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Genome instability and aging: Cause or effect?
Accepted Manuscript Genome instability and aging: cause or effect? Jan Vijg, Cristina Montagna PII:
S2468-5011(17)30010-X
DOI:
10.1016/j.tma.2017.09.003
Reference:
TMA 6
To appear in:
Translational Medicine of Aging
Received Date: 21 July 2017 Revised Date:
18 September 2017
Accepted Date: 18 September 2017
Please cite this article as: J. Vijg, C. Montagna, Genome instability and aging: cause or effect?, Translational Medicine of Aging (2017), doi: 10.1016/j.tma.2017.09.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Genome instability and aging: cause or effect? Jan Vijg, Cristina Montagna, Department of Genetics, Albert Einstein College of Medicine, Michael F. Price Center, 1301 Morris Park Avenue, Bronx, NY 10461, USA
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*Correspondence: [email protected] [email protected]
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Abstract. Genome instability, i.e., the tendency of the genome to undergo alterations in DNA information content through mutation, is considered a hallmark of aging. While mutations can be analyzed in clonal lineages, such as tumors, normal tissues have thus far not been amenable to mutation analysis except for the largest type of mutations: chromosomal aberrations. This is because mutations are random events and, therefore, unique to a single cell. New, single-cell sequencing-based methods are now emerging and may soon provide quantitative assays for estimating the possible functional effects of mutations accumulating during aging in various tissues and organs. Here we briefly review the mechanisms of genome instability in normal cells, the accumulation of various types of genome instability with age and their possible physiological consequences. Introduction
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Genome instability is considered one of the hallmarks of aging (1). Genome instability refers to a range of DNA alterations, from point mutations and deletions and insertions to chromosomal rearrangements and whole chromosome numerical changes, which irreversibly change the information content of the genome. Already in the 1950s Failla (2) and Szilard (3) independently proposed that mutations, then realized to occur spontaneously as a consequence of, for example, cosmic radiation, would cause aging by randomly inactivating genes. Failla (2), as well as Osgood (4), also considered mutations as the primary cause of cancer, an idea that goes back all the way to Boveri's first proposition, in 1902, of chromosomal changes as the root cause of cancer (5). But it was only in the 1970s when the evidence became irrefutable that cancer was primarily a genetic disease caused by cycles of mutation, selection and new mutations, a process that can eventually result in a metastatic tumor (6). Indeed, the reason that cancer is able to survive treatment so often is because of its enormous capacity to generate mutational heterogeneity, providing a sheer infinite range of strategies to escape both natural defenses, such as the immune system, and almost any kind of therapy. Other than for cancer, an age-related disease, a causal role for various types of mutations in the many degenerative processes associated with aging is much less certain. This is mostly due to the fact that mutations in normal, non-clonal tissue are very difficult to measure. Nevertheless, it is conceptually possible that mutation accumulation, apart from increasing cancer risk, would gradually lead to loss of cell function with age (Fig. 1). Here we will briefly describe the mechanisms that lead to genome instability and then recapitulate the evidence for and against the various mutational events that comprise genome instability to act as a major cause of aging.
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DNA damage as a driver of genome instability
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Genomes are unstable because of the continuous induction of damage to DNA, mostly from endogenous sources. It should be noted that DNA damage is not the same as DNA mutations. DNA damage involves physical alterations in nucleic acid structure, e.g., breaks, depurination, depyrimidination, crosslinks, modified bases, while mutations arise as errors during repair or replication of DNA damage and sometimes spontaneously. Mutations are additions, deletions or substitutions of bits of genetic code, including chromosomal aberrations, transpositions and copy number variation. Together they are part of the phenomenon of genome instability. While DNA damage can be (and usually is) successfully repaired, DNA mutations cannot be recognized by repair enzymes, are irreversible and can only be removed through the death of the cell or the entire organism. Of note, accumulation of mutations with age is unavoidable and not dependent on a possible age-related decline in DNA repair capacity. In spite of the fact that as many as 100,000 lesions are thought to occur daily in each somatic cell (7), the steady state levels of the various types of DNA damage in cells and tissues is extremely low due to an extensive network of DNA repair mechanisms (8). Nevertheless, it is possible that DNA damage itself contributes to aging through impairment of transcription (9) and/or the induction of cellular responses, such as apoptosis and cellular senescence (10). However, as has been argued elsewhere in considerable detail (11), because DNA damage itself is reversible its molecular end points, i.e., the various types of mutations that collectively comprise genome instability (Fig. 1), may be more important causal contributors to aging, as they are in cancer. Results from multiple laboratories including our own have provided conclusive evidence that genomes in somatic tissues gradually lose their integrity during aging by accumulating mutations (12-17), a process that is delayed by dietary restriction (18, 19). Importantly, methods are available for quite some time to measure various types of DNA mutations in the genome, which is in striking contrast to spontaneous DNA damage, which is very difficult to measure (20). Nevertheless, also for genome instability truly quantitative measures, which would allow accurately predicting functional impacts of age-related increases in cellular mutation loads, are emerging only slowly. Below we discuss several types of genome instability in this context. Chromosomal alterations The simplest types of mutations in terms of available methodology suitable for their detection are chromosomal alterations. For example, G-banding enabled the analysis of chromosomal aneuploidy since the early 1900s (21). More recently, the method of choice for identifying and enumerating such events is fluorescence in situ hybridization (FISH), using locus specific probes or chromosome painting probes (22, 23) which provides much higher resolution than classical cytogenetic methods. Using FISH it has been confirmed that the frequency of lymphocytes with
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chromosomal aberrations in leukemia-free subjects increase with age in the blood of both humans and mice (12, 24). Interphase FISH has been applied to the analysis of post-mitotic, nondividing cells in tissues such as brain (25) for the detection of aneuploidy, i.e., gain or loss of entire chromosomes. Using interphase FISH it has been shown that in the developing nervous system of humans and mice the frequency of aneuploid cells is as high as 33% for all chromosomes combined (Fig. 2) (26, 27). However, after completion of development this extremely high level of aneuploidy was found to be significantly reduced suggesting selection against cells with high levels of genome instability. Yet, even in adult organisms, aneuploidy has been detected at levels as high as 4% per chromosome, as observed in human brain for chromosome 21 (28). Using interphase FISH it has also been demonstrated that human and murine hepatocytes are highly aneuploid at a frequency of about 3.7% per chromosome (Fig. 3) (29, 30). When extrapolated to all chromosomes combined this would mean that almost all cells in an organ such as brain or liver would be aneuploid for one chromosome. In mice, using a two-probe system, with aneuploidy only concluded when numerical change was indicated at both locations, we found that in the cerebral cortex of aging mice the frequency of aneuploid cells can rise to a level as high as 5% per chromosome (31). Overall, this would correspond to about half of all brain cells in old mice having at least one chromosomal aneuploidy. Taking into consideration that aneuploidy is merely the tip of the iceberg, genome instability in cells during development and aging may be very high. It should be noticed that more recent single-cell sequencing results do not show high levels of aneuploidy in the human brain (32, 33). Indeed, it has been argued that FISH overestimates aneuploidy frequency and that hybridization artifacts could easily account for 1-5% of cells showing numerical change, suggesting high overall levels of aneuploidy while the true frequency is not higher than about 5% of cells aneuploid for at least one chromosome (33). However, while it is true that interphase FISH is prone to artifacts due to suboptimal hybridization conditions, the use of dual probes targeting the same chromosome, as mentioned above (31), still indicated up to 5% of nuclei in mouse brain to be aneuploid. It is difficult to see how this can be due to coincidental hybridization artifacts indicating gain or loss for both probes. Indeed, single cell sequencing in detecting aneuploidy has drawbacks too. For example, this method requires whole genome amplification and overamplification would obscure aneuploidies, resulting in a significant underestimate. Nevertheless, even levels of 5% of cells aneuploid for at least one chromosome is still a surprisingly high level of instability, especially in combination with many other types of mutations which we will discuss below. Genome structural variation Very large genome structural variation, i.e., at the chromosomal level, can be detected using cytogenetic methods as discussed above. As we have seen, these events, which include aneuploidy and segmented chromosomal aberrations, do show an increase with age in blood of both humans and mice (12, 24), and aneuploidy even increases in the mouse brain (31). For this type of events it is clear,
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also from data generated using classical cytogenetics rather than FISH, that approximately 5% of cells in aged humans or mice have at least one stable chromosomal translocation (24) or chromosome numerical change. However, the resolution of FISH is in the Mb range and insufficient to detect smaller genome structural variation (SV) events, which is likely the vast majority. As yet, due to a lack of suitable methods, it is unclear what the total number of SVs in an average normal cell is, including fairly small deletions, i.e., down to a Kb or less. In the past we have used mouse models harboring a bacteriophage lambda or plasmids with the selectable marker gene lacZ stably integrated in the genome. By excising these constructs from tissue genomic DNA and recovering them in E. coli it was possible to select for mutational inactivation of the lacZ gene and characterize the mutation, including breakpoints associated with genome structural variation. We extrapolated the frequency of such break points to the genome overall and concluded that the average cell in a tissue from an aged mouse can contain from 1080 SVs (34). These SVs could be characterized as deletions, many fairly close to the lacZ target gene, as well as chromosomal translocations and much larger deletions. However, these kinds of estimates rely on the integrated bacterial construct to be representative for the endogenous mouse genome and could be substantially off. Moreover, copy number variation cannot be detected by the lacZ reporter system. Microarray-based comparative genomic hybridization (array CGH) and single-nucleotide polymorphism (SNP) arrays, allow testing for dosage-variant DNA Copy Number Variations (CNVs), a subtype of SVs. This has been done in tumors, in which mutational events are clonally expanded and detectable in bulk tumor DNA, but generally not in normal somatic cells, which would require single cell analysis. In normal, non-clonal somatic cell populations mutations are difficult to measure because of their random and infrequent occurrence. Analyzing DNA from bulk tissue for such low-abundant mutations is only possible when the mutational event is present in a substantial fraction of the cell population, e.g., through clonal expansion. Interestingly, an age-related accumulation of clonally expanded, copy number variants has been found (35, 36) in cancer-free blood and buccal cells from humans. While this conclusively demonstrated the occurrence of this type of genome instability with age, no quantitative information as to the total number of such events in a single cell of an aged organism can be derived from it. While single-cell array CGH methods have been developed, application of these methods has generally been limited to tumor cells. Using a substantially optimized procedure with a limit of resolution of 0.1 Mb, about 40 CNVs were detected in single breast cancer cells (37). To our knowledge, these methods have not yet been applied in studying normal cells during aging. More recently, CNVs and other genome structural variation have been detected by single cell sequencing, most notably in human brain. Single-cell sequencing of neurons from human frontal cortex showed that 13 to 41% of neurons contain at least one megabase-scale de novo CNV, while a subset of neurons displayed highly aberrant genomes with multiple alterations (38). Another study reported a lower frequency of neurons harboring CNVs, i.e., less than 5% (39). That study also reported clonally inherited CNVs indicating that they arose during neurogenesis. Hence, these studies suggest that like chromosomal aneuploidy CNVs
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do occur in the normal adult brain, probably at a frequency of at least 5%. No aging studies have as yet been done for this kind of analyses. Array CGH methods or low-coverage sequencing procedures are not capable of detecting dosage-invariant genome structural variation. Recently next-generation sequencing methods have been applied to identify SVs in tumors, based on breakpoint detection using discordant read pairs (40). Using these methods up to about 600 SVs (varying from deletions, inversions tandem duplications and interchromosomal translocations) were detected in tumors (41). But similar to the problem with CNVs, to detect de novo, somatic SVs spread at low abundance across normal somatic cell populations, single-cell assays are needed. Unlike the detection of CNVs, which is based on read depth after next-generation sequencing, the detection of SVs based on breakpoints is hampered by very high backgrounds. Indeed, such breakpoints can arise during single-cell whole genome amplification and/or library preparation. For this reason we do not yet know if SVs in normal cells are substantial and/or increase with age and the previously mentioned lacZ reporter data in the mouse remain the only evidence that such events are not infrequent in vivo. Other types of mutations, related to SVs, that have been studied include somatic LINE-1 (L1) retrotranspositions, which are suppressed at young age but have been shown to strongly increase with age in several mouse tissues (42). Using single-cell retrotransposon capture sequencing of human neurons, Upton et al (43) reported about 14 somatic L1 insertions per hippocampal neuron. However, these results were not reproduced by another study reporting less than one somatic L1 insertion per human neuron in cortex and caudate. As yet it is unclear if this discrepancy is due to a difference between hippocampus and cortex/caudate. In conclusion, no quantitative estimates for SVs, including CNVs and retrotranspositions, in normal tissues in relation to aging are available and even quantitative data on tumors can vary widely. However, when taking all data together the most conservative estimate is that at least 5% of normal cells in adults harbors at least one SV, CNV or L1 insertion. At old age this is probably higher. Base substitution mutations
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Like other somatic mutations in normal, non-clonal tissues, detecting base substitution mutations requires single cell technology. In turn this necessitates whole genome amplification, which is notorious for its many false positive base substitutions (44). We recently developed a protocol, including computational analysis, allowing to accurately determine the complete base substitution spectrum in a given cell; the protocol was validated by comparing mutation frequencies and spectra with unamplified clones grown from the same primary fibroblast population (45). This assay, for the first time, allowed an accurate estimate of the total number of mutations in primary somatic cells. The results indicate about 1000 somatic mutations in single human primary fibroblasts, in the same range as the numbers found in unamplified clones from the same population of cells. More recently, whole genome sequencing of clonal organoid cultures derived from mouse or human
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primary multipotent cells revealed base substitution mutations per genome in the same range and increasing with age (46, 47). While these numbers are quite high, most base substitutions have no functional consequence. Indeed, unlike aneuploidy, CNVs or SVs, in which one event can affect expression level of many genes, even 1000 base substitutions may have no functional consequences. Preliminary results show that while most mutations in individual fibroblasts are located in what most likely are non-functional regions of the genome, some occur in the exome and over 100 in regions that correspond to transcription factor binding sites (X. Dong, L. Zhang, unpublished). Hence, unlike the situation for SVs we now have reliable quantitative methods to study base substitution mutations in cells from aging organisms. Unfortunately, this type of mutations is the least likely to have a major adverse effect as a cause of aging. Conclusions: functional consequences of genome instability
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Driven by advances in genome technology we have now reached a stage where for the first time it has become possible to directly test one of the oldest theories of aging: the somatic mutation theory. Thus far, even the most conservative estimates indicate that at least 5% of somatic cells are aneuploid for at least one chromosome and about the same frequency of cells carry at least one stable chromosomal aberration. About the same percentage of cells, and possibly more, appear to suffer from large SVs, including CNVs and retrotransposon insertion. Assuming those are independent events, together almost 15% of cells would carry at least one large genomic mutation likely to have a major impact. Indeed, while we lack detailed information about the effects of genomic instability on the transcriptome, it seems highly likely that multiple genes would suffer from dose effects with such large mutational events present. As we have seen, we know much less about smaller genome structural variation that are not CNVs or retrotranspositions, i.e., probably mostly deletions affecting less than several Kb of sequence, but it seems highly unlikely that not all cells throughout the life span will at least accumulate several of those events. Taken together, and also including the several thousand base substitution mutations in most somatic cells at old age (46), this would still represent a substantial mutation load for an average cell and may have adverse effects on cell function that contribute to aging. It is possible that genome instability clusters in certain cells and that overall the cells in an average tissue remain relatively mutation-free. This requires more investigation at the single cell level. Importantly, the distributed functional organization of the genome and the high level of integration of the many sequence features that encode specific cellular functions provide it with robustness and a very high level of redundancy. Initially, therefore, genomes can tolerate fairly high levels of mutation. However, at some point this type of genome functional organization will begin to amplify effects of multiple random mutations. It is possible, therefore, that even at a linear increase of somatic mutations with age, their effects on health and mortality would be exponential, following Gompertz kinetics (48). In this respect somatic mutation
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accumulation could explain aging very well. Nevertheless, it seem highly unlikely that aging has a single cause. Other factors, such as the aforementioned effects of DNA damage, such as transcription interference and adverse cellular responses to DNA damage, as well as changes at the protein level, are likely to also play major roles as cell autonomous mechanisms of aging.
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Acknowledgements
Our research is supported by grants from the US National Institutes of Health (AG017242, CA180126, AG047200, AG038072) and the Glenn Foundation for Medical Research.
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Legends to the figures
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Figure 1: Schematic flow chart depicting how DNA damage could drive aging. DNA damage induced by a variety of endogenous and exogenous factors is mostly repaired. Occasionally errors are made, either during the repair of a lesion, when the cell replicates DNA containing lesions or during chromosomal segregation. Errors could also occur spontaneously, without DNA damage. Collectively, these mutational events represents genome instability. A direct consequence of genomic instability is cell cycle stress, changes in gene expression and changes in gene regulation. Ultimately, this could explain age-related cellular degeneration and functional decay. The ultimate outcome of genomic instability is aging, cancer and degenerative disease.
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Figure 2: Probe design in FISH analysis of aneuploidy. (A) Two differently labeled BAC clones (spectrum green and spectrum orange) mapping to mouse chromosome 7 at two distinct genomic loci (proximal and distal to the centromere) enable to distinguish diploid from aneuploid cells. (B) Schematic representation of the FISH hybridization results in metaphase chromosome preparations (left) and interphase cells (right). (C) Two-color interphase FISH: the identification of whole chromosome loss or gain is determined by the numerical correspondence between the two colors. Representative hybridizations of mouse cortical nuclei are shown. Left panel shows a nucleus with two copies for MMU7 (2n) and right panel shows an aneuploid nucleus that contains three copies (gain). Adapted from (49). Figure 3: FISH analysis for the detection of aneuploidy in hepatocytes. (A) Representative image of liver hepatocytes isolated from a 4-month adult mouse hybridized with a chromosome paint probe specific for chromosome Y (red) and a locus-specific probe specific for chromosome X (green). (B) Schematic representation of the hybridization signal present in A. References 1. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194-217.
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S2468-5011(17)30010-X
DOI:
10.1016/j.tma.2017.09.003
Reference:
TMA 6
To appear in:
Translational Medicine of Aging
Received Date: 21 July 2017 Revised Date:
18 September 2017
Accepted Date: 18 September 2017
Please cite this article as: J. Vijg, C. Montagna, Genome instability and aging: cause or effect?, Translational Medicine of Aging (2017), doi: 10.1016/j.tma.2017.09.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Genome instability and aging: cause or effect? Jan Vijg, Cristina Montagna, Department of Genetics, Albert Einstein College of Medicine, Michael F. Price Center, 1301 Morris Park Avenue, Bronx, NY 10461, USA
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*Correspondence: [email protected] [email protected]
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Abstract. Genome instability, i.e., the tendency of the genome to undergo alterations in DNA information content through mutation, is considered a hallmark of aging. While mutations can be analyzed in clonal lineages, such as tumors, normal tissues have thus far not been amenable to mutation analysis except for the largest type of mutations: chromosomal aberrations. This is because mutations are random events and, therefore, unique to a single cell. New, single-cell sequencing-based methods are now emerging and may soon provide quantitative assays for estimating the possible functional effects of mutations accumulating during aging in various tissues and organs. Here we briefly review the mechanisms of genome instability in normal cells, the accumulation of various types of genome instability with age and their possible physiological consequences. Introduction
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Genome instability is considered one of the hallmarks of aging (1). Genome instability refers to a range of DNA alterations, from point mutations and deletions and insertions to chromosomal rearrangements and whole chromosome numerical changes, which irreversibly change the information content of the genome. Already in the 1950s Failla (2) and Szilard (3) independently proposed that mutations, then realized to occur spontaneously as a consequence of, for example, cosmic radiation, would cause aging by randomly inactivating genes. Failla (2), as well as Osgood (4), also considered mutations as the primary cause of cancer, an idea that goes back all the way to Boveri's first proposition, in 1902, of chromosomal changes as the root cause of cancer (5). But it was only in the 1970s when the evidence became irrefutable that cancer was primarily a genetic disease caused by cycles of mutation, selection and new mutations, a process that can eventually result in a metastatic tumor (6). Indeed, the reason that cancer is able to survive treatment so often is because of its enormous capacity to generate mutational heterogeneity, providing a sheer infinite range of strategies to escape both natural defenses, such as the immune system, and almost any kind of therapy. Other than for cancer, an age-related disease, a causal role for various types of mutations in the many degenerative processes associated with aging is much less certain. This is mostly due to the fact that mutations in normal, non-clonal tissue are very difficult to measure. Nevertheless, it is conceptually possible that mutation accumulation, apart from increasing cancer risk, would gradually lead to loss of cell function with age (Fig. 1). Here we will briefly describe the mechanisms that lead to genome instability and then recapitulate the evidence for and against the various mutational events that comprise genome instability to act as a major cause of aging.
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DNA damage as a driver of genome instability
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Genomes are unstable because of the continuous induction of damage to DNA, mostly from endogenous sources. It should be noted that DNA damage is not the same as DNA mutations. DNA damage involves physical alterations in nucleic acid structure, e.g., breaks, depurination, depyrimidination, crosslinks, modified bases, while mutations arise as errors during repair or replication of DNA damage and sometimes spontaneously. Mutations are additions, deletions or substitutions of bits of genetic code, including chromosomal aberrations, transpositions and copy number variation. Together they are part of the phenomenon of genome instability. While DNA damage can be (and usually is) successfully repaired, DNA mutations cannot be recognized by repair enzymes, are irreversible and can only be removed through the death of the cell or the entire organism. Of note, accumulation of mutations with age is unavoidable and not dependent on a possible age-related decline in DNA repair capacity. In spite of the fact that as many as 100,000 lesions are thought to occur daily in each somatic cell (7), the steady state levels of the various types of DNA damage in cells and tissues is extremely low due to an extensive network of DNA repair mechanisms (8). Nevertheless, it is possible that DNA damage itself contributes to aging through impairment of transcription (9) and/or the induction of cellular responses, such as apoptosis and cellular senescence (10). However, as has been argued elsewhere in considerable detail (11), because DNA damage itself is reversible its molecular end points, i.e., the various types of mutations that collectively comprise genome instability (Fig. 1), may be more important causal contributors to aging, as they are in cancer. Results from multiple laboratories including our own have provided conclusive evidence that genomes in somatic tissues gradually lose their integrity during aging by accumulating mutations (12-17), a process that is delayed by dietary restriction (18, 19). Importantly, methods are available for quite some time to measure various types of DNA mutations in the genome, which is in striking contrast to spontaneous DNA damage, which is very difficult to measure (20). Nevertheless, also for genome instability truly quantitative measures, which would allow accurately predicting functional impacts of age-related increases in cellular mutation loads, are emerging only slowly. Below we discuss several types of genome instability in this context. Chromosomal alterations The simplest types of mutations in terms of available methodology suitable for their detection are chromosomal alterations. For example, G-banding enabled the analysis of chromosomal aneuploidy since the early 1900s (21). More recently, the method of choice for identifying and enumerating such events is fluorescence in situ hybridization (FISH), using locus specific probes or chromosome painting probes (22, 23) which provides much higher resolution than classical cytogenetic methods. Using FISH it has been confirmed that the frequency of lymphocytes with
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chromosomal aberrations in leukemia-free subjects increase with age in the blood of both humans and mice (12, 24). Interphase FISH has been applied to the analysis of post-mitotic, nondividing cells in tissues such as brain (25) for the detection of aneuploidy, i.e., gain or loss of entire chromosomes. Using interphase FISH it has been shown that in the developing nervous system of humans and mice the frequency of aneuploid cells is as high as 33% for all chromosomes combined (Fig. 2) (26, 27). However, after completion of development this extremely high level of aneuploidy was found to be significantly reduced suggesting selection against cells with high levels of genome instability. Yet, even in adult organisms, aneuploidy has been detected at levels as high as 4% per chromosome, as observed in human brain for chromosome 21 (28). Using interphase FISH it has also been demonstrated that human and murine hepatocytes are highly aneuploid at a frequency of about 3.7% per chromosome (Fig. 3) (29, 30). When extrapolated to all chromosomes combined this would mean that almost all cells in an organ such as brain or liver would be aneuploid for one chromosome. In mice, using a two-probe system, with aneuploidy only concluded when numerical change was indicated at both locations, we found that in the cerebral cortex of aging mice the frequency of aneuploid cells can rise to a level as high as 5% per chromosome (31). Overall, this would correspond to about half of all brain cells in old mice having at least one chromosomal aneuploidy. Taking into consideration that aneuploidy is merely the tip of the iceberg, genome instability in cells during development and aging may be very high. It should be noticed that more recent single-cell sequencing results do not show high levels of aneuploidy in the human brain (32, 33). Indeed, it has been argued that FISH overestimates aneuploidy frequency and that hybridization artifacts could easily account for 1-5% of cells showing numerical change, suggesting high overall levels of aneuploidy while the true frequency is not higher than about 5% of cells aneuploid for at least one chromosome (33). However, while it is true that interphase FISH is prone to artifacts due to suboptimal hybridization conditions, the use of dual probes targeting the same chromosome, as mentioned above (31), still indicated up to 5% of nuclei in mouse brain to be aneuploid. It is difficult to see how this can be due to coincidental hybridization artifacts indicating gain or loss for both probes. Indeed, single cell sequencing in detecting aneuploidy has drawbacks too. For example, this method requires whole genome amplification and overamplification would obscure aneuploidies, resulting in a significant underestimate. Nevertheless, even levels of 5% of cells aneuploid for at least one chromosome is still a surprisingly high level of instability, especially in combination with many other types of mutations which we will discuss below. Genome structural variation Very large genome structural variation, i.e., at the chromosomal level, can be detected using cytogenetic methods as discussed above. As we have seen, these events, which include aneuploidy and segmented chromosomal aberrations, do show an increase with age in blood of both humans and mice (12, 24), and aneuploidy even increases in the mouse brain (31). For this type of events it is clear,
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also from data generated using classical cytogenetics rather than FISH, that approximately 5% of cells in aged humans or mice have at least one stable chromosomal translocation (24) or chromosome numerical change. However, the resolution of FISH is in the Mb range and insufficient to detect smaller genome structural variation (SV) events, which is likely the vast majority. As yet, due to a lack of suitable methods, it is unclear what the total number of SVs in an average normal cell is, including fairly small deletions, i.e., down to a Kb or less. In the past we have used mouse models harboring a bacteriophage lambda or plasmids with the selectable marker gene lacZ stably integrated in the genome. By excising these constructs from tissue genomic DNA and recovering them in E. coli it was possible to select for mutational inactivation of the lacZ gene and characterize the mutation, including breakpoints associated with genome structural variation. We extrapolated the frequency of such break points to the genome overall and concluded that the average cell in a tissue from an aged mouse can contain from 1080 SVs (34). These SVs could be characterized as deletions, many fairly close to the lacZ target gene, as well as chromosomal translocations and much larger deletions. However, these kinds of estimates rely on the integrated bacterial construct to be representative for the endogenous mouse genome and could be substantially off. Moreover, copy number variation cannot be detected by the lacZ reporter system. Microarray-based comparative genomic hybridization (array CGH) and single-nucleotide polymorphism (SNP) arrays, allow testing for dosage-variant DNA Copy Number Variations (CNVs), a subtype of SVs. This has been done in tumors, in which mutational events are clonally expanded and detectable in bulk tumor DNA, but generally not in normal somatic cells, which would require single cell analysis. In normal, non-clonal somatic cell populations mutations are difficult to measure because of their random and infrequent occurrence. Analyzing DNA from bulk tissue for such low-abundant mutations is only possible when the mutational event is present in a substantial fraction of the cell population, e.g., through clonal expansion. Interestingly, an age-related accumulation of clonally expanded, copy number variants has been found (35, 36) in cancer-free blood and buccal cells from humans. While this conclusively demonstrated the occurrence of this type of genome instability with age, no quantitative information as to the total number of such events in a single cell of an aged organism can be derived from it. While single-cell array CGH methods have been developed, application of these methods has generally been limited to tumor cells. Using a substantially optimized procedure with a limit of resolution of 0.1 Mb, about 40 CNVs were detected in single breast cancer cells (37). To our knowledge, these methods have not yet been applied in studying normal cells during aging. More recently, CNVs and other genome structural variation have been detected by single cell sequencing, most notably in human brain. Single-cell sequencing of neurons from human frontal cortex showed that 13 to 41% of neurons contain at least one megabase-scale de novo CNV, while a subset of neurons displayed highly aberrant genomes with multiple alterations (38). Another study reported a lower frequency of neurons harboring CNVs, i.e., less than 5% (39). That study also reported clonally inherited CNVs indicating that they arose during neurogenesis. Hence, these studies suggest that like chromosomal aneuploidy CNVs
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do occur in the normal adult brain, probably at a frequency of at least 5%. No aging studies have as yet been done for this kind of analyses. Array CGH methods or low-coverage sequencing procedures are not capable of detecting dosage-invariant genome structural variation. Recently next-generation sequencing methods have been applied to identify SVs in tumors, based on breakpoint detection using discordant read pairs (40). Using these methods up to about 600 SVs (varying from deletions, inversions tandem duplications and interchromosomal translocations) were detected in tumors (41). But similar to the problem with CNVs, to detect de novo, somatic SVs spread at low abundance across normal somatic cell populations, single-cell assays are needed. Unlike the detection of CNVs, which is based on read depth after next-generation sequencing, the detection of SVs based on breakpoints is hampered by very high backgrounds. Indeed, such breakpoints can arise during single-cell whole genome amplification and/or library preparation. For this reason we do not yet know if SVs in normal cells are substantial and/or increase with age and the previously mentioned lacZ reporter data in the mouse remain the only evidence that such events are not infrequent in vivo. Other types of mutations, related to SVs, that have been studied include somatic LINE-1 (L1) retrotranspositions, which are suppressed at young age but have been shown to strongly increase with age in several mouse tissues (42). Using single-cell retrotransposon capture sequencing of human neurons, Upton et al (43) reported about 14 somatic L1 insertions per hippocampal neuron. However, these results were not reproduced by another study reporting less than one somatic L1 insertion per human neuron in cortex and caudate. As yet it is unclear if this discrepancy is due to a difference between hippocampus and cortex/caudate. In conclusion, no quantitative estimates for SVs, including CNVs and retrotranspositions, in normal tissues in relation to aging are available and even quantitative data on tumors can vary widely. However, when taking all data together the most conservative estimate is that at least 5% of normal cells in adults harbors at least one SV, CNV or L1 insertion. At old age this is probably higher. Base substitution mutations
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Like other somatic mutations in normal, non-clonal tissues, detecting base substitution mutations requires single cell technology. In turn this necessitates whole genome amplification, which is notorious for its many false positive base substitutions (44). We recently developed a protocol, including computational analysis, allowing to accurately determine the complete base substitution spectrum in a given cell; the protocol was validated by comparing mutation frequencies and spectra with unamplified clones grown from the same primary fibroblast population (45). This assay, for the first time, allowed an accurate estimate of the total number of mutations in primary somatic cells. The results indicate about 1000 somatic mutations in single human primary fibroblasts, in the same range as the numbers found in unamplified clones from the same population of cells. More recently, whole genome sequencing of clonal organoid cultures derived from mouse or human
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primary multipotent cells revealed base substitution mutations per genome in the same range and increasing with age (46, 47). While these numbers are quite high, most base substitutions have no functional consequence. Indeed, unlike aneuploidy, CNVs or SVs, in which one event can affect expression level of many genes, even 1000 base substitutions may have no functional consequences. Preliminary results show that while most mutations in individual fibroblasts are located in what most likely are non-functional regions of the genome, some occur in the exome and over 100 in regions that correspond to transcription factor binding sites (X. Dong, L. Zhang, unpublished). Hence, unlike the situation for SVs we now have reliable quantitative methods to study base substitution mutations in cells from aging organisms. Unfortunately, this type of mutations is the least likely to have a major adverse effect as a cause of aging. Conclusions: functional consequences of genome instability
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Driven by advances in genome technology we have now reached a stage where for the first time it has become possible to directly test one of the oldest theories of aging: the somatic mutation theory. Thus far, even the most conservative estimates indicate that at least 5% of somatic cells are aneuploid for at least one chromosome and about the same frequency of cells carry at least one stable chromosomal aberration. About the same percentage of cells, and possibly more, appear to suffer from large SVs, including CNVs and retrotransposon insertion. Assuming those are independent events, together almost 15% of cells would carry at least one large genomic mutation likely to have a major impact. Indeed, while we lack detailed information about the effects of genomic instability on the transcriptome, it seems highly likely that multiple genes would suffer from dose effects with such large mutational events present. As we have seen, we know much less about smaller genome structural variation that are not CNVs or retrotranspositions, i.e., probably mostly deletions affecting less than several Kb of sequence, but it seems highly unlikely that not all cells throughout the life span will at least accumulate several of those events. Taken together, and also including the several thousand base substitution mutations in most somatic cells at old age (46), this would still represent a substantial mutation load for an average cell and may have adverse effects on cell function that contribute to aging. It is possible that genome instability clusters in certain cells and that overall the cells in an average tissue remain relatively mutation-free. This requires more investigation at the single cell level. Importantly, the distributed functional organization of the genome and the high level of integration of the many sequence features that encode specific cellular functions provide it with robustness and a very high level of redundancy. Initially, therefore, genomes can tolerate fairly high levels of mutation. However, at some point this type of genome functional organization will begin to amplify effects of multiple random mutations. It is possible, therefore, that even at a linear increase of somatic mutations with age, their effects on health and mortality would be exponential, following Gompertz kinetics (48). In this respect somatic mutation
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accumulation could explain aging very well. Nevertheless, it seem highly unlikely that aging has a single cause. Other factors, such as the aforementioned effects of DNA damage, such as transcription interference and adverse cellular responses to DNA damage, as well as changes at the protein level, are likely to also play major roles as cell autonomous mechanisms of aging.
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Acknowledgements
Our research is supported by grants from the US National Institutes of Health (AG017242, CA180126, AG047200, AG038072) and the Glenn Foundation for Medical Research.
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Legends to the figures
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Figure 1: Schematic flow chart depicting how DNA damage could drive aging. DNA damage induced by a variety of endogenous and exogenous factors is mostly repaired. Occasionally errors are made, either during the repair of a lesion, when the cell replicates DNA containing lesions or during chromosomal segregation. Errors could also occur spontaneously, without DNA damage. Collectively, these mutational events represents genome instability. A direct consequence of genomic instability is cell cycle stress, changes in gene expression and changes in gene regulation. Ultimately, this could explain age-related cellular degeneration and functional decay. The ultimate outcome of genomic instability is aging, cancer and degenerative disease.
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Figure 2: Probe design in FISH analysis of aneuploidy. (A) Two differently labeled BAC clones (spectrum green and spectrum orange) mapping to mouse chromosome 7 at two distinct genomic loci (proximal and distal to the centromere) enable to distinguish diploid from aneuploid cells. (B) Schematic representation of the FISH hybridization results in metaphase chromosome preparations (left) and interphase cells (right). (C) Two-color interphase FISH: the identification of whole chromosome loss or gain is determined by the numerical correspondence between the two colors. Representative hybridizations of mouse cortical nuclei are shown. Left panel shows a nucleus with two copies for MMU7 (2n) and right panel shows an aneuploid nucleus that contains three copies (gain). Adapted from (49). Figure 3: FISH analysis for the detection of aneuploidy in hepatocytes. (A) Representative image of liver hepatocytes isolated from a 4-month adult mouse hybridized with a chromosome paint probe specific for chromosome Y (red) and a locus-specific probe specific for chromosome X (green). (B) Schematic representation of the hybridization signal present in A. References 1. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194-217.
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