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Longitudinal comparative transcriptomics reveals unique mechanisms underlying extended healthspan in bats

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Abstract

Bats are the longest-lived mammals, given their body size. However, the underlying molecular mechanisms of their extended healthspans are poorly understood. To address this question we carried out an eight-year longitudinal study of ageing in long-lived bats (Myotis myotis). We deep-sequenced ~1.7 trillion base pairs of RNA from 150 blood samples collected from known aged bats to ascertain the age-related transcriptomic shifts and potential microRNA-directed regulation that occurred. We also compared ageing transcriptomic profiles between bats and other mammals by analysis of 298 longitudinal RNA sequencing datasets. Bats did not show the same transcriptomic changes with age as commonly observed in humans and other mammals, but rather exhibited a unique, age-related gene expression pattern associated with DNA repair, autophagy, immunity and tumour suppression that may drive their extended healthspans. We show that bats have naturally evolved transcriptomic signatures that are known to extend lifespan in model organisms, and identify novel genes not yet implicated in healthy ageing. We further show that bats’ longevity profiles are partially regulated by microRNA, thus providing novel regulatory targets and pathways for future ageing intervention studies. These results further disentangle the ageing process by highlighting which ageing pathways contribute most to healthy ageing in mammals.

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Data availability

The raw data used in this study have been deposited in the National Center for Biotechnology Information’s BioProject under the accession PRJNA503704. The additional data supporting the conclusions in this paper can be available in the Supplementary Data 16.

Code availability

The custom scripts have been deposited in GitHub (https://github.com/UCDBatLab/Longitudinal_myoMyo_transcriptome).

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

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Acknowledgements

We acknowledge and thank the members of Bretagne Vivante and local volunteers and students from University College Dublin for their extensive help in sample collection, and the various owners/local authorities for allowing access to their sites. We would also like to thank M. Bekaert, M. Clarke, G. Hughes and J. Kacprzyk for helpful discussions of the analyses. We acknowledge the Irish Centre for High-End Computing for the provision of computational facilities and support. This project was funded by a European Research Council Research Grant (No. ERC-2012-StG311000 to E.C.T.), a UCD Wellcome Institutional Strategic Support Fund, financed jointly by University College Dublin and SFI-HRB-Wellcome Biomedical Research Partnership (No. 204844/Z/16/Z to E.C.T.), an Irish Research Council Consolidator Laureate Award (to E.C.T.) and a China Scholarship Council studentship (under the UCD-CSC funding programme to Z.H.). The French fieldwork was supported by a Contrat Nature Grant awarded to Bretagne Vivante.

Author information

E.C.T and Z.H. devised the study. M. myotis samples were collected by F.T., S.J.P., E.C.T., E.J.P., N.M.F., D.J., C.V.W. and Z.H. RNA extraction was performed by C.V.W. and Z.H. All data analyses were performed by Z.H. Z.H. is responsible for the Figures presented throughout. The manuscript was written by E.C.T. and Z.H. with input from all authors.

Competing interests

The authors declare no competing interests.

Correspondence to Emma C. Teeling.

Supplementary information

  1. Supplementary information

    Supplementary Methods, Supplementary Figs. 1–14 and Supplementary Tables 1–9

  2. Reporting Summary

  3. Supplementary Data 1

    Spearman’s correlation coefficients between gene expression and age (n = 12,263).

  4. Supplementary Data 2

    Top 20 genes that exhibited the strongest correlation (both positive and negative) with age in M. myotis.

  5. Supplementary Data 3

    The full list of GO term expression pattern with age across 4 species.

  6. Supplementary Data 4

    Spearman’s correlation coefficients between expression of 207 human aging-associated genes and age.

  7. Supplementary Data 5

    Raw gene expression counts (n = 12,263).

  8. Supplementary Data 6

    Raw mature miRNA expression counts (n = 117).

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Fig. 1: Overview of M. myotis blood transcriptome.
Fig. 2: Co-expression network analysis based on 6,692 age-associated candidate genes.
Fig. 3: Network analysis of the genes enriched in DNA repair.
Fig. 4: Comparative transcriptomic analyses between bat and human, mouse and wolf.
Fig. 5: Comparison of expression patterns of human ageing-associated genes between bat and human, mouse and wolf.
Fig. 6: miRNA analyses and their regulatory network.