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Identification of a longevity gene through evolutionary rate covariation of insect mito-nuclear genomes

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Abstract

Oxidative phosphorylation, essential for energy metabolism and linked to the regulation of longevity, involves mitochondrial and nuclear genes. The functions of these genes and their evolutionary rate covariation (ERC) have been extensively studied, but little is known about whether other nuclear genes not targeted to mitochondria evolutionarily and functionally interact with mitochondrial genes. Here we systematically examined the ERC of mitochondrial and nuclear benchmarking universal single-copy ortholog (BUSCO) genes from 472 insects, identifying 75 non-mitochondria-targeted nuclear genes. We found that the uncharacterized gene CG11837—a putative ortholog of human DIMT1—regulates insect lifespan, as its knockdown reduces median lifespan in five diverse insect species and Caenorhabditis elegans, whereas its overexpression extends median lifespans in fruit flies and C. elegans and enhances oxidative phosphorylation gene activity. Additionally, DIMT1 overexpression protects human cells from cellular senescence. Together, these data provide insights into the ERC of mito-nuclear genes and suggest that CG11837 may regulate longevity across animals.

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Fig. 1: Contrasting patterns of genomic properties between nuclear and mitochondrial genomes in insects.
Fig. 2: The classification of proteins and the landscape of ERC of nuclear and mitochondrial PCGs.
Fig. 3: Identification of non-mitochondria-targeted nuclear genes that exhibit strong ERC and potentially interact with mtOXPHOS genes.
Fig. 4: CG11837 is a conserved lifespan gene across animals.

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Data availability

All mitochondrial genomes, phylogenomic matrix, gene trees and summary statistics as well as 3D protein structures of 75 non-mitochondria-targeted nuclear proteins are available on the figshare repository (https://doi.org/10.6084/m9.figshare.22637761)54. Raw RNA sequencing data have been deposited in GenBank under BioProject ID PRJNA962685. The STRING version 11 resource45 is available at https://string-db.org/. Publicly available insect genome assemblies and RNA sequencing data are from National Center for Biotechnology Information (NCBI) Genome browser (https://www.ncbi.nlm.nih.gov/datasets/genome/) and the NCBI Sequence Read Archive browser (https://www.ncbi.nlm.nih.gov/sra/)55. The OrthoDB version 10.1 resource56 is available at https://v10-1.orthodb.org/. The lifespans of 43 insects are available at the Animal Diversity Web (https://animaldiversity.org/)57. All other data supporting the findings of this study are available as source data or from the corresponding authors upon reasonable request. Source data are provided with this paper.

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Acknowledgements

We thank X. Yin and J. Yang for constructive feedbacks. We also thank C. Tong and S. Liu for quantifying mitochondrial morphology. This work was conducted, in part, using the resources of the Information Technology Center and the State Key Laboratory of Computer-Aided Design & Computer Graphics at Zhejiang University. This work was supported by the National Key R&D Program of China (2022YFD1401600 to X.-X.S.); the National Science Foundation for Distinguished Young Scholars of Zhejiang Province (LR23C140001 to X.-X.S.); the National Natural Science Foundation of China (32071665 to X.-X.S., 32325044 to J.H., 32230015 and 32021001 to S.W. and 31922074 to Y.C.); the Fundamental Research Funds for the Central Universities (226-2023-00021 to X.-X.S. and 2021FZZX001-31 to Y.C.); the Zhejiang Provincial Natural Science Foundation of China (LZ23C140003 to J.H., LZ23C110002 to Y.Z., LZ23C020002 to R.P. and LR21C120002 to S.X.); the General Program of the National Natural Science Foundation of China (32371236 to Y.Z.); the New Cornerstone Science Foundation (S.W.); the Key International Joint Research Program of the National Natural Science Foundation of China (31920103005 to X.-x.C.); the National Science Foundation (DEB-2110404 to A.R.); and the National Institutes of Health/National Institute of Allergy and Infectious Diseases (R01 AI153356 to A.R.).

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Authors and Affiliations

Authors

Contributions

X.-X.S. conceived and designed the study. X.-X.S., M.T., J.C., C.C., Z.S., J.X., J.Y., J.Z., G.-Z.O., C.L. and Y.X. performed computational analyses and experiments. X.-X.S., J.H., A.R., S.W., M.T., J.C., Y.C., Z.-R.Z., R.P., S.X., X.-x.C. and Y.Z. interpreted results. X.-X.S. wrote the paper, with input from all authors. X.-X.S., M.T., J.H., A.R., S.W. and Y.Z. edited the paper.

Corresponding authors

Correspondence to Sibao Wang, Jianhua Huang or Xing-Xing Shen.

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A.R. is a scientific consultant for LifeMine Therapeutics, Inc. The other authors declare no competing interests.

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Nature Aging thanks Mark Harrison, David Rand, Li Zhao 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 Comparison of the higher-level phylogenies inferred from the analyses of 1,417 nuclear BUSCO amino acid genes (left panel) and 13 mitochondrial amino acid genes (right panel).

Branch support values near internodes / internal branches correspond to ultrafast bootstrap support. Only support values smaller than 95% are shown. The number in parenthesis is the number of species for each collapsed clade. The conflicting relationships between nuBUCSO phylogeny and mtPCG phylogeny indicate dashed lines. Two complete phylogenies are given in Supplementary Figs. 3 and 4.

Extended Data Fig. 2 The schematic workflow used for the calculation of the evolutionary rate covariation (ERC).

Evolutionary rate covariation (ERC) was the Pearson’s correlation coefficient (r) between relative evolutionary rates estimated from each branch of the phylogeny inferred from a given gene and those estimated from each branch of the phylogeny inferred from the mtOXPHOS supergene (concatenated 13 mtOXPHOS genes). The reference (or background) species tree is from the Fig. 1a. A detailed description of the analyses performed in each step of the workflow is provided in the Methods.

Extended Data Fig. 3 The histogram of connectivity for each of the 75 non-mt-nuProtein genes in three largest Clusters 1-3.

After identifying 75 non-mt-nuProtein genes based on the protein-protein association network (Fig. 3b), we further examined the connectivity (that is, the number of edges connecting the examined gene to other genes) for each of the 75 non-mt-nuProtein genes within three largest Clusters 1-3. Four genes with red stars, namely, CG13220, CG11837, and CG11788, which displayed the highest connectivity levels in their respective clusters, as well as Nop60B, which ranked as the second-highest in connectivity within the largest Cluster 2 that contains 50 genes ( ~ 67%) out of the 75 non-mt-nuProtein genes, were used to investigate their impact on mitochondrial morphology.

Extended Data Fig. 4 Effect of RNA interference of four non-mitochondria-targeted nuclear candidate genes on mitochondrial morphology.

We chose CG13220, CG11837, CG11788, and Nop60B to examine their effect on fruit fly mitochondrial morphologies. The Cg-GAL4 > UAS-mitoGFP line was crossed with the four UAS-RNAi lines (UAS-CG13220-RNAi, UAS-CG11837-RNAi, UAS-CG11788-RNAi, and UAS-Nop60B-RNAi), respectively. The early third-instar larval fat body was used for investigating mitochondrial morphology for each genotype. a, For each image, the boxes in the top right corners of the individual panels are magnifications that show mitochondrial morphology in greater detail. The genotypes are specified in the bottom left corners of the individual panels. We included three additional representative images for each genotype in Supplementary Fig. 10a, in addition to the one already provided. Scale bars = 10 μm. b, Illustration of the simplified organelle components, determined and stained from the control (Cg-GAL4 > UAS-mitoGFP) early third-instar larval fat body. c, For a given genotype’s confocal image, we measured and averaged the length of mitochondria from each of five randomly selected areas (25μm x 25μm) in the early third-instar larval fat body. n = 4 biologically independent experiments for each genotype. Data represent mean ± SD. P values were calculated using a two-tailed t test. ***P ≤ 0.001.

Source data

Extended Data Fig. 5 A superposition between two protein structures.

Protein structures of fruit fly CG11837 and human DIMT1 were retrieved from the Alphafold2 database (https://alphafold.ebi.ac.uk/), respectively. TM-score in structure similarity is 0.93.

Extended Data Fig. 6 The efficiencies of knockdown and overexpression of CG11837.

a, The efficiencies of knockdown of CG11837 and its orthologs on both sexes. Note that the worm was not assessed for two sexes, as worms are hermaphrodites. Fruit fly used D42-GAL4/+ and UAS-CG11837-RNAi/+ as two genetic controls; mosquito, beetle, bee, and brown planthopper used dsGFP as controls; worm used empty vector as a control. n = 3 biologically independent experiments for each genotype. Data represent mean ± SD. P values were calculated using a two-tailed t test. NS P > 0.05; *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001. b, The efficiency of overexpression of CG11837 in fruit fly and E02H1.1 (an ortholog of CG11837) in worm. Fruit fly used Actin-GAL4 tsGAL80/+ and UAS-CG11837/+ as two genetic controls; worm used the N2 Bristol strain as a control. Note that the worm was not assessed for two sexes, as worms are hermaphrodites. n = 3 biologically independent experiments for each genotype. Data represent mean ± SD. P values were calculated using a two-tailed t test. NS P > 0.05; *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001. All examined organisms are 3-day-old adults.

Source data

Extended Data Fig. 7 Overexpression of DIMT1 protects human cells in response to DNA damage.

a, Western blot verifying the overexpression of DIMT1 in human skin fibroblasts. n = 3 biologically independent experiments. Data represent mean ± SD. P value was calculated using a two-tailed t test. **P ≤ 0.01 and > 0.001. b, Senescence-associated β-galactosidase (SA-β-gal.) activity assay without (left panel) and with (right panel) X-ray. Cells were irradiated without or with 5 Gy X-ray. Ten days after irradiation, cells were re-seeded to 6-well plates and the next day, cells were stained for SA-β-gal. n = 3 biologically independent experiments. Scale bars = 200 μm.

Source data

Extended Data Fig. 8 Transcriptome data analysis for KEGG pathway enrichment and expression level of all 80 OXPHOS genes.

a, KEGG pathway enrichment (left panel) and expression level of all OXPHOS genes (right panel) between CG11837 knockdown and control male adults of 3-day-old mosquitos and 13-day-old beetles. A two paired Wilcoxon test was used to test whether the two sets of values (sample size = 80) are significantly different. ***P ≤ 0.001. b, KEGG pathway enrichment (left panel) and expression level of all OXPHOS genes (right panel) between CG11837 overexpression and control male adults of 3-day-old fruit files. A two paired Wilcoxon test was used to test whether the two sets of values (sample size = 80) are significantly different. ***P ≤ 0.001.

Source data

Extended Data Fig. 9 Comparison of expression levels of the all 80 OXPHOS genes between young and aged adult insects.

Note that expression levels of all OXPHOS genes for young and aged insects were calculated based on publicly available wild type adult whole body transcriptome data. Nilaparvata lugens: female; Apis cerana: female; Sitophilus oryzae: unknown sex; Helicoverpa armigera: unknown sex; Drosophila melanogaster: female + male. For each boxplot, the bottom, center and top represent 25th, 50th and 75th percentiles, respectively. Whiskers represent 1.5× interquartile range. Sample size = 80. Two-tailed paired Wilcoxon test was used to test whether the two sets of values are significantly different. ***P ≤ 0.001.

Supplementary information

Supplementary Information

Supplementary Methods, full legends for Supplementary Tables 1–8 in Excel format, Supplementary Tables 9–11, References and Figs. 1–15.

Reporting Summary

Supplementary Tables 1–8

Legends and descriptive captions of all the supplementary tables are listed in the Supplementary Information.

Source data

Source Data Fig. 4

Numerical source data for Fig. 4.

Source Data Extended Data Fig. 4

Numerical source data for Extended Data Fig. 4.

Source Data Extended Data Fig. 6

Numerical source data for Extended Data Fig. 6.

Source Data Extended Data Fig. 7

Unprocessed western blot for Extended Data Fig. 7.

Source Data Extended Data Fig. 7

Numerical source data for Extended Data Fig. 7.

Source Data Extended Data Fig. 8

Numerical source data for Extended Data Fig. 8.

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Tao, M., Chen, J., Cui, C. et al. Identification of a longevity gene through evolutionary rate covariation of insect mito-nuclear genomes. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00641-z

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