CALCULATION OF AGING: ANALYSIS OF SURVIVAL CURVES IN NORMAL AND IN PATHOLOGY, FLUCTUATIONS IN MORTALITY DYNAMICS, CHARACTERISTICS OF LIFE SPAN DISTRIBUTION AND INDICATORS OF ITS VARIATION

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The article describes the history of studies of survival data carried out at the Research Institute of Physico-Chemical Biology under the leadership of Academician V. P. Skulachev from 1970s until present, with special emphasis on the last decade. The use of accelerated failure time (AFT) model and analysis of coefficient of variation of lifespan (CVLS) in addition to the Gompertz methods of analysis, allows to assess survival curves for the presence of temporal scaling (i.e., manifestation of accelerated aging), without changing the shape of survival curve with the same coefficient of variation. A modification of the AFT model that uses temporal scaling as the null hypothesis made it possible to distinguish between the quantitative and qualitative differences in the dynamics of aging. It was also shown that it is possible to compare the data on the survival of species characterized by the survival curves of the original shape (i.e., “flat” curves without a pronounced increase in the probability of death with age typical of slowly aging species), when considering the distribution of lifespan as a statistical random variable and comparing parameters of such distribution. Thus, it was demonstrated that the higher impact of mortality caused by external factors (background mortality) in addition to the age-dependent mortality, the higher the disorder of mortality values and the greater its difference from the calculated value characteristic of developed countries (15-20%). For comparison, CVLS for the Paraguayan Ache Indians is 100% (57% if we exclude prepuberty individuals as suggested by Jones et al.). According to Skulachev, the next step is considering mortality fluctuations as a measure for the disorder of survival data. Visual evaluation of survival curves can already provide important data for subsequent analysis. Thus, Sokolov and Severin [1] found that mutations have different effects on the shape of survival curves. Type I survival curves generally retains their standard convex rectangular shape, while type II curves demonstrate a sharp increase in the mortality which makes them similar to a concave exponential curve with a stably high mortality rate. It is noteworthy that despite these differences, mutations in groups I and II are of a similar nature. They are associated (i) with “DNA metabolism” (DNA repair, transcription, and replication); (ii) protection against oxidative stress, associated with the activity of the transcription factor Nrf2, and (iii) regulation of proliferation, and (or these categories may overlap). However, these different mutations appear to produce the same result at the organismal level, namely, accelerated aging according to the Gompertz’s law. This might be explained by the fact that all these mutations, each in its own unique way, either reduce the lifespan of cells or accelerate their transition to the senescent state, which supports the concept of Skulachev on the existence of multiple pathways of aging (chronic phenoptosis).

Sobre autores

G. Shilovsky

Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University; Faculty of Biology, Lomonosov Moscow State University; Institute for Information Transmission Problems, Russian Academy of Sciences

Email: grgerontol@gmail.com
119992 Moscow, Russia; 119234 Moscow, Russia; 127051 Moscow, Russia

Bibliografia

  1. Sokolov, S. S., and Severin, F. F. (2023) Two types of survival curves in different lines of progeric mice, Biochemistry (Moscow), 89, 371-376, https://doi.org/10.1134/S0006297924020159.
  2. Kowald, A. (2002) Lifespan does not measure ageing, Biogerontology, 3, 187-190, https://doi.org/10.1023/a:1015659527013.
  3. Гаврилов Л. А., Гаврилова Н. С., Ягужинский Л. С. (1978) Основные закономерности старения и гибели животных с точки зрения теории надежности, Ж. общей биологии, 39, 734-742.
  4. Gavrilov, L. A., and Gavrilova, N. S. (1991) The Biology of Life Span: a Quantitative Approach, Harwood Academic Publisher, New York.
  5. Khaliavkin, A. V. (2001) Influence of environment on the mortality pattern of potentially non-senescent organisms. General approach and comparison with real populations, Adv. Gerontol., 7, 46-49.
  6. Tarkhov, A. E., Menshikov, L. I., and Fedichev, P. O. (2017) Strehler-Mildvan correlation is a degenerate manifold of Gompertz fit, J. Theor. Biol., 416, 180-189, doi: 10.1016/j.jtbi.2017.01.017.
  7. Shilovsky, G. A., Putyatina, T. S., Markov, A. V., and Skulachev, V. P. (2015) Contribution of quantitative methods of estimating mortality dynamics to explaining mechanisms of aging, Biochemistry (Moscow), 80, 1547-1559, https://doi.org/10.1134/S0006297915120020.
  8. Shilovsky, G. A., Putyatina, T. S., Lysenkov, S. N., Ashapkin, V. V., Luchkina, O. S., Markov, A. V., and Skulachev, V. P. (2016) Is it possible to prove the existence of an aging program by quantitative analysis of mortality dynamics? Biochemistry (Moscow), 81, 1461-1476, https://doi.org/10.1134/S0006297916120075.
  9. Shilovsky, G. A., Putyatina, T. S., Ashapkin, V. V., Luchkina, O. S., and Markov, A. V. (2017) Coefficient of variation of lifespan across the tree of life: Is it a signature of programmed aging? Biochemistry (Moscow), 82, 1480-1492, https://doi.org/10.1134/S0006297917120070.
  10. Skulachev, V. P., Shilovsky, G. A., Putyatina, T. S., Popov, N. A., Markov, A. V., et al. (2020) Perspectives of Homo sapiens lifespan extension: Focus on external or internal resources? Aging (Albany NY), 12, 5566-5584, doi: 10.18632/aging.102981.
  11. Goldman, R. D., Shumaker, D. K., Erdos, M. R., Eriksson, M., Goldman, A. E., Gordon, L. B., et al. (2004) Accumulation of mutant lamin A causes progressive changes in nuclear architecture in Hutchinson-Gilford progeria syndrome, Proc. Natl. Acad. Sci. USA, 101, 8963-8968, https://doi.org/10.1073/pnas.0402943101.
  12. Prasher, J. M., Lalai, A. S., Heijmans-Antonissen, C., Ploemacher, R. E., Hoeijmakers, J. H. J., Touw, I. P., et al. (2005) Reduced hematopoietic reserves in DNA interstrand crosslink repair-deficient Ercc1–/– mice, EMBO J., 24, 861-871, https://doi.org/10.1038/sj.emboj.7600542.
  13. Oh, Y. S., Kim, D. G., Kim, G., Choi, E.-C., Kennedy, B. K., Suh, Y., et al. (2010) Downregulation of lamin A by tumor suppressor AIMP3/p18 leads to a progeroid phenotype in mice, Aging Cell, 9, 810-822, https://doi.org/10.1111/j.1474-9726.2010.00614.x.
  14. Wijshake, T., Malureanu, L. A., Baker, D. J., Jeganathan, K. B., van de Sluis, B., and van Deursen, J. M. (2012) Reduced life- and healthspan in mice carrying a mono-allelic BubR1 MVA mutation, PLoS Genet., 8, e1003138, https://doi.org/10.1371/journal.pgen.1003138.
  15. Liao, C.-Y., and Kennedy, B. K. (2014) Mouse models and aging: longevity and progeria, Curr. Top. Dev. Biol., 109, 249-285, https://doi.org/10.1016/B978-0-12-397920-9.00003-2.
  16. Vermeij, W. P., Dollé, M. E. T., Reiling, E., Jaarsma, D., Payan-Gomez, C., Bombardieri, C. R., Wu, H., Roks, A. J. M., Botter, S. M., van der Eerden, B. C., Youssef, S. A., Kuiper, R. V., Nagarajah, B., van Oostrom, C. T. et al. (2016) Restricted diet delays accelerated ageing and genomic stress in DNA-repair-deficient mice, Nature, 537, 427-431, https://doi.org/10.1038/nature19329.
  17. Cabral, W. A., Tavarez, U. L., Beeram, I., Yeritsyan, D., Boku, Y. D., Eckhaus, M. A., et al. (2021) Genetic reduction of mTOR extends lifespan in a mouse model of Hutchinson-Gilford progeria syndrome, Aging Cell, 20, e13457, https://doi.org/10.1111/acel.13457.
  18. Gavrilova, N. S., Gavrilov, L. A., Severin, F. F., and Skulachev, V. P. (2012) Testing predictions of the programmed and stochastic theories of aging: comparison of variation in age at death, menopause, and sexual maturation, Biochemistry (Moscow), 77, 754-760, doi: 10.1134/S0006297912070085.
  19. Jones, O. R., Scheuerlein, A., Salguero-Gomez, R., Camarda, C. G., Schaible, R., et al. (2014) Diversity of ageing across the tree of life, Nature, 505, 169-173, https://doi.org/10.1038/nature12789.
  20. Vaupel, J. W., Carey, J. R., Christensen, K., Johnson, T. E., Yashin, A. I., et al. (1998) Biodemographic trajectories of longevity, Science, 280, 855860, doi: 10.1126/science.280.5365.855.
  21. Skulachev, V. P., Holtze, S., Vyssokikh, M. Y., Bakeeva, L. E., Skulachev, M. V., Markov, A. V., Hildebrandt, T. B., and Sadovnichii, V. A. (2017) Neoteny, prolongation of youth: from naked mole rats to “naked apes” (humans), Physiol. Rev., 97, 699-720, https://doi.org/10.1152/physrev.00040.2015.
  22. Скулачев В. П., Скулачев М. В., Фенюк Б. А. (2017) Жизнь без старости. М. МГУ.
  23. Cox, D. R. (1972) Regression models and lifetables, J. Roy. Statist. Soc. Ser., 34, 187-202.
  24. Collett, D. (2003) Modelling Survival Data in Medical Research, Vol. 2. CRC Press, Boca Raton.
  25. Swindell, W. R. (2009) Accelerated failure time models provide a useful statistical framework for aging research, Exp. Gerontol., 44, 190-200, https://doi.org/10.1016/j.exger.2008.10.005.
  26. Stroustrup, N., Anthony, W. E., Nash, Z. M., Gowda, V., Gomez, A., López-Moyado, I. F., Apfeld, J., and Fontana, W. (2016) The temporal scaling of Caenorhabditis elegans ageing, Nature, 530, 103-107, https://doi.org/10.1038/nature16550.
  27. Markov, A. V., Naimark, E. B., and Yakovleva, E. U. (2016) Temporal scaling of age-dependent mortality: dynamics of aging in Caenorhabditis elegans is easy to speed up or slow down, but its overall trajectory is stable, Biochemistry (Moscow), 81, 906-911, https://doi.org/10.1134/S0006297916080125.
  28. Asgharian, H., Chang, P. L., Lysenkov, S., Scobeyeva, V. A., Reisen, W. K., and Nuzhdin, S. V. (2015) Evolutionary genomics of Culex pipiens: global and local adaptations associated with climate, lifehistory traits and anthropogenic factors, Proc. Biol. Sci., 282, 20150728, https://doi.org/10.1098/rspb.2015.0728.
  29. Choudhury, A. R., Ju, Z., Djojosubroto, M. W., Schienke, A., Lechel, A., et al. (2007) Cdkn1a deletion improves stem cell function and lifespan of mice with dysfunctional telomeres without accelerating cancer formation, Nat. Genet., 39, 99-105, https://doi.org/10.1038/ng1937.
  30. Khokhlov, A. N., and Morgunova, G. V. (2017) Testing of geroprotectors in experiments on cell cultures: pros and cons, in Anti-Aging Drugs: From Basic Research to Clinical Practice (Vaiserman A. M., ed), Royal Society of Chemistry, https://doi.org/10.1039/9781782626602-00051.
  31. Khokhlov, A. N. (2010) From Carrel to Hayflick and back or what we got from the 100 years of cytogerontological studies, Radiats. Biol. Radioecol., 50, 304-311, https://doi.org/10.1134/S0006350910050313.
  32. Khokhlov, A. N. (2013) Impairment of regeneration in aging: appropriateness or stochastics? Biogerontology, 14, 703-708, https://doi.org/10.1007/s10522-013-9468-x.
  33. Shilovsky, G. A., Putyatina, T. S., Morgunova, G. V., Seliverstov, A. V., Ashapkin, V. V., Sorokina, E. V., Markov, A. V., and Skulachev, V. P. (2021) A crosstalk between the biorhythms and gatekeepers of longevity: dual role of glycogen synthase kinase-3, Biochemistry (Moscow), 86, 433-448, https://doi.org/10.1134/S0006297921040052.
  34. Shilovsky, G. A., Shram, S. I., Morgunova, G. V., and Khokhlov, A. N. (2017) Protein poly(ADP-ribosyl)ation system: Changes in development and aging as well as due to restriction of cell proliferation, Biochemistry (Moscow), 82, 1391-1401, https://doi.org/10.1134/S0006297917110177.
  35. Omotoso, O., Gladyshev, V. N., and Zhou, X. (2021) Lifespan extension in long-lived vertebrates rooted in ecological adaptation, Front. Cell. Dev. Biol., 9, 704966, https://doi.org/10.3389/fcell.2021.704966.
  36. Buescu, J., Oliveira, H. M., and Sousa, M. (2023) Growth rate, evolutionary entropy and ageing across the tree of life, J. Biol. Dyn., 17, 2256766, https://doi.org/10.1080/17513758.2023.2256766.
  37. Ebmeier, S., Thayabaran, D., Braithwaite, I., Bunamara, C., Weatherall, M., et al. (2017) Trends in international asthma mortality: analysis of data from the WHO Mortality Database from 46 countries (1993-2012), Lancet, 390, 935945, doi: 10.1016/S01406736(17)314484.
  38. Gavrilov, L. A., and Gavrilova, N. S. (2020) What can we learn about aging and COVID-19 by studying mortality? Biochemistry (Moscow), 85, 1499-1504, https://doi.org/10.1134/S0006297920120032.
  39. Shilovsky, G. A. (2022) Variability of mortality: Additional information on mortality and morbidity curves under normal and pathological conditions, Biochemistry (Moscow), 87, 294-299, https://doi.org/10.1134/S0006297922030087.

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