Gene expression and regulatory factors of the mechanistic target of rapamycin (mTOR) complex 1 predict mammalian longevity

Abstract

Species longevity varies significantly across animal species, but the underlying molecular mechanisms remain poorly understood. Recent studies and omics approaches suggest that phenotypic traits of longevity could converge in the mammalian target of rapamycin (mTOR) signalling pathway. The present study focuses on the comparative approach in heart tissue from 8 mammalian species with a ML ranging from 3.5 to 46 years. Gene expression, protein content, and concentration of regulatory metabolites of the mTOR complex 1 (mTORC1) were measured using droplet digital PCR, western blot, and mass spectrometry, respectively. Our results demonstrate (1) the existence of differences in species-specific gene expression and protein content of mTORC1, (2) that the achievement of a high longevity phenotype correlates with decreased and inhibited mTORC1, (3) a decreased content of mTORC1 activators in long-lived animals, and (4) that these differences are independent of phylogeny. Our findings, taken together, support an important role for mTORC1 downregulation in the evolution of long-lived mammals.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Antikainen H, Driscoll M, Haspel G, Dobrowolski R. TOR-mediated regulation of metabolism in aging. Aging Cell. 2017;16:1219–33. https://doi.org/10.1111/acel.12689.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. Bárcena C, López-Otín C, Kroemer G. Methionine restriction for improving progeria: another autophagy-inducing anti-aging strategy? Autophagy. 2019;15:558–9. https://doi.org/10.1080/15548627.2018.1533059.

    CAS  Article  PubMed  Google Scholar 

  3. Barja G. Mitochondrial free radical production and aging in mammals and birds. Ann N Y Acad Sci. 1998;854:224–38. https://doi.org/10.1111/j.1749-6632.1998.tb09905.x.

    CAS  Article  PubMed  Google Scholar 

  4. Barja G. The gene cluster hypothesis of aging and longevity. Biogerontology. 2008;9:57–66. https://doi.org/10.1007/s10522-007-9115-5.

    Article  PubMed  Google Scholar 

  5. Barja G. Longevity and evolution. New York: Nova Science Publishers, Inc.; 2010.

    Google Scholar 

  6. Barja G. Towards a unified mechanistic theory of aging. Exp Gerontol. 2019;124:110627. https://doi.org/10.1016/j.exger.2019.05.016.

    Article  PubMed  Google Scholar 

  7. Barja G, Cadenas S, Rojas C, Pérez-Campo R, López-Torres M. Low mitochondrial free radical production per unit O2 consumption can explain the simultaneous presence of high longevity and high aerobic metabolic rate in birds. Free Radic Res. 1994;21:317–27. https://doi.org/10.3109/10715769409056584.

    CAS  Article  PubMed  Google Scholar 

  8. Bowles JT. The evolution of aging: a new approach to an old problem of biology. Med Hypotheses. 1998;51:179–221. https://doi.org/10.1016/S0306-9877(98)90079-2.

    CAS  Article  PubMed  Google Scholar 

  9. Bozek K, Khrameeva EE, Reznick J, Omerbašić D, Bennett NC, Lewin GR, et al. Lipidome determinants of maximal lifespan in mammals. Sci Rep. 2017;7:1–5. https://doi.org/10.1038/s41598-017-00037-7.

    CAS  Article  Google Scholar 

  10. Cabré R, Jové M, Naudí A, Ayala V, Piñol-Ripoll G, Gil-Villar MP, et al. Specific metabolomics adaptations define a differential regional vulnerability in the adult human cerebral cortex. Front Mol Neurosci. 2016;9. https://doi.org/10.3389/fnmol.2016.00138.

  11. Caraveo G, Soste M, Cappelleti V, Fanning S, van Rossum DB, Whitesell L, et al. FKBP12 contributes to α-synuclein toxicity by regulating the calcineurin-dependent phosphoproteome. Proc Natl Acad Sci. 2017;114:311–22. https://doi.org/10.1073/pnas.1711926115.

    CAS  Article  Google Scholar 

  12. Caron A, Richard D, Laplante M. The roles of mTOR complexes in lipid metabolism. Annu Rev Nutr. 2015;35:321–48. https://doi.org/10.1146/annurev-nutr-071714-034355.

    CAS  Article  PubMed  Google Scholar 

  13. Chiang GG, Abraham RT. Phosphorylation of mammalian target of rapamycin (mTOR) at Ser-2448 is mediated by p70S6 kinase. J Biol Chem. 2005;280:25485–90. https://doi.org/10.1074/jbc.M501707200.

    CAS  Article  PubMed  Google Scholar 

  14. Chong J, Wishart DS, Xia J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr Protoc Bioinforma. 2019;68. https://doi.org/10.1002/cpbi.86.

  15. Cooper N, Thomas GH, FitzJohn RG. Shedding light on the ‘dark side’ of phylogenetic comparative methods. Methods Ecol Evol. 2016;7:693–9. https://doi.org/10.1111/2041-210X.12533.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Cunningham JT, Rodgers JT, Arlow DH, Vazquez F, Mootha VK, Puigserver P. mTOR controls mitochondrial oxidative function through a YY1–PGC-1α transcriptional complex. Nature. 2007;450:736–40. https://doi.org/10.1038/nature06322.

    CAS  Article  PubMed  Google Scholar 

  17. Düvel K, Yecies JL, Menon S, Raman P, Lipovsky AI, Souza AL, et al. Activation of a metabolic gene regulatory network downstream of mTOR complex 1. Mol Cell. 2010;39:171–83. https://doi.org/10.1016/j.molcel.2010.06.022.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. Figueiredo VC, Markworth JF, Cameron-Smith D. Considerations on mTOR regulation at serine 2448: implications for muscle metabolism studies. Cell Mol Life Sci. 2017;74:2537–45. https://doi.org/10.1007/s00018-017-2481-5.

    CAS  Article  PubMed  Google Scholar 

  19. Fontana L, Partridge L, Longo VD. Extending healthy life span--from yeast to humans. Science. 2010;328(80):321–6. https://doi.org/10.1126/science.1172539.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Fredslund J, Schauser L, Madsen LH, Sandal N, Stougaard J. PriFi: using a multiple alignment of related sequences to find primers for amplification of homologs. Nucleic Acids Res. 2005;33:516–20. https://doi.org/10.1093/nar/gki425.

    CAS  Article  Google Scholar 

  21. Fushan AA, Turanov AA, Lee S-G, Kim EB, Lobanov AV, Yim SH, et al. Gene expression defines natural changes in mammalian lifespan. Aging Cell. 2015;14:352–65. https://doi.org/10.1111/acel.12283.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Gomez A, Gomez J, Torres ML, Naudi A, Mota-Martorell N, Pamplona R, et al. Cysteine dietary supplementation reverses the decrease in mitochondrial ROS production at complex I induced by methionine restriction. J Bioenerg Biomembr. 2015;47. https://doi.org/10.1007/s10863-015-9608-x.

  23. Gu X, Orozco JM, Saxton RA, et al. SAMTOR is an S-adenosylmethionine sensor for the mTORC1 pathway. Science. 2017;358(80):813–8. https://doi.org/10.1126/science.aao3265.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. Guarente L, Kenyon C. Genetic pathways that regulate ageing in model organisms. Nature. 2000;408:255–62. https://doi.org/10.1038/35041700.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Hoeffer CA, Tang W, Wong H, Santillan A, Patterson RJ, Martinez LA, et al. Removal of FKBP12 enhances mTOR-Raptor interactions, LTP, memory, and perseverative/repetitive behavior. Neuron. 2008;60:832–45. https://doi.org/10.1016/j.neuron.2008.09.037.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Jeon JS, Oh J-J, Kwak HC, Yun HY, Kim HC, Kim YM, et al. Age-related changes in sulfur amino acid metabolism in male C57BL/6 mice. Biomol Ther (Seoul). 2018;26:167–74. https://doi.org/10.4062/biomolther.2017.054.

    CAS  Article  Google Scholar 

  27. Johnson SC, Rabinovitch PS, Kaeberlein M. mTOR is a key modulator of ageing and age-related disease. Nature. 2013;493:338–45. https://doi.org/10.1038/nature11861.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Jones OR, Scheuerlein A, Salguero-Gómez R, Camarda CG, Schaible R, Casper BB, et al. Diversity of ageing across the tree of life. Nature. 2014;505:169–73. https://doi.org/10.1038/nature12789.

    CAS  Article  PubMed  Google Scholar 

  29. Jové M, Naudí A, Aledo JC, Cabré R, Ayala V, Portero-Otin M, et al. Plasma long-chain free fatty acids predict mammalian longevity. Sci Rep. 2013;3:3346. https://doi.org/10.1038/srep03346.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kapahi P, Chen D, Rogers AN, Katewa SD, Li PWL, Thomas EL, et al. With TOR, less is more: a key role for the conserved nutrient-sensing TOR pathway in aging. Cell Metab. 2010;11:453–65. https://doi.org/10.1016/j.cmet.2010.05.001.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. Kenyon CJ. The genetics of ageing. Nature. 2010;464:504–12. https://doi.org/10.1038/nature08980.

    CAS  Article  PubMed  Google Scholar 

  32. Kim EB, Fang X, Fushan AA, Huang Z, Lobanov AV, Han L, et al. Genome sequencing reveals insights into physiology and longevity of the naked mole rat. Nature. 2011;479:223–7. https://doi.org/10.1038/nature10533.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Kumar S, Stecher G, Suleski M, Hedges SB. TimeTree: a resource for timelines, timetrees, and divergence times. Mol Biol Evol. 2017;34:1812–9. https://doi.org/10.1093/molbev/msx116.

    CAS  Article  PubMed  Google Scholar 

  34. Lewis KN, Rubinstein ND, Buffenstein R. A window into extreme longevity; the circulating metabolomic signature of the naked mole-rat, a mammal that shows negligible senescence. GeroScience. 2018;40:105–21. https://doi.org/10.1007/s11357-018-0014-2.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. Libertini G. An adaptive theory of the increasing mortality with increasing chronological age in populations in the wild. J Theor Biol. 1988;132:145–62. https://doi.org/10.1016/S0022-5193(88)80153-X.

    CAS  Article  PubMed  Google Scholar 

  36. Liu Y, Song D, Xu B, Li H, Dai X, Chen B. Development of a matrix-based candidate reference material of total homocysteine in human serum. Anal Bioanal Chem. 2017;409:3329–35. https://doi.org/10.1007/s00216-017-0272-3.

    CAS  Article  PubMed  Google Scholar 

  37. Longo VD, Mitteldorf J, Skulachev VP. Programmed and altruistic ageing. Nat Rev Genet. 2005;6:866–72. https://doi.org/10.1038/nrg1706.

    CAS  Article  PubMed  Google Scholar 

  38. Longo VD, Antebi A, Bartke A, Barzilai N, Brown-Borg HM, Caruso C, et al. Interventions to slow aging in humans: are we ready? Aging Cell. 2015;14:497–510. https://doi.org/10.1111/acel.12338.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. Lushchak O, Strilbytska O, Piskovatska V, Storey KB, Koliada A, Vaiserman A. The role of the TOR pathway in mediating the link between nutrition and longevity. Mech Ageing Dev. 2017;164:127–38. https://doi.org/10.1016/j.mad.2017.03.005.

    CAS  Article  PubMed  Google Scholar 

  40. Ma S, Gladyshev VN. Molecular signatures of longevity: insights from cross-species comparative studies. Semin Cell Dev Biol. 2017;70:190–203. https://doi.org/10.1016/j.semcdb.2017.08.007.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. Ma S, Yim SH, Lee S-G, Kim EB, Lee SR, Chang KT, et al. Organization of the mammalian metabolome according to organ function, lineage specialization and longevity. Cell Metab. 2015;22:332–43. https://doi.org/10.1016/j.cmet.2015.07.005.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. Ma S, Upneja A, Galecki A, Tsai YM, Burant CF, Raskind S, et al. Cell culture-based profiling across mammals reveals DNA repair and metabolism as determinants of species longevity. Elife. 2016;5:1–25. https://doi.org/10.7554/eLife.19130.

    CAS  Article  Google Scholar 

  43. Mitteldorf J. Aging is a group-selected adaptation: theory, evidence, and medical implications. Boca Ratón: CRC Press; 2016.

    Google Scholar 

  44. Mitteldorf J. Can aging be programmed? Biochem. 2018;83:1524–33. https://doi.org/10.1134/S0006297918120106.

    CAS  Article  Google Scholar 

  45. Miwa S, Jow H, Baty K, Johnson A, Czapiewski R, Saretzki G, et al. Low abundance of the matrix arm of complex I in mitochondria predicts longevity in mice. Nat Commun. 2014;5:1–12. https://doi.org/10.1038/ncomms4837.

    CAS  Article  Google Scholar 

  46. Mota-Martorell N, Pradas I, Jové M, Naudí A, Pamplona R. Biosíntesis de novo de glicerofosfolípidos y longevidad. Rev Esp Geriatr Gerontol. 2019;54:88–93. https://doi.org/10.1016/j.regg.2018.05.006.

    Article  PubMed  Google Scholar 

  47. Muntané G, Farré X, Rodríguez JA, Pegueroles C, Hughes DA, de Magalhães JP, et al. Biological processes modulating longevity across primates: a phylogenetic genome-phenome analysis. Mol Biol Evol. 2018;35:1990–2004. https://doi.org/10.1093/molbev/msy105.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. Nascimento EBM, Snel M, Guigas B, van der Zon GCM, Kriek J, Maassen JA, et al. Phosphorylation of PRAS40 on Thr246 by PKB/AKT facilitates efficient phosphorylation of Ser183 by mTORC1. Cell Signal. 2010;22:961–7. https://doi.org/10.1016/j.cellsig.2010.02.002.

    CAS  Article  PubMed  Google Scholar 

  49. Naudí A, Jové M, Ayala V, Portero-Otín M, Barja G, Pamplona R. Membrane lipid unsaturation as physiological adaptation to animal longevity. Front Physiol. 2013;4:372. https://doi.org/10.3389/fphys.2013.00372.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Pamplona R, Barja G. Mitochondrial oxidative stress, aging and caloric restriction: the protein and methionine connection. Biochim Biophys Acta Bioenerg. 2006;1757:496–508. https://doi.org/10.1016/j.bbabio.2006.01.009.

    CAS  Article  Google Scholar 

  51. Pamplona R, Barja G. Highly resistant macromolecular components and low rate of generation of endogenous damage: two key traits of longevity. Ageing Res Rev. 2007;6:189–210. https://doi.org/10.1016/j.arr.2007.06.002.

    CAS  Article  PubMed  Google Scholar 

  52. Pamplona R, Barja G. An evolutionary comparative scan for longevity-related oxidative stress resistance mechanisms in homeotherms. Biogerontology. 2011;12:409–35. https://doi.org/10.1007/s10522-011-9348-1.

    CAS  Article  PubMed  Google Scholar 

  53. Pamplona R, Barja G, Portero-Otín M. Membrane fatty acid unsaturation, protection against oxidative stress and maximum life span. Ann N Y Acad Sci. 2002;959:475–90. https://doi.org/10.1111/j.1749-6632.2002.tb02118.x.

    CAS  Article  PubMed  Google Scholar 

  54. Papadopoli D, Boulay K, Kazak L, et al. mTOR as a central regulator of lifespan and aging. F1000Research. 2019;8:998. https://doi.org/10.12688/f1000research.17196.1.

    Article  Google Scholar 

  55. Passtoors WM, Beekman M, Deelen J, van der Breggen R, Maier AB, Guigas B, et al. Gene expression analysis of mTOR pathway: association with human longevity. Aging Cell. 2013;12:24–31. https://doi.org/10.1111/acel.12015.

    CAS  Article  PubMed  Google Scholar 

  56. Perez-Campo R, López-Torres M, Cadenas S, Rojas C, Barja G. The rate of free radical production as a determinant of the rate of aging: evidence from the comparative approach. J Comp Physiol B Biochem Syst Environ Physiol. 1998;168:149–58. https://doi.org/10.1007/s003600050131.

    CAS  Article  Google Scholar 

  57. Ruckenstuhl C, Netzberger C, Entfellner I, Carmona-Gutierrez D, Kickenweiz T, Stekovic S, et al. Lifespan extension by methionine restriction requires autophagy-dependent vacuolar acidification. PLoS Genet. 2014;10:e1004347. https://doi.org/10.1371/journal.pgen.1004347.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. Sahm A, Bens M, Henning Y, et al. Higher gene expression stability during aging in long-lived giant mole-rats than in short-lived rats. Aging (Albany NY). 2018;10:3938–56. https://doi.org/10.18632/aging.101683.

    CAS  Article  Google Scholar 

  59. Sancak Y, Thoreen CC, Peterson TR, Lindquist RA, Kang SA, Spooner E, et al. PRAS40 is an insulin-regulated inhibitor of the mTORC1 protein kinase. Mol Cell. 2007;25:903–15. https://doi.org/10.1016/j.molcel.2007.03.003.

    CAS  Article  PubMed  Google Scholar 

  60. Schieke SM, Phillips D, McCoy JP, et al. The mammalian target of rapamycin (mTOR) pathway regulates mitochondrial oxygen consumption and oxidative capacity. J Biol Chem. 2006;281:27643–52. https://doi.org/10.1074/jbc.M603536200.

    CAS  Article  PubMed  Google Scholar 

  61. Selman C, Tullet JMA, Wieser D, et al. Ribosomal protein S6 kinase 1 signaling regulates mammalian life span. Science. 2009;326(80):140–4. https://doi.org/10.1126/science.1177221.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. Simonsen A, Cumming RC, Brech A, Isakson P, Schubert DR, Finley KD. Promoting basal levels of autophagy in the nervous system enhances longevity and oxidant resistance in adult Drosophila. Autophagy. 2008;4:176–84. https://doi.org/10.4161/auto.5269.

    CAS  Article  PubMed  Google Scholar 

  63. Singh PP, Demmitt BA, Nath RD, Brunet A. The henetics of aging: a vertebrate perspective. Cell. 2019;177:200–20. https://doi.org/10.1016/j.cell.2019.02.038.

    CAS  Article  PubMed  Google Scholar 

  64. Skulachev VP. Aging is a specific biological function rather than the result of a disorder in complex living systems: biochemical evidence in support of Weismann’s hypothesis. Biochemistry (Mosc). 1997;62:1191–5.

    CAS  Google Scholar 

  65. Tyshkovskiy A, Bozaykut P, Borodinova AA, et al. Identification and application of gene expression signatures associated with lifespan extension. Cell Metab. 2019;30:573–593.e8. https://doi.org/10.1016/j.cmet.2019.06.018.

    CAS  Article  PubMed  Google Scholar 

  66. Valvezan AJ, Manning BD. Molecular logic of mTORC1 signalling as a metabolic rheostat. Nat Metab. 2019;1:321–33. https://doi.org/10.1038/s42255-019-0038-7.

    CAS  Article  Google Scholar 

  67. Weichhart T. mTOR as regulator of lifespan, aging, and cellular senescence: a mini-review. Gerontology. 2018;64:127–34. https://doi.org/10.1159/000484629.

    CAS  Article  PubMed  Google Scholar 

  68. Wu JJ, Liu J, Chen EB, Wang JJ, Cao L, Narayan N, et al. Increased mammalian lifespan and a segmental and tissue-specific slowing of aging after genetic reduction of mTOR expression. Cell Rep. 2013;4:913–20. https://doi.org/10.1016/j.celrep.2013.07.030.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

MJ is a ‘Serra Hunter’ Fellow. NMM received a predoctoral fellowship from the Generalitat of Catalonia (AGAUR, ref 2018FI_B2_00104). RB received a predoctoral fellowship from the ‘Impuls Program’ funded by the University of Lleida and Banco Santander (UdL, ref 0864/2016). We thank Salvador Batolome, from the Laboratory of Luminescence and Biomolecular Spectroscopy (Autonomous University of Barcelona, Barcelona, Catalonia, Spain), for ddPCR technical support.

Funding

This work was supported by the Spanish Ministry of Economy and Competitiveness, Institute of Health Carlos III (grant number PI14/00328), the Spanish Ministry of Science, Innovation and Universities (RTI2018–099200-B-I00), and the Generalitat of Catalonia, Agency for Management of University and Research Grants (2017SGR696) and Department of Health (SLT002/16/00250) to RP. This study has been co-financed by FEDER funds from the European Union (‘A way to build Europe’).

Author information

Affiliations

Authors

Contributions

GB and RP designed the study. NMM., MJ, IP, RB, IS, AN, and EG performed experimental work. NMM and RP analysed the data. RP supervised the design and data interpretation. The manuscript was written by NMM, GB, and RP and edited by RP. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Gustavo Barja or Reinald Pamplona.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

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

Electronic supplementary material

ESM 1
figure9

mTORC1 core components are correlated in heart tissue from mammalian species. Pearson correlation was performed. Pearson r values are reported in Figure 3. Linear regression (LR) model was performed when significant relationships were found. Minimum signification level was set at p<0.05. Gene expression, protein content and phosphorylation were log-transformed to accomplish the assumptions of normality. (PNG 537 kb)

ESM 2
figure10

mTORC1 subunits and regulators that are not correlated with animal longevity after correcting for phylogenetic relationships. A) Effect of Pagel’s λ value on phylogeny branch length, when assuming absent (λ=0) or strong (λ=1) phylogenetic signal in the data. (PNG 1359 kb)

ESM 3
figure11

Individual mTORC1 protein content and phosphorylation from animal’s heart. A) mTOR total protein content, mTORSer2448 and its respective Coomassie. B) PRAS40 total protein content, PRAS40Thr246 and its respective Coomassie. C) Raptor protein content and its respective Coomassie. D) FKBP12 protein content and its respective Coomassie. M = Mouse; R = Rat; G = Gerbil; GP = Guinea pig; Rb = Rabbit; P = Pig; C = Cow; H = Horse. (PNG 2047 kb)

High Resolution Image (TIF 787 kb)

High Resolution Image (TIF 2118 kb)

High Resolution Image (TIF 2597 kb)

ESM 4

(DOCX 27 kb)

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mota-Martorell, N., Jove, M., Pradas, I. et al. Gene expression and regulatory factors of the mechanistic target of rapamycin (mTOR) complex 1 predict mammalian longevity. GeroScience (2020). https://doi.org/10.1007/s11357-020-00210-3

Download citation

Keywords

  • Arginine
  • FKBP12
  • Methionine cycle metabolites
  • mTOR
  • PRAS40
  • Raptor