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Maternal age enhances purifying selection on pathogenic mutations in complex I genes of mammalian mtDNA

Nature Aging (2024)Cite this article

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Abstract

Mitochondrial diseases, caused mainly by pathogenic mitochondrial DNA (mtDNA) mutations, pose major challenges due to the lack of effective treatments. Investigating the patterns of maternal transmission of mitochondrial diseases could pave the way for preventive approaches. In this study, we used DddA-derived cytosine base editors (DdCBEs) to generate two mouse models, each haboring a single pathogenic mutation in complex I genes (ND1 and ND5), replicating those found in human patients. Our findings revealed that both mutations are under strong purifying selection during maternal transmission and occur predominantly during postnatal oocyte maturation, with increased protein synthesis playing a vital role. Interestingly, we discovered that maternal age intensifies the purifying selection, suggesting that older maternal age may offer a protective effect against the transmission of deleterious mtDNA mutations, contradicting the conventional notion that maternal age correlates with increased transmitted mtDNA mutations. As collecting comprehensive clinical data is needed to understand the relationship between maternal age and transmission patterns in humans, our findings may have profound implications for reproductive counseling of mitochondrial diseases, especially those involving complex I gene mutations.

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Fig. 1: The mouse mtDNA 2820 G > A mutation causes mitochondrial dysfunction.
Fig. 2: Age enhances the purifying selection of m.2820 G > A mtDNA during transmission.
Fig. 3: M2 oocytes exhibit age-enhanced purifying selection against the m.2820 G > A mutation.
Fig. 4: Age-enhanced purifying selection on m.2820 G > A occurs during oocyte maturation.
Fig. 5: High ND1 mutations in mature oocytes from young mice can be transmitted to the next generation.
Fig. 6: Age enhances purifying selection on the mouse ND5 12918 G > A mutation during transmission.
Fig. 7: Enhanced protein synthesis in oocytes from ND1 mutant females elucidated by spatial transcriptomics.
Fig. 8: Enhancement of protein translation drives purifying selection in ND1 mutant oocytes.

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Data availability

All source data are provided with this paper. Stereo-seq data that support the findings of this study have been deposited into STOmis DB of the China National GeneBank DataBase68 with accession number STT0000087. Further information supporting the findings of this study is available from M.J. upon reasonable request.

References

  1. Picard, M. & Shirihai, O. S. Mitochondrial signal transduction. Cell Metab. 34, 1620–1653 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gustafsson, C. M., Falkenberg, M. & Larsson, N.-G. Maintenance and expression of mammalian mitochondrial DNA. Annu. Rev. Biochem. 85, 133–160 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Stewart, J. B. et al. Strong purifying selection in transmission of mammalian mitochondrial DNA. PLoS Biol. 6, e10 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Kauppila, J. H. K. et al. Base-excision repair deficiency alone or combined with increased oxidative stress does not increase mtDNA point mutations in mice. Nucleic Acids Res. 46, 6642–6669 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Stewart, J. B. & Chinnery, P. F. Extreme heterogeneity of human mitochondrial DNA from organelles to populations. Nat. Rev. Genet. 22, 106–118 (2021).

    Article  CAS  PubMed  Google Scholar 

  6. Wei, W. et al. Germline selection shapes human mitochondrial DNA diversity. Science 364, eaau6520 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. Jenuth, J. P., Peterson, A. C., Fu, K. & Shoubridge, E. A. Random genetic drift in the female germline explains the rapid segregation of mammalian mitochondrial DNA. Nat. Genet. 14, 146–151 (1996).

    Article  CAS  PubMed  Google Scholar 

  8. Brown, D. T., Samuels, D. C., Michael, E. M., Turnbull, D. M. & Chinnery, P. F. Random genetic drift determines the level of mutant mtDNA in human primary oocytes. Am. J. Hum. Genet. 68, 533–536 (2001).

    Article  CAS  PubMed  Google Scholar 

  9. Cree, L. M. et al. A reduction of mitochondrial DNA molecules during embryogenesis explains the rapid segregation of genotypes. Nat. Genet. 40, 249–254 (2008).

    Article  CAS  PubMed  Google Scholar 

  10. Burr, S. P., Pezet, M. & Chinnery, P. F. Mitochondrial DNA heteroplasmy and purifying selection in the mammalian female germ line. Dev. Growth Differ. 60, 21–32 (2018).

    Article  PubMed  Google Scholar 

  11. Zaidi, A. A. et al. Bottleneck and selection in the germline and maternal age influence transmission of mitochondrial DNA in human pedigrees. Proc. Natl Acad. Sci. USA 116, 201906331 (2019).

    Article  Google Scholar 

  12. Mok, B. Y. et al. A bacterial cytidine deaminase toxin enables CRISPR-free mitochondrial base editing. Nature 583, 631–637 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Mok, B. Y. et al. CRISPR-free base editors with enhanced activity and expanded targeting scope in mitochondrial and nuclear DNA. Nat. Biotechnol. 40, 1378–1387 (2021).

    Article  Google Scholar 

  14. Lee, H. et al. Mitochondrial DNA editing in mice with DddA-TALE fusion deaminases. Nat. Commun. 12, 1190 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Guo, J. et al. DdCBE mediates efficient and inheritable modifications in mouse mitochondrial genome. Mol. Ther. Nucleic Acids 27, 73–80 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Silva-Pinheiro, P. et al. A library of base editors for the precise ablation of all protein-coding genes in the mouse mitochondrial genome. Nat. Biomed. Eng. 7, 692–703 (2022).

  17. Agip, A.-N. A., Blaza, J. N., Fedor, J. G. & Hirst, J. Mammalian respiratory complex I through the lens of cryo-EM. Annu. Rev. Biophys. 48, 165–184 (2019).

    Article  CAS  PubMed  Google Scholar 

  18. Grba, D. N. & Hirst, J. Mitochondrial complex I structure reveals ordered water molecules for catalysis and proton translocation. Nat. Struct. Mol. Biol. 27, 892–900 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Freyer, C. et al. Variation in germline mtDNA heteroplasmy is determined prenatally but modified during subsequent transmission. Nat. Genet. 44, 1282–1285 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kauppila, J. H. K. et al. A phenotype-driven approach to generate mouse models with pathogenic mtDNA mutations causing mitochondrial disease. Cell Rep. 16, 2980–2990 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Filograna, R. et al. Modulation of mtDNA copy number ameliorates the pathological consequences of a heteroplasmic mtDNA mutation in the mouse. Sci. Adv. 5, eaav9824 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kremer, L. S. et al. A role for BCL2L13 and autophagy in germline purifying selection of mtDNA. PLoS Genet. 19, e1010573 (2022).

    Article  Google Scholar 

  23. Fan, W. et al. A mouse model of mitochondrial disease reveals germline selection against severe mtDNA mutations. Science 319, 958–962 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hill, J. H., Chen, Z. & Xu, H. Selective propagation of functional mitochondrial DNA during oogenesis restricts the transmission of a deleterious mitochondrial variant. Nat. Genet. 46, 389–392 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ma, H., Xu, H. & O’Farrell, P. H. Transmission of mitochondrial mutations and action of purifying selection in Drosophila melanogaster. Nat. Genet. 46, 393–397 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lieber, T., Jeedigunta, S. P., Palozzi, J. M., Lehmann, R. & Hurd, T. R. Mitochondrial fragmentation drives selective removal of deleterious mtDNA in the germline. Nature 570, 380–384 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Palozzi, J. M. et al. Mitochondrial DNA quality control in the female germline requires a unique programmed mitophagy. Cell Metab. 34, 1809–1823 (2022).

    Article  CAS  PubMed  Google Scholar 

  28. Zhang, Y. et al. PINK1 inhibits local protein synthesis to limit transmission of deleterious mitochondrial DNA mutations. Mol. Cell 73, 1127–1137 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chiang, A. C.-Y., McCartney, E., O’Farrell, P. H. & Ma, H. A genome-wide screen reveals that reducing mitochondrial DNA polymerase can promote elimination of deleterious mitochondrial mutations. Curr. Biol. 29, 4330–4336 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chen, Z. et al. Mitochondrial DNA segregation and replication restrict the transmission of detrimental mutation. J. Cell Biol. 219, e201905160 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Arbeithuber, B. et al. Advanced age increases frequencies of de novo mitochondrial mutations in macaque oocytes and somatic tissues. Proc. Natl Acad. Sci. USA 119, e2118740119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Rebolledo-Jaramillo, B. et al. Maternal age effect and severe germ-line bottleneck in the inheritance of human mitochondrial DNA. Proc. Natl Acad. Sci. USA 111, 15474–15479 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Blakely, E. L. et al. LHON/MELAS overlap syndrome associated with a mitochondrial MTND1 gene mutation. Eur. J. Hum. Genet. 13, 623–627 (2005).

    Article  CAS  PubMed  Google Scholar 

  34. Liu, Q. et al. Hi-TOM: a platform for high-throughput tracking of mutations induced by CRISPR/Cas systems. Sci. China Life Sci. 62, 1–7 (2019).

    Article  PubMed  Google Scholar 

  35. Kampjut, D. & Sazanov, L. A. The coupling mechanism of mammalian respiratory complex I. Science 370, eabc4209 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. Kravchuk, V. et al. A universal coupling mechanism of respiratory complex I. Nature 609, 808–814 (2022).

    Article  CAS  PubMed  Google Scholar 

  37. Stewart, J. B. & Chinnery, P. F. The dynamics of mitochondrial DNA heteroplasmy: implications for human health and disease. Nat. Rev. Genet. 16, 530–542 (2015).

    Article  CAS  PubMed  Google Scholar 

  38. Walker, M. A. et al. Purifying selection against pathogenic mitochondrial DNA in human T cells. N. Engl. J. Med. 383, 1556–1563 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Pulkes, T. et al. The mitochondrial DNA G13513A transition in ND5 is associated with a LHON/MELAS overlap syndrome and may be a frequent cause of MELAS. Ann. Neurol. 46, 916–919 (1999).

    Article  CAS  PubMed  Google Scholar 

  40. Chol, M. et al. The mitochondrial DNA G13513A MELAS mutation in the NADH dehydrogenase 5 gene is a frequent cause of Leigh-like syndrome with isolated complex I deficiency. J. Med. Genet. 40, 188–191 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Wang, S.-B. et al. Mutation of mitochondrial DNA G13513A presenting with Leigh syndrome, Wolff–Parkinson–White syndrome and cardiomyopathy. Pediatr. Neonatol. 49, 145–149 (2008).

    Article  PubMed  Google Scholar 

  42. Wang, Z. et al. Phenotypic patterns of MELAS/LS overlap syndrome associated with m.13513G>A mutation, and neuropathological findings in one autopsy case. Neuropathology 30, 606–614 (2010).

    Article  CAS  PubMed  Google Scholar 

  43. Sun, C., Bai, H., Xu, D., Xiao, Q. & Liu, Z. Mitochondrial 13513G>A mutation with low mutant load presenting as isolated Leber’s hereditary optic neuropathy assessed by next generation sequencing. Front. Neurol. 12, 601307 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792 (2022).

    Article  CAS  PubMed  Google Scholar 

  45. Fitzpatrick, S. L. et al. Expression of growth differentiation factor-9 messenger ribonucleic acid in ovarian and nonovarian rodent and human tissues. Endocrinology 139, 2571–2578 (1998).

    Article  CAS  PubMed  Google Scholar 

  46. McGrath, S. A., Esquela, A. F. & Lee, S. J. Oocyte-specific expression of growth/differentiation factor-9. Mol. Endocrinol. 9, 131–136 (1995).

    CAS  PubMed  Google Scholar 

  47. Sato, M. et al. Identification of PGC7, a new gene expressed specifically in preimplantation embryos and germ cells. Mech. Dev. 113, 91–94 (2002).

    Article  CAS  PubMed  Google Scholar 

  48. Thoreen, C. C. et al. A unifying model for mTORC1-mediated regulation of mRNA translation. Nature 485, 109–113 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Gross, J. D. & Pears, C. J. Possible involvement of the nutrient and energy sensors mTORC1 and AMPK in cell fate diversification in a non-metazoan organism. Front. Cell Dev. Biol. 9, 758317 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Goul, C., Peruzzo, R. & Zoncu, R. The molecular basis of nutrient sensing and signalling by mTORC1 in metabolism regulation and disease. Nat. Rev. Mol. Cell Biol. 24, 857–875 (2023).

    Article  CAS  PubMed  Google Scholar 

  51. Kim, J. & Guan, K.-L. mTOR as a central hub of nutrient signalling and cell growth. Nat. Cell Biol. 21, 63–71 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Zheng, W. et al. Two classes of ovarian primordial follicles exhibit distinct developmental dynamics and physiological functions. Hum. Mol. Genet. 23, 920–928 (2014).

    Article  CAS  PubMed  Google Scholar 

  53. Dou, X. et al. Short‐term rapamycin treatment increases ovarian lifespan in young and middle‐aged female mice. Aging Cell 16, 825–836 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Civiletto, G. et al. Rapamycin rescues mitochondrial myopathy via coordinated activation of autophagy and lysosomal biogenesis. EMBO Mol. Med. 10, e8799 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Jiang, C. et al. GGPP-mediated protein geranylgeranylation in oocyte is essential for the establishment of oocyte-granulosa cell communication and primary-secondary follicle transition in mouse ovary. PLoS Genet. 13, e1006535 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Rodríguez-Nuevo, A. et al. Oocytes maintain ROS-free mitochondrial metabolism by suppressing complex I. Nature 607, 756–761 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Cao, L. et al. The mitochondrial bottleneck occurs without reduction of mtDNA content in female mouse germ cells. Nat. Genet. 39, 386–390 (2007).

    Article  CAS  PubMed  Google Scholar 

  58. Wai, T., Teoli, D. & Shoubridge, E. A. The mitochondrial DNA genetic bottleneck results from replication of a subpopulation of genomes. Nat. Genet. 40, 1484–1488 (2008).

    Article  CAS  PubMed  Google Scholar 

  59. Guo, J. et al. Precision modeling of mitochondrial diseases in zebrafish via DdCBE-mediated mtDNA base editing. Cell Discov. 7, 78 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Clayton, D. A. & Shadel, G. S. Isolation of mitochondria from cells and tissues. Cold Spring Harb. Protoc. https://doi.org/10.1101/pdb.top074542 (2014).

  61. Griffin, J., Emery, B. R., Huang, I., Peterson, C. M. & Carrell, D. T. Comparative analysis of follicle morphology and oocyte diameter in four mammalian species (mouse, hamster, pig, and human). J. Exp. Clin. Assist. Reprod. 3, 2 (2006).

  62. Ye, S., Hu, J. J. & Yang, D. Tandem Payne/Dakin reaction: a new strategy for hydrogen peroxide detection and molecular imaging. Angew. Chem. Int. Ed. Engl. 57, 10173–10177 (2018).

  63. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb.) 2, 100141 (2021).

    CAS  PubMed  Google Scholar 

  64. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

  66. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    Article  CAS  PubMed  Google Scholar 

  67. Zhang, H. et al. Mitochondrial DNA heteroplasmy is modulated during oocyte development propagating mutation transmission. Sci. Adv. 7, eabi5657 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Chen, F. Z. et al. CNGBdb: China National GeneBank DataBase. Hereditas (Beijing) 42, 799–809 (2020).

    Google Scholar 

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Acknowledgements

We thank the Westlake Laboratory Animal Resources Center for microinjection and mouse husbandry and Westlake Biomedical Research Core Facilities for technical support. We thank S. Bin (Nanjing Medical University) for the DdCBE backbones (with puromycin and without TALE) as a kind gift. M.J. was supported by the National Key Research and Development Program of China (2022YFC2702702); the National Natural Science Foundation of China (82271897); 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.

Author information

Author notes

  1. These authors contributed equally: Yanfei Ru, Xiaoling Deng, Jiatong Chen, Leping Zhang.

Authors and Affiliations

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

    Yanfei Ru, Xiaoling Deng, Jiatong Chen, Leping Zhang, Zhe Xu, Qunyu Lv, Shiyun Long, Zijian Huang, Minghua Kong & Min Jiang

  2. Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences,Westlake University, Hangzhou, China

    Yanfei Ru, Xiaoling Deng, Jiatong Chen, Leping Zhang, Zhe Xu, Qunyu Lv, Shiyun Long, Zijian Huang, Minghua Kong & Min Jiang

  3. Fudan University, Shanghai, China

    Xiaoling Deng & Zijian Huang

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

    Jiatong Chen, Leping Zhang, Qunyu Lv & Shiyun Long

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

    Minghua Kong & Min Jiang

  6. Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, China

    Jing Guo

  7. NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Sciences, Central South University, Changsha, China

    Jing Guo

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Contributions

M.J. conceived and led the project. M.J., Y.R., X.D., J.C. and L.Z. designed and conducted most of the experiments. L.Z. and Z.X. constructed the mouse models. Q.L. sorted the immune cells from the blood and collected the sequencing results. J.G., S.L., Z.H. and M.K. assisted with the oocyte assay and Seahorse experiments. M.J. and Y.R. wrote the manuscript, with the help of X.D. and L.Z.

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Correspondence to Min Jiang.

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Extended data

Extended Data Fig. 1 Generation and characterization of mutant MEF lines.

(a) A schematic outlining the editing process of MEFs and the establishment of mutant, single-cell-derived MEF lines. Created with BioRender.com. (b) Depiction of the four pairs of DdCBE editing plasmids targeting the mouse ND1 gene, highlighting TALE-binding regions in gray-blue and the base editing window in black. Plasmid combinations are labeled according to their left or right positions, with -G1333 or −1397, and with -N or -C termini. Produced using BioRender. (c) Validation of Hi-TOM for quantifying mtDNA mutation loads, with the black line representing the line of unity (y = x) and the red dot/line indicating the mutation load of m.2820 G > A as analyzed by Hi-TOM and corresponding linear regression. (d) Mutation loads of m.2820 G > A across various MEF cell lines, each originating from individually sorted cell. (e) Comparative mutation loads of m.2820 G > A in the toe of 1 week of age and tails at the time of sacrifice (n = 37). Statistical significance assessed using a two-tailed unpaired t-test.

Source data

Extended Data Fig. 2 Tissue-specific ND1 mutation load variation across multiple ages.

(a–h) Mutation loads quantified across various somatic tissues and blood cells in the ND1 mutant mice: (a) Heart; (b) Liver; (c) Spleen; (d) Kidney; (e) Muscle; (f) Colon; (g) Brain; (h) Blood. Organ sample sizes are as follows: n = 3 for 8w; n = 3 for 9w; n = 4 for 12w; n = 5 for 14w; n = 2 for 22w; n = 2 for 23w; n = 3 for 25w; n = 4 for 31-32w; n = 3 for 41w; n = 5 for 43w; n = 3 for 80w; n = 3 for 98w. Data are mean ± SEM. (i–j) Mutation loads measured in blood and specific sorted cell types, including T cells, B cells, neutrophils, and monocytes. sample sizes: n = 17 animals (I) n = 13 animals (J). p-value from one-way ANOVA with Tukey’s multiple comparisons test.

Source data

Extended Data Fig. 3 Age-enhanced selection on ND1 mutations also occurs in F1 females.

(a) Correlation of F2 pup mutation loads (n = 186) with F1 female maternal mutation loads (n = 7), by litter. Different litters are color-coded. The y = x line shows expected transmission without selection, while y=ax+b is the linear regression of the mutation loads of F2 pups from different F1 females but produced in the same litter order. (b) Relationship between F1 female maternal age at conception and F2 pup normalized mutation load. Data are median ± 95% CI, with p-values from Kruskal-Wallis test and Dunn’s comparison. (c) Litter sizes of F1 females with varying mutation loads. Different colors indicate distinct litters. (d) Mutation load in M2 oocytes from F1 mothers at various ages. Data shown as median ± 95% CI; p-value via Kruskal-Wallis test with Dunn’s post hoc analysis. (e-f) Sperm counts (E) and sperm motility (F) from WT and ND1 mutant males at 24 weeks of age. Data are mean ± SEM. WT group n = 3; 77–79% mutation load group n = 3. Data are mean ± SEM; with p-values from two-tailed unpaired t-test.

Source data

Extended Data Fig. 4 Morphometry of mouse oocytes in primordial and antral follicles at multiple ages.

(a) Morphometric analysis of oocytes and surrounding somatic cells in primordial follicles from female mice at various developmental stages (P3, P7, P21, 8 weeks, 14 weeks, 22 weeks, and 32 weeks), and oocytes in antral follicles from P21 females. The diameters of oocytes (red) and somatic cells (soma) were measured. Oocytes are indicated in red, granulosa cells in black and red blood cells in purple. (b) The violin plots show the distribution of diameters for oocytes (n = 382 from 2 females) from primordial follicles in P3 mice, and granulosa cells (n = 314 from 2 females), delineating the variability in size of both cell types.

Source data

Extended Data Fig. 5 Age-enhanced purifying selection on m.2820 G > A occurs during oocyte maturation.

(a) Oocyte mutational loads in primordial follicles of P3 and P7 female mice. Oocytes with mutations >37% are marked in yellow. Individual female mutation loads are shown along the x-axis. Data presented as median ± 95% CI. P3 values correspond to those in Fig. 4e, now with expanded individual data. (b) Mutation loads in pups born to females of varying ages. Data are median ± 95% CI.This dataset correspond to those in Fig. 2e, with in-depth individual details. Female mutation loads are displayed along the x-axis. (c) Mutation load in M2 oocytes from females of different ages. Data are median ± 95% CI. This dataset correspond to those in Fig. 3a, with in-depth individual details. Female mutation loads are displayed along the x-axis. (d) Mutation loads throughout oocyte development and in pups from individual mothers. Data are median ± 95% CI. This dataset correspond to those in Fig. 4b, with in-depth individual details. Female mutation loads are displayed along the x-axis. (e) Representative PAS-stained ovarian section images from 14-week-old ND1 and WT mouse. (f) Counted follicle numbers at various developmental stages in ovarian sections from 14-week-old ND1 (n = 6 ovaries) and WT (n = 4 ovaries) mice. Data are mean ± SEM; with p-values from two-tailed unpaired t-test.

Source data

Extended Data Fig. 6 Age enhances purifying selection on the mouse m.12918 G > A mutation during maternal transmission.

(a) Mutation loads in pups born to individual females of varying ages. Data are median ± 95% CI. This dataset correspond to those in Fig. 6e, with in-depth individual details. Female mutation loads are displayed along the x-axis. (b) Mutation load in M2 oocytes from individual females of varying ages. Data are median ± 95% CI. This dataset correspond to those in Fig. 6g, with in-depth individual details. Female mutation loads are displayed along the x-axis. (c) Normalized mutation loads throughout oocyte development and in pups from multiple females at 22-week-old. Data are median ± 95% CI; with p-value from Kruskal-Wallis test followed by Dunn’s multiple comparisons. (d) Normalized mutation loads at different oocyte development and M2 from young females under 4 weeks old. Data are median ± 95% CI; with p-value from one-way ANOVA followed by Tukey’s multiple comparisons.

Source data

Extended Data Fig. 7 Spatial transcriptomic analysis of the ovary.

(a) Top row: H&E staining of ovarian sections from ND1 mutant and WT mice. Bottom row: Gdf9 gene expression profile obtained from spatial transcriptomics on adjacent sections for Stero-seq. Arrows indicate oocytes in secondary follicles (#1-10); while arrowheads point to oocytes in other follicles (#11, #12, #13, #16, #17), follicle-free regions (#15) or no mached follicle region (#14) in top H&E image (for detail in Supplementary Table 3). (b) Schematic diagram of the spatial transcriptomics analysis workflow applied in this study, detailing the step-by-step process from initial tissue sectioning to the final analysis and data interpretation.

Extended Data Fig. 8 Detailed assessment of rapamycin treatment efficacy in ovary and heart.

(a) Representative western blot of p-S6 levels in ovarian tissue following long-term treatment of either PBS or rapamycin in mice. (b) Quantitative analysis of (A). n = 4 for PBS-49d; n = 3 for Rapa-49d. Data are presented as the mean ± SEM, with p-values from two-tailed unpaired t-test. (c) Representative western blot of p-S6 levels in ovarian tissue following short-term treatment of either PBS or Rapamycin in mice. (d) Quantitative analysis of (C). n = 4 for PBS-49d; n = 3 for Rapa-21d. Data are mean ± SEM, with p-values from two-tailed unpaired t-test. (e) Representative western blot of p-S6 levels in heart tissue following short-, long-term treatment of rapamycin and PBS in mice. (f) Quantitative analysis of (E). n = 5 for PBS-49d; n = 5 for Rapa-49d; n = 3 for Rapa-21d. Data are mean ± SEM, with p-value from two-tailed unpaired t test. (g) The mutation load in heart tissues from mice treated with either PBS or Rapamycin. n = 5 for PBS-49d; n = 5 for Rapa-49d; n = 3 for Rapa-21d. Data are mean ± SEM, with p-values from two-tailed unpaired t-test. (h) Comparision of normalized mutational loads in M2 oocytes from treated (the same dataset as those in Fig. 8i) or non-treated mice (the same dataset as those in Fig. 3a). Each symbol represents an individual oocyte. Data are median ± 95% CI, with p-values via Kruskal-Wallis test and Dunn’s post hoc analysis.

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Extended Data Fig. 9 No observable mitophagy in the ND1 mutant oocytes.

(a) Heatmap presenting detailed gene expression profiles related to mitophagy pathway from Stero-seq analysis, log-normalized for clarity using Seurat. (b) Representative TEM image of oocytes in secondary follicles from ND1 and WT mice. Four distinct views from each mouse are presented. A zoomed-in view highlights a potential autophagic structure. (c) Quantitative analysis of autophagysome-like structures in (B). Data are mean ± SEM. n = 60 for WT; n = 71 for ND1; with p-value from two-tailed Mann Whitney test. (d) Representative images of LC3B and MTCO1 co-staining in oocytes in secondary follicle of WT and ND1 mutant mice. The experiment was repeated three times. (e) Representative images of LAMP1 and TOMM20 co-staining in oocytes in secondary follicle of WT and ND1 mutant mice. The experiment was repeated five times.

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Extended Data Fig. 10 Flow cytometry gating strategies.

(a) TMRM staining Gating: Initial gating excluded debris via FSC-A vs SSC-A, followed by doublet exclusion using FSC-A vs FSC-H. Finally, eFluor 780 positive dead cells were removed from the analysis. (b) Immune cell isolation Gating: Debris was first excluded using FSC-A vs SSC-A, with doublets subsequently removed via FSC-A vs FSC-H. Specific immune cell populations were identified using the following markers: B lymphocytes (CD45+CD3−CD19+), T lymphocytes (CD45+CD3+CD19−), Monocytes (CD45+CD3−CD19−CD11b+Ly6G−Ly6Chi), and Neutrophils (CD45+CD3−CD19−CD11b+Ly6G+).

Supplementary information

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Ru, Y., Deng, X., Chen, J. et al. Maternal age enhances purifying selection on pathogenic mutations in complex I genes of mammalian mtDNA. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00672-6

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