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Mitonuclear communication in metabolic homeostasis during aging and exercise
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Mitonuclear communication in metabolic homeostasis during aging and exercise
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MITONUCLEAR COMMUNICATION IN METABOLIC HOMEOSTASIS DURING AGING AND EXERCISE By Joseph C. Reynolds A Dissertation Presented To The FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the degree DOCTOR OF PHILOSOPHY Biology of Aging May 2020 Copyright 2020 Joseph C. Reynolds i Acknowledgements As I approach the conclusion of my doctoral work at USC, I have reflected upon the last five years and gained a new appreciation for all the help, guidance, and support to lead me to where I am today. There are countless people who have helped me throughout my studies, whether it be through technical guidance, conceptual advice, or just support through the times where things do not go exactly as planned. I would like to give a special thank you to my mentor throughout this entire process, Dr. Changhan Lee, who has constantly been there to bounce ideas off of and helped shape me into the researcher and professional I aim to be. Thank you for supporting me through the discoveries and the mistakes, and always providing ideas on how to move forward. While I’ve spent the last five years in your lab, you will always be someone I look up to as a mentor, and I thank you for your continual support. I want to thank my parents, Bob and Patty Reynolds for encouraging me to follow this path. Your constant belief in my success has been a reassuring driving force. Your support does not go unnoticed, and I appreciate all that you do to allow me to follow my instinct and turn my passion for science into a career. I have always looked up to you both and it makes me proud to share this accomplishment with you. I would like to thank my grandparents, Marlene and Bill Bertram, Ann and Bill Zeller, and Tina and Bob Reynolds. Thank you for your never-ending love and support. Their ongoing support is a comforting guidance in my pursuit of this goal. Thank you for always being there for me. I would like to thank Dr. Bérénice Benayoun for her support and advice over the years. Thank you for always making me laugh and for being an advocate for colorblind scientists. You ii provided me great guidance in data analysis and interpreting large datasets. I’ll keep the lessons you taught me for years to come. To Dr. Hassy Cohen, thank you for taking an interest in my research and your wisdom both in science and just with navigating the policies of the University. You’ve been a great help over my career at USC, including during my initial interviews at the school. I want to thank Dr. Ramanathan for always lending a helping hand. Even before I joined USC, you’ve been there to encourage me and guide me through the process of achieving my PhD. You and your lab members at The Buck Institute were essential in helping me earn my master’s degree, and that has continued through to USC. To Dr. Deepak Lamba, you were my first mentor after I began my science career. You taught me so much about how to do research, think of new approaches to problems, and form hypotheses. You took a chance on me when I came out of my undergrad with little lab experience and helped light a fire that still burns. Your encouragement to continue with my education has led me here today, and I will always be thankful for the years of support and genuine care. I want to thank everyone who helped me with experiments and technical guidance on my manuscript that helped shape my time at USC. Thank you to Dr. Rochelle Lai, Jonathan Woodhead, James Joly, Ryan Lu, Dr. Nicholas Graham and Dr. Troy Merry. Your research experience and guidance on the manuscript helped me achieve this degree, and I thank you for always being willing to help me do the best science possible. iii I want to thank the members of the Lee Lab who have stuck with me day in and day out. Thank you to Dr. Jyung Mean Son for teaching me techniques and providing me with a post-doc mentor. Thank you to Chan Yoon Park for your input on my experiments and analysis. Thank you to Maria Chun for helping me around the lab and for keeping our lab meetings running on time. Thank you to Princesse Bwiza for listening to all my practice presentations and offering your advice. Thank you to Arianna Chen for bringing some outside excitement and energy into the basement. I would also like to thank past members of the Lee Lab who have aided in my research. Thank you to Maggie Donovan for being there to help me with the mice and process the data, along with being a good partner to talk to and help me formulate my ideas. Thank you to Kathleen Tor and Emmeline Kim for helping to convince me to join the lab and providing endless entertainment during my time learning the ropes. I would like to say a special thank you to Annette Thomason. You helped unearth the scientist within me and taught me I was capable of mastering something if I put in the work. I don’t know how to thank you for instilling my confidence and determination in science. Finally, to my partner, Gina DeCesare, thank you for supporting me throughout this entire journey. From late nights to Christmas eve conference calls and weekend in the lab, your patience has been greatly appreciated. You’ve motivated me to continue doing my best work at times I felt less than optimistic, and your faith in me never wavered through this process. I cannot explain enough how much your support and love continue to drive me. I could not have done this without you, and this degree is just as much yours as it is mine. iv Table of Contents Acknowledgements ........................................................................................................................ i List of Figures ............................................................................................................................... vi List of Tables .............................................................................................................................. viii Abbreviations ................................................................................................................................ ix Abstract ....................................................................................................................................... xiii Chapter I: Introduction .................................................................................................................. 1 Multifaceted Mitochondria ................................................................................................. 1 Overview of the mitochondria ................................................................................ 1 Mitochondria: Origin and Genome ........................................................................ 3 Mitochondrial-Derived Peptides ........................................................................................ 5 Humanin ................................................................................................................ 7 Small Humanin-Like Peptides ............................................................................... 8 MOTS-c ................................................................................................................. 8 mtDNA Diversity .............................................................................................................. 12 NUMTs ............................................................................................................................ 15 mtDNA mutations and aging ........................................................................................... 17 Mitonuclear Gene Regulation ......................................................................................... 19 MOTS-c Regulation of Nuclear Genes ................................................................ 19 Communication Through Metabolic Intermediates .............................................. 20 Impaired Mitonuclear Communication ................................................................. 21 Cellular Homeostasis Through the Mitochondrial Unfolded Protein Response .. 22 Mechanisms of Exercise and Aging ................................................................................ 25 Beneficial Effects of Exercise .............................................................................. 25 Mitochondria and Exercise .................................................................................. 27 The Role of Skeletal Muscle in Exercise and Metabolism .................................. 28 Skeletal Muscle Structure.................................................................................... 29 Mechanisms associated with Exercise ................................................................ 32 Aging and Model Organisms ........................................................................................... 39 Restrictions of Invertebrate Models ..................................................................... 40 M. musculus as a Model Organism to Study Aging ............................................ 41 Limitations of Mice as Model Organisms ............................................................ 42 Sexual Dimorphism in Aging Biology .............................................................................. 43 Chapter II: Assessment of mouse fitness as determined through treadmill running and walking .................................................................................................................................................... 45 Abstract ........................................................................................................................... 45 Introduction ..................................................................................................................... 45 v Materials ......................................................................................................................... 47 Methods .......................................................................................................................... 48 Notes ............................................................................................................................... 51 Chapter III: MOTS-c is an Exercise-Induced Mitochondrial-Encoded Regulator of Age-Dependent Physical Decline and Muscle Homeostasis ................................................................................ 54 Abstract ........................................................................................................................... 54 Introduction ..................................................................................................................... 55 Results ............................................................................................................................ 56 Discussion ....................................................................................................................... 72 Materials and Methods .................................................................................................... 74 Mouse Care ......................................................................................................... 74 Physical Tests in Mice ......................................................................................... 74 Cognitive Tests .................................................................................................... 76 In vivo Metabolism Assessment .......................................................................... 77 Western Blots ...................................................................................................... 78 Cell Studies ......................................................................................................... 78 Human Studies .................................................................................................... 80 Liquid Chromatography-Mass Spectrometry Metabolomics ............................... 82 RNA-seq .............................................................................................................. 83 Functional Enrichment Analysis .......................................................................... 84 Principal Component Analysis ............................................................................ 85 Quantification and Statistical Analysis ................................................................ 85 Chapter IV: Conclusion and Final Thoughts ............................................................................... 87 MOTS-c improves exercise performance in mice ........................................................... 87 MOTS-c improves metabolic function under stress ........................................................ 91 Supplemental Information ........................................................................................................... 93 References ............................................................................................................................... 121 vi List of Figures Figure 1.1: Mitochondrial DNA. ..................................................................................................... 5 Figure 1.2: MOTS-c within the 12s region of mtDNA. ................................................................. 10 Figure 1.3: Mitochondrial regulation of nuclear-encoded stress response genes. ..................... 25 Figure 1.4: Organization of Skeletal Muscles. ............................................................................ 31 Figure 1.5: Brief Overview of Physiological Changes with Exercise. .......................................... 35 Figure 1.6: mTOR pathway and the hallmarks of aging. ............................................................ 37 Figure 2.1: Treadmill Equipment Overview. ................................................................................ 47 Figure 2.2: High Intensity Running Test Protocol. ...................................................................... 49 Figure 2.3: Set-up of Treadmill and Mice. ................................................................................... 51 Figure 3.1: MOTS-c responds to and regulates exercise in young adults .................................. 56 Figure 3.2: MOTS-c levels in human muscle and plasma. ......................................................... 57 Figure 3.3. Rotarod, grip strength, and Barnes Maze tests in MOTS-c treated old mice. .......... 58 Figure 3.4. Outline of HFD mouse experiments. ........................................................................ 59 Figure 3.5: MOTS-c treatment increases physical capacity in young mice regardless of diet. ... 59 Figure 3.6: Initial running time of MOTS-c-treated young mice. ................................................. 60 Figure 3.7: Body composition analysis on MOTS-c-treated young mice. ................................... 60 Figure 3.8: MOTS-c treatment increases physical capacity in young inbred mice regardless of diet. ............................................................................................................................................. 61 Figure 3.9: Body composition analysis on MOTS-c-treated young mice. ................................... 61 Figure 3.10: Timeline of aged mouse experiment. ..................................................................... 62 Figure 3.11: Acute MOTS-c treatment enhances physical capacity in old mice. ........................ 63 Figure 3.12: Circadian RER and feeding patterns in aged mice ................................................. 63 Figure 3.13: Metabolomic analysis of aged mouse skeletal muscle. .......................................... 64 Figure 3.14: MOTS-c treated old mice metabolomics and performance. ................................... 65 vii Figure 3.15: MOTS-c regulation of physical performance in aged mice. .................................... 66 Figure 3.16: MOTS-c increases lifespan in LLII mice. ................................................................ 66 Figure 3.17: MOTS-c regulates Aging Metabolism. .................................................................... 67 Figure 3.18: Total physical activity in MOTS-c-treated old mice. ................................................ 68 Figure 3.19: MOTS-c-dependent circadian fuel selection old mice. ........................................... 69 Figure 3.20: MOTS-c enhances adaptation to metabolic stress. ................................................ 70 Figure 3.21: MOTS-c-dependent glycolytic rate in lipid-stimulated mouse myoblasts. .............. 71 Figure 3.22: MOTS-c translocation under stress. ....................................................................... 72 Figure 3.23: C2C12 RNA-seq heatmap and protein interactions. .............................................. 73 Figure 4.1: Exercise mimetics in skeletal muscle. ...................................................................... 90 viii List of Tables Table 1.1: Skeletal Muscle Fiber Types ...................................................................................... 30 Table 1.2: Various Model Organisms in the Biology of Aging ..................................................... 40 Table 4.1: Exercise Mimetics ...................................................................................................... 89 Supplemental Table 1: Effects of MOTS-c in various cellular processes ................................... 96 Supplementary Table 2: Aged Mouse Skeletal Muscle Enrichment ........................................... 98 Supplementary Table 3: Aged Mouse Gene Ontology Biological Process (GO_BP) analysis . 101 Supplementary Table 4: C2C12 GO_BP enrichment table ...................................................... 114 Supplementary Table 5: GO_BP enrichment table skeletal muscle vs. C2C12 ....................... 117 ix Abbreviations aKG - Alpha-ketoglutarate, 22 AD - Alzheimer's Disease, 7 AICAR - 5-Aminoimidazole-4-carboxamide ribonucleotide, 34 AMPK - 5' adenosine monophosphate-activated protein kinase, xiv ANT1 - adenine nucleotide translocator 1, 13 AR - androgen receptor, 24 ATFS-1 - activating transcription factor associated with stress 1, 23 ATP - Adenosine triphosphate, xiii BAT - brown adipose tissue, 44 BER - base excision repair, 18 BSA - bovine serum albumin, 78 cDNA - complementary DNA, 16 CKD - chronic kidney disease, 10 CR - caloric restriction, 41 DAMPs - damage-associated molecular patterns, 23 DSBs - double-strand breaks, 17 DWORF - DWarf Open Reading Frame, 34 EB - escape box, 76 FDR - false discovery rate, 64 GH - growth hormone, 12 GO_BP - Gene Ontology Biological Process, 64, 99 GPS2 - G-Protein Pathway Suppressor 2, 23 GR - glucose restriction, 69 x GSEA - Gene Set Enrichment Analysis, 64 H1 and H2 - heavy chain 1 and 2, 4 HDL - high-density lipoproteins, 28 HFD - high-fat diet, xv HRP - horseradish peroxidase, 78 HSF1 - heat shock factor 1, 54 HSP60 - Heat shock protein 60, 24 IAPP - islet amyloid polypeptide, 8 IGF-1 - insulin-like growth factor-1, 12 IGFBP-3 - insulin-like growth factor-binding protein-3, 7 IGT - impaired glucose tolerance, 27 IP - intraperitoneal, xv KEGG - Kyoto Encyclopedia of Genes and Genomes, 85 L - light chain, 4 LLII - late-life initiated intermittent, 65 MDP - mitochondrial-derived peptide, xiv MFRTA - mitochondrial free radical theory of aging, 17 MHC - myosin heavy chain, 29 MI-R - myocardial ischemia-reperfusion, 7 MnSOD - Manganese Superoxide Dismutase, 44 MNX - mitochondrial nuclear exchange, 14 MOTS-c - mitochondrial open reading frame of the 12S rRNA type-c, xiv MRC - mitochondrial respiratory chain, 3, 5 MRPS5 - mitochondrial ribosomal protein S5, 24 MRT - mitochondrial replacement therapy, 13 xi mtDNA - mitochondrial DNA, xiv mTOR - mammalian target of rapamycin, 36 mTORC1 - mammalian target of rapamycin complex 1, 34 mtRNA - mitochondrial RNA, 8 MUL1 - mitochondrial E3 ubiquitin protein ligase 1, 12 NAD - nicotinamide adenine dinucleotide, 20 NHEJ - non-homologous end joining, 17 NMN - nicotinamide mononucleotide, 24 nuDNA - nuclear DNA, 3 NUMT - nuclear mitochondrial DNA, 9 OPG - osteoprotegerin, 10 ORF - open reading frame, 5 OXPHOS - oxidative phosphorylation, 3 PCA - principal component analysis, 70 PDH - pyruvate dehydrogenase, 20 PGC1a - Peroxisome proliferator-activated receptor gamma coactivator 1-alpha, 21 POLGA - DNA polymerase subunit gamma, 4 PPARd - peroxisome proliferator activated receptor delta, 87 PPP - pentose phosphate pathway, 60 PVDF - Polyvinylidene fluoride, 77 RANKL - receptor activator of nuclear factor kappa-Β ligand, 10 rDNA - ribosomal DNA, 9 RER - respiratory exchange ratio, 63 ROS - reactive oxygen species, 7 xii rRNA - ribosomal RNA, 3, 5 S6K - Ribosomal protein S6 kinase beta-1, 36 SASP - senescence-associated secretory phenotype, 38 SD - serum deprivation, 69 SERCA - SR-Ca 2+ -ATPase, 34 SHLP - small humanin-like peptide, 6 siRNA - small interfering RNA, 55 SIRT1 - Sirtuin 1, 10 smORF - small open reading frame, 5 sod - Superoxide dismutase, 18 sORF - short open reading frame, 6 SS-31 - D-Arg-2′6′-dimethylTyr-Lys-Phe-NH2, 34 STAT3 - signal transducer and activator of transcription 3, 38 STRING - Search Tool for the Retrieval of Interacting Genes/Proteins, 71 T2DM - type 2 diabetes mellitus, 8 TFAM - mitochondrial transcription factor A, 5 TGX - tris-glycine gels, 77 tRNA - transfer RNA, 3, 5 TWINKLE - TWINKLE mtDNA helicase, 4 UPR mt - mitochondrial unfolded protein response, 22 VDAC - voltage-dependent anion channel, 9 VO2 max - maximum O2 uptake, 35 xiii Abstract Metabolic alterations underlie the great majority of known hallmarks of aging (Lopez-Otin, Blasco, Partridge, Serrano, & Kroemer, 2013; C. Lopez-Otin, L. Galluzzi, J. M. P. Freije, F. Madeo, & G. Kroemer, 2016b). One organelle central to metabolic function is the mitochondrion. While classically known for providing energy for cells, mitochondria have diverse functions; a prominent function gaining increasing appreciation is mitochondrial communication. Proper mitochondrial function is essential for all organs, but particularly so in metabolically demanding tissues, such as skeletal muscle. Skeletal muscle takes up between 40%-50% of total body mass, positioning it as a key player in maintaining glucose homeostasis (Kohrt & Holloszy, 1995). Certain muscle fibers types are rich in mitochondria and rely on oxidative phosphorylation for adenosine triphosphate (ATP) production, which directly impact muscle performance (Kanzleiter et al., 2014; Picard, Hepple, & Burelle, 2012). Loss of skeletal muscle leads to many adverse health effects in the elderly, including physical competence and metabolism and is one of the strongest indicators of mortality and morbidity (Neufer et al., 2015). Taken together, overall metabolism is a major regulator of aging, and metabolic activity is heavily regulated by mitochondria. These issues are compounded in skeletal muscle, where energy demand is high, and the loss of homeostasis leads to poor health outcomes in the aging population. Physical activity and exercise can delay or reduce age-related metabolic dysfunction and age-related diseases, including cardiovascular disease, musculoskeletal disorders, frailty, and sarcopenia (Booth, Chakravarthy, Gordon, & Spangenburg, 2002; Cartee, Hepple, Bamman, & Zierath, 2016; Graber, Ferguson-Stegall, Liu, & Thompson, 2015; Peterson et al., 2009). Mitochondria are key metabolic organelles that not only energetically support physical activity, but also transmit adaptive regulatory signals to maintain homeostasis (T. L. Merry & Ristow, 2016). Mitochondrial function declines at multiple levels during aging, which is thought to contribute to xiv the age-related decline in physical capacity as well as skeletal muscle mass and regeneration potential (Bernet et al., 2014; Gonzalez-Freire et al., 2015; Joseph et al., 2012; Short et al., 2005). Therefore, targeting mitochondrial regulation may be an effective intervention to retain physical competence during aging and prevent age-associated diseases. Mitochondrial DNA (mtDNA) encodes for small peptides, termed mitochondrial-derived peptides (MDPs), which regulate various cellular functions (C. Lee, Yen, & Cohen, 2013a). The discovery of MDPs expanded our understanding of mitochondrial-centric signaling and regulation. We recently discovered a novel MDP named MOTS-c (mitochondrial open reading frame of the 12S rRNA type-c), that regulates insulin sensitivity and glucose homeostasis (C. Lee et al., 2015). Interestingly, MOTS-c expression is age-dependent and can be detected both in multiple tissues and in circulation. This evidence suggests a dual role as a cell-autonomous factor and hormone (C. Lee et al., 2015; T. L. Merry & Ristow, 2016). MOTS-c can target skeletal muscle and protect against age- and diet-dependent insulin resistance and diet-induced obesity in mice (C. Lee et al., 2015). At the cellular level, MOTS-c promotes homeostasis by translocating to the nucleus and directly regulating adaptive nuclear gene expression in response to metabolic stress; this is the first time a mitochondrial-encoded factor has been shown to actively regulate the nuclear genome (Kim, Son, Benayoun, & Lee, 2018a). Because MOTS-c can activate AMPK signaling and promote metabolic homeostasis through improving glucose utilization in skeletal muscle, it has been speculated to enhance performance or mimic exercise (R. Alis, A. Lucia, J. R. Blesa, & F. Sanchis-Gomar, 2015; Handschin, 2016; Hunter, 2016; S. Li & I. Laher, 2015; T. L. Merry & Ristow, 2016; Thevis & Schanzer, 2016). My work is the first known to show the effect of MOTS-c on physical capacity during aging and its role in regulating healthspan and lifespan. xv To address the question of whether MOTS-c acts as an exercise-induced mitochondrial signal that improves physical capacity we treated young mice (CD-1; outbred) daily with MOTS-c [5mg/kg/day; intraperitoneal injections (IP)] for 2 weeks. We found that MOTS-c treatment improved the exercise capacity as determined by treadmill running, reduced the relative fat mass of mice on a high-fat diet (HFD) and maintained the muscle mass of these mice. While the young CD-1 mice were injected with MOTS-c and received a HFD simultaneously, we wanted to test whether MOTS-c could improve the exercise capacity of mice that had already been on a HFD for several weeks. We fed young C57BL/6J mice a HFD, or a normal diet, for 2 weeks before initiating daily MOTS-c injections (15 mg/kg/day) for 2 weeks prior to a treadmill running test. Similar to the previous trial, MOTS-c treatment improved the running capacity of C57BL/6J mice regardless of the diet. Lastly, I sought to determine if promoting metabolic homeostasis by MOTS-c treatment could reverse age-dependent decline in physical capacity. Middle-aged (12 mo.) and old (22 mo.) C57BL/6N mice were treated daily with MOTS-c (15 mg/kg/day; IP) for 2 weeks, then subjected to a treadmill running test. MOTS-c treated mice showed an improvement in running times, a variety of metabolic parameters, and even an increase in lifespan. In order to determine the mechanism of MOTS-c treatment in mice, we utilized C2C12 cells. We exposed MOTS-c treated cells to a variety of stressors. MOTS-c treatment improved skeletal muscle adaptation to metabolic stress. We found that MOTS-c increases cellular viability, replicative capacity, and metabolic flexibility of these C2C12 mouse myoblasts. xvi In conclusion, my studies indicate that exercise induces mtDNA-encoded MOTS-c expression in humans. MOTS-c treatment significantly improved physical performance in young, middle-aged, and old mice. Thus, it is plausible that the physiological role of exercise-induced MOTS-c is to promote adaptive responses to exercise-related stress conditions (e.g. metabolic imbalance and heat shock) in the skeletal muscle and maintain cellular homeostasis. 1 Chapter I: Introduction The following chapter contains portions adapted from (J. C. Reynolds, Bwiza, & Lee, 2020). Multifaceted Mitochondria Overview of the mitochondria Our cells operate based on two distinct genomes that are enclosed in the nucleus and mitochondria. The mitochondrial genome presumably originates from endosymbiotic bacteria. With time, a large portion of the original genes in the bacterial genome is considered to have been lost or transferred to the nuclear genome, leaving a reduced 16.5Kb circular mtDNA. Traditionally only 37 genes, including 13 proteins, were thought to be encoded within mtDNA, its genetic repertoire is expanding with the identification of mitochondrial-derived peptides MDPs. The biology of aging has been largely unveiled to be regulated by genes that are encoded in the nuclear genome, whereas the mitochondrial genome remained more cryptic. However, recent studies position mitochondria and mtDNA as an important counterpart to the nuclear genome, whereby the 2 organelles constantly regulate each other. Thus, the genomic network that regulates lifespan and/or healthspan is likely constituted by two unique, yet co-evolved, genomes. Here, we will discuss aspects of mitochondrial biology, especially mitochondrial communication, that may add substantial momentum to aging research by accounting for both mitonuclear genomes to more comprehensively and inclusively map the genetic and molecular networks that govern aging and age-related diseases. Mitochondria are frequently labeled “the powerhouse” of the cell, reflecting their role as the primary bioenergetic source, yet their biological functions are remarkably extensive. They are increasingly being appreciated for their role in sensing environmental cues and 2 coordinating/communicating adaptive responses to other cellular compartments, including the nucleus. A wide range of cellular functions are now known to be regulated by mitochondria, including multiple age-related processes, such as metabolism, unfolded protein response, autophagy, and inflammation (Chandel, 2015; S. Hill, Sataranatarajan, & Remmen, 2018; Melber & Haynes, 2018; Quirós, Mottis, & Auwerx, 2016; Sun, Youle, & Finkel, 2016). Mitochondria are cogently thought to originate from endosymbiotic bacteria that emerged as early as 1.5 billion years ago (Martijn, Vosseberg, Guy, Offre, & Ettema, 2018; Quirós et al., 2016; Spang et al., 2015; Sunnucks, Morales, Lamb, Pavlova, & Greening, 2017). Mitochondria have retained many of their inherited prokaryotic properties, including a circular genome (mtDNA) with a unique genetic code, formylation of mitochondrial proteins, and binary fission and fusion. The modern mtDNA is estimated to be considerably reduced from the primeval bacterial genome, resulting from loss and lateral transfer to the nuclear genome. The survival advantage of maintaining two genomes is unclear, but the co-evolution of mitochondrial and nuclear (mitonuclear) genomes likely required, and likely still requires, continuous adaptation to each other to establish a unified singular genetic system. During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, providing a rich knowledge base (Longo et al., 2015; Lopez-Otin et al., 2013; C. Lopez-Otin, L. Galluzzi, J. M. Freije, F. Madeo, & G. Kroemer, 2016a; P. P. Singh, Demmitt, Nath, & Brunet, 2019). However, the focus on longevity pathways has been nuclear-centric and all known longevity genes are nuclear-encoded. Here, I will discuss key aspects of the mitochondrial genome and mitonuclear communication, which may add additional 3 momentum to aging research by accounting for both genomes to more comprehensively and inclusively map the genetic and molecular networks that govern lifespan and/or healthspan. Mitochondria: Origin and Genome Mitochondria presumably originate from endosymbiotic alpha-proteobacteria (Sagan, 1967) and continue to possess multiple prokaryotic remnants including their own unique circular genome and genetic code. While the specific evolutionary origin of mitochondria remains debatable, the integration of two free-living organisms likely required dynamic communication and coordination (Lane, 2017; Youle, 2019). The mtDNA and the nuclear DNA (nuDNA) conceivably have been evolving for over 1.5 billion years. It is estimated that a considerable amount of the original bacterial genome has been lost or transferred to the nuclear genome (Bock, 2017). Notably, mitochondria-to-nucleus gene transfer still occurs in modern eukaryotic cells (Ju et al., 2015). The mitochondrial genome has been traditionally described to encode for 37 genes; 13 proteins, 2 ribosomal RNAs (rRNAs), and 22 transfer RNAs (tRNAs) (Figure 1.1). All 13 proteins are components of the mitochondrial respiratory chain (MRC). For example, of the five complexes involved in oxidative phosphorylation (OXPHOS), complex I is composed of 45 polypeptides, of which seven are encoded in the mtDNA (Wallace, 2010). Only complex II is entirely assembled from nuclear-encoded subunits. The stoichiometry of the MRC subunits is critical for OXPHOS (Milenkovic, Blaza, Larsson, & Hirst, 2017) and the mitochondrial and cytosolic translation of the MRC components are tightly coordinated (Couvillion, Soto, Shipkovenska, & Churchman, 2016). Mitochondrial-encoded rRNAs and tRNAs are exclusive to mitochondrial translation; nuclear- encoded tRNAs are imported into mitochondria in a species-specific manner (Rubio et al., 2008; Salinas-Giegé, Giegé, & Giegé, 2015; Schneider, 2011). 4 The regulation of the mitochondrial genome also reflects its prokaryotic ancestry. While nuclear DNA undergoes replication during cell division, mtDNA replication occurs independently of cell cycle. The majority of the components for mtDNA replication are imported nuclear-encoded proteins, including the catalytic subunit of mtDNA polymerase (POLGA), and the replicative mitochondrial helicase (TWINKLE) (Cermakian, Ikeda, Cedergren, & Gray, 1996; Tyynismaa et al., 2004). Both the heavy and light strands of mtDNA contain genes, which are transcribed from three promoters; two are on the heavy chain (H1 and H2) and one is on the light chain (L) (Mercer et al., 2011; Shokolenko & Alexeyev, 2017). A single subunit RNA polymerase transcribes mitochondrial genes, while translation requires mitochondrial-specific ribosomes using a distinct genetic code (Faye & Sor, 1977; Kelly & Lehman, 1986; Masters, Stohl, & Clayton, 1987; Ringel et al., 2011). The H2 and L promoters transcribe almost the entire mitochondrial genome as a single polycistronic transcript. Genes in mtDNA lack introns and levels of unprocessed transcripts are low, indicating highly active co-transcriptional processing (Gustafsson, Falkenberg, & Larsson, 2016). Notably, mitochondrial genes are typically flanked by tRNAs, which are then cut to produce individual transcripts (Anderson et al., 1981; Falkenberg, Larsson, & Gustafsson, 2007; Mercer et al., 2011; Ojala, Montoya, & Attardi, 1981). Interestingly, the mitochondrial transcription machinery is considered to have originated from the endosymbiotic alpha- proteobacteria that eventually became replaced with bacteriophage-derived factors (Gustafsson et al., 2016; Shutt & Gray, 2006). 5 Figure 1.1: Mitochondrial DNA. The mtDNA houses 37 genes; 13 proteins, 2 ribosomal RNAs (rRNAs), and 22 transfer RNAs (tRNAs). All 13 proteins are components of the mitochondrial respiratory chain (MRC). All known MDPs currently are found in the 16s rRNA (Humanin and SHLPs) and the 12s rRNA (MOTS-c). Adapted from (Spelbrink, 2010). The mitochondrial genome is grouped and packaged in a nucleoid, which consists of DNA- binding proteins. Nucleoid architecture plays an important role in maintenance and transcription (Gilkerson et al., 2013; Kanki et al., 2004; Mercer et al., 2011), in which the mitochondrial transcription factor A (TFAM) is considered a key structural component (Kaufman et al., 2007). The overexpression and reduction of TFAM both affect mtDNA compaction level and interfere with mitochondrial function (Ekstrand et al., 2004). Additionally, TFAM deficiency has been shown to enhance nuclear DNA repair under chronic genotoxic stress by inducing a protective signaling response (Zheng Wu et al., 2019). Mitochondrial-Derived Peptides The human genome project annotated genes that encode for proteins generally >100 amino acids (International Human Genome Sequencing, 2004), leaving shorter peptides largely unknown. More recently, peptides that are encoded as short open reading frames [ORFs; a.k.a small ORFs (smORFs)] have been increasingly identified in the nuclear and mitochondrial genomes (Ingolia et al., 2014; Saghatelian & Couso, 2015). Such polycistronic genes (i.e. genes- within-genes) have been traditionally thought to exist in prokaryotes to allow genomic compaction 6 (B. A. P. Williams, Slamovits, Patron, Fast, & Keeling, 2005). Additionally, recent technological innovations in computational biology, sequencing, and proteomics have revealed a much larger portion of the genome that is transcribed and translated than was originally understood (Andrews & Rothnagel, 2014; Rothnagel & Menschaert, 2018; Ruiz-Orera & Albà, 2019; Saghatelian & Couso, 2015). In fact, accumulating evidence indicates that transcripts that harbor sORFs have been erroneously annotated as non-coding (Bazzini et al., 2014; Deng, Bamigbade, Hammad, Xu, & Liu, 2018; Galindo, Pueyo, Fouix, Bishop, & Couso, 2007; Ji, Song, Regev, & Struhl, 2015; Kondo et al., 2007; Kondo et al., 2010; Magny et al., 2013; Makarewich & Olson, 2017; Nicholas et al., 2014; Yeasmin, Yada, & Akimitsu, 2018). It is estimated that thousands of nuclear-encoded sORFs that yield bioactive peptides exist, enriching our proteome considerably (Raj et al., 2016). Several sORFs have been functionally described with diverse biological roles, including development (Chanut-Delalande et al., 2014; Kondo et al., 2010), DNA repair (Slavoff, Heo, Budnik, Hanakahi, & Saghatelian, 2014), muscle function (Bi et al., 2017), and immunity (Jackson et al., 2018). Mitochondria have been known to code for 13 mRNAs, which are all components of the oxidative phosphorylation complexes, 22 tRNAs, and 2 rRNAs. However, recent studies have shown that mtDNA also encodes for previously unknown sORFs that yield biologically active peptides, collectively referred to as MDPs (Cobb et al., 2016; Hashimoto, Ito, et al., 2001; Hashimoto, Niikura, et al., 2001; C. Lee et al., 2013a; C. Lee et al., 2015). Currently, there are eight distinct MDPs that have been published (S. Hill et al., 2018): humanin (B. Guo et al., 2003; Hashimoto, Niikura, et al., 2001; Ikonen et al., 2003), SHLP1-6 (small humanin-like peptide 1-6) (Cobb et al., 2016), and MOTS-c (C. Lee et al., 2015). 7 Humanin The first MDP to have been detected at the protein level with functional description is humanin, which is a 24-amino acid peptide encoded within the 16S rDNA of mitochondria (Hashimoto, Ito, et al., 2001; Hashimoto, Niikura, et al., 2001). Humanin has cytoprotective roles, including (i) enhanced resistance against Alzheimer’s disease (AD)-related toxins (e.g. β-amyloid) (Hashimoto, Niikura, et al., 2001), (ii) anti-apoptotic effects by directly inhibiting BAX (B. Guo et al., 2003) and by downregulating p38 MAP kinase (D. Wang et al., 2005), and (iii) by binding to insulin-like growth factor-binding protein-3 (IGFBP-3) and improving cell survival (Ikonen et al., 2003). Notably, humanin can be negatively regulated by IGF-1 and is positively correlated with longevity in mice and humans (C. Lee et al., 2014). Humanin also plays a protective role in several pathological conditions. Humanin has been shown to be cardioprotective against myocardial ischemia-reperfusion (MI-R) injury by AMPK- endothelial nitric oxide synthase-mediated signaling and regulation of apoptotic factors (Muzumdar et al., 2010), reducing oxidative stress and promoting mitochondrial structural integrity (Klein, Cui, Gong, Su, & Muzumdar, 2013), and reducing mitochondrial ROS levels and oxidative stress by targeting complex I (Thummasorn et al., 2016; Thummasorn et al., 2018). Reduced age- related myocardial fibrosis was observed in 18-month old female mice that were treated with a humanin analog (HNG; 4 mg/kg, 2x/week, intraperitoneal injections) for 14 months (Qing Qin et al., 2018). Further, humanin protected human aortic endothelial cells against oxidized LDL- induced oxidative stress (Bachar et al., 2010), and preserved endothelial function in hypercholesterolemic ApoE-deficient mice (Oh et al., 2011). Notably, in humans, circulating humanin levels were negatively correlated with coronary endothelial function (Widmer et al., 2013), whereas higher levels of humanin were detected in unstable carotid plaques (Zacharias et al., 2012). Humanin and its derivative (S14G-humanin; HNG)(Hashimoto, Ito, et al., 2001; 8 Hashimoto, Niikura, et al., 2001) also showed beneficial effects in neurodegenerative disease models, such as protection against scopolamine-induced learning and memory impairment in mice (Mamiya & Ukai, 2001), β-amyloid-induced hippocampal long-term potentiation in rats (F. Guo et al., 2010) and mice (W. Zhang et al., 2009), and β-amyloid-induced memory impairment in mice (Tajima et al., 2005). Small Humanin-Like Peptides Following the discovery of humanin, additional sORFs within the 16S rDNA have been identified and termed SHLPs (Small Humanin-Like Peptides). There are 6 SHLPs (SHLP1-6), which have unique and redundant biological effects, including cellular proliferation, apoptosis, and mitochondrial metabolism (Cobb et al., 2016). For example, SHLP2 and SHLP3 have antiapoptotic effects and promote cellular survival, while SLPH6 induces apoptosis in both murine beta-cells and prostate cancer cells (Cobb et al., 2016). Interestingly, humanin and SHLP2 have chaperone activities that can prevent the misfolding of islet amyloid polypeptide (IAPP), a pathogenic process in the development of type 2 diabetes mellitus (T2DM) (Okada et al., 2017). In addition, lower levels of circulating SHLP2 was associated with an increased risk for prostate cancer in white, but not in black, men (Xiao et al., 2017). MOTS-c Mitochondria have 2 rRNA genes (12S and 16S rRNA). Whereas humanin and the 6 SHLPs are encoded within the 16S rRNA, another MDP was identified from the 12S rRNA (Figure 1.2). MOTS-c is a 16 amino acid peptide that is expressed in multiple tissues and in circulation, indicating dual roles as an intracellular and endocrine factor (C. Lee et al., 2015), a characteristic shared with humanin and SHLP1-6. Notably, MOTS-c expression is lost in cells with selective depletion of mtDNA (using low-dose chronic ethidium bromide; HeLa-ρ0) or mtRNA (using actinonin) without affecting the nuclear counterparts (C. Lee et al., 2015). In fact, the discovery of 9 MOTS-c was inspired by the discovery that over 75% of mRNAs induced upon interferon activation in human myeloblasts mapped back to mitochondrial 12S and 16S rDNA loci (the paper didn’t identify specific genes) (Tsuzuki et al., 1983). Notably, NUMTs that are identical to the mtDNA-encoded MOTS-c sequence are not found, although there are few similar sequences. This is consistent with the NCBI database, whereby only the mtDNA sequence for MOTS-c, and none of the NUMTs, have been recorded as mRNAs (C. Lee et al., 2015). Further, small mtRNAs (annotated as non-coding) exclusively map to mtDNA sequences, rather than NUMTs (Mercer et al., 2011; Pozzi & Dowling, 2019). Whereas tissue-specific abundance of small mtRNA levels is strongly associated with mtDNA content, no association was observed with NUMT levels across 6 vertebrate species (Pozzi & Dowling, 2019). MOTS-c expression requires cytosolic ribosomes because translation using the mitochondrial genetic code would lead to tandem start and stop codons (C. Lee et al., 2015). Although the specific mechanisms of mitochondrial nucleotide export are unknown, VDAC oligomers can form pores to secrete mtDNA fragments (J. Kim et al., 2019), which have been increasingly appreciated as an adaptive mitochondrial stress response (Ingelsson et al., 2018; Trumpff et al., 2019; Yousefi et al., 2008). Upon leaving the mitochondria, it is plausible that the transcript may be translated using mitochondria-associated cytoplasmic ribosomes (C. C. Williams, Jan, & Weissman, 2014), thereby conferring mitochondrial specificity. Even then, it is possible that some NUMTs could encode for peptides, or express regulatory RNA, which would add another layer to the understanding of the evolution of our genomes as the origin of such sequences would still be mitochondrial. 10 Emerging studies continuously unveil the functions of MOTS-c in a wide range of pathophysiological processes as summarized in Supplementary Table 1. MOTS- c regulates cellular metabolic homeostasis by coordinating cellular glucose, fat, and protein metabolism. The key metabolic regulators AMPK and SIRT1 are required for several functions of MOTS-c, including metabolism (K. H. Kim et al., 2018a; Kim, Son, Benayoun, & Lee, 2018b; C. Lee et al., 2015; H. Lu et al., 2019b; Ming et al., 2016; Yan et al., 2019). In mice, MOTS-c has been shown to (i) enhance insulin sensitivity, largely by targeting skeletal muscle glucose metabolism (C. Lee et al., 2015), (ii) promote white fat browning and brown fat activation in ovariectomized mice and mice exposed to cold (Huanyu Lu et al., 2019; H. Lu et al., 2019b), (iii) reduce fat mass, plasma lipid, and adipocyte size, while enhancing the lipid catabolism in ovariectomized mice (H. Lu et al., 2019b), in part, by increasing mitochondrial β-oxidation (C. Lee et al., 2015), (iv) alleviate ovariectomy- induced bone loss by inhibiting RANKL-induced osteoclast formation (Ming et al., 2016) and osteoclastogenesis through osteocyte OPG/RANKL secretion (Yan et al., 2019). In humans, MOTS-c has been implicated in different metabolic syndromes and diseases, including diabetes, cardiovascular diseases, and chronic kidney disease (CKD). Circulating MOTS-c levels were reported to be lower in obese male children and adolescents, especially in those who were insulin-resistant (Du et al., 2018). However, in adults, plasma MOTS-c levels were similar in both lean and obese subjects, but a positive correlation to insulin resistance was observed in lean subjects (Luis Rodrigo Cataldo, Fernández-Verdejo, Santos, & Galgani, 2018). Figure 1.2: MOTS- c within the 12s region of mtDNA. MOTS-c lies within the 12s rRNA. It is a 16 amino acid peptide found in various tissues as well as in circulation. Adapted from (Yen, Lee, Mehta, & Cohen, 2013). 11 These data suggest that MOTS-c levels change dynamically in a context-specific manner. Further, it is unclear if the levels of MOTS-c reflect a mechanistic contribution to the metabolic dysfunction, or a positive response to such metabolic perturbations. The role of MOTS-c in fat metabolism (C. Lee et al., 2015; Huanyu Lu et al., 2019; H. Lu et al., 2019b) and its implication in obesity (C. Lee et al., 2015) are crucial since it is known that obesity is a risk factor for cardiovascular diseases (Eckel & Krauss, 1998; Hubert, Feinleib, McNamara, & Castelli, 1983). Moreover, adult patients with type 2 diabetes (Ramanjaneya, Bettahi, et al., 2019) showed reduced serum MOTS-c levels. Also, adult subjects with CKD, in which diabetes and cardiovascular diseases are major risk factors, patients exhibited a decrease in MOTS-c levels in both serum and skeletal muscle (Liu et al., 2019). Mitochondria dynamically communicate to other organelles, including the nucleus, to coordinate a myriad of vital cellular functions (Mottis, Herzig, & Auwerx, 2019; Quirós et al., 2016). Mitonuclear communication is especially interesting because it engages 2 organelles that hold independent genomes. However, traditionally, all known gene-encoded regulators of the mitonuclear genomes have been known to be nuclear-encoded. MOTS-c translocates to the nucleus in response to cellular stress in an AMPK-dependent manner to directly regulate adaptive nuclear gene expression by interacting with DNA and transcription factors (K. H. Kim et al., 2018b). MOTS-c provides evidence for cross-genomic regulation and extends the possibilities underlying the preservation of an independent mitochondrial genome. Several of these mitochondrial-derived peptides may play a role in aging. SHLP2, humanin and MOTS-c all are positively correlated with longevity, and their levels decline with age in certain tissues (Cobb et al., 2016; S. J. Kim, J. Xiao, J. Wan, P. Cohen, & K. Yen, 2017; C. Lee et al., 2015; Muzumdar et al., 2009). In both mice and humans, humanin is regulated through the 12 GH/IGF-1 axis, which is a major conserved longevity pathway (C. Lee et al., 2014; Tatar, Bartke, & Antebi, 2003). Humanin levels are lower in the short-lived GH-transgenic mice, yet higher in the long-lived GH-deficient mice (C. Lee et al., 2014). Interestingly, a MOTS-c polymorphism found in a Japanese population is related to exceptional longevity (Fuku et al., 2015; Zempo et al., 2016a). At the functional level, MOTS-c can reverse age-dependent insulin resistance in mice (C. Lee et al., 2015). The effect of MOTS-c on cellular metabolism is mediated, in part, by AMPK and SIRT1, which are key regulators of lifespan (Canto et al., 2009; Price et al., 2012). mtDNA Diversity Unlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage. It is unclear what advantage a uniparental mtDNA transmission confers, but one possibility is to minimize the number of distinct genomes to maximize the efficiency of a multi-genomic system (G. E. Hill et al., 2019). In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca & O'Farrell, 2012; Rojansky, Cha, & Chan, 2016). Paternal mitochondria from sperms that enter into the egg during fertilization are actively and selectively eliminated via mitophagy through two E3 ligases, PARKIN and MUL1 (Rojansky et al., 2016). PARKIN and MUL1 serve redundant purposes, and mitophagy becomes insufficient to eliminate paternal mtDNA only in the absence of both (Rojansky et al., 2016). Even though oocytes have at least a thousand-fold more mitochondria than a sperm cell (Rojansky et al., 2016) and heteroplasmy levels would be very low if paternal mtDNA were to contaminate the embryo, the results can still be non-trivial. However, challenging this notion, a recent study provides evidence of potential paternal transmission (Luo et al., 2018), but awaits further corroborating studies (Lutz-Bonengel & Parson, 2019). 13 MtDNA has a considerable impact on the regulation of nuclear genes (Kimberly J. Dunham-Snary et al., 2018; Jessica L. Fetterman & Ballinger, 2019; Kopinski et al., 2019; Morava, Kozicz, & Wallace, 2019; Mossman, Biancani, & Rand, 2019; Mottis et al., 2019; Quirós et al., 2016). MtDNA diversity is thought to influence the penetrance and phenotypic expression of pathogenic genetic variants, even within a given family (Morava et al., 2019). For example, a homozygous mutation (c.523delC) in the adenine nucleotide translocator 1 gene (SLC25A4, ANT1) can lead to cardiomyopathy with variable pathological degrees depending on the mtDNA lineage (McManus et al., 2019). Mitochondrial genotype also influences metabolic and epigenomic processes, thereby may underlie phenotypic variability of diseases (Kopinski et al., 2019). Further, mice with artificially matched mitonuclear genomes can exhibit altered physiology, including fertility, metabolism, and gene expression (Dobler, Dowling, Morrow, & Reinhardt, 2018). Based on these studies, compatibility between the mitochondrial and nuclear genomes is a key determining factor in organismal fitness. On this line, mitochondrial replacement therapy (MRT) is a specific form of human gene editing where a mother with known pathological mtDNA can replace her mitochondria with that from another woman. Thus, the baby will have 3 biological parents that each contributed half of the nuclear genome or the entire mitochondrial genome, often referred to as a “three-parent baby”. Combining these genomes would introduce novel mitonuclear combinations that have not undergone natural selection and may increase the risk of developing diseases, especially with age (DeLuca & O'Farrell, 2012; Dobler et al., 2018; G. E. Hill et al., 2019; Reinhardt, Dowling, & Morrow, 2013). In flies, artificial disruption of mitonuclear epistasis, by generating mutations in the mitochondrial tRNA tyr and its nuclear-encoded mitochondrial tyrosine synthetase, resulted in decreased oxygen consumption, higher mtDNA copy number, higher hydrogen peroxide production, and aggravated age-dependent mitochondrial dysfunction (Pichaud et al., 2019). 14 Notably, humanin has pleiotropic effects on mtDNA copy number (Kariya, Takahashi, Hirano, & Ueno, 2003; Sreekumar et al., 2016), suggesting a dynamic regulatory role in mitochondrial function and cellular health (Clay Montier, Deng, & Bai, 2009; Fazzini et al., 2018). In mice, cross- pairing mitonuclear genomes derived from different strains [mitochondrial nuclear exchange (MNX) mice], shifts cellular metabolism, oxidative stress levels, resistance to cardiac damage, and atherogenic diet (Betancourt et al., 2014; K. J. Dunham-Snary & Ballinger, 2015; J. L. Fetterman et al., 2013). Mitonuclear interactions associated with components of the MRC can influence function and aging itself in a sex-dependent manner (Immonen, Collet, Goenaga, & Arnqvist, 2016). Mitonuclear genomic compatibility may clinically manifest at different stages of life and have a considerable impact on aging and age-related disease. MtDNA exhibit a higher mutation rate than nuclear DNA, leading to significant population- level mtDNA polymorphisms (van Oven & Kayser, 2009; Wallace, 1999; Wallace & Chalkia, 2013). In fact, the co-evolution of the mitonuclear genomes has been proposed to be driven by mtDNA mutations that select for compensatory changes in the nuclear genome (Havird & Sloan, 2016). Populations that share similar mtDNA polymorphisms can be clustered into distinct haplogroups that are designated using all letters of the alphabet (i.e. A through Z). The mtDNA haplogroups represent major branch points on the mitochondrial phylogenetic tree that have strong regional ties around the globe, thus supporting the concept of a ‘mitochondrial eve’ (Wallace, 1999). Haplogroups present inherently different mitonuclear interactions (Zaidi & Makova, 2019), which eventually affect the aging process (Wolff et al., 2016). For example, one haplogroup commonly found in Ashkenazi Jews can interact with a specific enrichment of an amino acid sequence in complex I, and result in altered susceptibility to type 2 diabetes mellitus (Gershoni et al., 2014). The effect of mitonuclear compatibility on lifespan is influenced by environmental cues in flies (Drummond, Short, & Clancy, 2019). It is unclear if mitonuclear 15 compatibility is invariable throughout an organism’s life, or antagonistically pleiotropic during aging, making it a difficult moving target to understand. NUMTs The original genome of the endosymbiotic bacteria has been considered to be lost or transferred to the nuclear genome, leading to the current abridged mtDNA (Johnston & Williams, 2016). Proto-mitochondrial DNA sequences that have laterally transferred to the nuclear genome are known as NUMTs (nuclear mitochondrial DNA segment) (Lopez, Yuhki, Masuda, Modi, & O'Brien, 1994; Timmis, Ayliffe, Huang, & Martin, 2004). Further, long and short stretches of the mitochondrial genome are found to be copied into the nuclear DNA, albeit the sequences being degenerate. While the full comprehension of the number of NUMTs in eukaryotes is unknown, current sequencing technology is sufficient to understand NUMT evolution and comparative analyses across species. Interestingly, one study used phylogenetic analysis of NUMTs to show that primates had a greater occurrence of NUMTs than non-primates, and that the clusterizations of these primate NUMTs were intermingled, while non-primate NUMTs were separated by species (Calabrese et al., 2017). Given the relative mutation rates of mitochondrial vs nuclear DNA, NUMTs serve as a “molecular fossil”, and can be used to estimate the time of integration (Perna & Kocher, 1996). While there are certain periods of rapid NUMT integration, insertion appears to have been continuous over time leading to the current human genome (Bensasson, Feldman, & Petrov, 2003; Calabrese et al., 2017; Hazkani-Covo, Zeller, & Martin, 2010). Notably, mtDNA sequences are still continuously being integrated into the nuclear genome (Ju et al., 2015; Ricchetti, Tekaia, & Dujon, 2004; Srinivasainagendra et al., 2017). The integration of NUMTs into the nuclear genome can lead to problems. While most NUMTs are benign polymorphisms, there are a small number of human diseases associated with 16 NUMTs. The majority of these cases involve the insertion of the NUMT into a nuclear-encoded gene that disrupts proper function (Ahmed et al., 2002; Goldin et al., 2004; Turner et al., 2003). In each of these cases, the nuclear genome is compromised while the mtDNA is intact. Discovering these diseases pose additional challenges. Since NUMTs are of mitochondrial origin, it is difficult to discern mtDNA from nuclear DNA in common methods. When identifying mutations, it becomes easy to confuse nuclear mutations for the much more volatile mitochondrial mutations (Hazkani-Covo et al., 2010). Beside these insertion diseases, there is growing evidence involving NUMTs in cancer (K. K. Singh, Choudhury, & Tiwari, 2017). In one of the first reports on this issue, NUMTs that were nearly the size of the entire mitochondria genome were found in cancer cells (Ju et al., 2015). Another study found that colorectal tumor DNA had roughly four times the number of NUMTs compared to DNA taken from blood cells in the same individual (Srinivasainagendra et al., 2017). Given the emerging role of NUMTs in human diseases including cancer, combined with the increasing ease of sequencing, further findings on the role of NUMTs in disease and evolution are likely around the corner. Understanding the effects of NUMTs in human pathology involves understanding the mechanisms of their integration into the nuclear genome. This process involves mtDNA exiting the mitochondria, entering the nucleus, and recombination into the nuclear genome. While there is debate as to the frequency of NUMT integration, the frequency of mtDNA transfer to the nucleus is estimated to be 2 x 10 -5 per cell per generation (Thorsness & Fox, 1990). Furthermore, integration frequency may be that one cell in every 1,000-10,000 yeast cells may harbor a new mitochondrial insertion. NUMTs found in the human nuclear genome contain large fragments of non-coding regions of the mtDNA (Huang, Grunheit, Ahmadinejad, Timmis, & Martin, 2005). This data indicates that it is not cDNA or transcripts that integrate into the nuclear genome, but rather large unedited portions of mtDNA. NUMTs in humans are integrated into the genome through 17 double-strand breaks (DSBs), combined via non-homologous end joining (NHEJ) (Ricchetti et al., 2004). Interestingly, unlike normal NHEJ events, repair involving NUMTs rarely cause deletions, and these deletions are small when the do occur (Hazkani-Covo & Covo, 2008). Therefore, there is a trade-off between larger deletions to repair DSBs or utilizing mtDNA in the repair process in the form of NUMTs. Deletions may be catastrophic for cells, and insertion of NUMTs, while implicated in disease, may be preferential to the survival of the cell and organism. The number of NUMTs in the genome is small enough to indicate NUMTs are not utilized significantly to stabilize genomic integrity, but no other type of DNA fragments have been found that heal DSBs in a similar manner (Hazkani-Covo et al., 2010). This offers an intriguing role of NUMTs in evolution beyond the ability to regulate OXPHOS components through concerted mitonuclear communication. However, small mtRNA levels are not associated with NUMT abundance across 6 vertebrate species, but are rather strongly associated with mtDNA content in a tissue-specific manner within species (Pozzi & Dowling, 2019). mtDNA mutations and aging One of the major components of mitonuclear communication comes as a direct byproduct of OXPHOS activity. Electrons can leak from the MRC and combine with surrounding oxygen molecules to create free radicals and ROS (Adam-Vizi, 2005; Boveris & Chance, 1973). These molecules can damage cell components such as protein, lipids, and DNA. Given the high production of ROS in the mitochondria, mtDNA was considered to be particularly susceptible to this damage (Harman, 1956). For nearly 50 years, this idea led many to believe that free radicals were largely responsible for mtDNA damage and, consequently, a major driver of the aging process. This became known as the mitochondrial free radical theory of aging (MFRTA) (Harman, 1956, 2009). However, the effect of antioxidants on longevity has largely been inconclusive (Pomatto & Davies, 2018). Only a handful of studies that inactivated various antioxidant systems 18 in model organisms shortened lifespan. These include sod1 and sod2 in yeast (Longo, Gralla, & Valentine, 1996; Unlu & Koc, 2007), various sod isoforms in worms (Doonan et al., 2008), sod1 and sod2 in flies (Martin et al., 2009; Wicks, Bain, Duttaroy, Hilliker, & Phillips, 2009), and sod1 in mice (Y. Zhang et al., 2017). Conversely, the overexpression of these same genes can increase lifespan in these species (Fabrizio et al., 2003; Melov et al., 2000; Y. Zhang et al., 2016). MFRTA was further reinforced by the fact that mitochondrial repair mechanisms were inferior to their nuclear counterparts, making mtDNA more vulnerable to ROS-induced DNA mutations (Yakes & Van Houten, 1997). ROS causes base modifications (hydroxylation) that are effectively fixed by base excision repair (BER) mechanisms. Unlike previously thought, mitochondria are proficient in BER and can effectively repair oxidative mtDNA lesions (Bohr, Stevnsner, & De Souza-Pinto, 2002). Further, mtDNA quality is controlled and maintained through numerous mechanisms including mitochondrial fission and fusion (Chen et al., 2010; Prevost et al., 2018), mitophagy (Pickles, Vigie, & Youle, 2018), distance from MRCs (Cogliati, Enriquez, & Scorrano, 2016; Cogliati et al., 2013; Kopek, Shtengel, Xu, Clayton, & Hess, 2012), and physical shielding the mtDNA through clustering in nucleoids (S. R. Lee & Han, 2017). Even with these levels of mtDNA protection, mtDNA mutation frequency increase with age in animal models and humans alike (Cortopassi & Arnheim, 1990; Larsson, 2010), although the role of mtDNA mutations remains unclear (Khrapko & Vijg, 2009; Pohjoismaki, Forslund, Goffart, Torregrosa-Munumer, & Wanrooij, 2018; Theurey & Pizzo, 2018). However, recent reports have shown that mtDNA point mutations in aged tissues largely arise from replication infidelity (i.e. DNA polymerase errors), rather than ROS-induced damage (Ameur et al., 2011; S. R. Kennedy, Salk, Schmitt, & Loeb, 2013; Vermulst et al., 2007). To test if replicative infidelity causes aging, mice with mutant mitochondrial DNA polymerase g that are deficient in proofreading during DNA replication, causing supraphysiological mutation loads (roughly 2,500-fold in the homozygous 19 polg mut/mut compared to 500-fold higher in the polg +/mut ), were examined (Vermulst et al., 2007). While the homozygous mice (polg mut/mut ) showed signs of accelerated aging phenotypes and significantly reduced lifespan, the heterozygous mice (polg +/mut ) had a normal lifespan albeit exhibiting premature aging phenotypes (Trifunovic et al., 2004). One plausible explanation for this discrepancy lies with increased mtDNA deletions in the homozygous mice (polg mut/mut ) (Vermulst et al., 2007; Vermulst et al., 2008). These cumulative results suggest that the connections between oxidative stress, mtDNA mutations, and aging are more complicated than originally appreciated and require further investigation to fully understand their relation (Pomatto & Davies, 2018). It is evident, however, that the mtDNA mutations are linked to more than 300 diseases connected to aging, including Alzheimer’s Disease, and that proper communication between the mitochondria and the nucleus plays a key role (DeBalsi, Hoff, & Copeland, 2017; Grazina et al., 2006; Lane, 2011; Onyango et al., 2006; Quirós et al., 2016; Swerdlow et al., 2017). Mitonuclear Gene Regulation MOTS-c Regulation of Nuclear Genes Human cells are based on a bi-genomic system that compartmentalizes each genome in the nucleus and mitochondria. Historically, the nuclear genome was considered to encode for regulators of gene expression for both mitonuclear genomes, whereas mtDNA exclusively encoded for respiratory machinery subunits. However, we recently reported that the mitochondrial-encoded MOTS-c peptide can translocate to the nucleus and directly regulate adaptive nuclear gene expression in response to metabolic stress (K. H. Kim et al., 2018b; Mangalhara & Shadel, 2018; Wong, 2018; Yong & Tang, 2018) (Figure 1.3). The stress-induced nuclear translocation of MOTS-c occurred rapidly (<30 minutes) and dynamically and required the co-activation of AMPK. MOTS-c can bind DNA and interact with major stress-responsive 20 transcription factors, including Nrf2 and ATF1. A broad range of genes were regulated by MOTS- c under glucose restricted conditions, especially including those related to interferon pathways. Ultimately, the overexpression MOTS-c increased its nuclear presence and significantly protected HEK293 cells from glucose and serum starvation. This study suggests the existence of additional mitochondrial-encoded regulators of nuclear gene expression, where MDPs are prime candidates, especially considering that the mitonuclear genomes co-evolved for over 1.5 billion years as a unified and integrated genetic system. Communication Through Metabolic Intermediates Mitochondria can also communicate to the nucleus using metabolic intermediates, largely products of the Krebs cycle, that serve as substrates for key regulators of nuclear gene expression. Acetyl-CoA is produced by pyruvate dehydrogenase (PDH), a complex normally residing in the mitochondria (Menzies, Zhang, Katsyuba, & Auwerx, 2016). PDH can also translocate to the nucleus and produce acetyl-CoA in situ. Acetyl-CoA levels are higher in the nucleus and cytosol under growth conditions, where it is used for histone acetylation and lipid synthesis. Conversely, under low-nutrient conditions, mitochondrial acetyl-CoA levels increase to drive ATP production (L. Shi & Tu, 2015; Sutendra et al., 2014). Other metabolites serve similar functions in regulating genetic and epigenetic reprogramming, including oxaloacetate, fumarate, a-ketoglutarate, and malate (B. A. Benayoun, Pollina, & Brunet, 2015). NAD + is another mitochondrial metabolite involved in mitonuclear communication through its central role in ATP production (Karpac & Jasper, 2013; Mouchiroud et al., 2013). Reduced NAD + activity is related to lower levels of deacetylase sirtuin activity, which impacts communication between the nucleus and mitochondria (Imai & Guarente, 2016). Additionally, NAD + levels decline with age, and the resulting decrease in mitonuclear communication results in reduced longevity (Mouchiroud et al., 2013; Yoshino, Mills, Yoon, & Imai, 2011). 21 Mitochondrial ATP and ROS levels also act as signaling molecules that relay metabolic cues to the nucleus. Reduced ATP synthesis can stimulate AMPK, which in turn activates PGC1a, which then serves to increase mitochondrial energy metabolism and biogenesis (Garcia-Roves, Osler, Holmstrom, & Zierath, 2008; Quirós et al., 2016). Activation of the AMPK pathway also induces the mitochondrial quality control system and mitophagy (D. F. Egan et al., 2011). ROS levels act as a surrogate gauge of mitochondrial respiration activity and efficiency (Murphy, 2009). While ROS is often associated with macromolecule damage at higher concentrations, they are key signaling molecules under physiological levels (Sena & Chandel, 2012). For instance, antioxidant supplementation can reduce organismal fitness and lifespan by inducing an adaptive stress response (Ristow & Schmeisser, 2014; Ristow & Schmeisser, 2011) and dampen skeletal muscle adaptation to exercise training (Troy L. Merry & Ristow, 2015). Also, a mild increase in ROS production delays the aging process in worms (Schulz et al., 2007) and mice (Ristow & Schmeisser, 2011), in part, through the activation of array genes that regulate cellular homeostasis under stress (Shadel & Horvath, 2015). Impaired Mitonuclear Communication MtDNA variation can influence the expression and progression of nuclear DNA mutations (McManus et al., 2019). In this study, researchers knocked out ANT1 in mice. They found that ANT1 -/- resulted in decreased OXPHOS complex I amount, as well as complex V assembly. Additionally, these knockout mice showed that mtDNA mutations enhance the deleterious impact of communication between the mitochondrial and nuclear genomes (McManus et al., 2019). The adverse effects include impaired complex I activity, increased ROS damage, altered mitochondrial morphology, changes to the mitochondrial permeability transition pore, increased mtDNA 22 mutation and shortened lifespan. Overall, researchers discovered the crucial role that mtDNA variants play in autosomal diseases (McManus et al., 2019). Multiple studies have linked mtDNA heteroplasmy to nuclear epigenomic changes (Bellizzi, D'Aquila, Giordano, Montesanto, & Passarino, 2012; Kimberly J. Dunham-Snary et al., 2018; Kopinski et al., 2019; W. T. Lee et al., 2017), highlighting the importance of heteroplasmy in proper communication between the genomes. For instance, using cells of the same nuclear background, a mitochondrial genome with increasing levels of the pathogenic mutation (tRNA Leu(UUR) 3243A > G) can be introduced to achieve a gradient of heteroplasmy ranging from 0% to 100% (Kopinski et al., 2019). Interestingly, different levels of heteroplasmy had various effects on nuclear gene expression. Under conditions of high heteroplasmy, the amount of acetyl- CoA decreased, indicative of decreased acetylation of histone H4. Samples with 30% to 70% of the A3243G heteroplasmy had higher levels of aKG/succinate, which is linked to reduced histone 3 methylation (Kopinski et al., 2019). Additionally, between heteroplasmy levels of 60% to 70%, the ratio of NAD + /NADH is elevated, indicating an increase in OXPHOS genes, possibly as a countermeasure to respond to declining mitochondrial function (Jessica L. Fetterman & Ballinger, 2019; Kopinski et al., 2019). This finding directly links mtDNA polymorphism to nuclear gene expression. Cellular Homeostasis Through the Mitochondrial Unfolded Protein Response The mitochondrial unfolded protein response (UPR mt ) is an adaptive transcriptional response to mitochondrial stress that promotes cellular homeostasis. Initially, UPR mt was described in mammalian cells and referred to the selective induction of nuclear-encoded genes involved in stress response to mtDNA depletion (Martinus et al., 1996) or accumulation of misfolded proteins in the mitochondrial matrix (Abbott & Turcotte, 2014). More recently, G-Protein 23 Pathway Suppressor 2 (GPS2) has been shown to be involved in mitonuclear communication in mammals, regulating insulin signaling, lipid metabolism and inflammation (Cardamone et al., 2012; Cederquist et al., 2017; Jakobsson et al., 2009). GPS2 translocates to the nucleus upon mitochondrial perturbation and directly activates nuclear-encoded mitochondrial genes, including mitochondrial biogenesis, particularly in brown adipose tissue (Cardamone et al., 2018) (Figure 1.3). In C. elegans, the activating transcription factor associated with stress 1 (ATFS-1) is a key mediator of UPR mt (Amrita, Christopher, Mark, Deng, & Cole, 2015; Nargund, Pellegrino, Fiorese, Baker, & Haynes, 2012). Under normal conditions, ATFS-1 enters the mitochondria and is degraded through proteolysis. However, under stress conditions, ATFS-1 translocates to the nucleus where it upregulates a number of stress response genes, and also plays a role in chromatin remodeling to promote longevity (Nargund et al., 2012) (Figure 1.3). Notably, ATF5, a mammalian homolog of ATFS-1, also induces mitochondrial proteostasis gene transcription (Fiorese et al., 2016; Tian et al., 2016). In addition, UPR mt can induce chromatin remodeling by specific histone modifications; H3K9 methylation by the histone methyltransferase MET-2 and the nuclear co-factor LIN-65 (Tian et al., 2016) and H3K27 demethylation by histone demethylases (jmjd-1.2 and jmjd-3.1) (Merkwirth et al., 2016). UPR mt is currently used more inclusively and can refer to adaptive nuclear responses to various types of mitochondrial perturbations, including nutrient availability, iron-sulfur cluster assembly, immune response, and dysfunctional metabolism (Nargund, Fiorese, Pellegrino, Deng, & Haynes, 2015; Shpilka & Haynes, 2018; Tauffenberger, Vaccaro, & Parker, 2016; C. T. Zhu, Ingelmo, & Rand, 2014). Notably, whereas UPR mt recognizes the loss of mitochondrial proteostasis, the release of bacterial-like mitochondrial components, including formylated proteins and mtDNA, can act as damage-associated molecular patterns (DAMPs) and trigger an immune response (Grazioli & Pugin, 2018; Wenceslau et al., 2014; Q. Zhang et al., 2010). Notably, mtDNA 24 levels in circulation increase with stress and age and are associated with higher levels of inflammatory markers (Pinti et al., 2014; Trumpff et al., 2019). In skeletal muscle, which is metabolically highly active, silencing of miRNA-382 results in UPR mt activation through an imbalance in mitonuclear proteins, induction of HSP60, and downregulation of mitochondrial ribosomal proteins (Dahlmans et al., 2019). Further, nicotinamide mononucleotide (NMN) treatment prevents mitonuclear protein imbalance in mouse muscles (Mills et al., 2016). UPR mt activation in worms, by genetic perturbation of mitochondrial ribosomal protein S5 (MRPS5) or pharmacological treatment (ethidium bromide, rapamycin, and resveratrol), extended lifespan. (Houtkooper et al., 2013). Further, mitochondrial stress increases the expression and mitochondrial localization of androgen receptor (AR), which then regulates nuclear-encoded mitochondrial ribosomal proteins and the mitochondrial translation machinery, indicating an adaptive mitonuclear cooperation (Bajpai, Koc, Sonpavde, Singh, & Singh, 2019). Collectively, these findings, and many others, highlight the tight-knit cellular system balancing nuclear and mitochondrial proteins coordinated through UPR mt . 25 Figure 1.3: Mitochondrial regulation of nuclear-encoded stress response genes. (A) ATFS-1 has a mitochondrial targeting sequence. Under normal conditions, ATFS-1 enters the mitochondria and is degraded by the Lon protease. Under stress, the nuclear localization sequence helps ATFS-1 enter the nucleus, triggering transcription of mitochondrial proteases and chaperones, along with antioxidant enzymes. (B) Under stress conditions, GPS2 translocates to the nucleus in response to depolarization. This activates nuclear-encoded mitochondrial genes and triggers a stress response and mitochondrial biogenesis. (C) MOTS-c translocates from the mitochondria to the nucleus under stress conditions. MOTS-c can bind to chromatin and promotes resistance against metabolic stress. Adapted from (Cardamone et al., 2018; K. H. Kim et al., 2018a; Nargund et al., 2012). Mechanisms of Exercise and Aging Beneficial Effects of Exercise Physical activity and exercise have been a key evolutionary force in human life and physiology. For decades, researchers have investigated the role of regular physical activity on A B C ATFS-1 GPS2 GPS2 MOTS-c NUCLEUS Adaptive Gene Regulation 26 numerous diseases, such as T2D, cardiovascular disease and AD (Booth, Roberts, & Laye, 2012; Goodpaster & Sparks, 2017; Morris, Heady, Raffle, Roberts, & Parks, 1953; Mullers, Taubert, & Muller, 2019; Warburton, Nicol, & Bredin, 2006). Although the public is generally aware of the overwhelming health benefits associated with exercise and physical activity, sedentary lifestyles and inactivity are becoming more common. Diseases linked to inactivity, such as obesity and T2D represent an increasing strain on healthcare services and is regarded as preventable. Regular physical exertion has improved numerous health outcomes including cardiovascular function, improved oxidative capacity, increased insulin resistance and a generally improved quality of life (Fan et al., 2017; Graber et al., 2015; Holloszy, Rennie, Hickson, Conlee, & Hagberg, 1977; M. L. Johnson, Robinson, & Nair, 2013; Joseph, Adhihetty, & Leeuwenburgh, 2016). There is a strong inverse relationship between exercise, in terms of intensity, frequency, and duration, and morbidity risk (Bouchard, Blair, & Katzmarzyk, 2015; Hawley, Hargreaves, Joyner, & Zierath, 2014; Lavie, Carbone, Kachur, O'Keefe, & Elagizi, 2019). These findings highlight the multitude of biological systems that are impacted by exercise. Modern advances in research technologies has allowed a more expansive assessment of genes and proteins impacted with exercise (Carbone, McClung, & Pasiakos, 2019; Kienzler, Hargreaves, & Patel, 2017). T2D is one disease tightly associated with exercise and physical activity. Studies have indicated that T2D is due in part to dysfunctional mitochondria in skeletal muscle (Moller et al., 2017; Zabielski et al., 2016). Further, OXPHOS genes are downregulated in muscle of obese patients, or patients with T2D (Gomez-Serrano et al., 2017; Petersen, Dufour, & Shulman, 2005). During exercise, concerted coordination between the nucleus and mitochondria are necessary to ensure sufficient energy supplies for the various tissues and organs involved. Therefore, proper mitochondrial function is paramount in any physical activity. 27 Mitochondria and Exercise Physical activity and exercise can delay or reduce age-related metabolic dysfunction and age-related diseases, including cardiovascular disease, musculoskeletal disorders, frailty, and sarcopenia (Booth et al., 2002; Cartee et al., 2016; Graber et al., 2015; Peterson et al., 2009). Mitochondria are key metabolic organelles that not only energetically support physical activity, but also transmit adaptive regulatory signals to maintain homeostasis (T. L. Merry & Ristow, 2016). Mitochondrial function declines at multiple levels during aging, which is thought to contribute to the age-related decline in physical capacity as well as skeletal muscle mass and regeneration potential (Bernet et al., 2014; Gonzalez-Freire et al., 2015; Joseph et al., 2012; Short et al., 2005). Therefore, targeting mitochondrial regulation may be an effective intervention to retain physical competence during aging and prevent age-associated diseases. Glucose levels in the blood during a post-prandial period or during fasting are normal in early stages of metabolic dysfunction. However, as the disease progresses, impaired glucose tolerance (IGT) is generally the diagnosis (Gong et al., 2019). Disease progression eventually leads to T2D. Throughout this progression, these metabolic dysfunctions are sensitive to changes in lifestyle changes along with pharmacological interventions targeting dyslipidemia, insulin resistance and glucose levels (Nuffer, Trujillo, & Megyeri, 2016). Physical activity is a highly effective preventative measure for metabolic dysfunction and T2D (J. L. Johnson, Slentz, Ross, Huffman, & Kraus, 2019; Koh, 2016; Yaribeygi, Butler, & Sahebkar, 2019). While numerous tissues in the body contribute to maintaining glucose levels, the skeletal muscle in particular is of great importance in maintaining metabolic homeostasis. The prevalence of sedentary lifestyles paired with poor nutritional choices and other poor health habits, paired with genetic predispositions, culminates in a global increase in metabolic disorders. Inactivity and obesity are two factors that may initiate metabolic dysregulation and 28 metabolic disorders. These are generally characterized by high levels of triglycerides and low levels of high-density lipoproteins (HDL) in serum (Hui, Barter, Ong, & Rye, 2019). The Role of Skeletal Muscle in Exercise and Metabolism Skeletal muscle tissue is the largest organ in the body by mass, constituting 40-50% of total body mass. Skeletal muscle not only allows for movement, it is a highly metabolically active tissue that regulates nutrients such as electrolytes, glucose, and plays a role in protein storage (Holloszy & Booth, 1976). Skeletal muscle is also the largest glycogen storage organ with nearly a four-fold increased capacity over the liver (Koopman et al., 2006). Further, skeletal muscle is the main target of insulin-dependent and non-dependent uptake of glucose, with an emphasis on maintaining glucose homeostasis (Kjobsted et al., 2019). Due to its central role in metabolism, skeletal muscle is suggested to have its own secretome, positioning it as an endocrine organ (Karstoft & Pedersen, 2016; Trayhurn, Drevon, & Eckel, 2011). The secretome of skeletal muscle includes hundreds of peptides termed myokines that may be involved in communication with distal tissues such as the liver, brain, pancreas, and adipose tissues (Bostrom et al., 2012; Colaianni et al., 2015; Pedersen, 2019). Given their numerous roles in multiple tissues, myokines may contribute to the beneficial effects of exercise in disease prevention. The combination of traditional experimental approaches and bioinformatics revealed that skeletal muscles contain over 1000 proteins capable of secretion, including growth factors and other known myokines (Deshmukh, Cox, Jensen, Meissner, & Mann, 2015). Given the role of skeletal muscle in exercise and any type of physical exertion, these myokines represent a robust system of communication with other organs critical for movement and nutrient availability, such as the lungs, heart, and liver. 29 Metabolic alterations underlie the great majority of known hallmarks of aging (Lopez-Otin et al., 2013). While classically known for providing energy for cells, mitochondria have diverse functions; a prominent function gaining increasing appreciation is mitochondrial communication. Proper mitochondrial function is essential for all organs, but particularly so in metabolically demanding tissues, such as skeletal muscle. Skeletal muscle takes up between 40%-50% of total body mass, positioning it as a key player in maintaining glucose homeostasis (Kohrt & Holloszy, 1995). Certain muscle fibers types are rich in mitochondria and rely on oxidative phosphorylation for ATP production, which directly impact muscle performance (Kanzleiter et al., 2014; Picard et al., 2012). Loss of skeletal muscle leads to many adverse health effects in the elderly, including physical competence and metabolism, and is one of the strongest indicators of mortality and morbidity (Neufer et al., 2015). Taken together, overall metabolism is a major regulator of aging, and metabolic activity is heavily regulated by mitochondria. These issues are compounded in skeletal muscle, where energy demand is high, and the loss of homeostasis leads to poor health outcomes in the aging population. Skeletal Muscle Structure Unlike other cells, skeletal muscle cells are multinucleated, and highly organized into bundles of muscle fibers. Even among these fibers, there is great variability in terms of mitochondrial content, metabolic profile, contractile speed and time to exhaustion. These variables within skeletal muscle structure highlights the range of physiological functions skeletal muscles serve, and their ability to adapt to lifestyle demands. Classification of muscle fibers are established through the isoforms of myosin heavy chain (MHC) (Essen, Jansson, Henriksson, Taylor, & Saltin, 1975; Schiaffino, Reggiani, & Murgia, 2019; Spangenburg & Booth, 2003). Human muscle fibers come in three types: type I, type IIa, and type IIb (Table 1.1). Type I fibers are slow-twitch fibers that are rich in mitochondria, making them 30 highly oxidative and are capable of producing low to moderate force over a long duration. In contrast, both type IIa and IIb fibers are fast twitch fibers that have a high force capacity, but low endurance. They are mitochondrial poor and highly glycolytic (Spangenburg & Booth, 2003). As contractile force demand increases, type IIa fiber recruitment is prioritized over type IIb (B. Egan & Zierath, 2013). Table 1.1: Skeletal Muscle Fiber Types Muscle Type Description Type I Slowest Contractile Speed, highest aerobic capacity. Dark in color, dense with mitochondria Type IIa Intermediate between type I and IIb Type IIb Fastest contractile speed, highly anaerobic. Largest cross-sectional area. A single muscle fiber consists of multiple myofibrils. The sarcomere, the smallest and highly organized contractile unit in skeletal muscle, is located in this myofibril. Muscle fibers are arranged in parallel and organized in tight bundles surrounded by the fascia which acts as connective tissue for the bound fibers (Figure 1.4). Sarcomeres contain both actin and myosin, the two crucial myofilaments that allow for locomotion. It is ultimately these myofilaments that produce the contractile mechanical force of contracting muscles. The sarcoplasmic reticulum surrounds the myofibers, and stores Ca 2+ for release as needed by the muscle. Calcium release is the key step that allows contraction and relaxation of the muscle, along with ATP. This concerted effort is extremely energy demanding; going from rest to fully active induces a 20-fold increase in the metabolic rate of the whole body, and causes a 100-fold increase in skeletal 31 muscle energy demand (B. Egan & Zierath, 2013; Hale, 2008; Periasamy, Herrera, & Reis, 2017). During the initial stages of exercise, energy comes directly from stored ATP molecules. Simultaneously, creatine phosphate is converted to ATP for immediate use. This supply of ATP lasts for roughly 90 seconds, after which ATP is primarily supplied from OXPHOS in the mitochondria. For this reason, type IIa and type IIb skeletal muscle fibers allow for short, intense bouts of exercise, after which the mitochondria rich type I fibers are responsible for the bulk of ATP production (Bangsbo, Krustrup, Gonzalez-Alonso, & Saltin, 2001). Figure 1.4: Organization of Skeletal Muscles. Parallel muscle fibers are bound tightly together and surrounded by the fascia connective tissue. A single unit of muscle fascicle is composed of smaller muscle fibers. These fibers contain the sarcomere unit primarily responsible for contraction. Muscle fibers themselves are multinucleated and made up of smaller myofibrils. Adapted from (Frontera & Ochala, 2015) By nature, skeletal muscle is highly adaptive and responds to a wide range of stimuli such as exercise. In both acute and chronic activity, skeletal muscle demonstrates a range of functional and structural adaptations and self-repairs in response to contraction and increased in force exertion. All muscle fiber types require metabolic adaptations including increased mitochondrial function and mitochondrial density, along with angiogenesis to provide increased oxygen and nutrient delivery to the muscle tissue (B. Egan & Zierath, 2013). Exercise depletes skeletal muscle Skeletal Muscle Muscle Fascicles Muscle Fascicle Muscle Fibers Muscle Fiber Sarcolemma 32 energy stores and causes micro-tears in the tissue itself, necessitating an adaptive response in order to grow and repair itself. Resting metabolic homeostasis is disrupted by exercise and is highly dependent on the type of exercise (i.e. aerobic vs. anaerobic, eccentric vs. isometric, etc.). This disruption of homeostasis results in adaptation. Intensity and duration of exercise also plays an important factor in the adaptive response from muscles (Coffey & Hawley, 2007; Hawley, 2002). While both aerobic and anaerobic exercise provide health benefits associated with exercise such as lowered resting blood pressure and improved glucose control, aerobic training more efficiently lowers cardiovascular risk factors, while resistance training increases the basal metabolic rate and increase muscle mass (Spangenburg & Booth, 2003). Combinations of both types of exercise provide the greatest health benefit and functional improvements in patients with obesity of metabolic syndrome (Colberg et al., 2010). Mechanisms associated with Exercise Mitochondria play key roles in metabolic homeostasis, and their dynamics are tightly regulated. Creation of new mitochondria and the breakdown of older, damaged mitochondria involves a concerted feedback system. There is a connection between extended lifespan and both increased mitochondrial degeneration and synthesis (Finley et al., 2012; Palikaras, Lionaki, & Tavernarakis, 2015). Regulation of this sort requires coordinated communication. Given the interdependent relationship between the mitochondria and nucleus, a robust system of communication is maintained for proper cell function (Vivian et al., 2017). Furthermore, mitochondrial dysfunctions in one tissue can have impacts on mitochondria in various other tissues as well, implying an organism-wide communication network (Song et al., 2017). Many of the communication pathways that involve the mitochondria are induced by stress. As nutrient 33 availability declines, the mitochondria can signal the nucleus and aid in the restoration of homeostasis. One such stress is exercise. Exercise results in a decrease in ATP synthesis, and activates the AMPK pathway, eventually leading to increased mitochondrial biogenesis (Tanner et al., 2013). Mitochondrial dynamics also shift with exercise. Mitochondrial fission and fusion dynamics are altered during the stress of exercise to provide ATP where required, as well as triggering a signal cascade to aid in adaptation to exercise. Intense bouts of exercise not only alter the oxidative capacity of mitochondria in skeletal muscle, but also induce changes in protein acetylation and alters metabolism in response to nutrient availability (Overmyer et al., 2015). Overall, exercise alters not only mitochondrial dynamics required for immediate energy supply, but also influences metabolite levels used in signaling and initiating several pathways to stimulate autophagy and recapture metabolic homeostasis (Clark-Matott et al., 2015; Marino et al., 2014). Collectively, these data further elucidate the complex role of mitochondria as a signaling peptide and a key player in metabolic processes far beyond the traditional role of the mitochondria as simply the powerhouse of the cell. One benefit of exercise is the improvement of ATP production in the mitochondria. Low levels of ROS resulting from exercise bouts helps to increase components of the electron transport chain to more effectively provide ATP for future muscle exertion (Greggio et al., 2017). Further research has homed in on exercise mimetics, or pharmacological interventions that activate certain pathways normally stimulated through exercise. Some of the well-known exercise mimetics gained public attention for their use in athletic competitions. Performance enhancing drugs are a type of exercise mimetic, however the commercially or illegally available compounds are largely untested and have severe side effects (de Hon, Kuipers, & van Bottenburg, 2015). It is worth noting, however, that many of these compounds work through activation of many of the pathways described above, including the AMPK-PGC1∝ pathways. Experiments involving 34 PGC1∝ transgenic mice shift towards type I muscle fibers, increased oxidative metabolism and an improved fatigue resistance (Lin et al., 2002). AMPK has long been studied for its role in preventing or treating metabolic disorders, such as insulin resistance. AMPK can be activated through exercise or caloric restriction, both conditions that induce metabolic stress. Under conditions of low nutrient availability, AMPK, SIRT1 and mTORC1 pathways are all activated in induce autophagy (Eisenberg et al., 2014). It has been shown that activation of these pathways is sufficient to extent lifespan in mice (Mercken et al., 2014). Conversely, inhibition of these pathways disrupts the beneficial effects of exercise or caloric restriction (Abbott & Turcotte, 2014). Given the relationship between activated AMPK levels and insulin sensitivity, MOTS-c is a viable candidate for exercise induced AMPK activation. This would position MOTS-c as a potential exercise mimetic. Similar to metabolism as a whole, exercise impacts all nine of the hallmarks of aging. Altering nutrient sensing and metabolic function through a mitochondrial derived peptide is a paradigm shifting concept that may have direct benefit to the aging community. While MOTS-c may be the first mitochondrial derived peptide that influences exercise performance, many studies have found nuclear encoded peptides that activate pathways associated with the beneficial effects of exercise. Like MOTS-c, one group showed that inhibition of AICAR leads to AMPK activation and improved insulin sensitivity and obesity prevention (Asby et al., 2015). Beyond AMPK, another group recently discovered DWORF, a peptide that enhances SERCA activity, which increases muscle performance through improving calcium cycling (Nelson et al., 2016). While peptides targeting nutrient sensing pathways and impacting the beneficial effects of exercise, peptides that target the mitochondria are of particular interest. SS-31, a nuclear encoded peptide that targets the mitochondria energetics, increases muscle performance in aged mice and has translational potential (Siegel et al., 2013). These are just a few examples of the variety of peptides and other signaling molecules capable of stimulating an increase in 35 muscle performance. Exercise mimetics remain an intriguing approach to improving physical output and endurance through stimulating specific pathways. These mimetics revolve around regulating homeostasis in muscle cells and preventing mitochondrial dysfunction. Figure 1.5: Brief Overview of Physiological Changes with Exercise. Exercise induces a multitude of pathways. AMPK is a major factor that results in increased fatty acid oxidation, mitochondrial biogenesis and angiogenesis. This helps bring the physiological benefits of exercise. Simultaneously, HSPs help prevent oxidative stress and apoptosis. Adapted from (T. L. Merry & Ristow, 2016). Exercise and skeletal muscle performance involve a complex network of various inputs that must remain tightly regulated. The physiological benefits of exercise therefore should encompass multiple adaptations that improve (i) motion and fatigue, (ii) maintaining tighter metabolic control through maintaining appropriate ATP levels (iii) minimizing interruptions in metabolic homeostasis from the oxygen produced from exercising muscles and (iv) maintaining the highest rate of energy yield (Hawley, 2002) (Figure 1.5). Essentially, one must maintain the highest rate of aerobic metabolism. The maximum value for this is represented by VO2 max. Increasing VO2 max through endurance training increases mitochondrial protein expression and respiratory capacity by up to 15% (Lundby & Jacobs, 2016). Regular exercise allows cells to experience a reduced stress given the same relative amount of exertion. Training-induced adaptations require less output from the muscles given the same absolute intensity of the exercise performed, resulting in a reduced amplitude of change in metabolic homeostasis (Holloszy et al., 1977) and requires a greater emphasis on oxidizing fat relative to carbohydrates (Brooks & Mercier, 1994). Additionally, cells develop an increased capacity to both create and clear lactate, allowing for improved performance and faster recovery (Messonnier et al., 2013). Additionally, enhanced endurance likely involved ROS created during exercise itself (T. L. Merry & Ristow, Benefits of Exercise Metabolic Control Decreased Fatigue Increase VO 2 Max Increased Homeostasis Exercise Oxidative Stress Apoptosis Fatty Acid Oxidation Mitochondrial Biogenesis Angiogenesis HSF1 AMPK HSPs PGC1a 36 2016). This, in part, can alter the expression of certain HSPs, including HSP27, HSP60, HSP72 and HSP90 (Horowitz, 2010, 2014). The ability of skeletal muscle to acclimate to exercise relies on a variety of signaling pathways (Hawley, Lundby, Cotter, & Burke, 2018) and requires tight coordination with the mitochondria. The mTOR pathway also plays key roles in both exercise and the cellular response and adaptation to exercise. Activation of mTOR through exercise can help increase skeletal muscle fiber size (Bodine, 2006; Bolster et al., 2003; Nader & Esser, 2001). mTOR is a highly conserved protein kinase with roles in nutrient sensing and regulates cell cycle progression (Blenis, 2017). After exercise, mTOR activity in rats led to an increase in activated S6K, which led to an increase in muscle mass after 6 weeks of training (Baar & Esser, 1999). In another study, male rats were trained to mimic resistance exercise training through ankle extensions while wearing a weighted vest. This led to increased protein synthesis, but this increase was completely prevented when administering rapamycin, an mTOR inhibitor, two hours prior to exercise (Kubica, Bolster, Farrell, Kimball, & Jefferson, 2005). Conversely, maintaining proper skeletal muscle homeostasis is essential for function. Inhibition of mTOR during nutrient stress prevents muscle growth and increases autophagy in order to maintain proper muscle function through optimizing energy stores (Bujak et al., 2015). Skeletal muscle activation of mTOR is part of a coordinated network designed to optimize muscle function. Signals from distal tissues have been shown to activate mTOR signaling, resulting in changes in skeletal muscle performance. For example, BAT secretes interferon regulatory factor 4 (IRF4) in response to exercise, resulting in increased mTOR activation and increased exercise performance (Kong et al., 2018). Additionally, knockdown of IRF4 reduced performance, while acute IRF4 injections resulted in boosted exercise performance (Kong et al., 37 2018). In addition to extracellular factors altering mTOR activation, intracellular signals also play a key role. Factors originating from the mitochondria have been shown to active mTOR as well, indicating that mTOR activity is determined in part by retrograde signaling (Quirós et al., 2016; Soledad, Charles, & Samarjit, 2019). Under exercise stress, mTOR activation helps to increase cell growth in order to adapt skeletal muscles to the given stressor. Figure 1.6: mTOR pathway and the hallmarks of aging. The mTOR pathway directly impacts energy homeostasis, cellular senescence and stem cell maintenance, each of which is a hallmark of aging. Proteostasis is linked to each of these pathways. Similarly, energy homeostasis and proteostasis influence mitochondrial function, protein degradation and synthesis, autophagy and the unfolded protein response. Adapted from (B. K. Kennedy & Lamming, 2016; Lindqvist, Tandoc, Topisirovic, & Furic, 2018; Su & Dai, 2017). Similar to exercise, mTOR plays an important role in aging. mTOR is essential for muscle function, mitochondrial function and metabolic homeostasis. In fact, mTOR signaling influences many of the traditional hallmarks of aging (Figure 1.6). Of note, mitochondrial function deteriorates mTOR Energy Homeostasis Cellular Senescence Stem Cell Maintenance Proteostasis Mitochondrial Function Proteasome Degradation Protein Synthesis Autophagy Unfolded Protein Response 38 with age (Lopez-Otin et al., 2016a). The exerkine Apelin activates mTOR in aged mice, resulting in increased protein synthesis in sarcopenic myofibers (Vinel et al., 2018). It is well established that ROS increases with age. However, mTOR activation can increase levels of signal transducer and activator of transcription 3 (STAT3) leading to the reduction of harmful ROS (Meier et al., 2017). In mammals, mTOR activity is altered by stress caused by mtDNA deletions as well, working through ATF5 (Khan et al., 2017; Youle, 2019). Depletion of TOR, resulting in decreased protein synthesis and muscle growth, has been linked to lifespan extension in a variety of species (Anisimov et al., 2011; Harrison et al., 2009; Pan & Finkel, 2017; Papadopoli et al., 2019; Powers, Kaeberlein, Caldwell, Kennedy, & Fields, 2006; Saxton & Sabatini, 2017). The key element of mTOR’s lifespan extending activity lies in its role as a nutrient sensor. mTOR is essential for the benefits of CR in multiple species (Hwangbo, Gershman, Tu, Palmer, & Tatar, 2004; Mulvey, Sands, Salin, Carr, & Selman, 2017; Selman et al., 2008). Other hallmarks of aging are also impacted by mTOR. Proteostasis largely revolves around mTOR, given its role in protein synthesis. mTOR signaling coordinates not only protein synthesis but also autophagy, making it partially responsible for energy maintenance and protein homeostasis (Lindqvist et al., 2018). Further, mTOR increases protein ubiquitination, resulting in increased degradation by the proteasome (Zhao, Zhai, Gygi, & Goldberg, 2015). Mitochondrial function is impacted by mTOR through increasing translation of OXPHOS components and TFAM, paired with its role in regulating mitophagy (Bartolome et al., 2017; Morita et al., 2013). In skeletal muscle, hyperactivation of mTOR led to abnormal and dysfunctional mitochondria, increased oxidative stress and damage of muscle fibers, combined with skeletal muscle stem cell dysfunction (Takayama et al., 2017; Tang et al., 2019). Senescence is yet another hallmark of aging influenced by mTOR. Senescence-associated secretory phenotype (SASP) is promoted by mTOR activity. Rapamycin inhibits mTOR activity and results in interleukin-1 mRNA which 39 reduced the expression of inflammatory genes (Laberge et al., 2015). Inhibition of mTOR also leads to decreased mitochondrial mass and reduces stress induced ROS (Correia-Melo et al., 2016). While senescent cells no longer divide, they are still highly metabolically active, given their robust role is SASP signaling (Papadopoli et al., 2019). Taken together, these results highlight the vast diversity of roles played by mTOR in stress response, both in exercise and in aging. TOR is conserved in many species utilized in aging studies, making it a useful tool to study the aging process. Aging and Model Organisms A major hurdle in studying the Biology of Aging is simply the fixed rate at which aging occurs. Humans require decades to show an aging phenotype under normal conditions. Due to this, studying human aging chronologically is nearly impossible. Cross-sectional studies, in which groups of individuals as a certain age are compared to a separate group at a different age, allow a limited understanding of the aging process due to the extensive factors different between the groups. Longitudinal studies, where a single group is followed over time, provide great insight, but requires a great deal of time and following individuals presents another set of unique challenges. Model organisms provide an opportunity to avoid these pitfalls. Worms (C. elegans) and fruit flies (D. melanogaster) allowed researchers to study aging, and greatly accelerate the rate of biological discoveries. The relatively simple genetics and short lifespan of these organisms allowed them to be readily studies at great numbers for limited cost (Table 1.2). These findings were then observed in mice (M. musculus) to test a mammalian model. Recently, the African Killifish (N. Furzeri) (Valenzano et al., 2015) is gaining popularity as a vertebrate model organism due to its number of conserved genes with humans paired with its relatively short lifespan. 40 Researchers have successfully extended the lifespan and healthspan of these organisms through various interventions, lending credence to the idea of extending lifespan and healthspan in humans through biological interventions. Table 1.2: Various Model Organisms in the Biology of Aging Restrictions of Invertebrate Models While invertebrate model organisms have provided a great knowledge base in the aging process and age-associated molecular pathways, their similarity to humans is limited. It is Model Organisms in Aging C. elegans D. melanogaster N. furzeri M. musculus Practicality Progeny Time 3-5 Days 10-14 Days 3-4 Weeks 3-4 Weeks Lifespan 10 Days 6-14 Weeks 4-6 Months Years Size 1 mm 3 mm 2-5 cm 10 cm Maintenance Costs Low Low Medium High Human Similarity Total Number of Genes 19000 13000 28000 25000 Conserved Genes > 50% > 60% > 70% > 90% Anatomical Similarity Low Low Medium High Genetic Tools Generation of Transgenic Lines Weeks Yes Yes Yes Targeted Gene Knockout No Weeks Weeks Month 41 necessary to employ other models to identify additional age-related pathways and potential longevity genes. In both worms and flies, a great deal of evolutionary divergence has taken place between these organisms and humans, creating a high degree of genetic loss (Austad, 2009). Worm larvae also can enter a dauer state in response to various stressors, including nutrient stress, while humans do not have this biological characteristic (Chute et al., 2019; Murthy & Ram, 2015). Further, worms and flies both have limited tissue repair and renewal mechanisms that are crucial for human mammalian homeostasis in various tissues (Austad, 2009; Vizcaya-Molina et al., 2018; Z. Wu et al., 2007). Due to these limitations, it is necessary to seek other model organisms to build up the genetic profile relating to the biology of aging in humans. M. musculus as a Model Organism to Study Aging Due to its high genetic similarity to humans, the mouse is arguably the primary mammalian model in aging research (Zainabadi, 2018). Due to their extensive use in biology, a number of mutant strains and genetic strains are available. Further, mice have many similarities to humans including the presence of two sexes and similar physiological functions, making them a desirable candidate to study human aging processes (Vanhooren & Libert, 2013). While invertebrate models have contributed largely to our understanding of specific pathways associated with aging, many researchers argue that mammalian models are necessary to make a strong link to human aging. Additionally, clinical trials require mammalian data to determine safety and efficacy, and mice make up the large majority of these data sets (Howe et al., 2018). As predicted, mouse studies have indeed provided insight into lifespan and healthspan interventions that have provided a foundation for human studies. Caloric restriction (CR), specifically, is one of the most well-established method for aging intervention. Originally discovered as a lifespan extension tool in rats (McCay, Crowell, & Maynard, 1989), CR extends 42 lifespan in multiple mouse strains (Barger et al., 2017; Forster, Morris, & Sohal, 2003; Speakman & Mitchell, 2011; Vaughan et al., 2017). Further, CR delays the onset of some age-related diseases including cancer (Raffaghello et al., 2008), neurodegenerative diseases (Halagappa et al., 2007) and cardiovascular disease (Madeo, Carmona-Gutierrez, Hofer, & Kroemer, 2019). However, recent evidence has shown that CR may be limited in it benefits to specific strains, and may be sex-dependent (Astafev, Patel, & Kondratov, 2017; Boldrin et al., 2017). Nonetheless, mice have been used extensively in aging studies, and provide an invaluable tool in understanding the complex networks involved in mammalian aging. Limitations of Mice as Model Organisms While model organisms provide valuable insight into various biological processes, each model has their unique drawbacks. For example, although mice share over 90% homology with humans, there are clear differences between species. Interventions that show success in mice often do not translate to human biology. Over 85% of novel drugs shown to have success in mice fail within Phase II of human clinical trials (Ledford, 2011). The glaring downside to this fact is the extraordinary amount of time and financial investment required to even get a drug into a clinical trial stage. For example, in cancer therapies, less than 8% of pre-clinical drugs ever make it to humans (Mak, Evaniew, & Ghert, 2014). This discrepancy presents a growing issue for research. Humans as a whole are living longer (Crimmins, 2015), but are facing a growing threat of chronic diseases as they age. The need for affordable translational solutions to these chronic diseases is paramount. Further, utilizing mice to study human diseases and human aging presents another set of obstacles. Mice do not share perfect disease phenotypes with humans and relying on mice to study diseases they are not prone to develop is of growing concern. For example, aged mice can 43 develop cognitive impairment similar to humans as they age, but they do not have an AD phenotype to match what humans experience (Mullane & Williams, 2019). Further, mice are nocturnal creatures, and have circadian metabolic shifts that differ from humans (Asher & Sassone-Corsi, 2015). For these reasons, the proper selection of model organisms to study human aging and disease must lay a strong foundation to provide a similar outcome in humans, and is a constantly evolving field of study (Justice & Dhillon, 2016; Normand et al., 2018). Sexual Dimorphism in Aging Biology A challenge presented in studying aging is that across species, females typically outlive males (Holzenberger et al., 2003; Maklakov & Lummaa, 2013; Rochelle, Yeung, Bond, & Li, 2015; Sampathkumar et al., 2019; F. Zhu et al., 2015). This disparity implies an underlying mechanism at the root of lifespan differences between sexes. Although a majority of lifespan studies currently involve only male organisms, or do not distinguish between male and female subjects (Le Couteur, Anderson, & de Cabo, 2018; Pomatto, Wong, Tower, & Davies, 2017), the majority of lifespan interventions are have differential effects in males and females (Shen, Landis, & Tower, 2017). One prominent example of this is SIRT6, where male mice showed decreased IGF1 levels in serum, and altered phosphorylation levels of several components of the IGF1 signaling pathway (Kanfi et al., 2012). Interestingly, a genetic target frequently knocked out in mice is S6K, which has been demonstrated to increase lifespan in females (Selman et al., 2009). As previously discussed, S6K is part of the mTOR pathway and its role in lifespan extension mimics the effects of rapamycin (Lesniewski et al., 2017; Miller et al., 2014). Sexual dimorphism and its impact on lifespan may be directly related to the interaction between the maternally inherited mtDNA and the nuclear DNA, with contributions from both 44 parents (Birky, 1995; Drummond et al., 2019). This type of co-evolution between the mitochondrial and nuclear genomes results in mtDNA to be best adapted for females, at the expense of males (Camus, Clancy, & Dowling, 2012). Indeed, mtDNA mutations, which have little impact on nuclear gene expression in females, modify nearly one-tenth of the nuclear transcripts in male flies (Innocenti, Morrow, & Dowling, 2011). Further, researchers established a direct link between single mtDNA point mutations in males and decreased lifespan (Camus, Wolf, Morrow, & Dowling, 2015). This evidence supports the notion that mutations causing positive or neutral changes in females may lead to accelerated aging in males. The disparity between male and female mitonuclear communication is evident in tissues that are highly metabolically active. These tissues include the liver (Sunny, Bril, & Cusi, 2017; Von Schulze et al., 2018), muscle (Hevener, Zhou, Moore, Drew, & Ribas, 2018), and brown adipose tissue (BAT) (Rodriguez-Cuenca et al., 2002; Valencak, Osterrieder, & Schulz, 2017; Vatner et al., 2018). Experiments using rats found that females had a higher mitochondrial membrane potential under basal conditions, increased substrate oxidation capacity and lower ROS production compared to males (Justo et al., 2005; Nye, Sakellariou, Degens, & Lightfoot, 2017). In combination, females also have higher activity of antioxidant genes such as Manganese Superoxide Dismutase (MnSOD) which are encoded in the nuclear genome and may contribute to the mitochondrial damage from oxygen in males (Cunningham et al., 2018; Pinto & Bartley, 1969) 45 Chapter II: Assessment of mouse fitness as determined through treadmill running and walking Abstract Endurance testing simultaneously assesses a wide variety of physiological systems including the cardiovascular, respiratory, metabolic, and the neuromuscular system (Gabriel & Zierath, 2017). Treadmill running is a non-invasive method to evaluate fitness capacity in a longitudinal or cross-sectional manner. High-intensity exercise tests can be used to determine peak physical capacity in mice. However, because aging is associated with a progressive loss of physical capacity the running protocols can be adapted and optimized for aged mice. Introduction Treadmill running in mice provides a functional measure of exercise tolerance. While voluntary wheel running is a useful indicator of overall activity level, forced treadmill running utilizes different physiological realms, and has been extensively tested in both mice and humans (Richardson et al., 2016). Mice are placed on a motorized treadmill and encouraged to run to reach the dark covered end of the treadmill, which they prefer to the bright exposed area. Manual stimulation, such as gently prodding with a stick, encourages mice to re-engage with the treadmill after they fall back on the resting platform. While some protocols utilize a shock plate to encourage running, this has been shown to potentially lead to a decrease in running performance or increased escape behavior (Conner, Wolden-Hanson, & Quinn, 2014; Dougherty, Springer, & Gershengorn, 2016). One major benefit of motorized treadmill running is that the procedures are easily adjustable. Exercise intensity can be varied by increasing or decreasing the running speed and incline. Increasing the incline of the treadmill places more strain on the mouse muscle and is more energy intensive. Running on a decline, meanwhile can be used to measure eccentric 46 muscle contraction (Batra et al., 2019; Castro & Kuang, 2017). This degree of sensitivity has benefits over swimming tests, or wheel running analysis (Feng et al., 2019). Another key benefit of using treadmill running as a way to measure exercise performance is the ability to test mice over time. This non-invasive measure allows flexibility in experimental design, and the ability to follow mice longitudinally. Several studies utilize repeated treadmill training to study a wide variety of biological benefits associated with long-term running protocols (Hollinski et al., 2018; Mees et al., 2019; Nguemeni et al., 2018; Walton et al., 2016). Conversely, treadmill running can also be used for short-term assessment of single bouts of exercise, or comparison between groups (Hoene et al., 2016; Y. Shi et al., 2019). Forced treadmill running is frequently used as a method to test alterations in skeletal muscle, as adaptive stress response to exercise occurs quickly in this tissue (Brandt, Dethlefsen, Bangsbo, & Pilegaard, 2017; Ikeda et al., 2016; Richardson et al., 2016; Sako, Yada, & Suzuki, 2016). However, while skeletal muscle certainly plays an important role in exercise capacity, treadmill running combines performance of several different biological systems (Richardson et al., 2016). For this reason, other assays, such as grip strength or rotarod performance, should be used in combination with exercise capacity to determine skeletal muscle specific contributions to physical capacity (Gill, Santos, Schnyder, & Handschin, 2018; Jin et al., 2019). Here, we developed a treadmill running protocol which allows for testing the running capacity of mice at a variety of ages. Young and middle-aged mice (up to 22 months) have been tested using a high intensity running protocol (Das et al., 2018). Older mice (30 months) are generally incapable of running to the same degree as their younger counterparts, so a 60 second walking test can be sufficient to determine their physical capacity. This protocol has been used 47 for mice of the C57BL/6 and CD-1 (ICR) background. Additional optimization for other mouse strains is recommended. Materials While minimal materials are required, it is crucial to keep the treadmill and all supplies well maintained (see Note 1). 1. Mouse Treadmill Apparatus: 4-lane treadmill systems are available from TSE Systems, USA. Treadmills connect to the manufacturer’s software via USB ports, so multiple treadmill systems may be used simultaneously. Treadmill set-up is shown in Figure 2.1. 2. Paintbrush (or similar): A long tool with a soft end is used to gently encourage mice to re- engage with the treadmill. 3. Ethanol: 70% solution in water. Figure 2.1: Treadmill Equipment Overview. This is the complete treadmill set-up. The box on the left is the power source, as well as the indicator of mouse resting. The light for each lane illuminates if the mouse is on the resting platform. Mice should be encouraged to reengage with the treadmill when this occurs. This treadmill has four lanes, with the vertical pole in the back controlling the angle of the treadmill. Treadmills are connected to the computer software via USB, and multiple treadmills can be operated at once. 48 Methods 3.1 Acclimating Mice: Before any physical exercise testing can begin, mice must be properly and thoroughly acclimated to the treadmill. This will help to minimize escape behavior and allow for proper assessment of physical capacity. It will also help the mice familiarize themselves with tactics to encourage reengagement with the treadmill. 1. Day 1: Place the mice on the stationary treadmill without any belt movement. If possible, place the mice on the same single track, and with the same neighboring mice that they will experience during the physical test. Allow the mice to freely explore the stationary treadmill for 10 minutes. Do not interact with the mice and allow them to move (or remain stationary) without any involvement (see Note 2). 2. Place the mice back in their home cage. If the study design allows, positive reinforcement can be given at this point. If the mice are on a fasting, or caloric restriction, feeding the mice after returning to their home cage may help improve their interaction with the treadmill in future tests (see Note 3). 3. Day 2: Repeat the 10-minute stationary exposure to the treadmill. Return the mice to their home cage. 4. Day 3: Allow the mice to rest with no exposure to the treadmill for one full day (see Note 4). 5. Day 4: Place the mice on the treadmill. Using the TSE Systems software, program the treadmill to run at 10 m/min for 20 minutes (see Note 5). This is a relatively low intensity speed and is intended to get the mice acclimated to moving on the treadmill. Adjust the treadmill to be level (or the appropriate incline/decline as desired). 6. Start the 20-minute running protocol. Mice are allowed to momentarily fall back onto the resting platform; however, we find that less time between the mice resting, and intervening 49 to encourage reengagement usually has the most success in sustained time on the treadmill (see Note 6). 7. As mice use the resting platform, use the paintbrush or other tools to encourage reengagement with the treadmill. There are three methods that we found most effective. First, open the lid to the lane of the treadmill with the resting mouse. Using a paintbrush or a similar tool, gently poke the mouse near the hind legs to nudge the mouse towards the treadmill belt. Alternatively, if the tail of the mouse is sticking out from the back of the treadmill, a gentle tail squeeze often works to the same effect. Finally, tapping the wooden end of the paintbrush on the lid of the treadmill, followed by nudging the mouse to run creates a learned behavior. Eventually, simply tapping the end of the brush of the lid becomes sufficient to get the mouse to reengage. A combination of these techniques will likely be required to achieve sustained running from all mice. 8. After the 20-minute run, return the mice to their home cages. 9. Day 5: Allow the mice to rest with no exposure to the treadmill. 10. Day 6: Repeat the 20-minute run at a fixed speed of 10 m/min. The mice should now be fully acclimatized, and ready for the high intensity running test following a day of rest. 3.2 High Intensity Running Test This test allows the assessment of physical capacity of the mice to be tested. 1. Using the TSE Systems software, prepare the running protocol. There are four stages of the protocol as described in Figure Figure 2.2: High Intensity Running Test Protocol. Time and speed of the four stages of forced treadmill running. Mice run through all four stages and are considered exhausted after 30 seconds of refusing to reengage with the treadmill. Running stages A-D are labeled and described in section 3.2. 50 2.2 Stage A) 13 m/min for 5 minutes. Stage B) Increase the speed from 13 m/min to 18 m/min over 5 minutes. Stage C) mice run at the fixed speed of 18 m/min for 30 minutes. Stage D) Increase the treadmill speed to a fixed speed of 23 m/min until mice reach exhaustion. 2. As mice rest on the stationary platform, encourage reengagement as previously described. 3. Any mouse that refuses to reengage with the treadmill after 30 seconds of encouragement should be considered to have reached exhaustion. Record this time and move this mouse from the treadmill lane to its home cage (see Note 7). 3.3 Low Intensity Walking Test Aged mice will reach a point where performing the same high intensity running test becomes impossible. Therefore, we have developed a simple test to measure the physical capabilities of mice no longer able to run on a treadmill. 1. Acclimate the mice to the treadmill by exposing them to the stationary apparatus (3.1 steps 1-4). 2. Program the TSE System software to run for 1 minute at 13 m/min. 3. Start the treadmill and encourage the mice to stay on the treadmill for the full 60 seconds. This is a binary test and is recorded simply as “pass” or “fail”. This test is not intended to measure physical performance, but rather to determine if a mouse has reached a point of frailty where they can no longer run. 4. After the test, return the mice to their home cages. This test should not be repeated without adequate recovery times for the mice. This time period should be determined by close observation of the mice, and with input from the veterinary staff. 51 Notes 1. Make sure all equipment is well cleaned. Use 70% ethanol to sterilize the treadmill and any tools used. Additionally, allow ample time for the scent of alcohol to vanish. Lingering odors may impair performance and increase escape behavior by the mice. 2. Some treadmill models come equipped with a barrier that runs perpendicular to the treadmill track Figure 2.3. We find that this barrier, intended to keep the mouse on the treadmill, increases escape behavior. Proper training allows for this barrier to be removed. Acclimate the mice in the same condition as the anticipated test. Figure 2.3: Set-up of Treadmill and Mice. This is the view of the treadmill with all four lanes occupied. Note the position of the resting platform. Since the mouse in Lane 3 has its hind limbs on the platform, that mouse is considered resting. Also note the removable barriers. Removing these during acclimation and running help limit escape behavior. 3. In some cases, we gave the mice food that is 70% calories by fat, which the mice show preference for when given as a reward. 4. While the treadmill training bouts are not designed to train the mice physically, it is designed to get them comfortable with the experimental procedures. Other protocols give detail on how to train mice physically (Moore et al., 2016). Regardless, the days in between training are important for acclimation. If desired, the mice can be placed on the treadmill that is not moving to further expose them to the apparatus itself. 52 5. The TSE Software system can be used to control multiple treadmills simultaneously. To do this, plug in one treadmill via USB, and open the software. Enter the experiment details and continue to get to the page to start the treadmill. Next, plug in the next treadmill and repeat this process. Repeat for any additional treadmills. Finally, start each running window separately, so that there is one experimental timer per treadmill. 6. Often times, the mouse will sit on the resting platform and “run” using only its front limbs. This does not count as running and should be considered resting. 7. Some mice will simply refuse to ever engage with the treadmill to begin the run. If this occurs, do not apply too much force to the mouse. Rather, return the mouse to its home cage and try again after 30 minutes. If the mouse still refuses, try again later in the day, or the next day. For the High Intensity Running Test, if a mouse refuses to begin running, it should not be counted as a sample for that experiment. This should count as a fail for the Low Intensity Walking Test. 8. Mice may continue to display escape behavior during the treadmill run. While using the paintbrush to encourage running, mice may use this opportunity to escape out the top of the treadmill, now that the plastic barrier is lifted. If a mouse continues to do this, use the tapping method or gentle tail squeeze to encourage running. A mouse that spends considerable time attempting to escape should be omitted from the results and tested again at a later time. 9. It is helpful to do all treadmill training and tests at the same time of day for each trial. Circadian effects can influence both the willingness and abilities of the mice running on the treadmill (Tahara & Shibata, 2018). Keep in mind that the High Intensity Running Test can take over 40 minutes for high-performing mice. If multiple rounds of tests are needed, take this timing into account during the training stages. 53 10. We use a dedicated room to do the treadmill tests to avoid disruptions. Excessive noise such as the opening and closing of doors may distract the mice and cause them to retreat to the resting platform. 11. It works best to sterilize the treadmill immediately after a round of tests. This allows the treadmill to fully dry and rid of residual odors prior to the next round of running tests or acclimation periods. 12. Mice are most likely to fall back onto the resting platform during certain stages of the High Intensity Running Test. Stage B has the speed increasing over a 5-minute period. When the treadmill speed increases, even by small amounts, the mice are more likely to rest. This is particularly true when entering Stage D, where the speed goes from 18 m/min to 23 m/min. Extra attention must be paid during these times to ensure the mice remain engaged with the treadmill. 54 Chapter III: MOTS-c is an Exercise-Induced Mitochondrial-Encoded Regulator of Age- Dependent Physical Decline and Muscle Homeostasis The following chapter is adapted from (J. Reynolds et al., 2019). Abstract Aging is the leading risk factor for multiple non-communicable chronic diseases. Age- related physiological deterioration is underlined by a progressive loss of cellular homeostasis and physical capacity. Healthy aging can be promoted by preemptively enhancing physical capacity and metabolic fitness. Mitochondria are chief metabolic organelles with strong implications in aging. In addition to their prominent role in bioenergetics, mitochondria also coordinate broad physiological functions by communicating to other cellular compartments or distal cells, using multiple factors including peptides that are encoded within their own independent genome. However, it is unknown if aging is actively regulated by factors encoded in the mitochondrial genome. MOTS-c is a mitochondrial-encoded peptide that regulates metabolic homeostasis, in part, by translocating to the nucleus to regulate adaptive nuclear gene expression in response to cellular stress. Here, we report that MOTS-c is an exercise-induced mitochondrial-encoded peptide that significantly enhanced physical performance when administered to young (2 mo.), middle-aged (12 mo.), and old (22 mo.) mice. In humans, we found that endogenous MOTS-c levels significantly increased in response to exercise in skeletal muscle (5-fold) and in circulation (1.5-fold). Systemic MOTS-c treatment in mice significantly enhanced the performance on a treadmill of all age groups (~2-fold). MOTS-c regulated (i) nuclear genes, including those related to metabolism and protein homeostasis, (ii) glucose and amino acid metabolism in skeletal muscle, and (iii) myoblast adaptation to metabolic stress. Notably, our RNA-seq data revealed HSF1 as a commonly enriched transcription factor in both MOTS-c-treated skeletal muscle and 55 myoblasts. Indeed, siRNA-mediated HSF1 knockdown reversed MOTS-c-dependent stress resistance against glucose restriction/serum deprivation. Ultimately, late-life initiated intermittent MOTS-c treatment (23.5 months; 3x/week) improved overall physical capacity and trended towards increasing lifespan. Our data indicate that aging is regulated by genes that are encoded not only in the nuclear genome, but also in the mitochondrial genome. Considering that aging is the major risk factor for multiple chronic diseases, our study provides new grounds for further investigation into mitochondrial-encoded regulators of healthy lifespan that could also provide novel therapeutic targets of mitochondrial basis. Introduction The progressive loss of metabolic homeostasis is a hallmark of aging, which impedes parenchymal function and ultimately diminishes physical capacity (Lopez-Otin et al., 2013; Lopez- Otin et al., 2016a). In fact, aging is a leading risk factor for a myriad of non-communicable chronic diseases (Barzilai, Cuervo, & Austad, 2018; Campisi et al., 2019; De Magalhães, Stevens, & Thornton, 2017; Dzau, Inouye, Rowe, Finkelman, & Yamada, 2019; Kaeberlein, Rabinovitch, & Martin, 2015; Brian K. Kennedy et al., 2014; Olshansky, 2018). Organismal fitness requires continuous adaptive cellular stress responses to the ever-shifting internal and external environment. Mitochondria not only produce the bulk of cellular energy, but also coordinate adaptive cellular homeostasis by dynamically communicating to the nucleus (Quirós et al., 2016) and other subcellular compartments (Gottschling & Nyström, 2017). Mitochondrial communication is mediated by multiple nuclear-encoded proteins, transient molecules, and mitochondrial metabolites (Matilainen, Quirós, & Auwerx, 2017). Mitochondria possess a distinct circular genome that has been traditionally known to host only 13 protein-coding genes. However, short open reading frames (sORFs) encoded in the mitochondrial genome have been recently identified. Such sORFs produce bioactive peptides, 56 collectively referred to as mitochondrial-derived peptides (MDPs), with broad physiological functions (S.-J. Kim, J. Xiao, J. Wan, P. Cohen, & K. Yen, 2017; C. Lee, Yen, & Cohen, 2013b). MOTS-c (mitochondrial ORF of the 12S rDNA type-c) is an MDP that promotes metabolic homeostasis, in part, via AMPK (C. Lee et al., 2015; Zarse & Ristow, 2015) and by directly regulating adaptive nuclear gene expression following nuclear translocation (K. H. Kim et al., 2018b; Mangalhara & Shadel, 2018). MOTS-c expression is age-dependent and detected in multiple tissues, including skeletal muscle, and in circulation (C. Lee et al., 2015; Ramanjaneya, Bettahi, et al., 2019; Zarse & Ristow, 2015), thus it has been dubbed a “mitochondrial hormone” (Zarse & Ristow, 2015) or “mitokine” (Rafael Alis, Alejandro Lucia, Jose R. Blesa, & Fabian Sanchis-Gomar, 2015; Yong & Tang, 2018). In fact, systemic MOTS-c treatment reversed diet- induced obesity and diet- and age-dependent insulin resistance in mice (C. Lee et al., 2015). We tested if MOTS-c functions as a mitochondrial-encoded regulator of physical capacity and performance (Knoop, Thomas, & Thevis, 2019; Shunchang Li & Ismail Laher, 2015; Lopez-Otin et al., 2016a) in young (2 mo.), middle-aged (12 mo.), and old (22 mo.) mice. Figure 3.1: MOTS-c responds to and regulates exercise in young adults. a Schedule of exercise on a stationary bicycle and blood and skeletal muscle collection in young male subjects (n=10). b, c Representative western blot of MOTS-c from skeletal muscle and quantification d Quantification of serum MOTS-c levels by ELISA. Data expressed as mean +/- SEM. Wilcoxon matched-pairs signed rank test was used for (c, d). Results To determine if endogenous MOTS-c responds to physical exertion, and thus may be involved in driving adaptation to enhance physical capacity, we collected skeletal muscle and plasma from sedentary healthy young male volunteers (24.5 ± 3.7 years old and BMI 24.1 ± 2.1) 57 that exercised on a stationary bicycle (Figure 3.1). Samples were collected before, during (plasma only), and after exercise and following a 4-hour rest. Western blotting revealed that endogenous MOTS-c levels in skeletal muscle significantly increased after exercise (5-fold) and remained elevated after a 4-hour rest, albeit exhibiting a trend to return to baseline (3.8-fold) (Figure 3.1b, c; Figure 3.2; Supplementary Figure 3.1). ELISA revealed that circulating endogenous MOTS-c levels also significantly increased during (1.6-fold) and after (1.5-fold) exercise, which then returned to baseline after 4 hours of resting (Figure 3.1d; Figure 3.2b). These findings suggest that exercise induces the expression of mitochondrial-encoded regulatory peptides in humans. Figure 3.2: MOTS-c levels in human muscle and plasma. MOTS-c levels measured by a Western blotting on human skeletal muscle collected pre-, post-exercise and 4- hours of resting and b ELISA on plasma from same individuals collected pre-, mid-, post- exercise and 4-hours of resting (n=10). Statistics by Wilcox-on matched-pairs signed rank test. **P<0.01 We next probed if MOTS-c functions as an exercise-induced mitochondrial signal that improves physical capacity by treating young mice (CD-1; outbred) daily with MOTS-c [5mg/kg/day; intraperitoneal injections (IP)] for 2 weeks. The rotarod performance test, whereby mice are placed on a rotating rod, revealed that daily MOTS-c significantly improved physical 58 capacity (Figure 3.3), but not grip strength (Figure 3.3b) in young mice. Because the rotarod test can also be affected by cognitive capacity, we assessed learning and memory using the Barnes maze and found no improvement (Figure 3.3c and 3.3d). Figure 3.3. Rotarod, grip strength, and Barnes Maze tests in MOTS-c treated old mice. a Summary of latency time to fall on the Rotarod test (n=15). The speed of the rotations increased from a starting speed of 24 rpm by 1 rpm every 10 seconds. b grip strength test. c,d Barned Maze performance in control and MOTS-c treated 12-week old CD-1 mice (n=15). c There was no changes in average time to find the escape box (latency) between control and MOTS-c treated mice. d There was no change in the number of errors made prior to discovering the escape box between groups. Errors were defined as nose-pokes or head deflections over false holes. Data expressed as mean +/- SEM of three 24-hour acquisition cycles. Student’s t- test. *P<0.05, **P<0.01, ***P<0.001. A treadmill running test confirmed that MOTS-c treatment can enhance physical performance. Because MOTS-c is regulator of metabolic homeostasis that prevented high-fat diet (HFD)-induced obesity and insulin resistance (C. Lee et al., 2015), we tested if MOTS-c also improved running performance under metabolic (dietary) stress. We fed young mice (CD-1) a HFD (60% calories from fat) and treated them with 2 doses of MOTS-c (5 and 15 mg/kg/day; IP) (Figure 3.4). Mice on the higher dose of MOTS-c showed significantly superior running capacity following 10 days of treatment (Figure 3.5 a-c), but not 7 days of treatment (Figure 3.6). We progressively increased the treadmill speed to test both endurance and speed. The final stage, which required mice to sprint (23m/s), was reached by 100% of mice on the higher dose of MOTS- c, but only 16.6% in the lower dose and control (vehicle) groups (Figure 3.5d). Body composition analysis using a time-domain NMR analyzer revealed that both doses of MOTS-c significantly 59 retarded fat gain and that the high dose significantly increased lean mass in young mice (CD-1) (Figure 3.7), in accord with prior reports (C. Lee et al., 2015). Figure 3.4. Outline of HFD mouse experiments. Timeline of experiment for 12-week old male CD-1 (outbred) and C57BL/6J (inbred) mice fed a HFD or defined control diet. a CD-1 mice were fed a HFD and given daily intraperitoneal injections (IP) of MOTS-c (0, 5, or 15 mg/kg/day) from Day 0. Treadmill running tests were performed on Day 7 and Day 10. Daily MOTS-c injections ceased at Day 16. b C57BL/6J mice were started on either a HFD or a defined control diet on Day 0 and continued uninterruptedly throughout the experiment. Daily MOTS-c treatment (15 mg/kg; IP) started on Day 14. Treadmill running tests were performed on Day 24 and Day 28 (10 days and 14 days after the start of MOTS-c treatment). Mice were treated daily until Day 56, at which time metabolomics was performed. Figure 3.5: MOTS-c treatment increases physical capacity in young mice regardless of diet. a-c Treadmill performance of 12-week old male CD-1 (outbred) mice fed a normal diet (n=5-6); a running curves, b total time on treadmill, c total distance ran, and d percent capable of reaching the highest speed (sprint). In young CD-1 mice, we simultaneously initiated MOTS-c treatment and a HFD (Figure 3.4a). To test if MOTS-c can improve physical performance in mice that have been on a HFD, we fed young C57BL/6J mice a HFD, or a normal diet, for 2 weeks before initiating daily MOTS-c injections (15 mg/kg/day) for 2 weeks prior to a treadmill running test (Figure 3.4b). MOTS-c treatment significantly enhanced running performance on the treadmill regardless of the diet 60 (Figure 3.8a-d). MOTS-c treatment enabled 25% of the young C57BL/6J mice to enter the final running stage (highest speed) on a normal diet, but none on a HFD (Figure 3.8d). Consistent with our prior study (C. Lee et al., 2015), MOTS-c treatment curbed HFD-induced weight gain in C57BL/6J mice (Figure 3.9), which was largely driven by reduced fat accumulation (Figure 3.9b), but not loss of lean mass (Figure 3.9c), as determined by an NMR-based body composition analysis. Further, targeted metabolomics revealed that MOTS-c treatment significantly regulated (i) glycolysis/PPP (pentose phosphate pathway) and (ii) amino acid metabolism (Figure 3.9d; Figure 3.4b) in skeletal muscle, but not in liver, consistent with our previous study (C. Lee et al., 2015). Together, these data indicate that MOTS-c treatment can improve overall physical performance, in part, by targeting skeletal muscle metabolism in young mice. Figure 3.7: Body composition analysis on MOTS-c-treated young mice. Body composition was measured non-invasively using a time-domain NMR analyzer. a-c Young CD-1 mice were treated daily with MOTS-c (0, 5, or 15 mg/kg/day;IP) for 16 days (n=5-6) and percent a body weight, b fat mass, and c lean muscle mass were measured. Data expressed as mean +/- SEM. Significance determined by using two-way ANOVA (repeated measures). *P<0.05, **P<0.01, ***P<0.001. Figure 3.6: Initial running time of MOTS-c-treated young mice. a Running time of CD-1 mice following seven days of MOTS-c treatment (n=5-6). MOTS-c (15 mg/kg/day) treatment showed a trend towards enhanced running performance. 61 Figure 3.8: MOTS-c treatment increases physical capacity in young inbred mice regardless of diet. a-d Treadmill performance of 12-week old male C57BL/6J (inbred) mice fed a HFD (n=8); a running curves, b total time on treadmill c total distance ran d percent capable of reaching the highest speed (final stage). Data expressed as mean +/- SEM. Log- rank (Mantel-Cox) test was used for a. Otherwise, all statistics were performed using the Student’s t-test. *P<0.05, ** P<0.01, *** P<0.001. Figure 3.9: Body composition analysis on MOTS-c-treated young mice. Body composition was measured non-invasively using a time-domain NMR analyzer. a-c C57BL/6J mice either on a HFD or a defined control diet and treated daily with MOTS-c (15 mg/kg/day; IP) or saline control (n=8) and percent a body weight, b fat mass, and c lean muscle mass were measured. The dotted line at Day 14 represents the start of MOTS-c treatment. D PCA and MSEA on metabolomic data from skeletal muscle and liver of C57BL/6J mice that were fed a HFD, treated with MOTS-c, and exercised. Data expressed as mean +/- SEM. Significance determined by using two-way ANOVA (repeated measures). *P<0.05, **P<0.01, ***P<0.001. 62 Aging is accompanied by a progressive decline in mitochondrial function (Jang, Blum, Liu, & Finkel, 2018; Lopez-Otin et al., 2013) and loss of metabolic homeostasis, in which MOTS-c may play a role (Lopez-Otin et al., 2016a; Quirós et al., 2016). Aging is associated with reduced MOTS- c levels in certain tissues, including the skeletal muscle, and in circulation (C. Lee et al., 2015; Ramanjaneya, Bettahi, et al., 2019). We previously showed that an acute one-week MOTS-c treatment reversed age-dependent insulin resistance in mouse skeletal muscle (C. Lee et al., 2015). Thus, we investigated if promoting metabolic homeostasis by MOTS-c treatment could reverse age-dependent decline in physical capacity. Middle-aged (12 mo.) and old (22 mo.) C57BL/6N mice were treated daily with MOTS-c (15 mg/kg/day; IP) for 2 weeks, then subjected to a treadmill running test (Figure 3.10). Both middle-aged and old mice ran significantly longer following MOTS-c treatment (Figure 3.11a). Old mice ran longer (2-fold) (Figure 3.11b) and farther (2.16-fold) (Figure 3.11c) when treated with MOTS-c. Further, MOTS-c enabled 17% of the old mice to enter the final running stage (highest speed), whereas none in the untreated group were successful (Figure 3.11d). Figure 3.10: Timeline of aged mouse experiment. Schedule of MOTS-c treatment and assays in middle-aged and old C57BL/6N mice (n=10 and 16-19, respectively). 63 Figure 3.11: Acute MOTS-c treatment enhances physical capacity in old mice. Aged C57Bl/6N mice treadmill running tests shows a treadmill running curve, b total time on treadmill, c total distance ran on treadmill and d percent capable of reaching the highest speed (final stage). Data expressed as mean +/- SEM. Log-rank (Mantel-Cox) test was used for a. Otherwise, all statistics were performed using the Student’s t-test. *P<0.05, ** P<0.01, *** P<0.001. Notably, MOTS-c treatment enabled old mice to outperform untreated middle-aged mice, suggesting a more pervasive physical re-programming rather than just rejuvenation. Respiratory exchange ratio (RER), measured using a metabolic cage, indicates fuel preference (1.0: carbohydrates, 0.7: fat). “Metabolic flexibility”, which refers to the overall adaptive capacity to a shift in metabolic supply-demand equilibrium (e.g. exercise), declines with age (Goodpaster & Sparks, 2017; Smith, Soeters, Wüst, & Houtkooper, 2018). Indeed, old mice relied on carbohydrates regardless of the time of day (Figure 3.12a), whereas middle-aged mice, and MOTS-c-treated old mice, exhibited a circadian-dependent shift in fuel usage that favored fat during the daytime (Figure 3.12a), coinciding with the low-feeding hours (Figure 3.12b,c). Figure 3.12: Circadian RER and feeding patterns in aged mice. a Respiratory exchange ratio (RER) following 2 weeks of daily MOTS-c injection (n=4). b The sum of continuous food intake measurements using metabolic cages divided into daily quartiles in MOTS-c-treated middle-age (14 months) and old (24 months) mice (n=4). c Measurements were taken 6 month later in same mice. Data expressed as mean +/- SEM. Student’s t-test. *P<0.05, **P<0.01, ***P<0.001. 64 Metabolomic analysis on skeletal muscle collected immediately post-exercise (a 30- minute run at a fixed moderate speed) in MOTS-c-treated (2 weeks) mice revealed that MOTS-c significantly regulated glycolysis and amino acid metabolism (Figure 3.13a); the skeletal muscles of non-exercised mice did not show significant alterations in response to MOTS-c (Figure 3.14a), suggesting that MOTS-c induces an adaptive metabolic response to exercise. To begin to understand the molecular mechanisms underlying the effects of MOTS-c, we performed RNA-seq Figure 3.13: Metabolomic analysis of aged mouse skeletal muscle. a metabolomics and analyzed using PCA and MSEA and b GSEA analysis of muscle RNA- seq analysis. Balloon plots of select enriched terms using Gene Ontology Biological Process (GO_BP) database at false discovery rate (FDR) < 15%. Full GSEA results are available in Supplementary Table 2. Data expressed as mean +/- SEM. GSEA statistics from R package 19 ‘clusterProfiler’ were used for b. Otherwise, all statistics were performed using the Student’s t- test. *P<0.05, ** P<0.01, *** P<0.001. analysis on the same skeletal muscles used for metabolomics. Although individual-to-individual variability was high, Gene Set Enrichment Analysis (GSEA) using the KEGG pathway database revealed that MOTS-c regulated processes related to (i) metabolism, including those known to be regulated by MOTS-c (e.g. AMPK signaling, glycolysis, and central carbon metabolism (K. H. Kim et al., 2018b; C. Lee et al., 2015), and (ii) longevity (FDR < 15%; select pathways in Figure 65 3.13b; full analysis in Supplementary Table 2). Gene Ontology Biological Process (GO_BP) analysis revealed a broader range of processes, including metabolism (lipid, carbohydrate, amino acid, and nucleotides), oxidative stress response, immune response, and nuclear transport (FDR < 15%; select pathways in Supplementary Figure 3.1; full analysis in Supplementary Table 3), again, consistent with our previous studies (K. H. Kim et al., 2018b; C. Lee et al., 2015). The rotarod performance test confirmed that MOTS-c treatment improved physical capacity in old mice (Figure 3.14b), while learning and memory was not affected as determined using the Y-maze test (Figure 3.14c), consistent with our observations in young mice (Figure 3.5). Together, these data suggest that MOTS-c treatment can significantly improve physical capacity in old mice, in part, by regulating skeletal function and improving “metabolic flexibility”. Figure 3.14: MOTS-c treated old mice metabolomics and performance. a Skeletal muscle from sedentary (not treadmill- exercised) old mice (22.5 months) treated daily with MOTS- c (15 mg/kg/day) for 2 weeks (n=10) were subject to metabolomics and analyzed using PCA. Middle-aged (14 mo.; n=5-6) and old (24 mo.; n=17-19) mice were treated daily with MOTS-c (15 mg/kg/day; IP) and subject to b a rotarod test and c Y- maze test. Data expressed as mean +/- SEM. Student’s t-test. ***P<0.001. 66 Anti-aging interventions that are applied later in life would be more translationally feasible compared to life-long treatments (Harrison et al., 2009; Mao et al., 2018; Rae et al., 2010). Building on the treadmill running tests, we tested if a late-life initiated (~24 mo.) intermittent (LLII) MOTS-c treatment (3x/week; 15mg/kg/day) would improve healthy lifespan (Figure 3.10). To assess healthspan, towards the end-of-life (>30 mo.), we performed a battery of physical tests to further probe the effect of MOTS-c on reversing age-dependent physical decline (Figure 3.10). LLII MOTS-c improved (i) grip strength (Figure 3.15a), (ii) gait, assessed by stride length (Figure 3.15b), and (iii) physical performance, assessed by a 60-second walking test (running was not possible at this age) (Figure 3.15c). In humans, reduced stride length and walking capacity are strongly linked to mortality and morbidity (Neufer et al., 2015). Together, these data indicate that LLII MOTS-c treatment improves physical capacity in old mice. Figure 3.16: MOTS-c increases lifespan in LLII mice. Lifespan curve; P=0.05 until 31.8 months of age. Overall curve trended towards increased median and maximum lifespan (P=0.23). Data expressed as mean +/- SEM. Log-rank (Mantel- Cox) test was used. Figure 3.15: MOTS-c regulation of physical performance in aged mice. a grip strength test (n=11), b gait analysis (stride length) (n=5), c 60-second walking test (n=11-12), and d blood glucose levels (n=11). All statistics were performed using the Student’s t-test. *P<0.05, ** P<0.01, *** P<0.001. 67 Independent lines of research have shown that MOTS-c is a mitochondrial- encoded metabolic regulator at the cellular and organismal level (L. R. Cataldo, Fernandez- Verdejo, Santos, & Galgani, 2018; Du et al., 2018; K. H. Kim et al., 2018b; C. Lee et al., 2015; Qingyang Li et al., 2019; H. Lu et al., 2019a; Ramanjaneya, Bettahi, et al., 2019; Ramanjaneya, Jerobin, et al., 2019). We posited that LLII MOTS-c treatment would cause metabolic reprogramming in old mice. Consistent with our previous report (C. Lee et al., 2015), non-fasting blood glucose was better maintained in LLII MOTS-c-treated old mice (30 mo.; Figure 3.15d). Over course of their life, LLII MOTS-c-treated mice showed comparable body weight to their untreated counterparts (Figure 3.17a and b) However, total food intake was significantly reduced (Figure 3.12; Figure 3.17c and d), whereas total activity was significantly higher (Figure 3.18). Body composition analysis using a time-domain NMR analyzer revealed significant reduction of fat mass (Figure 3.17e and f) and a modest increase in lean mass (Figure 3.17g and h). The RER, measured using metabolic cages, at 30 mo. revealed increased fat utilization, consistent with that obtained at ~23.5 mo. (Figure 3.12), but with a circadian shift (Figure 3.19); this is also consistent with reduced total fat mass (Figure 3.9) and increased lipid utilization (C. Lee et al., 2015; Ramanjaneya, Jerobin, et al., 2019). Ultimately, LLII MOTS-c Figure 3.17: MOTS-c regulates Aging Metabolism. e, f Body weight e as a function of time and f the total sum (∑); g, h Food intake g as a function of time and h the total sum (∑); i, j Percent fat mass i as a function of time and j the total sum (∑); k, l Percent lean mass k as a function of time and l the total sum (∑). All statistics were performed using the Student’s t-test. *P<0.05, ** P<0.01, *** P<0.001. 68 treatment showed a trend towards increased median (6.4%) and maximum (7.0%) lifespan and reduced hazard ratio (0.654); P=0.05 until 31.8 months (Figure 3.16). Larger cohorts will be needed to confirm the broader significance of MOTS-c treatment on overall longevity. These data suggest that LLII MOTS-c treatment improves overall physical capacity in old mice and may compress morbidity and increase healthspan. Skeletal muscle must adapt to various exercise-induced challenges (John, Hargreaves, Michael, & Juleen, 2014), including nutrient (e.g. metabolic supply-demand imbalance) (Brendan Egan & Juleen, 2013), oxidative (Troy L. Merry & Ristow, 2015; Packer, Cadenas, & Davies, 2008), and heat stress (Chrétien et al., 2018; John et al., 2014), which share mitochondria as a common denominator. Because MOTS-c enhanced cellular resistance against metabolic/oxidative stress (K. H. Kim et al., 2018b), we tested if MOTS-c treatment improved skeletal muscle adaptation to metabolic stress using C2C12 mouse myoblast cells. Using crystal Figure 3.18: Total physical activity in MOTS-c-treated old mice. Total movement [horizontal and vertical movement (XYZ-axis)] of MOTS-c-treated a middle-aged (14 mo.) and b old (24 mo.) mice were continuously measured using metabolic cages throughout the day for three days (n=4). c The sum of all measured movements is shown. d-f The procedure was repeated on the same mice after 6 months of LLII MOTS-c treatment. Data expressed as mean +/- SEM of three 24-hour acquisition cycles. Student’s t-test. *P<0.05, **P<0.01, ***P<0.001. 69 violet staining to determine cellular viability, we found that MOTS-c (10µM) treatment significantly protected C2C12 cells (~2- fold) from 48 hours of metabolic stress [glucose restriction (GR; 0.5 g/L) and serum deprivation (SD; 1% FBS)] (Figure 3.20a). Next, we tested the replicative capacity of C2C12 cells following prolonged metabolic stress as a functional marker of protection. C2C12 cells were metabolically stressed (GR/SD) for one week with daily MOTS-c (10µM) treatment, then replenished with complete medium for 2 days and stained with crystal violet. MOTS-c-treated C2C12 cells showed significantly enhanced proliferative capacity within 2 days (~6-fold) (Figure 3.20b). Because MOTS-c promotes fat utilization, which may underly its effect on “metabolic flexibility” (Figure 3.12; Figure 3.17e and f; Figure 3.9) (Su‐Jeong Kim et al., 2019; C. Lee et al., 2015; Ramanjaneya, Jerobin, et al., 2019), we tested if MOTS-c-treated C2C12 cells could survive on lipids without glucose (0 g/L). As expected, most control cells died without glucose even with lipid supplementation, whereas MOTS-c treatment provided significant protection (~2- fold) (Figure 3.20c). Real-time metabolic flux analysis revealed that MOTS-c treatment significantly increased lipid utilization capacity (Figure 3.21a) and lipid-dependent glycolysis (Figure 3.21b) in C2C12 cells. Figure 3.19: MOTS-c-dependent circadian fuel selection old mice. Respiratory Exchange Ratio (RER) measurements in LLII MOTS-c-treated, or control, old mice (30 mo.; n=4). Shaded region represents daytime (light cycle). Data expressed as mean +/- SEM of three 24- hour acquisition cycles. Two-way ANOVA (repeated measures). 70 We previously reported that endogenous MOTS-c translocates to the nucleus to directly regulate adaptive nuclear gene expression in response to cellular stress (K. H. Kim et al., 2018b). Using fluorescently labeled MOTS-c peptide (MOTS-c-FITC), we confirmed that exogenously treated MOTS-c also dynamically translocated to the nucleus in a time-dependent manner (Figure 3.22) (K. H. Kim et al., 2018b), indicating a direct nuclear role. We performed RNA-seq on C2C12 cells treated with MOTS-c or vehicle control (10µM) under GR/SD for 48 hours and found (i) clustering by treatment type using a principal component analysis (PCA) (Figure 3.23a) and (ii) 69 genes that were differentially regulated at FDR < 5% (Figure 3.23b). Further, using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database to assess putative changes in protein-protein interaction networks based on our RNA-seq results, we found that a cluster related to heat-shock responses, including Hsp40 (DNAJ) and Hsp70s (HSPA), were prominently regulated by MOTS-c in C2C12 cells under GR/SD; we also identified previously reported MOTS-c targets, including Atf3, Jun, Fosl1, and Mafg (K. H. Kim et al., 2018b) (Figure 3.23c). Figure 3.20: MOTS-c enhances adaptation to metabolic stress. a- c Survival of MOTS-c-treated (10μM; equal-volume vehicle control) C2C12 myoblasts assessed by crystal violet staining following a 48 hours of glucose restriction (GR; 0.5 g/L) and serum deprivation (SD; 1% FBS) with MOTS-c treated only once initially (n=12), b 7 days of GR/SD with daily MOTS-c treatment, followed by a 2-day recovery in full media with MOTS-c (n=10), and c 48 hours of complete GR (0 g/L) with chemically-defined lipid supplementation and daily MOTS-c treatment (n=6). 71 Consistently, select GO_BP analysis revealed protein regulation as a major target process of MOTS-c in myoblasts (select terms in Supplementary Figure 3.2; full results in Supplementary Table 4). To identify common pathways in in vitro and in vivo models, we overlaid RNA-seq data from MOTS-c-treated (i) old mouse skeletal muscle and (ii) metabolically stressed (GR/SD) C2C12. Select GO_BP analysis revealed several commonly targeted processes, including protein regulation/metabolism, cellular metabolism, and oxidative stress response (select terms in Supplementary Figure 3.3a; full results in Supplementary Table 5). Notably, enrichment analysis of putative transcription factors whose target genes were differentially regulated (FDR < 5%), inferred from the ChEA knowledge base (Kuleshov et al., 2016), revealed HSF1 as a commonly enriched transcription factor upon MOTS-c treatment in mouse skeletal muscle and myoblasts (Supplementary Figure 3.3b). HSF1 is a master nutrient sensor and regulator of stress responses, in part, by inducing the expression of a myriad of heat shock proteins, including Hsp40 (DNAJ) and Hsp70s (HSPA), which were induced by MOTS-c (Figure 3.23c) (Anckar & Sistonen, 2011; Li, Labbadia, & Morimoto, 2017). Exercise induces HSF1 activation in skeletal muscle (Vasilaki, Figure 3.21: MOTS-c-dependent glycolytic rate in lipid-stimulated mouse myoblasts. a Real-time oxygen consumption rate (OCR) in response to fatty acid (palmitate-BSA) in C2C12 myoblasts treated with MOTS-c (10μM) for 48 hours (n=11-12). b C2C12 mouse myoblasts were treated with MOTS-c (10μM) or saline control in nutrient- limited media (n=11-12). Real- time glycolytic flux determined by the extracellular acidification rate was measured using the XF96 Seahorse bioanalyzer. Prior to the start of the assay, nutrient-deprived cells were given either BSA alone or palmitate bound to BSA (palmitate-BSA) to determine the capacity to metabolize fatty acids. Data expressed as mean +/- SEM. Student’s t-test. *P<0.05, **P<0.01, ***P<0.001. 72 McArdle, Iwanejko, & McArdle, 2006) and cardiac muscle (Sakamoto et al., 2006) in rodents. Indeed, siRNA-mediated HSF1 knockdown reversed the protective effects of MOTS-c against GR/SD in myoblasts (Supplementary Figure 3.3c). Together, these data suggest that MOTS-c improves metabolic homeostasis/flexibility and protein homeostasis in skeletal muscle under exercise-induced stress conditions. Discussion Our study shows that exercise induces mtDNA-encoded MOTS-c expression in humans. MOTS-c treatment significantly (i) improved physical performance in young, middle-aged, and old mice, (ii) regulated skeletal muscle metabolism and gene expression, and (iii) enhanced adaptation to metabolic stress in C2C12 cells in a HSF1-dependent manner. Thus, it is plausible that the physiological role of exercise-induced MOTS-c is to promote adaptive responses to exercise-related stress conditions (e.g. metabolic imbalance and heat shock) in the skeletal muscle and maintain cellular homeostasis. Figure 3.22: MOTS-c translocation under stress. Time- dependent subcellular localization pattern of exogenously treated MOTS-c-FITC (10μM) in C2C12 myoblasts. Scale bar: 10μm. 73 Mitochondria are strongly implicated in aging at multiple levels (Jang et al., 2018; Kauppila, Kauppila, & Larsson, 2017; Lopez-Otin et al., 2013; Lopez-Otin et al., 2016a; Son & Lee, 2019; Y. Wang & Hekimi, 2015). Here, we present evidence that the mitochondrial genome encodes for instructions to maintain physical capacity (i.e. performance and metabolism) during aging and thereby increase healthspan. MOTS-c treatment initiated in late-life, proximal to the age at which the lifespan curve rapidly descends for C57BL/6N mice, significantly delayed the onset of age-related physical disabilities, suggesting “compression of morbidity” in later life (Crimmins, 2015). Interestingly, an exceptionally long-lived Japanese population harbors a mitochondrial DNA (mtDNA) SNP (m.1382A>C) that yields a functional variant of MOTS-c (Fuku et al., 2015; Zempo et al., 2016b). Our study shows that exogenously treated MOTS-c enters the nucleus and regulates nuclear gene expression, including those involved in heat shock response and metabolism. Thus, age-related gene networks are comprised of integrated factors encoded by both genomes, which Figure 3.23: C2C12 RNA-seq heatmap and protein interactions. a Principle Component Analysis (PCA) and b heatmap of significantly differentially regulated genes by MOTS-c at false discovery rate (FDR) < 5% by DESeq2 analysis. c Protein-protein interaction network analysis based on genes that were significantly differentially regulated by MOTS-c (FDR < 5%) using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database version 11.0. 74 entails a bi-genomic basis for the evolution of aging. Although the detailed molecular mechanism(s) underlying the functions of MOTS-c is an active field of research, we provide a “proof-of-principle” study that realizes the mitochondrial genome as a source for instructions that can regulate physical capacity and healthy aging. Materials and Methods Mouse Care All animal work was performed in accordance with the University of Southern California (USC) Institutional Animal Care and Use Committee. MOTS-c (New England Peptide, USA) was administered daily at 5 or 15 mg/kg via intraperitoneal injections. 12-week old male CD-1 (outbred) mice (Charles River, USA), 12-week old male C57BL/6J mice (Jackson Laboratory) and 8- and 18-month old male C57BL/6N mice (National Institute on Aging; NIA) were obtained. All mice were fed either a HFD (60% calories from fat) or matching control diet (Research Diets, USA, #D12492 and D12450J, respectively). NIA mice were sufficiently acclimated for 4 months in our vivarium until they were considered middle-aged (12 mo.) and old (22 mo.) at the start of MOTS-c injections. Body weight and food consumption were recorded daily, while body composition was analyzed twice weekly using an LF90II time-domain NMR minispec (Bruker, USA). After eight weeks of injections (23.5 months of age), mice were transitioned to receive MOTS-c injections three times weekly. No live mouse was censored. Physical Tests in Mice Running Test: Prior to running training/testing, mice were acclimated to the stationary treadmill apparatus (TSE-Systems, USA) for ten minutes on two consecutive days (Days 1 and 2). Both the high intensity test and training protocols were adapted from previously published protocols (Das et al., 2018). Running training was given twice on non-consecutive days and 75 consisted of a fixed speed run of 10 m/min for 20 minutes (Days 4 and 6) on a level treadmill. The treadmill test on Day 10 consisted of three stages. Stage one was a five-minute run at 13 m/min. For the next five minutes, the speed was increased by 1 m/min. The mice run at a fixed speed of 18 m/min for the next 30 minutes. Finally, after 40 minutes of total run time, the running speed is increased to 23 m/min until exhaustion is reached. All training and testing were done on a level treadmill. Mice resting on the platform were gently prodded to encourage re-engagement. Any mouse that resisted prodding and remained on the platform for 30 seconds was considered to be exhausted, and time was recorded. Walking Test: When the mice reached 30 months of age, they were no longer capable of performing the same treadmill routine. We developed a measure of mobility in the aged mice consisting of a 60 second walking test. The treadmill was set at 13 m/min for 60 seconds. We recorded whether the mouse was able to walk, or not, on the treadmill for 60 seconds, with gentle prodding as needed. Mice remaining on the stationary platform, refusing to engage in the treadmill walking, for more than five seconds were considered to have failed the test. Rotarod: The Rotarod test was performed by placing the mice on the apparatus (TSE- Systems), all facing the opposite direction of rotation. The initial speed of rotation was 24 rpm and accelerated at 1 rpm every 10 seconds. Time to fall was recorded for each mouse, and three trials per mouse was run. Mice received no less than five minutes of recovery time between trials. Grip Strength: We measured grip strength using a horizontal bar connected to the grip strength meter (TSE-Systems) as a high precision force sensor for the forelimbs. After allowing the mouse to properly grip the bar they were firmly and quickly pulled in the opposite direction. 76 Only trials where the mouse released its paws from the bar simultaneously were counted as successful. Mice underwent three trials, with at least 30 seconds recovery time between trials. Gait Analysis: To perform gait analysis, we applied a different color of non-toxic ink (BLICK®, USA) to the front and hind paw of the mice to record footprints. Barriers were constructed to guide the mice to walk straight on the recording paper. The home cage was kept at the end of the recording paper to encourage completion of the test. Only trials in which the mouse made a continuous, direct path to its home cage were counted. Stride length was measured as the average forward movement of three full strides as previously described (Carter, Morton, & Dunnett, 2001). Cognitive Tests Y-maze tests were performed as previously described (Brandhorst et al., 2015). Briefly, mice were placed in a maze consisting of three arms equally spaced 120° apart. Mice were placed in one arm of the maze and allowed to freely explore the maze for five minutes. Total arm entries and arm choices were recorded for each mouse. An arm entry was defined as a mouse having both front and hind paws entering the arm fully. Percent alternations was defined as an arm choice differing from the previous two compared to the total number of alternation opportunities. Barnes maze tests were performed as previously described (Brandhorst et al., 2015). 12- week old male CD-1 mice were tested twice daily for 7 days. Mice were placed in a start chamber in the middle of the maze and allowed to habituate (30 seconds), then the mouse was released to explore the maze and find the escape box (EB). Latency (time to enter the EB) and number of 77 errors (nose pokes and head deflections over false holes) were recorded. A maximum of 2 minutes was allowed for each trial. In vivo Metabolism Assessment Metabolic Cages: Metabolic activity in mice was measured using the PhenoMaster system (TSE-Systems) equipped to detect indirect calorimetry, measure food and water intake, and monitor activity. Prior to metabolic analysis, mice were housed 3-4 per cage in a facility with a 12:12 hour light- dark cycle (light period 0600-1800) at 24ºC. Food and water were available ad libitum. For metabolic assessment, mice were moved into individual PhenoMaster cages in an isolated room under the same environmental conditions. Mice were automatedly monitored for 36 hours to record physiological parameters. To measure O2 intake and CO2 production, gas sensors were calibrated prior to the study using primary gas standards of known concentrations of O 2, CO2, and N2. Room air was passed through the animal chambers at a rate of 0.5 L/min. Exhaust air from individual cages were sampled at 30-minute intervals for 3 minutes. Sample air was passed through sensors to determine oxygen consumption (VO2) and carbon dioxide production (VCO2). The respiratory exchange ratio (RER) was calculated as the ratio of carbon dioxide produced to oxygen consumption. The PhenoMaster system allows for activity monitoring using a triple beam IR technology system. Breaking the IR beams through movement was considered a “count”. The three-beam system allows XYZ monitoring that considers both ambulatory activity around the cage as well as rearing activity. All data are expressed as the mean of three 24-hour acquisition cycles. Blood glucose: Blood was collected via a single tail-nick and immediately analyzed using a glucometer (Freestyle, Abbott). Blood collection was performed by trained professionals and in 78 accordance with the University of Southern California Institutional Animal Care and Use Committee. Western Blots Protein samples were lysed in 1% Triton X-100 (Thermo Fisher Scientific, USA, #21568- 2500) with 1 mM EDTA (Promega Life Sciences, USA, #V4231) and 100 mM Tris-HCl pH 7.5 (Quality Biological, USA, #351-006-101) and protease inhibitors (Roche, Germany, #118636170001) and sonicated using a Sonic Dismembrator (Fisher Scientific, USA). Samples were heated at 95ºC for five minutes. Samples were ran on 4-20% gradient tris-glycine gels (TGX; Bio-Rad, USA, #456-1104) and transferred onto 0.2 µM PVDF membranes (Bio-Rad #162-0184) using a Transblot Turbo semi-dry transfer system (Bio-Rad) at 9 volts for 15 minutes. Membranes were blocked for 1 hour using 5% BSA (Akron Biotech, USA, #AK8905-0100) in tris-buffered saline containing 0.05% Tween-20 (Bio-Rad #161-0781) and incubated in primary antibodies against MOTS-c (rabbit polyclonal; YenZym, USA) and GAPDH (cat# 5174; Cell Signaling, USA) overnight at 4ºC. Secondary HRP-conjugated antibodies (#7074; Cell Signaling, USA) were then added (1:30,000) for one hour at room temperature. Chemiluminescence was detected and imaged using Clarity western ECL substrate (Bio-Rad #1705060) and Chemidoc XRS system (Bio-Rad). Western blots were quantified using ImageJ version 1.52k. Cell Studies Cell culture: C2C12 cells were cultured in DMEM with 4.5 g/L glucose (Corning, USA #10- 017-CV) and 10% FBS (Millipore-Sigma, USA, #F0926-500). All cells were stored at 37°C and 5% CO2. Cells were passaged when they reached 75-80% confluence using TrypLE (Thermo Fisher Scientific #12605-010). 79 Cell survival assays: Protection against glucose restriction (GR) and serum deprivation (SD) was tested by culturing cells in DMEM (Thermo Fisher Scientific #11966-025) with 0.5 g/L glucose (Millipore-Sigma #G8769) and 1% FBS. MOTS-c (10µM) or vehicle (PBS). MOTS-c (10µM) was added to the media every 24 hours. After 48 hours of GR/SD, we performed crystal violet (Thermo Fisher Scientific #C581-25) staining as before (K. H. Kim et al., 2018b) to determine cell survival. We also tested cellular proliferation, following prolonged (7-day) GR/SD (DMEM with 1% FBS and 0.5 g/L glucose), as a measure of cellular fitness. In this case, MOTS- c-containing (10µM) media was changed once every two days; no additional MOTS-c supplementation was given between media changes. After 7 days of GR/SD, we returned the cells to full growth media (10% FBS and 4.5 g/L glucose) for 48 hours with MOTS-c (10µM), then stained them with crystal violet. To determine the metabolic flexibility to utilize fatty acids, we cultured cells in DMEM with 1% FBS, 0.5 g/L glucose, and 1% chemically defined lipid mixture (Millipore-Sigma #L0288) for 48 hours, then stained them with crystal violet. Metabolic flux: Real-time oxygen consumption and extracellular acidification rates in C2C12 myoblasts treated with 16% palmitate-BSA (1mM palmitate conjugated to 0.17mM BSA) or 16% BSA (0.17 mM; Seahorse Bioscience #102720-100) were obtained using the XF96 Bioanalyzer (Seahorse Bioscience) at the USC Leonard Davis School of Gerontology Seahorse Core. All values were normalized to relative protein concentration using a BCA protein assay kit (Thermo Fisher Scientific #23227). 80 Confocal microscopy: Confocal images were obtained using a Zeiss Confocal Laser Scanning Microscope 700 (Zeiss, Germany). C2C12 myoblasts were cultured on glass coverslips (Chemglass, USA, #CLS-1760-015). Cells were treated with FITC-MOTS-c (New England Peptide) for either 0 hours (immediate), 30 minutes, 4 hours, or 24 hours. Some cells were left as untreated controls (data not shown). All cells were treated with Hoeschst (Biotium, USA, #40045) for 15 minutes and then washed three times with PBS. Cells were fixed in 10% formalin (Millipore- Sigma #EM-R04586-82) and washed an additional three times in PBS. Coverslips were affixed to glass slides (VWR, USA, #48300-025) using ProLong Gold antifade reagent (Life Technologies Corporation, USA, #P36934). Human Studies Study outline: Participants gave written consent before the commencement of the study, which was approved by the Northern Health and Disability Ethics Committee (New Zealand) (16/STH/116/AM01). 10 sedentary (<4h aerobic exercise/week) healthy young males (24.5 ± 3.7 years old and BMI 24.1 ± 2.1) were recruited to take part in a two-visit exercise trial. Recruited participants were free of cardiovascular, metabolic and blood diseases and were not taking any medication or supplements. The trial was separated into two visits, each involving exercise bouts that were carried out on an electromagnetically braked cycle ergometer (Velotron, RacerMate, USA). Determination of peak oxygen uptake (VO2 peak) and maximal power output (visit 1): Peak oxygen uptake was determined using a ramped cycling exercise protocol. Prior to testing, participants warmed up for five minutes at a self-selected workload between 60 and 80W. The ramp protocol began at 60W, with the cycling power output set to increase by 1W every 4 seconds 81 (15W/min) continuously until the participant was unable to maintain cycling workload (cycling cadence<minimum 60 revolutions per minute) or maximal volitional fatigue was reached. Mean Peak oxygen uptake of participants was reported as 38.4 ± 7.3 ml.kg.min. Acute high intensity cycling exercise session (visit 2): Prior to visit 2, participants were asked to fast overnight (from 10PM) and were instructed to abstain from physical activity for at least 48h prior. Upon arrival to the laboratory, participants lay supine for 15-minutes and then had an intravenous cannula inserted into a forearm vein. A resting plasma sample was collected followed by a pre-exercise muscle biopsy taken from the vastus lateralis muscle (quadriceps muscle). Approximately 10-minutes later, participants completed ten, 60-second cycling intervals at individually-specified peak power workloads (determined from peak oxygen uptake test) followed by 75-seconds of rest/low intensity cycling (<30W) per interval as previously described (Hedges et al., 2019). A mid-exercise blood sample was taken following the completion of the 5 th exercise interval as well as immediately following the completion of the exercise bout. In addition, an immediately-post exercise muscle biopsy was taken (within ~5-minutes of completion of the exercise bout). Participants remained supine and resting in the procedure bed for a 4-hour recovery period. Following four-hours of recovery, a final blood and muscle biopsy sample was collected. Muscle biopsy and blood sampling: Muscle biopsies were extracted under local anesthesia (1% xylocaine) using the Bergstrom needle with manual suction technique (Bergstrom, 1975). Biopsies were snap-frozen in liquid nitrogen and stored at −80°C until analyzed. Blood was drawn through a 20-gauge cannula, collected in 10-mL EDTA vacutainers 82 and then centrifuged immediately upon collection at 4°C at 2,000 g for 10 minutes. Plasma was extracted and then stored at −80°C until further analysis using an in-house ELISA as described before (C. Lee et al., 2015). Human skeletal muscle was processed for western blotting by soaking the samples in lysis buffer (above) and minced using a razor blade. Once the sample was evenly minced, we proceeded with the Sonic Dismembrator step as described above. Liquid Chromatography-Mass Spectrometry Metabolomics Metabolites were extracted from randomly selected tissue samples by adding 1 mL of 80:20 methanol:water solution on dry ice. Samples were incubated at -80C for 4 hours and centrifuged at 4C for 5 minutes at 15k rpm. Supernatants were transferred into LoBind Eppendorf microcentrifuge tubes and the cell pellets were re-extracted with 200 µL ice-cold 80% MeOH, spun down and the supernatants were combined. Metabolites were dried at room temperature under vacuum and re-suspended in water for injection. Samples were randomized and analyzed on a Q-Exactive Plus hybrid quadrupole-Orbitrap mass spectrometer coupled to an UltiMate 3000 UHPLC system (Thermo Scientific). The mass spectrometer was run in polarity switching mode (+3.00 kV/-2.25 kV) with an m/z window ranging from 65 to 975. Mobile phase A was 5 mM NH4AcO, pH 9.9, and mobile phase B was acetonitrile. Metabolites were separated on a Luna 3 μm NH2 100 Å (150 × 2.0 mm) column (Phenomenex). The flowrate was 300 μl/min, and the gradient was from 15% A to 95% A in 18 min, followed by an isocratic step for 9 min and re-equilibration for 7 min. All samples were injected twice for technical duplicates. Metabolites were detected and quantified as area under the curve based on retention time and accurate mass (≤ 5 ppm) using the TraceFinder 3.3 (Thermo Scientific) software. 83 RNA-seq RNA purification from tissue and cells: Total RNA extraction from skelatal muscle tissue or C2C12 mouse myoblasts was done using TRI Reagent (Millipore-Sigma #T9424). Muscle tissue samples were flash-frozen in liquid nitrogen until further processing. Tissues were resuspended in 600µL of TRI Reagent, then homogenized on Lysing Matrix D 2mL tubes (MP Biomedicals) on a BeadBug homogenizer (Benchmark Scientific). For both skeletal muscle and C2C12 cells, total RNA was purified using the Direct-zol RNA MiniPrep (Zymo Research #R2052). RNA-seq library preparation: Total RNA was subjected to rRNA depletion using the NEBNext rRNA Depletion Kit (New England Biolabs), according to the manufacturer’s protocol. Strand specific RNA-seq libraries were then constructed using the SMARTer Stranded RNA-Seq Kit (Clontech # 634839), according to the manufacturer’s protocol. Based on rRNA-depleted input amount, 13-15 cycles of amplification were performed to generate RNA-seq libraries. Paired-end 150bp reads were sent for sequencing on the Illumina HiSeq-Xten platform at the Novogene Corporation (USA). The raw sequencing data was deposited to the NCBI Sequence Read Archive (accession: PRJNA556045). The resulting data was then analyzed with a standardized RNA-seq data analysis pipeline (described below). RNA-seq analysis pipeline: To avoid the mapping issues due to overlapping sequence segments in paired end reads, reads were hard trimmed to 75bp using the Fastx toolkit v0.0.13. Reads were then further quality-trimmed using Trimgalore 0.4.4 (github.com/FelixKrueger/TrimGalore) to retain high-quality bases with Phred score > 20. All reads were also trimmed by 6 bp from their 5' end to avoid poor qualities or biases. cDNA 84 sequences of protein coding and lincRNA genes were obtained through ENSEMBL Biomart for the GRCm38 build of the mouse genome (Ensemble release v94). Trimmed reads were mapped to this reference using kallisto 0.43.0-1 and the –fr-stranded option (Bray, Pimentel, Melsted, & Pachter, 2016). All subsequent analyses were performed in the R statistical software (https://cran.r-project.org/). Read counts were imported into R, and summarized at the gene level, to estimate differential gene expression as a function of age. Because of high sample variability, we used surrogate variable analysis to remove experimental noise from the muscle RNA-seq dataset (Leek & Storey, 2007). R package ‘sva’ v3.24.4 (Leek et al., 2019) was used to estimate surrogate variable, and the effects of surrogate variables were regressed out using ‘limma’. Corrected read counts were then used for downstream analyses. DEseq2 normalized fold-changes were then used to estimate differential gene expression between control and MOTS-c treated muscle or cell samples using the ‘DESeq2’ R package (DESeq2 1.16.1) (Love, Huber, & Anders, 2014). The heatmap of expression across samples for significant genes (Figure 3.23b) was plotted using the R package ‘pheatmap’ 1.0.10 (Raivo Kolde, 2015-12-11; https://CRAN.R-project.org/package=pheatmap). Putative protein-protein interaction was derived using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database version 11.0 (Szklarczyk et al., 2019) (https://string-db.org/). Functional Enrichment Analysis To perform functional enrichment analysis, we used the Gene Set Enrichment Analysis 85 [GSEA] paradigm through its implementation in the R package ‘ClusterProfiler’ v3.10.1 (Yu, Wang, Han, & He, 2012), and Bioconductor annotation package ‘org.Mm.eg.db’ v3.7.0. Balloon plots representing the output were generated using R packages ‘ggplot2’ v3.1.0 and ‘scales’ 1.0.0. GSEA analysis was conducted using the Genome Ontology information from the R ‘org.Mm.eg.db’ package, as well as the ChEA knowledge base derived from EnrichR (Kuleshov et al., 2016), as formatted in our previous study (Bérénice A. Benayoun et al., 2019). Principal Component Analysis Metabolites: Principal component analysis (PCA) was performed using the mean-centered matrix of metabolite values per each mouse. Principal components that separated sample groups were identified with visual inspection. Loadings from principal components that stratify experimental samples versus controls were then queried against metabolic pathways using a Kolmogorov-Smirnov statistic against the expected distribution of metabolites. Metabolic pathway enrichment analysis (gene set enrichment analysis, GSEA) (Subramanian et al., 2005) were performed using 28 metabolic pathways defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) database using pathways with four or more measured metabolites. RNA-seq: PCA was performed using the R base package function ‘prcomp’. The first 2 principal components were used. Quantification and Statistical Analysis Unless otherwise noted, statistical significance was determined using the Student t-test. Statistical tests were performed using GraphPad Prism version 8.1.2. Results of t-tests are indicated in all figures as *p<0.05, ** p<0.01, ***p <0.001 and ns for not significant (p>0.05). 86 The RNA-seq analytical code will be made available on the Benayoun lab github (https://github.com/BenayounLaboratory/MOTSc_Exercise). 87 Chapter IV: Conclusion and Final Thoughts MOTS-c improves exercise performance in mice I have demonstrated that treatment of exogenous MOTS-c in mice has improved their exercise performance both in an acute and chronic treatment. Acute MOTS-c injected daily allowed mice to run nearly twice as long as the mice receiving saline injections. Additionally, MOTS-c treatment prevented the accumulation of fat mass in mice of a HFD and maintained lean muscle mass. MOTS-c treatment increased performance in treadmill running and rotarod performance, but not grip strength. This may indicate that the performance boost is related to energy metabolism rather than a surge in overall strength associated with increased skeletal muscle mass. Interestingly, MOTS-c also improves physical performance in 30-month mice when treated LLII. This improvement may be attributed to an overall optimization of metabolic processes. Proteostasis is a key hallmark of aging that may be targeted in this result. The lifespan-extending mechanism may be linked to improved proteostasis and a more efficient response to various cellular stressors. Given the improved movement of the MOTS-c treated mice at 30 months compared to the saline treated mice, MOTS-c may well extend healthspan as well as lifespan. MOTS-c’s ability to improve exercise performance is not in itself a unique characteristic. The novelty of this enhancement comes from the fact that this peptide is mitochondrial-derived. The classically viewed “powerhouse of the cell” is in fact capable of communicating to the nucleus, causing the upregulation of genes that optimize energy efficiency. Rapid responses to dynamic energy demands is a criteria that must be met by cells to ensure survival. Tight regulation between 88 the mitochondrial and nuclear genomes helps fulfill this requirement, and MOTS-c acts as one of these messengers. While MOTS-c is the first established mitochondrial-encoded regulator of exercise metabolism that improves exercise performance, several other factors have been discovered capable of increasing physical performance using a variety of mechanisms (Figure 4.1; Table 4.1). MOTS-c acts through activation of AICAR and continues to AMPK (C. Lee et al., 2015). AICAR itself has been implicated as an exercise mimetic targeting skeletal muscle due to its role in mitochondrial biogenesis, expanding into changing muscle fiber types (S. Li & I. Laher, 2015; Narkar et al., 2008; Osler & Zierath, 2008; Sabina, Holmes, & Becker, 1984; Winder, Taylor, & Thomson, 2006). Similar to AICAR, GW501516 also increases fiber type switches through mitochondrial biogenesis (Narkar et al., 2008). However, rather than acting through AMPK, GW501516 targets peroxisome proliferator activated receptor delta (PPARd) in skeletal muscle (Y. X. Wang et al., 2004). Recently, PPARd has been shown to delay exhaustion from exercise through limiting the rate us glucose usage during physical exertion (Fan et al., 2017). Limits of exercise performance are generally dictated by glucose levels, while PPARd limits glucose consumption in skeletal muscles, allowing for greater substrate availability later in the exercise (Fan et al., 2017). Another class of exercise mimetics deal with PGC1a. Irisin, for example, is secreted from skeletal muscle during exercise and is therefore considered a myokine (Alvehus, Boman, Soderlund, Svensson, & Buren, 2014; Bostrom et al., 2012; Kraemer, Shockett, Webb, Shah, & Castracane, 2014; Mai et al., 2020; Norheim et al., 2014; Tsuchiya et al., 2014). PGC1a is an important regulator of various processes associated with energy metabolism, acting to prevent 89 obesity and extend lifespan (Wenz, Rossi, Rotundo, Spiegelman, & Moraes, 2009). Further, PGC1a expression in skeletal muscle drives the conversion of white adipose tissue to brown adipose tissue in a manner resembling exercise (Bostrom et al., 2012; Mai et al., 2020). Irisin is involved in thermogenesis and optimizes energy expenditure even in small increases in circulating irisin levels (Bostrom et al., 2012). Table 4.1: Exercise Mimetics This table is modified from (S. Li & I. Laher, 2015). Exercise Mimetic Classification Target Molecular Target Changes Reference AICAR AMPK Agonist Skeletal Muscle AMPK Mitochondrial Biogenesis (S. Li & I. Laher, 2015) (Narkar et al., 2008) Fiber type switch (Osler & Zierath, 2008) (Sabina et al., 1984) (Winder et al., 2006) GW501516 PPARd Skeletal Muscle PPARd Mitochondrial Biogenesis (Y. X. Wang et al., 2004) FIber type switch Irisin Myokine Adipose Tissue PGC1a Thermogenesis (Alvehus et al., 2014) (Bostrom et al., 2012) (Kraemer et al., 2014) Enhanced energy expenditure (Mai et al., 2020) (Norheim et al., 2014) (Tsuchiya et al., 2014) Resveratrol Phytochemical Cardiovasular PGC1a Angiogenesis (Canto et al., 2009) Mitochondrial Biogenesis (Lagouge et al., 2006) 90 Resveratrol also targets PGC1a and acts as an exercise mimetic but acts in a unique manner. Resveratrol impacts the cardiovascular system and increases exercise performance through mitochondrial biogenesis and angiogenesis (Canto et al., 2009; Lagouge et al., 2006). While resveratrol has shown promise, there has been little success in human clinical trials (Furrer & Handschin, 2020). However, the concept of targeting vascular aging as a method of improving physical capacity has remained a hot topic of research. For example, nicotinamide mononucleotide (MNM) has been shown to increase angiogenesis and blood flow, resulting in increased exercise performance (Das et al., 2018). Taken together, these exercise mimetics all home in on a single mechanism to simulate exercise itself. Exercise involves various biological systems, coordinated communication between organs and rapid cellular communication. While the field of exercise mimetics shows promise, there has yet to be a solution to combining these pathways. MOTS-c likely does not represent a “silver bullet” in mimicking the beneficial effects of Figure 4.1: Exercise mimetics in skeletal muscle. Various exercise mimetics (red diamonds) interact with AMPK and PPARd resulting in improved physical capacity. Irisin is secreted from skeletal muscle to improve performance through enhanced energy expenditure. MOTS-c is the only mitochondrial-encoded factor show to improve exercise performance. Adapted from (Fan et al., 2017). AMPK PGC1a PPARd AICAR GW1516 MOTS-c Irisin Resveratrol 91 exercise, yet its mitochondrial origin presents a critical difference between MOTS-c and other exercise mimetics. MOTS-c improves metabolic function under stress Another important finding of my research is the ability of MOTS-c to maintain metabolic homeostasis under various stress conditions. Our lab previously established the translocation of MOTS-c from the mitochondria to the nucleus under stress to upregulate a number of stress- response genes (K. H. Kim et al., 2018a). My research extends on this findings to include in vivo stress response models. We used both dietary stress induced by a HFD (Figures 3.5, 3.8, and 3.11) and metabolic stress through mouse aging. In both cases, MOTS-c improved exercise performance and metabolic function as determined by RER and body composition (Figures 3.7, 3.9, 3.12, 3.17 and 3.19). Aged mice treated with MOTS-c more resemble their younger counterparts rather than untreated age matched mice. This finding also applies to C2C12 mouse myoblasts. These cells were exposed to both GR and SD and MOTS-c improved their stress response under several conditions. First, in acute exposure, MOTS-c improved survival, displaying its protective capabilities. Second, we exposed the cells to chronic stress, and tested replicative capacity. MOTS-c treated cells showed increased replicative capacity after a weeklong exposure to GR/SD stress. To test the metabolic flexibility of MOTS-c, mirroring the RER studies in vivo, we removed glucose completely, and examined the ability of the cells to survive on lipid supplementation alone. Once again, MOTS-c improved survival in this condition where nearly all of the control treated cells dies (Figure 3.20). These findings highlight the critical role of MOTS-c in stress response, both on a cellular level as 92 well as organismal. Given the co-evolution of the mitochondria and nucleus and their need to develop efficient communication techniques to ensure survival, MOTS-c may act as a key regulator of a coordinated mitonuclear stress response. This finding I exemplified by the increase in both mean and maximal lifespan in mice receiving MOTS-c treatment as late as 22-months old. Such a late-life intervention has only previously been seen with rapamycin treatment (Harrison et al., 2009). Lifespan extension, however, is of less importance of extending healthspan, or the functional and disease-free period of life (Hansen & Kennedy, 2016). MOTS-c not only extends lifespan, but given the improvements in exercise performance, body composition, mobility at advanced age and improved maintenance of metabolic homeostasis, may very well extend healthspan as well. 93 Supplemental Information Supplementary Figure 3.1: Gene expression analysis on skeletal muscle from exercised MOTS-c-treated old mice. RNA-seq was performed on skeletal muscles from MOTS-c-treated old mice. Balloon plots of biological processes derived from Gene Set Enrichment Analysis (GSEA) using the Gene Ontology (Biological Process) database at a false discovery rate (FDR) < 15% (n=6). 94 Supplementary Figure 3.2: Gene expression analysis on MOTS-c-treated mouse myoblasts under metabolic stress. RNA-seq was performed on C2C12 myoblasts following 48 hours of GR/SD with MOTS-c (10μM) treatment only once initially (n=6). Balloon plots of biological processes derived from Gene Set Enrichment Analysis (GSEA) using the Gene Ontology (Biological Process) database at a false discovery rate (FDR) < 15% (n=6). 95 Supplementary Figure 3.3: MOTS-c regulates myoblast gene expression and cell growth. a Balloon plots of common biological processes derived from RNA-seq data between MOTS-c-treated (i) skeletal muscle from old mice and (ii) C2C12 myoblasts, based on gene set enrichment analysis (GSEA) using gene ontology biological process (GO_BP) b Balloon plots of common transcription factors derived from RNA-seq data between MOTS-c- treated (i) skeletal muscle from old mice and (ii) C2C12 myoblasts, based on ChEA database. c Relative cell number (n=3-6) at 48 vs 72 hours of GR/SD assessed by crystal violet staining. Data expressed as mean +/- SEM. Student’s t-test. *P<0.05, ** P<0.01, *** P<0.001. 96 Supplemental Table 1: Effects of MOTS-c in various cellular processes Effects of MOTS-c Models Ref. Metabolism Targets the methionine-folate cycle, increases AICAR levels, and activates AMPK. Regulates cellular glucose, mitochondrial, and fatty acid metabolism In vitro (C. Lee et al., 2015) Targets skeletal muscles and regulates insulin sensitivity. Prevents high-fat diet-induced obesity and insulin resistance. Mice (C. Lee et al., 2015) Reduces fat mass and improved OVX-induced lipid deposition in the liver. Reduced adipocyte size and suppresses adipose- inflammatory response, enhances lipid catabolism, and activates the brown adipose, in OVX mice Mice (H. Lu et al., 2019b) Circulating levels of MOTS-c is decreased in T2D Human (Ramanja neya, Bettahi, et al., 2019) Lean and obese people have similar plasma MOTS-c concentrations, but MOTS-c levels are associated with insulin sensitivity in lean, but not in obese people. Human (Luis Rodrigo Cataldo et al., 2018) Plasma MOTS-c decreases in obese male children and adolescents and decreases more significantly when they are already obese and insulin resistant. Plasma MOTS-c negatively correlates with body mass index, waist circumference, and fasting insulin in male obese children and adolescents. Human (Du et al., 2018) Low endogenous plasma MOTS-c is associated with impaired coronary endothelial function (human), and MOTS- c treatment improves endothelial function in rodents. Human Rodent (Q. Qin et al., 2018) MOTS-c treatment regulates plasma metabolites, reduces fat accumulation in muscle, improves insulin sensitivity in diet- induced obese mice. Mice (S. J. Kim et al., 2019) 97 MOTS-c treatment improves metabolic status and dermal aging in D-gal-induced aging mice and alleviates lipid accumulation in liver. Mice (Q. Li et al., 2019) Lipid enhances circulating MOTS-c while insulin attenuates the MOTS-c response in human. Human (Ramanja neya, Jerobin, et al., 2019) Circulating and skeletal muscle MOTS-c levels are decreased in chronic kidney disease patients. Human (Liu et al., 2019) Bone Biology MOTS-c treatment alleviates bone erosion by inhibiting osteoclastogenesis through the regulation of osteocyte OPG/RANKL secretion in an ultra-high molecular weight polyethylene (UHMWPE) particle-induced osteolysis mouse model. Mice (Yan et al., 2019) MOTS-c treatment alleviates bone loss and inhibits (RANKL) osteoclast differentiation. Mice (Ming et al., 2016) Gene Variants K14Q-MOTS-c is specific for the Northeast Asian population who are known to have long lifespan. Human (Fuku et al., 2015) K14Q-MOTS-c is associated with type 2 diabetes with lower MVPA in men, but not in women. K14Q-MOTS-c affects glucose tolerance in male mice. These suggest that K14Q-MOTS-c by m.1382 A>C polymorphism may influence prevalence of type 2 diabetes. Human (Zempo et al., 2016a) Males, but not females, with K14Q-MOTS-c exhibit higher prevalence of T2D K14Q-MOTS-c has reduced insulin sensitization effects compared to MOTS-c, and is less effective in reducing the body weight, fat mass, and glucose tolerance in CD-1 male mice exposed to high fat diet. Human Mice (Zempo et al., 2019) Senescence MOTS-c is increased in senescent primary human fibroblasts, and MOTS-c treatment increases mitochondrial respiration and selected components of the SASPs in doxorubicin-induced senescent cells partially via the JAK signaling pathway. Human (S. J. Kim et al., 2018) Immunity MOTS-c improves the survival in mice with MRSA infection and enhances bactericidal function of macrophages. Mice (Zhai et al., 2017) MOTS-c has anti-inflammatory effects in macrophages stimulated with MRSA. Mice (Zhai et al., 2017) 98 MOTS-c treatment in ultra-high molecular weight polyethylene particle-induced osteolysis mouse model alleviates inflammation by restraining NF-κB and STAT1 pathway. Mice (Yan et al., 2019) There is a decrease in MDP-coding genes MT- RNR1 (MOTS-c) expression in chronic fatigue syndrome (CFS), Q fever fatigue syndrome (QFS), and, to a lesser extent, in Q fever seropositive controls. Human (Raijmak ers et al., 2019) Adaptive Stress Response MOTS-c translocates to the nucleus to regulate the adaptive nuclear genome expression in response to metabolic stress. In vitro (K. H. Kim et al., 2018b) MOTS-c alleviates mitochondrial dysfunction caused by PM2.5 nanoparticle exposure and higher methylation in MT-RNR1 of the mtDNA D-loop is associated with higher MOTS-c level suggesting that MOTS-c may be regulated partially by mtDNA methylation in humans. In vitro (Breton et al., 2019) MOTS-c treatment promotes cold adaptation, decreases lipid accumulation upon acute cold exposure, and increases the white fat browning and brown fat activation upon acute cold exposure in mice. Mice (Huanyu Lu et al., 2019) Signaling Pathway MOTS-c functions that are dependent on AMPK activity. Misc. (K. H. Kim et al., 2018b; C. Lee et al., 2015; H. Lu et al., 2019b; Ming et al., 2016; Yan et al., 2019) Supplementary Table 2: Aged Mouse Skeletal Muscle Enrichment Pathway Names signed_enrichment minusLog10Pval 1 Carbon metabolism 2.039589448 1.562409499 2 Oxidative phosphorylation 2.033199169 1.562409499 3 Non-alcoholic fatty liver disease (NAFLD) 2.027337162 1.562409499 4 Biosynthesis of amino acids 1.991473497 1.562409499 5 Central carbon metabolism in cancer 1.989923011 1.562409499 6 Staphylococcus aureus infection 1.949091195 1.562409499 7 Parkinson disease 1.920518305 1.562409499 99 8 Huntington disease 1.90645093 1.562409499 9 AMPK signaling pathway 1.873324681 1.562409499 10 Phagosome 1.853352669 1.562409499 11 Citrate cycle (TCA cycle) 1.849538236 1.493006749 12 PI3K-Akt signaling pathway 1.821015267 1.562409499 13 Glycolysis / Gluconeogenesis 1.809358859 1.562409499 14 2-Oxocarboxylic acid metabolism 1.799356502 1.321745948 15 EGFR tyrosine kinase inhibitor resistance 1.782783948 1.562409499 16 Longevity regulating pathway 1.774263289 1.562409499 17 Influenza A 1.770004645 1.562409499 18 Protein digestion and absorption 1.765198706 1.562409499 19 Prostate cancer 1.74730538 1.562409499 20 Thermogenesis 1.723736659 1.562409499 21 Galactose metabolism 1.720941617 1.321745948 22 Alanine, aspartate and glutamate metabolism 1.703977923 1.256878031 23 AGE-RAGE signaling pathway in diabetic complications 1.696881371 1.562409499 24 Linoleic acid metabolism 1.678172377 1.02600155 25 Chemokine signaling pathway 1.675649721 1.493006749 26 Malaria 1.67283079 1.321745948 27 Arginine biosynthesis 1.657328651 0.994369395 28 Focal adhesion 1.650727676 1.493006749 29 TNF signaling pathway 1.642644135 1.493006749 30 Bladder cancer 1.62286423 1.224395938 31 ECM-receptor interaction 1.621791374 1.493006749 32 Glutathione metabolism 1.62084714 1.493006749 33 Glycosaminoglycan biosynthesis - chondroitin sulfate / dermatan sulfate 1.615090686 1.010654627 34 Epstein-Barr virus infection 1.614230238 1.396537406 35 Complement and coagulation cascades 1.600482813 1.396537406 36 JAK-STAT signaling pathway 1.598655256 1.562409499 37 Human papillomavirus infection 1.596146939 1.562409499 38 Gap junction 1.59419158 1.396537406 39 Apoptosis 1.592400197 1.493006749 40 Cysteine and methionine metabolism 1.585753066 1.224395938 41 Longevity regulating pathway - multiple species 1.580126466 1.321745948 42 Kaposi sarcoma-associated herpesvirus infection 1.575253726 1.396537406 43 Proteoglycans in cancer 1.565699488 1.396537406 44 Thyroid hormone synthesis 1.564276753 1.162582812 45 Insulin signaling pathway 1.561612458 1.493006749 100 46 Human T-cell leukemia virus 1 infection 1.561401694 1.396537406 47 Antigen processing and presentation 1.561203268 1.224395938 48 Mineral absorption 1.553908289 0.918170869 49 Arginine and proline metabolism 1.545262776 0.971624692 50 Small cell lung cancer 1.542118902 1.493006749 51 Relaxin signaling pathway 1.540266272 1.493006749 52 Insulin resistance 1.539217297 1.493006749 53 Melanoma 1.532284278 1.224395938 54 Measles 1.525060662 1.493006749 55 Acute myeloid leukemia 1.524255967 1.224395938 56 Platelet activation 1.519570571 1.396537406 57 Allograft rejection 1.517573758 0.838853421 58 Cytokine-cytokine receptor interaction 1.486987252 1.260912772 59 Alzheimer disease 1.483581026 1.321745948 60 Amoebiasis 1.47823062 1.010654627 61 Cell adhesion molecules (CAMs) 1.463867253 1.224395938 62 Chronic myeloid leukemia 1.461592772 0.953408173 63 Glioma 1.455410432 0.953408173 64 Tuberculosis 1.452427952 1.321745948 65 Adipocytokine signaling pathway 1.444625573 0.995003952 66 Hepatitis C 1.432221079 1.086375341 67 Endocrine resistance 1.430757313 0.953408173 68 Toxoplasmosis 1.420384759 0.946584966 69 Cellular senescence 1.390351028 1.043901391 70 Fluid shear stress and atherosclerosis 1.371181078 0.946798914 71 Pathways in cancer 1.366048258 1.493006749 72 Human cytomegalovirus infection 1.363219209 1.000827115 73 Estrogen signaling pathway 1.360908191 0.846110618 74 Hepatocellular carcinoma 1.333147274 0.892221953 101 Supplementary Table 3: Aged Mouse Gene Ontology Biological Process (GO_BP) analysis Pathway Names signed_enrichment minusLog10Pval 1 extracellular matrix organization 2.180429875 1.645776938 2 extracellular structure organization 2.13014533 1.645776938 3 cellular respiration 1.980983501 1.645776938 4 protein folding 1.972403521 1.645776938 5 leukocyte migration 1.96504037 1.645776938 6 tumor necrosis factor superfamily cytokine production 1.955684622 1.645776938 7 cytokine-mediated signaling pathway 1.948665395 1.645776938 8 tumor necrosis factor production 1.948409029 1.645776938 9 regulation of tumor necrosis factor superfamily cytokine production 1.941885555 1.645776938 10 regulation of tumor necrosis factor production 1.919206674 1.645776938 11 reactive oxygen species metabolic process 1.888591916 1.645776938 12 energy derivation by oxidation of organic compounds 1.885357934 1.645776938 13 regulation of reactive oxygen species metabolic process 1.874364869 1.645776938 14 regulation of lipid localization 1.85234645 1.645776938 15 myeloid leukocyte differentiation 1.841623627 1.645776938 16 positive regulation of small molecule metabolic process 1.841590348 1.645776938 17 cellular response to tumor necrosis factor 1.836374906 1.645776938 18 leukocyte chemotaxis 1.821818259 1.645776938 19 angiogenesis 1.821320285 1.645776938 20 generation of precursor metabolites and energy 1.806860938 1.645776938 21 regulation of leukocyte migration 1.805988177 1.645776938 22 cellular response to acid chemical 1.784463959 1.645776938 23 fat cell differentiation 1.775942912 1.645776938 24 negative regulation of cellular response to growth factor stimulus 1.770838936 1.645776938 25 positive regulation of ERK1 and ERK2 cascade 1.770153472 1.645776938 26 blood coagulation 1.751284016 1.645776938 27 positive regulation of cell motility 1.750357647 1.645776938 28 carboxylic acid biosynthetic process 1.747260629 1.645776938 29 organic acid biosynthetic process 1.744892451 1.645776938 30 pyridine nucleotide metabolic process 1.735519482 1.645776938 31 regulation of ERK1 and ERK2 cascade 1.735022277 1.645776938 32 oxidoreduction coenzyme metabolic process 1.734527417 1.645776938 33 ERK1 and ERK2 cascade 1.73333185 1.645776938 102 34 neurotransmitter metabolic process 1.732971314 1.645776938 35 inflammatory response 1.732942168 1.645776938 36 response to tumor necrosis factor 1.732666618 1.645776938 37 hemostasis 1.729455885 1.645776938 38 regulation of fat cell differentiation 1.723256576 1.645776938 39 response to bacterium 1.722274051 1.645776938 40 positive regulation of leukocyte migration 1.721189502 1.645776938 41 response to wounding 1.718498156 1.645776938 42 regulation of tube size 1.717926586 1.645776938 43 regulation of blood vessel size 1.717926586 1.645776938 44 nucleotide biosynthetic process 1.717582278 1.645776938 45 positive regulation of locomotion 1.717002358 1.645776938 46 positive regulation of cell migration 1.708323247 1.645776938 47 positive regulation of cellular component movement 1.700027173 1.645776938 48 cell chemotaxis 1.699940065 1.645776938 49 ribose phosphate biosynthetic process 1.698638602 1.645776938 50 regulation of tube diameter 1.69169952 1.645776938 51 regulation of blood vessel diameter 1.69169952 1.645776938 52 nicotinamide nucleotide metabolic process 1.691186296 1.537993895 53 endothelial cell proliferation 1.689767866 1.565060338 54 phagocytosis 1.689566948 1.645776938 55 regulation of lipid biosynthetic process 1.684622265 1.537993895 56 wound healing 1.680007058 1.565060338 57 carbohydrate catabolic process 1.674185181 1.565060338 58 drug metabolic process 1.670454876 1.645776938 59 protein import 1.66397164 1.565060338 60 regulation of endothelial cell proliferation 1.6609865 1.645776938 61 negative regulation of cell development 1.659173992 1.645776938 62 myeloid leukocyte migration 1.658486052 1.537993895 63 pyridine-containing compound metabolic process 1.657947436 1.537993895 64 regulation of protein kinase B signaling 1.656166658 1.537993895 65 monocarboxylic acid biosynthetic process 1.655438525 1.565060338 66 positive regulation of lipid metabolic process 1.654039467 1.565060338 67 hexose metabolic process 1.653305632 1.565060338 68 negative regulation of defense response 1.647560488 1.645776938 69 nucleoside phosphate biosynthetic process 1.647019752 1.565060338 70 purine ribonucleotide biosynthetic process 1.646969093 1.537993895 71 positive regulation of cell-substrate adhesion 1.646919062 1.565060338 72 vascular process in circulatory system 1.642577452 1.565060338 73 ribonucleotide biosynthetic process 1.63540794 1.565060338 103 74 organophosphate biosynthetic process 1.632799346 1.645776938 75 protein kinase B signaling 1.632070657 1.565060338 76 positive regulation of angiogenesis 1.631079437 1.537993895 77 regulation of inflammatory response 1.628019609 1.565060338 78 alpha-amino acid metabolic process 1.627187114 1.565060338 79 coagulation 1.623533922 1.537993895 80 bone development 1.623058862 1.537993895 81 response to acid chemical 1.617457639 1.565060338 82 positive regulation of chemotaxis 1.612578867 1.537993895 83 coenzyme metabolic process 1.610742331 1.565060338 84 coenzyme biosynthetic process 1.608769832 1.537993895 85 response to peptide 1.607939172 1.565060338 86 positive regulation of peptidyl-tyrosine phosphorylation 1.606728501 1.565060338 87 purine-containing compound biosynthetic process 1.60643096 1.565060338 88 mitochondrion organization 1.605294388 1.645776938 89 negative regulation of protein phosphorylation 1.60500478 1.645776938 90 regulation of peptidyl-tyrosine phosphorylation 1.604426707 1.565060338 91 purine nucleotide biosynthetic process 1.603997207 1.565060338 92 negative regulation of secretion 1.600034306 1.537993895 93 transmembrane receptor protein serine/threonine kinase signaling pathway 1.598840032 1.565060338 94 leukocyte differentiation 1.598749686 1.645776938 95 regulation of cell shape 1.597717758 1.537993895 96 negative regulation of response to external stimulus 1.597249549 1.565060338 97 cell-substrate adhesion 1.596882099 1.565060338 98 negative regulation of cytokine production 1.590890749 1.537993895 99 actin filament bundle organization 1.588537328 1.565060338 100 connective tissue development 1.584738901 1.565060338 101 regulation of cellular response to growth factor stimulus 1.584581137 1.565060338 102 negative regulation of peptidase activity 1.584144237 1.537993895 103 lipid biosynthetic process 1.583738811 1.645776938 104 positive regulation of response to external stimulus 1.582840075 1.565060338 105 ATP biosynthetic process 1.581906212 1.537993895 106 regulation of body fluid levels 1.580135165 1.565060338 107 regulation of angiogenesis 1.579408599 1.565060338 108 monocarboxylic acid metabolic process 1.579207908 1.645776938 109 small molecule biosynthetic process 1.577842367 1.645776938 110 cellular amino acid metabolic process 1.577272075 1.565060338 104 111 myeloid cell differentiation 1.576287716 1.645776938 112 positive regulation of cysteine-type endopeptidase activity 1.575302407 1.537993895 113 regulation of peptidase activity 1.57406154 1.565060338 114 sulfur compound metabolic process 1.573121276 1.565060338 115 monosaccharide metabolic process 1.572287613 1.537993895 116 purine nucleoside triphosphate biosynthetic process 1.564850217 1.537993895 117 actin filament bundle assembly 1.563466131 1.537993895 118 negative regulation of secretion by cell 1.562952925 1.537993895 119 regulation of cell-substrate adhesion 1.560939127 1.537993895 120 negative regulation of cellular component movement 1.560379525 1.565060338 121 negative regulation of MAPK cascade 1.558704778 1.537993895 122 cofactor metabolic process 1.557556149 1.645776938 123 positive regulation of proteolysis 1.554771957 1.565060338 124 regulation of chemotaxis 1.554751666 1.537993895 125 nucleotide metabolic process 1.551337188 1.645776938 126 nucleoside triphosphate metabolic process 1.551149468 1.565060338 127 tissue remodeling 1.550594251 1.537993895 128 chondrocyte differentiation 1.549645121 1.420780618 129 negative regulation of immune system process 1.548437037 1.645776938 130 negative regulation of cell adhesion 1.547904739 1.565060338 131 positive regulation of protein complex assembly 1.54622487 1.565060338 132 negative regulation of peptide secretion 1.546167362 1.537993895 133 regulation of intrinsic apoptotic signaling pathway 1.546059129 1.481489075 134 positive regulation of vasculature development 1.545011251 1.537993895 135 negative regulation of inflammatory response 1.543535805 1.481489075 136 purine ribonucleoside triphosphate biosynthetic process 1.543526209 1.537993895 137 ATP metabolic process 1.541111156 1.537993895 138 negative regulation of protein kinase activity 1.54050217 1.537993895 139 nucleoside phosphate metabolic process 1.539360881 1.645776938 140 negative regulation of protein complex assembly 1.536911309 1.481489075 141 response to oxidative stress 1.534029312 1.645776938 142 negative regulation of locomotion 1.533363351 1.565060338 143 mesenchymal cell differentiation 1.531850097 1.537993895 144 purine nucleoside triphosphate metabolic process 1.53024108 1.565060338 145 cellular response to nitrogen compound 1.527333445 1.645776938 105 146 ribose phosphate metabolic process 1.527108726 1.645776938 147 regulation of response to wounding 1.525202443 1.481489075 148 maintenance of location 1.520749106 1.565060338 149 regulation of transmembrane receptor protein serine/threonine kinase signaling pathway 1.517244674 1.537993895 150 purine ribonucleoside triphosphate metabolic process 1.514587521 1.565060338 151 negative regulation of Wnt signaling pathway 1.51385085 1.537993895 152 cellular response to peptide 1.513743073 1.565060338 153 innate immune response 1.511554795 1.645776938 154 negative regulation of phosphorylation 1.511391796 1.645776938 155 ribonucleoside triphosphate biosynthetic process 1.509100505 1.422361817 156 positive regulation of endocytosis 1.508870316 1.537993895 157 cellular response to growth factor stimulus 1.504886921 1.645776938 158 response to peptide hormone 1.502766767 1.565060338 159 regulation of cysteine-type endopeptidase activity 1.502440029 1.537993895 160 peptidyl-tyrosine modification 1.502372507 1.565060338 161 defense response to other organism 1.501284802 1.537993895 162 ribonucleotide metabolic process 1.50097899 1.645776938 163 ribonucleoside triphosphate metabolic process 1.500460041 1.565060338 164 regulation of protein localization to cell periphery 1.500311612 1.422361817 165 mesenchyme development 1.5001317 1.537993895 166 purine ribonucleotide metabolic process 1.498351496 1.645776938 167 endothelial cell migration 1.496491291 1.537993895 168 defense response to bacterium 1.496329976 1.422361817 169 cellular modified amino acid metabolic process 1.495614991 1.481489075 170 regulation of endothelial cell migration 1.495400828 1.537993895 171 cellular response to transforming growth factor beta stimulus 1.495181229 1.537993895 172 negative regulation of intracellular signal transduction 1.493737989 1.645776938 173 fatty acid metabolic process 1.493473909 1.537993895 174 negative regulation of neurogenesis 1.492176699 1.565060338 175 cellular response to organonitrogen compound 1.488825781 1.645776938 176 regulation of cellular carbohydrate metabolic process 1.488560518 1.378364248 177 positive regulation of endopeptidase activity 1.488511308 1.378364248 178 purine-containing compound metabolic process 1.487657579 1.645776938 179 phosphatidylinositol-mediated signaling 1.485945329 1.378364248 180 response to antibiotic 1.485030741 1.537993895 106 181 peptidyl-tyrosine phosphorylation 1.484238044 1.565060338 182 multicellular organismal homeostasis 1.482632467 1.645776938 183 regulation of cytokine production 1.481419329 1.645776938 184 response to transforming growth factor beta 1.481298561 1.537993895 185 positive regulation of supramolecular fiber organization 1.4773725 1.537993895 186 carbohydrate metabolic process 1.47730691 1.645776938 187 import into nucleus 1.476361633 1.480957949 188 negative regulation of apoptotic signaling pathway 1.475138339 1.537539265 189 protein import into nucleus 1.474199002 1.537993895 190 inositol lipid-mediated signaling 1.474050267 1.254904976 191 purine nucleotide metabolic process 1.473951994 1.645776938 192 transmembrane receptor protein tyrosine kinase signaling pathway 1.473888569 1.645776938 193 negative regulation of immune response 1.472820259 1.378083714 194 leukocyte cell-cell adhesion 1.472019683 1.565060338 195 ribonucleoside monophosphate biosynthetic process 1.468606757 1.378364248 196 regulation of cell-matrix adhesion 1.467840664 1.376278358 197 cellular carbohydrate metabolic process 1.467413864 1.565060338 198 positive regulation of cellular protein catabolic process 1.467132077 1.282609526 199 regulation of small molecule metabolic process 1.46477438 1.481489075 200 nucleoside triphosphate biosynthetic process 1.463998673 1.326140715 201 establishment of protein localization to organelle 1.463632109 1.565060338 202 fatty acid biosynthetic process 1.4620851 1.325761016 203 regulation of cell-cell adhesion 1.461294544 1.481489075 204 positive regulation of peptidase activity 1.460454946 1.378364248 205 nucleoside monophosphate biosynthetic process 1.460146169 1.481489075 206 regulation of cysteine-type endopeptidase activity involved in apoptotic process 1.458818654 1.537993895 207 regulation of endopeptidase activity 1.457039293 1.481489075 208 positive regulation of cytokine production 1.45563701 1.537993895 209 nucleoside monophosphate metabolic process 1.453811319 1.565060338 210 negative regulation of canonical Wnt signaling pathway 1.453685053 1.378364248 211 response to growth factor 1.453263071 1.645776938 212 regulation of vasculature development 1.452823381 1.481489075 213 positive regulation of cell adhesion 1.451224442 1.537993895 214 positive regulation of MAPK cascade 1.450059587 1.645776938 107 215 cofactor biosynthetic process 1.446855497 1.537993895 216 blood vessel endothelial cell migration 1.446670629 1.378083714 217 negative regulation of cell-cell adhesion 1.445994551 1.481489075 218 positive regulation of protein catabolic process 1.44588536 1.480957949 219 negative regulation of cell motility 1.443769403 1.481489075 220 ribonucleoside monophosphate metabolic process 1.44151114 1.481489075 221 organ growth 1.439876607 1.537993895 222 organic hydroxy compound transport 1.439696302 1.481489075 223 negative regulation of nervous system development 1.439405671 1.481489075 224 regulation of smooth muscle cell proliferation 1.437823926 1.378364248 225 positive regulation of cellular catabolic process 1.436808482 1.537993895 226 intrinsic apoptotic signaling pathway 1.433443697 1.481489075 227 cellular response to external stimulus 1.433049283 1.481489075 228 ameboidal-type cell migration 1.432917066 1.537993895 229 regulation of lipid metabolic process 1.432611597 1.422361817 230 cellular response to drug 1.43113047 1.537993895 231 endocytosis 1.430526101 1.645776938 232 negative regulation of protein modification process 1.429587842 1.645776938 233 response to starvation 1.425375054 1.378364248 234 regulation of leukocyte cell-cell adhesion 1.425110145 1.481489075 235 actin filament organization 1.424329694 1.537993895 236 response to extracellular stimulus 1.42380151 1.423631134 237 regulation of anatomical structure size 1.423790794 1.645776938 238 regulation of MAP kinase activity 1.423015529 1.537993895 239 glucose metabolic process 1.422777093 1.480957949 240 regulation of myeloid cell differentiation 1.420444031 1.481489075 241 ossification 1.419549805 1.481489075 242 response to nutrient levels 1.419456233 1.481489075 243 negative regulation of phosphorus metabolic process 1.419242645 1.645776938 244 negative regulation of phosphate metabolic process 1.419242645 1.645776938 245 cartilage development 1.419093185 1.537993895 246 smooth muscle cell proliferation 1.418573553 1.420780618 247 mammary gland development 1.415743957 1.184203733 248 purine nucleoside monophosphate biosynthetic process 1.414229666 1.184203733 249 purine ribonucleoside monophosphate biosynthetic process 1.414229666 1.184203733 250 muscle cell proliferation 1.411614979 1.481489075 108 251 carbohydrate homeostasis 1.411413237 1.420780618 252 glucose homeostasis 1.411413237 1.420780618 253 myeloid leukocyte activation 1.40980465 1.282609526 254 chemotaxis 1.406739762 1.565060338 255 response to toxic substance 1.406676094 1.339204232 256 protein localization to nucleus 1.40666628 1.378364248 257 regulation of defense response 1.406462277 1.565060338 258 negative regulation of transferase activity 1.406071185 1.481489075 259 transforming growth factor beta receptor signaling pathway 1.405700916 1.420780618 260 negative regulation of neuron projection development 1.40503376 1.481489075 261 biomineral tissue development 1.405018708 1.184203733 262 temperature homeostasis 1.404022155 1.250701046 263 endothelium development 1.403197183 1.098941154 264 negative regulation of protein secretion 1.402348271 1.127137114 265 taxis 1.401579172 1.565060338 266 regulation of carbohydrate metabolic process 1.400270439 1.420780618 267 positive regulation of cell activation 1.398317335 1.338300991 268 positive regulation of cellular protein localization 1.397077913 1.378364248 269 purine ribonucleoside monophosphate metabolic process 1.39587243 1.481489075 270 positive regulation of protein kinase activity 1.395552871 1.481489075 271 regulation of protein complex assembly 1.393482926 1.565060338 272 cell-matrix adhesion 1.393232654 1.378364248 273 cyclic-nucleotide-mediated signaling 1.390205658 1.184203733 274 purine nucleoside monophosphate metabolic process 1.390048757 1.378364248 275 T cell activation 1.389566604 1.481489075 276 negative regulation of transport 1.389026367 1.565060338 277 lymphocyte differentiation 1.38483899 1.427413972 278 cellular response to peptide hormone stimulus 1.384068238 1.420780618 279 circulatory system process 1.384055067 1.537993895 280 negative regulation of cell migration 1.3809467 1.284007513 281 carboxylic acid transport 1.379050495 1.242718784 282 response to oxygen levels 1.378281193 1.278917594 283 negative regulation of neuron apoptotic process 1.377850575 1.378364248 284 nucleocytoplasmic transport 1.376880939 1.378364248 285 nuclear transport 1.376880939 1.378364248 286 cell junction organization 1.376330533 1.284007513 109 287 negative regulation of cell projection organization 1.375198587 1.378364248 288 regulation of leukocyte differentiation 1.373265316 1.282609526 289 organic acid transport 1.370472256 1.278917594 290 positive regulation of cytoskeleton organization 1.370183742 1.278917594 291 regulation of protein localization to nucleus 1.36755115 1.01239316 292 regulation of actin cytoskeleton organization 1.36570653 1.427413972 293 cytokine secretion 1.365028612 1.278917594 294 blood circulation 1.364737891 1.481489075 295 glycerolipid biosynthetic process 1.363992307 1.070486816 296 alcohol metabolic process 1.362612765 1.252283542 297 alpha-beta T cell activation 1.360553414 1.046370179 298 regulation of actin filament-based process 1.360489934 1.427413972 299 cellular response to lipopolysaccharide 1.359906612 1.031564298 300 regulation of supramolecular fiber organization 1.359836138 1.378364248 301 negative regulation of kinase activity 1.359271993 1.242718784 302 response to alcohol 1.35925351 0.976239561 303 carbohydrate derivative biosynthetic process 1.358893789 1.565060338 304 protein homooligomerization 1.358773904 1.378364248 305 organic anion transport 1.356584089 1.378364248 306 regulation of cell activation 1.355082499 1.537993895 307 carbohydrate biosynthetic process 1.354986502 1.325761016 308 lymphocyte activation involved in immune response 1.352672266 1.024709012 309 epithelial cell migration 1.351765423 1.291434632 310 regulation of endocytosis 1.350975353 1.255123544 311 cellular response to molecule of bacterial origin 1.350765339 1.046370179 312 response to hypoxia 1.35048003 1.218185267 313 epithelial cell proliferation 1.350384955 1.427413972 314 leukocyte proliferation 1.34990578 1.280759583 315 regulation of actin filament organization 1.348250487 1.291434632 316 negative regulation of neuron death 1.347063021 1.212133326 317 negative regulation of multi-organism process 1.346780296 1.03010975 318 tissue homeostasis 1.346450308 1.250701046 319 regulation of protein serine/threonine kinase activity 1.344606026 1.481489075 320 positive regulation of catabolic process 1.342843226 1.481489075 321 cellular response to oxidative stress 1.341199421 1.225184796 322 response to decreased oxygen levels 1.340862211 1.184203733 323 tissue migration 1.339745403 1.230406341 324 epithelium migration 1.339745403 1.230406341 325 nucleic acid transport 1.338136533 1.067962401 110 326 RNA transport 1.338136533 1.067962401 327 cellular response to starvation 1.337940772 0.95898375 328 response to molecule of bacterial origin 1.337193983 1.183485534 329 regulation of apoptotic signaling pathway 1.336512019 1.481489075 330 Notch signaling pathway 1.336292099 0.99459411 331 regulation of T cell proliferation 1.335707061 0.973089575 332 positive regulation of cellular component biogenesis 1.335508587 1.537993895 333 mitochondrial transport 1.332363904 1.155701451 334 positive regulation of hemopoiesis 1.33173869 1.128363363 335 regulation of proteasomal ubiquitin-dependent protein catabolic process 1.331131099 1.011082996 336 epithelial to mesenchymal transition 1.330158442 0.98042424 337 cellular response to antibiotic 1.329504154 0.924355874 338 regulation of protein catabolic process 1.329454046 1.427413972 339 skeletal system morphogenesis 1.328624774 1.154496294 340 protein complex oligomerization 1.327775508 1.537993895 341 lipid catabolic process 1.327204837 1.103896439 342 cellular response to biotic stimulus 1.32608042 0.989931605 343 anatomical structure maturation 1.326066296 0.98042424 344 cellular response to insulin stimulus 1.326017481 1.100206381 345 response to lipopolysaccharide 1.325345863 1.087256607 346 posttranscriptional regulation of gene expression 1.323597361 1.428122789 347 regulation of T cell activation 1.322717608 1.162069996 348 cell growth 1.32200436 1.537993895 349 negative regulation of neuron differentiation 1.32062255 1.183485534 350 positive regulation of hydrolase activity 1.318683741 1.481489075 351 gland morphogenesis 1.318304719 0.97136645 352 sensory organ development 1.315950109 1.565060338 353 regulation of cellular protein catabolic process 1.315796239 1.13076432 354 regulation of ubiquitin-dependent protein catabolic process 1.313293119 0.97136645 355 gland development 1.311296998 1.340868852 356 lymphocyte mediated immunity 1.310850541 1.070486816 357 gliogenesis 1.310840319 1.114268023 358 regulation of epithelial cell migration 1.310542732 1.046370179 359 positive regulation of binding 1.310355096 0.98042424 360 regulation of neurotransmitter levels 1.310203387 1.225452873 361 positive regulation of neurogenesis 1.309742898 1.481489075 362 positive regulation of kinase activity 1.309344389 1.353978783 363 negative regulation of cell growth 1.309161724 1.046370179 111 364 receptor-mediated endocytosis 1.308995541 1.01239316 365 cellular response to lipid 1.307768229 1.428122789 366 cellular glucose homeostasis 1.307519708 0.899050009 367 regulation of hemopoiesis 1.307195237 1.237760105 368 cellular response to reactive oxygen species 1.305827637 0.917565799 369 establishment of RNA localization 1.304848012 0.888782522 370 cellular response to organic cyclic compound 1.30468889 1.265603876 371 regulation of synapse organization 1.303850225 1.087256607 372 lipid localization 1.302532984 1.163201419 373 regulation of multi-organism process 1.301875655 1.150283995 374 regulation of synapse structure or activity 1.301555528 1.087256607 375 negative regulation of hydrolase activity 1.30056449 1.095466402 376 negative regulation of leukocyte activation 1.300473604 0.881519108 377 cell activation involved in immune response 1.300263939 0.947835305 378 response to insulin 1.299723102 0.937993123 379 positive regulation of protein serine/threonine kinase activity 1.299602098 1.096605363 380 positive regulation of leukocyte cell-cell adhesion 1.299054288 0.881519108 381 nucleobase-containing small molecule biosynthetic process 1.297832216 0.914236141 382 defense response to virus 1.296689821 0.978227228 383 T cell proliferation 1.296527128 0.881519108 384 eye morphogenesis 1.295942551 0.882797781 385 regulation of cytokine secretion 1.295022173 0.882797781 386 regulation of ossification 1.294611353 0.914236141 387 ear development 1.293699166 0.931373105 388 axon extension 1.28825349 0.856142296 389 regulation of leukocyte activation 1.287159767 1.270312133 390 negative regulation of cell activation 1.286570983 0.907065911 391 regulation of cell growth 1.284339813 1.351681807 392 regulation of epithelial cell proliferation 1.28417232 1.046370179 393 regulation of leukocyte proliferation 1.282338755 0.936771974 394 lymphocyte activation 1.282008993 1.250701046 395 protein localization to membrane 1.279999723 1.429958524 396 negative regulation of catalytic activity 1.279854999 1.372298158 397 negative regulation of growth 1.279615024 1.006117262 398 regulation of protein binding 1.279541987 0.992430776 399 eye development 1.278961481 1.031408464 400 visual system development 1.278961481 1.031408464 401 activation of immune response 1.277889731 1.078854396 402 regulation of binding 1.27648599 1.242472408 112 403 nucleoside phosphate catabolic process 1.275745094 0.832524566 404 RNA localization 1.274467275 0.917565799 405 positive regulation of secretion 1.272573417 1.103896439 406 response to virus 1.272141871 0.967522764 407 symbiont process 1.271325933 0.998618755 408 adaptive thermogenesis 1.270806559 0.857635065 409 cellular response to inorganic substance 1.269529831 0.825269808 410 positive regulation of protein transport 1.266439184 1.087256607 411 negative regulation of hemopoiesis 1.266420788 0.832524566 412 positive regulation of leukocyte activation 1.266306628 0.989931605 413 positive regulation of epithelial cell migration 1.266258707 0.83032584 414 regulation of system process 1.26518747 1.218185267 415 regulation of mononuclear cell proliferation 1.265001013 0.859953156 416 positive regulation of MAP kinase activity 1.264354068 0.845994713 417 positive regulation of cell-cell adhesion 1.261254255 0.840928019 418 muscle system process 1.260690442 1.066435554 419 adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains 1.260237645 0.881519108 420 sensory system development 1.259115591 0.98042424 421 cellular process involved in reproduction in multicellular organism 1.256140661 0.976239561 422 positive regulation of protein secretion 1.25478629 0.871985313 423 T cell differentiation 1.251896057 0.868869294 424 positive regulation of nervous system development 1.251862096 1.143182147 425 positive regulation of establishment of protein localization 1.251796598 1.070486816 426 regulation of cytoskeleton organization 1.251320415 1.157138499 427 positive regulation of secretion by cell 1.251247802 1.046579057 428 leukocyte mediated immunity 1.250306242 0.881519108 429 developmental process involved in reproduction 1.249251038 1.117909314 430 cellular response to hormone stimulus 1.248125271 1.09367294 431 cellular response to abiotic stimulus 1.247837628 0.888782522 432 cellular response to environmental stimulus 1.247837628 0.888782522 433 mononuclear cell proliferation 1.246947795 0.845994713 434 regulation of developmental growth 1.245157736 0.942898706 435 lipid transport 1.24501301 0.896268799 436 regulation of cellular component size 1.240460531 1.006572998 437 response to organic cyclic compound 1.239031393 1.087256607 438 regulation of neuron death 1.23870435 0.937993123 439 regulation of translation 1.23521777 0.89294708 113 440 positive regulation of peptide secretion 1.233688575 0.839458877 441 regulation of cellular amide metabolic process 1.224794117 0.917949746 442 positive regulation of transferase activity 1.22346995 0.989931605 443 regulation of peptide secretion 1.22334433 1.00062189 444 epithelial cell differentiation 1.223040854 0.973089575 445 nucleobase-containing compound catabolic process 1.220750301 0.914236141 446 reproductive system development 1.20813652 0.881519108 447 positive regulation of immune response 1.206270277 0.89062281 448 cellular nitrogen compound catabolic process 1.206202779 0.881519108 449 heterocycle catabolic process 1.204478349 0.888782522 450 neuron death 1.204168544 0.831476091 451 organelle fission 1.203927531 0.86403005 452 regulation of protein secretion 1.201129842 0.839966607 453 peptide secretion 1.200841087 0.86458299 114 Supplementary Table 4: C2C12 GO_BP enrichment table Pathway names signed_enrichment minusLog10Pval protein folding 2.423566381 1.476666352 ribosome biogenesis 1.867124448 1.476666352 response to topologically incorrect protein 1.842514013 1.476666352 regulation of ubiquitin-dependent protein catabolic process 1.775644814 1.476666352 rRNA processing 1.759094231 1.476666352 ribonucleoprotein complex biogenesis 1.753075615 1.476666352 mRNA processing 1.739696025 1.476666352 regulation of proteolysis involved in cellular protein catabolic process 1.728331894 1.476666352 regulation of proteasomal protein catabolic process 1.724760347 1.476666352 regulation of mRNA processing 1.705331304 1.447781121 negative regulation of proteolysis 1.673309507 1.476666352 proteasome-mediated ubiquitin- dependent protein catabolic process 1.669740047 1.476666352 extrinsic apoptotic signaling pathway 1.65202011 1.476666352 response to oxidative stress 1.638495113 1.476666352 intrinsic apoptotic signaling pathway 1.633497147 1.476666352 regulation of cellular protein catabolic process 1.629318489 1.476666352 mRNA metabolic process 1.629284423 1.476666352 protein stabilization 1.626283198 1.41314385 RNA splicing 1.619200221 1.476666352 RNA splicing, via transesterification reactions 1.615829714 1.476666352 RNA splicing, via transesterification reactions with bulged adenosine as nucleophile 1.615829714 1.476666352 mRNA splicing, via spliceosome 1.615829714 1.476666352 cellular response to steroid hormone stimulus 1.606852706 1.342769739 nuclear export 1.601008716 1.342769739 regulation of intrinsic apoptotic signaling pathway 1.596963599 1.41314385 regulation of mRNA metabolic process 1.591011729 1.476666352 apoptotic signaling pathway 1.590776929 1.476666352 proteasomal protein catabolic process 1.580074319 1.476666352 regulation of protein catabolic process 1.572963031 1.476666352 cofactor biosynthetic process 1.567428805 1.41314385 115 response to reactive oxygen species 1.566416216 1.342769739 rRNA metabolic process 1.556527635 1.45610651 RNA localization 1.531057254 1.41314385 regulation of cellular catabolic process 1.528861935 1.476666352 modification-dependent protein catabolic process 1.524175152 1.45610651 modification-dependent macromolecule catabolic process 1.519580093 1.45610651 ubiquitin-dependent protein catabolic process 1.517671928 1.45610651 regulation of protein modification by small protein conjugation or removal 1.514420525 1.45610651 regulation of protein ubiquitination 1.514258215 1.342769739 regulation of proteolysis 1.512283997 1.45610651 negative regulation of apoptotic signaling pathway 1.508242101 1.30473511 cellular response to oxidative stress 1.499620074 1.41314385 positive regulation of protein catabolic process 1.49596851 1.305243316 regulation of neuron death 1.490976373 1.41314385 regulation of neuron apoptotic process 1.49037602 1.305243316 negative regulation of catalytic activity 1.477066343 1.45610651 negative regulation of protein modification process 1.473775513 1.45610651 positive regulation of cellular catabolic process 1.473329609 1.41314385 cofactor metabolic process 1.460949961 1.45610651 negative regulation of phosphorylation 1.458519754 1.41314385 cellular response to hormone stimulus 1.455060067 1.45610651 regulation of apoptotic signaling pathway 1.450610801 1.41314385 response to hormone 1.449045019 1.41314385 positive regulation of catabolic process 1.444474702 1.45610651 negative regulation of protein phosphorylation 1.440759699 1.41314385 positive regulation of establishment of protein localization 1.434126 1.45610651 ncRNA processing 1.433968866 1.347060198 regulation of establishment of protein localization 1.431042107 1.41314385 neuron death 1.422286279 1.45610651 nucleocytoplasmic transport 1.421886495 1.342769739 nuclear transport 1.421886495 1.342769739 proteolysis involved in cellular protein catabolic process 1.412573535 1.45610651 116 positive regulation of programmed cell death 1.407207269 1.347060198 cellular nitrogen compound catabolic process 1.400227393 1.347060198 organic cyclic compound catabolic process 1.38994683 1.305243316 positive regulation of apoptotic process 1.389144402 1.347060198 heterocycle catabolic process 1.38575495 1.305243316 response to lipid 1.384882658 1.41314385 negative regulation of phosphorus metabolic process 1.371743655 1.347060198 negative regulation of phosphate metabolic process 1.371743655 1.347060198 117 Supplementary Table 5: GO_BP enrichment table skeletal muscle vs. C2C12 Pathway Name Sk. Muscle C2C12 protein folding 1.972403521 2.406355897 reactive oxygen species metabolic process 1.888591916 1.312735452 response to oxidative stress 1.534029312 1.645671871 regulation of intrinsic apoptotic signaling pathway 1.546059129 1.586955007 intrinsic apoptotic signaling pathway 1.433443697 1.646525853 regulation of ubiquitin-dependent protein catabolic process 1.313293119 1.7650411 carboxylic acid biosynthetic process 1.747260629 1.329111738 organic acid biosynthetic process 1.744892451 1.322136988 negative regulation of protein phosphorylation 1.60500478 1.44636014 cofactor metabolic process 1.557556149 1.469570702 cofactor biosynthetic process 1.446855497 1.562723604 protein import 1.66397164 1.324336928 negative regulation of phosphorylation 1.511391796 1.470400173 negative regulation of apoptotic signaling pathway 1.475138339 1.499435723 mitochondrion organization 1.605294388 1.359581778 alpha-amino acid metabolic process 1.627187114 1.337623642 small molecule biosynthetic process 1.577842367 1.370318786 regulation of cellular protein catabolic process 1.315796239 1.63006854 positive regulation of protein catabolic process 1.44588536 1.492360172 positive regulation of cellular catabolic process 1.436808482 1.483474128 regulation of protein catabolic process 1.329454046 1.584113701 negative regulation of protein modification process 1.429587842 1.475553751 lipid biosynthetic process 1.583738811 1.309919067 negative regulation of MAPK cascade 1.558704778 1.331448805 regulation of peptidase activity 1.57406154 1.293909023 negative regulation of transferase activity 1.406071185 1.452369873 negative regulation of protein kinase activity 1.54050217 1.31353229 regulation of endopeptidase activity 1.457039293 1.384003494 cellular response to oxidative stress 1.341199421 1.498085748 regulation of cysteine-type endopeptidase activity involved in apoptotic process 1.458818654 1.374991347 nucleic acid transport 1.338136533 1.492164677 RNA transport 1.338136533 1.492164677 negative regulation of neuron death 1.347063021 1.481662523 response to toxic substance 1.406676094 1.420500778 nucleocytoplasmic transport 1.376880939 1.433227403 118 nuclear transport 1.376880939 1.433227403 establishment of protein localization to organelle 1.463632109 1.343824037 RNA localization 1.274467275 1.525920851 positive regulation of catabolic process 1.342843226 1.453189134 regulation of apoptotic signaling pathway 1.336512019 1.458428873 negative regulation of phosphorus metabolic process 1.419242645 1.373693317 negative regulation of phosphate metabolic process 1.419242645 1.373693317 regulation of cytokine production 1.481419329 1.308868706 protein import into nucleus 1.474199002 1.315050527 import into nucleus 1.476361633 1.306422601 establishment of RNA localization 1.304848012 1.474991494 protein localization to nucleus 1.40666628 1.365944929 negative regulation of catalytic activity 1.279854999 1.479756892 response to starvation 1.425375054 1.317084193 regulation of neuron death 1.23870435 1.50354387 negative regulation of kinase activity 1.359271993 1.368432254 negative regulation of intracellular signal transduction 1.493737989 1.215828937 cellular response to hormone stimulus 1.248125271 1.460514772 positive regulation of establishment of protein localization 1.251796598 1.441737418 neuron death 1.204168544 1.434762765 leukocyte mediated immunity 1.250306242 1.373531759 cellular nitrogen compound catabolic process 1.206202779 1.403479451 cell growth 1.32200436 1.287657861 positive regulation of protein secretion 1.25478629 1.35123407 negative regulation of hydrolase activity 1.30056449 1.305166545 positive regulation of peptide secretion 1.233688575 1.364385699 heterocycle catabolic process 1.204478349 1.388973396 cellular response to lipid 1.307768229 1.284579289 activation of immune response 1.277889731 1.312553117 reproductive system development 1.20813652 1.366724491 positive regulation of protein transport 1.266439184 1.284685487 response to organic cyclic compound 1.239031393 1.299325599 nucleobase-containing compound catabolic process 1.220750301 1.31746664 developmental process involved in reproduction 1.249251038 1.282505608 angiogenesis 1.821320285 -1.350235788 response to wounding 1.718498156 -1.305923959 fat cell differentiation 1.775942912 -1.369171477 119 extracellular structure organization 2.13014533 -1.768466628 positive regulation of locomotion 1.717002358 -1.356043397 positive regulation of cell motility 1.750357647 -1.434318517 positive regulation of cell migration 1.708323247 -1.395179786 wound healing 1.680007058 -1.3681031 positive regulation of cellular component movement 1.700027173 -1.392838688 extracellular matrix organization 2.180429875 -1.882566728 mesenchymal cell differentiation 1.531850097 -1.326875666 actin filament organization 1.424329694 -1.227504276 cellular response to growth factor stimulus 1.504886921 -1.316679044 transmembrane receptor protein serine/threonine kinase signaling pathway 1.598840032 -1.420947418 response to growth factor 1.453263071 -1.282516846 negative regulation of cell development 1.659173992 -1.498310638 regulation of cellular response to growth factor stimulus 1.584581137 -1.465708763 ameboidal-type cell migration 1.432917066 -1.314387818 positive regulation of cell adhesion 1.451224442 -1.334686383 regulation of transmembrane receptor protein serine/threonine kinase signaling pathway 1.517244674 -1.405575373 regulation of actin filament organization 1.348250487 -1.243762055 negative regulation of neurogenesis 1.492176699 -1.401188362 blood circulation 1.364737891 -1.275406746 circulatory system process 1.384055067 -1.302365667 negative regulation of cell motility 1.443769403 -1.377435737 negative regulation of cellular component movement 1.560379525 -1.500387557 negative regulation of locomotion 1.533363351 -1.492638969 ossification 1.419549805 -1.379683535 mesenchyme development 1.5001317 -1.461752025 connective tissue development 1.584738901 -1.560527822 regulation of actin filament-based process 1.360489934 -1.356424887 negative regulation of cell migration 1.3809467 -1.37949009 negative regulation of nervous system development 1.439405671 -1.443312617 macroautophagy -1.3412265 1.327637998 organelle fission 1.203927531 -1.223572756 muscle system process 1.260690442 -1.308768462 eye development 1.278961481 -1.339943636 visual system development 1.278961481 -1.339943636 cell junction organization 1.376330533 -1.455298328 sensory system development 1.259115591 -1.339943636 120 regulation of system process 1.26518747 -1.362528946 chemotaxis 1.406739762 -1.525440766 regulation of cell-substrate adhesion 1.560939127 -1.680497235 taxis 1.401579172 -1.534835682 cell-matrix adhesion 1.393232654 -1.543202883 bone development 1.623058862 -1.77803918 organ growth 1.439876607 -1.618061011 regulation of synapse structure or activity 1.301555528 -1.488070375 regulation of synapse organization 1.303850225 -1.50232854 positive regulation of neurogenesis 1.309742898 -1.522741253 cell-substrate adhesion 1.596882099 -1.835492665 positive regulation of nervous system development 1.251862096 -1.614375039 gliogenesis 1.310840319 -1.693589763 skeletal system morphogenesis 1.328624774 -1.968452911 cilium assembly -1.224225772 -1.426922897 cilium organization -1.207991145 -1.452774619 pattern specification process -1.285011317 -1.401232722 121 References Abbott, M. J., & Turcotte, L. P. (2014). AMPK-alpha2 is involved in exercise training-induced adaptations in insulin-stimulated metabolism in skeletal muscle following high-fat diet. J Appl Physiol (1985), 117(8), 869-879. doi:10.1152/japplphysiol.01380.2013 Adam-Vizi, V. (2005). Production of Reactive Oxygen Species in Brain Mitochondria: Contribution by Electron Transport Chain and Non–Electron Transport Chain Sources. 7(9-10), 1140- 1149. doi:10.1089/ars.2005.7.1140 Ahmed, Z. M., Smith, T. N., Riazuddin, S., Makishima, T., Ghosh, M., Bokhari, S., . . . Wilcox, E. R. (2002). Nonsyndromic recessive deafness DFNB18 and Usher syndrome type IC are allelic mutations of USHIC. Hum Genet, 110(6), 527-531. doi:10.1007/s00439-002-0732- 4 Alis, R., Lucia, A., Blesa, J. R., & Sanchis-Gomar, F. (2015). The role of mitochondrial derived peptides (MDPs) in metabolism. J Cell Physiol, 230(12), 2903-2904. doi:10.1002/jcp.25023 Alis, R., Lucia, A., Blesa, J. R., & Sanchis-Gomar, F. (2015). The role of mitochondrial derived peptides (MDPs) in metabolism. J Cell Physiol, 230(12), 2903-2904. doi:10.1002/jcp.25023 Alvehus, M., Boman, N., Soderlund, K., Svensson, M. B., & Buren, J. (2014). Metabolic adaptations in skeletal muscle, adipose tissue, and whole-body oxidative capacity in response to resistance training. Eur J Appl Physiol, 114(7), 1463-1471. doi:10.1007/s00421-014-2879-9 Ameur, A., Stewart, J. B., Freyer, C., Hagstrom, E., Ingman, M., Larsson, N. G., & Gyllensten, U. (2011). Ultra-deep sequencing of mouse mitochondrial DNA: mutational patterns and their origins. PLoS Genet, 7(3), e1002028. doi:10.1371/journal.pgen.1002028 Amrita, Christopher, Mark, Deng, P., & Cole. (2015). Mitochondrial and Nuclear Accumulation of the Transcription Factor ATFS-1 Promotes OXPHOS Recovery during the UPRmt. Mol Cell, 58(1), 123-133. doi:10.1016/j.molcel.2015.02.008 Anckar, J., & Sistonen, L. (2011). Regulation of HSF1 Function in the Heat Stress Response: Implications in Aging and Disease. 80(1), 1089-1115. doi:10.1146/annurev-biochem- 060809-095203 Anderson, S., Bankier, A. T., Barrell, B. G., de Bruijn, M. H., Coulson, A. R., Drouin, J., . . . Young, I. G. (1981). Sequence and organization of the human mitochondrial genome. Nature, 290(5806), 457-465. doi:10.1038/290457a0 Andrews, S. J., & Rothnagel, J. A. (2014). Emerging evidence for functional peptides encoded by short open reading frames. Nat Rev Genet, 15(3), 193-204. doi:10.1038/nrg3520 Anisimov, V. N., Zabezhinski, M. A., Popovich, I. G., Piskunova, T. S., Semenchenko, A. V., Tyndyk, M. L., . . . Blagosklonny, M. V. (2011). Rapamycin increases lifespan and inhibits 122 spontaneous tumorigenesis in inbred female mice. Cell Cycle, 10(24), 4230-4236. doi:10.4161/cc.10.24.18486 Asby, D. J., Cuda, F., Beyaert, M., Houghton, F. D., Cagampang, F. R., & Tavassoli, A. (2015). AMPK Activation via Modulation of De Novo Purine Biosynthesis with an Inhibitor of ATIC Homodimerization. Chem Biol, 22(7), 838-848. doi:10.1016/j.chembiol.2015.06.008 Asher, G., & Sassone-Corsi, P. (2015). Time for food: the intimate interplay between nutrition, metabolism, and the circadian clock. Cell, 161(1), 84-92. doi:10.1016/j.cell.2015.03.015 Astafev, A. A., Patel, S. A., & Kondratov, R. V. (2017). Calorie restriction effects on circadian rhythms in gene expression are sex dependent. Sci Rep, 7(1), 9716. doi:10.1038/s41598- 017-09289-9 Austad, S. N. (2009). Is there a role for new invertebrate models for aging research? J Gerontol A Biol Sci Med Sci, 64(2), 192-194. doi:10.1093/gerona/gln059 Baar, K., & Esser, K. (1999). Phosphorylation of p70(S6k) correlates with increased skeletal muscle mass following resistance exercise. Am J Physiol, 276(1), C120-127. doi:10.1152/ajpcell.1999.276.1.C120 Bachar, A. R., Scheffer, L., Schroeder, A. S., Nakamura, H. K., Cobb, L. J., Oh, Y. K., . . . Lerman, A. (2010). Humanin is expressed in human vascular walls and has a cytoprotective effect against oxidized LDL-induced oxidative stress. Cardiovasc Res, 88(2), 360-366. doi:10.1093/cvr/cvq191 Bajpai, P., Koc, E., Sonpavde, G., Singh, R., & Singh, K. K. (2019). Mitochondrial localization, import, and mitochondrial function of the androgen receptor. J Biol Chem, 294(16), 6621- 6634. doi:10.1074/jbc.RA118.006727 Bangsbo, J., Krustrup, P., Gonzalez-Alonso, J., & Saltin, B. (2001). ATP production and efficiency of human skeletal muscle during intense exercise: effect of previous exercise. Am J Physiol Endocrinol Metab, 280(6), E956-964. doi:10.1152/ajpendo.2001.280.6.E956 Barger, J. L., Vann, J. M., Cray, N. L., Pugh, T. D., Mastaloudis, A., Hester, S. N., . . . Prolla, T. A. (2017). Identification of tissue-specific transcriptional markers of caloric restriction in the mouse and their use to evaluate caloric restriction mimetics. Aging Cell, 16(4), 750- 760. doi:10.1111/acel.12608 Bartolome, A., Garcia-Aguilar, A., Asahara, S. I., Kido, Y., Guillen, C., Pajvani, U. B., & Benito, M. (2017). MTORC1 Regulates both General Autophagy and Mitophagy Induction after Oxidative Phosphorylation Uncoupling. Mol Cell Biol, 37(23). doi:10.1128/MCB.00441-17 Barzilai, N., Cuervo, A. M., & Austad, S. (2018). Aging as a Biological Target for Prevention and Therapy. JAMA, 320(13), 1321. doi:10.1001/jama.2018.9562 Batra, A., Vohra, R. S., Chrzanowski, S. M., Hammers, D. W., Lott, D. J., Vandenborne, K., . . . Forbes, S. C. (2019). Effects of PDE5 inhibition on dystrophic muscle following an acute bout of downhill running and endurance training. J Appl Physiol (1985), 126(6), 1737- 1745. doi:10.1152/japplphysiol.00664.2018 123 Bazzini, A. A., Johnstone, T. G., Christiano, R., Mackowiak, S. D., Obermayer, B., Fleming, E. S., . . . Giraldez, A. J. (2014). Identification of small ORFs in vertebrates using ribosome footprinting and evolutionary conservation. EMBO J, 33(9), 981-993. doi:10.1002/embj.201488411 Bellizzi, D., D'Aquila, P., Giordano, M., Montesanto, A., & Passarino, G. (2012). Global DNA methylation levels are modulated by mitochondrial DNA variants. Epigenomics, 4(1), 17- 27. doi:10.2217/epi.11.109 Benayoun, B. A., Pollina, E. A., & Brunet, A. (2015). Epigenetic regulation of ageing: linking environmental inputs to genomic stability. Nat Rev Mol Cell Biol, 16(10), 593-610. doi:10.1038/nrm4048 Benayoun, B. A., Pollina, E. A., Singh, P. P., Mahmoudi, S., Harel, I., Casey, K. M., . . . Brunet, A. (2019). Remodeling of epigenome and transcriptome landscapes with aging in mice reveals widespread induction of inflammatory responses. Genome Res. doi:10.1101/gr.240093.118 Bensasson, D., Feldman, M. W., & Petrov, D. A. (2003). Rates of DNA duplication and mitochondrial DNA insertion in the human genome. J Mol Evol, 57(3), 343-354. doi:10.1007/s00239-003-2485-7 Bergstrom, J. (1975). Percutaneous needle biopsy of skeletal muscle in physiological and clinical research. Scand J Clin Lab Invest, 35(7), 609-616. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/1108172 Bernet, J. D., Doles, J. D., Hall, J. K., Kelly Tanaka, K., Carter, T. A., & Olwin, B. B. (2014). p38 MAPK signaling underlies a cell-autonomous loss of stem cell self-renewal in skeletal muscle of aged mice. Nat Med, 20(3), 265-271. doi:10.1038/nm.3465 Betancourt, A. M., King, A. L., Fetterman, J. L., Millender-Swain, T., Finley, R. D., Oliva, C. R., . . . Bailey, S. M. (2014). Mitochondrial-nuclear genome interactions in non-alcoholic fatty liver disease in mice. Biochem J, 461(2), 223-232. doi:10.1042/BJ20131433 Bi, P., Ramirez-Martinez, A., Li, H., Cannavino, J., McAnally, J. R., Shelton, J. M., . . . Olson, E. N. (2017). Control of muscle formation by the fusogenic micropeptide myomixer. Science, 356(6335), 323-327. doi:10.1126/science.aam9361 Birky, C. W., Jr. (1995). Uniparental inheritance of mitochondrial and chloroplast genes: mechanisms and evolution. Proc Natl Acad Sci U S A, 92(25), 11331-11338. doi:10.1073/pnas.92.25.11331 Blenis, J. (2017). TOR, the Gateway to Cellular Metabolism, Cell Growth, and Disease. Cell, 171(1), 10-13. doi:10.1016/j.cell.2017.08.019 Bock, R. (2017). Witnessing Genome Evolution: Experimental Reconstruction of Endosymbiotic and Horizontal Gene Transfer. Annu Rev Genet, 51(1), 1-22. doi:10.1146/annurev-genet- 120215-035329 124 Bodine, S. C. (2006). mTOR signaling and the molecular adaptation to resistance exercise. Med Sci Sports Exerc, 38(11), 1950-1957. doi:10.1249/01.mss.0000233797.24035.35 Bohr, V. A., Stevnsner, T., & De Souza-Pinto, N. C. (2002). Mitochondrial DNA repair of oxidative damage in mammalian cells. Gene, 286(1), 127-134. doi:10.1016/s0378-1119(01)00813- 7 Boldrin, L., Ross, J. A., Whitmore, C., Doreste, B., Beaver, C., Eddaoudi, A., . . . Morgan, J. E. (2017). The effect of calorie restriction on mouse skeletal muscle is sex, strain and time- dependent. Sci Rep, 7(1), 5160. doi:10.1038/s41598-017-04896-y Bolster, D. R., Kubica, N., Crozier, S. J., Williamson, D. L., Farrell, P. A., Kimball, S. R., & Jefferson, L. S. (2003). Immediate response of mammalian target of rapamycin (mTOR)- mediated signalling following acute resistance exercise in rat skeletal muscle. J Physiol, 553(Pt 1), 213-220. doi:10.1113/jphysiol.2003.047019 Booth, F. W., Chakravarthy, M. V., Gordon, S. E., & Spangenburg, E. E. (2002). Waging war on physical inactivity: using modern molecular ammunition against an ancient enemy. J Appl Physiol (1985), 93(1), 3-30. doi:10.1152/japplphysiol.00073.2002 Booth, F. W., Roberts, C. K., & Laye, M. J. (2012). Lack of exercise is a major cause of chronic diseases. Compr Physiol, 2(2), 1143-1211. doi:10.1002/cphy.c110025 Bostrom, P., Wu, J., Jedrychowski, M. P., Korde, A., Ye, L., Lo, J. C., . . . Spiegelman, B. M. (2012). A PGC1-alpha-dependent myokine that drives brown-fat-like development of white fat and thermogenesis. Nature, 481(7382), 463-468. doi:10.1038/nature10777 Bouchard, C., Blair, S. N., & Katzmarzyk, P. T. (2015). Less Sitting, More Physical Activity, or Higher Fitness? Mayo Clin Proc, 90(11), 1533-1540. doi:10.1016/j.mayocp.2015.08.005 Boveris, A., & Chance, B. (1973). The mitochondrial generation of hydrogen peroxide. General properties and effect of hyperbaric oxygen. Biochemical Journal, 134(3), 707-716. doi:10.1042/bj1340707 Brandhorst, S., In, Wei, M., Chia, Sedrakyan, S., Navarrete, G., . . . Valter. (2015). A Periodic Diet that Mimics Fasting Promotes Multi-System Regeneration, Enhanced Cognitive Performance, and Healthspan. Cell Metab, 22(1), 86-99. doi:10.1016/j.cmet.2015.05.012 Brandt, N., Dethlefsen, M. M., Bangsbo, J., & Pilegaard, H. (2017). PGC-1alpha and exercise intensity dependent adaptations in mouse skeletal muscle. PLoS One, 12(10), e0185993. doi:10.1371/journal.pone.0185993 Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol, 34(5), 525-527. doi:10.1038/nbt.3519 Breton, C. V., Song, A. Y., Xiao, J., Kim, S. J., Mehta, H. H., Wan, J., . . . Cohen, P. (2019). Effects of air pollution on mitochondrial function, mitochondrial DNA methylation, and mitochondrial peptide expression. Mitochondrion, 46, 22-29. doi:10.1016/j.mito.2019.04.001 125 Brooks, G. A., & Mercier, J. (1994). Balance of carbohydrate and lipid utilization during exercise: the "crossover" concept. J Appl Physiol (1985), 76(6), 2253-2261. doi:10.1152/jappl.1994.76.6.2253 Bujak, A. L., Crane, J. D., Lally, J. S., Ford, R. J., Kang, S. J., Rebalka, I. A., . . . Steinberg, G. R. (2015). AMPK activation of muscle autophagy prevents fasting-induced hypoglycemia and myopathy during aging. Cell Metab, 21(6), 883-890. doi:10.1016/j.cmet.2015.05.016 Calabrese, F. M., Balacco, D. L., Preste, R., Diroma, M. A., Forino, R., Ventura, M., & Attimonelli, M. (2017). NumtS colonization in mammalian genomes. Sci Rep, 7(1), 16357. doi:10.1038/s41598-017-16750-2 Campisi, J., Kapahi, P., Lithgow, G. J., Melov, S., Newman, J. C., & Verdin, E. (2019). From discoveries in ageing research to therapeutics for healthy ageing. Nature, 571(7764), 183- 192. doi:10.1038/s41586-019-1365-2 Camus, M. F., Clancy, D. J., & Dowling, D. K. (2012). Mitochondria, maternal inheritance, and male aging. Curr Biol, 22(18), 1717-1721. doi:10.1016/j.cub.2012.07.018 Camus, M. F., Wolf, J. B., Morrow, E. H., & Dowling, D. K. (2015). Single Nucleotides in the mtDNA Sequence Modify Mitochondrial Molecular Function and Are Associated with Sex- Specific Effects on Fertility and Aging. Curr Biol, 25(20), 2717-2722. doi:10.1016/j.cub.2015.09.012 Canto, C., Gerhart-Hines, Z., Feige, J. N., Lagouge, M., Noriega, L., Milne, J. C., . . . Auwerx, J. (2009). AMPK regulates energy expenditure by modulating NAD+ metabolism and SIRT1 activity. Nature, 458(7241), 1056-1060. doi:10.1038/nature07813 Carbone, J. W., McClung, J. P., & Pasiakos, S. M. (2019). Recent Advances in the Characterization of Skeletal Muscle and Whole-Body Protein Responses to Dietary Protein and Exercise during Negative Energy Balance. Adv Nutr, 10(1), 70-79. doi:10.1093/advances/nmy087 Cardamone, M. D., Krones, A., Tanasa, B., Taylor, H., Ricci, L., Ohgi, K. A., . . . Perissi, V. (2012). A protective strategy against hyperinflammatory responses requiring the nontranscriptional actions of GPS2. Mol Cell, 46(1), 91-104. doi:10.1016/j.molcel.2012.01.025 Cardamone, M. D., Tanasa, B., Cederquist, C. T., Huang, J., Mahdaviani, K., Li, W., . . . Perissi, V. (2018). Mitochondrial Retrograde Signaling in Mammals Is Mediated by the Transcriptional Cofactor GPS2 via Direct Mitochondria-to-Nucleus Translocation. Mol Cell, 69(5), 757-772 e757. doi:10.1016/j.molcel.2018.01.037 Cartee, G. D., Hepple, R. T., Bamman, M. M., & Zierath, J. R. (2016). Exercise Promotes Healthy Aging of Skeletal Muscle. Cell Metab, 23(6), 1034-1047. doi:10.1016/j.cmet.2016.05.007 Carter, R. J., Morton, J., & Dunnett, S. B. (2001). Motor coordination and balance in rodents. Curr Protoc Neurosci, Chapter 8, Unit 8 12. doi:10.1002/0471142301.ns0812s15 126 Castro, B., & Kuang, S. (2017). Evaluation of Muscle Performance in Mice by Treadmill Exhaustion Test and Whole-limb Grip Strength Assay. Bio Protoc, 7(8). doi:10.21769/BioProtoc.2237 Cataldo, L. R., Fernandez-Verdejo, R., Santos, J. L., & Galgani, J. E. (2018). Plasma MOTS-c levels are associated with insulin sensitivity in lean but not in obese individuals. J Investig Med, 66(6), 1019-1022. doi:10.1136/jim-2017-000681 Cataldo, L. R., Fernández-Verdejo, R., Santos, J. L., & Galgani, J. E. (2018). Plasma MOTS-c levels are associated with insulin sensitivity in lean but not in obese individuals. Journal of Investigative Medicine, 66(6), 1019-1022. doi:10.1136/jim-2017-000681 Cederquist, C. T., Lentucci, C., Martinez-Calejman, C., Hayashi, V., Orofino, J., Guertin, D., . . . Perissi, V. (2017). Systemic insulin sensitivity is regulated by GPS2 inhibition of AKT ubiquitination and activation in adipose tissue. Mol Metab, 6(1), 125-137. doi:10.1016/j.molmet.2016.10.007 Cermakian, N., Ikeda, T. M., Cedergren, R., & Gray, M. W. (1996). Sequences homologous to yeast mitochondrial and bacteriophage T3 and T7 RNA polymerases are widespread throughout the eukaryotic lineage. Nucleic Acids Res, 24(4), 648-654. doi:10.1093/nar/24.4.648 Chandel, N. S. (2015). Evolution of Mitochondria as Signaling Organelles. Cell Metab, 22(2), 204- 206. doi:10.1016/j.cmet.2015.05.013 Chanut-Delalande, H., Hashimoto, Y., Pelissier-Monier, A., Spokony, R., Dib, A., Kondo, T., . . . Payre, F. (2014). Pri peptides are mediators of ecdysone for the temporal control of development. Nat Cell Biol, 16(11), 1035-1044. doi:10.1038/ncb3052 Chen, H., Vermulst, M., Wang, Y. E., Chomyn, A., Prolla, T. A., McCaffery, J. M., & Chan, D. C. (2010). Mitochondrial fusion is required for mtDNA stability in skeletal muscle and tolerance of mtDNA mutations. Cell, 141(2), 280-289. doi:10.1016/j.cell.2010.02.026 Chrétien, D., Bénit, P., Ha, H.-H., Keipert, S., El-Khoury, R., Chang, Y.-T., . . . Rak, M. (2018). Mitochondria are physiologically maintained at close to 50 °C. PLoS Biol, 16(1), e2003992. doi:10.1371/journal.pbio.2003992 Chute, C. D., DiLoreto, E. M., Zhang, Y. K., Reilly, D. K., Rayes, D., Coyle, V. L., . . . Srinivasan, J. (2019). Co-option of neurotransmitter signaling for inter-organismal communication in C. elegans. Nat Commun, 10(1), 3186. doi:10.1038/s41467-019-11240-7 Clark-Matott, J., Saleem, A., Dai, Y., Shurubor, Y., Ma, X., Safdar, A., . . . Simon, D. K. (2015). Metabolomic analysis of exercise effects in the POLG mitochondrial DNA mutator mouse brain. Neurobiol Aging, 36(11), 2972-2983. doi:10.1016/j.neurobiolaging.2015.07.020 Clay Montier, L. L., Deng, J. J., & Bai, Y. (2009). Number matters: control of mammalian mitochondrial DNA copy number. Journal of Genetics and Genomics, 36(3), 125-131. doi:10.1016/s1673-8527(08)60099-5 127 Cobb, L. J., Lee, C., Xiao, J., Yen, K., Wong, R. G., Nakamura, H. K., . . . Cohen, P. (2016). Naturally occurring mitochondrial-derived peptides are age-dependent regulators of apoptosis, insulin sensitivity, and inflammatory markers. Aging (Albany NY), 8(4), 796- 809. doi:10.18632/aging.100943 Coffey, V. G., & Hawley, J. A. (2007). The molecular bases of training adaptation. Sports Med, 37(9), 737-763. doi:10.2165/00007256-200737090-00001 Cogliati, S., Enriquez, J. A., & Scorrano, L. (2016). Mitochondrial cristae: where beauty meets functionality. Trends Biochem Sci, 41(3), 261-273. Cogliati, S., Frezza, C., Soriano, M. E., Varanita, T., Quintana-Cabrera, R., Corrado, M., . . . Gomes, L. C. (2013). Mitochondrial cristae shape determines respiratory chain supercomplexes assembly and respiratory efficiency. Cell, 155(1), 160-171. Colaianni, G., Cuscito, C., Mongelli, T., Pignataro, P., Buccoliero, C., Liu, P., . . . Grano, M. (2015). The myokine irisin increases cortical bone mass. Proc Natl Acad Sci U S A, 112(39), 12157-12162. doi:10.1073/pnas.1516622112 Colberg, S. R., Albright, A. L., Blissmer, B. J., Braun, B., Chasan-Taber, L., Fernhall, B., . . . American Diabetes, A. (2010). Exercise and type 2 diabetes: American College of Sports Medicine and the American Diabetes Association: joint position statement. Exercise and type 2 diabetes. Med Sci Sports Exerc, 42(12), 2282-2303. doi:10.1249/MSS.0b013e3181eeb61c Conner, J. D., Wolden-Hanson, T., & Quinn, L. S. (2014). Assessment of murine exercise endurance without the use of a shock grid: an alternative to forced exercise. J Vis Exp(90), e51846. doi:10.3791/51846 Correia-Melo, C., Marques, F. D., Anderson, R., Hewitt, G., Hewitt, R., Cole, J., . . . Passos, J. F. (2016). Mitochondria are required for pro-ageing features of the senescent phenotype. EMBO J, 35(7), 724-742. doi:10.15252/embj.201592862 Cortopassi, G. A., & Arnheim, N. (1990). Detection of a specific mitochondrial DNA deletion in tissues of older humans. Nucleic Acids Res, 18(23), 6927-6933. doi:10.1093/nar/18.23.6927 Couvillion, M. T., Soto, I. C., Shipkovenska, G., & Churchman, L. S. (2016). Synchronized mitochondrial and cytosolic translation programs. Nature, 533(7604), 499-503. doi:10.1038/nature18015 Crimmins, E. M. (2015). Lifespan and Healthspan: Past, Present, and Promise. The Gerontologist, 55(6), 901-911. doi:10.1093/geront/gnv130 Cunningham, G. M., Flores, L. C., Roman, M. G., Cheng, C., Dube, S., Allen, C., . . . Ikeno, Y. (2018). Thioredoxin overexpression in both the cytosol and mitochondria accelerates age- related disease and shortens lifespan in male C57BL/6 mice. Geroscience, 40(5-6), 453- 468. doi:10.1007/s11357-018-0039-6 128 Dahlmans, D., Houzelle, A., Andreux, P., Wang, X., Jorgensen, J. A., Moullan, N., . . . Hoeks, J. (2019). MicroRNA-382 silencing induces a mitonuclear protein imbalance and activates the mitochondrial unfolded protein response in muscle cells. J Cell Physiol, 234(5), 6601- 6610. doi:10.1002/jcp.27401 Das, A., Huang, G. X., Bonkowski, M. S., Longchamp, A., Li, C., Schultz, M. B., . . . Sinclair, D. A. (2018). Impairment of an Endothelial NAD(+)-H2S Signaling Network Is a Reversible Cause of Vascular Aging. Cell, 173(1), 74-89 e20. doi:10.1016/j.cell.2018.02.008 de Hon, O., Kuipers, H., & van Bottenburg, M. (2015). Prevalence of doping use in elite sports: a review of numbers and methods. Sports Med, 45(1), 57-69. doi:10.1007/s40279-014- 0247-x De Magalhães, J. P., Stevens, M., & Thornton, D. (2017). The Business of Anti-Aging Science. Trends in Biotechnology, 35(11), 1062-1073. doi:10.1016/j.tibtech.2017.07.004 DeBalsi, K. L., Hoff, K. E., & Copeland, W. C. (2017). Role of the mitochondrial DNA replication machinery in mitochondrial DNA mutagenesis, aging and age-related diseases. Ageing Res Rev, 33, 89-104. doi:10.1016/j.arr.2016.04.006 DeLuca, S. Z., & O'Farrell, P. H. (2012). Barriers to male transmission of mitochondrial DNA in sperm development. Dev Cell, 22(3), 660-668. doi:10.1016/j.devcel.2011.12.021 Deng, Y., Bamigbade, A. T., Hammad, M. A., Xu, S., & Liu, P. (2018). Identification of small ORF- encoded peptides in mouse serum. Biophysics Reports, 4(1), 39-49. doi:10.1007/s41048- 018-0048-0 Deshmukh, A. S., Cox, J., Jensen, L. J., Meissner, F., & Mann, M. (2015). Secretome Analysis of Lipid-Induced Insulin Resistance in Skeletal Muscle Cells by a Combined Experimental and Bioinformatics Workflow. J Proteome Res, 14(11), 4885-4895. doi:10.1021/acs.jproteome.5b00720 Dobler, R., Dowling, D. K., Morrow, E. H., & Reinhardt, K. (2018). A systematic review and meta- analysis reveals pervasive effects of germline mitochondrial replacement on components of health. Hum Reprod Update, 24(5), 519-534. doi:10.1093/humupd/dmy018 Doonan, R., McElwee, J. J., Matthijssens, F., Walker, G. A., Houthoofd, K., Back, P., . . . Gems, D. (2008). Against the oxidative damage theory of aging: superoxide dismutases protect against oxidative stress but have little or no effect on life span in Caenorhabditis elegans. Genes Dev, 22(23), 3236-3241. doi:10.1101/gad.504808 Dougherty, J. P., Springer, D. A., & Gershengorn, M. C. (2016). The Treadmill Fatigue Test: A Simple, High-throughput Assay of Fatigue-like Behavior for the Mouse. J Vis Exp(111). doi:10.3791/54052 Drummond, E., Short, E., & Clancy, D. (2019). Mitonuclear gene X environment effects on lifespan and health: How common, how big? Mitochondrion, 49, 12-18. doi:10.1016/j.mito.2019.06.009 129 Du, C., Zhang, C., Wu, W., Liang, Y., Wang, A., Wu, S., . . . Luo, X. (2018). Circulating MOTS-c levels are decreased in obese male children and adolescents and associated with insulin resistance. Pediatr Diabetes. doi:10.1111/pedi.12685 Dunham-Snary, K. J., & Ballinger, S. W. (2015). GENETICS. Mitochondrial-nuclear DNA mismatch matters. Science, 349(6255), 1449-1450. doi:10.1126/science.aac5271 Dunham-Snary, K. J., Sandel, M. W., Sammy, M. J., Westbrook, D. G., Xiao, R., McMonigle, R. J., . . . Ballinger, S. W. (2018). Mitochondrial – nuclear genetic interaction modulates whole body metabolism, adiposity and gene expression in vivo. EBioMedicine, 36, 316-328. doi:10.1016/j.ebiom.2018.08.036 Dzau, V. J., Inouye, S. K., Rowe, J. W., Finkelman, E., & Yamada, T. (2019). Enabling Healthful Aging for All — The National Academy of Medicine Grand Challenge in Healthy Longevity. New England Journal of Medicine. doi:10.1056/nejmp1912298 Eckel, R. H., & Krauss, R. M. (1998). American Heart Association Call to Action: Obesity as a Major Risk Factor for Coronary Heart Disease. Retrieved from https://www.ahajournals.org/doi/full/10.1161/01.cir.97.21.2099 Egan, B., & Juleen. (2013). Exercise Metabolism and the Molecular Regulation of Skeletal Muscle Adaptation. Cell Metab, 17(2), 162-184. doi:10.1016/j.cmet.2012.12.012 Egan, B., & Zierath, J. R. (2013). Exercise metabolism and the molecular regulation of skeletal muscle adaptation. Cell Metab, 17(2), 162-184. doi:10.1016/j.cmet.2012.12.012 Egan, D. F., Shackelford, D. B., Mihaylova, M. M., Gelino, S., Kohnz, R. A., Mair, W., . . . Shaw, R. J. (2011). Phosphorylation of ULK1 (hATG1) by AMP-activated protein kinase connects energy sensing to mitophagy. Science, 331(6016), 456-461. doi:10.1126/science.1196371 Eisenberg, T., Schroeder, S., Andryushkova, A., Pendl, T., Kuttner, V., Bhukel, A., . . . Madeo, F. (2014). Nucleocytosolic depletion of the energy metabolite acetyl-coenzyme a stimulates autophagy and prolongs lifespan. Cell Metab, 19(3), 431-444. doi:10.1016/j.cmet.2014.02.010 Ekstrand, M. I., Falkenberg, M., Rantanen, A., Park, C. B., Gaspari, M., Hultenby, K., . . . Larsson, N. G. (2004). Mitochondrial transcription factor A regulates mtDNA copy number in mammals. Hum Mol Genet, 13(9), 935-944. doi:10.1093/hmg/ddh109 Essen, B., Jansson, E., Henriksson, J., Taylor, A. W., & Saltin, B. (1975). Metabolic characteristics of fibre types in human skeletal muscle. Acta Physiol Scand, 95(2), 153-165. doi:10.1111/j.1748-1716.1975.tb10038.x Fabrizio, P., Liou, L. L., Moy, V. N., Diaspro, A., Valentine, J. S., Gralla, E. B., & Longo, V. D. (2003). SOD2 functions downstream of Sch9 to extend longevity in yeast. Genetics, 163(1), 35-46. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/12586694 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1462415/pdf/12586694.pdf 130 Falkenberg, M., Larsson, N. G., & Gustafsson, C. M. (2007). DNA replication and transcription in mammalian mitochondria. Annu Rev Biochem, 76, 679-699. doi:10.1146/annurev.biochem.76.060305.152028 Fan, W., Waizenegger, W., Lin, C. S., Sorrentino, V., He, M. X., Wall, C. E., . . . Evans, R. M. (2017). PPARdelta Promotes Running Endurance by Preserving Glucose. Cell Metab, 25(5), 1186-1193 e1184. doi:10.1016/j.cmet.2017.04.006 Faye, G., & Sor, F. (1977). Analysis of mitochondrial ribosomal proteins of Saccharomyces cerevisiae by two dimensional polyacrylamide gel electrophoresis. Mol Gen Genet, 155(1), 27-34. doi:10.1007/bf00268557 Fazzini, F., Schöpf, B., Blatzer, M., Coassin, S., Hicks, A. A., Kronenberg, F., & Fendt, L. (2018). Plasmid-normalized quantification of relative mitochondrial DNA copy number. Sci Rep, 8(1). doi:10.1038/s41598-018-33684-5 Feng, R., Wang, L., Li, Z., Yang, R., Liang, Y., Sun, Y., . . . Xu, X. (2019). A systematic comparison of exercise training protocols on animal models of cardiovascular capacity. Life Sci, 217, 128-140. doi:10.1016/j.lfs.2018.12.001 Fetterman, J. L., & Ballinger, S. W. (2019). Mitochondrial genetics regulate nuclear gene expression through metabolites. Proceedings of the National Academy of Sciences, 116(32), 15763-15765. doi:10.1073/pnas.1909996116 Fetterman, J. L., Zelickson, B. R., Johnson, L. W., Moellering, D. R., Westbrook, D. G., Pompilius, M., . . . Ballinger, S. W. (2013). Mitochondrial genetic background modulates bioenergetics and susceptibility to acute cardiac volume overload. Biochem J, 455(2), 157-167. doi:10.1042/BJ20130029 Finley, L. W., Lee, J., Souza, A., Desquiret-Dumas, V., Bullock, K., Rowe, G. C., . . . Haigis, M. C. (2012). Skeletal muscle transcriptional coactivator PGC-1alpha mediates mitochondrial, but not metabolic, changes during calorie restriction. Proc Natl Acad Sci U S A, 109(8), 2931-2936. doi:10.1073/pnas.1115813109 Fiorese, C. J., Schulz, A. M., Lin, Y. F., Rosin, N., Pellegrino, M. W., & Haynes, C. M. (2016). The Transcription Factor ATF5 Mediates a Mammalian Mitochondrial UPR. Curr Biol, 26(15), 2037-2043. doi:10.1016/j.cub.2016.06.002 Forster, M. J., Morris, P., & Sohal, R. S. (2003). Genotype and age influence the effect of caloric intake on mortality in mice. FASEB J, 17(6), 690-692. doi:10.1096/fj.02-0533fje Frontera, W. R., & Ochala, J. (2015). Skeletal muscle: a brief review of structure and function. Calcif Tissue Int, 96(3), 183-195. doi:10.1007/s00223-014-9915-y Fuku, N., Pareja-Galeano, H., Zempo, H., Alis, R., Arai, Y., Lucia, A., & Hirose, N. (2015). The mitochondrial-derived peptide MOTS-c: a player in exceptional longevity? Aging Cell, 14(6), 921-923. doi:10.1111/acel.12389 Furrer, R., & Handschin, C. (2020). Lifestyle vs. pharmacological interventions for healthy aging. Aging (Albany NY), 12(1), 5-7. doi:10.18632/aging.102741 131 Gabriel, B. M., & Zierath, J. R. (2017). The Limits of Exercise Physiology: From Performance to Health. Cell Metab, 25(5), 1000-1011. doi:10.1016/j.cmet.2017.04.018 Galindo, M. I., Pueyo, J. I., Fouix, S., Bishop, S. A., & Couso, J. P. (2007). Peptides encoded by short ORFs control development and define a new eukaryotic gene family. PLoS Biol, 5(5), e106. doi:10.1371/journal.pbio.0050106 Garcia-Roves, P. M., Osler, M. E., Holmstrom, M. H., & Zierath, J. R. (2008). Gain-of-function R225Q mutation in AMP-activated protein kinase gamma3 subunit increases mitochondrial biogenesis in glycolytic skeletal muscle. J Biol Chem, 283(51), 35724- 35734. doi:10.1074/jbc.M805078200 Gershoni, M., Levin, L., Ovadia, O., Toiw, Y., Shani, N., Dadon, S., . . . Mishmar, D. (2014). Disrupting mitochondrial-nuclear coevolution affects OXPHOS complex I integrity and impacts human health. Genome Biol Evol, 6(10), 2665-2680. doi:10.1093/gbe/evu208 Gilkerson, R., Bravo, L., Garcia, I., Gaytan, N., Herrera, A., Maldonado, A., & Quintanilla, B. (2013). The Mitochondrial Nucleoid: Integrating Mitochondrial DNA into Cellular Homeostasis. Cold Spring Harb Perspect Biol, 5(5), a011080-a011080. doi:10.1101/cshperspect.a011080 Gill, J. F., Santos, G., Schnyder, S., & Handschin, C. (2018). PGC-1alpha affects aging-related changes in muscle and motor function by modulating specific exercise-mediated changes in old mice. Aging Cell, 17(1). doi:10.1111/acel.12697 Goldin, E., Stahl, S., Cooney, A. M., Kaneski, C. R., Gupta, S., Brady, R. O., . . . Schiffmann, R. (2004). Transfer of a mitochondrial DNA fragment to MCOLN1 causes an inherited case of mucolipidosis IV. Hum Mutat, 24(6), 460-465. doi:10.1002/humu.20094 Gomez-Serrano, M., Camafeita, E., Lopez, J. A., Rubio, M. A., Breton, I., Garcia-Consuegra, I., . . . Peral, B. (2017). Differential proteomic and oxidative profiles unveil dysfunctional protein import to adipocyte mitochondria in obesity-associated aging and diabetes. Redox Biol, 11, 415-428. doi:10.1016/j.redox.2016.12.013 Gong, Q., Zhang, P., Wang, J., Ma, J., An, Y., Chen, Y., . . . Da Qing Diabetes Prevention Study, G. (2019). Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study. Lancet Diabetes Endocrinol, 7(6), 452-461. doi:10.1016/S2213-8587(19)30093-2 Gonzalez-Freire, M., de Cabo, R., Bernier, M., Sollott, S. J., Fabbri, E., Navas, P., & Ferrucci, L. (2015). Reconsidering the Role of Mitochondria in Aging. J Gerontol A Biol Sci Med Sci, 70(11), 1334-1342. doi:10.1093/gerona/glv070 Goodpaster, B. H., & Sparks, L. M. (2017). Metabolic Flexibility in Health and Disease. Cell Metabolism, 25(5), 1027-1036. doi:10.1016/j.cmet.2017.04.015 Gottschling, D. E., & Nyström, T. (2017). The Upsides and Downsides of Organelle Interconnectivity. Cell, 169(1), 24-34. doi:10.1016/j.cell.2017.02.030 132 Graber, T. G., Ferguson-Stegall, L., Liu, H., & Thompson, L. V. (2015). Voluntary Aerobic Exercise Reverses Frailty in Old Mice. J Gerontol A Biol Sci Med Sci, 70(9), 1045-1058. doi:10.1093/gerona/glu163 Grazina, M., Pratas, J., Silva, F., Oliveira, S., Santana, I., & Oliveira, C. (2006). Genetic basis of Alzheimer's dementia: role of mtDNA mutations. Genes Brain Behav, 5 Suppl 2, 92-107. doi:10.1111/j.1601-183X.2006.00225.x Grazioli, S., & Pugin, J. (2018). Mitochondrial Damage-Associated Molecular Patterns: From Inflammatory Signaling to Human Diseases. Front Immunol, 9, 832. doi:10.3389/fimmu.2018.00832 Greggio, C., Jha, P., Kulkarni, S. S., Lagarrigue, S., Broskey, N. T., Boutant, M., . . . Amati, F. (2017). Enhanced Respiratory Chain Supercomplex Formation in Response to Exercise in Human Skeletal Muscle. Cell Metab, 25(2), 301-311. doi:10.1016/j.cmet.2016.11.004 Guo, B., Zhai, D., Cabezas, E., Welsh, K., Nouraini, S., Satterthwait, A. C., & Reed, J. C. (2003). Humanin peptide suppresses apoptosis by interfering with Bax activation. Nature, 423(6938), 456-461. doi:10.1038/nature01627 Guo, F., Jing, W., Ma, C. G., Wu, M. N., Zhang, J. F., Li, X. Y., & Qi, J. S. (2010). [Gly(14)]- humanin rescues long-term potentiation from amyloid beta protein-induced impairment in the rat hippocampal CA1 region in vivo. Synapse, 64(1), 83-91. doi:10.1002/syn.20707 Gustafsson, C. M., Falkenberg, M., & Larsson, N. G. (2016). Maintenance and Expression of Mammalian Mitochondrial DNA. Annu Rev Biochem, 85, 133-160. doi:10.1146/annurev- biochem-060815-014402 Halagappa, V. K., Guo, Z., Pearson, M., Matsuoka, Y., Cutler, R. G., Laferla, F. M., & Mattson, M. P. (2007). Intermittent fasting and caloric restriction ameliorate age-related behavioral deficits in the triple-transgenic mouse model of Alzheimer's disease. Neurobiol Dis, 26(1), 212-220. doi:10.1016/j.nbd.2006.12.019 Hale, T. (2008). History of developments in sport and exercise physiology: A. V. Hill, maximal oxygen uptake, and oxygen debt. J Sports Sci, 26(4), 365-400. doi:10.1080/02640410701701016 Handschin, C. (2016). Caloric restriction and exercise "mimetics'': Ready for prime time? Pharmacol Res, 103, 158-166. doi:10.1016/j.phrs.2015.11.009 Hansen, M., & Kennedy, B. K. (2016). Does Longer Lifespan Mean Longer Healthspan? Trends Cell Biol, 26(8), 565-568. doi:10.1016/j.tcb.2016.05.002 Harman, D. (1956). Aging: a theory based on free radical and radiation chemistry. J Gerontol, 11(3), 298-300. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/13332224 Harman, D. (2009). Origin and evolution of the free radical theory of aging: a brief personal history, 1954-2009. Biogerontology, 10(6), 773-781. doi:10.1007/s10522-009-9234-2 133 Harrison, D. E., Strong, R., Sharp, Z. D., Nelson, J. F., Astle, C. M., Flurkey, K., . . . Miller, R. A. (2009). Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature, 460(7253), 392-395. doi:10.1038/nature08221 Hashimoto, Y., Ito, Y., Niikura, T., Shao, Z., Hata, M., Oyama, F., & Nishimoto, I. (2001). Mechanisms of neuroprotection by a novel rescue factor humanin from Swedish mutant amyloid precursor protein. Biochem Biophys Res Commun, 283(2), 460-468. doi:10.1006/bbrc.2001.4765 Hashimoto, Y., Niikura, T., Tajima, H., Yasukawa, T., Sudo, H., Ito, Y., . . . Nishimoto, I. (2001). A rescue factor abolishing neuronal cell death by a wide spectrum of familial Alzheimer's disease genes and Abeta. Proc Natl Acad Sci U S A, 98(11), 6336-6341. doi:10.1073/pnas.101133498 Havird, J. C., & Sloan, D. B. (2016). The Roles of Mutation, Selection, and Expression in Determining Relative Rates of Evolution in Mitochondrial versus Nuclear Genomes. Mol Biol Evol, 33(12), 3042-3053. doi:10.1093/molbev/msw185 Hawley, J. A. (2002). Adaptations of skeletal muscle to prolonged, intense endurance training. Clin Exp Pharmacol Physiol, 29(3), 218-222. doi:10.1046/j.1440-1681.2002.03623.x Hawley, J. A., Hargreaves, M., Joyner, M. J., & Zierath, J. R. (2014). Integrative biology of exercise. Cell, 159(4), 738-749. doi:10.1016/j.cell.2014.10.029 Hawley, J. A., Lundby, C., Cotter, J. D., & Burke, L. M. (2018). Maximizing Cellular Adaptation to Endurance Exercise in Skeletal Muscle. Cell Metab, 27(5), 962-976. doi:10.1016/j.cmet.2018.04.014 Hazkani-Covo, E., & Covo, S. (2008). Numt-mediated double-strand break repair mitigates deletions during primate genome evolution. PLoS Genet, 4(10), e1000237. doi:10.1371/journal.pgen.1000237 Hazkani-Covo, E., Zeller, R. M., & Martin, W. (2010). Molecular poltergeists: mitochondrial DNA copies (numts) in sequenced nuclear genomes. PLoS Genet, 6(2), e1000834. doi:10.1371/journal.pgen.1000834 Hedges, C. P., Woodhead, J. S. T., Wang, H. W., Mitchell, C. J., Cameron-Smith, D., Hickey, A. J. R., & Merry, T. L. (2019). Peripheral blood mononuclear cells do not reflect skeletal muscle mitochondrial function or adaptation to high-intensity interval training in healthy young men. J Appl Physiol (1985), 126(2), 454-461. doi:10.1152/japplphysiol.00777.2018 Hevener, A. L., Zhou, Z., Moore, T. M., Drew, B. G., & Ribas, V. (2018). The impact of ERalpha action on muscle metabolism and insulin sensitivity - Strong enough for a man, made for a woman. Mol Metab, 15, 20-34. doi:10.1016/j.molmet.2018.06.013 Hill, G. E., Havird, J. C., Sloan, D. B., Burton, R. S., Greening, C., & Dowling, D. K. (2019). Assessing the fitness consequences of mitonuclear interactions in natural populations. Biol Rev Camb Philos Soc, 94(3), 1089-1104. doi:10.1111/brv.12493 134 Hill, S., Sataranatarajan, K., & Remmen, H. V. (2018). Role of Signaling Molecules in Mitochondrial Stress Response. Frontiers in Genetics, 9. doi:10.3389/fgene.2018.00225 Hoene, M., Li, J., Li, Y., Runge, H., Zhao, X., Haring, H. U., . . . Weigert, C. (2016). Muscle and liver-specific alterations in lipid and acylcarnitine metabolism after a single bout of exercise in mice. Sci Rep, 6, 22218. doi:10.1038/srep22218 Hollinski, R., Osterberg, A., Polei, S., Lindner, T., Cantre, D., Mittlmeier, T., . . . Muller-Hilke, B. (2018). Young and healthy C57BL/6 J mice performing sprint interval training reveal gender- and site-specific changes to the cortical bone. Sci Rep, 8(1), 1529. doi:10.1038/s41598-018-19547-z Holloszy, J. O., & Booth, F. W. (1976). Biochemical adaptations to endurance exercise in muscle. Annu Rev Physiol, 38, 273-291. doi:10.1146/annurev.ph.38.030176.001421 Holloszy, J. O., Rennie, M. J., Hickson, R. C., Conlee, R. K., & Hagberg, J. M. (1977). Physiological consequences of the biochemical adaptations to endurance exercise. Ann N Y Acad Sci, 301, 440-450. doi:10.1111/j.1749-6632.1977.tb38220.x Holzenberger, M., Dupont, J., Ducos, B., Leneuve, P., Geloen, A., Even, P. C., . . . Le Bouc, Y. (2003). IGF-1 receptor regulates lifespan and resistance to oxidative stress in mice. Nature, 421(6919), 182-187. doi:10.1038/nature01298 Horowitz, M. (2010). Genomics and proteomics of heat acclimation. Front Biosci (Schol Ed), 2, 1068-1080. doi:10.2741/s118 Horowitz, M. (2014). Heat acclimation, epigenetics, and cytoprotection memory. Compr Physiol, 4(1), 199-230. doi:10.1002/cphy.c130025 Houtkooper, R. H., Mouchiroud, L., Ryu, D., Moullan, N., Katsyuba, E., Knott, G., . . . Auwerx, J. (2013). Mitonuclear protein imbalance as a conserved longevity mechanism. Nature, 497(7450), 451-457. doi:10.1038/nature12188 Howe, D. G., Blake, J. A., Bradford, Y. M., Bult, C. J., Calvi, B. R., Engel, S. R., . . . Smith, C. (2018). Model organism data evolving in support of translational medicine. Lab Anim (NY), 47(10), 277-289. doi:10.1038/s41684-018-0150-4 Huang, C. Y., Grunheit, N., Ahmadinejad, N., Timmis, J. N., & Martin, W. (2005). Mutational decay and age of chloroplast and mitochondrial genomes transferred recently to angiosperm nuclear chromosomes. Plant Physiol, 138(3), 1723-1733. doi:10.1104/pp.105.060327 Hubert, H. B., Feinleib, M., McNamara, P. M., & Castelli, W. P. (1983). Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation, 67(5), 968-977. doi:10.1161/01.cir.67.5.968 Hui, N., Barter, P. J., Ong, K. L., & Rye, K. A. (2019). Altered HDL metabolism in metabolic disorders: insights into the therapeutic potential of HDL. Clin Sci (Lond), 133(21), 2221- 2235. doi:10.1042/CS20190873 135 Hunter, P. (2016). Exercise in a bottle: Elucidating how exercise conveys health benefits might lead to new therapeutic options for a range of diseases from cancer to metabolic syndrome. EMBO Rep, 17(2), 136-138. doi:10.15252/embr.201541835 Hwangbo, D. S., Gershman, B., Tu, M. P., Palmer, M., & Tatar, M. (2004). Drosophila dFOXO controls lifespan and regulates insulin signalling in brain and fat body. Nature, 429(6991), 562-566. doi:10.1038/nature02549 Ikeda, S. I., Tamura, Y., Kakehi, S., Sanada, H., Kawamori, R., & Watada, H. (2016). Exercise- induced increase in IL-6 level enhances GLUT4 expression and insulin sensitivity in mouse skeletal muscle. Biochem Biophys Res Commun, 473(4), 947-952. doi:10.1016/j.bbrc.2016.03.159 Ikonen, M., Liu, B., Hashimoto, Y., Ma, L., Lee, K. W., Niikura, T., . . . Cohen, P. (2003). Interaction between the Alzheimer's survival peptide humanin and insulin-like growth factor-binding protein 3 regulates cell survival and apoptosis. Proc Natl Acad Sci U S A, 100(22), 13042- 13047. doi:10.1073/pnas.2135111100 Imai, S. I., & Guarente, L. (2016). It takes two to tango: NAD(+) and sirtuins in aging/longevity control. NPJ Aging Mech Dis, 2, 16017. doi:10.1038/npjamd.2016.17 Immonen, E., Collet, M., Goenaga, J., & Arnqvist, G. (2016). Direct and indirect genetic effects of sex-specific mitonuclear epistasis on reproductive ageing. Heredity (Edinb), 116(3), 338- 347. doi:10.1038/hdy.2015.112 Ingelsson, B., Söderberg, D., Strid, T., Söderberg, A., Bergh, A.-C., Loitto, V., . . . Rosén, A. (2018). Lymphocytes eject interferogenic mitochondrial DNA webs in response to CpG and non-CpG oligodeoxynucleotides of class C. Proceedings of the National Academy of Sciences, 115(3), E478-E487. doi:10.1073/pnas.1711950115 Ingolia, N. T., Brar, G. A., Stern-Ginossar, N., Harris, M. S., Talhouarne, G. J., Jackson, S. E., . . . Weissman, J. S. (2014). Ribosome profiling reveals pervasive translation outside of annotated protein-coding genes. Cell Rep, 8(5), 1365-1379. doi:10.1016/j.celrep.2014.07.045 Innocenti, P., Morrow, E. H., & Dowling, D. K. (2011). Experimental evidence supports a sex- specific selective sieve in mitochondrial genome evolution. Science, 332(6031), 845-848. doi:10.1126/science.1201157 International Human Genome Sequencing, C. (2004). Finishing the euchromatic sequence of the human genome. Nature, 431(7011), 931-945. doi:10.1038/nature03001 Jackson, R., Kroehling, L., Khitun, A., Bailis, W., Jarret, A., York, A. G., . . . Flavell, R. A. (2018). The translation of non-canonical open reading frames controls mucosal immunity. Nature, 564(7736), 434-438. doi:10.1038/s41586-018-0794-7 Jakobsson, T., Venteclef, N., Toresson, G., Damdimopoulos, A. E., Ehrlund, A., Lou, X., . . . Treuter, E. (2009). GPS2 is required for cholesterol efflux by triggering histone demethylation, LXR recruitment, and coregulator assembly at the ABCG1 locus. Mol Cell, 34(4), 510-518. doi:10.1016/j.molcel.2009.05.006 136 Jang, J. Y., Blum, A., Liu, J., & Finkel, T. (2018). The role of mitochondria in aging. J Clin Invest, 128(9), 3662-3670. doi:10.1172/jci120842 Ji, Z., Song, R., Regev, A., & Struhl, K. (2015). Many lncRNAs, 5’UTRs, and pseudogenes are translated and some are likely to express functional proteins. Elife, 4. doi:10.7554/elife.08890 Jin, Q., Qiao, C., Li, J., Xiao, B., Li, J., & Xiao, X. (2019). A GDF11/myostatin inhibitor, GDF11 propeptide-Fc, increases skeletal muscle mass and improves muscle strength in dystrophic mdx mice. Skelet Muscle, 9(1), 16. doi:10.1186/s13395-019-0197-y John, Hargreaves, M., Michael, & Juleen. (2014). Integrative Biology of Exercise. Cell, 159(4), 738-749. doi:10.1016/j.cell.2014.10.029 Johnson, J. L., Slentz, C. A., Ross, L. M., Huffman, K. M., & Kraus, W. E. (2019). Ten-Year Legacy Effects of Three Eight-Month Exercise Training Programs on Cardiometabolic Health Parameters. Front Physiol, 10, 452. doi:10.3389/fphys.2019.00452 Johnson, M. L., Robinson, M. M., & Nair, K. S. (2013). Skeletal muscle aging and the mitochondrion. Trends Endocrinol Metab, 24(5), 247-256. doi:10.1016/j.tem.2012.12.003 Johnston, I. G., & Williams, B. P. (2016). Evolutionary Inference across Eukaryotes Identifies Specific Pressures Favoring Mitochondrial Gene Retention. Cell Syst, 2(2), 101-111. doi:10.1016/j.cels.2016.01.013 Joseph, A. M., Adhihetty, P. J., Buford, T. W., Wohlgemuth, S. E., Lees, H. A., Nguyen, L. M., . . . Leeuwenburgh, C. (2012). The impact of aging on mitochondrial function and biogenesis pathways in skeletal muscle of sedentary high- and low-functioning elderly individuals. Aging Cell, 11(5), 801-809. doi:10.1111/j.1474-9726.2012.00844.x Joseph, A. M., Adhihetty, P. J., & Leeuwenburgh, C. (2016). Beneficial effects of exercise on age- related mitochondrial dysfunction and oxidative stress in skeletal muscle. J Physiol, 594(18), 5105-5123. doi:10.1113/JP270659 Ju, Y. S., Tubio, J. M. C., Mifsud, W., Fu, B., Davies, H. R., Ramakrishna, M., . . . Stratton, M. R. (2015). Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells. Genome Res, 25(6), 814-824. doi:10.1101/gr.190470.115 Justice, M. J., & Dhillon, P. (2016). Using the mouse to model human disease: increasing validity and reproducibility. Dis Model Mech, 9(2), 101-103. doi:10.1242/dmm.024547 Justo, R., Boada, J., Frontera, M., Oliver, J., Bermudez, J., & Gianotti, M. (2005). Gender dimorphism in rat liver mitochondrial oxidative metabolism and biogenesis. Am J Physiol Cell Physiol, 289(2), C372-378. doi:10.1152/ajpcell.00035.2005 Kaeberlein, M., Rabinovitch, P. S., & Martin, G. M. (2015). Healthy aging: The ultimate preventative medicine. Science, 350(6265), 1191-1193. doi:10.1126/science.aad3267 137 Kanfi, Y., Naiman, S., Amir, G., Peshti, V., Zinman, G., Nahum, L., . . . Cohen, H. Y. (2012). The sirtuin SIRT6 regulates lifespan in male mice. Nature, 483(7388), 218-221. doi:10.1038/nature10815 Kanki, T., Ohgaki, K., Gaspari, M., Gustafsson, C. M., Fukuoh, A., Sasaki, N., . . . Kang, D. (2004). Architectural Role of Mitochondrial Transcription Factor A in Maintenance of Human Mitochondrial DNA. 24(22), 9823-9834. doi:10.1128/mcb.24.22.9823-9834.2004 Kanzleiter, T., Rath, M., Penkov, D., Puchkov, D., Schulz, N., Blasi, F., & Schurmann, A. (2014). Pknox1/Prep1 regulates mitochondrial oxidative phosphorylation components in skeletal muscle. Mol Cell Biol, 34(2), 290-298. doi:10.1128/MCB.01232-13 Kariya, S., Takahashi, N., Hirano, M., & Ueno, S. (2003). Humanin improves impaired metabolic activity and prolongs survival of serum-deprived human lymphocytes. Mol Cell Biochem, 254(1-2), 83-89. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/14674685 Karpac, J., & Jasper, H. (2013). Aging: seeking mitonuclear balance. Cell, 154(2), 271-273. doi:10.1016/j.cell.2013.06.046 Karstoft, K., & Pedersen, B. K. (2016). Skeletal muscle as a gene regulatory endocrine organ. Curr Opin Clin Nutr Metab Care, 19(4), 270-275. doi:10.1097/MCO.0000000000000283 Kaufman, B. A., Durisic, N., Mativetsky, J. M., Costantino, S., Hancock, M. A., Grutter, P., & Shoubridge, E. A. (2007). The mitochondrial transcription factor TFAM coordinates the assembly of multiple DNA molecules into nucleoid-like structures. Mol Biol Cell, 18(9), 3225-3236. doi:10.1091/mbc.e07-05-0404 Kauppila, T. E. S., Kauppila, J. H. K., & Larsson, N.-G. (2017). Mammalian Mitochondria and Aging: An Update. Cell Metab, 25(1), 57-71. doi:https://doi.org/10.1016/j.cmet.2016.09.017 Kelly, J. L., & Lehman, I. R. (1986). Yeast mitochondrial RNA polymerase. Purification and properties of the catalytic subunit. J Biol Chem, 261(22), 10340-10347. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/3525543 http://www.jbc.org/content/261/22/10340.full.pdf Kennedy, B. K., Berger, S. L., Brunet, A., Campisi, J., Cuervo, A. M., Epel, E. S., . . . Sierra, F. (2014). Geroscience: Linking Aging to Chronic Disease. Cell, 159(4), 709-713. doi:10.1016/j.cell.2014.10.039 Kennedy, B. K., & Lamming, D. W. (2016). The Mechanistic Target of Rapamycin: The Grand ConducTOR of Metabolism and Aging. Cell Metab, 23(6), 990-1003. doi:10.1016/j.cmet.2016.05.009 Kennedy, S. R., Salk, J. J., Schmitt, M. W., & Loeb, L. A. (2013). Ultra-sensitive sequencing reveals an age-related increase in somatic mitochondrial mutations that are inconsistent with oxidative damage. PLoS Genet, 9(9), e1003794. doi:10.1371/journal.pgen.1003794 138 Khan, N. A., Nikkanen, J., Yatsuga, S., Jackson, C., Wang, L., Pradhan, S., . . . Suomalainen, A. (2017). mTORC1 Regulates Mitochondrial Integrated Stress Response and Mitochondrial Myopathy Progression. Cell Metab, 26(2), 419-428 e415. doi:10.1016/j.cmet.2017.07.007 Khrapko, K., & Vijg, J. (2009). Mitochondrial DNA mutations and aging: devils in the details? Trends Genet, 25(2), 91-98. doi:10.1016/j.tig.2008.11.007 Kienzler, A. K., Hargreaves, C. E., & Patel, S. Y. (2017). The role of genomics in common variable immunodeficiency disorders. Clin Exp Immunol, 188(3), 326-332. doi:10.1111/cei.12947 Kim, J., Gupta, R., Blanco, L. P., Yang, S., Shteinfer-Kuzmine, A., Wang, K., . . . Kerkhofs, M. (2019). VDAC oligomers form mitochondrial pores to release mtDNA fragments and promote lupus-like disease. Science, 366(6472), 1531-1536. Retrieved from https://science.sciencemag.org/content/sci/366/6472/1531.full.pdf Kim, K. H., Son, J. M., Benayoun, B. A., & Lee, C. (2018a). The Mitochondrial-Encoded Peptide MOTS-c Translocates to the Nucleus to Regulate Nuclear Gene Expression in Response to Metabolic Stress. Cell Metab. doi:10.1016/j.cmet.2018.06.008 Kim, K. H., Son, J. M., Benayoun, B. A., & Lee, C. (2018b). The Mitochondrial-Encoded Peptide MOTS-c Translocates to the Nucleus to Regulate Nuclear Gene Expression in Response to Metabolic Stress. Cell Metab, 28(3), 516-524 e517. doi:10.1016/j.cmet.2018.06.008 Kim, S.-J., Xiao, J., Wan, J., Cohen, P., & Yen, K. (2017). Mitochondrially derived peptides as novel regulators of metabolism. J Physiol, 595(21), 6613-6621. doi:10.1113/jp274472 Kim, S. J., Mehta, H. H., Wan, J., Kuehnemann, C., Chen, J., Hu, J. F., . . . Cohen, P. (2018). Mitochondrial peptides modulate mitochondrial function during cellular senescence. Aging (Albany NY), 10(6), 1239-1256. doi:10.18632/aging.101463 Kim, S. J., Miller, B., Mehta, H. H., Xiao, J., Wan, J., Arpawong, T. E., . . . Cohen, P. (2019). The mitochondrial-derived peptide MOTS-c is a regulator of plasma metabolites and enhances insulin sensitivity. Physiol Rep, 7(13), e14171. doi:10.14814/phy2.14171 Kim, S. J., Miller, B., Mehta, H. H., Xiao, J., Wan, J., Arpawong, T. E., . . . Cohen, P. (2019). The mitochondrial‐derived peptide MOTS‐c is a regulator of plasma metabolites and enhances insulin sensitivity. Physiological Reports, 7(13). doi:10.14814/phy2.14171 Kim, S. J., Xiao, J., Wan, J., Cohen, P., & Yen, K. (2017). Mitochondrially derived peptides as novel regulators of metabolism. J Physiol, 595(21), 6613-6621. doi:10.1113/JP274472 Kjobsted, R., Roll, J. L. W., Jorgensen, N. O., Birk, J. B., Foretz, M., Viollet, B., . . . Wojtaszewski, J. F. P. (2019). AMPK and TBC1D1 Regulate Muscle Glucose Uptake After, but Not During, Exercise and Contraction. Diabetes, 68(7), 1427-1440. doi:10.2337/db19-0050 Klein, L. E., Cui, L., Gong, Z., Su, K., & Muzumdar, R. (2013). A humanin analog decreases oxidative stress and preserves mitochondrial integrity in cardiac myoblasts. Biochem Biophys Res Commun, 440(2), 197-203. doi:10.1016/j.bbrc.2013.08.055 139 Knoop, A., Thomas, A., & Thevis, M. (2019). Development of a mass spectrometry based detection method for the mitochondrion-derived peptide MOTS-c in plasma samples for doping control purposes. Rapid Commun Mass Spectrom, 33(4), 371-380. doi:10.1002/rcm.8337 Koh, H. J. (2016). Regulation of exercise-stimulated glucose uptake in skeletal muscle. Ann Pediatr Endocrinol Metab, 21(2), 61-65. doi:10.6065/apem.2016.21.2.61 Kohrt, W. M., & Holloszy, J. O. (1995). Loss of skeletal muscle mass with aging: effect on glucose tolerance. J Gerontol A Biol Sci Med Sci, 50 Spec No, 68-72. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/7493222 Kondo, T., Hashimoto, Y., Kato, K., Inagaki, S., Hayashi, S., & Kageyama, Y. (2007). Small peptide regulators of actin-based cell morphogenesis encoded by a polycistronic mRNA. Nat Cell Biol, 9(6), 660-665. doi:10.1038/ncb1595 Kondo, T., Plaza, S., Zanet, J., Benrabah, E., Valenti, P., Hashimoto, Y., . . . Kageyama, Y. (2010). Small peptides switch the transcriptional activity of Shavenbaby during Drosophila embryogenesis. Science, 329(5989), 336-339. doi:10.1126/science.1188158 Kong, X., Yao, T., Zhou, P., Kazak, L., Tenen, D., Lyubetskaya, A., . . . Rosen, E. D. (2018). Brown Adipose Tissue Controls Skeletal Muscle Function via the Secretion of Myostatin. Cell Metab, 28(4), 631-643 e633. doi:10.1016/j.cmet.2018.07.004 Koopman, R., Manders, R. J., Jonkers, R. A., Hul, G. B., Kuipers, H., & van Loon, L. J. (2006). Intramyocellular lipid and glycogen content are reduced following resistance exercise in untrained healthy males. Eur J Appl Physiol, 96(5), 525-534. doi:10.1007/s00421-005- 0118-0 Kopek, B. G., Shtengel, G., Xu, C. S., Clayton, D. A., & Hess, H. F. (2012). Correlative 3D superresolution fluorescence and electron microscopy reveal the relationship of mitochondrial nucleoids to membranes. Proceedings of the National Academy of Sciences, 109(16), 6136-6141. doi:10.1073/pnas.1121558109 Kopinski, P. K., Janssen, K. A., Schaefer, P. M., Trefely, S., Perry, C. E., Potluri, P., . . . Wallace, D. C. (2019). Regulation of nuclear epigenome by mitochondrial DNA heteroplasmy. Proceedings of the National Academy of Sciences, 116(32), 16028-16035. doi:10.1073/pnas.1906896116 Kraemer, R. R., Shockett, P., Webb, N. D., Shah, U., & Castracane, V. D. (2014). A transient elevated irisin blood concentration in response to prolonged, moderate aerobic exercise in young men and women. Horm Metab Res, 46(2), 150-154. doi:10.1055/s-0033- 1355381 Kubica, N., Bolster, D. R., Farrell, P. A., Kimball, S. R., & Jefferson, L. S. (2005). Resistance exercise increases muscle protein synthesis and translation of eukaryotic initiation factor 2Bepsilon mRNA in a mammalian target of rapamycin-dependent manner. J Biol Chem, 280(9), 7570-7580. doi:10.1074/jbc.M413732200 140 Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., . . . Ma'Ayan, A. (2016). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res, 44(W1), W90-W97. doi:10.1093/nar/gkw377 Laberge, R. M., Sun, Y., Orjalo, A. V., Patil, C. K., Freund, A., Zhou, L., . . . Campisi, J. (2015). MTOR regulates the pro-tumorigenic senescence-associated secretory phenotype by promoting IL1A translation. Nat Cell Biol, 17(8), 1049-1061. doi:10.1038/ncb3195 Lagouge, M., Argmann, C., Gerhart-Hines, Z., Meziane, H., Lerin, C., Daussin, F., . . . Auwerx, J. (2006). Resveratrol improves mitochondrial function and protects against metabolic disease by activating SIRT1 and PGC-1alpha. Cell, 127(6), 1109-1122. doi:10.1016/j.cell.2006.11.013 Lane, N. (2011). Mitonuclear match: optimizing fitness and fertility over generations drives ageing within generations. Bioessays, 33(11), 860-869. doi:10.1002/bies.201100051 Lane, N. (2017). Serial endosymbiosis or singular event at the origin of eukaryotes? J Theor Biol, 434, 58-67. doi:10.1016/j.jtbi.2017.04.031 Larsson, N. G. (2010). Somatic mitochondrial DNA mutations in mammalian aging. Annu Rev Biochem, 79, 683-706. doi:10.1146/annurev-biochem-060408-093701 Lavie, C. J., Carbone, S., Kachur, S., O'Keefe, E. L., & Elagizi, A. (2019). Effects of Physical Activity, Exercise, and Fitness on Obesity-Related Morbidity and Mortality. Curr Sports Med Rep, 18(8), 292-298. doi:10.1249/JSR.0000000000000623 Le Couteur, D. G., Anderson, R. M., & de Cabo, R. (2018). Sex and Aging. J Gerontol A Biol Sci Med Sci, 73(2), 139-140. doi:10.1093/gerona/glx221 Ledford, H. (2011). Translational research: 4 ways to fix the clinical trial. Nature, 477(7366), 526- 528. doi:10.1038/477526a Lee, C., Wan, J., Miyazaki, B., Fang, Y., Guevara-Aguirre, J., Yen, K., . . . Cohen, P. (2014). IGF- I regulates the age-dependent signaling peptide humanin. Aging Cell, 13(5), 958-961. doi:10.1111/acel.12243 Lee, C., Yen, K., & Cohen, P. (2013a). Humanin: a harbinger of mitochondrial-derived peptides? Trends Endocrinol Metab, 24(5), 222-228. doi:10.1016/j.tem.2013.01.005 Lee, C., Yen, K., & Cohen, P. (2013b). Humanin: a harbinger of mitochondrial-derived peptides? Trends Endocrinol Metab. doi:10.1016/j.tem.2013.01.005 Lee, C., Zeng, J., Drew, B. G., Sallam, T., Martin-Montalvo, A., Wan, J., . . . Cohen, P. (2015). The mitochondrial-derived peptide MOTS-c promotes metabolic homeostasis and reduces obesity and insulin resistance. Cell Metab, 21(3), 443-454. doi:10.1016/j.cmet.2015.02.009 Lee, S. R., & Han, J. (2017). Mitochondrial Nucleoid: Shield and Switch of the Mitochondrial Genome. Oxid Med Cell Longev, 2017, 8060949. doi:10.1155/2017/8060949 141 Lee, W. T., Sun, X., Tsai, T. S., Johnson, J. L., Gould, J. A., Garama, D. J., . . . St John, J. C. (2017). Mitochondrial DNA haplotypes induce differential patterns of DNA methylation that result in differential chromosomal gene expression patterns. Cell Death Discov, 3, 17062. doi:10.1038/cddiscovery.2017.62 Leek, J. T., Johnson, W. E., Parker, H. S., Fertig, E. J., Jaffe, A. E., Storey, J. D., . . . Torres, L. C. (2019). sva: Surrogate Variable Analysis. R package version 3.32.1. Leek, J. T., & Storey, J. D. (2007). Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genet, 3(9), e161. doi:10.1371/journal.pgen.0030161 Lesniewski, L. A., Seals, D. R., Walker, A. E., Henson, G. D., Blimline, M. W., Trott, D. W., . . . Donato, A. J. (2017). Dietary rapamycin supplementation reverses age-related vascular dysfunction and oxidative stress, while modulating nutrient-sensing, cell cycle, and senescence pathways. Aging Cell, 16(1), 17-26. doi:10.1111/acel.12524 Li, J., Labbadia, J., & Morimoto, R. I. (2017). Rethinking HSF1 in Stress, Development, and Organismal Health. Trends Cell Biol, 27(12), 895-905. doi:10.1016/j.tcb.2017.08.002 Li, Q., Lu, H., Hu, G., Ye, Z., Zhai, D., Yan, Z., . . . Lu, Z. (2019). Earlier changes in mice after D- galactose treatment were improved by mitochondria derived small peptide MOTS-c. Biochem Biophys Res Commun, 513(2), 439-445. doi:10.1016/j.bbrc.2019.03.194 Li, Q., Lu, H., Hu, G., Ye, Z., Zhai, D., Yan, Z., . . . Lu, Z. (2019). Earlier changes in mice after D- galactose treatment were improved by mitochondria derived small peptide MOTS-c. Biochem Biophys Res Commun. doi:10.1016/j.bbrc.2019.03.194 Li, S., & Laher, I. (2015). Exercise Pills: At the Starting Line. Trends Pharmacol Sci. Li, S., & Laher, I. (2015). Exercise Pills: At the Starting Line. Trends Pharmacol Sci, 36(12), 906- 917. doi:10.1016/j.tips.2015.08.014 Lin, J., Wu, H., Tarr, P. T., Zhang, C. Y., Wu, Z., Boss, O., . . . Spiegelman, B. M. (2002). Transcriptional co-activator PGC-1 alpha drives the formation of slow-twitch muscle fibres. Nature, 418(6899), 797-801. doi:10.1038/nature00904 Lindqvist, L. M., Tandoc, K., Topisirovic, I., & Furic, L. (2018). Cross-talk between protein synthesis, energy metabolism and autophagy in cancer. Curr Opin Genet Dev, 48, 104- 111. doi:10.1016/j.gde.2017.11.003 Liu, C., Gidlund, E. K., Witasp, A., Qureshi, A. R., Soderberg, M., Thorell, A., . . . von Walden, F. (2019). Reduced skeletal muscle expression of mitochondrial derived peptides humanin and MOTS-C and Nrf2 in chronic kidney disease. Am J Physiol Renal Physiol. doi:10.1152/ajprenal.00202.2019 Longo, V. D., Antebi, A., Bartke, A., Barzilai, N., Brown-Borg, H. M., Caruso, C., . . . Fontana, L. (2015). Interventions to Slow Aging in Humans: Are We Ready? Aging Cell, 14(4), 497- 510. doi:10.1111/acel.12338 142 Longo, V. D., Gralla, E. B., & Valentine, J. S. (1996). Superoxide dismutase activity is essential for stationary phase survival in Saccharomyces cerevisiae. Mitochondrial production of toxic oxygen species in vivo. J Biol Chem, 271(21), 12275-12280. doi:10.1074/jbc.271.21.12275 Lopez, J. V., Yuhki, N., Masuda, R., Modi, W., & O'Brien, S. J. (1994). Numt, a recent transfer and tandem amplification of mitochondrial DNA to the nuclear genome of the domestic cat. J Mol Evol, 39(2), 174-190. doi:10.1007/bf00163806 Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013). The hallmarks of aging. Cell, 153(6), 1194-1217. doi:10.1016/j.cell.2013.05.039 Lopez-Otin, C., Galluzzi, L., Freije, J. M., Madeo, F., & Kroemer, G. (2016a). Metabolic Control of Longevity. Cell, 166(4), 802-821. doi:10.1016/j.cell.2016.07.031 Lopez-Otin, C., Galluzzi, L., Freije, J. M. P., Madeo, F., & Kroemer, G. (2016b). Metabolic Control of Longevity. Cell, 166(4), 802-821. doi:10.1016/j.cell.2016.07.031 Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 15(12). doi:10.1186/s13059-014-0550-8 Lu, H., Tang, S., Xue, C., Liu, Y., Wang, J., Zhang, W., . . . Chen, J. (2019). Mitochondrial-Derived Peptide MOTS-c Increases Adipose Thermogenic Activation to Promote Cold Adaptation. International Journal of Molecular Sciences, 20(10), 2456. doi:10.3390/ijms20102456 Lu, H., Wei, M., Zhai, Y., Li, Q., Ye, Z., Wang, L., . . . Lu, Z. (2019a). MOTS-c peptide regulates adipose homeostasis to prevent ovariectomy-induced metabolic dysfunction. J Mol Med (Berl). doi:10.1007/s00109-018-01738-w Lu, H., Wei, M., Zhai, Y., Li, Q., Ye, Z., Wang, L., . . . Lu, Z. (2019b). MOTS-c peptide regulates adipose homeostasis to prevent ovariectomy-induced metabolic dysfunction. J Mol Med (Berl), 97(4), 473-485. doi:10.1007/s00109-018-01738-w Lundby, C., & Jacobs, R. A. (2016). Adaptations of skeletal muscle mitochondria to exercise training. Exp Physiol, 101(1), 17-22. doi:10.1113/EP085319 Luo, S., Valencia, C. A., Zhang, J., Lee, N.-C., Slone, J., Gui, B., . . . Huang, T. (2018). Biparental Inheritance of Mitochondrial DNA in Humans. Proceedings of the National Academy of Sciences, 115(51), 13039-13044. doi:10.1073/pnas.1810946115 Lutz-Bonengel, S., & Parson, W. (2019). No further evidence for paternal leakage of mitochondrial DNA in humans yet. Proceedings of the National Academy of Sciences, 116(6), 1821- 1822. doi:10.1073/pnas.1820533116 Madeo, F., Carmona-Gutierrez, D., Hofer, S. J., & Kroemer, G. (2019). Caloric Restriction Mimetics against Age-Associated Disease: Targets, Mechanisms, and Therapeutic Potential. Cell Metab, 29(3), 592-610. doi:10.1016/j.cmet.2019.01.018 143 Magny, E. G., Pueyo, J. I., Pearl, F. M., Cespedes, M. A., Niven, J. E., Bishop, S. A., & Couso, J. P. (2013). Conserved regulation of cardiac calcium uptake by peptides encoded in small open reading frames. Science, 341(6150), 1116-1120. doi:10.1126/science.1238802 Mai, S., Grugni, G., Mele, C., Vietti, R., Vigna, L., Sartorio, A., . . . Marzullo, P. (2020). Irisin levels in genetic and essential obesity: clues for a potential dual role. Sci Rep, 10(1), 1020. doi:10.1038/s41598-020-57855-5 Mak, I. W., Evaniew, N., & Ghert, M. (2014). Lost in translation: animal models and clinical trials in cancer treatment. Am J Transl Res, 6(2), 114-118. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/24489990 Makarewich, C. A., & Olson, E. N. (2017). Mining for Micropeptides. Trends Cell Biol. doi:10.1016/j.tcb.2017.04.006 Maklakov, A. A., & Lummaa, V. (2013). Evolution of sex differences in lifespan and aging: causes and constraints. Bioessays, 35(8), 717-724. doi:10.1002/bies.201300021 Mamiya, T., & Ukai, M. (2001). [Gly(14)]-Humanin improved the learning and memory impairment induced by scopolamine in vivo. Br J Pharmacol, 134(8), 1597-1599. doi:10.1038/sj.bjp.0704429 Mangalhara, K. C., & Shadel, G. S. (2018). A Mitochondrial-Derived Peptide Exercises the Nuclear Option. Cell Metab, 28(3), 330-331. doi:10.1016/j.cmet.2018.08.017 Mao, K., Quipildor, G. F., Tabrizian, T., Novaj, A., Guan, F., Walters, R. O., . . . Huffman, D. M. (2018). Late-life targeting of the IGF-1 receptor improves healthspan and lifespan in female mice. Nat Commun, 9(1). doi:10.1038/s41467-018-04805-5 Marino, G., Pietrocola, F., Eisenberg, T., Kong, Y., Malik, S. A., Andryushkova, A., . . . Kroemer, G. (2014). Regulation of autophagy by cytosolic acetyl-coenzyme A. Mol Cell, 53(5), 710- 725. doi:10.1016/j.molcel.2014.01.016 Martijn, J., Vosseberg, J., Guy, L., Offre, P., & Ettema, T. J. G. (2018). Deep mitochondrial origin outside the sampled alphaproteobacteria. Nature, 557(7703), 101-105. doi:10.1038/s41586-018-0059-5 Martin, I., Jones, M. A., Rhodenizer, D., Zheng, J., Warrick, J. M., Seroude, L., & Grotewiel, M. (2009). Sod2 knockdown in the musculature has whole-organism consequences in Drosophila. Free Radic Biol Med, 47(6), 803-813. doi:10.1016/j.freeradbiomed.2009.06.021 Martinus, R. D., Garth, G. P., Webster, T. L., Cartwright, P., Naylor, D. J., Høj, P. B., & Hoogenraad, N. J. (1996). Selective Induction of Mitochondrial Chaperones in Response to Loss of the Mitochondrial Genome. 240(1), 98-103. doi:10.1111/j.1432- 1033.1996.0098h.x Masters, B. S., Stohl, L. L., & Clayton, D. A. (1987). Yeast mitochondrial RNA polymerase is homologous to those encoded by bacteriophages T3 and T7. Cell, 51(1), 89-99. doi:10.1016/0092-8674(87)90013-4 144 Matilainen, O., Quirós, P. M., & Auwerx, J. (2017). Mitochondria and Epigenetics – Crosstalk in Homeostasis and Stress. Trends Cell Biol, 27(6), 453-463. doi:10.1016/j.tcb.2017.02.004 McCay, C. M., Crowell, M. F., & Maynard, L. A. (1989). The effect of retarded growth upon the length of life span and upon the ultimate body size. 1935. Nutrition, 5(3), 155-171; discussion 172. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/2520283 McManus, M. J., Picard, M., Chen, H. W., De Haas, H. J., Potluri, P., Leipzig, J., . . . Wallace, D. C. (2019). Mitochondrial DNA Variation Dictates Expressivity and Progression of Nuclear DNA Mutations Causing Cardiomyopathy. Cell Metab, 29(1), 78-90 e75. doi:10.1016/j.cmet.2018.08.002 Mees, L. M., Coulter, M. M., Chrenek, M. A., Motz, C. T., Landis, E. G., Boatright, J. H., & Pardue, M. T. (2019). Low-Intensity Exercise in Mice Is Sufficient to Protect Retinal Function During Light-Induced Retinal Degeneration. Invest Ophthalmol Vis Sci, 60(5), 1328-1335. doi:10.1167/iovs.18-25883 Meier, J. A., Hyun, M., Cantwell, M., Raza, A., Mertens, C., Raje, V., . . . Larner, A. C. (2017). Stress-induced dynamic regulation of mitochondrial STAT3 and its association with cyclophilin D reduce mitochondrial ROS production. Sci Signal, 10(472). doi:10.1126/scisignal.aag2588 Melber, A., & Haynes, C. M. (2018). UPR(mt) regulation and output: a stress response mediated by mitochondrial-nuclear communication. Cell Res, 28(3), 281-295. doi:10.1038/cr.2018.16 Melov, S., Ravenscroft, J., Malik, S., Gill, M. S., Walker, D. W., Clayton, P. E., . . . Lithgow, G. J. (2000). Extension of life-span with superoxide dismutase/catalase mimetics. Science, 289(5484), 1567-1569. doi:10.1126/science.289.5484.1567 Menzies, K. J., Zhang, H., Katsyuba, E., & Auwerx, J. (2016). Protein acetylation in metabolism - metabolites and cofactors. Nat Rev Endocrinol, 12(1), 43-60. doi:10.1038/nrendo.2015.181 Mercer, T. R., Neph, S., Dinger, M. E., Crawford, J., Smith, M. A., Shearwood, A. M., . . . Mattick, J. S. (2011). The human mitochondrial transcriptome. Cell, 146(4), 645-658. doi:10.1016/j.cell.2011.06.051 Mercken, E. M., Hu, J., Krzysik-Walker, S., Wei, M., Li, Y., McBurney, M. W., . . . Longo, V. D. (2014). SIRT1 but not its increased expression is essential for lifespan extension in caloric- restricted mice. Aging Cell, 13(1), 193-196. doi:10.1111/acel.12151 Merkwirth, C., Jovaisaite, V., Durieux, J., Matilainen, O., Sabine, Pedro, . . . Dillin, A. (2016). Two Conserved Histone Demethylases Regulate Mitochondrial Stress-Induced Longevity. Cell, 165(5), 1209-1223. doi:10.1016/j.cell.2016.04.012 Merry, T. L., & Ristow, M. (2015). Do antioxidant supplements interfere with skeletal muscle adaptation to exercise training? , n/a-n/a. doi:10.1113/jp270654 145 Merry, T. L., & Ristow, M. (2016). Mitohormesis in exercise training. Free Radic Biol Med, 98, 123-130. doi:10.1016/j.freeradbiomed.2015.11.032 Messonnier, L. A., Emhoff, C. A., Fattor, J. A., Horning, M. A., Carlson, T. J., & Brooks, G. A. (2013). Lactate kinetics at the lactate threshold in trained and untrained men. J Appl Physiol (1985), 114(11), 1593-1602. doi:10.1152/japplphysiol.00043.2013 Milenkovic, D., Blaza, J. N., Larsson, N. G., & Hirst, J. (2017). The Enigma of the Respiratory Chain Supercomplex. Cell Metab, 25(4), 765-776. doi:10.1016/j.cmet.2017.03.009 Miller, R. A., Harrison, D. E., Astle, C. M., Fernandez, E., Flurkey, K., Han, M., . . . Strong, R. (2014). Rapamycin-mediated lifespan increase in mice is dose and sex dependent and metabolically distinct from dietary restriction. Aging Cell, 13(3), 468-477. doi:10.1111/acel.12194 Mills, K. F., Yoshida, S., Stein, L. R., Grozio, A., Kubota, S., Sasaki, Y., . . . Imai, S. I. (2016). Long-Term Administration of Nicotinamide Mononucleotide Mitigates Age-Associated Physiological Decline in Mice. Cell Metab, 24(6), 795-806. doi:10.1016/j.cmet.2016.09.013 Ming, W., Lu, G., Xin, S., Huanyu, L., Yinghao, J., Xiaoying, L., . . . Zifan, L. (2016). Mitochondria related peptide MOTS-c suppresses ovariectomy-induced bone loss via AMPK activation. Biochem Biophys Res Commun, 476(4), 412-419. doi:10.1016/j.bbrc.2016.05.135 Moller, A. B., Kampmann, U., Hedegaard, J., Thorsen, K., Nordentoft, I., Vendelbo, M. H., . . . Jessen, N. (2017). Altered gene expression and repressed markers of autophagy in skeletal muscle of insulin resistant patients with type 2 diabetes. Sci Rep, 7, 43775. doi:10.1038/srep43775 Moore, K. M., Girens, R. E., Larson, S. K., Jones, M. R., Restivo, J. L., Holtzman, D. M., . . . Timson, B. F. (2016). A spectrum of exercise training reduces soluble Abeta in a dose- dependent manner in a mouse model of Alzheimer's disease. Neurobiol Dis, 85, 218-224. doi:10.1016/j.nbd.2015.11.004 Morava, E., Kozicz, T., & Wallace, D. C. (2019). The phenotype modifier: is the mitochondrial DNA background responsible for individual differences in disease severity. J Inherit Metab Dis, 42(1), 3-4. doi:10.1002/jimd.12050 Morita, M., Gravel, S. P., Chenard, V., Sikstrom, K., Zheng, L., Alain, T., . . . Sonenberg, N. (2013). mTORC1 controls mitochondrial activity and biogenesis through 4E-BP-dependent translational regulation. Cell Metab, 18(5), 698-711. doi:10.1016/j.cmet.2013.10.001 Morris, J. N., Heady, J. A., Raffle, P. A., Roberts, C. G., & Parks, J. W. (1953). Coronary heart- disease and physical activity of work. Lancet, 262(6796), 1111-1120; concl. doi:10.1016/s0140-6736(53)91495-0 Mossman, J. A., Biancani, L. M., & Rand, D. M. (2019). Mitochondrial genomic variation drives differential nuclear gene expression in discrete regions of Drosophila gene and protein interaction networks. BMC Genomics, 20(1). doi:10.1186/s12864-019-6061-y 146 Mottis, A., Herzig, S., & Auwerx, J. (2019). Mitocellular communication: Shaping health and disease. Science, 366(6467), 827-832. doi:10.1126/science.aax3768 Mouchiroud, L., Houtkooper, R. H., Moullan, N., Katsyuba, E., Ryu, D., Canto, C., . . . Auwerx, J. (2013). The NAD(+)/Sirtuin Pathway Modulates Longevity through Activation of Mitochondrial UPR and FOXO Signaling. Cell, 154(2), 430-441. doi:10.1016/j.cell.2013.06.016 Mullane, K., & Williams, M. (2019). Preclinical Models of Alzheimer's Disease: Relevance and Translational Validity. Curr Protoc Pharmacol, 84(1), e57. doi:10.1002/cpph.57 Mullers, P., Taubert, M., & Muller, N. G. (2019). Physical Exercise as Personalized Medicine for Dementia Prevention? Front Physiol, 10, 672. doi:10.3389/fphys.2019.00672 Mulvey, L., Sands, W. A., Salin, K., Carr, A. E., & Selman, C. (2017). Disentangling the effect of dietary restriction on mitochondrial function using recombinant inbred mice. Mol Cell Endocrinol, 455, 41-53. doi:10.1016/j.mce.2016.09.001 Murphy, M. P. (2009). How mitochondria produce reactive oxygen species. Biochem J, 417(1), 1- 13. doi:10.1042/BJ20081386 Murthy, M., & Ram, J. L. (2015). Invertebrates as model organisms for research on aging biology. Invertebr Reprod Dev, 59(sup1), 1-4. doi:10.1080/07924259.2014.970002 Muzumdar, R. H., Huffman, D. M., Atzmon, G., Buettner, C., Cobb, L. J., Fishman, S., . . . Cohen, P. (2009). Humanin: a novel central regulator of peripheral insulin action. PLoS One, 4(7), e6334. doi:10.1371/journal.pone.0006334 Muzumdar, R. H., Huffman, D. M., Calvert, J. W., Jha, S., Weinberg, Y., Cui, L., . . . Lefer, D. J. (2010). Acute humanin therapy attenuates myocardial ischemia and reperfusion injury in mice. Arterioscler Thromb Vasc Biol, 30(10), 1940-1948. doi:10.1161/ATVBAHA.110.205997 Nader, G. A., & Esser, K. A. (2001). Intracellular signaling specificity in skeletal muscle in response to different modes of exercise. J Appl Physiol (1985), 90(5), 1936-1942. doi:10.1152/jappl.2001.90.5.1936 Nargund, A. M., Fiorese, C. J., Pellegrino, M. W., Deng, P., & Haynes, C. M. (2015). Mitochondrial and nuclear accumulation of the transcription factor ATFS-1 promotes OXPHOS recovery during the UPR(mt). Mol Cell, 58(1), 123-133. doi:10.1016/j.molcel.2015.02.008 Nargund, A. M., Pellegrino, M. W., Fiorese, C. J., Baker, B. M., & Haynes, C. M. (2012). Mitochondrial import efficiency of ATFS-1 regulates mitochondrial UPR activation. Science, 337(6094), 587-590. doi:10.1126/science.1223560 Narkar, V. A., Downes, M., Yu, R. T., Embler, E., Wang, Y. X., Banayo, E., . . . Evans, R. M. (2008). AMPK and PPARdelta agonists are exercise mimetics. Cell, 134(3), 405-415. doi:10.1016/j.cell.2008.06.051 147 Nelson, B. R., Makarewich, C. A., Anderson, D. M., Winders, B. R., Troupes, C. D., Wu, F., . . . Olson, E. N. (2016). A peptide encoded by a transcript annotated as long noncoding RNA enhances SERCA activity in muscle. Science, 351(6270), 271-275. doi:10.1126/science.aad4076 Neufer, P. D., Bamman, M. M., Muoio, D. M., Bouchard, C., Cooper, D. M., Goodpaster, B. H., . . . Laughlin, M. R. (2015). Understanding the Cellular and Molecular Mechanisms of Physical Activity-Induced Health Benefits. Cell Metab, 22(1), 4-11. doi:10.1016/j.cmet.2015.05.011 Nguemeni, C., McDonald, M. W., Jeffers, M. S., Livingston-Thomas, J., Lagace, D., & Corbett, D. (2018). Short- and Long-term Exposure to Low and High Dose Running Produce Differential Effects on Hippocampal Neurogenesis. Neuroscience, 369, 202-211. doi:10.1016/j.neuroscience.2017.11.026 Nicholas, Gloria, Stern-Ginossar, N., Michael, Gaëlle, Sarah, . . . Jonathan. (2014). Ribosome Profiling Reveals Pervasive Translation Outside of Annotated Protein-Coding Genes. Cell Rep, 8(5), 1365-1379. doi:10.1016/j.celrep.2014.07.045 Norheim, F., Langleite, T. M., Hjorth, M., Holen, T., Kielland, A., Stadheim, H. K., . . . Drevon, C. A. (2014). The effects of acute and chronic exercise on PGC-1alpha, irisin and browning of subcutaneous adipose tissue in humans. FEBS J, 281(3), 739-749. doi:10.1111/febs.12619 Normand, R., Du, W., Briller, M., Gaujoux, R., Starosvetsky, E., Ziv-Kenet, A., . . . Shen-Orr, S. S. (2018). Found In Translation: a machine learning model for mouse-to-human inference. Nat Methods, 15(12), 1067-1073. doi:10.1038/s41592-018-0214-9 Nuffer, W., Trujillo, J. M., & Megyeri, J. (2016). A Comparison of New Pharmacological Agents for the Treatment of Obesity. Ann Pharmacother, 50(5), 376-388. doi:10.1177/1060028016634351 Nye, G. A., Sakellariou, G. K., Degens, H., & Lightfoot, A. P. (2017). Muscling in on mitochondrial sexual dimorphism; role of mitochondrial dimorphism in skeletal muscle health and disease. Clin Sci (Lond), 131(15), 1919-1922. doi:10.1042/CS20160940 Oh, Y. K., Bachar, A. R., Zacharias, D. G., Kim, S. G., Wan, J., Cobb, L. J., . . . Lerman, A. (2011). Humanin preserves endothelial function and prevents atherosclerotic plaque progression in hypercholesterolemic ApoE deficient mice. Atherosclerosis, 219(1), 65-73. doi:10.1016/j.atherosclerosis.2011.06.038 Ojala, D., Montoya, J., & Attardi, G. (1981). tRNA punctuation model of RNA processing in human mitochondria. Nature, 290(5806), 470-474. doi:10.1038/290470a0 Okada, A. K., Teranishi, K., Lobo, F., Isas, J. M., Xiao, J., Yen, K., . . . Langen, R. (2017). The Mitochondrial-Derived Peptides, HumaninS14G and Small Humanin-like Peptide 2, Exhibit Chaperone-like Activity. Sci Rep, 7(1). doi:10.1038/s41598-017-08372-5 Olshansky, S. J. (2018). From Lifespan to Healthspan. JAMA, 320(13), 1323. doi:10.1001/jama.2018.12621 148 Onyango, I., Khan, S., Miller, B., Swerdlow, R., Trimmer, P., & Bennett, P., Jr. (2006). Mitochondrial genomic contribution to mitochondrial dysfunction in Alzheimer's disease. J Alzheimers Dis, 9(2), 183-193. doi:10.3233/jad-2006-9210 Osler, M. E., & Zierath, J. R. (2008). Adenosine 5'-monophosphate-activated protein kinase regulation of fatty acid oxidation in skeletal muscle. Endocrinology, 149(3), 935-941. doi:10.1210/en.2007-1441 Overmyer, K. A., Evans, C. R., Qi, N. R., Minogue, C. E., Carson, J. J., Chermside-Scabbo, C. J., . . . Burant, C. F. (2015). Maximal oxidative capacity during exercise is associated with skeletal muscle fuel selection and dynamic changes in mitochondrial protein acetylation. Cell Metab, 21(3), 468-478. doi:10.1016/j.cmet.2015.02.007 Packer, L., Cadenas, E., & Davies, K. J. A. (2008). Free radicals and exercise: An introduction. Free Radical Biology and Medicine, 44(2), 123-125. doi:10.1016/j.freeradbiomed.2007.05.031 Palikaras, K., Lionaki, E., & Tavernarakis, N. (2015). Coordination of mitophagy and mitochondrial biogenesis during ageing in C. elegans. Nature, 521(7553), 525-528. doi:10.1038/nature14300 Pan, H., & Finkel, T. (2017). Key proteins and pathways that regulate lifespan. J Biol Chem, 292(16), 6452-6460. doi:10.1074/jbc.R116.771915 Papadopoli, D., Boulay, K., Kazak, L., Pollak, M., Mallette, F., Topisirovic, I., & Hulea, L. (2019). mTOR as a central regulator of lifespan and aging. F1000Res, 8. doi:10.12688/f1000research.17196.1 Pedersen, B. K. (2019). Physical activity and muscle-brain crosstalk. Nat Rev Endocrinol, 15(7), 383-392. doi:10.1038/s41574-019-0174-x Periasamy, M., Herrera, J. L., & Reis, F. C. G. (2017). Skeletal Muscle Thermogenesis and Its Role in Whole Body Energy Metabolism. Diabetes Metab J, 41(5), 327-336. doi:10.4093/dmj.2017.41.5.327 Perna, N. T., & Kocher, T. D. (1996). Mitochondrial DNA: molecular fossils in the nucleus. Curr Biol, 6(2), 128-129. doi:10.1016/s0960-9822(02)00441-4 Petersen, K. F., Dufour, S., & Shulman, G. I. (2005). Decreased insulin-stimulated ATP synthesis and phosphate transport in muscle of insulin-resistant offspring of type 2 diabetic parents. PLoS Med, 2(9), e233. doi:10.1371/journal.pmed.0020233 Peterson, M. J., Giuliani, C., Morey, M. C., Pieper, C. F., Evenson, K. R., Mercer, V., . . . Body Composition Study Research, G. (2009). Physical activity as a preventative factor for frailty: the health, aging, and body composition study. J Gerontol A Biol Sci Med Sci, 64(1), 61-68. doi:10.1093/gerona/gln001 Picard, M., Hepple, R. T., & Burelle, Y. (2012). Mitochondrial functional specialization in glycolytic and oxidative muscle fibers: tailoring the organelle for optimal function. Am J Physiol Cell Physiol, 302(4), C629-641. doi:10.1152/ajpcell.00368.2011 149 Pichaud, N., Berube, R., Cote, G., Belzile, C., Dufresne, F., Morrow, G., . . . Blier, P. U. (2019). Age Dependent Dysfunction of Mitochondrial and ROS Metabolism Induced by Mitonuclear Mismatch. Front Genet, 10, 130. doi:10.3389/fgene.2019.00130 Pickles, S., Vigie, P., & Youle, R. J. (2018). Mitophagy and Quality Control Mechanisms in Mitochondrial Maintenance. Curr Biol, 28(4), R170-R185. doi:10.1016/j.cub.2018.01.004 Pinti, M., Cevenini, E., Nasi, M., De Biasi, S., Salvioli, S., Monti, D., . . . Cossarizza, A. (2014). Circulating mitochondrial DNA increases with age and is a familiar trait: Implications for "inflamm-aging". Eur J Immunol, 44(5), 1552-1562. doi:10.1002/eji.201343921 Pinto, R. E., & Bartley, W. (1969). The effect of age and sex on glutathione reductase and glutathione peroxidase activities and on aerobic glutathione oxidation in rat liver homogenates. Biochem J, 112(1), 109-115. doi:10.1042/bj1120109 Pohjoismaki, J. L. O., Forslund, J. M. E., Goffart, S., Torregrosa-Munumer, R., & Wanrooij, S. (2018). Known Unknowns of Mammalian Mitochondrial DNA Maintenance. Bioessays, 40(9), e1800102. doi:10.1002/bies.201800102 Pomatto, L. C. D., & Davies, K. J. A. (2018). Adaptive homeostasis and the free radical theory of ageing. Free Radic Biol Med, 124, 420-430. doi:10.1016/j.freeradbiomed.2018.06.016 Pomatto, L. C. D., Wong, S., Tower, J., & Davies, K. J. A. (2017). Sexual dimorphism in oxidant- induced adaptive homeostasis in multiple wild-type D. melanogaster strains. Arch Biochem Biophys, 636, 57-70. doi:10.1016/j.abb.2017.10.021 Powers, R. W., 3rd, Kaeberlein, M., Caldwell, S. D., Kennedy, B. K., & Fields, S. (2006). Extension of chronological life span in yeast by decreased TOR pathway signaling. Genes Dev, 20(2), 174-184. doi:10.1101/gad.1381406 Pozzi, A., & Dowling, D. K. (2019). The Genomic Origins of Small Mitochondrial RNAs: Are They Transcribed by the Mitochondrial DNA or by Mitochondrial Pseudogenes within the Nucleus (NUMTs)? Genome Biol Evol, 11(7), 1883-1896. doi:10.1093/gbe/evz132 Prevost, C. T., Peris, N., Seger, C., Pedeville, D. R., Wershing, K., Sia, E. A., & Sia, R. A. L. (2018). The influence of mitochondrial dynamics on mitochondrial genome stability. Curr Genet, 64(1), 199-214. doi:10.1007/s00294-017-0717-4 Price, N. L., Gomes, A. P., Ling, A. J., Duarte, F. V., Martin-Montalvo, A., North, B. J., . . . Sinclair, D. A. (2012). SIRT1 is required for AMPK activation and the beneficial effects of resveratrol on mitochondrial function. Cell Metab, 15(5), 675-690. doi:10.1016/j.cmet.2012.04.003 Qin, Q., Delrio, S., Wan, J., Jay Widmer, R., Cohen, P., Lerman, L. O., & Lerman, A. (2018). Downregulation of circulating MOTS-c levels in patients with coronary endothelial dysfunction. Int J Cardiol, 254, 23-27. doi:10.1016/j.ijcard.2017.12.001 Qin, Q., Mehta, H., Yen, K., Navarrete, G., Brandhorst, S., Wan, J., . . . Lerman, A. (2018). Chronic treatment with the mitochondrial peptide humanin prevents age-related myocardial fibrosis in mice. American Journal of Physiology-Heart and Circulatory Physiology, 315(5), H1127- H1136. doi:10.1152/ajpheart.00685.2017 150 Quirós, P. M., Mottis, A., & Auwerx, J. (2016). Mitonuclear communication in homeostasis and stress. Nature Reviews Molecular Cell Biology, 17(4), 213-226. doi:10.1038/nrm.2016.23 Rae, M. J., Butler, R. N., Campisi, J., De Grey, A. D. N. J., Finch, C. E., Gough, M., . . . Logan, B. J. (2010). The Demographic and Biomedical Case for Late-Life Interventions in Aging. Sci Transl Med, 2(40), 40cm21-40cm21. doi:10.1126/scitranslmed.3000822 Raffaghello, L., Lee, C., Safdie, F. M., Wei, M., Madia, F., Bianchi, G., & Longo, V. D. (2008). Starvation-dependent differential stress resistance protects normal but not cancer cells against high-dose chemotherapy. Proc Natl Acad Sci U S A, 105(24), 8215-8220. doi:10.1073/pnas.0708100105 Raijmakers, R. P. H., Jansen, A. F. M., Keijmel, S. P., Ter Horst, R., Roerink, M. E., Novakovic, B., . . . Bleeker-Rovers, C. P. (2019). A possible role for mitochondrial-derived peptides humanin and MOTS-c in patients with Q fever fatigue syndrome and chronic fatigue syndrome. J Transl Med, 17(1), 157. doi:10.1186/s12967-019-1906-3 Raj, A., Wang, S. H., Shim, H., Harpak, A., Li, Y. I., Engelmann, B., . . . Pritchard, J. K. (2016). Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling. Elife, 5. doi:10.7554/eLife.13328 Ramanjaneya, M., Bettahi, I., Jerobin, J., Chandra, P., Abi Khalil, C., Skarulis, M., . . . Abou- Samra, A.-B. (2019). Mitochondrial-Derived Peptides Are Down Regulated in Diabetes Subjects. Frontiers in endocrinology, 10. doi:10.3389/fendo.2019.00331 Ramanjaneya, M., Bettahi, I., Jerobin, J., Chandra, P., Abi Khalil, C., Skarulis, M., . . . Abou- Samra, A. B. (2019). Mitochondrial-Derived Peptides Are Down Regulated in Diabetes Subjects. Front Endocrinol (Lausanne), 10, 331. doi:10.3389/fendo.2019.00331 Ramanjaneya, M., Jerobin, J., Bettahi, I., Bensila, M., Aye, M., Siveen, K. S., . . . Atkin, S. L. (2019). Lipids and insulin regulate mitochondrial-derived peptide (MOTS-c) in PCOS and healthy subjects. Clin Endocrinol (Oxf). doi:10.1111/cen.14007 Ramanjaneya, M., Jerobin, J., Bettahi, I., Bensila, M., Aye, M., Siveen, K. S., . . . Atkin, S. L. (2019). Lipids and insulin regulate mitochondrial‐derived peptide (MOTS‐c) in PCOS and healthy subjects. Clinical Endocrinology. doi:10.1111/cen.14007 Reinhardt, K., Dowling, D. K., & Morrow, E. H. (2013). Medicine. Mitochondrial replacement, evolution, and the clinic. Science, 341(6152), 1345-1346. doi:10.1126/science.1237146 Reynolds, J., Lai, R. W., Woodhead, J. S. T., Joly, J. H., Mitchell, C. J., Cameron-Smith, D., . . . Lee, C. (2019). MOTS-c is an Exercise-Induced Mitochondrial-Encoded Regulator of Age- Dependent Physical Decline and Muscle Homeostasis. BioRxIV Preprint. doi:10.1101/2019.12.22.886432 Reynolds, J. C., Bwiza, C. P., & Lee, C. (2020). Mitonuclear genomics and aging. Human Genetics. doi:10.1007/s00439-020-02119-5 Ricchetti, M., Tekaia, F., & Dujon, B. (2004). Continued colonization of the human genome by mitochondrial DNA. PLoS Biol, 2(9), E273. doi:10.1371/journal.pbio.0020273 151 Richardson, A., Fischer, K. E., Speakman, J. R., de Cabo, R., Mitchell, S. J., Peterson, C. A., . . . Austad, S. N. (2016). Measures of Healthspan as Indices of Aging in Mice-A Recommendation. J Gerontol A Biol Sci Med Sci, 71(4), 427-430. doi:10.1093/gerona/glv080 Ringel, R., Sologub, M., Morozov, Y. I., Litonin, D., Cramer, P., & Temiakov, D. (2011). Structure of human mitochondrial RNA polymerase. Nature, 478(7368), 269-273. doi:10.1038/nature10435 Ristow, M., & Schmeisser, K. (2014). Mitohormesis: Promoting Health and Lifespan by Increased Levels of Reactive Oxygen Species (ROS). Dose-Response, 12(2), 288-341. doi:10.2203/dose-response.13-035.Ristow Ristow, M., & Schmeisser, S. (2011). Extending life span by increasing oxidative stress. Free Radical Biology and Medicine, 51(2), 327-336. doi:https://doi.org/10.1016/j.freeradbiomed.2011.05.010 Rochelle, T. L., Yeung, D. K., Bond, M. H., & Li, L. M. (2015). Predictors of the gender gap in life expectancy across 54 nations. Psychol Health Med, 20(2), 129-138. doi:10.1080/13548506.2014.936884 Rodriguez-Cuenca, S., Pujol, E., Justo, R., Frontera, M., Oliver, J., Gianotti, M., & Roca, P. (2002). Sex-dependent thermogenesis, differences in mitochondrial morphology and function, and adrenergic response in brown adipose tissue. J Biol Chem, 277(45), 42958-42963. doi:10.1074/jbc.M207229200 Rojansky, R., Cha, M. Y., & Chan, D. C. (2016). Elimination of paternal mitochondria in mouse embryos occurs through autophagic degradation dependent on PARKIN and MUL1. Elife, 5. doi:10.7554/eLife.17896 Rothnagel, J., & Menschaert, G. (2018). Short Open Reading Frames and Their Encoded Peptides. PROTEOMICS, 18(10), 1700035. doi:10.1002/pmic.201700035 Rubio, M. A. T., Rinehart, J. J., Krett, B., Duvezin-Caubet, S., Reichert, A. S., Söll, D., & Alfonzo, J. D. (2008). Mammalian mitochondria have the innate ability to import tRNAs by a mechanism distinct from protein import. Proceedings of the National Academy of Sciences, 105(27), 9186-9191. doi:10.1073/pnas.0804283105 Ruiz-Orera, J., & Albà, M. M. (2019). Translation of Small Open Reading Frames: Roles in Regulation and Evolutionary Innovation. Trends in genetics, 35(3), 186-198. doi:10.1016/j.tig.2018.12.003 Sabina, R. L., Holmes, E. W., & Becker, M. A. (1984). The enzymatic synthesis of 5-amino-4- imidazolecarboxamide riboside triphosphate (ZTP). Science, 223(4641), 1193-1195. doi:10.1126/science.6199843 Sagan, L. (1967). On the origin of mitosing cells. J Theor Biol, 14(3), 255-274. doi:10.1016/0022- 5193(67)90079-3 152 Saghatelian, A., & Couso, J. P. (2015). Discovery and characterization of smORF-encoded bioactive polypeptides. Nat Chem Biol, 11(12), 909-916. doi:10.1038/nchembio.1964 Sakamoto, M., Minamino, T., Toko, H., Kayama, Y., Zou, Y., Sano, M., . . . Komuro, I. (2006). Upregulation of Heat Shock Transcription Factor 1 Plays a Critical Role in Adaptive Cardiac Hypertrophy. 99(12), 1411-1418. doi:10.1161/01.res.0000252345.80198.97 Sako, H., Yada, K., & Suzuki, K. (2016). Genome-Wide Analysis of Acute Endurance Exercise- Induced Translational Regulation in Mouse Skeletal Muscle. PLoS One, 11(2), e0148311. doi:10.1371/journal.pone.0148311 Salinas-Giegé, T., Giegé, R., & Giegé, P. (2015). tRNA Biology in Mitochondria. Int J Mol Sci, 16(3), 4518-4559. doi:10.3390/ijms16034518 Sampathkumar, N. K., Bravo, J. I., Chen, Y., Danthi, P. S., Donahue, E. K., Lai, R. W., . . . Benayoun, B. A. (2019). Widespread sex dimorphism in aging and age-related diseases. Hum Genet. doi:10.1007/s00439-019-02082-w Saxton, R. A., & Sabatini, D. M. (2017). mTOR Signaling in Growth, Metabolism, and Disease. Cell, 169(2), 361-371. doi:10.1016/j.cell.2017.03.035 Schiaffino, S., Reggiani, C., & Murgia, M. (2019). Fiber type diversity in skeletal muscle explored by mass spectrometry-based single fiber proteomics. Histol Histopathol, 18170. doi:10.14670/HH-18-170 Schneider, A. (2011). Mitochondrial tRNA Import and Its Consequences for Mitochondrial Translation. Annu Rev Biochem, 80(1), 1033-1053. doi:10.1146/annurev-biochem- 060109-092838 Schulz, T. J., Zarse, K., Voigt, A., Urban, N., Birringer, M., & Ristow, M. (2007). Glucose restriction extends Caenorhabditis elegans life span by inducing mitochondrial respiration and increasing oxidative stress. Cell Metab, 6(4), 280-293. doi:10.1016/j.cmet.2007.08.011 Selman, C., Lingard, S., Choudhury, A. I., Batterham, R. L., Claret, M., Clements, M., . . . Withers, D. J. (2008). Evidence for lifespan extension and delayed age-related biomarkers in insulin receptor substrate 1 null mice. FASEB J, 22(3), 807-818. doi:10.1096/fj.07-9261com Selman, C., Tullet, J. M., Wieser, D., Irvine, E., Lingard, S. J., Choudhury, A. I., . . . Withers, D. J. (2009). Ribosomal protein S6 kinase 1 signaling regulates mammalian life span. Science, 326(5949), 140-144. doi:10.1126/science.1177221 Sena, L. A., & Chandel, N. S. (2012). Physiological roles of mitochondrial reactive oxygen species. Mol Cell, 48(2), 158-167. doi:10.1016/j.molcel.2012.09.025 Shadel, G. S., & Horvath, T. L. (2015). Mitochondrial ROS signaling in organismal homeostasis. Cell, 163(3), 560-569. doi:10.1016/j.cell.2015.10.001 Shen, J., Landis, G. N., & Tower, J. (2017). Multiple Metazoan Life-span Interventions Exhibit a Sex-specific Strehler-Mildvan Inverse Relationship Between Initial Mortality Rate and Age- 153 dependent Mortality Rate Acceleration. J Gerontol A Biol Sci Med Sci, 72(1), 44-53. doi:10.1093/gerona/glw005 Shi, L., & Tu, B. P. (2015). Acetyl-CoA and the regulation of metabolism: mechanisms and consequences. Curr Opin Cell Biol, 33, 125-131. doi:10.1016/j.ceb.2015.02.003 Shi, Y., Shi, H., Nieman, D. C., Hu, Q., Yang, L., Liu, T., . . . Chen, P. (2019). Lactic Acid Accumulation During Exhaustive Exercise Impairs Release of Neutrophil Extracellular Traps in Mice. Front Physiol, 10, 709. doi:10.3389/fphys.2019.00709 Shokolenko, I. N., & Alexeyev, M. F. (2017). Mitochondrial transcription in mammalian cells. Front Biosci (Landmark Ed), 22, 835-853. doi:10.2741/4520 Short, K. R., Bigelow, M. L., Kahl, J., Singh, R., Coenen-Schimke, J., Raghavakaimal, S., & Nair, K. S. (2005). Decline in skeletal muscle mitochondrial function with aging in humans. Proc Natl Acad Sci U S A, 102(15), 5618-5623. doi:10.1073/pnas.0501559102 Shpilka, T., & Haynes, C. M. (2018). The mitochondrial UPR: mechanisms, physiological functions and implications in ageing. Nat Rev Mol Cell Biol, 19(2), 109-120. doi:10.1038/nrm.2017.110 Shutt, T. E., & Gray, M. W. (2006). Bacteriophage origins of mitochondrial replication and transcription proteins. Trends Genet, 22(2), 90-95. doi:10.1016/j.tig.2005.11.007 Siegel, M. P., Kruse, S. E., Percival, J. M., Goh, J., White, C. C., Hopkins, H. C., . . . Marcinek, D. J. (2013). Mitochondrial-targeted peptide rapidly improves mitochondrial energetics and skeletal muscle performance in aged mice. Aging Cell, 12(5), 763-771. doi:10.1111/acel.12102 Singh, K. K., Choudhury, A. R., & Tiwari, H. K. (2017). Numtogenesis as a mechanism for development of cancer. Semin Cancer Biol, 47, 101-109. doi:10.1016/j.semcancer.2017.05.003 Singh, P. P., Demmitt, B. A., Nath, R. D., & Brunet, A. (2019). The Genetics of Aging: A Vertebrate Perspective. Cell, 177(1), 200-220. doi:10.1016/j.cell.2019.02.038 Slavoff, S. A., Heo, J., Budnik, B. A., Hanakahi, L. A., & Saghatelian, A. (2014). A human short open reading frame (sORF)-encoded polypeptide that stimulates DNA end joining. J Biol Chem, 289(16), 10950-10957. doi:10.1074/jbc.C113.533968 Smith, R. L., Soeters, M. R., Wüst, R. C. I., & Houtkooper, R. H. (2018). Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease. Endocrine Reviews, 39(4), 489-517. doi:10.1210/er.2017-00211 Soledad, R. B., Charles, S., & Samarjit, D. (2019). The secret messages between mitochondria and nucleus in muscle cell biology. Arch Biochem Biophys, 666, 52-62. doi:10.1016/j.abb.2019.03.019 154 Son, J. M., & Lee, C. (2019). Mitochondria: multifaceted regulators of aging. BMB Rep, 52(1), 13- 23. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386233/pdf/bmb-52- 013.pdf Song, W., Owusu-Ansah, E., Hu, Y., Cheng, D., Ni, X., Zirin, J., & Perrimon, N. (2017). Activin signaling mediates muscle-to-adipose communication in a mitochondria dysfunction- associated obesity model. Proc Natl Acad Sci U S A. doi:10.1073/pnas.1708037114 Spang, A., Saw, J. H., Jørgensen, S. L., Zaremba-Niedzwiedzka, K., Martijn, J., Lind, A. E., . . . Ettema, T. J. G. (2015). Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature, 521, 173. doi:10.1038/nature14447 https://www.nature.com/articles/nature14447#supplementary-information Spangenburg, E. E., & Booth, F. W. (2003). Molecular regulation of individual skeletal muscle fibre types. Acta Physiol Scand, 178(4), 413-424. doi:10.1046/j.1365-201X.2003.01158.x Speakman, J. R., & Mitchell, S. E. (2011). Caloric restriction. Mol Aspects Med, 32(3), 159-221. doi:10.1016/j.mam.2011.07.001 Spelbrink, J. N. (2010). Functional organization of mammalian mitochondrial DNA in nucleoids: history, recent developments, and future challenges. IUBMB Life, 62(1), 19-32. doi:10.1002/iub.282 Sreekumar, P. G., Ishikawa, K., Spee, C., Mehta, H. H., Wan, J., Yen, K., . . . Hinton, D. R. (2016). The Mitochondrial-Derived Peptide Humanin Protects RPE Cells From Oxidative Stress, Senescence, and Mitochondrial Dysfunction. 57(3), 1238. doi:10.1167/iovs.15-17053 Srinivasainagendra, V., Sandel, M. W., Singh, B., Sundaresan, A., Mooga, V. P., Bajpai, P., . . . Singh, K. K. (2017). Migration of mitochondrial DNA in the nuclear genome of colorectal adenocarcinoma. Genome Med, 9(1), 31. doi:10.1186/s13073-017-0420-6 Su, K. H., & Dai, C. (2017). mTORC1 senses stresses: Coupling stress to proteostasis. Bioessays, 39(5). doi:10.1002/bies.201600268 Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., . . . Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545-15550. doi:10.1073/pnas.0506580102 Sun, N., Youle, R. J., & Finkel, T. (2016). The Mitochondrial Basis of Aging. Mol Cell, 61(5), 654- 666. doi:10.1016/j.molcel.2016.01.028 Sunnucks, P., Morales, H. E., Lamb, A. M., Pavlova, A., & Greening, C. (2017). Integrative Approaches for Studying Mitochondrial and Nuclear Genome Co-evolution in Oxidative Phosphorylation. Front Genet, 8, 25. doi:10.3389/fgene.2017.00025 Sunny, N. E., Bril, F., & Cusi, K. (2017). Mitochondrial Adaptation in Nonalcoholic Fatty Liver Disease: Novel Mechanisms and Treatment Strategies. Trends Endocrinol Metab, 28(4), 250-260. doi:10.1016/j.tem.2016.11.006 155 Sutendra, G., Kinnaird, A., Dromparis, P., Paulin, R., Stenson, T. H., Haromy, A., . . . Michelakis, E. D. (2014). A nuclear pyruvate dehydrogenase complex is important for the generation of acetyl-CoA and histone acetylation. Cell, 158(1), 84-97. doi:10.1016/j.cell.2014.04.046 Swerdlow, R. H., Koppel, S., Weidling, I., Hayley, C., Ji, Y., & Wilkins, H. M. (2017). Mitochondria, Cybrids, Aging, and Alzheimer's Disease. Prog Mol Biol Transl Sci, 146, 259-302. doi:10.1016/bs.pmbts.2016.12.017 Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., . . . Christian. (2019). STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 47(D1), D607-D613. doi:10.1093/nar/gky1131 Tahara, Y., & Shibata, S. (2018). Entrainment of the mouse circadian clock: Effects of stress, exercise, and nutrition. Free Radic Biol Med, 119, 129-138. doi:10.1016/j.freeradbiomed.2017.12.026 Tajima, H., Kawasumi, M., Chiba, T., Yamada, M., Yamashita, K., Nawa, M., . . . Nishimoto, I. (2005). A humanin derivative, S14G-HN, prevents amyloid-beta-induced memory impairment in mice. J Neurosci Res, 79(5), 714-723. doi:10.1002/jnr.20391 Takayama, K., Kawakami, Y., Lavasani, M., Mu, X., Cummins, J. H., Yurube, T., . . . Huard, J. (2017). mTOR signaling plays a critical role in the defects observed in muscle-derived stem/progenitor cells isolated from a murine model of accelerated aging. J Orthop Res, 35(7), 1375-1382. doi:10.1002/jor.23409 Tang, H., Inoki, K., Brooks, S. V., Okazawa, H., Lee, M., Wang, J., . . . Shrager, J. B. (2019). mTORC1 underlies age-related muscle fiber damage and loss by inducing oxidative stress and catabolism. Aging Cell, 18(3), e12943. doi:10.1111/acel.12943 Tanner, C. B., Madsen, S. R., Hallowell, D. M., Goring, D. M., Moore, T. M., Hardman, S. E., . . . Thomson, D. M. (2013). Mitochondrial and performance adaptations to exercise training in mice lacking skeletal muscle LKB1. Am J Physiol Endocrinol Metab, 305(8), E1018- 1029. doi:10.1152/ajpendo.00227.2013 Tatar, M., Bartke, A., & Antebi, A. (2003). The endocrine regulation of aging by insulin-like signals. Science, 299(5611), 1346-1351. Tauffenberger, A., Vaccaro, A., & Parker, J. A. (2016). Fragile lifespan expansion by dietary mitohormesis in C. elegans. Aging (Albany NY), 8(1), 50-61. doi:10.18632/aging.100863 Theurey, P., & Pizzo, P. (2018). The Aging Mitochondria. Genes (Basel), 9(1). doi:10.3390/genes9010022 Thevis, M., & Schanzer, W. (2016). Emerging drugs affecting skeletal muscle function and mitochondrial biogenesis - Potential implications for sports drug testing programs. Rapid Commun Mass Spectrom, 30(5), 635-651. doi:10.1002/rcm.7470 Thorsness, P. E., & Fox, T. D. (1990). Escape of DNA from mitochondria to the nucleus in Saccharomyces cerevisiae. Nature, 346(6282), 376-379. doi:10.1038/346376a0 156 Thummasorn, S., Apaijai, N., Kerdphoo, S., Shinlapawittayatorn, K., Chattipakorn, S. C., & Chattipakorn, N. (2016). Humanin exerts cardioprotection against cardiac ischemia/reperfusion injury through attenuation of mitochondrial dysfunction. Cardiovasc Ther, 34(6), 404-414. doi:10.1111/1755-5922.12210 Thummasorn, S., Shinlapawittayatorn, K., Khamseekaew, J., Jaiwongkam, T., Chattipakorn, S. C., & Chattipakorn, N. (2018). Humanin directly protects cardiac mitochondria against dysfunction initiated by oxidative stress by decreasing complex I activity. Mitochondrion, 38, 31-40. doi:10.1016/j.mito.2017.08.001 Tian, Y., Garcia, G., Bian, Q., Steffen, K. K., Joe, L., Wolff, S., . . . Dillin, A. (2016). Mitochondrial Stress Induces Chromatin Reorganization to Promote Longevity and UPR(mt). Cell, 165(5), 1197-1208. doi:10.1016/j.cell.2016.04.011 Timmis, J. N., Ayliffe, M. A., Huang, C. Y., & Martin, W. (2004). Endosymbiotic gene transfer: organelle genomes forge eukaryotic chromosomes. Nat Rev Genet, 5(2), 123-135. doi:10.1038/nrg1271 Trayhurn, P., Drevon, C. A., & Eckel, J. (2011). Secreted proteins from adipose tissue and skeletal muscle - adipokines, myokines and adipose/muscle cross-talk. Arch Physiol Biochem, 117(2), 47-56. doi:10.3109/13813455.2010.535835 Trifunovic, A., Wredenberg, A., Falkenberg, M., Spelbrink, J. N., Rovio, A. T., Bruder, C. E., . . . Larsson, N. G. (2004). Premature ageing in mice expressing defective mitochondrial DNA polymerase. Nature, 429(6990), 417-423. doi:10.1038/nature02517 Trumpff, C., Marsland, A. L., Basualto-Alarcón, C., Martin, J. L., Carroll, J. E., Sturm, G., . . . Picard, M. (2019). Acute psychological stress increases serum circulating cell-free mitochondrial DNA. Psychoneuroendocrinology, 106, 268-276. doi:10.1016/j.psyneuen.2019.03.026 Tsuchiya, Y., Ando, D., Goto, K., Kiuchi, M., Yamakita, M., & Koyama, K. (2014). High-intensity exercise causes greater irisin response compared with low-intensity exercise under similar energy consumption. Tohoku J Exp Med, 233(2), 135-140. doi:10.1620/tjem.233.135 Tsuzuki, T., Nomiyama, H., Setoyama, C., Maeda, S., Shimada, K., & Pestka, S. (1983). The majority of cDNA clones with strong positive signals for the interferon-induction-specific sequences resemble mitochondrial ribosomal RNA genes. Biochem Biophys Res Commun, 114(2), 670-676. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/6192820 Turner, C., Killoran, C., Thomas, N. S., Rosenberg, M., Chuzhanova, N. A., Johnston, J., . . . Biesecker, L. G. (2003). Human genetic disease caused by de novo mitochondrial-nuclear DNA transfer. Hum Genet, 112(3), 303-309. doi:10.1007/s00439-002-0892-2 Tyynismaa, H., Sembongi, H., Bokori-Brown, M., Granycome, C., Ashley, N., Poulton, J., . . . Suomalainen, A. (2004). Twinkle helicase is essential for mtDNA maintenance and regulates mtDNA copy number. Hum Mol Genet, 13(24), 3219-3227. doi:10.1093/hmg/ddh342 157 Unlu, E. S., & Koc, A. (2007). Effects of deleting mitochondrial antioxidant genes on life span. Ann N Y Acad Sci, 1100, 505-509. doi:10.1196/annals.1395.055 Valencak, T. G., Osterrieder, A., & Schulz, T. J. (2017). Sex matters: The effects of biological sex on adipose tissue biology and energy metabolism. Redox Biol, 12, 806-813. doi:10.1016/j.redox.2017.04.012 Valenzano, D. R., Benayoun, B. A., Singh, P. P., Zhang, E., Etter, P. D., Hu, C. K., . . . Brunet, A. (2015). The African Turquoise Killifish Genome Provides Insights into Evolution and Genetic Architecture of Lifespan. Cell, 163(6), 1539-1554. doi:10.1016/j.cell.2015.11.008 van Oven, M., & Kayser, M. (2009). Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation. Hum Mutat, 30(2), E386-394. doi:10.1002/humu.20921 Vanhooren, V., & Libert, C. (2013). The mouse as a model organism in aging research: usefulness, pitfalls and possibilities. Ageing Res Rev, 12(1), 8-21. doi:10.1016/j.arr.2012.03.010 Vasilaki, A., McArdle, F., Iwanejko, L. M., & McArdle, A. (2006). Adaptive responses of mouse skeletal muscle to contractile activity: The effect of age. 127(11), 830-839. doi:10.1016/j.mad.2006.08.004 Vatner, D. E., Zhang, J., Oydanich, M., Guers, J., Katsyuba, E., Yan, L., . . . Vatner, S. F. (2018). Enhanced longevity and metabolism by brown adipose tissue with disruption of the regulator of G protein signaling 14. Aging Cell, 17(4), e12751. doi:10.1111/acel.12751 Vaughan, K. L., Kaiser, T., Peaden, R., Anson, R. M., de Cabo, R., & Mattison, J. A. (2017). Caloric Restriction Study Design Limitations in Rodent and Nonhuman Primate Studies. J Gerontol A Biol Sci Med Sci, 73(1), 48-53. doi:10.1093/gerona/glx088 Vermulst, M., Bielas, J. H., Kujoth, G. C., Ladiges, W. C., Rabinovitch, P. S., Prolla, T. A., & Loeb, L. A. (2007). Mitochondrial point mutations do not limit the natural lifespan of mice. Nat Genet, 39(4), 540-543. doi:10.1038/ng1988 Vermulst, M., Wanagat, J., Kujoth, G. C., Bielas, J. H., Rabinovitch, P. S., Prolla, T. A., & Loeb, L. A. (2008). DNA deletions and clonal mutations drive premature aging in mitochondrial mutator mice. Nat Genet, 40(4), 392-394. doi:10.1038/ng.95 Vinel, C., Lukjanenko, L., Batut, A., Deleruyelle, S., Pradere, J. P., Le Gonidec, S., . . . Dray, C. (2018). The exerkine apelin reverses age-associated sarcopenia. Nat Med, 24(9), 1360- 1371. doi:10.1038/s41591-018-0131-6 Vivian, C. J., Brinker, A. E., Graw, S., Koestler, D. C., Legendre, C., Gooden, G. C., . . . Welch, D. R. (2017). Mitochondrial genomic backgrounds affect nuclear DNA methylation and gene expression. Cancer Res. doi:10.1158/0008-5472.CAN-17-1473 Vizcaya-Molina, E., Klein, C. C., Serras, F., Mishra, R. K., Guigo, R., & Corominas, M. (2018). Damage-responsive elements in Drosophila regeneration. Genome Res, 28(12), 1852- 1866. doi:10.1101/gr.233098.117 158 Von Schulze, A., McCoin, C. S., Onyekere, C., Allen, J., Geiger, P., Dorn, G. W., 2nd, . . . Thyfault, J. P. (2018). Hepatic mitochondrial adaptations to physical activity: impact of sexual dimorphism, PGC1alpha and BNIP3-mediated mitophagy. J Physiol, 596(24), 6157-6171. doi:10.1113/JP276539 Wallace, D. C. (1999). Mitochondrial diseases in man and mouse. Science, 283(5407), 1482- 1488. doi:10.1126/science.283.5407.1482 Wallace, D. C. (2010). Mitochondrial DNA mutations in disease and aging. Environ Mol Mutagen, 51(5), 440-450. doi:10.1002/em.20586 Wallace, D. C., & Chalkia, D. (2013). Mitochondrial DNA Genetics and the Heteroplasmy Conundrum in Evolution and Disease. Cold Spring Harb Perspect Biol, 5(11), a021220- a021220. doi:10.1101/cshperspect.a021220 Walton, R. D., Jones, S. A., Rostron, K. A., Kayani, A. C., Close, G. L., McArdle, A., & Lancaster, M. K. (2016). Interactions of Short-Term and Chronic Treadmill Training With Aging of the Left Ventricle of the Heart. J Gerontol A Biol Sci Med Sci, 71(8), 1005-1013. doi:10.1093/gerona/glv093 Wang, D., Li, H., Yuan, H., Zheng, M., Bai, C., Chen, L., & Pei, X. (2005). Humanin delays apoptosis in K562 cells by downregulation of P38 MAP kinase. Apoptosis, 10(5), 963-971. doi:10.1007/s10495-005-1191-x Wang, Y., & Hekimi, S. (2015). Mitochondrial dysfunction and longevity in animals: Untangling the knot. Science, 350(6265), 1204-1207. doi:10.1126/science.aac4357 Wang, Y. X., Zhang, C. L., Yu, R. T., Cho, H. K., Nelson, M. C., Bayuga-Ocampo, C. R., . . . Evans, R. M. (2004). Regulation of muscle fiber type and running endurance by PPARdelta. PLoS Biol, 2(10), e294. doi:10.1371/journal.pbio.0020294 Warburton, D. E., Nicol, C. W., & Bredin, S. S. (2006). Health benefits of physical activity: the evidence. CMAJ, 174(6), 801-809. doi:10.1503/cmaj.051351 Wenceslau, C. F., McCarthy, C. G., Szasz, T., Spitler, K., Goulopoulou, S., Webb, R. C., & Working Group on, D. i. C. D. (2014). Mitochondrial damage-associated molecular patterns and vascular function. Eur Heart J, 35(18), 1172-1177. doi:10.1093/eurheartj/ehu047 Wenz, T., Rossi, S. G., Rotundo, R. L., Spiegelman, B. M., & Moraes, C. T. (2009). Increased muscle PGC-1alpha expression protects from sarcopenia and metabolic disease during aging. Proc Natl Acad Sci U S A, 106(48), 20405-20410. doi:10.1073/pnas.0911570106 Wicks, S., Bain, N., Duttaroy, A., Hilliker, A. J., & Phillips, J. P. (2009). Hypoxia rescues early mortality conferred by superoxide dismutase deficiency. Free Radic Biol Med, 46(2), 176- 181. doi:10.1016/j.freeradbiomed.2008.09.036 Widmer, R. J., Flammer, A. J., Herrmann, J., Rodriguez-Porcel, M., Wan, J., Cohen, P., . . . Lerman, A. (2013). Circulating humanin levels are associated with preserved coronary 159 endothelial function. American Journal of Physiology-Heart and Circulatory Physiology, 304(3), H393-H397. doi:10.1152/ajpheart.00765.2012 Williams, B. A. P., Slamovits, C. H., Patron, N. J., Fast, N. M., & Keeling, P. J. (2005). A high frequency of overlapping gene expression in compacted eukaryotic genomes. Proceedings of the National Academy of Sciences, 102(31), 10936-10941. doi:10.1073/pnas.0501321102 Williams, C. C., Jan, C. H., & Weissman, J. S. (2014). Targeting and plasticity of mitochondrial proteins revealed by proximity-specific ribosome profiling. Science, 346(6210), 748-751. doi:10.1126/science.1257522 Winder, W. W., Taylor, E. B., & Thomson, D. M. (2006). Role of AMP-activated protein kinase in the molecular adaptation to endurance exercise. Med Sci Sports Exerc, 38(11), 1945- 1949. doi:10.1249/01.mss.0000233798.62153.50 Wolff, J. N., Pichaud, N., Camus, M. F., Cote, G., Blier, P. U., & Dowling, D. K. (2016). Evolutionary implications of mitochondrial genetic variation: mitochondrial genetic effects on OXPHOS respiration and mitochondrial quantity change with age and sex in fruit flies. J Evol Biol, 29(4), 736-747. doi:10.1111/jeb.12822 Wong, W. (2018). Going nuclear with stress. Science Signaling, 11(548). doi:10.1126/scisignal.aav4285 Wu, Z., Ghosh-Roy, A., Yanik, M. F., Zhang, J. Z., Jin, Y., & Chisholm, A. D. (2007). Caenorhabditis elegans neuronal regeneration is influenced by life stage, ephrin signaling, and synaptic branching. Proc Natl Acad Sci U S A, 104(38), 15132-15137. doi:10.1073/pnas.0707001104 Wu, Z., Oeck, S., West, A. P., Mangalhara, K. C., Sainz, A. G., Newman, L. E., . . . Bosenberg, M. (2019). Mitochondrial DNA stress signalling protects the nuclear genome. Nature Metabolism, 1-10. Xiao, J., Howard, L., Wan, J., Wiggins, E., Vidal, A., Cohen, P., & Freedland, S. J. (2017). Low circulating levels of the mitochondrial-peptide hormone SHLP2: novel biomarker for prostate cancer risk. Oncotarget, 8(55), 94900-94909. doi:10.18632/oncotarget.20134 Yakes, F. M., & Van Houten, B. (1997). Mitochondrial DNA damage is more extensive and persists longer than nuclear DNA damage in human cells following oxidative stress. Proc Natl Acad Sci U S A, 94(2), 514-519. doi:10.1073/pnas.94.2.514 Yan, Z., Zhu, S., Wang, H., Wang, L., Du, T., Ye, Z., . . . Cao, X. (2019). MOTS-c inhibits Osteolysis in the Mouse Calvaria by affecting osteocyte-osteoclast crosstalk and inhibiting inflammation. Pharmacol Res, 147, 104381. doi:10.1016/j.phrs.2019.104381 Yaribeygi, H., Butler, A. E., & Sahebkar, A. (2019). Aerobic exercise can modulate the underlying mechanisms involved in the development of diabetic complications. J Cell Physiol, 234(8), 12508-12515. doi:10.1002/jcp.28110 160 Yeasmin, F., Yada, T., & Akimitsu, N. (2018). Micropeptides Encoded in Transcripts Previously Identified as Long Noncoding RNAs: A New Chapter in Transcriptomics and Proteomics. Front Genet, 9. doi:10.3389/fgene.2018.00144 Yen, K., Lee, C., Mehta, H., & Cohen, P. (2013). The emerging role of the mitochondrial-derived peptide humanin in stress resistance. J Mol Endocrinol, 50(1), R11-19. doi:10.1530/JME- 12-0203 Yong, C. Q. Y., & Tang, B. L. (2018). A Mitochondrial Encoded Messenger at the Nucleus. Cells, 7(8). doi:10.3390/cells7080105 Yoshino, J., Mills, K. F., Yoon, M. J., & Imai, S. (2011). Nicotinamide mononucleotide, a key NAD(+) intermediate, treats the pathophysiology of diet- and age-induced diabetes in mice. Cell Metab, 14(4), 528-536. doi:10.1016/j.cmet.2011.08.014 Youle, R. J. (2019). Mitochondria-Striking a balance between host and endosymbiont. Science, 365(6454). doi:10.1126/science.aaw9855 Yousefi, S., Gold, J. A., Andina, N., Lee, J. J., Kelly, A. M., Kozlowski, E., . . . Simon, H.-U. (2008). Catapult-like release of mitochondrial DNA by eosinophils contributes to antibacterial defense. Nat Med, 14(9), 949-953. doi:10.1038/nm.1855 Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012). clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. OMICS: A Journal of Integrative Biology, 16(5), 284-287. doi:10.1089/omi.2011.0118 Zabielski, P., Lanza, I. R., Gopala, S., Heppelmann, C. J., Bergen, H. R., 3rd, Dasari, S., & Nair, K. S. (2016). Altered Skeletal Muscle Mitochondrial Proteome As the Basis of Disruption of Mitochondrial Function in Diabetic Mice. Diabetes, 65(3), 561-573. doi:10.2337/db15- 0823 Zacharias, D. G., Kim, S. G., Massat, A. E., Bachar, A. R., Oh, Y. K., Herrmann, J., . . . Lerman, A. (2012). Humanin, a cytoprotective peptide, is expressed in carotid artherosclerotic plaques in humans. PLoS One, 7(2), e31065. doi:10.1371/journal.pone.0031065 Zaidi, A. A., & Makova, K. D. (2019). Investigating mitonuclear interactions in human admixed populations. Nature Ecology & Evolution, 3(2), 213-222. doi:10.1038/s41559-018-0766-1 Zainabadi, K. (2018). A brief history of modern aging research. Exp Gerontol, 104, 35-42. doi:10.1016/j.exger.2018.01.018 Zarse, K., & Ristow, M. (2015). A mitochondrially encoded hormone ameliorates obesity and insulin resistance. Cell Metab, 21(3), 355-356. doi:10.1016/j.cmet.2015.02.013 Zempo, H., Fuku, N., Nishida, Y., Higaki, Y., Naito, H., Hara, M., & Tanaka, K. (2016a). Relation between type 2 diabetes and m.1382 A>C polymorphism which occurs amino acid replacement (K14Q) of mitochondria-derived MOTS-c. doi:956.1 Zempo, H., Fuku, N., Nishida, Y., Higaki, Y., Naito, H., Hara, M., & Tanaka, K. (2016b). Relation between type 2 diabetes and m. 1382 A> C polymorphism which occurs amino acid 161 replacement (K14Q) of mitochondria-derived MOTS-c. The FASEB Journal, 30(1 Supplement), 956.951-956.951. Zempo, H., Kim, S.-J., Fuku, N., Nishida, Y., Higaki, Y., Wan, J., . . . Cohen, P. (2019). A Pro- Diabetogenic mtDNA Polymorphism in the Mitochondrial-Derived Peptide, MOTS-c. doi:10.1101/695585 Zhai, D., Ye, Z., Jiang, Y., Xu, C., Ruan, B., Yang, Y., . . . Lu, Z. (2017). MOTS-c peptide increases survival and decreases bacterial load in mice infected with MRSA. Mol Immunol, 92, 151- 160. doi:10.1016/j.molimm.2017.10.017 Zhang, Q., Raoof, M., Chen, Y., Sumi, Y., Sursal, T., Junger, W., . . . Hauser, C. J. (2010). Circulating mitochondrial DAMPs cause inflammatory responses to injury. Nature, 464(7285), 104-107. doi:10.1038/nature08780 Zhang, W., Miao, J., Hao, J., Li, Z., Xu, J., Liu, R., . . . Chen, J. (2009). Protective effect of S14G- humanin against beta-amyloid induced LTP inhibition in mouse hippocampal slices. Peptides, 30(6), 1197-1202. doi:10.1016/j.peptides.2009.02.017 Zhang, Y., Liu, Y., Walsh, M., Bokov, A., Ikeno, Y., Jang, Y. C., . . . Richardson, A. (2016). Liver specific expression of Cu/ZnSOD extends the lifespan of Sod1 null mice. Mech Ageing Dev, 154, 1-8. doi:10.1016/j.mad.2016.01.005 Zhang, Y., Unnikrishnan, A., Deepa, S. S., Liu, Y., Li, Y., Ikeno, Y., . . . Richardson, A. (2017). A new role for oxidative stress in aging: The accelerated aging phenotype in Sod1(-/)(-) mice is correlated to increased cellular senescence. Redox Biol, 11, 30-37. doi:10.1016/j.redox.2016.10.014 Zhao, J., Zhai, B., Gygi, S. P., & Goldberg, A. L. (2015). mTOR inhibition activates overall protein degradation by the ubiquitin proteasome system as well as by autophagy. Proc Natl Acad Sci U S A, 112(52), 15790-15797. doi:10.1073/pnas.1521919112 Zhu, C. T., Ingelmo, P., & Rand, D. M. (2014). GxGxE for lifespan in Drosophila: mitochondrial, nuclear, and dietary interactions that modify longevity. PLoS Genet, 10(5), e1004354. doi:10.1371/journal.pgen.1004354 Zhu, F., Li, Q., Zhang, F., Sun, X., Cai, G., Zhang, W., & Chen, X. (2015). Chronic lithium treatment diminishes the female advantage in lifespan in Drosophila melanogaster. Clin Exp Pharmacol Physiol, 42(6), 617-621. doi:10.1111/1440-1681.12393
Abstract (if available)
Abstract
Metabolic alterations underlie the great majority of known hallmarks of aging (Lopez-Otin, Blasco, Partridge, Serrano, & Kroemer, 2013
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Reynolds, Joseph Cassidy
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Mitonuclear communication in metabolic homeostasis during aging and exercise
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Leonard Davis School of Gerontology
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Doctor of Philosophy
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Biology of Aging
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10/01/2020
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03/06/2020
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aging,Exercise,homeostasis,metabolism,mitochondria,MOTS-c,muscle,OAI-PMH Harvest,peptides
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homeostasis
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mitochondria
MOTS-c
muscle
peptides