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).

Figure 1 with 4 supplements see all

Download asset Open asset

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.

Figure 2

Download asset Open asset

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.

Figure 3 with 3 supplements see all

Download asset Open asset

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).

Figure 4 with 1 supplement see all

Download asset Open asset

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.

Figure 5

Download asset Open asset

Figure 5—source data 1

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

https://cdn.elifesciences.org/articles/83395/elife-83395-fig5-data1-v2.xlsx

Download elife-83395-fig5-data1-v2.xlsx

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).

Figure 6 with 5 supplements see all

Download asset Open asset

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
   3. Kennedy SR

   (2021) NCBI BioProject

   ID PRJNA727407. Mitochondrial DNA isolated from different tissues with two different ages (4-5 months and 24-26 months) and two different aging interventions.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA727407

1. 1. Arbeithuber B
   2. Makova KD

   (2019) NCBI BioProject

   ID PRJNA563921. Duplex sequencing of mtDNA from mouse somatic tissues (brain and skeletal muscle), single oocytes, and oocyte pools was performed to study the effect of aging on mtDNA substitution frequencies.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA563921

1. 1. Abascal F
   2. Harvey LMR
   3. Mitchell E
   4. Lawson ARJ
   5. Lensing SV
   6. Ellis P
   7. Russell AJC
   8. Alcantara RE
   9. Baez-Ortega A
   10. Wang Y
   11. Kwa EJ
   12. Lee-Six H
   13. Cagan A
   14. Coorens THH
   15. Chapman MS
   16. Olafsson S
   17. Leonard S
   18. Jones D
   19. Machado HE
   20. Davies M
   21. Øbro NF
   22. Mahubani KT
   23. Allinson K
   24. Gerstung M
   25. Saeb-Parsy K
   26. Kent DG
   27. Laurenti E
   28. Stratton MR
   29. Rahbari R
   30. Campbell PJ
   31. Osborne RJ
   32. Martincorena I

   (2021) Somatic mutation landscapes at single-molecule resolution

   Nature 593:405–410.

   https://doi.org/10.1038/s41586-021-03477-4

   • PubMed
   • Google Scholar
2. 1. Ahn EH
   2. Lee SH

   (2019) Detection of low-frequency mutations and identification of heat-induced artifactual mutations using duplex sequencing

   International Journal of Molecular Sciences 20:199.

   https://doi.org/10.3390/ijms20010199

   • PubMed
   • Google Scholar
3. 1. Alexandrov LB
   2. Jones PH
   3. Wedge DC
   4. Sale JE
   5. Campbell PJ
   6. Nik-Zainal S
   7. Stratton MR

   (2015) Clock-Like mutational processes in human somatic cells

   Nature Genetics 47:1402–1407.

   https://doi.org/10.1038/ng.3441

   • PubMed
   • Google Scholar
4. 1. Ameur A
   2. Stewart JB
   3. Freyer C
   4. Hagström E
   5. Ingman M
   6. Larsson NG
   7. Gyllensten U

   (2011) Ultra-deep sequencing of mouse mitochondrial DNA: mutational patterns and their origins

   PLOS Genetics 7:e1002028.

   https://doi.org/10.1371/journal.pgen.1002028

   • PubMed
   • Google Scholar
5. 1. Andreazza S
   2. Samstag CL
   3. Sanchez-Martinez A
   4. Fernandez-Vizarra E
   5. Gomez-Duran A
   6. Lee JJ
   7. Tufi R
   8. Hipp MJ
   9. Schmidt EK
   10. Nicholls TJ
   11. Gammage PA
   12. Chinnery PF
   13. Minczuk M
   14. Pallanck LJ
   15. Kennedy SR
   16. Whitworth AJ

   (2019) Mitochondrially-targeted apobec1 is a potent mtdna mutator affecting mitochondrial function and organismal fitness in Drosophila

   Nature Communications 10:3280.

   https://doi.org/10.1038/s41467-019-10857-y

   • PubMed
   • Google Scholar
6. 1. Arbeithuber B
   2. Hester J
   3. Cremona MA
   4. Stoler N
   5. Zaidi A
   6. Higgins B
   7. Anthony K
   8. Chiaromonte F
   9. Diaz FJ
   10. Makova KD

   (2020) Age-related accumulation of de novo mitochondrial mutations in mammalian oocytes and somatic tissues

   PLOS Biology 18:e3000745.

   https://doi.org/10.1371/journal.pbio.3000745

   • PubMed
   • Google Scholar
7. 1. Baker KT
   2. Nachmanson D
   3. Kumar S
   4. Emond MJ
   5. Ussakli C
   6. Brentnall TA
   7. Kennedy SR
   8. Risques RA

   (2019) Mitochondrial DNA mutations are associated with ulcerative colitis preneoplasia but tend to be negatively selected in cancer

   Molecular Cancer Research 17:488–498.

   https://doi.org/10.1158/1541-7786.MCR-18-0520

   • PubMed
   • Google Scholar
8. 1. Birk AV
   2. Liu S
   3. Soong Y
   4. Mills W
   5. Singh P
   6. Warren JD
   7. Seshan SV
   8. Pardee JD
   9. Szeto HH

   (2013) The mitochondrial-targeted compound SS-31 re-energizes ischemic mitochondria by interacting with cardiolipin

   Journal of the American Society of Nephrology 24:1250–1261.

   https://doi.org/10.1681/ASN.2012121216

   • PubMed
   • Google Scholar
9. 1. Calabrese FM
   2. Simone D
   3. Attimonelli M

   (2012) Primates and mouse NuMts in the UCSC genome browser

   BMC Bioinformatics 13 Suppl 4:S15.

   https://doi.org/10.1186/1471-2105-13-S4-S15

   • PubMed
   • Google Scholar
10. 1. Campbell MD
    2. Duan J
    3. Samuelson AT
    4. Gaffrey MJ
    5. Merrihew GE
    6. Egertson JD
    7. Wang L
    8. Bammler TK
    9. Moore RJ
    10. White CC
    11. Kavanagh TJ
    12. Voss JG
    13. Szeto HH
    14. Rabinovitch PS
    15. MacCoss MJ
    16. Qian WJ
    17. Marcinek DJ

    (2019) Improving mitochondrial function with SS-31 reverses age-related redox stress and improves exercise tolerance in aged mice

    Free Radical Biology & Medicine 134:268–281.

    https://doi.org/10.1016/j.freeradbiomed.2018.12.031

    • PubMed
    • Google Scholar
11. 1. Chen H
    2. Vermulst M
    3. Wang YE
    4. Chomyn A
    5. Prolla TA
    6. McCaffery JM
    7. Chan DC

    (2010) Mitochondrial fusion is required for mtDNA stability in skeletal muscle and tolerance of mtDNA mutations

    Cell 141:280–289.

    https://doi.org/10.1016/j.cell.2010.02.026

    • PubMed
    • Google Scholar
12. 1. Chen Z
    2. Qi Y
    3. French S
    4. Zhang G
    5. Covian Garcia R
    6. Balaban R
    7. Xu H

    (2015) Genetic mosaic analysis of a deleterious mitochondrial DNA mutation in Drosophila reveals novel aspects of mitochondrial regulation and function

    Molecular Biology of the Cell 26:674–684.

    https://doi.org/10.1091/mbc.E14-11-1513

    • PubMed
    • Google Scholar
13. 1. Chiao YA
    2. Zhang H
    3. Sweetwyne M
    4. Whitson J
    5. Ting YS
    6. Basisty N
    7. Pino LK
    8. Quarles E
    9. Nguyen NH
    10. Campbell MD
    11. Zhang T
    12. Gaffrey MJ
    13. Merrihew G
    14. Wang L
    15. Yue Y
    16. Duan D
    17. Granzier HL
    18. Szeto HH
    19. Qian WJ
    20. Marcinek D
    21. MacCoss MJ
    22. Rabinovitch P

    (2020) Late-life restoration of mitochondrial function reverses cardiac dysfunction in old mice

    eLife 9:e55513.

    https://doi.org/10.7554/eLife.55513

    • PubMed
    • Google Scholar
14. 1. Chrétien D
    2. Bénit P
    3. Ha HH
    4. Keipert S
    5. El-Khoury R
    6. Chang YT
    7. Jastroch M
    8. Jacobs HT
    9. Rustin P
    10. Rak M

    (2018) Mitochondria are physiologically maintained at close to 50 °C

    PLOS Biology 16:e2003992.

    https://doi.org/10.1371/journal.pbio.2003992

    • PubMed
    • Google Scholar
15. 1. Colom B
    2. Alcolea MP
    3. Piedrafita G
    4. Hall MWJ
    5. Wabik A
    6. Dentro SC
    7. Fowler JC
    8. Herms A
    9. King C
    10. Ong SH
    11. Sood RK
    12. Gerstung M
    13. Martincorena I
    14. Hall BA
    15. Jones PH

    (2020) Spatial competition shapes the dynamic mutational landscape of normal esophageal epithelium

    Nature Genetics 52:604–614.

    https://doi.org/10.1038/s41588-020-0624-3

    • PubMed
    • Google Scholar
16. 1. Felsenstein J

    (1974) The evolutionary advantage of recombination

    Genetics 78:737–756.

    https://doi.org/10.1093/genetics/78.2.737

    • PubMed
    • Google Scholar
17. 1. Fernández-Vizarra E
    2. Enríquez JA
    3. Pérez-Martos A
    4. Montoya J
    5. Fernández-Silva P

    (2011) Tissue-Specific differences in mitochondrial activity and biogenesis

    Mitochondrion 11:207–213.

    https://doi.org/10.1016/j.mito.2010.09.011

    • PubMed
    • Google Scholar
18. 1. Fieller EC

    (1954) Some problems in interval estimation

    Journal of the Royal Statistical Society 16:175–185.

    https://doi.org/10.1111/j.2517-6161.1954.tb00159.x

    • Google Scholar
19. 1. Frederico LA
    2. Kunkel TA
    3. Shaw BR

    (1990) A sensitive genetic assay for the detection of cytosine deamination: determination of rate constants and the activation energy

    Biochemistry 29:2532–2537.

    https://doi.org/10.1021/bi00462a015

    • PubMed
    • Google Scholar
20. 1. Gates KS

    (2009) An overview of chemical processes that damage cellular DNA: spontaneous hydrolysis, alkylation, and reactions with radicals

    Chemical Research in Toxicology 22:1747–1760.

    https://doi.org/10.1021/tx900242k

    • PubMed
    • Google Scholar
21. 1. Gitschlag BL
    2. Kirby CS
    3. Samuels DC
    4. Gangula RD
    5. Mallal SA
    6. Patel MR

    (2016) Homeostatic responses regulate selfish mitochondrial genome dynamics in C. elegans

    Cell Metabolism 24:91–103.

    https://doi.org/10.1016/j.cmet.2016.06.008

    • PubMed
    • Google Scholar
22. 1. Greaves LC
    2. Preston SL
    3. Tadrous PJ
    4. Taylor RW
    5. Barron MJ
    6. Oukrif D
    7. Leedham SJ
    8. Deheragoda M
    9. Sasieni P
    10. Novelli MR
    11. Jankowski JAZ
    12. Turnbull DM
    13. Wright NA
    14. McDonald SAC

    (2006) Mitochondrial DNA mutations are established in human colonic stem cells, and mutated clones expand by crypt fission

    PNAS 103:714–719.

    https://doi.org/10.1073/pnas.0505903103

    • PubMed
    • Google Scholar
23. 1. Greaves LC
    2. Barron MJ
    3. Plusa S
    4. Kirkwood TB
    5. Mathers JC
    6. Taylor RW
    7. Turnbull DM

    (2010) Defects in multiple complexes of the respiratory chain are present in ageing human colonic crypts

    Experimental Gerontology 45:573–579.

    https://doi.org/10.1016/j.exger.2010.01.013

    • PubMed
    • Google Scholar
24. 1. Greaves LC
    2. Nooteboom M
    3. Elson JL
    4. Tuppen HAL
    5. Taylor GA
    6. Commane DM
    7. Arasaradnam RP
    8. Khrapko K
    9. Taylor RW
    10. Kirkwood TBL
    11. Mathers JC
    12. Turnbull DM

    (2014) Clonal expansion of early to mid-life mitochondrial DNA point mutations drives mitochondrial dysfunction during human ageing

    PLOS Genetics 10:e1004620.

    https://doi.org/10.1371/journal.pgen.1004620

    • PubMed
    • Google Scholar
25. 1. Guan Y
    2. Wang S-R
    3. Huang X-Z
    4. Xie Q-H
    5. Xu Y-Y
    6. Shang D
    7. Hao C-M

    (2017) Nicotinamide mononucleotide, an NAD+ precursor, rescues age-associated susceptibility to AKI in a sirtuin 1-dependent manner

    Journal of the American Society of Nephrology 28:2337–2352.

    https://doi.org/10.1681/ASN.2016040385

    • PubMed
    • Google Scholar
26. 1. Guo X
    2. Xu W
    3. Zhang W
    4. Pan C
    5. Thalacker-Mercer AE
    6. Zheng H
    7. Gu Z

    (2023) High-frequency and functional mitochondrial DNA mutations at the single-cell level

    PNAS 120:e2201518120.

    https://doi.org/10.1073/pnas.2201518120

    • PubMed
    • Google Scholar
27. 1. Hamilton ML
    2. Guo Z
    3. Fuller CD
    4. Van Remmen H
    5. Ward WF
    6. Austad SN
    7. Troyer DA
    8. Thompson I
    9. Richardson A

    (2001) A reliable assessment of 8-oxo-2-deoxyguanosine levels in nuclear and mitochondrial DNA using the sodium iodide method to isolate DNA

    Nucleic Acids Research 29:2117–2126.

    https://doi.org/10.1093/nar/29.10.2117

    • PubMed
    • Google Scholar
28. 1. Harman D

    (1972) The biologic clock: the mitochondria?

    Journal of the American Geriatrics Society 20:145–147.

    https://doi.org/10.1111/j.1532-5415.1972.tb00787.x

    • PubMed
    • Google Scholar
29. 1. Hoekstra JG
    2. Hipp MJ
    3. Montine TJ
    4. Kennedy SR

    (2016) Mitochondrial DNA mutations increase in early stage Alzheimer disease and are inconsistent with oxidative damage

    Annals of Neurology 80:301–306.

    https://doi.org/10.1002/ana.24709

    • PubMed
    • Google Scholar
30. 1. Hong W
    2. Mo F
    3. Zhang Z
    4. Huang M
    5. Wei X

    (2020) Nicotinamide mononucleotide: a promising molecule for therapy of diverse diseases by targeting NAD+ metabolism

    Frontiers in Cell and Developmental Biology 8:246.

    https://doi.org/10.3389/fcell.2020.00246

    • PubMed
    • Google Scholar
31. 1. Horvath S

    (2013) DNA methylation age of human tissues and cell types

    Genome Biology 14:R115.

    https://doi.org/10.1186/gb-2013-14-10-r115

    • PubMed
    • Google Scholar
32. 1. Itsara LS
    2. Kennedy SR
    3. Fox EJ
    4. Yu S
    5. Hewitt JJ
    6. Sanchez-Contreras M
    7. Cardozo-Pelaez F
    8. Pallanck LJ

    (2014) Oxidative stress is not a major contributor to somatic mitochondrial DNA mutations

    PLOS Genetics 10:e1003974.

    https://doi.org/10.1371/journal.pgen.1003974

    • PubMed
    • Google Scholar
33. 1. Ju YS
    2. Alexandrov LB
    3. Gerstung M
    4. Martincorena I
    5. Nik-Zainal S
    6. Ramakrishna M
    7. Davies HR
    8. Papaemmanuil E
    9. Gundem G
    10. Shlien A
    11. Bolli N
    12. Behjati S
    13. Tarpey PS
    14. Nangalia J
    15. Massie CE
    16. Butler AP
    17. Teague JW
    18. Vassiliou GS
    19. Green AR
    20. Du M-Q
    21. Unnikrishnan A
    22. Pimanda JE
    23. Teh BT
    24. Munshi N
    25. Greaves M
    26. Vyas P
    27. El-Naggar AK
    28. Santarius T
    29. Collins VP
    30. Grundy R
    31. Taylor JA
    32. Hayes DN
    33. Malkin D
    34. Foster CS
    35. Warren AY
    36. Whitaker HC
    37. Brewer D
    38. Eeles R
    39. Cooper C
    40. Neal D
    41. Visakorpi T
    42. Isaacs WB
    43. Bova GS
    44. Flanagan AM
    45. Futreal PA
    46. Lynch AG
    47. Chinnery PF
    48. McDermott U
    49. Stratton MR
    50. Campbell PJ
    51. ICGC Breast Cancer Group
    52. ICGC Chronic Myeloid Disorders Group
    53. ICGC Prostate Cancer Group

    (2014) Origins and functional consequences of somatic mitochondrial DNA mutations in human cancer

    eLife 3:e02935.

    https://doi.org/10.7554/eLife.02935

    • PubMed
    • Google Scholar
34. 1. Kauppila JHK
    2. Stewart JB

    (2015) Mitochondrial DNA: radically free of free-radical driven mutations

    Biochimica et Biophysica Acta 1847:1354–1361.

    https://doi.org/10.1016/j.bbabio.2015.06.001

    • PubMed
    • Google Scholar
35. 1. Kauppila JHK
    2. Bonekamp NA
    3. Mourier A
    4. Isokallio MA
    5. Just A
    6. Kauppila TES
    7. Stewart JB
    8. Larsson NG

    (2018) Base-excision repair deficiency alone or combined with increased oxidative stress does not increase mtdna point mutations in mice

    Nucleic Acids Research 46:6642–6669.

    https://doi.org/10.1093/nar/gky456

    • PubMed
    • Google Scholar
36. 1. Kennedy SR
    2. Salk JJ
    3. Schmitt MW
    4. Loeb LA

    (2013) Ultra-sensitive sequencing reveals an age-related increase in somatic mitochondrial mutations that are inconsistent with oxidative damage

    PLOS Genetics 9:e1003794.

    https://doi.org/10.1371/journal.pgen.1003794

    • PubMed
    • Google Scholar
37. 1. Kennedy SR
    2. Schmitt MW
    3. Fox EJ
    4. Kohrn BF
    5. Salk JJ
    6. Ahn EH
    7. Prindle MJ
    8. Kuong KJ
    9. Shen J-C
    10. Risques R-A
    11. Loeb LA

    (2014) Detecting ultralow-frequency mutations by duplex sequencing

    Nature Protocols 9:2586–2606.

    https://doi.org/10.1038/nprot.2014.170

    • PubMed
    • Google Scholar
38. 1. Khrapko K
    2. Coller H
    3. André P
    4. Li XC
    5. Foret F
    6. Belenky A
    7. Karger BL
    8. Thilly WG

    (1997) Mutational spectrometry without phenotypic selection: human mitochondrial DNA

    Nucleic Acids Research 25:685–693.

    https://doi.org/10.1093/nar/25.4.685

    • PubMed
    • Google Scholar
39. 1. Kowaltowski AJ

    (2000) Alternative mitochondrial functions in cell physiopathology: beyond ATP production

    Brazilian Journal of Medical and Biological Research = Revista Brasileira de Pesquisas Medicas e Biologicas 33:241–250.

    https://doi.org/10.1590/s0100-879x2000000200014

    • PubMed
    • Google Scholar
40. 1. Lareau CA
    2. Ludwig LS
    3. Sankaran VG

    (2019) Longitudinal assessment of clonal mosaicism in human hematopoiesis via mitochondrial mutation tracking

    Blood Advances 3:4161–4165.

    https://doi.org/10.1182/bloodadvances.2019001196

    • PubMed
    • Google Scholar
41. 1. Lareau CA
    2. Ludwig LS
    3. Muus C
    4. Gohil SH
    5. Zhao T
    6. Chiang Z
    7. Pelka K
    8. Verboon JM
    9. Luo W
    10. Christian E
    11. Rosebrock D
    12. Getz G
    13. Boland GM
    14. Chen F
    15. Buenrostro JD
    16. Hacohen N
    17. Wu CJ
    18. Aryee MJ
    19. Regev A
    20. Sankaran VG

    (2021) Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling

    Nature Biotechnology 39:451–461.

    https://doi.org/10.1038/s41587-020-0645-6

    • PubMed
    • Google Scholar
42. 1. Larsson NG

    (2010) Somatic mitochondrial DNA mutations in mammalian aging

    Annual Review of Biochemistry 79:683–706.

    https://doi.org/10.1146/annurev-biochem-060408-093701

    • PubMed
    • Google Scholar
43. 1. Li R
    2. Di L
    3. Li J
    4. Fan W
    5. Liu Y
    6. Guo W
    7. Liu W
    8. Liu L
    9. Li Q
    10. Chen L
    11. Chen Y
    12. Miao C
    13. Liu H
    14. Wang Y
    15. Ma Y
    16. Xu D
    17. Lin D
    18. Huang Y
    19. Wang J
    20. Bai F
    21. Wu C

    (2021) A body map of somatic mutagenesis in morphologically normal human tissues

    Nature 597:398–403.

    https://doi.org/10.1038/s41586-021-03836-1

    • PubMed
    • Google Scholar
44. 1. Lin YF
    2. Schulz AM
    3. Pellegrino MW
    4. Lu Y
    5. Shaham S
    6. Haynes CM

    (2016) Maintenance and propagation of a deleterious mitochondrial genome by the mitochondrial unfolded protein response

    Nature 533:416–419.

    https://doi.org/10.1038/nature17989

    • PubMed
    • Google Scholar
45. 1. López-Otín C
    2. Blasco MA
    3. Partridge L
    4. Serrano M
    5. Kroemer G

    (2013) The hallmarks of aging

    Cell 153:1194–1217.

    https://doi.org/10.1016/j.cell.2013.05.039

    • PubMed
    • Google Scholar
46. 1. Lujan SA
    2. Williams JS
    3. Pursell ZF
    4. Abdulovic-Cui AA
    5. Clark AB
    6. Nick McElhinny SA
    7. Kunkel TA

    (2012) Mismatch repair balances leading and lagging strand DNA replication fidelity

    PLOS Genetics 8:e1003016.

    https://doi.org/10.1371/journal.pgen.1003016

    • PubMed
    • Google Scholar
47. 1. Luo C
    2. Ding W
    3. Zhu S
    4. Chen Y
    5. Liu X
    6. Deng H

    (2022) Nicotinamide mononucleotide administration amends protein acetylome of aged mouse liver

    Cells 11:1654.

    https://doi.org/10.3390/cells11101654

    • PubMed
    • Google Scholar
48. 1. Ma H
    2. Lee Y
    3. Hayama T
    4. Van Dyken C
    5. Marti-Gutierrez N
    6. Li Y
    7. Ahmed R
    8. Koski A
    9. Kang E
    10. Darby H
    11. Gonmanee T
    12. Park Y
    13. Wolf DP
    14. Jai Kim C
    15. Mitalipov S

    (2018) Germline and somatic mtdna mutations in mouse aging

    PLOS ONE 13:e0201304.

    https://doi.org/10.1371/journal.pone.0201304

    • PubMed
    • Google Scholar
49. 1. Machiraju P
    2. Wang X
    3. Sabouny R
    4. Huang J
    5. Zhao T
    6. Iqbal F
    7. King M
    8. Prasher D
    9. Lodha A
    10. Jimenez-Tellez N
    11. Ravandi A
    12. Argiropoulos B
    13. Sinasac D
    14. Khan A
    15. Shutt TE
    16. Greenway SC

    (2019) SS-31 peptide reverses the mitochondrial fragmentation present in fibroblasts from patients with DCMA, a mitochondrial cardiomyopathy

    Frontiers in Cardiovascular Medicine 6:167.

    https://doi.org/10.3389/fcvm.2019.00167

    • PubMed
    • Google Scholar
50. 1. Marcelino LA
    2. Thilly WG

    (1999) Mitochondrial mutagenesis in human cells and tissues

    Mutation Research 434:177–203.

    https://doi.org/10.1016/s0921-8777(99)00028-2

    • PubMed
    • Google Scholar
51. 1. Martin AS
    2. Abraham DM
    3. Hershberger KA
    4. Bhatt DP
    5. Mao L
    6. Cui H
    7. Liu J
    8. Liu X
    9. Muehlbauer MJ
    10. Grimsrud PA
    11. Locasale JW
    12. Payne RM
    13. Hirschey MD

    (2017) Nicotinamide mononucleotide requires SIRT3 to improve cardiac function and bioenergetics in a friedreich’s ataxia cardiomyopathy model

    JCI Insight 2:e93885.

    https://doi.org/10.1172/jci.insight.93885

    • PubMed
    • Google Scholar
52. 1. Martincorena I
    2. Roshan A
    3. Gerstung M
    4. Ellis P
    5. Van Loo P
    6. McLaren S
    7. Wedge DC
    8. Fullam A
    9. Alexandrov LB
    10. Tubio JM
    11. Stebbings L
    12. Menzies A
    13. Widaa S
    14. Stratton MR
    15. Jones PH
    16. Campbell PJ

    (2015) Tumor evolution. high burden and pervasive positive selection of somatic mutations in normal human skin

    Science 348:880–886.

    https://doi.org/10.1126/science.aaa6806

    • PubMed
    • Google Scholar
53. 1. Martincorena I
    2. Raine KM
    3. Gerstung M
    4. Dawson KJ
    5. Haase K
    6. Van Loo P
    7. Davies H
    8. Stratton MR
    9. Campbell PJ

    (2017) Universal patterns of selection in cancer and somatic tissues

    Cell 171:1029–1041.

    https://doi.org/10.1016/j.cell.2017.09.042

    • PubMed
    • Google Scholar
54. 1. Martincorena I
    2. Fowler JC
    3. Wabik A
    4. Lawson ARJ
    5. Abascal F
    6. Hall MWJ
    7. Cagan A
    8. Murai K
    9. Mahbubani K
    10. Stratton MR
    11. Fitzgerald RC
    12. Handford PA
    13. Campbell PJ
    14. Saeb-Parsy K
    15. Jones PH

    (2018) Somatic mutant clones colonize the human esophagus with age

    Science 362:911–917.

    https://doi.org/10.1126/science.aau3879

    • PubMed
    • Google Scholar
55. 1. Masser DR
    2. Clark NW
    3. Van Remmen H
    4. Freeman WM

    (2016) Loss of the antioxidant enzyme cuznsod (sod1) mimics an age-related increase in absolute mitochondrial DNA copy number in the skeletal muscle

    Age 38:323–333.

    https://doi.org/10.1007/s11357-016-9930-1

    • PubMed
    • Google Scholar
56. 1. Mikhailova AG
    2. Mikhailova AA
    3. Ushakova K
    4. Tretiakov EO
    5. Iliushchenko D
    6. Shamansky V
    7. Lobanova V
    8. Kozenkov I
    9. Efimenko B
    10. Yurchenko AA
    11. Kozenkova E
    12. Zdobnov EM
    13. Makeev V
    14. Yurov V
    15. Tanaka M
    16. Gostimskaya I
    17. Fleischmann Z
    18. Annis S
    19. Franco M
    20. Wasko K
    21. Denisov S
    22. Kunz WS
    23. Knorre D
    24. Mazunin I
    25. Nikolaev S
    26. Fellay J
    27. Reymond A
    28. Khrapko K
    29. Gunbin K
    30. Popadin K

    (2022) A mitochondria-specific mutational signature of aging: increased rate of a > G substitutions on the heavy strand

    Nucleic Acids Research 50:10264–10277.

    https://doi.org/10.1093/nar/gkac779

    • PubMed
    • Google Scholar
57. 1. Mills KF
    2. Yoshida S
    3. Stein LR
    4. Grozio A
    5. Kubota S
    6. Sasaki Y
    7. Redpath P
    8. Migaud ME
    9. Apte RS
    10. Uchida K
    11. Yoshino J
    12. Imai S-I

    (2016) Long-Term administration of nicotinamide mononucleotide mitigates age-associated physiological decline in mice

    Cell Metabolism 24:795–806.

    https://doi.org/10.1016/j.cmet.2016.09.013

    • PubMed
    • Google Scholar
58. 1. Mitchell W
    2. Ng EA
    3. Tamucci JD
    4. Boyd KJ
    5. Sathappa M
    6. Coscia A
    7. Pan M
    8. Han X
    9. Eddy NA
    10. May ER
    11. Szeto HH
    12. Alder NN

    (2020) The mitochondria-targeted peptide SS-31 binds lipid bilayers and modulates surface electrostatics as a key component of its mechanism of action

    The Journal of Biological Chemistry 295:7452–7469.

    https://doi.org/10.1074/jbc.RA119.012094

    • PubMed
    • Google Scholar
59. 1. Muller HJ

    (1964) The relation of recombination to mutational advance

    Mutation Research 106:2–9.

    https://doi.org/10.1016/0027-5107(64)90047-8

    • PubMed
    • Google Scholar
60. 1. Nekhaeva E
    2. Bodyak ND
    3. Kraytsberg Y
    4. McGrath SB
    5. Van Orsouw NJ
    6. Pluzhnikov A
    7. Wei JY
    8. Vijg J
    9. Khrapko K

    (2002) Clonally expanded mtdna point mutations are abundant in individual cells of human tissues

    PNAS 99:5521–5526.

    https://doi.org/10.1073/pnas.072670199

    • PubMed
    • Google Scholar
61. 1. Obi C
    2. Smith AT
    3. Hughes GJ
    4. Adeboye AA

    (2022) Targeting mitochondrial dysfunction with elamipretide

    Heart Failure Reviews 27:1925–1932.

    https://doi.org/10.1007/s10741-021-10199-2

    • PubMed
    • Google Scholar
62. 1. Pickles S
    2. Vigié P
    3. Youle RJ

    (2018) Mitophagy and quality control mechanisms in mitochondrial maintenance

    Current Biology 28:R170–R185.

    https://doi.org/10.1016/j.cub.2018.01.004

    • PubMed
    • Google Scholar
63. 1. Pickrell AM
    2. Huang C-H
    3. Kennedy SR
    4. Ordureau A
    5. Sideris DP
    6. Hoekstra JG
    7. Harper JW
    8. Youle RJ

    (2015) Endogenous parkin preserves dopaminergic substantia nigral neurons following mitochondrial DNA mutagenic stress

    Neuron 87:371–381.

    https://doi.org/10.1016/j.neuron.2015.06.034

    • PubMed
    • Google Scholar
64. 1. Richly E
    2. Leister D

    (2004) NUMTs in sequenced eukaryotic genomes

    Molecular Biology and Evolution 21:1081–1084.

    https://doi.org/10.1093/molbev/msh110

    • PubMed
    • Google Scholar
65. 1. Rossignol R
    2. Malgat M
    3. Mazat JP
    4. Letellier T

    (1999) Threshold effect and tissue specificity

    Journal of Biological Chemistry 274:33426–33432.

    https://doi.org/10.1074/jbc.274.47.33426

    • PubMed
    • Google Scholar
66. 1. Rossignol R
    2. Faustin B
    3. Rocher C
    4. Malgat M
    5. Mazat JP
    6. Letellier T

    (2003) Mitochondrial threshold effects

    The Biochemical Journal 370:751–762.

    https://doi.org/10.1042/BJ20021594

    • PubMed
    • Google Scholar
67. 1. Salk JJ
    2. Schmitt MW
    3. Loeb LA

    (2018) Enhancing the accuracy of next-generation sequencing for detecting rare and subclonal mutations

    Nature Reviews. Genetics 19:269–285.

    https://doi.org/10.1038/nrg.2017.117

    • PubMed
    • Google Scholar
68. 1. Samstag CL
    2. Hoekstra JG
    3. Huang C-H
    4. Chaisson MJ
    5. Youle RJ
    6. Kennedy SR
    7. Pallanck LJ

    (2018) Deleterious mitochondrial DNA point mutations are overrepresented in Drosophila expressing a proofreading-defective DNA polymerase γ

    PLOS Genetics 14:e1007805.

    https://doi.org/10.1371/journal.pgen.1007805

    • PubMed
    • Google Scholar
69. 1. Samuels DC
    2. Li C
    3. Li B
    4. Song Z
    5. Torstenson E
    6. Boyd Clay H
    7. Rokas A
    8. Thornton-Wells TA
    9. Moore JH
    10. Hughes TM
    11. Hoffman RD
    12. Haines JL
    13. Murdock DG
    14. Mortlock DP
    15. Williams SM

    (2013) Recurrent tissue-specific mtdna mutations are common in humans

    PLOS Genetics 9:e1003929.

    https://doi.org/10.1371/journal.pgen.1003929

    • PubMed
    • Google Scholar
70. 1. Sanchez-Contreras M
    2. Sweetwyne MT
    3. Kohrn BF
    4. Tsantilas KA
    5. Hipp MJ
    6. Schmidt EK
    7. Fredrickson J
    8. Whitson JA
    9. Campbell MD
    10. Rabinovitch PS
    11. Marcinek DJ
    12. Kennedy SR

    (2021) A replication-linked mutational gradient drives somatic mutation accumulation and influences germline polymorphisms and genome composition in mitochondrial DNA

    Nucleic Acids Research 49:11103–11118.

    https://doi.org/10.1093/nar/gkab901

    • PubMed
    • Google Scholar
71. 1. Sanchez-Contreras M
    2. Kennedy SR

    (2022) The complicated nature of somatic mtDNA mutations in aging

    Frontiers in Aging 2:805126.

    https://doi.org/10.3389/fragi.2021.805126

    • PubMed
    • Google Scholar
72. 1. Schmitt MW
    2. Kennedy SR
    3. Salk JJ
    4. Fox EJ
    5. Hiatt JB
    6. Loeb LA

    (2012) Detection of ultra-rare mutations by next-generation sequencing

    PNAS 109:14508–14513.

    https://doi.org/10.1073/pnas.1208715109

    • PubMed
    • Google Scholar
73. 1. Suen DF
    2. Narendra DP
    3. Tanaka A
    4. Manfredi G
    5. Youle RJ

    (2010) Parkin overexpression selects against a deleterious mtdna mutation in heteroplasmic cybrid cells

    PNAS 107:11835–11840.

    https://doi.org/10.1073/pnas.0914569107

    • PubMed
    • Google Scholar
74. 1. Sweetwyne MT
    2. Pippin JW
    3. Eng DG
    4. Hudkins KL
    5. Chiao YA
    6. Campbell MD
    7. Marcinek DJ
    8. Alpers CE
    9. Szeto HH
    10. Rabinovitch PS
    11. Shankland SJ

    (2017) The mitochondrial-targeted peptide, SS-31, improves glomerular architecture in mice of advanced age

    Kidney International 91:1126–1145.

    https://doi.org/10.1016/j.kint.2016.10.036

    • PubMed
    • Google Scholar
75. 1. Szczepanowska K
    2. Trifunovic A

    (2017) Origins of mtDNA mutations in ageing

    Essays in Biochemistry 61:325–337.

    https://doi.org/10.1042/EBC20160090

    • PubMed
    • Google Scholar
76. 1. Vermulst M
    2. Bielas JH
    3. Kujoth GC
    4. Ladiges WC
    5. Rabinovitch PS
    6. Prolla TA
    7. Loeb LA

    (2007) Mitochondrial point mutations do not limit the natural lifespan of mice

    Nature Genetics 39:540–543.

    https://doi.org/10.1038/ng1988

    • PubMed
    • Google Scholar
77. 1. Wallace DC

    (1999) Mitochondrial diseases in man and mouse

    Science 283:1482–1488.

    https://doi.org/10.1126/science.283.5407.1482

    • PubMed
    • Google Scholar
78. 1. Wei W
    2. Tuna S
    3. Keogh MJ
    4. Smith KR
    5. Aitman TJ
    6. Beales PL
    7. Bennett DL
    8. Gale DP
    9. Bitner-Glindzicz MAK
    10. Black GC
    11. Brennan P
    12. Elliott P
    13. Flinter FA
    14. Floto RA
    15. Houlden H
    16. Irving M
    17. Koziell A
    18. Maher ER
    19. Markus HS
    20. Morrell NW
    21. Newman WG
    22. Roberts I
    23. Sayer JA
    24. Smith KGC
    25. Taylor JC
    26. Watkins H
    27. Webster AR
    28. Wilkie AOM
    29. Williamson C
    30. Ashford S
    31. Penkett CJ
    32. Stirrups KE
    33. Rendon A
    34. Ouwehand WH
    35. Bradley JR
    36. Raymond FL
    37. Caulfield M
    38. Turro E
    39. Chinnery PF
    40. NIHR BioResource–Rare Diseases
    41. 100,000 Genomes Project–Rare Diseases Pilot

    (2019) Germline selection shapes human mitochondrial DNA diversity

    Science 364:eaau6520.

    https://doi.org/10.1126/science.aau6520

    • PubMed
    • Google Scholar
79. 1. Whitson JA
    2. Bitto A
    3. Zhang H
    4. Sweetwyne MT
    5. Coig R
    6. Bhayana S
    7. Shankland EG
    8. Wang L
    9. Bammler TK
    10. Mills KF
    11. Imai S-I
    12. Conley KE
    13. Marcinek DJ
    14. Rabinovitch PS

    (2020) Ss-31 and NMN: two paths to improve metabolism and function in aged hearts

    Aging Cell 19:e13213.

    https://doi.org/10.1111/acel.13213

    • PubMed
    • Google Scholar
80. 1. Williams SL
    2. Mash DC
    3. Züchner S
    4. Moraes CT

    (2013) Somatic mtdna mutation spectra in the aging human putamen

    PLOS Genetics 9:e1003990.

    https://doi.org/10.1371/journal.pgen.1003990

    • PubMed
    • Google Scholar
81. 1. Xiong K
    2. Shea D
    3. Rhoades J
    4. Blewett T
    5. Liu R
    6. Bae JH
    7. Nguyen E
    8. Makrigiorgos GM
    9. Golub TR
    10. Adalsteinsson VA

    (2022) Duplex-repair enables highly accurate sequencing, despite DNA damage

    Nucleic Acids Research 50:e1.

    https://doi.org/10.1093/nar/gkab855

    • PubMed
    • Google Scholar
82. 1. Yoshino J
    2. Mills KF
    3. Yoon MJ
    4. Imai S

    (2011) Nicotinamide mononucleotide, a key NAD (+) intermediate, treats the pathophysiology of diet- and age-induced diabetes in mice

    Cell Metabolism 14:528–536.

    https://doi.org/10.1016/j.cmet.2011.08.014

    • PubMed
    • Google Scholar
83. 1. Yoshino J
    2. Baur JA
    3. Imai S-I

    (2018) Nad+ intermediates: the biology and therapeutic potential of NMN and NR

    Cell Metabolism 27:513–528.

    https://doi.org/10.1016/j.cmet.2017.11.002

    • PubMed
    • Google Scholar
84. 1. Youle RJ
    2. Narendra DP

    (2011) Mechanisms of mitophagy

    Nature Reviews. Molecular Cell Biology 12:9–14.

    https://doi.org/10.1038/nrm3028

    • PubMed
    • Google Scholar
85. 1. Zhang L
    2. Vijg J

    (2018) Somatic mutagenesis in mammals and its implications for human disease and aging

    Annual Review of Genetics 52:397–419.

    https://doi.org/10.1146/annurev-genet-120417-031501

    • PubMed
    • Google Scholar
86. 1. Zhang H
    2. Alder NN
    3. Wang W
    4. Szeto H
    5. Marcinek DJ
    6. Rabinovitch PS

    (2020) Reduction of elevated proton leak rejuvenates mitochondria in the aged cardiomyocyte

    eLife 9:e60827.

    https://doi.org/10.7554/eLife.60827

    • PubMed
    • Google Scholar
87. 1. Zheng W
    2. Khrapko K
    3. Coller HA
    4. Thilly WG
    5. Copeland WC

    (2006) Origins of human mitochondrial point mutations as DNA polymerase gamma-mediated errors

    Mutation Research 599:11–20.

    https://doi.org/10.1016/j.mrfmmm.2005.12.012

    • PubMed
    • Google Scholar
88. 1. Zhu M
    2. Lu T
    3. Jia Y
    4. Luo X
    5. Gopal P
    6. Li L
    7. Odewole M
    8. Renteria V
    9. Singal AG
    10. Jang Y
    11. Ge K
    12. Wang SC
    13. Sorouri M
    14. Parekh JR
    15. MacConmara MP
    16. Yopp AC
    17. Wang T
    18. Zhu H

    (2019) Somatic mutations increase hepatic clonal fitness and regeneration in chronic liver disease

    Cell 177:608–621.

    https://doi.org/10.1016/j.cell.2019.03.026

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "The multi-tissue landscape of somatic mtDNA mutations indicates tissue-specific accumulation and removal in aging" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Molly Przeworski as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Konstantin Khrapko (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Please address these essential items in your revised manuscript.

1. Please clarify that your claim of the largest collection of mtDNA mutations in a single study is limited to 'point' mutations only. An early study of mtDNA deletions detected much more mtDNA alterations.

2. Please address reviewer 1's concerns about how DNA mutations in NUMTs are excluded from your study.

3. Reviewer 1 has a concern that the effect of the drug treatment is independent of point mutagenesis for ROS-derived mutations. Given that the low amount of ROS-linked mutations is already well below a physiological level that would cause loss of mitochondrial function, please explain how reducing these mutations improves mitochondrial function.

4. Both of the reviewers agree that your study finds that, unlike conventional mtDNA mutations, the transversions are not involved in clonal expansion and do not accumulate with age and their relative presence varies very significantly between tissues and is affected by drug interventions. This unusual behavior of transversions is difficult to explain and thus this study is thought-provoking and will likely lead to further developments in mtDNA field. However, Reviewer 2 is concerned that the detection of transversions, in particular, may be due to an artifact reported by Abascal 2021. Please address their comments.

Reviewer #1 (Recommendations for the authors):

p.9. What is the sensitivity of In/Del detection compared to pt mutations?

"255 determined the frequency of both SNVs and small InDels (≲15bp) in aged mice."

Please reiterate this indel size range in the Discussion.

p. 16:

"414 Late-life treatment with mitochondrially-targeted interventions eliminate ROS-linked mutations".

p. 17:

"(Control: 9.7{plus minus}3.0x10-7 vs. ELAM: 5.5{plus minus}1.5x10-7, p=0.019; Control: 9.7{plus minus}3.0x10-7 vs. NMN: 440 4.7{plus minus}0.9x10-7, p=0.017)"

Beware of multiple hypothesis testing. In the Methods, they state that they accept tests with p-values < 0.05. If they are testing 6 pairs of substitution types and 2 treatments, then for a family-wise error rate of 0.05 their cutoff should really be p < 0.00427 (Šidák correction assuming 6x2=12 tests).

p. 21:

"This last scenario is a blind spot in Duplex-Seq methodology, such that large deletions are difficult to detect due to the reliance on the alignment of DNA fragments of ~200-400 nucleotides."

Studies exist that fill this blind spot. Please reference them, especially in light of the unreferenced assertion in the previous sentence.

"Our study is the first to demonstrate that ROS-linked mtDNA mutations can be specifically decreased pharmacologically at a late age and within a short treatment period in some tissues."

This assertion requires acceptance of the very marginal p-values on page 17.

p. 22:

"Previous overarching assumptions of how mtDNA mutations do, or do not, contribute to aging may have been premature when based on limited information from few tissues and with previous technical hurdles of poor accuracy in detection of low-level mutation burdens."

Wholeheartedly agreed. Do you think that the rates found herein are high enough to cause any of the phenotypes of aging? Would you care to defend your answer either way in the manuscript?

Reviewer #2 (Recommendations for the authors):

Historical comment:

Previous studies in the field are described in the following paragraph: line 503 "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 towards 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)"

We note, for the sake of fairness, that back in 1997 we published a study on somatic mtDNA mutations in human tissues (Khrapko et al. PNAS Vol. 94, pp. 13798-13803). In this work, we established the correct ballpark of mtDNA mutational frequency for SNVs (~3x10-6), demonstrated the strong bias towards G→A/C→T transitions, and, additionally, inferred the existence of clonal expansions/clusters of somatic mtDNA mutations within tissues. Later we also established the extreme strand asymmetry of somatic mtDNA mutations – Zheng 2006 – (doi:10.1016/j.mrfmmm.2005.12.012).

Interestingly, one of the controls we used to validate our mutational spectra was based on an asymmetric PCR test, in which we separately amplified either Watson or Crick strand. We then compared the Watson and the Crick mutational spectra to detect and exclude any single-stranded non-mutational base changes, i.e. those present in one but not the other spectrum (Khrapko et al., Nucleic Acids Research, 1997, Vol. 25, No. 4 685-693). This approach is conceptually very similar to the ds sequencing. It would be nice to cite and discuss this 'prior art research' in this publication.

Additionally, I would also recommend making other data more accessible to the reader, e.g., publishing the full list of mutations (and half-mutations) as a simple downloadable excel sheet.

Reviewer #1 (Recommendations for the authors):

p.9. What is the sensitivity of In/Del detection compared to pt mutations?

We can’t provide a specific ratio for this question, but Duplex-Seq is undoubtedly better at detecting point mutations. This is for several reasons. First, we perform targeted DNA capture using probes against the “wild-type” mtDNA sequence and the more sequence that is missing or added by an In/Del, the less efficient the capture will be. For small In/Dels, there is likely little loss, but for larger structural variations it’s likely to be a major source of loss. Second, the main aligner we use, bwa, is not very sensitive to larger In/Dels. An alternative aligner, of which there are many, would need to be used to get at this information. As the reviewer pointed out, Lujan et al. have developed a nice computational method to detect large structural variants from short read technologies. As we perform a reference free consensus calling algorithm any structural variations present in the samples will be found in the generated fastq files, provided that they were captured during target enrichment.

"255 determined the frequency of both SNVs and small InDels (≲15bp) in aged mice."

Please reiterate this indel size range in the Discussion.

We have done so.

p. 16:

"414 Late-Life treatment with mitochondrially-targeted interventions eliminate ROS-linked mutations".

There doesn’t seem to be a specific comment here, but we have changed “eliminates” to “reduces” to better reflect the fact that ROS-linked mutations are still present.

p. 17:

"(Control: 9.7{plus minus}3.0x10-7 vs. ELAM: 5.5{plus minus}1.5x10-7, p=0.019; Control: 9.7{plus minus}3.0x10-7 vs. NMN: 440 4.7{plus minus}0.9x10-7, p=0.017)"

Beware of multiple hypothesis testing. In the Methods, they state that they accept tests with p-values < 0.05. If they are testing 6 pairs of substitution types and 2 treatments, then for a family-wise error rate of 0.05 their cutoff should really be p < 0.00427 (Šidák correction assuming 6x2=12 tests).

We apologize for the confusion on this. The stated p-values are in fact adjusted p-values and we do perform multiple testing correction (specifically Tukey’s HSD or Dunnet’s test, where applicable). We now refer to the multiple testing corrected p-value as p_adj.

p. 21:

"This last scenario is a blind spot in Duplex-Seq methodology, such that large deletions are difficult to detect due to the reliance on the alignment of DNA fragments of ~200-400 nucleotides."

Studies exist that fill this blind spot. Please reference them, especially in light of the unreferenced assertion in the previous sentence.

We have revised this statement slightly and have also cited Krishnan et al., 2008 and Lujan et al., 2012 in the preceding sentence.

"Our study is the first to demonstrate that ROS-linked mtDNA mutations can be specifically decreased pharmacologically at a late age and within a short treatment period in some tissues."

This assertion requires acceptance of the very marginal p-values on page 17.

We have toned down this assertion to read

“Our study suggests that mtDNA mutations can be specifically decreased pharmacologically at late age and within a short treatment period in some tissues.”

And

“This response provides a potential explanation for why ROS-linked mtDNA mutations are rarely found to accumulate with age.”

See also our reply to the reviewer’s concern regarding p-values in comment #9.

p. 22:

"Previous overarching assumptions of how mtDNA mutations do, or do not, contribute to aging may have been premature when based on limited information from few tissues and with previous technical hurdles of poor accuracy in detection of low-level mutation burdens."

Wholeheartedly agreed. Do you think that the rates found herein are high enough to cause any of the phenotypes of aging? Would you care to defend your answer either way in the manuscript?

As noted in Reply #3, we need to know both how many cells harbor a mutation, as well as what the heteroplasmic level is within the cell, before making claims on their pathological impact. We have included a paragraph in the Discussion about this important issue starting on line 582.

Reviewer #2 (Recommendations for the authors):

Historical comment:

Previous studies in the field are described in the following paragraph: line 503 "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 towards 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)"

We note, for the sake of fairness, that back in 1997 we published a study on somatic mtDNA mutations in human tissues (Khrapko et al. PNAS Vol. 94, pp. 13798-13803). In this work, we established the correct ballpark of mtDNA mutational frequency for SNVs (~3x10-6), demonstrated the strong bias towards G→A/C→T transitions, and, additionally, inferred the existence of clonal expansions/clusters of somatic mtDNA mutations within tissues. Later we also established the extreme strand asymmetry of somatic mtDNA mutations – Zheng 2006 – (doi:10.1016/j.mrfmmm.2005.12.012).

Interestingly, one of the controls we used to validate our mutational spectra was based on an asymmetric PCR test, in which we separately amplified either Watson or Crick strand. We then compared the Watson and the Crick mutational spectra to detect and exclude any single-stranded non-mutational base changes, i.e. those present in one but not the other spectrum (Khrapko et al., Nucleic Acids Research, 1997, Vol. 25, No. 4 685-693). This approach is conceptually very similar to the ds sequencing. It would be nice to cite and discuss this 'prior art research' in this publication.

We apologize for not including these citations. We are definitely aware of them and have cited them in prior publications. This was an oversight on our part, and we have now included them in the revised manuscript.

Additionally, I would also recommend making other data more accessible to the reader, e.g., publishing the full list of mutations (and half-mutations) as a simple downloadable excel sheet.

An excellent suggestion. It is available as Supplementary File 2.

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

• 1,653

  Page views

• 238

  Downloads

• 3

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.