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m6A epitranscriptomic regulation of tissue homeostasis during primate aging

Nature Aging volume 3, pages 705–721 (2023)Cite this article

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Abstract

How N6-methyladenosine (m6A), the most abundant mRNA modification, contributes to primate tissue homeostasis and physiological aging remains elusive. Here, we characterize the m6A epitranscriptome across the liver, heart and skeletal muscle in young and old nonhuman primates. Our data reveal a positive correlation between m6A modifications and gene expression homeostasis across tissues as well as tissue-type-specific aging-associated m6A dynamics. Among these tissues, skeletal muscle is the most susceptible to m6A loss in aging and shows a reduction in the m6A methyltransferase METTL3. We further show that METTL3 deficiency in human pluripotent stem cell-derived myotubes leads to senescence and apoptosis, and identify NPNT as a key element downstream of METTL3 involved in myotube homeostasis, whose expression and m6A levels are both decreased in senescent myotubes. Our study provides a resource for elucidating m6A-mediated mechanisms of tissue aging and reveals a METTL3–m6A–NPNT axis counteracting aging-associated skeletal muscle degeneration.

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Fig. 1: Characterization of aging-associated phenotypes of liver, skeletal muscle and heart from young and old primates.
Fig. 2: Profiling of m6A epitranscriptomes of liver, skeletal muscle and heart from primates.
Fig. 3: m6A modification dynamics in the liver, skeletal muscle and heart during primate aging.
Fig. 4: Downregulation of METTL3 and m6A contributes to skeletal muscle dysregulation during primate aging.
Fig. 5: NPNT is a key downstream effector of METTL3 in regulating skeletal muscle aging.
Fig. 6: METTL3 promotes NPNT expression and myotube homeostasis in an m6A-dependent manner.

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Data availability

High-throughput sequencing data generated in this study have been deposited in the Genome Sequence Archive (GSA) in the National Genomics Data Center, Beijing Institute of Genomics (China National Center for Bioinformation) of the Chinese Academy of Sciences under the accession numbers CRA005942 (monkey tissues) and HRA002143 (human myotubes) and are publicly accessible. In addition, the relevant processed data are also publicly accessible at the Aging Atlas database (https://bigd.big.ac.cn/aging/index)92. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

All codes used for the analysis of m6A and RNA sequencing in this study are available at https://github.com/lumm11/M6a-Primate-Aging.

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Acknowledgements

We are grateful to L. Bai, Q. Chu, R. Bai, S. Ma, J. Lu, L. Tian, Y. Yang, J. Chen and X. Jin for their administrative assistance. We thank Q. Ji, Y. Zhang, S. Yang, L. Li, P. Yan, X. Jiang, J. Li, S. Li, H. Zhao, C. Liang, Y. He, H. Hu, Z. Wang, L. Fu, S. Ma, H. Yan and Z. Diao for their technical support. We also thank the Data Center for Stem Cell and Regeneration (DCSCR) for supporting the data analysis. This work was supported by the National Key Research and Development Program of China (2022YFA1103700 to W.Z. and J.Q.), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16000000 to G.-H.L., J.Q., W.Z., W.C., J.R., S.W. and Y.-G.Y.), the National Key Research and Development Program of China (2020YFA0804000 to G.-H.L. and S.W., 2018YFC2000100 to J.Q., 2018YFA0107203 to J.Q., 2020YFA0112200 to G.-H.L., 2021YFF1201005 to W.Z., 2019YFA0110900 to W.C., 2022YFA1103800 to Z.W. and T.W., 2019YFA0802202 to J.R., 2020YFA0803401 to J.R.), the National Natural Science Foundation of China (32100937 to Z.W., 81921006 to G.-H.L. and J.Q., 92149301 to G.-H.L. and S.W., 92168201 to G.-H.L., 82125011 to J.Q., 92049304 to J.Q., 91949209 to J.Q., 92049116 to W.Z., 32121001 to W.Z. and J.R., 82192863 to W.Z., 82122024 to S.W., 82071588 to S.W., 31970597 to J.R., 82173061 to W.C., 82001477 to Q.Z.), the STI2030-Major Projects (2021ZD0202400 to S.W.), CAS Project for Young Scientists in Basic Research (YSBR-076 to G.-H.L., J.Q. and J.R., YSBR-012 to W.Z.), CAS Special Research Assistant Program (to Z.W.), the Program of the Beijing Natural Science Foundation (Z190019 to G.-H.L. and W.Z.), K. C. Wong Education Foundation (GJTD-2019-06 to J.Q., GJTD-2019-08 to W.Z.), the Pilot Project for Public Welfare Development and Reform of Beijing-affiliated Medical Research Institutes (11000022T000000461062 to S.W.), Youth Innovation Promotion Association of CAS (E1CAZW0401 to W.Z.), Young Elite Scientists Sponsorship Program by CAST (YESS20200012 to S.W.), the Informatization Plan of Chinese Academy of Sciences (CAS-WX2021SF-0301 to G.-H.L., CAS-WX2022SDC-XK14 to G.-H.L., CAS-WX2021SF-0101 to J.Q.) and the Tencent Foundation (2021-1045 to G.-H.L.).

Author information

Author notes

  1. These authors contributed equally: Zeming Wu, Mingming Lu, Di Liu, Yue Shi, Jie Ren, Si Wang.

Authors and Affiliations

  1. State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Zeming Wu, Di Liu, Sheng Zhang, Hongyu Li & Guang-Hui Liu

  2. Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China

    Zeming Wu, Jie Ren, Tuo Wei, Yun-Gui Yang, Jing Qu, Weiqi Zhang, Weimin Ci & Guang-Hui Liu

  3. Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China

    Zeming Wu, Tuo Wei, Jing Qu & Guang-Hui Liu

  4. National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China

    Mingming Lu & Jingfa Xiao

  5. China National Center for Bioinformation, Beijing, China

    Mingming Lu, Yue Shi, Jie Ren, Zihui Yu, Yun-Gui Yang, Jingfa Xiao, Weiqi Zhang & Weimin Ci

  6. University of Chinese Academy of Sciences, Beijing, China

    Mingming Lu, Di Liu, Jie Ren, Ying Jing, Sheng Zhang, Hongyu Li, Zihui Yu, Zunpeng Liu, Shijia Bi, Tuo Wei, Yun-Gui Yang, Jingfa Xiao, Jing Qu, Weiqi Zhang, Weimin Ci & Guang-Hui Liu

  7. CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China

    Yue Shi, Jie Ren, Zihui Yu, Yun-Gui Yang, Weiqi Zhang & Weimin Ci

  8. Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China

    Jie Ren & Yun-Gui Yang

  9. Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China

    Si Wang, Qian Zhao & Guang-Hui Liu

  10. Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, China

    Si Wang, Qian Zhao & Guang-Hui Liu

  11. The Fifth People’s Hospital of Chongqing, Chongqing, China

    Si Wang

  12. State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Ying Jing, Zunpeng Liu, Shijia Bi, Tuo Wei & Jing Qu

  13. State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Brain-Intelligence Technology (Shanghai), Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    Sheng Zhang

  14. National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    Hongyu Li

  15. Altos Labs, San Diego, CA, USA

    Juan Carlos Izpisua Belmonte

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  1. Zeming Wu
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Contributions

G.-H.L., W.C., W.Z. and J.Q. conceived the project and supervised overall experiments. Z.W. performed the phenotypic and mechanistic analyses with assistance from D.L., J.R. and S.W. M.L. and Y.S. performed bioinformatic analyses with assistance from J.R., S.W., Z.W. and Z.L. Y.S. performed the m6A-seq and RNA-seq library construction with the help from Z.Y., D.L., S.Z. and S.B. Y.J., Q.Z. and H.L. performed the plasmid construction and lentivirus packaging. G.-H.L., W.C., W.Z., J.Q., Z.W., M.L., D.L., Y.S., J.R. and S.W. performed the data analysis. G.-H.L., W.C., W.Z., J.Q., Z.W., M.L., D.L., Y.S., J.R., S.W., T.W., Y.-G.Y., J.X. and J.C.I.B. wrote the initial draft of this manuscript. All the authors participated in editing both the form and content of this manuscript and approved the final version.

Corresponding authors

Correspondence to Jing Qu, Weiqi Zhang, Weimin Ci or Guang-Hui Liu.

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Competing interests

The authors declare no competing interests.

Peer review

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Nature Aging thanks Mary McMahon and the other, anonymous, reviewers for their contribution to the peer review of this work.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Characterization of mRNA m6A modification in liver, skeletal muscle, and heart from young and old primates.

a. Heatmaps showing the mRNA levels of IFN-I and SASP-related genes (IL1A, IL1B, IFNB1, and IL6) in liver, skeletal muscle, and heart from young and old primates. The color keys from blue to brown represent the mRNA levels from low to high. Eight animals each group were used and experiments were repeated three times independently. b. Motif analysis for m6A peaks in liver, skeletal muscle, and heart from young and old primates. c. Distribution of m6As along the 5′UTR, CDS, and 3′UTR regions of the total mRNAs across all tissues after normalization to transcript length. d. Heatmap showing Pearson’s correlation of m6A modification levels across all tissue samples from old primates. e. Heatmap showing the Pearson’s correlation of the RNA expression levels across all tissue samples from young primates. f. Heatmap showing the Pearson’s correlation of the RNA expression levels across all tissue samples from old tissues. g. Venn diagram showing the overlap of m6A peaks identified in liver, skeletal muscle, and heart from old primates. h. Functional pathway enrichment analysis of genes encoding mRNAs with common m6As shared by liver, skeletal muscle, and heart from old primates. i. Functional pathway enrichment analysis of genes encoding mRNAs with tissue-specific m6As in liver, skeletal muscle, and heart from old primates. The number of genes for each term is indicated in grey round shadow. j. Violin plots showing the distribution of expression level of mRNAs with common m6As and tissue-specific m6As in liver, skeletal muscle, and heart from old primates. k. Violin plots showing the distribution of expression divergence of mRNAs with common m6As and tissue-specific m6As in liver, skeletal muscle, and heart from old primates. The one-sided binomial test was used for statistical analysis in b. The accumulative hypergeometric test was used for statistical analysis in h and i. The two-tailed Mann-Whitney U-test was used for statistical analysis in j and k. Bioinformatic analyses: n = 8 (liver and skeletal muscle) or 5 (heart) biological replicates. P values are indicated in the figure. The color keys from light to dark represent the similarities (Pearson’s correlation) from low to high in d-f.

Source data

Extended Data Fig. 2 The correlation between m6A modification and gene expression dynamics during primate aging.

a. Venn diagram showing the overlap of m6A peaks or genes encoding mRNAs with m6A modification between young and old tissues. b. Venn diagram showing the overlap of genes encoding mRNAs with stable, old-gain or old-loss m6As in liver, skeletal muscle, and heart. c. Boxplots showing the expression level of genes encoding mRNAs with stable, old-loss and old-gain m6As at the 5′ UTR, CDS, and 3′ UTR of their transcripts. d. Boxplots showing the expression divergence of genes encoding mRNAs with stable, old-loss and old-gain m6As at the 5′ UTR, CDS, and 3′ UTR of their transcripts. The two-tailed Mann-Whitney U-test was used for statistical analysis in c and d. Box plots indicate median values and interquartile ranges; whiskers indicate 1.5x interquartile range. n = 8 (for liver and skeletal muscle) or 5 (for heart) biological replicates (see Methods and Supplementary Table 1 for details). P values are indicated in the figure.

Extended Data Fig. 3 Expression alterations in genes encoding mRNAs with m6A loss or gain during aging in the liver, skeletal muscle, and heart.

a-c, Cumulative plots showing the expression alterations in genes encoding mRNAs with m6A loss (left) or gain (right) between young and old livers (a), skeletal muscles (b), and hearts (c). The two-tailed Mann-Whitney U-test was used for statistical analysis. n = 8 (for liver and skeletal muscle) or 5 (for heart) biological replicates (see Methods and Supplementary Table 1 for details). P values are indicated in the figure.

Extended Data Fig. 4 Analysis of m6A modification and expression levels of m6A-related enzymes in young and old tissues.

a. Cumulative plot showing the m6A peak intensity in young and old livers. b. Cumulative plot showing the m6A peak intensity in young and old hearts. c. Dot blot analysis of m6A in mRNAs of young and old livers.d. Dot blot analysis of m6A in mRNAs of young and old hearts. e. Statistical results of the protein levels of METTL14, FTO and ALKBH5 in young and old skeletal muscles (corresponding to Fig. 4c). Triangles indicate female samples, and filled circles indicate male ones. f. Western blot analysis of the m6A-related enzymes in young and old livers. Triangles indicate female samples, and filled circles indicate male ones. g. Western blot analysis of the m6A-related enzymes in young and old hearts. Triangles indicate female samples, and filled circles indicate male ones. The two-tailed Mann-Whitney U-test was used for statistical analysis in a and b. Two-tailed Student’s t-test was used for statistical analysis in c-g. Data are presented as the mean ± s.e.m. n = 8 biological replicates in c-g. P values are indicated in the figure. For dot blot analysis in c and d, MB staining was used as the loading control. For western blot analysis in f and g, GAPDH was used as the loading control.

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Extended Data Fig. 5 Characterization of mRNA m6A modification in control and METTL3-deficient myotubes.

a. Motif analysis for m6A peaks in control (sgNTC) and METTL3-deficient (sgMETTL3) myotubes. b. Distribution of m6As along the 5′UTR, CDS, and 3′UTR regions of the total mRNAs after normalization to transcript length in sgNTC and sgMETTL3 myotubes. c. Pie chart showing the fraction of m6As in different transcript segments in sgNTC and sgMETTL3 myotubes. d. Functional pathway enrichment analysis of genes encoding mRNAs with m6A loss in their transcripts in sgMETTL3 myotubes. Gene numbers are indicated on the right. The color key from light to dark indicates the -log10(P value) from low to high, respectively. e. Heatmap showing the clusters of differentially expressed genes (DEGs) encoding mRNAs with m6A loss in their transcripts in sgMETTL3 myotubes relative to sgNTC myotubes. f. Functional pathway enrichment analysis of differentially expressed genes encoding mRNAs with m6A loss in their transcripts in sgMETTL3 myotubes relative to sgNTC myotubes. The number of genes for each term is indicated in grey round shadow. g. Heatmap showing the differential expression of SASP-related genes in sgNTC and sgMETTL3 myotubes. h. Heatmap showing the differential expression of adipogenesis-related genes in sgNTC and sgMETTL3 myotubes. i. Heatmaps showing the m6A and mRNA levels of NPNT in young and old skeletal muscles. Eight animals each group were used and experiments were repeated three times independently. j. IGV views showing the m6A signals in NPNT in sgNTC and sgMETTL3 myotubes and in young and old skeletal muscles. The color from light to dark represents the m6A signal from low to high, respectively. The one-sided binomial test was used for statistical analysis in a. The accumulative hypergeometric test was used for statistical analysis in d and f. For bioinformatic analyses in sgNTC and sgMETTL3 myotubes, two biological replicates were used. The color keys from blue to brown indicate the normalized RPKM (e, g and h) or the m6A and mRNA levels of NPNT (i) from low to high.

Source data

Extended Data Fig. 6 NPNT is a key downstream effector of METTL3 in counteracting myotube senescence.

a. Heatmap showing the Pearson’s correlation of RNA-seq data in replicates of control (siNC) and NPNT-knockdown (siNPNT) myotubes. The color key from blue to red indicates the Pearson’s correlation from low to high, respectively. b. Heatmap showing the expression level of SASP-related genes in siNC and siNPNT myotubes. The color key from blue to red indicates the expression level (normalized RPKM) from low to high, respectively. c. Heatmap showing the differential expression of adipogenesis-related genes in siNC and siNPNT myotubes. The color key from blue to red indicates the normalized RPKM from low to high, respectively. d. Functional pathway enrichment analysis of downregulated genes in siNPNT myotubes relative to siNC myotubes. e. Heatmaps showing the expression level of genes related to terms including “regulation of muscle contraction” (left), “regulation of muscle system process” (middle), and “muscle cell differentiation” (right) in siNC and siNPNT myotubes. The color key from blue to red indicates the expression level (normalized RPKM) from low to high, respectively. The gene symbol of NPNT is highlighted in red. f. Venn diagram showing the overlap of downregulated genes in METTL3-deficient myotubes and NPNT-knockdown myotubes. g. Functional pathway enrichment analysis of genes showing downregulation in both METTL3-deficient myotubes and NPNT-knockdown myotubes. h. Western blot analysis of NPNT and P16 in METTL3-deficient myotubes transduced with lentiviruses expressing Flag-Luc or Flag-NPNT. i. RT-qPCR analysis of CDKN2A (P16), IL6 and CXCL8 in METTL3-deficient myotubes transduced with lentiviruses expressing Flag-Luc or Flag-NPNT. j. SA-β-gal staining in METTL3-deficient myotubes transduced with lentiviruses expressing Flag-Luc or Flag-NPNT. The accumulative hypergeometric test was used for statistical analysis in d and g. For bioinformatic analyses in siNC and siNPNT myotubes, three biological replicates were used. Two-tailed Student’s t-test was used for statistical analysis in h-j. Data are presented as the mean ± s.e.m. n = 3 biological replicates. P values are indicated in the figure. GAPDH was used as the loading control in h. Scale bars in j, 50 μm.

Source data

Extended Data Fig. 7 METTL3 regulates NPNT expression and myotube homeostasis in a catalytic activity-dependent manner.

a. Immunostaining of m6A in wild-type myotubes treated with vehicle or STM2457. b. RT-qPCR analysis of NPNT in wild-type myotubes treated with vehicle or STM2457. c. SA-β-gal staining in wild-type myotubes treated with vehicle or STM2457. d. Immunostaining of MYHC in wild-type myotubes treated with vehicle or STM2457. The yellow line segments indicate the representative diameters of indicated myotubes. Statistical analysis of myotube diameter is shown on the right. e. Western blot analysis of METTL3 and NPNT in wild-type myotubes cultured for indicated times. f. SA-β-gal staining in wild-type myotubes cultured for 8 and 14 days. Two-tailed Student’s t-test was used for statistical analysis. Data are presented as the mean ± s.e.m. n = 15 random fields from three biological replicates in a. n = 4 biological replicates in b. n = 3 biological replicates in c-f. P values are indicated in the figure. Scale bars in a, c, d and f, 50 μm. GAPDH was used as the loading control in e.

Source data

Extended Data Fig. 8 METTL3 promotes the stability of NPNT mRNA via IGF2BP1.

a. Schematic diagram showing the potential transcripts of NPNT with different skipping exons (indicated by dashed black box). b. Sashimi plots showing the major splicing events of the indicated skipping exons of NPNT in control (sgNTC) and METTL3-deficient (sgMETTL3) myotubes (left), as well as in young and old skeletal muscles (right). c. Heatmaps showing the IncLevel values of differential alternative splicing skipping-exon events between sgNTC and sgMETTL3 myotubes (left), as well as between young and old skeletal muscles (right). The color keys from blue to red indicate the IncLevel from low to high. d. mRNA stability analysis of NPNT in sgNTC and sgMETTL3 myotubes upon treatment with ACTD for the indicated times. e. RIP-qPCR analysis showing the enrichment of IgG or IGF2BP1 on NPNT mRNA. f. Western blot analysis of IGF2BP1 in control (siNC) and IGF2BP1-knockdown (siIGF2BP1) myotubes. GAPDH was used as the loading control. g. mRNA stability analysis of NPNT in siNC and siIGF2BP1 myotubes upon treatment with ACTD for the indicated times. Two-tailed Student’s t-test was used for statistical analysis in d-g. Data are presented as the mean ± s.e.m. n = 3 biological replicates in d-f. n = 4 biological replicates in g. P values are indicated in the figure. For alternative splicing analyses with RNA-seq data in b and c, two biological replicates were used in sgNTC and sgMETTL3 myotubes and eight biological replicates were used in young and old skeletal muscles.

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Supplementary information

Supplementary Information

Legends for Supplementary Tables 1–5.

Reporting Summary

Supplementary Tables 1–5

Supplementary Table 1. Information of cynomolgus monkeys used in this study. Table 2. Differential m6A peaks during primate aging in liver, skeletal muscle and heart. Table 3. Functional annotation of DEGs with m6A loss in METTL3-deficient myotubes. Table 4. Functional annotation of genes with m6A loss in both aged skeletal muscles and METTL3-deficient myotubes. Table 5. Sequences of sgRNA, siRNA and primers for construction of lentiviral expression vector and RT–qPCR analysis.

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Wu, Z., Lu, M., Liu, D. et al. m6A epitranscriptomic regulation of tissue homeostasis during primate aging. Nat Aging 3, 705–721 (2023). https://doi.org/10.1038/s43587-023-00393-2

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