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date: 17 November 2019

Integrated Theories of Biological Aging

Summary and Keywords

Aging is a progressive process with multiple biological processes collectively deteriorating with time, ultimately causing loss of physiological functions necessary for survival and reproduction. It is also thought to have a strong evolutionary basis, largely resulting from the lack of selection force. Here, we discuss the evolutionary aspects of aging and a selection of theories founded on a variety of biological functions that have been shown to be involved in aging in multiple model organisms, ranging from the simple yeast, worms, flies, killifish, and rodents, to non-human primates and humans. The conglomerate of distinct theories has together revolutionized aging research in the past several decades, far more than what humankind has known since the dawn of civilization. However, not one theory alone can independently explain aging and should not be interpreted out of context of the cell and organism in its entirety. That said, the 21st century has been and will be an exciting time in the field of aging, with scientific advances on health span and lifespan being made at multiple fronts of biology and medicine in an unprecedented scale.

Keywords: aging, evolution, natural selection, genomic instability, epigenetics, transposable elements, telomeres, mutations, mitochondria, mitochondrial DNA (mtDNA), free radicals, oxidative stress, neuroendocrine, hormones, inflammation, inflammaging, senescence

Introduction

Lifespan extension is an ancient quest of humankind. The large body of mythical stories and anecdotal accounts related to longevity reflect both our interest in and inability to achieve lifespan extension. It was only during the mid-20th century that the field of biogerontology started to take shape, paving the way to scientific investigation of the enigmatic process of aging—the biological process of life itself.

During the past decades, our understanding of the molecular mechanisms of aging has advanced remarkably. Genetic and molecular pathways and dietary interventions that regulate lifespan have been identified, providing a rich knowledge base for aging research to expand in an unprecedented scale. Discussed here are some of the key theories and concepts of aging, based on studies of a wide range of biological topics in multiple model organisms, including yeast, worms, flies, killifish, rodents, and non-human primates, as well as mathematical derivations. We begin by reviewing key evolutionary theories of aging and then discuss how genomic instability, mitochondria and free radicals, the neuroendocrine system, inflammation, and cellular senescence all contribute to the aging process.

However, it is imperative to remember that we should not attempt to understand or explain a highly complex process such as aging by a single theorem or a particular biological system. Further, direct evidence that a given theory is a driver of normal aging is difficult to assess and should be carefully weighed, with all possible caveats considered. The theories discussed in this review all contribute to our understanding of the biological process of aging, but not one can stand alone.

Aging: An Evolutionary Perspective

Two key questions underlie modern aging research: why and how do we age? The evolutionary theories of aging provide a point of reference for why we age. Although these theories do not provide direct clues to how we age, they have provided a conceptual framework to advance aging research. Each of these mathematical and logical derivatives provides complimentary paradigms that can partially explain recent experimental and empirical data.

The idea that aging is a product of evolution was first set forth by August Weismann, who theorized that soma was, to a certain degree, nothing but a secondary appendage to the reproductive cells, which are the real bearer of life (Kirkwood & Cremer, 1982; Weismann, Poulton, & Shipley, 1891). Reproduction, to Weismann, was the most important duty of an organism, and once completed, the individual becomes of no value. Along this line, aging could be viewed as a means to making way for the next generation and thus maximizing the chances of preserving the species.

On the level of an individual organism, the aging process is thought to arise due to the declining force of natural selection with age. Because natural selection acts through reproduction (i.e., progeny), the selection pressure is strong up to our reproductive age and declines thereafter. Thus, genes that reduce our fitness early on would be selected against and consequently reduced or removed from the population, whereas those that arise later in life (past the reproductive stage) would not be subject to selection. The mutation accumulation and antagonistic pleiotropy theories explain aging based on such an inability of natural selection to act on detrimental late-life traits (Figure 1). The disposable soma theory explains the evolution of aging from an energetic perspective.

Integrated Theories of Biological AgingClick to view larger

Figure 1. The evolutionary basis of aging. (A) The “mutation accumulation” theory posits that aging results from the declining force of selection, which leads to an accumulation of late-life mutations that would be in an “evolutionary blind spot.” Such late-life mutations would be manifest in organisms that have escaped extrinsic mortality (e.g., infection, predation, starvation, etc.) and reduce their fitness intrinsically. (B) The “antagonistic pleiotropy” theory posits that aging is driven by genes that are initially beneficial to an organism’s fitness up to its reproductive years, when the selection force is strong. However, the same genes could then have deleterious effects on the organism later in life, yet still remain in the population because of the weakened selection force.

Mutation accumulation theory: The first modern evolutionary theory of aging was proposed by Peter Medawar in 1952 (Medawar, 1952). This theory suggests that late-life mutations/dysfunction will not be purified because of the weak force of natural selection past the reproductive stage of one’s life. Constantly arising mutations from external and internal insults will inevitably reduce an organism’s fitness. The inability of natural selection to act on detrimental late-life mutations indicates that affected genes could continue to be passed from generation to generation. Accumulation of unrepaired damages and mutations leads to genetic instability, as described in more detail in the “Genomic Instability and Aging” section, that can cause progressive cellular dysfunction and age-related diseases (e.g., cancer).

Antagonistic pleiotropy theory: Proposed by George C. Williams in 1957 as an evolutionary explanation for senescence (Williams, 1957), this theory suggests that a given gene can exert multiple traits that could be beneficial or detrimental to an organism’s fitness at different stages of life (hence, pleiotropic). In other words, certain genes that can be beneficial to an organism early in life, and thus favored and selected for, could act negatively on the same organism’s fitness later in life. Some examples that support this idea include genes that are important for growth, reproduction, and tumor suppression. For instance, pathways that promote growth (e.g., GH/IGF-1 axis, TOR) or are required for reproduction (e.g., testosterone) are necessary and beneficial early in life but may contribute to cancer later in life. Also, senescence could suppress cancer and promote wound healing early in life but reduce stem cell renewal later in life (Childs, Durek, Baker, & Deursen, 2015; Oh, Lee, & Wagers, 2014). These genes, again, would not be filtered out by natural selection because of the decline of selection force past one’s reproductive age.

Disposable soma theory: Proposed by Thomas Kirkwood in 1977 (Kirkwood, 1977), this theory is founded on the idea that organisms have to balance the finite cellular resources between repair/maintenance of their body cells, or soma, and growth/reproduction. This builds on Weismann’s earlier concept that soma merely exists to support the reproductive cells (Kirkwood & Cremer, 1982; Weismann et al., 1891). During the growth/reproductive period of one’s life, fewer resources would inevitably be available for repair/maintenance. As a result, mutations and other cellular damages accumulate in the soma, causing cellular dysfunction. Several long-lived model organisms, achieved by either genetic (e.g., deficiencies in the GH/IGF-1 axis and their homologs) or dietary (e.g., dietary restriction) interventions exhibit common features that support this theory: long-lived organisms tend to (a) be smaller in size, (b) exhibit delayed/increased reproduction, and (c) have increased stress resistance. For instance, dwarfism has been consistently documented in long-lived yeast, flies, and mice (Longo & Finch, 2003). Interestingly, people with Laron’s syndrome (GH insensitivity) exhibit dwarfism and a remarkably low incidence of cancer and diabetes (Guevara-Aguirre et al., 2011). On the other hand, mice whose growth is unusually large because of the overexpression of growth hormone (GH) have a reduced lifespan (Bartke, 2003). Also, cells from long-lived animals show enhanced resistance to stress (Pickering, Lehr, Kohler, Han, & Miller, 2014). Dietary restriction (DR) is the most well-studied and reproducible intervention that increases resilience and health/lifespan in various model organisms, including non-human primates (Lee & Longo, 2016; Mattison et al., 2017). Although the precise mechanism of DR is unclear, the shift of energy allocation from growth/reproduction during the restricted period to repair/maintenance is considered to underlie its beneficial effects.

There is room for these evolutionary theories of aging themselves to evolve based on the leap of knowledge acquired since they were established. For instance, we now know that our cells possess two independent genomes, each located in the nucleus and mitochondria (discovered in the 1960s [Ernster & Schatz, 1981]). But these theories have been developed, to the best of their knowledge at the time, based on the nuclear genome. Thus, as discussed in more detail later, the role of the mitochondrial genome, and especially the coordination between the two genomes, in the context of the evolution of aging would provide a more comprehensive understanding of the past, present, and future of human longevity. Notably, because mitochondrial DNA is strictly inherited maternally in humans, its role in the evident sex disparity evolution of aging may be of significant interest.

Genomic Instability and Aging

DNA is the basic unit that holds the genetic blueprint of a cell. Our DNA is constantly damaged by various external (e.g., cosmic rays, toxic molecules, etc.) and endogenous factors (e.g., oxidative stress, replication error, etc.) that can ultimately alter our genetic information, ranging from point mutations to chromosome rearrangements (Aguilera & Gómez-González, 2008; Vijg & Suh, 2013). Fortunately, to counter these insults, our DNA is wrapped around proteins, known as histones, and packaged into chromatin that provides added protection. Further, sophisticated DNA repair mechanisms exist to counter these detrimental events. However, although these repair systems are highly efficient, allowing approximately fewer than one mistake every billion nucleotides, unrepaired mutations can accumulate with age, causing our genome to lose stability (Hoeijmakers, 2001). Genome instability is a term that encompasses a range of DNA lesions that alter our genetic information (e.g., point mutations, deletions, double strand breaks, chromosomal rearrangements, aneuploidy, etc.). Genomic instability can lead to cellular degeneration or functional decline, ultimately contributing to aging or oncogenesis. Here, we discuss several relevant cellular aspects that are thought to contribute to genomic instability and aging.

Somatic Mutations and Aging

There are about 10 trillion cells in our body, and each suffers approximately 70,000 DNA lesions every day (Lindahl & Barnes, 2000). Our cells are under constant exposure to external insults, including ionizing radiation (e.g., UV, cosmic rays) and environmental toxins, and internal insults, including free radicals and replication errors. A sophisticated set of repair mechanisms has evolved to repair different types of DNA lesions (Kunkel, 2015). However, even in the presence of an intact repair system, DNA mutations still accumulate with age. In human cortex, 8‑oxoguanine, a damage caused by reactive oxygen species (ROS), increases with age (Lu et al., 2004). H2AX, which is a histone subtype that gets phosphorylated and recruited to the site of double strand breaks (DSB) (Paull et al., 2000), increases with age in humans (Scaffidi & Misteli, 2006), mice (Sedelnikova et al., 2004), and monkeys (Herbig, Ferreira, Condel, Carey, & Sedivy, 2006). The phosphorylated H2Av (a homologue of H2AX in flies) foci persist in germ cells of old compared to young flies after the induction of DSB (Delabaere et al., 2017), which implies that DNA damage repair slows down with age in flies. Also, p53 binding protein (53BP1), another marker of DSB, accumulates with age in the skin of living baboons (Herbig et al., 2006). The persistent activation of repairing proteins may imply that the increased level of DNA damage exceeds the capacity of the DNA repair system or that DNA repair efficiency declines with age. In either case, evidence holds that genomic instability increases with age, but its role as a driver of aging, as likely as it seems, is yet to be experimentally scrutinized.

Progeria, a syndrome described by accelerated aging or premature aging, is driven by genomic instability; the Hutchinson-Gilford Progeria syndrome (HGPS) and Werner syndrome are the best studied. HGPS is known to be caused by de novo mutations in the LMNA gene, which encode for A-type lamins (lamin A/C) (Eriksson et al., 2003). Such mutations alter nuclear morphology and function, and cause genomic instability (Gonzalo & Kreienkamp, 2015). HGPS patients often die in their teens (Hennekam, 2006), but their cells exhibit age-related dysfunction, including genomic instability (Liu et al., 2005), senescence (Liu et al., 2005), altered gene expression (Prokocimer, Barkan, & Grunebaum, 2013), and nuclear envelope irregularities (Goldman et al., 2004). Notably, the de novo mutation found in HGPS syndrome patients also accumulates with age in normal aging human skin from different sites of the body (McClintock et al., 2007). Werner syndrome is caused by mutations in the WRN gene, which encodes for a protein known to act as a helicase and exonuclease, which are key processes in DNA replication and repair (Gray et al., 1997). Further, recent evidence suggests that the WRN protein is important in maintaining telomeres (described below in the “Telomeres” subsection) (Crabbe, Verdun, Haggblom, & Karlseder, 2004; Edwards, Machwe, Chen, Bohr, & Orren, 2015; Sun et al., 2017). Although it is debatable whether progeroid syndromes are an accelerated version of normal aging, there is much to be learned about the molecular mechanisms of aging from these unusual conditions (Burtner & Kennedy, 2010).

Epigenetics

Epigenetics, in a broad sense, refers to chromosomal modifications that regulate genes without altering the underlying DNA. Waddington coined the term “epigenetics” to describe changes in phenotype without changes in genotype (Waddington, 1942a, 1942b). Epigenetic regulation can largely happen at the DNA, protein (histone), and/or chromatin level, which are subject to changes during aging (Benayoun, Pollina, & Brunet, 2015). For instance, aging can lead to chromatin remodeling, histone loss, histone variant incorporation, histone modification alterations, and DNA methylation pattern shifts (Pal & Tyler, 2016; Sen, Shah, Nativio, & Berger, 2016). Here we discuss the role of epigenetics in genomic instability.

In eukaryotes, DNA methylation is a major epigenetic modification that occurs on cytosines, especially in clusters known as “CpG islands,” which are GC-rich regions with high densities of CpGs that typically occur at or near the transcription start site of genes. Imbalanced DNA methylation may dysregulate genes involved in DNA repair and chromosomal stability, and hence contribute to genomic instability (Dodge et al., 2005; Karpf & Matsui, 2005; Meng et al., 2015). Recently, studies on age-dependent DNA methylation changes in multiple tissues provide insight into a more accurate prediction of “biological age” as compared to “chronological age” (Horvath & Raj, 2018), which may be a more accurate predictor of mortality (Levine, 2012). These studies stem from a collective interest in identifying reliable biomarkers of aging (Warner, 2004) and the fact that DNA methylation patterns change with age (Bell et al., 2012; Christensen et al., 2009; Hernandez et al., 2011; Maegawa et al., 2010; Rakyan et al., 2010). Currently, there are three major “clocks” that provide comparable and complementary estimates of biological age (Horvath & Raj, 2018) that are based on DNA methylation data derived from (a) blood-derived DNA (71 CpGs) (Hannum et al., 2013); (b) multiple tissues types (353 CpGs), including 51 healthy tissues and cell types (Horvath, 2013); and (c) clinical data regressed on blood-derived DNA (513 CpGs) (Levine et al., 2018). The connection between the epigenetic clock and genomic instability has been speculated, but further research is needed to provide experimental evidence (Horvath & Raj, 2018).

The DNA in the nucleus wraps around the core histone proteins, forming a nucleosome that in turn compacts into a higher-level structure known as chromatin (Luger, Mäder, Richmond, Sargent, & Richmond, 1997). Such complex nucleoprotein structures confer not only high-level organization to contain a large amount of data that can be dynamically and rapidly accessed as needed, but also protection of our DNA code from damaging insults. Histone proteins (H2A, H2B, H3, and H4) possess a lysine-rich tail that projects from the nucleosome, and are subject to several post-translational modifications (PTMs) that regulate genetic processes, including chromatin compaction/dynamics and transcription. Well-studied histone PTMs include acetylation, methylation, phosphorylation, and ubiquitylation, which work together under a so-called “histone code” (Jenuwein & Allis, 2001). Although it is not clear whether histone methylation directly regulates aging, much correlative evidence of its involvement in aging exists. Methylation of lysine residues of histone 3 at positions 4 and 36 (H3K4 and H3K36) are “active” marks that are enriched in euchromatin, whereas H3K9 and H3K27 methylation are “repressive” marks that are enriched in heterochromatin. A wide redistribution of these methylated histone markers has been reported in senescent human cells and fibroblasts from HGPS patients (Shah et al., 2013). Histone acetylation can directly regulate DNA-histone interaction by neutralizing the positively charged lysine residues of the histone N termini, decreasing their affinity for DNA (Grunstein, 1997). Histone acetyltransferases (HATs) and deacetylases (HDACs) dynamically regulate the acetyl status of histones and, in turn, chromatin architecture. Notably, sirtuins are HDACs that are also key regulators of aging and age-related diseases (Longo & Kennedy, 2006). In yeast, the histone deacetylase silent information regulator 2 (Sir2), which is the founding member of the sirtuin family, silences transcription and stabilizes repetitive DNA (rDNA), but relocalizes to sites of genomic instability during aging or DNA damage; Sir2 overexpression extended yeast replicative life span, whereas its loss shortened it (Sinclair & Guarente, 1997). SIRT1, the mammalian homolog of Sir2 (Oberdoerffer et al., 2008; Wang et al., 2008), and SIRT6 (Mao et al., 2011; Mostoslavsky et al., 2006) are required for genomic stability.

Transposable Elements

Transposable elements (TEs) are genetic units that can jump around the genome, rearrange the chromosome by homologous recombination, and control gene expression (Rebollo, Romanish, & Mager, 2012; Slotkin & Martienssen, 2007). There are two major types of TEs: (a) retrotransposons that transcribe from DNA and then reverse transcribe into a new location, using their inherently encoded reverse transcriptase (copy-and-paste mechanism), and (b) DNA transposons that jump out of the genome and self-propagate using their inherently encoded transposase (cut-and-paste mechanism) (Huang, Burns, & Boeke, 2012). The large majority of TEs in humans consist of retrotransposons (Huang et al., 2012). TEs make up nearly 50% of our genome (de Koning, Wanjun, Todd, Batzer, & Pollock, 2011; Mills, Bennett, Iskow, & Devine, 2007), and although previously considered by some to be “junk” or “selfish” DNA fragments, they can contribute to the evolution of our genome (Biémont & Vieira, 2006). However, their “jumpy” nature can also have an obvious negative impact on our genome, including gene inactivation and genomic instability. Therefore, our cells have developed a highly regulated system to keep TE mobilization well under control. TEs are mostly found in heterochromatin, the suppressed areas of the genome, and are thus kept inert under healthy conditions (Riddle et al., 2011). In fact, although TE sequences can take up as much as 50% of our genome, only a fraction (<0.05%) remain active (Mills et al., 2007).

Notably, recent studies have shown that TE activity can increase with age, and contribute to genomic instability, in various model organisms. In yeasts, retrotransposon mobility is increased and associated with chromosome rearrangement in old cells (Maxwell, Burhans, & Curcio, 2011). In worms, capsids, products of a retrotransposon, accumulate in the gonads with age at 15°C (Dennis, Sheth, Feldman, English, & Priess, 2012). In flies, many TEs become activated with age, which can be blocked by stabilizing heterochromatin and prevented by dietary restriction (DR) (Orr, 2016; Wood et al., 2016). Also, in fly brains, several TEs showed increased activity with age, and the inactivation of a gene that is involved in silencing TEs further exacerbated brain TE activity and caused progressive age-dependent memory decline and, ultimately, shortened lifespan (Li et al., 2013).

Telomeres

Human telomeres are short DNA repeats that “cap” the ends of linear chromosomes to provide genomic stability (Blackburn & Gall, 1978) as a solution to their inherent end-protection and replication problems (de Lange, 2009; Olovnikov, 1996). Telomeres were first reported by Barbara McClintock (McClintock, 1941) and by Hermann Muller (Muller, 1938) as unique chromosomal ends that differ from those of broken lesions. Telomeric ends consist of repeated DNA sequences (TTAGGG) with an overhang at the lagging strand. This resembles a double-stranded DNA break lesion, and to avoid DNA repair mechanisms acting on them, telomeric ends assume a protective loop formation, mediated by telomeric repeat-binding factor 2 (TRF2) (Griffith et al., 1999). General DNA polymerases are incapable of completely replicating the telomeric ends and require special DNA polymerases known as telomerases. Notably, telomerases are generally not expressed in somatic cells. This leads to a progressive and cumulative loss of telomere length with each cell division. In fact, Alexey Olovnikov proposed in 1971 that the limited division potential of a cell (known as the Hayflick limit; see also the “Cellular Senescence and Aging” section) may be driven by the progressive shortening of the telomeric length with every replication (Olovnikov, 1996).

However, telomere attrition with the uncapping of linear chromosomal ends per se does not appear to be the driver of aging. For instance, a comparative analysis of over 60 mammalian species shows that there is an inverse correlation between telomere length and lifespan (Gomes, et al., 2011). Also, laboratory mice have longer telomeres compared to human telomeres (> 30 Kb vs. 10–15 Kb, respectively) (Campisi, Kim, Lim, & Rubio, 2001), yet live substantially less time than humans. In addition, telomerase activity has been suggested to have co-evolved with body mass rather than lifespan in 15 different rodent species (Seluanov et al., 2007).

Another interesting paradigm suggests that replicative senescence is driven by persistent DNA damage responses (DDR) at critically short telomeres that exhibit irreparable DNA damage (d’Adda di Fagagna et al., 2003; Fumagalli et al., 2012; Herbig, Jobling, Chen, Chen, & Sedivy, 2004). Indeed, the uncapped telomere recruits activated proteins involved in DSB repair at the telomeric site (Takai et al., 2003) and causes homologous recombination (HR)-dependent deletions (Wang et al., 2004). Notably, neither the DNA damage checkpoint nor DSB repair occur during mitosis, which appears to be an inherent mechanism to prevent sister telomere fusions that produce dicentric chromosomes and aneuploidy (Orthwein et al., 2014). This accords with Barbara McClintock’s earlier report that the chromatin ends are different from DSB in that HR was never observed (McClintock, 1941). Telomeric uncapping–induced recruitment of DSB repair proteins is also seen in human fibroblasts (d’Adda di Fagagna et al., 2003).

The African turquoise killifish, an emerging vertebrate model to study aging and age-related diseases (Kim, Nam, & Velenzano, 2016; Valdesalici & Cellerino, 2003), also show age-dependent shortening of telomeres, which are of comparable length to humans (5–7 Kb), in muscles and liver (Harel et al., 2015; Hartmann, Reichwald, & Lechel, 2009). Further, forced telomere attrition by mutating a component of telomerase (i.e., TERT) recapitulated key aspects of the human dyskeratosis congenita syndrome. TERT mutant killifish exhibited dysfunction in proliferative tissues, including the blood, intestine, and gonads, and showed precancerous changes in the gut (Harel et al., 2015). With the killifish genome now assembled and annotated (Valenzano et al., 2015), and powerful genetic tools made available (Harel, Valenzano, & Brunet, 2016), this shortest-lived laboratory model of vertebrate aging may provide deeper insight on genomic instability and aging. Telomeres have also been implicated in senescence, as discussed in the “Cellular Senescence and Aging” section.

mtDNA Instability

mtDNA is present in hundreds to thousands of copies per cell and replicated independent of the cell cycle (Bogenhagen & Clayton, 1977). Numerous studies have demonstrated that mitochondrial mutations increase with age in both animal models and in human tissues, including brain, heart, colon, and skeletal muscle (Bua et al., 2006; Cortopassi & Arnheim, 1990; Gardner, Payne, Hovarth, & Chinnery, 2015; Greaves et al., 2014; Kennedy, Salk, Schmitt, & Loeb, 2013; Larsson, 2010; Pikó, Hougham, & Bulpitt, 1988), although the levels and kind of mutations appear to differ between tissues and even within tissues (Soong, Hinton, Cortopassi, & Anheim, 1992). It has been documented that both point mutations and deletions of mtDNA accumulate with age (Ameur et al., 2011; Bua et al., 2006; Cortopassi & Arnheim, 1990; Gardner et al., 2015; Greaves et al., 2014; Kennedy et al., 2013; Larsson, 2010; Pikó et al., 1988; Vermulst et al., 2007). The major source of mtDNA point mutations with age has been shown to be replicative infidelity in rodents and humans, rather than oxidative stress as once considered by many (discussed in detail in the “Mitochondria, Free Radicals, and Aging” section) (Ameur et al., 2011; Vermulst et al., 2007). Further, some mutations can persist and cause heteroplasmy, whereby a mix of wild-type and mutant mtDNA co-exist in a cell (Kennedy et al., 2013; Stewart & Chinnery, 2015) or in a single mitochondrion (Morris et al., 2017). Even low-level heteroplasmy, which is common in humans (Payne et al., 2012), in the context of somatic mutations can contribute to aging and neurological defects (Ross et al., 2013).

Mitochondria, Free Radicals, and Aging

Over its lifespan, it is critical for the organism to effectively cope with diverse cellular and environmental stressors in order to preserve its functions and promote healthy aging. Of the variety of theories that have been proposed to explain the fundamental mechanisms mediating aging and age-related diseases, the mitochondrial free radical theory of aging has served as a key concept. However, increasing evidence that is inconsistent with the theory has recently led to controversies surrounding it (i.e., ROS-induced mtDNA damage accumulation with age), begging the question of what the role(s) of mitochondria is in aging.

The Mitochondria Free Radical Theory of Aging

ROS and mtDNA Damage

The free radical theory of aging (FRTA) was conceived by Denham Harman in the 1950s (Harman, 2009). Based on his chemical background in free radicals and interest in aging, Harman proposed that organisms age because cells accumulate damages incurred from deleterious free radicals (which are normally produced in the course of cellular metabolism) on cell constituents over time (Harmaan, 1955). Free radicals are atoms or molecules with at least one unpaired electron, and the most common source of free radicals in biological systems is oxygen (Halliwell, 1991). Free radicals (e.g., superoxide anion radical, hydroxyl radical, etc.) and related oxidants such as hydrogen peroxide are often called reactive oxygen species (ROS) (Halliwell & Gutteridge, 2015). These sources inspired Harman to expand the idea that ROS generated under normal biological processes could cause cumulative damage to cellular macromolecules such as lipids, protein, and DNA, resulting in aging and ultimately death. His theory was well received by several prominent scientists, including Peter Medawar, who proposed the mutation accumulation theory.

ROS can be generated from both exogenous and endogenous sources. Exogenous ROS can be generated from environmental stress such as ultraviolet (UV), heat exposure, and radiation. However, ROS can also be produced endogenously from normal metabolic processes, prominently by the mitochondrial electron transport chain and various oxidases (Cadenas & Davies, 2000). The mitochondrial electron transport chain is key in transforming the chemical energy potential of ingested food to electrochemical energy by passing electrons along a series of protein complexes until they are accepted by oxygen molecules to form water. In fact, over 90% of inhaled oxygen is estimated to be utilized by mitochondria. The mitochondrial electron transport chain is highly efficient in reducing oxygen; however, it is estimated that approximately 0.1–2% of oxygen is converted to ROS in vitro, but much less is expected in vivo (Hansford, Hogue, & Mildaziene, 1997; Murphy, 2009). ROS are generated during the process of the four-electron reduction of molecular oxygen to water, which is the terminal electron acceptor for energy generation. Electron leakage from the electron transport chain can react with oxygen, resulting in the production of ROS (Halliwell, 1991). ROS can indiscriminately damage DNA, proteins, and lipids. In the 1970s, recognizing that mitochondria are major sources of ROS, and that mitochondrial components, including mitochondrial DNA (mtDNA), are at the closest proximity to the production site and thus most prone to damage (Harman, 1972), FRTA has been referred to as the “mitochondrial theory of aging (MFRTA)” (Miquel, Economos, Fleming, & Johnson, 1980). Further, mtDNA has been thought to be more vulnerable to ROS-induced damages because the DNA repair mechanisms in mitochondria are inferior to those of the nucleus. However, recent studies indicate that mtDNA repair mechanisms are very effective against several types of mutations (Kazak, Reyes, & Holt, 2012) and that mtDNA exists bound to various proteins, rather than naked DNA, which provides physical protection (Kang, Kim, & Kamasaki, 2007; Lee & Han, 2017).

Perhaps the most recent and direct challenge to MFRTA comes from the failure to detect age-dependent increase in ROS-induced mtDNA damage in mice and humans, using high-resolution sequencing technology. ROS causes a very specific type of DNA mutation that changes a guanine to a thymidine (G to T) (Cooke, Evans, Dizdaroglu, & Lunec, 2003). In a recent report, mtDNA mutation frequency was ~10 times higher in the brain and heart mitochondria of old (< 24 months) mice compared to their young (< 10 months) counterparts (Vermulst et al., 2007). Eighty-one percent of all mutations were guanine to adenine (G to A) and not G to T. Notably, G-to-A mutations are largely driven by mistakes made during DNA replication, suggesting that replicative infidelity, not ROS, was the major age-dependent mtDNA mutation in mice. Similar results were obtained by ultra-deep sequencing of the mtDNA from purified mouse liver mitochondria, which showed a ~10-fold increase in mutation frequency with age and also supported replicative infidelity, rather than ROS, as the major cause of mutations (Ameur et al., 2011). In humans, similar results were obtained from the brain of either young (< 1 year) or old (> 75 years) with no known pathologies (Kennedy et al., 2013).

Mitochondrial “mutator mice” can artificially increase replicative infidelity in mitochondria by mutating the proofreading function of the protein that replicates mtDNA (polg). Homozygous mutants (polgmut/mut) were reported to have three- to eight-fold higher mutation frequency and showed significantly reduced lifespan and several clinical signs associated with aging (Kujoth et al., 2005; Trifunovic et al., 2004). Notably, recalculating the mutation frequency with updated methodologies put the mutation frequency increase to be ~2,500-fold and ~500-fold higher in the homozygous (polgmut/mut) and heterozygous (polg+/mut) mutant mice, respectively, with both far exceeding physiological ranges reached by normal aging (Vermulst et al., 2007). Despite the supra-physiological levels of mutations generated in both mouse types, the homozygous mutants were significantly short-lived, whereas heterozygous mutants exhibited a normal lifespan, indicating that mtDNA point mutations per se do not cause aging (Vermulst et al., 2007).

There has been debate whether mtDNA mutations that occur during normal aging may be sufficient to directly drive aging (Khrapko & Vijg, 2009). The role of mtDNA mutations as a driver of aging, and not as a secondary bystander, is currently inconclusive, and further experiments with the aid of emerging technological advances will provide further clues.

ROS and Cellular Signaling

Increasing evidence that ROS-dependent mtDNA mutations do not accumulate with age has significantly undermined the MFRTA. However, ROS has a dose-dependent pleiotropic nature, acting as a signaling molecule at lower concentrations and as a potent toxin at higher concentrations (Nathan & Cunningham-Bussel, 2013; Ristow & Schmeisser, 2011; Shadel & Horvath, 2015), indicating an alternative perspective that may shed new light on the MFRTA beyond mtDNA damage. The pleiotropic nature of ROS could explain why some have reported deleterious and beneficial effects of ROS on lifespan. Reduced ROS levels have been reported to extend the lifespan of various model organisms such as yeast, worms, fruit flies, and mice (Larsen, 1993; Magwere et al., 2006; Schriner et al., 2005; Wei et al., 2008) and increased ROS generation can reduce lifespan (Kirkwood & Kowald, 2012; Pomatto & Davies, 2018). In contrast, moderate increase in ROS can promote longevity in model organisms, which is consistent with enhanced nonlinear adaptability promoted by sublethal ROS, also referred to as “mitohormesis” (Ristow & Schmeisser, 2014). Further, antioxidant supplementations, including various vitamins, have been shown to have not only weak or neutral health benefits but also detrimental side effects, such as cancer promotion in mice (Harris et al., 2015; Sayin et al., 2014) and humans (Bjelakovic, Nikolova, Simonett, & Gluud, 2004; Bjelakovic, Nikolova, & Gluud, 2013; Alpha-Tocopherol, Beta Carotene Cancer Prevention Study Group, 1994; Kryscio et al., 2017).

Genetic interventions that inactivate or overexpress components of the cellular antioxidant system have also shown mixed results in various model organisms, including yeast (Fabrizio et al., 2003; Longo, Gralla, & Valentine, 1996; Unlu & Koç, 2007), worms (Cabreiro et al., 2011; Doonan et al., 2008; Melov et al., 2000), flies (Curtis et al., 2007; Duttaroy, Paul, Kundu, & Belton, 2003; Kirby, Hu, Hilliker, & Phillips, 2002; Martin et al., 2009; Parkes et al., 1998; Sun, Folk, Bradley, & Tower, 2002; Wicks, Bain, Dutteroy, Hilliker, & Phillips, 2009), and mice (Lee et al., 2010; Pérez et al., 2009; Schriner et al., 2005); in fact, the majority of genes involved in the antioxidant system didn’t have significant effects on lifespan in these model organisms. The genetic manipulation of antioxidant factors has led to much insight but evidently also to mixed results that beg further investigation. An important aspect to consider is that the cellular stress response system is highly adaptive and plastic; thus a cell may regain a new balance based on the chronic deficit or overexpression of certain antioxidant factors (Pomatto & Davies, 2018).

Emerging Mitochondrial Roles in Aging

Increasing evidence that mitochondria act as signaling organelles favors the concept that mitochondrial communication may be involved in important cellular processes (Chandel, 2015; Quiros, Mottis, & Auwerx, 2016), and ultimately aging (Bratic & Larsson, 2013; Durieux, Wolff, & Dillin, 2011; Hill, Sataranatarajan, & Remmen, 2018; Karpac & Jasper, 2013; López-Otin, Galluzzi, Freije, Madeo, & Kroemer, 2016; Melber & Haynes, 2018; Shadel & Horvath, 2015; Sun, Youle, & Finkel, 2016). Eukaryotic cells are functionally compartmentalized into organelles that have specific roles. Mitochondria and the nucleus are unique in the sense that they contain genomes. To dynamically coordinate the complex cellular components, a highly sophisticated communication system must exist. Indeed, mitochondria and the nucleus are known to orchestrate a wide range of cellular functions, and the privileged communication between these two is widely known as “mitonuclear communication” (Quiros et al., 2016). Although the messages transmitted through mitonuclear communication is predicted to be broad, cellular homeostasis is a common theme in recent literature (Hill et al., 2018). For example, ROS signaling (Shadel & Horvath, 2015), the mitochondrial unfolded protein response (UPRmt) (Nargund et al., 2012; Qureshi, Haynes, & Pellegrino, 2017), and mitochondrial damage-associated molecular patterns (mtDAMPs) that signal mitochondrial damage (Galluzzi, Kepp, & Kroemer, 2012; Wilkins, Weidling, Ji, & Swerdlow, 2017) all strive to bring balance back to the cell. Notably, mitochondrial signals can also regulate distal tissues in worms (Durieux et al., 2011; Woo & Shadel, 2011).

Until the early 2000s, mitochondrial retrograde signaling (i.e., signals transmitted from mitochondria) was entirely considered to be mediated by nuclear-encoded factors, secondary metabolites, or transient molecules (Lee, Yen, & Cohen, 2013; Quiros et al., 2016). However, identification of small genes in the mtDNA, collectively referred to as mitochondrial-derived peptides (MDPs), has provided another layer of mitochondrial communication (Kim, Son, Benayoun, & Lee, 2017; Lee et al., 2013). In fact, the past several years have witnessed increasing discoveries of very small peptides in both the nuclear and mitochondrial genomes (Kim et al., 2017; Makarewich & Olson, 2017). In the mtDNA, there are now eight peptides, including humanin (Guo et al., 2003; Hashimoto et al., 2001; Ikonen et al., 2003), MOTS-c (Lee et al., 2015), and SHLP1-6 (Cobb et al., 2016). Notably, metabolic stress can trigger MOTS-c to directly regulate the nuclear genome to enhance cellular resilience, indicating that the mitochondrial and nuclear genomes are fundamentally integrated at the genetic level (Kim, Son, Benayoun, & Lee, 2018). In fact, the compatibility between the mitochondrial and nuclear genomes has been shown to be important. Mice engineered to host mitochondria from a different genetic background (i.e., forced mismatch of mitochondrial and nuclear genomes) have altered cellular bioenergetics, mitochondrial ROS generation, and resistance to cardiac stress and atherogenic diet (Betancourt et al., 2014; Dunham-Snary & Ballinger, 2015; Fetterman et al., 2013). Although still on hold in the United States, the United Kingdom has approved mitochondrial replacement therapy, which would allow the genetic adoption of mtDNA from another woman (also referred to as “three-parent babies”), without understating the consequences of incompatible mtDNA-nDNA combinations and dysregulated mitonuclear communication (Hamilton et al., 2015). On that line, aging may be underwritten by the decline in communication robustness and efficiency, which reduces the cells’ ability to dynamically respond and adapt to the environment, and ultimately cellular fitness.

Lastly, our understanding of the evolutionary roots of mitochondria in aging is underdeveloped. This is especially interesting in the context of genomic co-evolution of the mitochondrial and nuclear genomes that have establish a tightly coordinated bi-genomic system—how would the force of natural selection apply under these conditions?

The Neuroendocrine Hypothesis of Aging

The coordinated actions of the nervous and endocrine systems are crucial for an organism to orchestrate the various tissues to dynamically respond to its changing environment. The hypothesis that aging could be regulated by the neuroendocrine system was first proposed by Vladimir Dilman in 1954, when he described a progressive age-dependent loss of hypothalamic receptor sensitivity to negative feedback inhibition (Ward, 1999). Of the many factors involved in neuroendocrine regulation, the growth hormone (GH) and insulin-like growth factor 1 (IGF-1) axis are the most impactful and well-studied regulators of aging (Milman, Huffman, & Barzlai, 2016). In addition, seminal studies in model organisms have identified genes that regulate lifespan, of which many are direct or indirect homologs of the conserved GH/IGF-1 or insulin/IGF-1 signaling (IIS) pathways that have greatly propelled the field of gerontology (Altintas, Park, & Lee, 2016; Fontana & Partridge, 2015; Tatar, Bartke, & Antebi, 2003). Mutations in the homologs of the IGF-1 signaling pathways have been shown to increase resistance to stress and lifespan in various model organisms, including yeast (> 2-fold) (Longo & Finch, 2003), worms (2-fold) (Kleemann & Murphy, 2009), and flies (1.5-fold) (Giannakou & Partridge, 2007).

The Somatotropic Axis and Aging

Growth hormone is a key regulator of somatic growth and metabolism either directly or indirectly via effectors such as IGF-1 (Vijayakumar, Yakar, & LeRoith, 2011). GH blocks insulin action, stimulates lipolysis, and inhibits lipogenesis, whereas IGF-1 has opposite effects on glucose and lipid metabolism (Vijayakumar et al., 2011). GH is produced and secreted by somatotropic cells in the anterior pituitary under the regulation of hypothalamic neuropeptides (i.e., growth hormone–releasing hormone and somatostatin). GH deficiency at the level of production or recognition by GH receptors (GHR) of its target tissues causes dwarfism but confers remarkable lifespan extension in mice (Bartke, Sun, & Longo, 2013). Ames, Snell, and Little mice are GH-deficient mutants that exhibit dwarfism and lifespan extension (> 50%, > 40%, and > 20%, respectively) (Brown-Borg, Borg, Meliska, & Bartke, 1996; Flurkey,Papaconstantinou, Miller, & Harrison, 2001). Also, mice that are GH insensitive because of mutations in GHR (GHRKO) are dwarfs and live approximately 50% longer than their wild-type counterparts (Coschigano, Clemmons, Belush, & Kopchick, 2000; Zhou et al., 1997). On the contrary, transgenic mice that overexpress GH show a 50% reduction in lifespan (Bartke, 2003) and increased cancer incidence, and exhibit various age-related dysfunctions in the kidney and liver (Wolf et al., 1993). The GHRKO mice model the Laron syndrome in humans, which is also described by GH insensitivity due to GHR mutations, leading to dwarfism (Guevara-Aguirre et al., 2011; Guevara-Aguirre et al., 2015). Notably, an Ecuadorian cohort with Laron syndrome was found to have only one nonlethal malignancy and no cases of diabetes, in contrast to a prevalence of 17% for cancer and 5% for diabetes in control subjects (Guevara-Aguirre et al., 2011). Further, those with Laron syndrome had greater regional cortical surface area, enhanced cognitive performance, and greater task-related activation in frontal, parietal, and hippocampal regions compared to their unaffected counterparts (Nashiro, Guevara-Aguirre, Braskie, Hafzalla, &Velasco, 2017). Unlike GHR disruption, homozygous inactivation of IGF-1 receptor (IGF-1R) is neonatal lethal in mice; homozygous IGF-1 knockout had a variable effect on lifespan (J.-P. Liu, Baker, Perkins, Robertson, & Efstratiadis, 1993). However, heterozygous knockout of IGF-1R (Igf1r+/–) from birth showed a 26% increase in mean lifespan in mice; female Igf1r+/– mice lived 33% longer, whereas male Igf1r+/– mice only had a 16% increase (not statistically significant) (Holzenberger et al., 2003). Interestingly, Igf1r+/– mice were normal in their size, metabolism, physical capacity, and fertility, but were more resilient to oxidative stress, a hallmark of longevity (Holzenberger et al., 2003). Recently, late-life inactivation of IGF-1R has also been shown effective in extending health/lifespan of female mice (K. Mao et al., 2018). In humans, overrepresented heterozygous mutations in the IGF1R gene in centenarians revealed a functional variant that reduces IGF-1R activity, providing a correlation between IGF-1 signaling and longevity (Suh et al., 2008).

Interestingly, the GH/IGF-1 axis is also linked to mitochondria via MDPs. Circulating humanin levels were higher in those with Laron syndrome, mouse models with deficiencies in GH/IGF-1, and after GH injection in children; humanin levels seems to be regulated at the level of IGF-1 (C. Lee et al., 2014).

Inflammation and Aging

Inflammation is an immune response that is essential for clearing infection, foreign matters, tissue debris, and wound healing. Such acute inflammation is adaptive and responds to certain events before returning to its resting state (Serhan, Chang, & Dalli, 2015). However, the level of resting inflammatory response increases with age, leading to chronic inflammation that is maladaptive and persistent. Such progressive age-dependent chronic and sterile low-grade inflammation has been referred to as “inflammaging” (Franceschi et al., 2000). Inflammaging is thought to be the result of the chronic physiological stimulation of the innate immune system, especially macrophages (Franceschi et al., 2000). Such detrimental late-life actions of inflammation would not have been purified during evolution and can be, in part, explained by the antagonistic pleiotropy hypothesis (Franceschi et al., 2000). The bow tie architecture of the inflammaging machinery suggests that a wide range of exogenous and endogenous danger stimuli (non-self, self, or quasi-self) can converge on an evolutionarily conserved set of sensors and trigger a limited number of inflammatory responses (Franceschi, Garagnani, Parini, Giulani, & Santoro, 2018). In fact, older individuals have been shown to have consistently elevated levels of inflammatory cytokines, especially interleukin-6 (IL-6) and tumor necrosis factor-α‎ (TNF-α‎) (Ershler et al., 1993; Fagiolo et al., 1993; Singh & Newman, 2011; J. Wei, Xu, Davies, & Hemmings, 1992). Also, pro-inflammatory cytokines are associated with age-related diseases like atherosclerosis (Libby, 2002), dementia (Bruunsgaard et al., 1999), Alzheimer’s disease (Sutinen, Pirttilä, Anderson, Salminen, & Ojala, 2012), obesity-induced insulin resistance (H. Xu et al., 2003), diabetes (Wellen & Hotamisligil, 2005), and cancer (Balkwill & Mantovani, 2001; Coussens & Werb, 2002), indicating inflammaging as an underlying factor of age-related diseases (Franceschi et al., 2018; Howcroft et al., 2013).

Two key sources of chronic inflammation are leukocytes and senescent cells. As mentioned above, macrophages have been highlighted as the most important executer of chronic inflammation (Sarkar & Fisher, 2006). Obesity increases the proliferation of non-monocyte-derived macrophages preferentially in adipose tissues that then secrete many pro-inflammatory mediators (Amano et al., 2014; Tilg & Moschen, 2006). Moreover, senescent cells can also contribute to chronic inflammation via senescence-associated secretory phenotypes (SASP), which include various pro-inflammatory growth factors, proteases, chemokines, and cytokines (Coppé et al., 2008). Cellular senescence increases with age (Childs et al., 2015; Deursen, 2014; Jeyapalan, Ferreira, Sedivy, & Herbig, 2007), indicating that SASP likely contributes to inflammaging.

Environmental Factors

We are continually exposed to toxic agents in the air we breathe. In a recent review from the World Health Organization (WHO), more than 80% of the population in the WHO European Region lives in cities with particulate matter (PM) levels that are higher than the WHO Air Quality Guidelines (2013). Exposure to airborne combustion engine–derived PM has been associated with cardiovascular and respiratory dysfunctions (Künzli et al., 2010). Exposure to PM was also positively correlated with increased inflammation (e.g., C-reactive protein [CRP]) and oxidative stress in humans (Chuang, Chan, Su, Lee, & Tang, 2007; Hajat et al., 2015; Ostro et al., 2014). PM smaller than 2.5 μ‎m in aerodynamic diameter (PM2.5) are of special concern and have been linked to many diseases, including cardiovascular and respiratory diseases, diabetes, and neurodevelopment and cognitive function, as well as increased mortality (Jerrett et al., 2013). Interestingly, mice exposed to nanoscale (< 200 nm) PM (nPM) derived from urban freeway air (Los Angeles, CA) showed elevated cytokines levels (IL-1α‎ and TNFα‎) in the cerebral cortex (Morgan et al., 2011).

Microbiome

The human body is colonized by as many bacterial cells as its own (near 1:1 ratio) (Sender, Fuchs, & Milo, 2016). Most commensal bacteria reside in the colon, and thus much interest has been placed on the gut microbiome. The gut microbiota composition of the elderly differs from that of younger individuals (Claesson et al., 2011), and loss of core microbiota diversity is associated with increased frailty (O’Toole & Jeffery, 2015). Our immune system is deeply connected to the microbiome; the microbiome-host interactions can regulate inflammatory cytokine production capacity (~10% in cytokine variability) (Schirmer et al., 2016). Further, intestinal permeability may increase with age, leading to a “leaky gut.” In fact, Elie Metchnikoff, who coined the term “gerontology,” suggested that “leaky gut” results in displaced bacteria, and that in turn causes chronic systemic inflammation (Metchnikoff, 2004). In a recent study, ileal biopsies of young and old individuals showed increased inflammatory cytokine expression (IL‑6), reduced barrier integrity, and reduced inflammatory response to bacterial challenges (IL‑6 and -8) (Man et al., 2015). Age-dependent loss of intestinal integrity is also observed in flies and is associated with chronic inflammation and death (Rera, Clark, & Walker, 2012). In mice, age-dependent microbial dysbiosis promoted loss of intestinal barrier integrity and systemic inflammation; mice under germ-free conditions were protected from age-dependent inflammation (Thevaranjan et al., 2017).

Drugs could also affect the gut microbiome. For instance, in addition to conventional antibiotics, other pro-longevity drugs, such as rapamycin (Bitto et al., 2016) and metformin (F.Cabreiro et al., 2013; Wu et al., 2017), also have antibacterial actions that may have mediated their effects on lifespan. Another study has shown that antibiotic-induced bacterial change in the guts of mice under a high-fat diet or control lowered the inflammation markers (Cani et al., 2008). Microbes have been shown to modulate the rate of aging in invertebrate model organisms, including worms and flies (Heintz & Mair, 2014). Further studies to understand the role of the gut microbiota in mediating pharmacological interventions, especially those administered orally, that extend lifespan and/or health span in model organisms may provide much insight.

Cellular Senescence and Aging

Senescence can refer to both organismal senescence and cellular senescence, whereby the latter is often simply, and erroneously, assumed to be a precursor to the former. Here, we focus on cellular senescence and discuss the recent discoveries that connect the two.

Cellular Senescence

Cellular senescence has been traditionally described as a permanent cell cycle arrest. Cells were once thought to be immortal and capable of dividing indefinitely (Witkowski, 1985), indicating that organismal aging was a problem of a multicellular body as a whole. Hayflick and Moorehead challenged this notion with their publication that described the first demonstration of finite cellular replicative capacity (Hayflick & Moorhead, 1961). They showed that human fibroblast cultures could divide 40 to 50 times before entering a nondividing state, and speculated that they had a role in aging. Interestingly, cells from older individuals had reduced dividing capacity compared to cells from younger individuals, suggesting that the replicative limitations of a given cell were predetermined from birth (Hayflick, 1965).

Hayflick’s proposal has been referred to as replicative senescence, which is associated with critical shortening of telomeres, segments at the end of nuclear chromosomal DNA. Telomere attrition, which induces persistent DDR, is further discussed in the “Genomic Instability and Aging” section. Telomeric shortening can induce cellular senescence (Herbig et al., 2004), and increasing telomerase activity can immortalize human cells (Counter et al., 1992; Kiyono et al., 1998). Therefore, long telomeres may seem to be a preventative factor against aging by reducing replicative cellular senescence. However, murine cells senesce regardless of their long telomeres (Itahana & Dimri, 2004), and an inverse relationship is observed in the wild between telomeric length and lifespan (Gomes et al., 2011). Therefore, multiple mechanisms may be involved in determining the relationship between telomere length and senescence.

Senescent cells in vitro can be characterized by enlarged and flat morphology (Lee et al., 2006), growth arrest, resistance to apoptosis, and altered gene expression (Campisi & d’Adda di Fagagna, 2007). There are biomarkers to detect cellular senescence, including senescence-associated beta-galactosidase (B. Y. Lee et al., 2006), loss of lamin B1 (Freund, Laberge, Demaria, & Campisi, 2012), and the complex SASP secretome (Coppé et al., 2008), which are best used in combination. However, senescence is much more difficult to identify in vivo, largely because of technical limitations, and thus poorly understood in living organisms (Childs, Durik, Baker, & Deursen, 2015). In addition, tissue specificity may further complicate senescence assessment in vivo, based on the fact that SASP gene expression at the transcriptional level varies by tissue type in both young and old mice (Hudgins et al., 2018).

Depending on the driver and the mechanism of activation, senescent cells can be classified into either acute or chronic senescent cells (Sturmlechner, Durik, Sieben, Baker, & van Deursen, 2017). Acute senescent cells arise from a defined set of regulated processes and are then cleared upon completing their physiological duties during a transient existence (e.g., embryonic development [Muñoz-Espín et al., 2013] and wound healing [Demaria et al., 2014]) (Muñoz-Espín & Serrano, 2014). However, chronic senescent cells are driven by diverse stressors over time and are poorly cleared, leading to their accumulation and continuous SASP production, ultimately accompanying pathological consequences. DNA damage, oxidative stress, suboptimal culture conditions, therapies, and other possible stressors, other than telomere erosion, can induce cellular senescence (Acosta et al., 2008; Acosta et al., 2013; D. J. Baker, Alimarah, van Deursen, Campisi, & Hildesheim, 2017; Parrinello et al., 2003). Such alternative mechanisms could partially explain how murine cells that possess long telomeres also undergo senescence (Parrinello et al., 2003). Further, senescence is a dynamic process that can be divided into three stages: early, full, and deep senescence, depending on the state of chromatin remodeling, SASP expression profiles, and phenotypic diversification, suggesting the possible reversion of early senescent cells back to their previous state (Deursen, 2014).

Senolytics

It is premature to conclude a causal connection between cellular senescence and aging (Jeyapalan & Sedivy, 2008). However, from an antagonistic pleiotropy perspective, cellular senescence can increase the fitness of young individuals by suppressing tumors and regulating embryonic development (Muñoz-Espín et al., 2013) and wound healing (Demaria et al., 2014). However, two major aspects of cellular senescence can directly reduce the fitness in late life: (a) a loss of tissue regeneration due to cell cycle arrest in progenitor cells (cell autonomous) and (b) the production of pro-inflammatory SASP (non-cell autonomous) that can promote various pathologies, including atherogenesis (Childs et al., 2016); kidney disease (Sturmlechner et al., 2017); and, paradoxically, cancer (Ana et al., 2001; Demaria et al., 2017; Jean-Philippe. Kauser, Campsi, & Beauséjour, 2006).

Therefore, if cellular senescence can promote tissue dysfunction during aging and may even directly drive age-related pathology, it may be therapeutically beneficial to eliminate it late in life. Clearance of senescent cells in a progeroid mouse model using a transgenic approach delayed several age-associated pathologies (Baker et al., 2011). Senolytics are drugs that can selectively eliminate senescent cells. The small-molecule senolytic ABT263 can selectively eliminate senescent bone marrow hematopoietic stem cells and senescent muscle stem cells in mice and are able to ameliorate stem cell function during aging (Chang et al., 2015). A recent study supports the concept that senescent cells can be contagious and cause physical impairment (M. Xu et al., 2018). In the study, senescent cells (pre-adipocytes) were transplanted into young mice, which then led to persistent physical dysfunction and triggered cellular senescence to other tissues; old mice that received senescent cell transplant exhibited an even stronger decline of physical capacity. Notably, intermittent administration of a senolytic cocktail, dasatinib plus quercetin, alleviated physical dysfunction and increased post-treatment survival by 36% while reducing mortality hazard to 65%.

In Closing

Aging is a complex process that involves various biological mechanisms. To add to the complexity, these biological systems are “moving targets” that change with age, and thus understanding them at one age group may not apply to understanding another. Further, from a strictly evolutionary and biological perspective, aging begins from one’s reproductive age and thus is a cumulative and progressive event with limitless and unpredictable inputs over one’s adult life. We, as a scientific community, have made leaps in exploring the boundaries of aging and closing knowledge gaps, but, as discussed repeatedly, recent research advances and breakthroughs have also brought more questions. As depicted by the three blind men and an elephant, aging is evidently larger than the sum of individual (and focused) studies. On that line, emphasis on interdisciplinary and convergent science will move the field forward and provide a more accurate and comprehensive picture of aging.

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