Genetic instability is a hallmark of aging (López-Otín et al., 2013). A mechanistic link between somatic mutations and age-related diseases such as cancer is clear, but their importance in other aging phenotypes, long hypothesized, is poorly understood (Zhang and Vijg, 2018). Recent surveys of non-diseased somatic tissues have shown that mutations are pervasive in the nuclear genome (nDNA), increase with age, and vary considerably between tissues (Abascal et al., 2021; Li et al., 2021). Additionally, these nDNA mutations commonly occur in cancer-associated genes, show evidence of selection and clonal expansion, and may play important roles in tissue regeneration and tumor suppression (Colom et al., 2020; Martincorena et al., 2018; Martincorena et al., 2017; Martincorena et al., 2015; Zhu et al., 2019). Collectively, these studies indicate a growing realization that somatic mutagenesis and clonal dynamics are likely an important determinant of human health during aging. While the accumulation of somatic mutations in the mitochondrial genome (mtDNA) with age has long been documented, the specific nature of their occurrence, and the consequences for aging, have remained unclear (reviewed in Sanchez-Contreras and Kennedy, 2022).

In vertebrates, mtDNA is a maternally inherited ~16–17 kb circular DNA molecule encoding 37 genes: 13 essential polypeptides of the electron transport chain (ETC), two ribosomal RNA genes, and 22 tRNAs. Mitochondria are involved in a broad range of crucial processes, including ATP generation via oxidative phosphorylation (OXPHOS), calcium homeostasis, iron-sulfur cluster biogenesis, regulation of apoptosis, and the biosynthesis of a wide variety of small molecules (Kowaltowski, 2000). These processes rely on mitochondria such that disruption of the genetic information encoded in mtDNA by mutation leads to dysfunction of these important processes and subsequently induces disease (Wallace, 1999). Unlike nDNA, mtDNA replication is largely independent of the cell cycle. The higher level of mtDNA replication, the absence of several cellular DNA repair pathways, and the lack of protection from histones results in mtDNA mutation rates ~100–1000× higher than that of nDNA (Khrapko et al., 1997; Marcelino and Thilly, 1999). Moreover, due to the coding density of mtDNA being higher than nDNA (~91% vs. ~1%), the probability that a mutation disrupts protein function is greater.

Observational studies have shown that the genetic instability of mtDNA in somatic cells is a fundamental phenotype of aging and may be involved in the pathogenesis of several diseases (reviewed in Larsson, 2010). Collectively, studies examining endogenous mtDNA mutations have shown low levels of G→T/C→A mutations and a preponderance of G→A/C→T and T→C/A→G transitions. This has been interpreted as being contrary to free radical theories of aging by suggesting that reactive oxygen species (ROS) are not the primary driver of mutagenesis in mtDNA (Arbeithuber et al., 2020; Ju et al., 2014; Kennedy et al., 2013; Williams et al., 2013; Zheng et al., 2006). Other notable patterns include an over-abundance of mutations in the mitochondrial Control Region (mCR), an unusual strand bias, a mutational gradient in transition mutations, and a unique trinucleotide mutational signature (Ju et al., 2014; Kennedy et al., 2013; Sanchez-Contreras et al., 2021; Wei et al., 2019). However, while the presence of somatic mtDNA mutations is well documented, a clear causative role in aging remains controversial (reviewed in Sanchez-Contreras and Kennedy, 2022).

One reason for this controversy stems from a poor understanding of when, where, and how somatic mtDNA mutations arise during the normal aging process. Most conclusions regarding the accumulation of mtDNA mutations during aging are based on a limited number of experimental models and tissue types, with data largely focused on brain and muscle due to their perceived sensitivity to mitochondrial dysfunction. Only a small number of pan-tissue surveys have been performed (Li et al., 2021; Ma et al., 2018; Samuels et al., 2013). Importantly, most of these prior studies made use of either ‘clone and sequence’ or conventional next-generation sequencing (NGS) to detect mutations. These approaches are technically limited in their ability to detect heteroplasmy below a variant allele fraction (VAF) of 1–2% (reviewed in Salk et al., 2018). The advent of ultrahigh-accuracy sequencing methods has shown that most heteroplasmies are present far below this analytical threshold (Arbeithuber et al., 2020; Kennedy et al., 2013). As such, determining the burden of somatic mtDNA mutations in the context of normal aging lags well behind the efforts focused on nDNA. This is especially pertinent given the heterogeneous nature of tissue decline during aging.

Like the nDNA, somatic mutations in mtDNA have been proposed to be under selection (Suen et al., 2010). Cells have evolved several mitochondrial quality control pathways such as removal of damaged mitochondria by mitophagy and fusion/fission to maintain a healthy mitochondrial pool (Youle and Narendra, 2011). The formation and expression of deleterious mtDNA mutations is hypothesized to lead to a loss of mitochondrial membrane potential, mitochondrial dysfunction, and induction of mitophagy. This is a potential mechanism by which cells prevent mtDNA mutations from reaching a phenotypic threshold capable of altering cell homeostasis (Rossignol et al., 2003; Rossignol et al., 1999). Evidence for involvement of quality control machinery in removing somatic mtDNA mutations has been contradictory, with some indicating a clear role for mitophagy and fission/fusion, while other evidence indicates no effect (Chen et al., 2010; Chen et al., 2015; Pickrell et al., 2015; Suen et al., 2010). Thus, the role, if any, of the mitochondrial quality control pathways in targeting mtDNA mutations for removal remains unclear.

We and others have previously identified a mitochondrially targeted synthetic peptide, elamipretide (Elam; previously referred to as SS-31 and Bendavia), and the NADH precursor nicotinamide mononucleotide (NMN) as interventions that restore mitochondrial function and tissue homeostasis late in life (reviewed in Yoshino et al., 2018, and Obi et al., 2022). The specific mechanism(s) by which these two compounds ameliorate age-related mitochondrial dysfunction differ. Elam interacts directly with the inner mitochondrial membrane and membrane-associated proteins, stabilizing the mitochondrial ultrastructure and influencing cardiolipin-dependent protein interactions to improve ETC function leading to reduced oxidant production, preservation of membrane potential, and enhanced ATP production (Campbell et al., 2019; Mitchell et al., 2020; Zhang et al., 2020). In contrast, NMN is an NAD+ precursor molecule and acts by elevating NAD+ levels and providing additional substrate for mitochondrial ATP generation (Guan et al., 2017; Martin et al., 2017; Yoshino et al., 2011). Neither intervention is expected to directly alter mtDNA repair mechanisms. Therefore, we sought to test whether these interventions would reduce the prevalence of mtDNA mutations in aged tissues because of their demonstrated ability to improve mitochondrial structure and/or function.

We first addressed the relative dearth of high-accuracy data regarding age-related accumulation of mtDNA in mice across multiple tissue types. To that end, we used ultraaccurate Duplex Sequencing (Duplex-Seq) to identify organ-specific mtDNA mutation burden in heart, skeletal muscle, eye, kidney, liver, and brain in naturally aged mice (Kennedy et al., 2014; Schmitt et al., 2012). Intra-animal comparison allowed us to determine whether mtDNA mutation rates differ between organs while still accounting for inter-animal variation. Our findings point to the accumulation of somatic mtDNA mutations being a dynamic and highly tissue-specific process that can be modulated by one or more cellular pathways amenable to small molecule intervention.

To study the effects of aging on the accumulation of somatic mtDNA mutations across tissues, we used Duplex-Seq to obtain high-accuracy variant information across the entire mtDNA. We examined six different organ systems (heart, kidney, liver, skeletal muscle, brain, and eye) at two different ages (young = 4.5 months; N=5 and old = 26 months; N=6). These two age groups were chosen for their representation of the two extremes of the adult mouse lifespan while mitigating potential confounders related to development, sexual maturation, and survival selection at more advanced ages. These tissues vary on their dependence of mitochondria function and OXPHOS (Fernández-Vizarra et al., 2011). To minimize variation of cell type substructure within tissues between animals, care was taken to isolate similar regions of each organ, as described in the Materials and methods section. In total, we sequenced over 27.9 billion high-accuracy bases, corresponding to a grand mean post-consensus depth of 10,125× for all samples with reasonably uniform coverage among experimental groups and mice, with the exception of the Ori_L (5160–5191) and several masked regions with high G/C content and/or repetitive sequences (Figure 1—figure supplement 1 and Figure 1—figure supplement 2, Figure 1—figure supplement 2; Supplementary file 1—Table 5). We observed a combined total of 77,017 single-nucleotide variants (SNVs) and 12,031 small insertion/deletions (In/Dels) (≲15 bp in size) across all tissue, age, and intervention groups. Collectively, these data represent the largest collection of somatic mtDNA point mutations obtained in a single study to date and is second only to Lujan et al. in terms of overall In/Del counts (Lujan et al., 2012). A summary of the data for each sample is reported in Supplementary file 1—Table 2 and Supplementary file 2.

Frequency of somatic mtDNA mutations increase with age and is tissue-specific

To better understand the effects of aging on somatic mtDNA mutations across tissues, we determined the frequency of both SNVs and small In/Dels (≲15 bp) in aged mice. To minimize the contribution of mtDNA mutations that could be either maternally inherited or early clonal expansions established in development, we limited our analysis to mutations occurring at or below a VAF of 1%. In young mice, an initial comparison of the frequency of mtDNA SNVs revealed a mutation frequency on the order of ~1 × 10^–6, with low variability between tissues (Figure 1A). Kidney and liver were notable exceptions, exhibiting significantly higher SNV frequencies compared to the other tissues in the young cohort (Figure 1A and B). With age, we observed significant increases in SNV frequency in all tissues we surveyed (Figure 1A). Moreover, mutation frequencies varied considerably between tissues in the aged cohort, with kidney having the highest SNV frequency (6.60±0.56 × 10^–6) and heart having the lowest (1.74±0.16 × 10^–6) (Figure 1A and C). The observed changes in frequency with age or tissue type did not correlate with differences in mtDNA copy number, as the mtDNA:nDNA ratio did not change with age (Figure 1—figure supplement 3A, B; Supplementary file 1—Table 3). In/Dels were approximately 10-fold less prevalent than SNVs in young mice, with a mean frequency of ~1.5 × 10^–7, and virtually no differences between tissues (Figure 1D and E). Like SNVs, In/Dels increased with age in most tissues we surveyed and did not correlate with copy number, but unlike SNVs, they did not significantly differ between tissue types, likely due to the high variability between samples (Figure 1D and F; Figure 1—figure supplement 3C).

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Due to mutation burdens being tissue-specific, we considered whether these differences could be driven by variation in the contribution of mitochondrial mutations in leukocytes of circulating blood. To determine this, we analyzed Duplex-Seq in a small subset of tissues from aged mice perfused with PBS to remove the blood. Duplex-Seq of mtDNA from blood collected prior to the perfusion showed that in aged mice, the average frequency of SNV in blood was 3.05±0.15 × 10^–6, comparable to the frequency detected in aged hippocampus. Comparisons of perfused (no/low blood) to non-perfused tissues from liver, kidney, skeletal muscle, hippocampus, and cerebellum (the retina, retinal pigmented epithelium [RPE]/choroid, and heart were not sequenced), showed no significant difference in the frequency of SNV mutations (Figure 1—figure supplement 4). Thus, our mutation profiles are likely driven primarily by organ-specific cell types.

Although little is known about the kinetics of somatic mtDNA mutation accumulation during aging, they have been reported to increase exponentially during aging in mice (Vermulst et al., 2007). Both this current study and a prior study by Arbeithuber et al. report only two time points each (4.5 months vs. 26 months and 20 days vs. 10 months, respectively), making it impossible to confirm exponential increase in either study (Arbeithuber et al., 2020). However, the combination of our data with the previously published data by Arebiethuber et al. indicates a linear increase in overall mutation frequencies across the lifespan in the three tissue types common to both studies (brain, muscle, and liver). This indicates a likely constant ‘clock-like’ accumulation analogous to what is seen in the nDNA (Abascal et al., 2021; Alexandrov et al., 2015; Arbeithuber et al., 2020; Figure 2). Together, these data demonstrate that mtDNA mutations accumulate at tissue-specific rates during aging and indicate use of a single tissue source to draw broad organism level conclusions regarding the interaction between mtDNA mutations and aging is not scientifically supported.

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Mutation spectra of somatic mtDNA mutations demonstrate tissue-specific distribution of mutation types

Previous work by us and others indicates that somatic mtDNA mutations are strongly biased toward transitions (i.e. G→A/C→T and T→C/A→G), with low levels of transversions (Ameur et al., 2011; Arbeithuber et al., 2020; Ju et al., 2014; Kennedy et al., 2013; Pickrell et al., 2015; Williams et al., 2013). Moreover, due to their low prevalence, transversions associated with oxidative lesions (i.e. G→T/C→A and G→C/C→G) have been largely discounted as contributing to age-associated mtDNA mutagenesis (Arbeithuber et al., 2020; Hoekstra et al., 2016; Itsara et al., 2014; Kauppila et al., 2018; Kennedy et al., 2013; Zheng et al., 2006). However, these findings are based on a limited number of tissue types, specifically muscle and brain. Given the wide range of SNV frequencies and known metabolic activities of the tissues we assayed, we examined the mutational spectra for each tissue. Our data show that the overall bias toward G→A/C→T transitions remains broadly true for most tissues, but the extent of this bias varies considerably, with kidney and heart being the notable extremes (Figure 3A). In agreement with prior studies, a single mutation class, G→A/C→T, is the most abundant mutation type and accounts for more than 50% all mutations in most young tissues (Figure 3—figure supplement 1). In contrast, ROS-linked G→T/C→A and G→C/C→G mutations exhibited substantial variation in the level of mutations between tissues. In the central nervous system (CNS) tissues (hippocampus, cerebellum, retina), G→T/C→A and G→C/C→G, combined, accounted for an average of 23% of the total mutation burden (retina = 18%, hippocampus 18%, cerebellum 33%) (Figure 3—figure supplement 1). These data are consistent with prior Duplex-Seq-based studies that focused on neural tissues (Arbeithuber et al., 2020; Hoekstra et al., 2016; Kennedy et al., 2013). In contrast, skeletal muscle and heart in young animals have a relatively high frequency of ROS-linked mutations, with 43% and 66% of all mutations, respectively, resulting from these two types of mutations. This suggests that ROS is a greater source of mtDNA mutagenesis earlier in life and is tissue-dependent.

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In comparison to the young tissues, mutation loads became more weighted toward transitions across the aged tissues we surveyed (Figure 3B). Significant differences between tissues within mutation classes also became more pronounced (Figure 3B, heatmaps). The fold-increase in most mutation types were remarkably uniform despite significant differences in SNV frequency between them (Figure 3C). Aging led to an average 3.2-fold increase of G→A/C→T and T→C/A→G transitions. Similarly, a significant 2.4-fold increase of T→A /A→T transversions was also observed (Figure 3C). A→C /T→G mutations were not evaluated due to their extreme paucity. In contrast to the other mutation types, G→T/C→A and G→C/C→G mutations did not significantly increase with age (Figure 3C).

The mtDNA has been documented to accumulate 8-oxo-dG in a tissue-specific manner (Hamilton et al., 2001). In addition, manipulations and high temperatures during library construction can further increase DNA damage (Ahn and Lee, 2019). It has been noted that DNA damage can, if present at sufficiently high levels, give rise to apparent G→T/C→A mutations in Duplex-Seq data, resulting from shearing induced DNA damage (Abascal et al., 2021; Xiong et al., 2022). We typically control for this class of artifacts by clipping the post-consensus read. However, artifacts have been documented to occur up to 30 cycles into DNA that is highly degraded (Xiong et al., 2022). To investigate the potential for DNA damage to explain the presence of G→T/C→A and G→C/C→G mutations, we performed two analyses. First, we examined the single-strand consensus sequence (SSCS) data that comprise the final high-accuracy duplex consensus sequence (DCS). Apparent variants in SSCS have sequencer-based errors removed and are comprised of both real DNA mutations and PCR-derived errors. Most of the PCR-derived errors are the result of base misincorporation across from DNA damage events, such as 8-oxo-dG and cytidine deamination to uracil (Schmitt et al., 2012). Our data show tissues significantly vary in the frequencies of G→T/C→A and G→C/C→G mutations. Therefore, if ROS-linked mutations were the result of fixation of DNA damage during PCR, then we would expect that the frequency of ROS-linked variants in the SSCS data should mirror the variability that is seen the final DCS data. Plotting the G→T and C→A SSCS frequency reveals that signal from ROS damage is uniform across all tissue types (Figure 3—figure supplement 2). Therefore, in order for false variants in the SSCS data to result in false DCS variants, the rate at which this occurred would need to be tissue-specific. Importantly, we randomized the library preparation and sequencing steps across tissues and ages to avoid batch effects, making it difficult to explain how tissue-level effects would occur. Second, we treated total DNA purified from aged skeletal muscle with formamidopyrimidine DNA glycosylase (Fpg) immediately after sonication. Fpg recognizes damaged purines, including 2,6-diamino-4-hydroxy-5-formamidopyrimidine and 8-oxo-dG, to generate an apurinic (AP) site that it then cleaves via its AP lyase activity, leaving a one nucleotide DNA gap. This effectively prevents the damaged DNA strand from PCR amplifying during library construction, thus preventing fragmented DNA from being able to form a final duplex consensus. Encouragingly, we observed no significant difference in our Fpg-treated and non-treated skeletal muscle samples, indicating that shearing-induced artifacts are unlikely to account for our observations (Figure 3—figure supplement 3). Taken together, our data suggest that ROS damage to the mtDNA is unlikely to explain our observations of tissue-related variability in G→T/C→A and G→C/C→G mutations.

Clonal expansion of somatic mtDNA mutations is tissue-specific

Mutagenesis has been described as an irreversible process that results in increasing levels of mutations in a population over time, termed ‘Muller’s ratchet’ (Felsenstein, 1974; Muller, 1964). Consequently, absent any compensatory mechanisms, mutations should increase during life. In the case of mtDNA, this should appear as an increase in the burden of apparent heteroplasmies (or clones) within a tissue over time. Importantly, because mtDNA replicates independently of nDNA, apparent heteroplasmies can increase during aging even in the absence of substantial cell proliferation. Moreover, mitochondria are subject to surveillance by mitophagy, which may affect the age-dependent mutational dynamics in tissue-specific ways (Pickles et al., 2018). Expansion of mtDNA mutations sufficient to warrant the term ‘clonal’ has been documented in human tissues, but the prevalence of this phenomenon remains poorly documented in mice (Greaves et al., 2014; Greaves et al., 2010; Greaves et al., 2006; Nekhaeva et al., 2002). The set of tissues we examined comprise a range of varying proliferative and replicative potentials, with heart, brain, and retina being limited, while many kidney and liver cell types proliferate throughout life. Therefore, we sought to determine the tissue-specific burden and dynamics of age-related clonal expansion of mtDNA mutations.

We defined a ‘heteroplasmic clone’ as a variant supported by three or more error-corrected reads and then calculated both the frequency and percentage of total mutations corresponding to these clones. We observed considerable tissue-specific variation in the effects of age on the presence of heteroplasmic clones, with all tissues exhibiting a significant increase in the frequency of total heteroplasmic clones with age (Figure 4A and C). Clones in all tissues were distributed relatively uniformly across the mtDNA coding region, but with a striking clustering of variants in the mCR (Figure 4C, green region), consistent with prior reports (Arbeithuber et al., 2020; Kennedy et al., 2013; Sanchez-Contreras et al., 2021).

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The mutation composition of the clones varied between tissues. In the RPE/choroid, brain, skeletal muscle, and heart, the relative percentage of SNVs found as heteroplasmic clones did not change with age (~2–3% of total SNV). In contrast, kidney, liver, and retina exhibited a disproportionate increase in the number of clonally expanded variants with age. In old kidney, the percentage of SNVs detected as heteroplasmic clones increased to 10.4%, while clones in liver increased from ~1% of SNVs in young to 5.6% in old (Figure 4B). In retina, the percentage of SNVs detected as clones increased from 3.6% in young to 5.6% in old mice. In kidney and liver, the expansion of mtDNA mutations was pervasive across the genome and suggestive of a relationship to the high proliferative and regenerative capacity of these tissues. Retina, however, is a post-mitotic tissue and displayed a very different pattern, with the age-associated increase in clonality being attributed almost entirely to variants clustered in the mCR (Figure 4C).

Importantly, several of the tissues we examined are highly vascular and the hematological compartment has been documented to exhibit significant heteroplasmy and shifts in clonal expansions with age (Lareau et al., 2019). With the exception of kidney, which showed a modest effect, we observed no significant changes between the perfused and non-perfused samples, indicating that blood is unlikely a significant source of age-dependent changes across tissues (Figure 4—figure supplement 1). Collectively, these data suggest that the importance of mtDNA heteroplasmic clones in aging phenotypes is tissue-dependent. Very few studies have examined somatic mtDNA heteroplasmic clones in any tissue, and this remains an area for future work.

Spectral analysis of clonal somatic mtDNA mutations suggests removal of ROS-linked mutations

Having established the tissue-specific profile of heteroplasmic clones, we reasoned that we could distinguish between mutations arising from a transient process earlier in life and an active clearance of mtDNA and/or whole mitochondria containing mutation types by examining which mutation types became more heteroplasmic with age. Specifically, an ongoing or early transient mutational process would be expected to result in expansion of a subset of variants across all mutation types. In contrast, evidence of active clearance would appear as either a lack of heteroplasmic clone expansion or a bias in the specific variants that underwent expansion. Because five of the eight tissues showed no significant change in the proportion of clonal SNV with aging, we expected that clones would be distributed across the spectra in a pattern similar to non-clonal mutations. Instead, we observed that the spectrum of heteroplasmic clones demonstrates a nearly complete suppression of heteroplasmic clones derived from G→T/C→A and G→C/C→G mutations in both young and old mice (Figure 5A and B). Remarkably, suppression of ROS-linked heteroplasmic clones was true even in the heart, which carried the highest combined G→T/C→A and G→C/C→G SNV mutation burden of any tissue, with 65% of total SNV in young and 34% of SNV in old mice (Figure 3—figure supplement 1). Thus, we asked whether this lack of G→T/C→A and G→C/C→G clones was a significant finding or merely a consequence of low sampling due to the relative paucity of heteroplasmic clones and the lower frequency of ROS-associated mutations. Under the assumption that heteroplasmic clones arise randomly, we tested whether our dataset of detected SNV clones differed from the expected number of clones based on the spectral distribution of non-clonal SNV mutations. To ensure that we had sufficient power, we combined SNV mutations and clones detected in all eight tissues of the six old mice for a total of 24,244 de novo SNVs and 1461 heteroplasmic clones. To account for differences in depth between samples, the spectral distribution of total SNV mutations was calculated for each tissue of each mouse. The expected contribution of clones in each tissue was then weighted based on the average percentage of ‘clonality’ measured in the aged dataset (Figure 5B). For example, more clones were expected to form in kidney and liver (10.4% and 6% clonality, respectively) than would be expected in brain or muscle tissues (~3% clonality). Using this method, our model predicted the detection of 1341 heteroplasmic clones in total for combined aged tissues, which is within 10% of the detected total of 1461.

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Speadsheet containing the calculation of expected vs observed clone counts for Figure 5C.

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We modeled the expected spectrum of these clones among the six mutation types using a Poisson process to model random sampling error due to the low abundance of clones. We compared the expected number to the observed spectrum and found that the clonal spectra differed significantly from the distribution of non-clonal SNVs (Figure 5C and D). G→T/C→A and C→G/G→C mutations were predicted to form ~146 and ~69 clones respectively, however, only eight G→T/C→A clones and two C→G/G→C clones were detected in the entire aged mouse dataset, corresponding to an 18- and 34-fold under-representation, respectively. Conversely, G→A/C→T and T→C/A→G transitions only deviated from the expected values by less than twofold (Figure 5D). Although we used a combined aged tissue dataset to ensure that we were not under sampling, this under/over-representation by mutation spectra was detected in every tissue type as shown by their observed spectral distribution (Figure 5E). Taken together, these results suggest that expansions of heteroplasmic clones in mtDNA unlikely arises as a random consequence of somatic mutation formation. The uneven distribution of clones relative to non-clonal SNVs suggests that the lack of G→T/C→A and G→C/C→G mutation accumulation with age does not reflect differences in the formation of these mutations, but rather is consistent with a steady-state level of ROS-linked mutations that is susceptible to a constant generation and removal.

Late-life treatment with mitochondrially targeted interventions reduces ROS-linked mutations

Like in the germline, mtDNA mutations have been hypothesized to be selectively removed in somatic tissue through a mechanism involving a still poorly understood interaction between the unfolded protein response, mitophagy, and mitochondrial fission/fusion (Chen et al., 2010; Gitschlag et al., 2016; Lin et al., 2016; Suen et al., 2010). Therefore, we hypothesized that compounds known to improve function and/or ultrastructure in aged mitochondria would impact the burden of aging mtDNA mutations by shifting the steady state of ROS damage toward removal of damaged/dysfunctional mitochondria and their accompanying mtDNA. To this end we sequenced tissues from mice treated systemically for 8 weeks with either Elam or NMN in old mice that had accumulated somatic mtDNA mutations throughout their lives. Functionally, both Elam and NMN improve mitochondrial energetics and the mitochondrial network in aged mice across multiple aged tissues within an 8-week time frame (Campbell et al., 2019; Chiao et al., 2020; Sweetwyne et al., 2017; Whitson et al., 2020), albeit through different mechanisms. Specifically, Elam improves mitochondria structure and integrity of the inner mitochondrial membrane cristae (Birk et al., 2013; Machiraju et al., 2019), whereas NMN acts as a precursor molecule for NAD⁺/NADP production (Hong et al., 2020). This allowed us to examine mice within such a narrow window of time that it was more likely to detect changes in mutation turnover/removal, rather than significant prevention of mutation accumulation during aging.

All treated samples were sequenced to a similar mean ‘duplex’ depth and detected comparable numbers of mutations as the controls (Figure 1—figure supplement 2; Figure 6—figure supplement 1 and Figure 1—figure supplement 2; Supplementary file 1—Table 2). We did not observe a significant change in the overall mutation frequencies between aged mice and treated mice, regardless of tissue, indicating that these interventions are unlikely to indiscriminately affect mtDNA mutations (Figure 6—figure supplement 3). In support of this observation, the non-synonymous to synonymous ratio (dN/dS), which is a measure of positive or negative selection, shows no significant deviation from the expected ratio of one for any age, tissue, or intervention (Figure 6—figure supplement 4). However, consistent with our hypothesis, mice treated with Elam or NMN showed a trend for reduction of ROS-associated G→T/C→A and G→C/C→G mutations in heart, kidney, liver, and skeletal muscle. In heart, this reached significance for reduction of G→T/C→A mutations (control: 9.7±3.0 × 10^–7 vs. ELAM: 5.5±1.5 × 10^–7, p_adj = 0.019; control: 9.7±3.0 × 10^–7 vs. NMN: 4.7±0.9 × 10^–7, p_adj = 0.017; one-way ANOVA within each mutation class with Dunnett’s test against saline control) (Figure 6A). In kidney, reduction of G→C/C→G mutations reached significance in NMN-treated animals (control: 7.3±2.0 × 10^–7 vs. NMN: 3.7±1.7 × 10^–7, p_adj = 0.038; one-way ANOVA within each mutation class with Dunnett’s test against saline control) (Figure 6B). Both liver and skeletal muscle trended toward significance (Figure 6C and D). No effect was seen in the remaining tissues (Figure 6—figure supplement 5). G→T/C→A and G→C/C→G mutations do not significantly increase between young and old age control animals, but are reduced in aged animals with these interventions, thus indicating that these changes are not simply due to the prevention of these mutation types during the treatment window. We interpret these findings to mean that ROS-linked mutations do occur throughout life, but that either mitochondria or the mtDNA harboring these mutations are more likely than other mutation classes to be eliminated via mechanisms of cellular organelle maintenance. Although we cannot discount that the lack of efficacy in some tissues may be due to tissue-specific differences in biodistribution of the two compounds tested, the efficacy of Elam and NMN to reduce ROS-linked mutations in these organs is consistent with other reports demonstrating functional improvements to aged tissues specifically in skeletal muscle, heart, and kidney for Elam (Chiao et al., 2020; Sweetwyne et al., 2017; Whitson et al., 2020) and heart, liver, and skeletal muscle for NMN (Luo et al., 2022; Mills et al., 2016; Whitson et al., 2020).

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In contrast to the pattern seen in peripheral organs of treated mice, a reduction of ROS-linked mutations was not observed in tissues of the brain or eye (Figure 6—figure supplement 5). Both ELAM and NMN can cross the blood-brain barrier with ease and so are expected to be available to both brain and eye with systemic treatment. Our findings are consistent with the lower overall G→T/C→A and G→C/C→G mutation burden in these latter tissues and may indicate that their mtDNA is more protected from ROS damage with age than is the case in peripheral organs. When considered in the context of the data presented in Figure 4, which demonstrates a lack of clone formation for G→T/C→A and G→C/C→G mutations, the selective reduction of these same mutations in tissues with high oxidative mutation burden further supports the hypothesis that ROS-linked mtDNA mutation accumulation is regulated through life-long and specific removal of ROS-damaged mtDNA genomes.

The processes that drive and influence somatic mtDNA mutagenesis in aging and disease has proved to be nuanced and controversial (Kauppila and Stewart, 2015; Sanchez-Contreras and Kennedy, 2022; Szczepanowska and Trifunovic, 2017). Enhancement of accuracy by NGS and newer methods, such as Duplex-Seq, have begun to shed light on these processes. In this report, we took advantage of our continued improvement in the Duplex-Seq protocol to perform a multi-tissue survey of somatic mtDNA mutations in a naturally aging mouse cohort. The very high depths we achieved (depth grand mean: 10,125×) allowed us to detect hundreds to thousands of mutations per sample (variant count mean: 453, total: 77,017 SNVs and 12,031 In/Dels) across all mutation types, representing an ~8.3-fold increase in depth and a 38.5-fold increase in mean mutation count per sample compared to the next largest currently published Duplex-Seq dataset for mouse mtDNA (depth grand mean: 1231×; variant count mean: 12.7, total: 2488) (Arbeithuber et al., 2020). This substantially expanded dataset allowed us to examine the types and classes of de novo mtDNA mutations with unprecedented sensitivity and, as a result, we observed unexpected patterns that would have been missed with a lower coverage of mutations.

The findings reported here broadly recapitulate those from smaller studies reporting that somatic mtDNA point mutations occur at a frequency on the order of 10^–6, increase with age, and are biased toward G→A/C→T transitions (Ameur et al., 2011; Andreazza et al., 2019; Arbeithuber et al., 2020; Hoekstra et al., 2016; Kennedy et al., 2013; Samstag et al., 2018; Williams et al., 2013). However, our expanded dataset found an unexpected level of variation between tissues in both overall mutation frequency and spectrum, indicating tissue-specific effects of aging on mtDNA mutation burden. While in young mouse tissues, we observed minimal variation in mtDNA SNV frequency with only kidney, liver, and RPE/choroid showing significantly increased levels compared to other tissues, with advancing age were distinct differences between all tissues apparent. Our data indicate that the accumulation of somatic mtDNA mutation is highly segmental in nature and therefore its impact on aging or disease risk may be tissue-dependent.

The Duplex-Seq-Pipeline is written in Python and R, but has dependencies written in other languages. The DuplexSeq-Pipeline software has been tested to run on Linux, Windows WSL1, Windows WSL2 and Apple OSX. The software can be obtained at https://github.com/Kennedy-Lab-UW/Duplex-Seq-Pipeline, (copy archived at swh:1:rev:878457d08f3272db01550fbf85da631ae11b713c). Raw mouse sequencing data used in this study are available at SRA accession PRJNA727407. The data from Arbeithuber et al. are available at SRA accession PRJNA563921. The final post-processed data, including variant call files, depth information, data summaries, and mutation frequencies, as well as the scripts to perform reproducible production of statistics and figure generation (with the exception of Figure 5C-E) are available at https://github.com/Kennedy-Lab-UW/Sanchez_Contreras_etal_2022, (copy archived at swh:1:rev:ff5f6a2bf4b30f40fd80ad4136ceea58f1b2dd52).

1. 1. Sanchez-Conteras M
   2. Sweetwyne MT
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Author details

1. Monica Sanchez-Contreras

   Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Mariya T Sweetwyne

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3092-2781

2. Mariya T Sweetwyne

   Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Formal analysis, Supervision, Investigation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Monica Sanchez-Contreras

   For correspondence

   sandu@uw.edu

   Competing interests

   No competing interests declared

3. Kristine A Tsantilas

   Department of Biochemistry, University of Washington, Seattle, United States

   Contribution

   Resources, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4274-6930

4. Jeremy A Whitson

   Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States

   Contribution

   Resources, Investigation

   Competing interests

   No competing interests declared

5. Matthew D Campbell

   Department of Radiology, University of Washington, Seattle, United States

   Contribution

   Resources, Investigation

   Competing interests

   No competing interests declared

6. Brenden F Kohrn

   Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States

   Contribution

   Software

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9948-2131

7. Hyeon Jeong Kim

   Department of Biology, University of Washington, Seattle, United States

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

8. Michael J Hipp

   Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1904-0670

9. Jeanne Fredrickson

   Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Megan M Nguyen

    Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

11. James B Hurley

    Department of Biochemistry, University of Washington, Seattle, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7754-0705

12. David J Marcinek

    Department of Radiology, University of Washington, Seattle, United States

    Contribution

    Funding acquisition, Writing – review and editing

    Competing interests

    No competing interests declared

13. Peter S Rabinovitch

    Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States

    Contribution

    Funding acquisition, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7169-3543

14. Scott R Kennedy

    Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States

    Contribution

    Data curation, Software, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing – original draft, Writing – review and editing

    For correspondence

    scottrk@uw.edu

    Competing interests

    is an equity holder and paid consultant for Twinstrand Biosciences, a for-profit company commercializing Duplex Sequencing. No Twinstrand products were used in the generation of the data

    "This ORCID iD identifies the author of this article:" 0000-0002-4444-1145

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