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Age-dependent accumulation of mitochondrial tRNA mutations in mouse kidneys linked to mitochondrial kidney diseases

Nature Aging volume 5, pages 1317–1339 (2025)Cite this article

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Abstract

Heteroplasmic pathogenic mitochondrial DNA (mtDNA) mutations are key drivers of mitochondrial diseases, yet their tissue-specific and cell-specific accumulation patterns during aging and the mechanistic links to pathology remain poorly understood. In this study, we employed DddA-derived cytosine base editor technology to generate three mouse models harboring distinct pathogenic mitochondrial tRNA mutations. These mutations exhibited age-dependent accumulation in the kidneys, leading to severe kidney defects that well recapitulate human mitochondrial kidney disease. Mitochondrial single-cell assay for transposase-accessible chromatin with sequencing (mtscATAC–seq) revealed unique heteroplasmy dynamics across different kidney cell types: podocytes exhibited a positive selection for mutant mtDNA, whereas tubular epithelial cells displayed neutral drift of mutations during aging. Integrative analyses combining mtscATAC–seq, single-cell RNA sequencing and spatially enhanced resolution omics sequencing further identified molecular changes in high-mutant defective cells, including increased AP-1 family transcription factor activity, tubular epithelial cell proliferation and immune activation, which contribute to disease progression. Our study underscores the importance of kidney function monitoring in patients with mitochondrial disease, particularly in older adults, and establishes robust preclinical models to facilitate the development of therapeutic strategies.

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Fig. 1: Generation of three mt-tRNA point mutation cell lines.
Fig. 2: Age-related mutation load changes in organs of TrnE mutant mouse models.
Fig. 3: Aging-related kidney defect progression in TrnE high-mutant mice.
Fig. 4: The activation of immune and integrated stress responses in TrnE mutation.
Fig. 5: Single-cell transcriptional profiling of control and TrnE mutant kidneys.
Fig. 6: mtscATAC–seq analysis of three TrnE mutant kidneys.
Fig. 7: Integrated analysis of scRNA-seq and mtscATAC–seq data.
Fig. 8: Stereo-seq analysis of control and TrnE mutant kidneys.

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Data availability

Source data are provided with this paper. Sequencing datasets, including RNA-seq, scRNA-seq and mtscATAC–seq, have been deposited in the Gene Expression Omnibus repository with accession number GSE282116. Stereo-seq data have been deposited into the STOmis DB of the China National GeneBank Database with accession number STT0000128. All other data that support the findings of this study are available from M.J. upon reasonable request.

Code availability

The codes for data analysis are available at https://github.com/JiangLab-mitochondria/NATAGING-A08421-T.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2022YFC2702702 to M.J.; 2022YFA1302700 to Y.Z.), the Westlake University Research Center for Industries of the Future (WU2022C013 to M.J.), the National Natural Science Foundation of China (82271897 to M.J.; 32370710 to Y.Z.), the Zhejiang Provincial Natural Science Foundation of China (LZYQ25C070001 to M.J.), the Key R&D Program of Zhejiang (2024SSYS0033) and the Westlake Education Foundation. The funders had no role in study design, data collection and analysis, preparation of the manuscript or decision to publish.

We thank J. Yang, Y. Dou, Y. Zhen and D. He from Westlake University for helpful discussions. We thank the Westlake Laboratory Animal Resources Center for microinjection and mouse husbandry and the Westlake Biomedical Research Core Facilities, the Radioisotope Laboratory, the Flow Cytometry Core and the High-Performance Computing Center at Westlake University for technical support. We thank X. Fan and X. Lu from Zhejiang University for the laser capture microdissection experiments.

Author information

Author notes

  1. These authors contributed equally: Leping Zhang, Zhe Xu, Jia Jing, Guoshi Chai.

Authors and Affiliations

  1. College of Life Sciences, Zhejiang University, Hangzhou, China

    Leping Zhang, Qunyu Lv, Jiatong Chen & Ning Liu

  2. Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China

    Leping Zhang, Zhe Xu, Jia Jing, Guoshi Chai, Guanglei Xie, Yanfei Ru, Qunyu Lv, Qian Zhang, Jiatong Chen, He Jin, Ning Liu, Xi Wang, Yanxiao Zhang & Min Jiang

  3. Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, Hangzhou, China

    Leping Zhang, Zhe Xu, Jia Jing, Guoshi Chai, Yanfei Ru, Qunyu Lv, Qian Zhang, Jiatong Chen, He Jin, Ning Liu & Min Jiang

  4. School of Life Sciences, Westlake University, Hangzhou, China

    Leping Zhang, Zhe Xu, Jia Jing, Guoshi Chai, Guanglei Xie, Yanfei Ru, Qunyu Lv, Qian Zhang, Jiatong Chen, He Jin, Ning Liu, Minghua Kong, Xi Wang, Yanxiao Zhang & Min Jiang

  5. Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China

    Leping Zhang, Zhe Xu, Jia Jing, Guoshi Chai, Yanfei Ru, Qunyu Lv, Qian Zhang, Jiatong Chen, Ning Liu, Minghua Kong, Xi Wang & Min Jiang

  6. College of Life Sciences, Nankai University, Tianjin, China

    Xiang Zuo

  7. Fudan University, Shanghai, China

    Qian Zhang

  8. State Key Laboratory of Reproductive Medicine and Offspring Health, Women’s Hospital of Nanjing Medical University, Nanjing Women and Children’s Healthcare Hospital, Center for Global Health, Gusu School, Nanjing Medical University, Nanjing, China

    Bin Shen

  9. State Key Laboratory of Reproductive Medicine and Offspring Health, Department of Histology and Embryology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China

    Mingxi Liu

  10. Renal Division, Department of Medicine, Peking University First Hospital; Peking University Institute of Nephrology, Beijing, China

    Lei Jiang

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  1. Leping Zhang
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  2. Zhe Xu
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  19. Min Jiang
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Contributions

M.J. conceived and led the project. M.J., L.Z., J.J. and Z.X. designed and conducted most of the experiments. L.Z. and Z.X. constructed the mouse models. Y.Z. and G.C. conducted bioinformatics analysis of single-cell sequencing. X.W. and G.X. conducted bioinformatics analysis of spatial transcriptomics sequencing. Y.R., Q.L., X.Z., Q.Z., J.C., H.J., N.L., M.K., B.S., M.L. and L.J. assisted with experiments. M.J. wrote the manuscript, with the help of L.Z. and Z.X.

Corresponding authors

Correspondence to Yanxiao Zhang or Min Jiang.

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The authors declare no competing interests.

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Nature Aging thanks Katalin Susztak and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Mt-tRNA mutant cell lines construction and mitochondrial function validation.

a, Pipeline of the double-fluorescence in vitro mtDNA editing system. Created with BioRender. b, Editing efficiency of DdCBE targeting m.5081, m.7743, m.14107 and m.14102 in MEF cells via Hi-TOM. High editing efficiency was achieved at m.G5081A and m.G7743A (96% and 85%, respectively), but no G-to-A conversion at m.14107, but ~10% efficiency at m.14102G>A. Red arrow: m.14107; green arrow: m.14102. The last figure indicated that 100% G-to-A conversion was achieved when the DdCBE targeting m.14102 (red arrow) in MEF cells. c, Mutation load quantification of mouse TrnA cells (m.G5081A), TrnK cells (m.G7743A), TrnE cells (m.G14102A) and human TrnE cells (m.G14705A) derived from single cells via mtDNA editing (mean ± SEM). d, Oxygen Consumption Rate (OCR) measurement of WT and different TrnE mutant MEF cell lines by Seahorse assay (mean ± SD, n = 6–8 per group). e, Quantification of OCR from d (mean ± SD, n = 6–8 per group. Unpaired two-tailed Student’s t-test). f, Western blot of WT and mutant TrnE HEK 293T cell lines (derived from different single cells) for OXPHOS complex subunits. ACTB as loading control. g, Quantification of OXPHOS complex subunits from f (mean ± SD, n = 3–4 per group. Unpaired two-tailed Student’s t-test). h, OCR measurement of WT and different TrnE mutant HEK 293T cell lines by Seahorse assay (mean ± SD, n = 8). i, Quantification of OCR from h (mean ± SD, n = 6–8 per group. Unpaired two-tailed Student’s t-test).

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Extended Data Fig. 2 Metabolic analysis and breeding performance of control and mutant TrnE mouse models.

a, Summary of birth rates and mutation load for in vivo DdCBE editing at three sites. b–d, Metabolic assessment in 20-week-old and 75-week-old mice. O2 consumption (b) and CO2 elimination (c) are shown for daytime and nighttime. Locomotion (d) is shown as ped meters and total meters (mean ± SEM, n = 9 (20w, control), n = 4–5 (20w, high-mutant TrnE), n = 8 (75w, control and high-mutant TrnE). Unpaired two-tailed Student’s t-test e, Breeding performance (pup number per litter) of TrnE mutant mothers (n = 43) with varying mutation loads, showing no obvious effect on female fertility. The black line (y = mx + c) represents the line of best fit with 95% confidence intervals (gray). f, Total pup number in a 6-month fertility test of control (TrnE 0–17%, n = 4) and high-mutant (TrnE 72–81%, n = 5) TrnE females (mean ± SD, unpaired two-tailed Student’s t-test). g, Male fertility comparison between WT and TrnE high-mutant group. Total pup number over 6 months is shown as the accumulation of litter index. h, Testis weight comparison between control and TrnE high-mutant groups at different ages (3, 8, 24, 50, and 75 weeks) (mean ± SD, n = 3 (3w), n = 5 (8w), n = 7 (24w), n = 8–9 (50w) and n = 6–7 (75w). Unpaired two-tailed Student’s t-test).

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Extended Data Fig. 3 TrnE mutation segregation in multiple organs and blood during aging.

a, Tail mutation load remained stable with aging (Two-tailed Wilcoxon test). b, Normalized mutation load of different organs from mutant TrnE mice at various age groups (3, 8, 24, 50, 75, and 100 weeks). The normalized organ mutation load was calculated as the organ’s mutation load divided by the tail’s mutation load from the same mouse. c, NBTx staining of different tissues (heart, liver, muscle, kidney and colon) from 50-week-old WT and TrnE high-mutant group.

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Extended Data Fig. 4 Kidney pathological analysis in mice and evaluation of urinary parameters.

a, Kidney morphology from 100-week-old TrnE male and female mice. The first row shows the mutation load in the tails of the mice, and the second row shows the mutation load in the kidneys. Scale bar = 1 cm. b, PAS staining of the kidneys from 100-week-old male and female mice. The annotated mutation load represents the tail mutation load at the time of dissection. c, Comparison of dilated tubular lumen sizes from b. The size of the dilated tubular lumen was measured as (long axis + short axis) / 2 (mean ± SD, n = 29 (Male), n = 63 (Female). Two-tailed Mann-Whitney test). d, Glomerulus size (μm) quantification from PAS staining data. Kidney tissues were from WT and TrnE high-mutant groups at 50, 75, and 100 weeks (mean ± SD. Two-tailed Mann-Whitney test). e, Quantification of fibrosis-related gene expression via qPCR, ACTB as housekeeping gene (mean ± SD, n = 4. Unpaired two-tailed Student’s t-test). TGFb1, Transforming Growth Factor Beta 1; Vim, Vimentin; Col1, Collagen Type I; Fn, Fibronectin. f, g, Urine protein levels in the control and TrnE high-mutant groups at 50 weeks (f) and 75 weeks (g). Higher protein levels indicate defect in kidney reabsorption function (mean ± SEM, n = 3–5 per group in (f) and n = 7–8 per group in (g). Unpaired two-tailed Student’s t-test). NAG, N-Acetylglucosamine; β2-MG1: β2-Microglobulin 1; ALB, Albumin. h, Urine phosphate levels in the control and TrnE high-mutant groups at 75 weeks (mean ± SD, n = 6. Unpaired two-tailed Student’s t-test). i, Urine glucose levels in the control and TrnE high-mutant groups at 75 weeks (mean ± SD, n = 8. Unpaired two-tailed Student’s t-test). j, Blood glucose levels in the control and TrnE high-mutant groups at 50 and 70 weeks in males and females (mean ± SD, n = 3–5 per group. Unpaired two-tailed Student’s t-test). k, Blood electrolyte levels in the control and TrnE high-mutant groups at 75 weeks (mean ± SD, n = 3. Unpaired two-tailed Student’s t-test).

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Extended Data Fig. 5 Pathogenic mutations accumulate in other two mouse models with aging.

a, NBTx staining of whole kidney sections from 75-week-old control and TrnE high-mutant mice. b, NBTx staining of different areas (renal cortex, outer medulla, and inner medulla) of kidney sections from 75-week-old control and TrnE high-mutant groups. Damage varied from the renal cortex to the medulla. c, Percentage of NBT positive blue area in kidney sections from 50-week-old and 75-week-old TrnE high-mutant males and females (mean ± SD, n = 14 (50w) and n = 16 (75w) in males, n = 25 (50w) and n = 15 (75w) in females). d, mtDNA copy number quantification in bulk kidney from control and TrnE high-mutant mice at different ages (8, 50, and 75 weeks) (mean ± SEM, n = 3–4 per group. Unpaired two-tailed Student’s t-test, ** P < 0.01). e, f, Kidney mutation load in mutant TrnA (e) and mutant TrnK (f) mice over time (8, 24, 50, and 100 weeks). The black line represents the line of best fit with 95% confidence intervals (gray). (mean ± SEM, n = 3 (8w, 24w), n = 7–8 (50w) and n = 5–7 (100w)). g, Kidney morphology from 100-week-old mice with WT, high TrnA, and high TrnK mutations. Scale bar = 5 mm. h, NBTx staining of kidney sections from 24-week-old WT, TrnA, and TrnK high-mutant mice. Cells with normal COX activity do not retain NBT, while those with COX deficiency appear blue. (section thickness = 10 μm) i, Transmission electron microscopy (TEM) images of mitochondria from the kidney epithelial cells of 100-week-old WT, TrnA, and TrnK high-mutant mice.

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Extended Data Fig. 6 TrnE mutation in the liver doesn’t cause detectable defects.

a, Liver morphology from 100-week-old mice with low and high TrnE mutation. The annotated mutation load represents the tail’s mutation at the time of dissection. Scale bar = 1 cm. b, First row: HE staining of paraformaldehyde-fixed, paraffin-embedded liver sections (5 μm thickness) from 100-week-old WT and TrnE mice. Second row: Oil-red O staining of liver frozen sections from 100-week-old WT and TrnE mice. (section thickness = 10 μm). Third row: NBTx staining of liver frozen sections from 100-week-old WT and TrnE mice (section thickness = 10 μm). Fourth row: TEM images of hepatocyte mitochondria from 100-week-old WT and TrnE high-mutant mice livers. Fifth row: high magnification view of mitochondria from the fourth row. c, Western blot of 100-week-old control (0–4%) and TrnE high-mutant group (75–81%) livers for OXPHOS complex subunits. TOM20 and ACTB shown as the loading control. d, Quantification of OXPHOS complex subunits in c (mean ± SD, n = 4. Unpaired two-tailed Student’s t-test). e, Plasma levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), cholesterol (CHOL), and triglyceride (TG) (mean ± SD, n = 5–7 per group. Unpaired two-tailed Student’s t-test) in 100-week-old mice. f, mtDNA copy number quantification in bulk liver from control and TrnE high-mutant mice at 3 and 75 weeks of age (mean ± SEM, n = 3–4 per group. Unpaired two-tailed Student’s t-test).

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Extended Data Fig. 7 Biochemistry analysis in TrnE mutant mouse kidneys.

a, Northern blot analysis of mitochondrial tRNA levels under denaturing conditions. 2 μg of total RNA from 100-week-old control and TrnE high-mutant group kidneys were used for loading, and hybridized with DIG-labeled oligonucleotide probes specific for TrnE, TrnP, TrnF, TrnL1, and 5.8S (loading control). b, Quantification of tRNA levels from a (mean ± SD, n = 4. Unpaired two-tailed Student’s t-test) c, qPCR of relative gene expression of mitochondrial aminoacyl-tRNA synthetases (mt-aaRS) between the control group and the high-mutant TrnE group of 100-week-old, ACTB as the internal control gene (mean ± SD, n = 5–6 per group. Unpaired two-tailed Student’s t-test). d, qPCR quantification of fused RNA transcripts in 100-week-old control and TrnE groups. Increased TrnE-ND6 levels indicated RNA processing defects. 18S was internal control gene for normalization, and TrnM-ND2 was used as a negative control (mean ± SD, n = 4. Unpaired two-tailed Student’s t-test). e, In organello translation of mitochondria isolated from 100-week-old mouse kidneys. Coomassie blue staining of proteins after SDS-PAGE is shown as a loading control. f, Quantification of in organello translation proteins from e (mean ± SD, n = 3–5 per group. Unpaired two-tailed Student’s t-test). Since the trends for pulse and chase were consistent, we only quantified the proteins for pulse here. g, Western blot analysis of OXPHOS complex subunits in WT and TrnE high-mutant mice at various ages (3, 24, 50, and 75 weeks). TOM20 served as a mitochondrial protein control. h, Quantification of OXPHOS complex subunits between the control and TrnE high-mutant groups at different ages (3, 24, 50, and 75 weeks) (mean ± SEM, n = 6 (3w), n = 7 (24w), n = 3 (50w), n = 9–10 (75w). Unpaired two-tailed Student’s t-test).

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Extended Data Fig. 8 ND1 mutant mouse models did not exhibit severe kidney disease at 100 weeks of age.

a, Blue-native PAGE and in-gel activity analysis of OXPHOS CI and CIV complexes using kidney mitochondria of 75 weeks old control and TrnE high-mutant mice. Coomassie staining is shown as a loading control. b, IHC staining of CIV (COX1) and CI (NDUFB8) protein levels in WT and TrnE high-mutant kidneys. Black box: high magnification view of the glomerulus. c, Western blot of 100-week-old WT and ND1 high-mutant mice (75–81%) kidneys for OXPHOS complex subunits. TOM20 and ACTB served as protein loading controls. d, Quantification of OXPHOS complex subunits from c (mean ± SEM, n = 3. Unpaired two-tailed Student’s t-test). e, First row: PAS staining of paraformaldehyde-fixed, paraffin-embedded kidney sections (5 μm thickness) from 100-week-old WT and ND1 mutant mice. Second row: NBTx staining of kidney frozen sections from 100-week-old WT and ND1 mutant mice (section thickness = 10 μm). f, TEM images of mitochondria from 40-week-old WT and ND1 high-mutant mice in the kidney cortex (Proximal tubule, Distal tubule, and Glomerulus).

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Extended Data Fig. 9 RNA-seq and scRNA-seq identify defects in TrnE mutant kidneys.

a, Relative gene expression of 13 mitochondrial mRNA genes between 100-week-old control and TrnE high-mutant groups from RNA-seq (mean ± SD, n = 3. Unpaired two-tailed Student’s t-test). b, Western blot of 3-week-old WT and TrnE high-mutant group (72–80%) kidneys for UPRmt related genes encoded proteins. Tubulin shown as loading control. c, Quantification of UPRmt protein levels from b (mean ± SEM, n = 3. Unpaired two-tailed Student’s t-test). d, Gene set enrichment analysis (GSEA) from RNA-seq of TrnE high-mutant groups vs. low-mutant control groups. The top 15 enriched MitoPathway terms from the MitoCarta3.0 database are displayed. Permutation test (two-sided) is used to calculate P value, and P value is adjusted by Benjamini-Hochberg. e, Single-cell RNA sequencing gene expression profile for selected marker genes in the immune cell cluster. The color scale represents relative expression across all clusters, and the dot size represents the percentage of cells in that cluster expressing the gene. f, g, The gene ontology (GO) terms enriched for up-regulated (f) and down-regulated (g) genes in TrnE mutant samples. GO biological process enriched in at least five cell types are displayed. Hypergeometric test (one-sided) is used to calculate P value, and P value is adjusted by Benjamini-Hochberg. h, Mutation load shift of various cell types. The difference represents the mutation load of different cell types minus the mutation load of tail samples.

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Extended Data Fig. 10 Correlation analysis between single-cell mtDNA coverage and mutation load, and heteroplasmy distribution analysis.

a, The correlation between single cell mtDNA coverage inferred from mtscATAC-seq data and the TrnE mutation load. Cell types with fewer than 5 cells are not displayed. Spearman correlation coefficient (R) and P value (two-sided) are calculated. The blue line represents the line of best fit with 95% confidence intervals (gray). b, c, Heteroplasmy distribution in PT and DT cells from 3-week-old (b) and 75-week-old (c) TrnE kidney mtscATAC-seq compared with the two-parameter Kimura distribution. The blue line represents the practical heteroplasmy distribution, while the gray line represents the integrated Kimura distribution. Goodness-of-fit was tested using a Monte Carlo Kolmogorov-Smirnov test, and the P value (two-tailed) is provided. d, DT cells at 75 weeks of age are classified into three categories based on mutation load: High-mutation cells (mutation load > 90%), moderate-mutation cells (mutation load < 70%), and intermediate mutation cells (mutation load between 70% and 90%). The median and mean values of the mutation load for each of these three cell groups are presented. The median, quartile, lower and upper whiskers are shown in boxplot. e, The correlation between Spp1 and Clu gene expression levels and the TrnE mutation load in DT and PT cells. Spearman correlation coefficient (R) and P value (two-sided) are calculated. The blue line represents the line of best fit with 95% confidence intervals (gray).

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Supplementary information

Supplementary information

Supplementary Methods, Supplementary Sequences, Supplementary Lists 1 and 2 and Supplementary Figs. 1–6.

Reporting Summary

Supplementary Tables 1–7

mtDNA off-target, organ mutation load, scRNA-seq and mtscATAC–seq analysis.

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Zhang, L., Xu, Z., Jing, J. et al. Age-dependent accumulation of mitochondrial tRNA mutations in mouse kidneys linked to mitochondrial kidney diseases. Nat Aging 5, 1317–1339 (2025). https://doi.org/10.1038/s43587-025-00909-y

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