Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Feb 12.
Published in final edited form as: Science. 2025 Sep 4;390(6769):164–172. doi: 10.1126/science.adk7978

Reversible compromise of physiological resilience by accumulation of heteroplasmic mtDNA mutations

Huihui Huang 1, Yi Wang 2, Zsuzsanna K Zsengeller 1, Joshua M Gorham 13, Vamsidhara Vemireddy 3, Amanda J Clark 1,4, Hui Pan 5, Jonathan M Dreyfuss 5, Vasantha Jotwani 6, Michael G Shlipak 6, Mark J Sarnak 7, Chirag R Parikh 8, Heather Thiessen-Philbrook 8, Ronit Katz 9, Sushrut S Waikar 10, Nicole J Lake 11, Monkol Lek 11, Wen Shi 12, Daniela Puiu 12, Yun Soo Hong 12, Jonathan G Seidman 13, Dan E Arking 12, Samir M Parikh 1,3,*
PMCID: PMC12892176  NIHMSID: NIHMS2138758  PMID: 40906782

Abstract

Somatically acquired mitochondrial DNA mutations accumulate with age, but the mechanisms and consequences are poorly understood. Here we show that transient injuries induce a burst of persistent mtDNA mutations that impair resilience to future injuries. mtDNA mutations suppressed energy-intensive nucleotide metabolism. Repletion of adenosine, but not other nucleotides, restored ATP generation, which required a nuclear-encoded purine biosynthetic enzyme, adenylate kinase 4 (AK4). Analysis of 369,912 UK Biobank participants revealed a graded association between mutation burden and chronic kidney disease severity as well as an independent increase in the risk of future acute kidney injury events (p < 10−7). Heteroplasmic mtDNA mutations may therefore reflect the cumulative effect of acute injuries to metabolically active cells, impairing major functions in a fashion amenable to nuclear-controlled purine biosynthesis.

INTRODUCTION

As the cell’s powerhouse, individual mitochondria contain two to ten copies of mitochondrial DNA (mtDNA), and each cell contains 10–1000s of mitochondria. This circular genome encodes 37 products that include core components of oxidative phosphorylation (OXPHOS), the final biochemical pathway for ATP production, as well as ribosomal RNAs and transfer RNAs required for translation (1). Both inherited and acquired mtDNA mutations give rise to the state of mtDNA called heteroplasmy, in which two or more mtDNA alleles exist within the same cell. Heteroplasmic mtDNA mutations rise markedly after age 70 in humans, largely attributable to somatic changes (2). Emerging evidence shows that mtDNA mutations increase in frequency with age in mice, with the kidney possessing the highest mtDNA mutation frequency among major organs (3).

The theory of mtDNA mutations driving aging has been, on one hand, supported by a mouse genetic model of accelerated mtDNA mutations that exhibits accelerated multi-organ aging (4–6). However, the burden of mtDNA mutations in this genetic model exceeded normal mouse aging (7). The mechanisms by which an organ like the kidney could accumulate mtDNA mutations and the consequences of this accumulation have not been studied.

Here we conducted a multi-omic interrogation of CRISPR-modified genetic models and analyses in a large human genomic dataset to investigate the relationships among mtDNA mutations, acute injuries, and chronic organ damage in the kidney. The results suggest a positive feedback loop in which acute injury induces mtDNA mutations that persist and impair energy metabolism and physiological resilience to future stressors in a fashion amenable to metabolic intervention; and that peripheral assessment of mtDNA heteroplasmy may be both a quantitative risk factor for susceptibility to acute kidney injury (AKI) and, independently, an indicator of chronic kidney disease (CKD) severity.

RESULTS

Acute injury induces persistent mtDNA mutations that are associated with functional and structural tissue damage

We studied unrelated models of AKI progressing to CKD: ischemia-reperfusion injury (IRI) and crystalline damage following parenteral folic acid (FA). We used 24 hours as the acute time point; chronic results were assessed 30 days after IRI and 14 days after FA (Figure 1A). In both models, tubular injury and functional impairment—measured by serum creatinine (Scr) and blood urea nitrogen (BUN)—were evident at the acute time point; by the chronic time point, fibrosis had developed and was associated with increases in canonical markers fibronectin (Fn), collagen 1-alpha-1 (Col1α1), and alpha-smooth muscle actin (α-SMA) (Figure S1).

Figure 1: Transient injury induces persistent mtDNA mutations in animals and across CKD stages in patients undergoing nephrectomy.

Figure 1:

Open in a new tab

(A) Schematic depicting time points for evaluation after kidney ischemia reperfusion injury (IRI) or parenteral folic acid injection (FA, 250 mg/kg IP × 1) to model acute kidney injury (AKI) and chronic kidney disease (CKD). (B-G) Characterization of acute and chronic mtDNA mutation burden after IRI: (B) mtDNA sequencing heatmap, row 1 is nucleotide position #1 in mtDNA genome, and color is indicative of alternative allele fraction at the position; (C) number of mtDNA sites with an alternative allele, where each symbol represents a single animal’s kidney specimen; (D) fraction of variant alleles per 50 bp across the mtDNA genome; (E) classification of mutation type; (F) correlation of acute histological injury with acute mtDNA mutation burden; and (G) correlation of chronic histological fibrosis score with chronic mtDNA mutation burden. (H-M) Characterization of acute and chronic mtDNA mutation burden after FA as in B-G, respectively. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 vs control. ###p < 0.001 vs FA-Acute. (N) Circular plot showing genome-wide distribution of sites with alternative alleles among human kidney samples. (O) Representative immunohistochemistry comparing kidney expression of mitochondrially encoded cytochrome c oxidase subunit I (MTCOI in control vs. CKD human kidney with scale bar 100 μm. (P) Correlation of mtDNA mutation burden with eGFR in human kidneys (n=44 patients). (Q) mtDNA mutation burden in CKD cohort above or below KDIGO stage 3, 60 ml/min/1.73m2. (R) Annotation of mutation type in CKD cohort. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 vs. control.

Assessment of the mtDNA mutation hotspot D-loop region (1) revealed lesions induced acutely by IRI or FA that persisted to the chronic time point; in contrast, no consistent changes were observed in mtDNA copy number in either injury model (Figure S2). Sequencing demonstrated that alternative alleles were distributed across the mtDNA genome; that the number of sites with alternative alleles increased approximately two-fold acutely; and that this mutational burden persisted to the chronic time point (Figure 1B, 1C), patterns corroborated by the fraction of alternative mtDNA alleles (Figure 1D). Mutations spanned synonymous, non-synonymous, and nonsense changes; and the burden of mutations at each time point was associated respectively with the extent of acute tissue injury and chronic fibrosis (Figure 1E–1G). The FA model revealed similar results (Figure 1H–1M). At either time point in either model, mutations were observed across the mtDNA genome with a distribution of alternative alleles not significantly different from random, consistent with spread beyond the D-loop hotspot (Figure S3).

We next studied kidney tissue obtained from individuals with varying levels of CKD undergoing nephrectomy, for example, for cancer treatment (Table S1). Circular plot and heatmap of mtDNA mutations confirmed distribution of alternative alleles throughout the genome region (Figure 1N & S4A-S4C). CKD kidneys exhibited mitochondrial impairment (Figure 1O). Both the abundance of alternative mtDNA alleles and the fraction present in the coding region were linearly associated with eGFR, and there was no significant correlation between mtDNA copy number and eGFR (Figure 1P & S4D, S4E). When we dichotomized the cohort by a common threshold for specialty care referral, mutational burden was higher in the more severe group (Figure 1Q,1R).

Oxidative stress may drive acute injury and chronic impairment (8). To model this, we applied H2O2 for 30 minutes or 72 hours to renal tubular epithelial cells, representing acute and chronic exposure. After 30 minutes, four readouts of mtDNA damage were increased: mitochondrial 8-oxo-2′-deoxyguanosine (8-OHdG) (9); D-loop lesions; number of sites with alternative alleles; and fraction of alternative alleles (Figure S5A-S5F). mtDNA mutations represented a mixture of mutation types, reactive oxygen species (ROS) were elevated, and ATP generation was suppressed (Figure S5G-S5I). Chronic exposure increased fibrosis markers (Figure S5J).

mtDNA mutations exacerbate energetic and physiological responses to transient stress in cells and mice

To examine sufficiency of somatic mtDNA mutation accumulation to impair cellular functions, we introduced a proofreading defect in the mitochondrial DNA polymerase, POLG (Figure 2A, 2B, S6A), that creates SNVs and larger deletions (6, 10). Compared to isogenic control cells, PolgD257A/− cells displayed a two-fold increase in alternative mtDNA allele abundance and fraction, decreased mitochondrial respiration, increased glycolysis, and decreased expression of mitochondrially encoded respiratory chain subunits (Figure 2C–2E & S6B-S6D).

Figure 2. mtDNA mutations exacerbate responses to transient stress in cells and mice.

Figure 2.

Open in a new tab

(A) Schematic depicting acute and chronic models of oxidative stress on cultured PolgD257A/− or Polg+/+ isogenic renal tubular cells and time points for evaluation after either kidney ischemia reperfusion injury (IRI) or parenteral folic acid injection (FA, 250 mg/kg IP × 1) in PolgD257A mice vs. littermate wildtype control Polg+/+ mice. (B) Schematic to develop clonal PolgD257A/− cells. (C-E) Characterization of PolgD257A/− cells vs. Polg+/+ control cells at baseline: (C) mtDNA sequencing heatmap; (D) number of mtDNA sites with alternative alleles; and (E) fraction of variant alleles per 50 bp across the mtDNA genome. (F-H) Characterization of PolgD257A/− cells vs. Polg+/+ control cells with and without H2O2 500 μM 24h: (F) ROS generation; (G) ATP generation; and (H) fibrosis marker expression. (I-L) Acute outcomes after IRI: (I) PAS-stained representative photomicrographs of kidney cortex with arrows to dilated tubules and scale bar 50 μm; (J) blinded quantification of histological tubular injury; (K) serum creatinine (SCr); and (L) blood urea nitrogen (BUN). (M-O) Chronic outcomes after IRI: (M) picrosirius red-stained representative photomicrographs of kidney cortex with arrows to areas of fibrosis and scale bar 50 μm; (N) blinded quantification of histological fibrosis; and (O) PCR of kidney cortex mRNA for fibrosis markers fibronectin (Fn), collagen1α1 (Col1α1), and α-smooth muscle actin (αSMA). (P-S) Acute outcomes after FA as in I-L, respectively. (T-V) Chronic outcomes after FA as in M-O, respectively. *p < 0.05, **p < 0.01, ***p < 0.001, vs. control. #p < 0.05, ##p<0.01, ###p < 0.001 vs Polg+/+-Acute or Polg+/+-Chronic as indicated.

In agreement with prior PolgD257A work (10), ROS generation was not increased in PolgD257A/− cells (Figure 2F). ATP generation, however, was reduced and further suppressed following transient H2O2 exposure (Figure 2G). At baseline, PolgD257A/− cells displayed induction of fibrotic genes, which H2O2 exposure exacerbated further (Figure 2H).

PolgD257A mice had normal kidney histology and function at baseline, but more mtDNA lesions and mtDNA SNVs and decreased expression of oxidative phosphorylation complexes compared to Polg+/+ mouse kidneys (Figure S7). After IRI, PolgD257A mice developed worse acute tissue injury and kidney function with more collagen deposition, scar formation, and fibrotic gene expression chronically compared to Polg+/+ mice (Figure 2I–2O). In the FA model, PolgD257A mice similarly developed worse outcomes at both time points (Figure 2P–2V). These findings collectively indicate that increased acquired mtDNA mutation burden exacerbates energetic and physiological outcomes after transient stress.

mtDNA mutations suppress nucleotide biosynthesis, and adenosine restores metabolic functions via AK4

Metabolomics on IRI kidneys from PolgD257A and Polg+/+ littermates unveiled both genotype- and injury-driven differences including a decrease in many nucleotide intermediates and an increase in lipid species in PolgD257A kidneys (Figure 3A–3D). The latter change was consistent with decreased oxidative metabolism we observed in PolgD257A/− cells (Figure S6B-S6D) and published results in kidney injury (11, 12). Several amino acids involved in purine biosynthesis—glutamate, glutamine, and glycine (13)—were decreased in IRI PolgD257A kidneys (Figure 3D). Metabolomics of PolgD257A/− cells and isogenic controls reproduced the whole-kidney pattern of lipid accumulation and nucleotide metabolite depletion in PolgD257A/− cells (Figure 3E–3F & S8A). Adenosine, AMP, and adenosine diphosphate (ADP) were all decreased in PolgD257A/− cells (Figure S8B).

Figure 3: Adenosine restores ATP generation in mtDNA mutation induced nucleotide biosynthesis suppression cells via AK4.

Figure 3:

Open in a new tab

(A) Schematic depicting metabolomics and RNAseq analysis of PolgD257A kidneys vs. Polg+/+ controls, and metabolomics analysis of PolgD257A/− cells vs. isogenic controls. (B) Principal component analysis and (C) volcano plot of kidney metabolomes following acute IRI in PolgD257A kidneys vs. Polg+/+ controls. (D) Representative functional classification heatmap of metabolites confirming reduction of purine metabolites and accumulation of fatty acids following IRI in PolgD257A kidneys vs. Polg+/+ controls. (E) Principal component analysis and (F) volcano plot of kidney metabolomes following acute IRI in PolgD257A cells vs. isogenic Polg+/+ controls. (G) ATP generation at 24h in control, adenosine (480 μM), and/or H2O2 (100 or 500 μM) treatment conditions in PolgD257A/− cells. (H) PCR for fibrosis markers fibronectin (Fn) and collagen1α1 (Col1α1) at 72h in the indicated condition in PolgD257A/− cells. *p < 0.05, **p < 0.01, ***p < 0.001 vs. control. (I-K) RNAseq and analysis of PolgD257A kidneys vs. Polg+/+ controls: (I) Principal component analysis and (J) volcano plot of acute IRI transcripts; and (K) heatmap of components of “ATP Metabolic Process” gene set. (L) Schematic of reactions catalyzed by AKs and actions of the AK inhibitor compound AP5A. (M) ATP generation in PolgD257A/− cells treated with or without adenosine (Ado) (480 μM) /AK4 siRNA under H2O2 condition (500 μM × 24h). (N) ATP generation in PolgD257A/− cells 24h after adenosine (480 μM) and increasing concentrations of AK inhibitor AP5A. *p < 0.05, **p < 0.01, ***p < 0.001 vs. control. ###p < 0.001 vs second condition from left in M-N. #p < 0.05, ##p < 0.01 vs. Ado condition in N.

To test whether ATP could be restored by nucleoside supplementation, we next applied adenosine, guanosine, cytidine, or thymidine to PolgD257A/− cells or controls. Only adenosine increased basal ATP generation (Figure S8C-S8F). Adenosine also counteracted the deleterious effects of oxidative stress on ATP generation, mitochondrial membrane potential, mitochondrial respiration, and expression of fibrosis markers in PolgD257A/− cells without itself alleviating oxidative stress or affecting isogenic controls (Figure 3G, 3H & Figure S8G-S8O).

Like metabolomes, transcriptomes of IRI kidneys from PolgD257A and Polg+/+ littermates were also highly correlated (r >0.9) within each experimental group and unveiled both genotype- and injury-driven differences (Figure 3I & S9A, S9B). Adenylate kinase 4 (AK4) was decreased in PolgD257A kidneys compared to Polg+/+ (Figure 3J,3K & S9C). This enzyme facilitates phosphate group transfer between adenosine triphosphate (ATP) and adenosine monophosphate (AMP, Figure 3L) and localizes to mitochondria (14, 15). Gene set enrichment and pathway analyses highlighted “ATP metabolic process” and decreased oxidative phosphorylation in PolgD257A compared to Polg+/+ after injury (Figure S9D, S9E) (16).

Adenosine can act through surface receptors or by cellular import (17). Three structurally distinct adenosine uptake inhibitors—cilostazol, dipyridamole, and NBMPR—each reduced adenosine-dependent ATP generation (Figure S10A-S10D). In contrast, neither adenosine receptor 2A (ANR94) nor adenosine receptor 2B (MRS1754) inhibitors reduced adenosine-induced ATP generation in PolgD257A/− cells (Figure S10E, S10F).

Excess or reduced AK4 exerted a bidirectional gene dose effect on ATP generation under oxidant stress. (Figure S11A-S11C). In the absence of oxidant stress, the AK inhibitor AP5A had no discernible effect on ATP generation on PolgD257A/− cells or controls (Figure S11D, S11E). Either pharmacological inhibition or genetic reduction of AK4 attenuated the ability of adenosine to increase ATP generation in PolgD257A/− cells (Figure 3M, 3N & S11F). Together, these results show that AK4 is sufficient to increase ATP generation and required for adenosine-dependent ATP induction in cells bearing mtDNA mutations.

Adenosine restores resistance to injury in mtDNA-mutated animals via adenylate kinase

We next tested whether adenosine could restore injury resistance when mtDNA mutations were elevated. Histological injury and physiological impairment were more severe in PolgD257A mice than littermate controls (Figure 4A). Adenosine’s protective effects were abrogated by concomitant AK inhibition (Figure 4B–4D). Adenosine supplementation improved both scar formation and fibrotic gene expression, and concomitant AP5A once again abrogated these effects (Figure 4E, 4F).

Figure 4: Adenosine restores injury resistance in mtDNA mutated animals via adenylate kinase.

Figure 4:

Open in a new tab

(A) Schematic depicting time points for evaluation after kidney ischemia reperfusion injury (IRI) in PolgD257A mice vs. littermate wildtype control Polg+/+ mice receiving adenosine (Ado, 17.5 mg/kg IP × 1) ± adenylate kinase inhibitor (AP5A, 5mg/kg IP × 1) at the time of surgery. (B) PAS (left 2 columns) and picrosirius red (right 2 columns) stained representative photomicrographs of kidney cortex from indicated conditions with scale bar 50 μm. (C) blinded quantification of histological tubular injury. (D) serum creatinine (SCr). (E) blinded quantification of histological fibrosis. (F) PCR of kidney cortex mRNA for fibrosis markers fibronectin (Fn), collagen1α1 (Col1α1), and α-smooth muscle actin (αSMA). (G) UMAP plots of single nucleus RNA sequencing from the following kidneys: Polg+/+ undergoing sham surgery and treated with vehicle (left); PolgD257A undergoing IRI and treated with vehicle (middle); and PolgD257A undergoing IRI and treated with adenosine (right). (H) Cell population proportions from G: CD, collecting duct; IC intercalated cell; PC principal cell; DCT distal convoluted tubule, Endo endothelial cell; LOH Loop of Henle; Macro, macrophage; Mesan, mesangial; Podo, podocyte; PT, proximal tubule; RV, renal vesicle. (I) Pathway analysis results with net enrichment score (NES) PT population from kidneys: PolgD257A undergoing IRI and treated with adenosine vs. PolgD257A undergoing IRI and treated with vehicle. *p < 0.05, **p < 0.01, ***p < 0.001 as indicated.

Single nucleus RNA sequencing of kidneys from control vs. PolgD257A mice showed that injury substantially reduced the proximal tubule population; in contrast, immune cell populations were low at baseline and rose only modestly following injury (Figure 4G–4H & S12A, S12B). Neither whole-blood cellular mtDNA nor CD11b+ myeloid cells isolated from kidneys displayed an increase in mtDNA mutations following injury (Figure S12C-S12H). Adenosine treatment upregulated genes involved with mitochondrial metabolism among proximal tubular epithelium cells (Figure 4I), consistent with the metabolic actions of adenosine in PolgD257A/− cells in Figure 3.

mtDNA mutation burden is independently associated with chronic disease severity and susceptibility to future acute kidney injury

Finally, we evaluated the relationships among mtDNA mutations, severity of CKD, and risk of AKI in the UK Biobank (Figure 5A). Mutational burden was assessed by heteroplasmy count after applying a 5% variant allele frequency (VAF) cutoff and through analysis of evolutionary mitochondrial local constraint (MLC) in order to annotate functional consequences of mutations (18–21). Briefly, MLC assigns each variant a score ranging from 0 for a synonymous change to 1 for a change predicted to be highly deleterious (Table S2). A modified MLC score sum (mMSS) was calculated for each individual sample, inversely weighted by the homoplasmic frequency of the variant.

Figure 5. mtDNA mutations in humans increase the risk of future acute kidney injury and independently associated with CKD severity.

Figure 5.

Open in a new tab

(A) Flow chart illustrating application of mitochondrial local constraint (MLC) model in UK Biobank (n=369,912 with 18,012 incident AKI events). (B, C) Forest plots showing the association of mtDNA heteroplasmy count (B) and mMSS (C) with estimated glomerular filtration rate (eGFR), respectively. (D) Kaplan-Meier survival curves for association between modified mitochondrial MLC score sum (mMSS) and acute kidney injury (AKI). (E) Proportion of participants with same mMSS at different increasingly severe clinical categories of CKD: G1 (normal), G2 (mild), G3+ (moderate to advanced). P=5.83×10−6. Adjusted for sex, age, BMI, center, smoking status.

Heteroplasmy count associated with lower eGFR (Figure 5B). Converting heteroplasmy count to functional assessment through mMSS, we found that mMSS was even more significantly associated with lower eGFR (−0.71 mL/min/1.73 m2 per SD of mMSS, 95% CI −0.96 to −0.47, P = 1.83 × 10−8, Figure 5C). Including heteroplasmy count in the model with mMSS only moderately attenuated the effect (0.59 mL/min/1.73 m2 per SD of mMSS, 95% CI −0.88 to −0.31, P = 1.83 × 10−8), suggesting a model in which heteroplasmy count increases with kidney function impairment and functionally damaging heteroplasmies may be linked to worsening kidney function.

To determine the impact of mitochondrial heteroplasmy on AKI, we performed Cox proportional hazards models adjusted for assessment center, age, sex, body mass index, and smoking status. The mMSS was associated with a 1.25-fold increased hazard ratio (HR) for incident AKI (95% CI 1.15–1.35, P = 4.50×10−8, Figure 5D). Including the number of heteroplasmies in the model only minimally attenuated the result (HR = 1.23, 95% CI 1.12–1.36, P = 1.13×10−5). Because mtDNA mutation burden was associated with both eGFR—a measure of persistent kidney impairment—and risk of future AKI, these findings support the observation that persistent GFR impairment is among the strongest risk factors for AKI (22). To determine whether the association of mtDNA heteroplasmy with AKI is independent of reduced eGFR, we then included both mMSS and eGFR in the same model. The mMSS was still associated with incident AKI (P = 1.70 × 10−6) with only modest attenuation of the effect estimate after inclusion of eGFR (HR = 1.21, 95% CI 1.12–1.31 vs. 1.25, 95% CI 1.15–1.35, Figure S13). When we adjusted separately for major risk factors and characteristics of chronic impairment—including type 2 diabetes, hypertension, and albumin-to-creatinine ratio—we found very modest attenuation of the effect of mMSS on AKI risk (HR=1.21, 95% CI 1.11–1.32). These analyses indicated that mMSS is associated with risk of AKI even after adjusting for severity of chronic kidney disease.

We also grouped patients by different levels of mMSS and categorized them within each group based on the clinical grades proposed by the clinical guidelines: G1 (normal), G2 (mildly decreased), and G3a+ (ranging from mild decrease to kidney failure). Individuals with higher mMSS levels exhibited more severe kidney function decline, with the proportion of G2 increasing from 33% to 52% and G3a+ from 1% to 4% (P = 5.83×10−6, adjusted for sex, age, BMI, center, smoking status, Figure 5E). Together, these findings suggest that mtDNA mutational burden is independently and proportionally associated with both the risk for future AKI and severity of CKD.

DISCUSSION

Here we have shown that unrelated transient insults lead to a burst of randomly distributed and persistent mutations throughout the mitochondrial genome that reduce gene expression, oxidative metabolism, and resistance to oxidative injury while increasing expression of fibrosis genes. A large human cohort study identified mtDNA burden as a proportional risk factor for future AKI as well as severity of CKD, and additional human results confirmed a relationship between in situ mtDNA mutations and CKD severity.

These results suggest a feed-forward loop in which initial injury leads to persistent mtDNA mutations, which in turn enhance susceptibility to future injury, culminating in irreversible fibrosis. Both in cells and in animals bearing mtDNA mutations, supplementation of adenosine, but not other nucleotide metabolites, acted through adenylate kinase to enhance resistance to early and late adverse sequelae of transient insults.

Overall, 1 out of 5000 adults are diagnosed with mitochondrial disease caused by a homoplasmic mtDNA mutation (23). Somatic mtDNA mutations—typically heteroplasmic—are present in ~30% of the adult population (24) and have been linked to the development of highly prevalent human conditions and overall mortality (20, 25–27). These mutations lead to biochemical defects within individual cells. Persistence of mtDNA mutations after transient stress implies an imperfect cellular machinery for monitoring and maintaining wild type mtDNA genomes.

A recent study of more than 250,000 individuals suggested roles for multiple nuclear-encoded genes in the regulation of maternally inherited mtDNA indel burden, including the mitochondrial DNA polymerase mutated in this study, POLG2, and AK3, a paralog of AK4 described herein (2). They also showed that heteroplasmic mutations appear to accumulate somatically with older age rather than as a quantitatively inherited feature. This aligns with the present thesis that mtDNA somatic mutations may be induced by transient stressors throughout life. It remains an open question whether common variation in nuclear encoded loci such as POLG2 can control somatic mtDNA mutation load. The kidney may be a model organ to explore this phenomenon, as suggested by a carefully conducted organ-based survey of mtDNA mutation burden across the lifespan (3).

Just as the mtDNA SNV burden is high in the kidney and rises with age (2, 3), there is a sharp rise in the risk of acute kidney injury with older age (28). A high mtDNA mutation burden may also contribute to adverse long-term outcomes following other age-associated transient insults such as stroke and myocardial infarction. These hypotheses need further study. Our results raise the possibility that variation in normal kidney aging reflects a complex interplay between repeated subclinical injuries and inherited or acquired changes in mtDNA mutation burden.

Both metabolomics and RNAseq identified defects in nucleotide metabolism arising from increased mtDNA mutations, aligning with recent reports linking mtDNA heteroplasmy with nucleotide balance (29, 30). For both pyrimidine and purine biosynthesis, intact mitochondrial form and function are necessary in at least three ways: (a) provision of sufficient ATP to drive energetically costly reactions necessary for nucleotide synthesis; (b) localization of individual biosynthetic enzymes or enzyme complexes on or in mitochondria (31); and (c) availability of mitochondrially generated amino acid building blocks for nucleobases (13). Purine biosynthesis bears an additional relationship by furnishing the precursor AMP necessary for AKs to generate ADP which, in turn, undergoes phosphorylation to ATP in the mitochondrion. We found that adenosine required AK4 to restore ATP in cells bearing excess mtDNA mutations, suggesting a reciprocal relationship of mtDNA mutations with AK loci. How mtDNA mutations impact purine biosynthesis requires further investigation, although one tracer study has shown that amino acid metabolism may be important (32).

Several areas for future investigation should be noted. First, mtDNA mutations could themselves be causal, but also likely indicate the extent of mtDNA damage, including indels, that together impair cellular metabolism and physiological resilience to stressors (6, 10).Second, the polyploid nature of mtDNA permits both inherited and acquired mutations (33). Whether both classes of variants are regulated through similar mechanisms merits further study. Third, adenosine can act in multiple ways and is unlikely to protect the kidney solely through substrate-level transformation to ATP. Finally, mtDNA mutation burden reflects a combination of factors favoring the development and persistence of alternative mtDNA clones vs. the elimination of such clones with cells or the cells in their entirety (34). For example, differences in mitophagy may contribute. More frequent sampling may help describe the temporal dynamics of mtDNA mutation appearance following cell injury, particularly with single cell resolution (35). Such methods may also describe tissue-, age-, and disease-related variation of mtDNA mutation burden in humans.

In summary, the present results reveal a burst of acquired mtDNA mutations arising after acute injury that persist to impair cellular metabolic health while promoting fibrosis. In animals, mtDNA mutation burden compromises responses to unrelated acute stressors. In humans, mtDNA mutation burden is independently and quantitatively associated with risk of future AKI and severity of CKD. A high burden of mtDNA mutation reduces nucleotide metabolism whereas adenosine administration restores cellular metabolic functions and injury resistance through adenylate kinase action. These results reveal mtDNA mutation burden as a genetic and tissue-based indicator of organ health and a nucleotide-dependent mechanism to enhance organ resilience. Considered broadly, the results suggest a means to assess the interaction of genetics and injury at the level of individual tissues in a quantifiable and targetable fashion.

Supplementary Material

Table S1 Characteristics of participants in quartiles of mt-SNVs
Table S3 Metabolomics data_animal and cell sample
Table S4 Primer List
Table S8 Patients mito genome annotation
Table S10 Allele fraction table for human biopsy 44 samples
Table S11 Sig bins table_pval0.05
Table S9 Allele fraction statistics analysis_ AKI-CKD mouse kidneys, cell lines, blood and CD11b+ cells from IRI kidneys
Table S12 Acute kidney injury, ICD9 and ICD10 diagnostic codes
Table S13 KEY RESOURCES TABLETable S2 Human mito genome annotation
Supplementary content and Figures
Table S2 Human mito genome annotation
Table S5 Genotype tables_animal, cell and human sample
Table S6 Mouse mito genome annotation_mutator mice and cells with/without H2O2
Table S7 Mouse mito genome annotation_AKI-CKD mouse kidneys and mutator cell lines

Supplementary Materials:

Materials and Methods

Figs S1 to S13

Tables S1 to S13

References (36–74)

ACKNOWLEDGMENTS

We thank Martin Pollak and Michael Brown for advice and support.

Funding:

This work was supported by awards from the National Heart, Lung and Blood Institutes, the National Institute on Aging, and the National Institute of Diabetes and Digestive and Kidney Disease: R01HL144569 (DEA, WS, DP), R01AG085753 (DEA, WS, YSH), R01AG027002 (MGS, MJS, and SMP), U01DK04308 (SSW), and R01DK095072 (SMP).

Footnotes

Competing interests: The authors declare that they have no competing interests.

Data and Materials Availability:

Requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Samir M. Parikh (samir.parikh@utsouthwestern.edu).

Reagents and methods in this study will be made available by the lead contact upon request. UK Biobank analyses were conducted using the UK Biobank Resource under Application Number 17731.

The NGS datasets generated during this study is available through the BioSample database (NCBI). RNA-Seq raw data are available in GEO (GSE239772). The mt-DNA seq raw data are available in Sequence Read Archive (SRA) (PRJNA1002252) Metabolomics peak data are available in Table S3. Analysis details are provided in the Materials and Methods section. Any additional information is available from the lead contact upon request.

REFERENCES AND NOTES

  • 1.Wei W et al. , Germline selection shapes human mitochondrial DNA diversity. Science 364, eaau6520 (2019). [DOI] [PubMed] [Google Scholar]
  • 2.Gupta R et al. , Nuclear genetic control of mtDNA copy number and heteroplasmy in humans. Nature 620, 839–848 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Sanchez-Contreras M et al. , The multi-tissue landscape of somatic mtDNA mutations indicates tissue-specific accumulation and removal in aging. eLife 12, e83395 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Linnane AW, Marzuki S, Ozawa T, Tanaka M, Mitochondrial DNA mutations as an important contributor to ageing and degenerative diseases. Lancet 1, 642–645 (1989). [DOI] [PubMed] [Google Scholar]
  • 5.Michikawa Y, Mazzucchelli F, Bresolin N, Scarlato G, Attardi G, Aging-dependent large accumulation of point mutations in the human mtDNA control region for replication. Science 286, 774–779 (1999). [DOI] [PubMed] [Google Scholar]
  • 6.Trifunovic A et al. , Premature ageing in mice expressing defective mitochondrial DNA polymerase. Nature 429, 417–423 (2004). [DOI] [PubMed] [Google Scholar]
  • 7.Vermulst M et al. , Mitochondrial point mutations do not limit the natural lifespan of mice. Nature Genetics 39, 540–543 (2007). [DOI] [PubMed] [Google Scholar]
  • 8.López-Otín C, Kroemer G, Hallmarks of Health. Cell 184, 33–63 (2021). [DOI] [PubMed] [Google Scholar]
  • 9.David SS, O’Shea VL, Kundu S, Base-excision repair of oxidative DNA damage. Nature 447, 941–950 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Kujoth GC et al. , Mitochondrial DNA mutations, oxidative stress, and apoptosis in mammalian aging. Science 309, 481–484 (2005). [DOI] [PubMed] [Google Scholar]
  • 11.Tran MT et al. , PGC1α drives NAD biosynthesis linking oxidative metabolism to renal protection. Nature 531, 528–532 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Kang HM et al. , Defective fatty acid oxidation in renal tubular epithelial cells has a key role in kidney fibrosis development. Nat Med 21, 37–46 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Lane AN, Fan TW, Regulation of mammalian nucleotide metabolism and biosynthesis. Nucleic Acids Res 43, 2466–2485 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Kerns SJ et al. , The energy landscape of adenylate kinase during catalysis. Nature Structural & Molecular Biology 22, 124–131 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Lanning NJ et al. , A mitochondrial RNAi screen defines cellular bioenergetic determinants and identifies an adenylate kinase as a key regulator of ATP levels. Cell Rep 7, 907–917 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Mootha VK et al. , PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34, 267–273 (2003). [DOI] [PubMed] [Google Scholar]
  • 17.Dwyer KM, Kishore BK, Robson SC, Conversion of extracellular ATP into adenosine: a master switch in renal health and disease. Nature Reviews Nephrology 16, 509–524 (2020). [DOI] [PubMed] [Google Scholar]
  • 18.Battle SL et al. , A bioinformatics pipeline for estimating mitochondrial DNA copy number and heteroplasmy levels from whole genome sequencing data. NAR Genom Bioinform 4, lqac034 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Stephanie LB et al. , Deleterious heteroplasmic mitochondrial mutations increase risk of overall and cancer-specific mortality. medRxiv, 2022.2009.2020.22280151 (2022). [Google Scholar]
  • 20.Hong YS et al. , Deleterious heteroplasmic mitochondrial mutations are associated with an increased risk of overall and cancer-specific mortality. Nat Commun 14, 6113 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Lake NJ et al. , Quantifying constraint in the human mitochondrial genome. Nature, (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Chawla LS, Eggers PW, Star RA, Kimmel PL, Acute kidney injury and chronic kidney disease as interconnected syndromes. N Engl J Med 371, 58–66 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Gorman GS et al. , Prevalence of nuclear and mitochondrial DNA mutations related to adult mitochondrial disease. Ann Neurol 77, 753–759 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Laricchia KM et al. , Mitochondrial DNA variation across 56,434 individuals in gnomAD. Genome Res 32, 569–582 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Corral-Debrinski M et al. , Marked changes in mitochondrial DNA deletion levels in Alzheimer brains. Genomics 23, 471–476 (1994). [DOI] [PubMed] [Google Scholar]
  • 26.Stewart JB, Chinnery PF, The dynamics of mitochondrial DNA heteroplasmy: implications for human health and disease. Nat Rev Genet 16, 530–542 (2015). [DOI] [PubMed] [Google Scholar]
  • 27.Bender A et al. , High levels of mitochondrial DNA deletions in substantia nigra neurons in aging and Parkinson disease. Nature Genetics 38, 515–517 (2006). [DOI] [PubMed] [Google Scholar]
  • 28.Saran R et al. , US Renal Data System 2017 Annual Data Report: Epidemiology of Kidney Disease in the United States. Am J Kidney Dis 71, A7 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Nikkanen J et al. , Mitochondrial DNA Replication Defects Disturb Cellular dNTP Pools and Remodel One-Carbon Metabolism. Cell Metab 23, 635–648 (2016). [DOI] [PubMed] [Google Scholar]
  • 30.Shintaku J et al. , RRM1 variants cause a mitochondrial DNA maintenance disorder via impaired de novo nucleotide synthesis. The Journal of Clinical Investigation 132, (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.French JB et al. , Spatial colocalization and functional link of purinosomes with mitochondria. Science 351, 733–737 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Lim EW et al. , Progressive alterations in amino acid and lipid metabolism correlate with peripheral neuropathy in Polg(D257A) mice. Sci Adv 7, eabj4077 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Schon EA, DiMauro S, Hirano M, Human mitochondrial DNA: roles of inherited and somatic mutations. Nat Rev Genet 13, 878–890 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Kirita Y, Wu H, Uchimura K, Wilson PC, Humphreys BD, Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury. Proceedings of the National Academy of Sciences 117, 15874–15883 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Ludwig LS et al. , Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics. Cell 176, 1325–1339 e1322 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Sudlow C et al. , UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12, e1001779 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Zinellu A et al. , Plasma creatinine and creatine quantification by capillary electrophoresis diode array detector. Anal Biochem 342, 186–193 (2005). [DOI] [PubMed] [Google Scholar]
  • 38.Wei W et al. , Nuclear-embedded mitochondrial DNA sequences in 66,083 human genomes. Nature 611, 105–114 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Sin STK et al. , Identification and characterization of extrachromosomal circular DNA in maternal plasma. Proceedings of the National Academy of Sciences 117, 1658–1665 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Chen S, Zhou Y, Chen Y, Gu J, fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Li H, Durbin R, Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.McKenna A et al. , The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20, 1297–1303 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Grünwald NJ, Everhart SE, Knaus BJ, Kamvar ZN, Best Practices for Population Genetic Analyses. Phytopathology 107, 1000–1010 (2017). [DOI] [PubMed] [Google Scholar]
  • 44.Cingolani P et al. , A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80–92 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Cingolani P, Variant Annotation and Functional Prediction: SnpEff. Methods Mol Biol 2493, 289–314 (2022). [DOI] [PubMed] [Google Scholar]
  • 46.Ritchie ME et al. , limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43, e47 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Song Y, Wang J, ggcoverage: an R package to visualize and annotate genome coverage for various NGS data. BMC Bioinformatics 24, 309 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Dobin A et al. , STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Danecek P et al. , Twelve years of SAMtools and BCFtools. GigaScience 10, (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Love MI, Huber W, Anders S, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Zhu A, Ibrahim JG, Love MI, Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35, 2084–2092 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Wickham H, ggplot2. WIREs Computational Statistics 3, 180–185 (2011). [Google Scholar]
  • 53.Wei R et al. , GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies. PLOS Computational Biology 14, e1005973 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Hoffman GE, Schadt EE, variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Tasdogan A et al. , Metabolic heterogeneity confers differences in melanoma metastatic potential. Nature 577, 115–120 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Battistone MA et al. , Proinflammatory P2Y14 receptor inhibition protects against ischemic acute kidney injury in mice. J Clin Invest 130, 3734–3749 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Nadelmann ER et al. , Isolation of Nuclei from Mammalian Cells and Tissues for Single-Nucleus Molecular Profiling. Curr Protoc 1, e132 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Fleming S, Marioni J, Babadi M, CellBender remove-background: A deep generative model for unsupervised removal of background noise from scRNA-seq datasets. (2019). [Google Scholar]
  • 59.Germain PL, Lun A, Garcia Meixide C, Macnair W, Robinson MD, Doublet identification in single-cell sequencing data using scDblFinder. F1000Res 10, 979 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Satija R, Farrell JA, Gennert D, Schier AF, Regev A, Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33, 495–502 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Hafemeister C, Satija R, Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology 20, 296 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Horn JL, A RATIONALE AND TEST FOR THE NUMBER OF FACTORS IN FACTOR ANALYSIS. Psychometrika 30, 179–185 (1965). [DOI] [PubMed] [Google Scholar]
  • 63.Blondel V, Guillaume J-L, Lambiotte R, Lefebvre E, Fast unfolding of community hierarchies in large networks. CoRR abs/0803.0476, (2008). [Google Scholar]
  • 64.Subramanian A et al. , Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545–15550 (2005). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Yu G, Wang LG, Han Y, He QY, clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Liberzon A et al. , Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Milacic M et al. , The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res 52, D672–d678 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Inker LA et al. , Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 367, 20–29 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Hong YS et al. , Mitochondrial heteroplasmy improves risk prediction for myeloid neoplasms. Nat Commun 15, 10133 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Rothfuss O, Gasser T, Patenge N, Analysis of differential DNA damage in the mitochondrial genome employing a semi-long run real-time PCR approach. Nucleic Acids Res 38, e24 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Conant D et al. , Inference of CRISPR Edits from Sanger Trace Data. Crispr j 5, 123–130 (2022). [DOI] [PubMed] [Google Scholar]
  • 72.Ohno M, Oka S, Nakabeppu Y, Quantitative analysis of oxidized guanine, 8-oxoguanine, in mitochondrial DNA by immunofluorescence method. Methods Mol Biol 554, 199–212 (2009). [DOI] [PubMed] [Google Scholar]
  • 73.Huang H et al. , Gentamicin-Induced Acute Kidney Injury in an Animal Model Involves Programmed Necrosis of the Collecting Duct. J Am Soc Nephrol 31, 2097–2115 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Huang H et al. , Lethal (3) malignant brain tumor-like 2 (L3MBTL2) protein protects against kidney injury by inhibiting the DNA damage-p53-apoptosis pathway in renal tubular cells. Kidney Int 93, 855–870 (2018). [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1 Characteristics of participants in quartiles of mt-SNVs
NIHMS2138758-supplement-Table_S1_Characteristics_of_participants_in_quartiles_of_mt-SNVs.docx (19.2KB, docx)
Table S3 Metabolomics data_animal and cell sample
NIHMS2138758-supplement-Table_S3_Metabolomics_data_animal_and_cell_sample.xlsx (1.1MB, xlsx)
Table S4 Primer List
NIHMS2138758-supplement-Table_S4_Primer_List.docx (14.8KB, docx)
Table S8 Patients mito genome annotation
NIHMS2138758-supplement-Table_S8_Patients_mito_genome_annotation.xlsx (909.9KB, xlsx)
Table S10 Allele fraction table for human biopsy 44 samples
NIHMS2138758-supplement-Table_S10_Allele_fraction_table_for_human_biopsy_44_samples.xlsx (677.9KB, xlsx)
Table S11 Sig bins table_pval0.05
NIHMS2138758-supplement-Table_S11_Sig_bins_table_pval0_05.xlsx (11.3KB, xlsx)
Table S9 Allele fraction statistics analysis_ AKI-CKD mouse kidneys, cell lines, blood and CD11b+ cells from IRI kidneys
NIHMS2138758-supplement-Table_S9_Allele_fraction_statistics_analysis__AKI-CKD_mouse_kidneys__cell_lines__blood_and_CD11b__cells_from_IRI_kidneys.xlsx (1.8MB, xlsx)
Table S12 Acute kidney injury, ICD9 and ICD10 diagnostic codes
NIHMS2138758-supplement-Table_S12_Acute_kidney_injury__ICD9_and_ICD10_diagnostic_codes.docx (13.5KB, docx)
Table S13 KEY RESOURCES TABLETable S2 Human mito genome annotation
NIHMS2138758-supplement-Table_S13_KEY_RESOURCES_TABLETable_S2_Human_mito_genome_annotation.docx (21.3KB, docx)
Supplementary content and Figures
NIHMS2138758-supplement-Supplementary_content_and_Figures.pdf (334.7MB, pdf)
Table S2 Human mito genome annotation
NIHMS2138758-supplement-Table_S2_Human_mito_genome_annotation.xlsx (4.9MB, xlsx)
Table S5 Genotype tables_animal, cell and human sample
NIHMS2138758-supplement-Table_S5_Genotype_tables_animal__cell_and_human_sample.xlsx (5.9MB, xlsx)
Table S6 Mouse mito genome annotation_mutator mice and cells with/without H2O2
NIHMS2138758-supplement-Table_S6_Mouse_mito_genome_annotation_mutator_mice_and_cells_with_without_H2O2.xlsx (2.8MB, xlsx)
Table S7 Mouse mito genome annotation_AKI-CKD mouse kidneys and mutator cell lines
NIHMS2138758-supplement-Table_S7_Mouse_mito_genome_annotation_AKI-CKD_mouse_kidneys_and_mutator_cell_lines.xlsx (7.9MB, xlsx)

Data Availability Statement

Requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Samir M. Parikh (samir.parikh@utsouthwestern.edu).

Reagents and methods in this study will be made available by the lead contact upon request. UK Biobank analyses were conducted using the UK Biobank Resource under Application Number 17731.

The NGS datasets generated during this study is available through the BioSample database (NCBI). RNA-Seq raw data are available in GEO (GSE239772). The mt-DNA seq raw data are available in Sequence Read Archive (SRA) (PRJNA1002252) Metabolomics peak data are available in Table S3. Analysis details are provided in the Materials and Methods section. Any additional information is available from the lead contact upon request.

RESOURCES