Aging: Somatic Mutations, Epigenetic Drift and Gene Dosage Imbalance

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Review

Aging: Somatic Mutations, Epigenetic Drift and Gene Dosage Imbalance Reiner A. Veitia,*,1 Diddahally R. Govindaraju,2,3 Samuel Bottani,4 and James A. Birchler5 Aging involves a progressive decline of metabolic function and an increased incidence of late-onset degenerative disorders and cancer. To a large extent, these processes are influenced by alterations affecting the integrity of genome architecture and, ultimately, its phenotypic expression. Despite the progress made towards establishing causal links between genomic and epigenomic changes and aging, mechanisms underlying metabolic dysregulation and age-related phenotypes remain obscure. Here, we present a model linking genome-wide changes and their age-related phenotypic consequences via the alteration of macromolecular complexes and cellular networks. This approach may provide a better understanding of the dynamically changing genome–phenome map with age, but also deeper insights to developing more targeted therapies to prevent and/or manage late-onset degenerative disorders as well as decelerate aging. Facts and Theories of Aging The aging process is a universal property of most organisms, accompanied by a subtle, progressive, and often irreversible decline of physiological and reproductive functions. In principle, this process starts shortly after the formation of the zygote and continues over various stages throughout the lifespan of the individual [1]. Both intrinsic and extrinsic factors affect genome integrity both in somatic and reproductive tissues over time [2]. A recent review has enumerated nine tentative hallmarks of aging that include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis (increased protein synthesis and decreased degradation) (see Glossary), deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication [3]. Age-associated genomic changes, which are[4_TD$IF] discussed in detail below, can lead to a general dysregulation of genome architecture, accessibility and expression [3–5]. Moreover, such somatic genomic changes that accumulate in tissues and organs, interact with inherited variation [6,7], and cumulatively or synergistically influence healthspan and lifespan. Mutations are responsible for genetic variation that results in evolutionary change. While a large number of these mutations are benign, a fraction of them profoundly affect all aspects of survival and reproduction of cells and organisms, and hence their fitness [8]. Somatic tissues also acquire genomic alterations, which could lead to both genetic [6] and epigenetic loads. These alterations are often cell specific and show cumulative or synergistic effects with the classical mutation load (i.e., inherited) of an individual or more broadly of a population. As argued below, somatic alterations may affect the relationship between genotype–phenotype, and as such, perturb the function of biochemical networks and macromolecular complexes with age [4,9].

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Trends Aging is a progressive phenomenon influenced by genetic and epigenetic alterations in interaction with the environment. The aging process is characterized by nine hallmarks that bear consequences at the molecular, cellular, tissue, and organismal levels. A perturbation of the crosstalk within the triangle genome, epigenome and macromolecular complexes may explain several hallmarks of aging. Experimental and clinical interventions can modulate aging and increase either lifespan or healthy aging.

1 Institute Jacques Monod, Paris, France and Université Paris Diderot, Paris, France 2 Department of Human Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA 3 Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY 10461, USA 4 Laboratory Complex Systems and Matter, Université Paris Diderot, Paris, France 5 Division of Biological Sciences, University of Missouri, Columbia, MO 65211, USA

*Correspondence: [email protected] (R.A. Veitia).

http://dx.doi.org/10.1016/j.tcb.2016.11.006 © 2016 Elsevier Ltd. All rights reserved.

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Such somatic alterations include single nucleotide variants (SNVs), copy number variants (CNVs), aneuploidy, and epigenetic changes (corresponding to the aging hallmarks known as genomic instability and epigenetic alterations) [3]. These processes are in accordance with the principles of the somatic mutation theory of aging, which posits that somatic mutations may be common to and underlie both aging and cancer [10]. How do mutations originate and accumulate in somatic tissues? DNA mutations are a general result of errors in DNA replication, in the processing of DNA damage or during chromosomal transactions. Hayflick and Moorhead proposed that each cell division is accompanied by replication errors, owing to the imperfect fidelity of DNA polymerases [11]. Although point mutations and aneuploidy are associated with replication and cell division, mutations can also occur in postmitotic (nondividing) cells. However, it is still unclear if DNA repair is more active or more accurate in germ cells, stem cells, or dividing cells in general than in postmitotic cells, such as neurons. Particular chromosomal regions, such as telomeres, are especially susceptible to age-related erosion and undergo attrition with the number of cell divisions [12]. Somatic cell division and the associated alterations also occur in stem cells and may affect their function (corresponding to the aging hallmark known as stem cell exhaustion [3]). A large number of harmful genetic and epigenetic alterations are eliminated during pre- and postzygotic stages, but those remaining accumulate in aging tissues and organs [13]. Thus, each mutated cell, upon expansion, generates a population of mutant cells that undergo further mutations, producing somatic mosaics [14]. The process of accumulation of deleterious mutations in somatic tissues mimics the well-known Muller's ratchet, through which, the genome of a cell irreversibly accumulates alterations [15] on a previously mutated background [6,7]. This is reflected by the increased frequency of somatic mutations found in aged tissues [6,16]. A meta-analysis of 36 studies strongly supports a link between DNA damage and aging. The analysis also found that other environmental variables such as smoking could inhibit the repair of DNA damage [17]. In line with this, age-related clonal mosaicism for chromosomal aberrations has been described [18,19]. The accumulation of somatic alterations with age has been suggested to result in a decrease of cellular function. For instance, Szilard [20] postulated that random somatic mutations would inactivate vegetative genes important for adult somatic cell function. Subsequently, Alexander hypothesized that DNA damage per se was important in aging [21], suggesting that the accumulation of DNA damage entails further damage accumulation [22,23]. Extrinsic factors also influence the expressed genome of a cell, which is modified epigenetically. Stable transmission of altered epigenetic patterns from mother to daughter cells are thought to occur more frequently than DNA mutations and may explain at least in part the epigenetic and metabolic changes observed during aging [24,25]. There are other complementary theories of aging such as the neuroendocrine theory, which posits that hormone release and receptivity decrease with age or the Free Radical Theory, which incriminates free radicals in the damage of DNA and other cellular components [26]. Linked to this is the Mitochondrial Decline Theory. Accordingly, age-related mitochondrial malfunctioning [27] and structural variations play a role in aging and age-associated disorders [23]. Interestingly, single-cell analyses have shown that the mitochondrial mutational load of individual aging cells increases to a high enough [25_TD$IF]level that the mutated genome prevails [28]. In line with this, mutator mice that are deficient in mitochondrial DNA polymerase g (Polga) display an accumulation of mtDNA mutations, reduced lifespan and some aspects of premature aging [29]. Also, there is evidence from several animal models that the stoichiometric balance between mitochondrial and nuclear gene products can impact aging [30].

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Glossary Aneuploidy: presence of an abnormal number of chromosomes in a cell (i.e., trisomy 21). Autophagy: mechanism that allows the degradation of cellular components. Cellular senescence: a phenomenon by which a cell ceases to divide. Chromatin: complex of DNA, protein, and RNA within the nucleus of eukaryotic cells. Chromatin remodeling: dynamic process of chromatin modification that allows regulation of gene expression. DNA methylation: process of addition of methyl (CH3-) groups to DNA. In mammals, it refers to methylation of cytosines lying in the context of CpG doublets. Epigenetic: refers, in the context of this review, to modifications of DNA or chromatin not changing DNA sequence but altering its expression. Histone: nuclear proteins closely associated with DNA that allow its compaction within the nucleus. Methylation pattern: distribution of DNA methylation across the genome (i.e., of methylated cytosines in the context of CpG doublets in mammals). Mutation load: number of damaging genetic variants carried in the genome that are transmitted to the offspring. Proteasome: proteolytic macromolecular complex able to degrade proteins in excess or damaged. Proteostasis: refers to protein homeostasis (from synthesis, folding, trafficking to degradation). Proteotoxic stress: toxicity caused often by misfolded proteins. Somatic mosaicism: presence of more than one genotype in the somatic cells of the body.

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Clearly, mutations and epimutations exert manifold and cascading effects in the genotype– epigenotype and phenotype spaces. Although the rate of aging can be decelerated to a limited extent with various interventions, it is ultimately irreversible. While the role of mutations in cancer and other diseases has been discussed (e.g., [23]) the consequences of genomic and epigenomic alterations on the phenotypic manifestations of aging has largely been ignored. In fact, the time-dependent accumulation of cellular damage is the general cause of aging but the mechanistic bases of the process are still unclear [31]. Here, we briefly (i) discuss the nature of genomic and epigenomic alterations in aging; (ii) present models, in an attempt to demonstrate that perturbations of the genetic and epigenetic spaces can result in alterations of the function of macromolecular complexes and signaling/metabolic networks that underlie[5_TD$IF] senescence and higher-level phenotypes; and (iii) emphasize the need to consider the impact of agerelated focal and global dosage imbalances on age-related phenomena. We pay particular attention to the primary hallmarks of aging: genomic instability, epigenetic alterations, and loss of proteostasis [3].

Epigenetic Gene Alterations and Dosage Imbalances Age-related epigenetic alterations involve changes in DNA methylation patterns, post-translational histone modifications and chromatin remodeling. Increases in histone H4K16 acetylation, H4K20 trimethylation, or H3K4 trimethylation, along with decreased H3K9 methylation or H3K27 trimethylation have been reported [32,33]. This, coupled with the loss of histones during aging supports a pangenomic increase in transcription and in transcriptional noise [24,34–36]. Such processes may amplify a self-perpetuating pathway of global epigenetic changes [24]. One of the most salient epigenetic changes is the modification and inheritance of methylation across cell generations. Aging mammalian cells undergo global DNA hypomethylation mainly in repetitive regions and local DNA hypermethylation, primarily at promoter CpGs doublets [37,38]. These alterations are part of an epigenetic drift defined as deviations from a normal epigenetic state; first documented for monozygotic twins [39] due to incomplete restoration of normal patterns after replication or repair. DNA methylation and histone acetylation patterns in twins are indistinguishable during the early years of life but change as a function of age, affecting gene expression [40]. The significance of epigenetic drift is unclear although it is a pangenomic event. Part of this drift is tissue[26_TD$IF]-specific, whereas another component is tissue[26_TD$IF]independent and affects stem cells and their differentiation [39]. This may be related to the observed decline of stem cell function with age. Epigenetic drift is also influenced by genetic factors[6_TD$IF] [39]. A comprehensive analysis of methylation patterns among cell types and tissues using publicly available gene expression data sets showed that methylation levels were [27_TD$IF]low for embryonic and induced pluripotent cells, but increased in relation to age among all organs [41]. Based on these findings, methylation patterns are becoming a promising biomarker for aging [39]. How can this epigenetic drift be explained? One possibility is random and unscheduled epigenetic changes arising in small cell populations (either during development or, later, in stems cells). In such conditions, it is likely that branching cell lineages leading to tissue clones arising from small primordia or cell populations will have different epigenetics patterns over the genome. Thus, depending on the number of initial cells harboring these random epigenetic alterations, DNA methylation patterns can diverge over time. However, to explain the prevalence of different clones, for instance in twins, we have to invoke (i) a potential clonal competition and selection that favors some clones over [28_TD$IF]others, and/or (ii) a positive feedback where epigenetic changes that affect chromatin modifiers will foster more epigenetic changes. The latter process affects many genes and effectively amounts to an epigenetic Muller[29_TD$IF]'s ratchet, provided that the epigenetic changes in question are stable, as reported for some human disorders [42].

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Box 1 displays a simple stochastic model of epigenetic drift in the case of gene inactivation. The existence of stochastic epigenetic silencing is supported by experiments showing variegation of the expression of a b-galactosidase reporter in erythroid cells of transgenic mice [43]. With age, there is a decline of transgene expression, with the coexistence of expressing and nonexpressing cells as a ‘clock within us’ (Box 1). In practice, inactivation of genes transferred into[3_TD$IF] stem cells challenges the success of gene therapy [44]. Another related explanation for epigenetic drift is the stochastic extinction of gene expression owing to a decrease of the signal that triggers gene expression. This epigenetic ‘use it or lose it’ scenario requires small induction rates (kI) in the Epigenetic Clock model outlined in Box 1. Thus, during a prolonged absence of the inducing signal, the time in the Ga state increases. This increases the probability of shifting to the G state, thereby leading to a time-dependent extinction of gene expression. Irrespective of the specific mechanism, age-dependent gene inactivation can affect the function of macromolecular complexes and cellular networks and pathways.

Genomic Imbalance and Aging Investigations in the early 1920s on the effect of structural variation on the fitness of organisms demonstrated that the addition of a single chromosome to an organism can be more detrimental than doubling the whole genome. Later, similar findings were reported in other eukaryotes (see [45] and references therein). This phenomenon is more striking when a diploid cell loses one chromosome. The molecular effects of such variation have been explained in terms of stoichiometric alterations of macromolecular complexes and cellular networks leading to their malfunction. This idea is currently known as the gene dosage balance hypothesis (GDBH) [45]. Aneuploidy is a common phenomenon in aging and age-related disorders [46], owing to perturbations of chromosome segregation during mitotic and meiotic processes [47]. According to the GDBH, aneuploidy disrupts gene networks and macromolecular complexes, as a result of dosage imbalances [48,49]. These effects can also be caused by more subtle mutations such gene-specific loss of function (LOF) mutations and epigenetic silencing events [45]. The impact of gene expression changes engendered by aneuploidy, gene silencing, or overexpression can be explored with a previously published model of macromolecular complex assembly [50]. As suggested elsewhere, aging is influenced by numerous genomic factors [51,52]. In this setting, consider the complex AaBb. . .Zz, where S and D are the synthesis and degradation rates of the monomers and K is an interaction constant [30_TD$IF]among the subunits. Note that the parameters S and D encapsulate information on mRNA and protein expression and that the multimer is dynamic and not degraded directly (but only its components). Under these assumptions, the concentration of AaBb. . .Zz in the steady state is:  a  b  z Sa Sb Sz ½ AaBb::Zz ¼ K ::: [1] Da Db Dz Such complexes obey stoichiometric constraints. For instance, it is known that the mRNA levels encoding interacting proteins display closer expression levels than expected randomly [53]. In addition, mRNAs encoding proteins involved in the same macromolecular complex have been proposed to form post-transcriptional operons through interaction with mRNA-binding proteins, which coordinate their localization, translation, and/or degradation [54]. Accordingly, the systematic analysis of protein complexes in the budding yeast Saccharomyces cerevisiae shows that synthesis levels of most of their components correlate with their stoichiometry within relevant complexes [55]. According to model behind Equation 1, small changes in the concentration of any single partner due to somatic alterations bear mild consequences on the concentration of the macromolecular complexes, although variations of the gene products represented as several copies per complex

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Box 1. [4_TD$IF]An Epigenetic Clock Transcription is driven by the recognition of a promoter by TFs that are present at low concentrations. In such cases, gene expression becomes probabilistic and stochastic fluctuations in time and space are observed [75]. Most active genes undergo on–off transcription cycles. In situ hybridization experiments on nascent RNA have shown that the two alleles of a locus are not constantly expressed [76]. Active transcription takes place in transcriptional factories enriched in RNA polymerase II and chromatin modifiers, which interact with chromatin loops [77]. A logical possibility is that these modifying enzymes help maintain an open chromatin state when the allele is being transcribed. Thus, a decrease in the activation or maintenance frequency may lead to an increase in DNA methylation and a decrease in activating histone marks and in gene expression with time. This process can be accounted for by a two-step gene activation model. Accordingly, the gene of interest can be either not accessible (G) or accessible (Ga) to the transcription machinery. The accessible gene can stochastically shift towards an induced[16_TD$IF]/expressed state (GI), allowing for transcription and subsequent protein expression (Figure IA). Parameters ka[17_TD$IF], g[1_TD$IF]a in Figure IA stand for accessibility gain and loss, and represent the epigenetic component of the model. Parameters kI[4_TD$IF], g I stand for transcription induction and arrest rates from open chromatin. Finally, kp is the protein expression rate and [15_TD$IF]gP is the protein degradation rate. If we focus only on the epigenetic component of the model (Figure IA) under irreversible conditions (ka = 0), we would expect an exponential decay of the expression of the alleles considered in isolation over the whole population. This is reminiscent of the ‘Clock in the Rocks’ process of radioactive decay. Because cells are diploid, the number of expressing cells can be modeled as a consecutive reaction: cells expressing two alleles ! cells expressing one allele ! nonexpressing cells [5_TD$IF](assuming that the proportions of the subpopulations of cells do not change with their proliferation[6_TD$IF]) (Figure IB). Figure IC, shows the probability distributions of protein abundance, represented as a fluorescence activated cell sorter (FACS) output, at different time points for diploid cells. During a transient period when both alleles are accessible the protein abundance is high. Subsequently, allele accessibility decreases, and at long time points, depending on the parameters (genetically and/or environmentally determined), the majority of cells display an extinction of gene expression. The minimalist model sketched here shows that changes in gene expression and the associated chromatin modifications over the life span of individual cells can be accounted for by elementary stochastic processes acting according to binary conditions (i.e., the allele is accessible or not; the accessible allele is induced/expressed or not).

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Figure I. Epigenetic Clock. (A) Model of gene expression with an exponential chromatin accessibility decay. G denotes the gene of interest in an inaccessible state due to, for instance, the presence of methylated DNA or repressive histone marks; Ga represents the gene in an accessible state and GI is the accessible gene induced for transcription[12_TD$IF] (i.e., expressed). ka and [13_TD$IF]ga control the exponential decay of accessibility whereas kI and [14_TD$IF]gI determine transcription rate of a gene embedded in open chromatin. Finally, kp and [15_TD$IF]gP are the protein production and degradation rates, respectively. (B) The graphic represents the percentage of cells in a population expressing two, one and zero alleles versus the age of the individual. We assumed that proportions of the cell subpopulations do not change as they proliferate. The allele inactivation rate was set to have only 1% of cells with two active alleles at age 100 years. (C) Idealized FACS profiles representing the evolution of protein abundance with time, on the way to gene extinction.

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Figure 1. Synergistic Effects of Deleterious Mutations in Complexes of Different Sizes. The graph displays the relative concentration of the complexes AB, ABC, and AABBCC for different expressions level of the monomers (for simplicity, expression loss was assumed to be equal for all monomers). The pathway leading to the synthesis of AB is displayed as an example. The curves were obtained using [9_TD$IF]Equation [10_TD$IF]1, main text (for otherwise unitary parameters except for monomer synthesis). Slight alterations of monomer synthesis lead to a synergistic decrease of multimeric concentration. The effects are more pronounced for the hexamer[1_TD$IF]. A similar behavior is expected for co-occurring wDNMs.

are predicted to have stronger effects than those of singleton components. More importantly, changes in the concentration of several partners are predicted to have synergistic (multiplicative) effects. Therefore, large multimeric complexes could more likely be affected by such combined and cumulative defects. Variation in the concentration of AaBb. . .Zz, owing to changes in the concentration of the monomers, is proportional to the number of times they appear in the complex and to the total number of components of the complex (Figure 1). This simplified model should draw similar qualitative conclusions as those from a more realistic, but less generalizable, model [56]. The effects of weakly dominant negative mutations, which can be described by a related model, are outlined in Box 2. Similar reasoning applies to transcriptional complexes. Consider the process of recognition of a promoter P with a, b. . ., z binding sites for the cognate transcription factors (TFs) A, B, . . ., Z. This process can be represented by the global reaction P + a(A) + b(B) + . . . + z(Z) = PAaBb. . .Zz. Assuming that the degree of saturation of promoter PAaBb..Zz/PTotal is proportional to transcription, the transcriptional response (TR) can be described by the equation: TR ¼

½Aa ½Bb ::::½zz K þ ½Aa ½Bb :::½Zz

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where K, a, b,. . .z are constants. The exponents depend on the number of binding sites per promoter. Here A, B,.., Z represent the free transcription factor concentrations but we assume that they are in excess to P (i.e., free = total concentration, see more details in [57]). Again, the accumulation of minor changes in expression or activity of the TFs involved in the reaction leads to multiplicative changes in TR. This can be seen in [3_TD$IF]Equation 2 for small TF concentrations so that the denominator becomes K (i.e., the rest disappears and the equation acquires the same form and properties as [3_TD$IF]Equation 1). This has to be viewed over the whole transcriptional network, which implies the existence of cascading and amplifying effects. In short, relative changes in the amount of components of transcriptional complexes will change the expression of target genes whose

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Box 2. Deleterious Somatic Mutations Leading to Weak Dominant Negative Effects Age-related genetic alterations may engender ‘mutant polypeptides that when (over)expressed disrupt the activity of the wild-type gene’ [78]. For example, if a mutant monomer m renders the dimer Mm inactive, a heterozygous individual will have only a quarter of active dimer (MM), which is likely to lead to an abnormal phenotype. This is a typical case of a dominant negative mutation (DNM) and its logic can be applied and extended to any macromolecular complex. Dominant negative effects (DNEs) are a common phenomenon. For instance, a huge proportion of in-frame gene fragments lead to toxic effects when overexpressed in Saccharomyces. Consistent with a DNE, gene fragments are more toxic than fulllength copies [79]. DNMs have been proposed to be more frequent than loss-of-function (LOF) mutations [80]. Thus, many somatic truncating and missense mutations are expected to be DNMs. However, most genetic alterations (especially, missense mutations) are expected to bear mild consequences on the function of such macromolecular complexes (i.e., the dimer Mm can retain a fraction of the normal activity). According to a previous model, similar to that described by [9_TD$IF]Equation 1,[21_TD$IF] in the main text, mutations that slightly alter the structure of a protein are also likely to have stronger effects in large multimers because the cumulative alterations of several subunits may have multiplicative effects on the concentration of active multimer [50]. In these instances, for the sake[7_TD$IF] of simplicity, we can use the term weakly dominant negative mutations (wDNMs) because, strictly speaking, the term DNM would be inappropriate as only the co-occurrence of a series of slightly defective alleles would lead to the relevant phenotype. In line with the existence of wDNMs, analysis of the effect of coding (nonsynonymous) single nucleotide polymorphisms (cSNPs) in humans on a series of structural parameters has shown that the number of such cSNPs is higher than the number of substitutions in the same proteins between species. This points to an accumulation of slightly deleterious alleles (both, partial LOF, and wDNM) in the human genome whose interaction can be modeled using a variant of [9_TD$IF]Equation 1 [50]. Indeed, such alleles are subjected to reduced selective pressures than disease-causing alterations and can persist in the population [81]. Many of the somatic alterations constantly arising during a lifetime may belong to the wDNMs class and their co-occurrence in the same cell, tissue or individual could have wide ranging effects on the phenotype.

products can be in turn components of other transcriptional complexes. Below, we discuss in more detail the biochemical consequences of local and global dosage imbalances.

Generalized Biochemical Effects of Dosage Imbalances Dosage balance is required in signaling pathways, which often involve enzymes with opposing activities, such as a kinase and a phosphatase, acting on a common substrate [58]. A relative decrease or increase of one or the other will introduce an alteration in the signaling process. Mutation and misexpression of metabolic enzymes are also likely to alter metabolism. The classical theory of metabolic control analysis defines the control coefficient, C, of a particular reaction step as the change of metabolic flux after a change of enzyme concentration. The sum of C values over the metabolic pathway is equal to 1 [59]. Hence, during the aging process, [8_TD$IF]isolated [9_TD$IF]alterations of genes encoding enzymes involved in long metabolic pathways should have a low impact on the phenotype, as decreasing their products should not drastically reduce flux (and fitness) because the C values of each step are small [60]. However, the accumulation of mutations or gene silencing events affecting several steps will lead to malfunction. Regarding macromolecular complexes, coexpression of their components implies that their misexpression will leave unpartnered elements. For gene/chromosome deletions, a deficit in a component will leave a relative excess of binding partners. When a single or multiple genes are overexpressed, the excess of the overexpressed components may induce the production of inactive subcomplexes, especially when they are molecular bridges capable of titrating other subunits. Such overexpressed elements may also engage in promiscuous protein–protein interactions. Indeed, proteins that become harmful when overexpressed in yeast suggest that intrinsic protein disorder is an important determinant of such effects. As a result of mass action, these regions can make promiscuous interactions when their concentrations are increased. They also contain potentially exposed short linear motifs that mediate transient molecular interactions (i.e., sites undergoing post-translational modifications, etc.), which can sequester proteins that recognize such motifs [61].

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Figure 2. Synergistic Effects of Decreased Proteolytic Activity for Complexes of Different Sizes. Note

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also that owing to global overtranscription, there can be an increase of synthesis. The graph displays the relative concentration of the AB, ABC, and AABBCC for different levels of proteolysis decrease of the monomers (for simplicity, expression[2_TD$IF] was assumed to be equal for all monomers). The pathway leading to the synthesis of AB is given as an example. The curves were obtained using [9_TD$IF]Equation [10_TD$IF]1, main text (for unitary parameters except for monomer degradation). Slight alterations of monomer proteolysis (less than 10%) lead to a disproportionate decrease of multimer concentration. The effects are more pronounced for the hexamer[1_TD$IF].

The assembly of a protein complex may involve unstable intermediates that are stabilized during the process. This process has been well studied for tubulins, which are typically found as //b heterodimers [62]. Thus, monomers in absolute or relative excess can be either in an intermediate or misfolded conformation, or expose regions that should be hidden within the complex [63]. The analysis of the aggregation propensities of protein complex interfaces and surfaces has shown that such regions tend to be more aggregation prone than other surface regions [64]. Chaperones are key players in protein folding and complex assembly and various animal models support a causal influence of chaperone levels on lifespan. For instance, mutant mice deficient for the co-chaperone CHIP (carboxyl terminus of Hsp70-interacting protein) display accelerated aging phenotypes by altered protein quality control [65], whereas long-lived dwarf mice upregulate some heat-shock proteins [66]. As expected, additional chromosomes in normally diploid cells lead to signs of proteotoxic stress [67,68] and initiate cellular senescence and age-related pathologies in the brain and liver [46]. Age-related transcription amplification as well as more focal dosage imbalances both lead to chronic proteomic stress. This is further enhanced by the decline in the activities of two principal proteolytic systems of the cell: autophagy and the proteasome [69,70] (corresponding to the aging hallmark known as loss of proteostasis) [3]. The comparison of wild-type and long-lived daf2 Caenorhabditis elegans individuals showed that[38_TD$IF] some factors regulating protein homeostasis were expressed at [39_TD$IF]higher levels in the latter, including genes regulating proteolysis, translation[8_TD$IF], and chaperones [71]. Decreasing the activity of the insulin/insulin-like growth factor (IGF)-1 signaling cascade is not only protective for the daf-2 mutant nematode, but also protects knockout mice from toxic protein aggregation. Accordingly, an IGF-1 signaling inhibitor can promote proteostasis in mammalian cells by enhancing the aggregation of hazardous proteins in quality control compartments known as aggresomes [72]. Interestingly, an increase in immunoproteasome and proteasome activity has been reported in the livers of long-lived Snell dwarf mice and in mice exposed to drugs that extend lifespan such as rapamycin or 17-/-estradiol. This suggests that increased (immuno)proteasome function may contribute to lifespan differences in mice and primates [73]. Figure 2 shows how a mild decrease of proteolytic rate can lead to disproportionate increases in the concentration of macromolecular complexes. According to [3_TD$IF]Equation. 1, small decreases in proteolytic activity have synergistic effects on multimer concentration. Again, it is expected that

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Young

Outstanding Questions

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Does the effect of the mutation load on macromolecular complexes lead to reactions such as hypo/hypermethylation (depending on CpG location) to compensate for the decrease of proteostasis? To what extent does the interaction between somatic mutations or epimutations influence health and lifespan? What is the relative weight of the perturbation of the structure–function of macromolecular complexes in the explanation of age-related phenotypes?

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What is the impact of proteostasis alteration on the in vivo concentrations of macromolecular complexes (which are harder to determine than the amounts of the individual components)? Does the systematic study of disorders due to genomic imbalances provide support to the perspective outlined here?

Figure 3. Effect of Altered Expression and Proteolysis with Age on the Concentrations of a Dimer and Trimer with Opposing Activities on Substrate A. Proteolysis is represented by the ribosome and the proteasome, respectively, while the dimer and trimer that act on substrate A [1_TD$IF]represent a kinase and a phosphatase, respectively. Decreased proteolysis has a greater impact on the trimer and alters the balance of the relative amounts of unmodified (A) and modified (A*) substrate potentially leading to malfunction. Several of such modules are represented in many signaling pathways, such as the mitogen-activated protein kinase pathway.

large complexes are more likely to be affected. Thus, the response of the concentration of AaBb. . .Zz to a decrease in proteolysis should be proportional to the total number of components of the complex and to the number of times they appear in the complex. This must be considered in the context of the cellular network where multimers of different sizes may have opposing activities. For instance, according to [3_TD$IF]Equation 1, a decrease in proteolytic activity will affect a trimeric phosphatase more than a dimeric kinase, which will automatically shift the balance and cause malfunction (Figure 3). A similar situation can emerge when net gene expression depends on the force of multisubunited activators and inhibitors. Although not treated in depth here, an increase in autophagy through rapamycin administration has been demonstrated to be beneficial for healthy aging [74].

Concluding Remarks Numerous inherited and acquired genomic and epigenomic changes have a cumulative effect on the aging phenotype. The role of epigenetic processes in aging and their contextual effect on the resulting phenotypes, in relation to biological and physical environments, is often underestimated. Here, we emphasize the need for examining the connections among genotype, epigenetics, and phenotype as an integrated system. We have proposed plausible models to examine the origin and consequences of genomic imbalances on metabolic output and their plausible effects on aging. This theoretical approach suggests that genomic changes could lead to linear and nonlinear responses [10_TD$IF]of important molecular effectors. From the perspective of a model of infinitely many alleles contributing to aging, the contribution of each of the (epi)genomic

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changes in isolation is often minute but individual contributions can be cumulative and synergistic. Because of the stoichiometric interactions in multisubunit regulatory complexes, accumulations in ratcheted changes of not necessarily strong mutations can affect aging. We are conscious of our current inability to systematically and comprehensively characterize the genome–phenome landscape and the interactions of somatic mutations and epimutations during aging. However, it seems likely that the increasing age-related mosaicism of the somatic genome, including methylation and chromatin changes, chromosomal alterations, and other types of mutations, could indeed lead to the type of imbalance at the RNA and protein levels described here. In fact, any alteration of the functional genome, [41_TD$IF]including regulatory RNAs and enhancers, etc. are potential contributors to this general genomic and epigenomic imbalance (see Outstanding Questions). The models explored suggest that the rate of aging (and thus, longevity) depends on the frequency of favorable alleles in the genome such as higher ka- and ki-inducing alleles according to the epigenetic clock model or those increasing proteolytic activity (i.e., increasing D in [3_TD$IF]Equation 1). However, artificial introduction of a favorable allele or genic segment may not necessarily have a positive effect on the phenotype because of the interaction with a complex genetic background. That said, there are opportunities for experimental interventions modulating the aging process such as the use of epidrugs increasing ka or by a systemic increase of proteolysis or autophagy. We hope that integrated approaches taking into account genomic– epigenomic and phenomic factors, in the light of what we have discussed, will illuminate many complex questions in aging research. Furthermore, as many Mendelian and complex disorders reduce healthy lifespan, the arguments presented here could be extended [43_TD$IF]towards managing aging in such conditions. Acknowledgments The authors thank two anonymous reviewers[1_TD$IF] and the editor for their helpful comments. Work in Veitia's laboratory is supported by the University Paris Diderot, the Centre National de la Recherche Scientifique, the Fondation pour le Recherche Medicale and the Agence National de la Recherche (Iceberg Project) Birchler's laboratory is supported by National Science Foundation grant I05-1545780.

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