, and in turn those studies have impacted new research in basic biology. mTOR inhibitors and senolytics are examples of pharmacological interventions that increase rodent life span and have already been translated to the clinic. Findings from clinical and translational studies have in turn stimulated new avenues in basic biology. We can expect this trend to continue in the future.

One of the more unfortunate consequences of these exciting developments is that it becomes impossible to produce a volume in the Handbook of the Biology of Aging series that comprehensively covers all aspects of the topic. The selection of topics offered in the current volume is an attempt to sample many of these exciting areas with contributions from leading scientists in their fields. But we also offer in advance our apologies to readers whose particular areas of interest we had to omit. Omission does not indicate that we felt the area of less importance, but we simply face the reality of an incredibly expanding and increasingly exciting field.

Part I

Basic mechanisms, underlying physiological changes, model organisms and interventions

Outline

Chapter 1 Longevity as a complex genetic trait

Chapter 2 DNA damage and repair in aging

Chapter 3 Mechanisms of cell senescence in aging

Chapter 4 The nature of aging and the geroscience hypothesis

Chapter 5 Sirtuins, healthspan, and longevity in mammals

Chapter 6 Integrative genomics of aging

Chapter 7 Thermogenesis and aging

Chapter 8 Yeast as a model organism for aging research

Chapter 9 Model organisms (invertebrates)

Chapter 10 NIA Interventions Testing Program: A collaborative approach for investigating interventions to promote healthy aging

Chapter 11 Aging in nonhuman primates

Chapter 1

Longevity as a complex genetic trait

George L. Sutphin¹ and Ron Korstanje²,    ¹ University of Arizona, Tucson, AZ, United States ,    ² The Jackson Laboratory, Bar Harbor, ME, United States

Abstract

Aging is influenced by many intrinsic and extrinsic factors including genetic background, epigenetics, diet, and environment. Both our ability to develop a complete model of the aging process and accurately predict outcomes designed to extend lifespan or treat age-associated pathology require the identification of the range of factors capable of influence aging and an understanding of how these factors interact. In this chapter we discuss longevity and other phenotypes related to aging as complex genetic traits. We first review past and ongoing efforts to comprehensively catalog genetic and nongenetic factors that impact lifespan in invertebrate and mammalian model systems and conclude by discussing emerging tools that will help the aging-research community encompass the complexities of the aging process.

Keywords

Longevity; complex trait; gene mapping; QTL; GWAS; genomics

Outline

Outline

Introduction 3

Defining the aging gene-space 4

Direct screens for genetic longevity determinants 4

Leveraging genetic diversity to identify aging loci 9

Nongenetic sources of complexity 17

Tissue-specific aging 17

Gene–environment interaction 18

Emerging tools for studying complex genetic traits 22

High-throughput life span assays in yeast and worms 23

Genome-scale mouse knockout collection 25

Collaborative cross and diversity outbred mice 26

Expression QTLs 28

Aging biomarkers 29

Conclusions 31

References 31

Introduction

Complex traits are phenotypic characteristics that result from the integration of many genetic loci and environmental factors. Longevity, along with the age-dependent decline in cellular and physiological processes that define aging, is quintessentially a complex genetic trait. A complete understanding of a complex trait requires both defining the range of factors that contribute to the trait and developing models for how the various factors interact. In the past several decades, hundreds of genes have been identified that are capable of influencing longevity or other age-associated phenotypes across a range of model systems. The majority of these genes can be broadly assigned to one or more of the following genetic pathways: (1) protein homeostasis, (2) insulin/IGF-1-like signaling (IIS), (3) mitochondrial metabolism, (4) sirtuins, (5) chemosensory function, or (6) dietary restriction. Pharmacologic agents targeting several of these pathways have been shown to increase lifespan and improve outcomes in age-associated disease in model systems and are either in use or in clinical trials for treatment of specific ailments. These include the mechanistic target of rapamycin (mTOR)-inhibitor rapamycin, the sirtuin activator resveratrol, and the glucose production suppressor metformin, and are discussed in greater detail in other chapters. Extragenetic but organism-intrinsic factors, such as tissue-specific gene expression, parentally inherited molecules, and epigenetics can also contribute to aging phenotypes.

Many environmental factors have been identified that impact longevity and age-associated disease. These include the abundance and composition of diet, exposure to various forms of stress, environmental temperature, social interaction, and even the presence or absence of a magnetic field. Among these, dietary restriction is by far the most widely studied. Reduction in total dietary intake or a change in the composition in the diet can have a profound impact on longevity in model systems. Short-term exposure to thermal, oxidative, endoplasmic reticulum (ER), or other forms of stress is sufficient to increase lifespan. In both worms and fruit flies, adjusting the culture temperature can dramatically influence lifespan. In each case, genes have been identified that mediate the organism’s response to the environmental stimuli.

This chapter examines aging as a complex trait. The following sections review past and ongoing efforts to define the scope of genetic, extragenetic, and environmental factors that influence aging, outline strategies for building interaction models, and discuss emerging tools that are furthering our ability to encompass the complexities of aging.

Defining the aging gene-space

A primary task in understanding the genetic complexity underlying any highly integrative phenotype is to identify the range of genes capable of impacting that phenotype. Three approaches are commonly employed to uncover novel aging factors. In models where targeted genome-scale genetic manipulation is possible and lifespan can be measured in a moderate- to high-throughput manner, screens have been carried out to identify single-gene manipulations capable of enhancing longevity. In longer-lived models and those less amenable to high-throughput targeted genetics, genetic mapping strategies are used to identify genetic loci at which natural variation is associated with differences in lifespan. A third approach is to leverage a secondary phenotype, such as stress resistance, that correlates with longevity but can be more rapidly screened to narrow the candidate gene list, and only screen genes that pass the primary threshold for longevity.

Direct screens for genetic longevity determinants

Among models commonly used in aging research, the nematode Caenorhabditis elegans and the budding yeast Saccharomyces cerevisiae possess three characteristics allowing for large-scale genetic screening for longevity: (1) genetic tools allowing for targeted genome-scale manipulation of individual genes, (2) relatively short lifespans, and (3) techniques to rapidly and inexpensively culture large populations in the laboratory. Complete genome sequences are available for both organisms (C. elegans Sequencing Consortium, 1998; Goffeau et al., 1996) and standardized lifespan assays can be completed in a matter of weeks (Murakami & Kaeberlein, 2009; Steffen, Kennedy, & Kaeberlein, 2009; Sutphin & Kaeberlein, 2009). Both models have been used in genome-scale screens for single-gene manipulations capable of increasing lifespan. In Drosophila melanogaster, while targeted gene-modification is not available at the genome-scale, random mutagenesis screens are used to identify novel longevity determinants and lifespan assays can similarly be completed in a matter of months (Linford, Bilgir, Ro, & Pletcher, 2013).

RNA interference screens in nematodes

In C. elegans, targeted gene knockdown by RNA interference (RNAi) can be accomplished by feeding animals bacteria expressing double-stranded RNA containing the target sequence (Timmons & Fire, 1998). Two RNAi feeding libraries targeting individual genes throughout the C. elegans genome have been constructed and are commercially available. The original Ahringer library contains 16,256 unique clones constructed by cloning genomic fragments targeting specific genes between two inverted T7 promoters (Fraser et al., 2000; Kamath et al., 2003). This library has recently been supplemented with an additional 3507 clones. The complete Ahringer library is commercially available through Source Bioscience (RNAi Resources | Source BioScience, n.d.). The Vidal library contains 11,511 clones produced using a full-length open reading frames (ORFs) gateway cloned into a double T7 vector (Rual et al., 2004) and is commercially available through either Source Bioscience (RNAi Resources | Source BioScience, n.d.) or Dharmacon Inc. (C. elegans | Dharmacon, 2019). Combined, these libraries provide single-gene clones targeting more than 20,000 unique sequences covering approximately 90% of known ORFs in C. elegans.

In total, more than 300 C. elegans genes have been identified for which reducing expression results in prolonged lifespan (Braeckman & Vanfleteren, 2007; Smith et al., 2008), the majority in longevity screens using the RNAi feeding libraries (reviewed by Yanos, Bennett, & Kaeberlein, 2012) or random mutagenesis screens (de Castro, Hegi de Castro, & Johnson, 2004; Muñoz & Riddle, 2003) (Table 1.1). These include three genome-wide screens using the Ahringer RNAi feeding library (Hamilton et al., 2005; Hansen, Hsu, Dillin, & Kenyon, 2005; Samuelson, Klimczak, Thompson, Carr, & Ruvkun, 2007), two partial screens targeting genes on specific chromosomes (Dillin et al., 2002; Lee et al., 2003), five screens of RNAi clones or mutant sets selected in a preliminary screen for a secondary longevity-associated phenotype, such as arrested development, upregulation of the mitochondrial unfolded protein response, or resistance to thermal or oxidative stress (Bennett et al., 2017; Chen, Pan, Palter, & Kapahi, 2007; Curran & Ruvkun, 2007; de Castro et al., 2004; Kim & Sun, 2007; Muñoz & Riddle, 2003), and one recent screen of C. elegans orthologs of human genes differentially expressed at different ages in human whole blood (Sutphin et al., 2017). Combined, these studies have identified aging factors in a range of biological processes including mitochondrial metabolism, mitochondrial unfolded protein response, cell structure, cell surface proteins, cell signaling, protein homeostasis, RNA processing, and chromatin binding.

Table 1.1

aThe authors only pursue four genes, but do not report the total number found to significantly affect lifespan.

bThe authors only report the number of significant hits on chromosome I.

cThe authors pursue the 90 genes with the largest change in chronological lifespan, but do not report how many are statistically significant.

d8736 of 27,157 lines were putatively classified as long-lived; the authors selected 45 and 15 remained long-lived after validation.

eThe authors tested 95 Drosophila strains with combined deletion of 130 miRNAs.

fGenes that increased lifespan in at least one sex under at least one expression mode (constitutive vs adult only, whole body vs neuronal).

Notably, while a large number of genes has been identified through longevity screening in C. elegans, there is little overlap between screens (Yanos et al., 2012). There are several possibilities that may account for this lack of overlap. RNAi is inherently noisy, which may result in a different degree of knockdown between experiments for a given clone. Many of these screens were designed to assess maximum lifespan, scoring only the number of worms alive after all control worms had died. Between these two factors, the low overlap may reflect a high false-negative rate inherent in the methodology. Another possibility is that subtle differences in experimental design may result in a different range of factors becoming prominent. These differences include culture temperature, strain background, age at RNAi induction, and the presence or absence of floxuridine (FUdR) to prevent reproduction (Table 1.2). Recent evidence suggests that the strain of Escherichia coli used in RNAi experiments (HT115) may have distinct interactions with the C. elegans genotype in the context of lifespan from the E. coli strain used in most non-RNAi experiments (OP50) (Xiao et al., 2015; Yen & Curran, 2016), further complicating comparisons between RNAi screens and other categories of intervention. The Caenorhabditis Interventions Testing Program (CITP) was established in 2013 as a platform to identify novel lifespan-extending pharmacological compounds through rigorous testing across three independent laboratories, modeled after the successful Interventions Testing Program in mice (Nadon, Strong, Miller, & Harrison, 2017). Initial testing revealed significant problems in reproducibility across test sites, resulting in a multiyear project to standardize reagents, materials, and methods (Lithgow, Driscoll, & Phillips, 2017; Lucanic et al., 2017) culminating in the recent release of a set of standardized protocols for measuring worm lifespan (Lucanic et al., 2017). Wide adoption of these protocols may go some way toward limiting reproducibility and variation across labs. Petrascheck and Miller (2017) recently employed statistical models of wild-type C. elegans lifespan data to simulate aspects of methodological variation (e.g., scoring frequency, sample size) and population structure (e.g., hazard distribution) in longevity studies and determine their influence on reproducibility under ideal conditions (i.e., in the absence of methodological error and environmental variation). They conclude that statistical detection of lifespan differences is surprisingly resilient to scoring frequency and shape of the lifespan distribution within a population. Perhaps unsurprisingly, they determined that sampling size was the primary driver of poor reproducibility and that most studies are underpowered to detect small (<10%) changes in lifespan (Petrascheck & Miller, 2017). Regardless of the cause, the small degree of overlap and the fact that these screens only identified pro-aging genes—genes for which reduced expression increases lifespan—suggests that the range of genetic factors involved in C. elegans aging has yet to be exhaustively bounded.

Table 1.2

Knockout screens in budding yeast

In the budding yeast S. cerevisiae, an analog to the C. elegans RNAi feeding libraries exists in the form of a genome-wide single-gene deletion strain collection. This collection contains ~4800 strains, each containing a complete open reading frame (ORF) deletion for a single nonessential gene in a common genetic background (Winzeler et al., 1999). Versions of this collection are available in both haploid mating types and in the homozygous diploid life stage. When conceptualizing longevity in a single-celled organism like S. cerevisiae, the first question to consider is the definition of lifespan. Two aging paradigms are commonly studied in the budding yeast (Steinkraus, Kaeberlein, & Kennedy, 2008). Replicative lifespan refers to the number of times a cell can divide prior to undergoing senescence (Kaeberlein, 2006; Mortimer & Johnston, 1959). In contrast, chronological lifespan refers to the length of time a cell can remain in a quiescent state while retaining the ability to re-enter the cell cycle (Fabrizio & Longo, 2003; Kaeberlein, 2006; Fabrizio, Pozza, Pletcher, Gendron, & Longo, 2001).

Until recently, high-throughput techniques had only been developed for measuring chronological lifespan in yeast. Chronological lifespan is typically measured by growing yeast cells in liquid culture until they enter stationary phase, maintaining the cells in the expired media, and periodically sampling the aging culture to assess viability (Kaeberlein, 2006). Viability has traditionally been measured by plating a defined culture volume onto rich solid media and counting the number of colonies to calculate colony-forming units (CFUs). Powers, Kaeberlein, Caldwell, Kennedy, and Fields (2006) dramatically increased throughput by replacing the labor-intensive (though quantitative) process of counting CFUs with the more qualitative approach of diluting a sample from the aging culture back into rich liquid media and measuring optical density at 600 nm (OD600) after a fixed outgrowth time. They used this approach to screen the homozygous diploid deletion collection, identifying 90 chronologically long-lived mutants (Powers et al., 2006; Table 1.1). This technique was later improved to quantitatively assess outgrowth using a combined instrument that provides continuous culture agitation, temperature control, and OD600 measurement (Burtner, Murakami, & Kaeberlein, 2009; Murakami & Kaeberlein, 2009; Olsen, Murakami, & Kaeberlein, 2010) and has been used to screen selected sets of mutants from the yeast ORF deletion collection for increased chronological lifespan (Burtner, Murakami, Olsen, Kennedy, & Kaeberlein, 2011). Campos, Avelar-Rivas, Garay, Juárez-Reyes, and DeLuna (2018) used a similar method adapted for a robotic platform to measure the chronological lifespan of 3718 strains from the yeast deletion collection in media with either glutamine (a preferred nitrogen source; considered nonrestricted) or γ-aminobutyric acid (GABA; a model of nitrogen restriction) as the sole nitrogen source. They identified 573 (15%) chronologically short-lived and 254 (7%) chronologically long-lived single-gene deletion strains in the nonrestricted media, and 510 (6%) chronologically short-lived and 228 (14%) chronologically long-lived single-gene deletion strains in the nitrogen-restricted media. In a modified approach, Garay et al. (2014) monitored the chronological lifespan of 3878 fluorescently labeled deletion collection strains, each pooled into a common culture during stationary phase arrest with the wild-type strain expressing a distinct fluorescent label. Viability was assessed for each mutant strain relative to the pooled wild-type based on the relative fluorescence during outgrowth. This method identified 516 (13%) chronologically short-lived and 262 (7%) chronologically long-lived strains. Fabrizio et al. (2010) and Matecic et al. (2010) both employed an alternative competitive strategy, chronologically aging a pooled culture containing cells from each of the single-gene deletion strains in the ORF deletion collection and using microarrays to genotype the longest surviving cells. A recent improvement to further increase throughput shifts the chronologically aging yeast cultures from culture tubes to 96-deep-well plates with outgrowth analysis performed in 384-well plates (Jung, Christian, Kay, Skupin, & Linster, 2015).

Similar to the RNAi screens for increased lifespan in C. elegans, the screens by Powers et al. (2006), Matecic et al. (2010), and Fabrizio et al. (2010) found a remarkable lack of overlap in genes identified to affect yeast chronological lifespan (Smith, Maharrey, Carey, White, & Hartman, 2016). These yeast screens varied methodologically to a greater extent than the C. elegans screens. Beyond using distinct methods for measuring chronological lifespan, the three studies used different subsets of the yeast deletion collection (diploid BY4743 vs haploid BY4741) and different media types (rich vs defined media). Smith et al. (2016) recently revisited these screens, examining the methodological differences in detail, concluding that differences in media components were likely a major contributor to lack of consensus in identified genes, but that other methodological factors were also involved (aeration, method of measuring chronological lifespan, competitive vs noncompetitive environments).

In contrast to chronological lifespan, the typical method for measuring replicative lifespan in yeast involves the manual and labor-intensive removal of daughter cells from a dividing mother. To bypass this problem, a moderate-throughput iterative strategy was devised to identify long-lived mutants in the yeast deletion collection by determining replicative lifespan initially for only five cells per strain and using statistical methods to select strains for further testing (Kaeberlein et al., 2005). A preliminary report identified 13 genes for which deletion extends replicative lifespan out of the first 564 strains initially tested in the ORF deletion collection (Kaeberlein et al., 2005). Of the 13 genes, five map to the mTOR signaling pathway (ROM2, RPL6B, RPL31A, TOR1, and URE2). This screen was recently completed, identifying 238 long-lived deletion strains among 4698 tested, including 189 that were not previously reported to influence lifespan (McCormick et al., 2015). Pathway enrichment among the final pro-aging gene set confirmed the importance of mTOR signaling and cytosolic translation, and identified a number of other pathways and cellular processes that play an important role in replicative aging: mitochondrial translation, the tricarboxylic acid (TCA) cycle, mannosyltransferases, and the SAGA complex. Two additional replicative lifespan screens have been reported examining gene sets selected for either orthology to known worm aging genes (Smith et al., 2008) or ribosomal components (Steffen et al., 2008). Combined, longevity screens in yeast have identified approximately 250 pro-aging genes related to a range of cellular processes including protein homeostasis, metabolism, stress resistance, and mitochondrial function (Table 1.1).

Overexpression and knockout screens in fruit flies

Tools for genome-scale targeted genetic modification have yet to be used in the context of aging in D. melanogaster. Drosophila does provide a unique tool among invertebrate aging models in the form of transposable enhancer and promoter elements that can be randomly inserted into the genome allowing for unbiased identification of genes that increase lifespan when overexpressed. In an early study using this method, Landis, Bhole, and Tower (2003) screened 10,000 lines and identified six genes for which overexpression increased longevity, including factors involved in vacuolar function, membrane transport, and cell structure (Table 1.1). Paik et al. (2012) initiated a longevity screen examining 27,157 lines and reported the first 15 long-lived transgenic strains overexpressing genes involved in transcription, translation, cell signaling, metabolism, and immunity (Table 1.1). Detailed mechanistic studies have now been published for two of these genes: (1) malic enzyme (Men), overexpression of which during larval development is sufficient to extend lifespan, drives a range of metabolic changes, and improves resistance to oxidative stress (Kim et al., 2015); and (2) NAD-dependent methylenetetrahydrofolate dehydrogenase-methenyltetrahydrofolate cyclohydrolase (Nmdmc), which increases lifespan and improves mitochondrial homeostasis when overexpressed either whole body or in fat (Yu, Jang, Paik, Lee, & Park, 2015).

A third study used growth impairment in the form of reduced wing and eye size as a surrogate marker for longevity in a screen of 716 transgenic Drosophila lines (Funakoshi et al., 2011). Two genes were identified with previous links to insulin/IGF-1-like and mTOR signaling (Table 1.1). Finally, Shaposhnikov, Proshkina, Shilova, Zhavoronkov, and Moskalev (2015) overexpressed nine genes involved in DNA damage detection and repair in both sexes under different temporal (constitutive vs adult-only) and tissue (whole-body vs neuronal) expression patterns and in different environmental contexts (well fed, starvation, heat stress, oxidative stress). The impact of each gene on lifespan was highly dependent on expression pattern and environmental context. Of the nine genes examined, overexpression of eight increased lifespan of well-fed, unstressed flies under at least one of the tested patterns of expression.

While genome-scale tools analogous to the C. elegans RNAi feeding library or the yeast deletion collection are not currently available in Drosophila, more targeted collections of genes have been examined in the context of aging. Chen et al. (2014) screened a collection of 95 Drosophila strains with deletions in 130 different microRNAs (miRNAs) (representing 99% of known Drosophila miRNAs) for lifespan and a variety of other phenotype. They report increased lifespan in 12 strains and decreased lifespan in 23 lines, demonstrating that miRNAs are a potentially rich source for genetic targets to modify longevity. Ostojic et al. (2014) found that knocking out four of six examined gustatory receptors resulted in increased lifespan in one or both sexes.

Genetic screening for lifespan variants using invertebrate models has been invaluable for defining the range of factors and biological processes involved in the determination of lifespan. Hundreds of genes have been identified across a range of central biological processes, the most prominent being mitochondrial metabolism, protein homeostasis, and stress resistance (Table 1.1). There is still work to be done in this area, particularly with respect to understanding how the range of factors important for lifespan is affected by different environmental conditions, such as changes in temperature or in response to dietary restriction.

Leveraging genetic diversity to identify aging loci

The previous sections describe reverse genetic approaches to identifying aging genes, in which large numbers of genes are knocked out or overexpressed individually and the effect on lifespan measured. Because of the scale, this approach has only been carried out in short-lived invertebrate models that are simple and inexpensive to maintain in the laboratory. Current genome-scale knockout efforts like the International Mouse Knockout Consortium (IMKC) and International Mouse Phenotyping Consortium (IMPC; see discussion of emerging tools later in this chapter) may lend themselves to a similar strategy in mice on a smaller scale, though the cost of maintaining statistically meaningful numbers of mice throughout life will still likely prevent full-genome mouse lifespan screens.

An alternative approach is to use forward genetics to leverage the natural phenotypic variation in genetically diverse populations to map candidate aging loci. This approach has the advantage of directly identifying genes even in long-lived mammalian systems. Genes identified in mammalian systems are more likely to be relevant to human aging than those identified in evolutionarily distant invertebrate screens. This approach also provides a complement to the screens carried out in invertebrate systems. Invertebrate screens tend to increase or decrease gene expression to levels outside of what is typically experienced from allelic variants in natural populations. Natural variants can result in large changes in gene activity, including complete inactivation of a gene, but more typically cause subtler changes in gene action or specificity. Gene mapping will therefore both identify longevity effects from less dramatic gene interventions, and point to genes that make the largest contributions to the variation in aging within the population examined.

Mapping longevity genes in human populations

The first gene-mapping studies to examine longevity employed linkage analysis in human families. Three studies of this type mapped loci associated with extreme longevity in 137 sibling pairs with one member being at least 98 years old and other members being at least 90 (males) or 95 (females) years old (Puca et al., 2001), 95 pairs of male fraternal twins with healthy aging (Reed, Dick, Uniacke, Foroud, & Nichols, 2004), and 279 families with multiple long-lived siblings (Boyden & Kunkel, 2010). All three studies identified one or more loci associated with variation in longevity, most notably a common locus on chromosome 4 (Table 1.3).

Table 1.3