Биомаркеры старения. Часть 1. Общее состояние проблемы и подходы к оценке биологического возраста

Учитывая гетерогенность процесса старения, его неоднородность для различных тканей, органов и систем человека и значительные различия в темпах старения между людьми, объективное представление о биологическом возрасте — индивидуальных темпах старения имеет большое клиническое значение. Выявление надежных биомаркеров, способных прогнозировать профиль старения и риск развития возрастной патологии может играть ключевую роль для профилактики ускоренного старения, геропротекторной терапии и своевременной диагностики и лечения возраст-ассоциированных заболеваний. В настоящее время разработан широкий спектр биомаркеров старения на клеточном, органном и молекулярном уровнях. На основе этих биомаркеров предложены модели «биологических часов» (фенотипические, эпигенетические, иммунные, метаболические и многие другие), которые с различной степенью точности позволяют определять биологический возраст человека и риски заболеваний конкретных органов и систем. Комбинации биомаркеров разных типов могут применяться для оценки потенциала здоровья и здорового долголетия у человека и являться объективными критериями эффективности геропротекторных стратегий при испытаниях новых методов и технологий. Однако к биомаркерам старения сегодня предъявляются очень высокие требования, которым, к сожалению, пока не полностью соответствуют предложенные маркеры и модели. Большинство современных биомаркеров отражает изменения, которые являются следствием старения, но не его причиной, что в свою очередь ограничивает точность биологических часов и их клиническую значимость. Это обусловливает актуальность разработки новых, доступных, неинвазивных, чувствительных к возрастным изменениям и надежных биомаркеров старения, которые будут полезны для раннего предупреждения возрастных заболеваний. В этой части обзора представлен краткий экскурс в состояние проблемы прогнозирования биологического возраста и описание подходов к моделям биологических часов. Вторая часть обзора будет посвящена маркерам нормального и патологического старения сетчатки и проблеме прогнозирования возраст-ассоциированной патологии зрительной системы. Фундаментальные знания о прогнозировании биологического возраста могут быть применены для перспективных исследований в офтальмологии, в частности с целью разработки биомаркеров старения сетчатки.

1. Chen R, Wang Y, Zhang Sh, et al. Biomarkers of ageing: Current state-of-art, challenges, and opportunities. MedComm — Future Medicine. 2023; 2: e50. https://doi.org/10.1002/mef2.50

2. Гильмутдинова И.Р., Кудряшова И.С., Костромина Е.Ю. и др. Современные подходы диагностики и коррекции биомаркеров старения. Вестник восстановительной медицины. 2021; 20 (6): 96–102. https://doi.org/10.38025/2078-1962-2021-20-6-96-102

3. Diebel LWM, Rockwood K. Determination of biological age: geriatric assessment vs. biological biomarkers. Curr Oncol Rep. 2021; 23 (9): 104. doi: 10.1007/s11912-021-01097-9

4. Yazdanyar A, Newman AB. The burden of cardiovascular disease in the elderly: morbidity, mortality, and costs. Clin Geriatr Med. 2009; 25: 563–77. doi: 10.1016/j.cger.2009.07.007

5. Lloyd-Jones DM, Hong Y, Labarthe D, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010; 121: 586–613. doi: 10.1161/CIRCULATIONAHA.109.192703

6. Lloyd-Jones DM, Allen NB, Anderson CAM, et al. Life’s Essential 8: updating and enhancing the American Heart Association’s construct of cardiovascular health: a presidential advisory from the American Heart Association. Circulation. 2022; 146(5): e18–e43. doi: 10.1161/CIR.0000000000001078

7. Kumar M, Orkaby A, Tighe C, et al. Life’s Essential 8: Optimizing Health in Older Adults. JACC Adv. 2023; 2 (7): 100560. doi: 10.1016/j.jacadv.2023.100560

8. Mander BA, Winer JR, Walker M.P. Sleep and human aging. Neuron. 2017; 94 (1): 19–36. doi: 10.1016/j.neuron.2017.02.004

9. Mahalakshmi AM, Ray B, Tuladhar S, et al. Sleep, brain vascular health and ageing. Geroscience. 2020; 42 (5): 1257–83. doi: 10.1007/s11357-020-00235-8

10. Hahad O, Frenis K, Kuntic M., Daiber A., Münzel T. Accelerated aging and age-related diseases (CVD and Neurological) due to air pollution and traffic noise exposure. Int J Mol Sci. 2021; 22 (5): 2419. doi: 10.3390/ijms22052419

11. Zhang R, Wu M, Zhang W, et al. Association between life’s essential 8 and biological ageing among US adults. J Transl Med. 2023; 21 (1): 622. doi: 10.1186/s12967-023-04495-8

12. World report on ageing and health. World Health Organization. 2015. https://iris.who.int/handle/10665/186463

13. Beard JR, Officer A, de Carvalho IA, et al. The World report on ageing and health: a policy framework for healthy ageing. Lancet. 2016; 387: 2145–54. doi: 10.1016/S0140-6736(15)00516-4

14. Zhou Y, Ma L. Intrinsic capacity in older adults: Recent advances. Aging Dis. 2022; 13 (2): 353–9. doi: 10.14336/AD.2021.0818

15. Zhou J, Chang H, Leng M, Wang Z. Intrinsic capacity to predict future adverse health outcomes in older adults: A scoping review. Healthcare (Basel). 2023; 11 (4): 450. doi: 10.3390/healthcare11040450

16. Wang J, Boehm L, Mion LC. Intrinsic capacity in older hospitalized adults: Implications for nursing practice. Geriatr Nurs. 2017; 38 (4): 359–61. doi: 10.1016/j.gerinurse.2017.06.008

17. Aging Biomarker Consortium; Bao H, Cao J, Chen M, et al. Biomarkers of aging. Sc. China Life Sci. 2023; 66 (5): 893–1066. doi: 10.1007/s11427-023-2305-0

18. Jylhävä J, Pedersen NL, Hägg S. Biological age predictors. EBioMedicine. 2017; 21: 29–36. doi: 10.1016/j.ebiom.2017.03.046

19. Rutledge J, Oh H, Wyss-Coray T. Measuring biological age using omics data. Nat Rev Genet. 2022; 23 (12): 715–27. doi: 10.1038/s41576-022-00511-7

20. Xia X, Chen X, Wu G, et al. Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle. Nature Metabolism. 2020; 2 (9): 946–57. doi: 10.1038/s42255-020-00270-x

21. Griffith JD, Comeau L, Rosenfield S, et al. Mammalian telomeres end in a large duplex loop. Cell. 1999; 97 (4): 503–14. doi: 10.1016/s0092-8674(00)80760-6

22. Bountziouka V, Nelson CP, Codd V, et al. Association of shorter leucocyte telomere length with risk of frailty. J Cachexia Sarcopenia Muscle. 2022 Jun; 13 (3): 1741–51. doi: 10.1002/jcsm.12971

23. Wang Q, Zhan Y, Pedersen NL, Fang F, Hägg S. Telomere length and all-cause mortality: a meta-analysis. Ageing Res Rev. 2018; 48: 11–20. doi: 10.1016/j.arr.2018.09.002

24. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: An expanding universe. Cell. 2023; 186: 243–78. doi: 10.1016/j.cell.2022.11.001

25. Jaenisch R, Bird A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat Genet. 2003; 33: 245–54. doi: 10.1038/ng1089

26. Бородкина А.В., Дерябин П.И., Грюкова А.А., Никольский Н.Н. «Социальная жизнь» стареющих клеток: что такое SASP и зачем его изучать? Acta Nature. 2018; 10/1 (36): 4–14. https://doi.org/10.32607/20758251-2018-10-1-4-14

27. Синёв В.В., Рыжкова А.И., Сазонова и др. Новый маркер старения человека: уровень гетероплазмии мутаций митохондриального генома. Патологическая физиология и экспериментальная терапия. 2021; 65 (2): 4–9. doi: 10.25557/0031-2991.2021.02.4-9

28. Molina A. Biomarkers and drivers of mitochondrial health and aging. Innovation in Aging. 2024; 8 (S1): 302, Abstract ID igae098.0984, https://doi.org/10.1093/geroni/igae098.0984

29. Migliavacca E, Tay SKH, Patel HP, et al. Mitochondrial oxidative capacity and NAD+ biosynthesis are reduced in human sarcopenia across ethnicities. Nat Commun. 2019; 10: 5808. doi: 10.1038/s41467-019-13694-1

30. Victorelli S, Salmonowicz H, Chapman J, et al. Apoptotic stress causes mtDNA release during senescence and drives the SASP. Nature. 2023; 622 (7983): 627–36. https://doi.org/10.1038/s41586-023-06621-4

31. Bucaciuc Mracica T, Anghel A, Ion CF, et al. MetaboAge DB: a repository of known ageing-related changes in the human metabolome. Biogerontology. 2020; 21: 763–71. doi: 10.1007/s10522-020-09892-w

32. Barbe-Tuana F, Funchal G, Schmitz CRR, Maurmann RM, Bauer ME. The interplay between immunosenescence and age-related diseases. Semin Immunopathol. 2020; 42 (5): 545–57. doi: 10.1007/s00281-020-00806-z

33. Chen Y, Klein SL, Garibaldi BT, et al. Aging in COVID-19: vulnerability, immunity and intervention. Ageing Res Rev. 2021; 65: 101205. doi: 10.1016/j.arr.2020.101205

34. Franceschi C, Bonaf M, Valensin S, et al. Inflamm-aging. An evolutionary perspective on immunosenescence. Ann NY Acad Sci. 2000; 908: 244–54. doi: 10.1111/j.1749-6632.2000.tb06651.x

35. Pawelec G, Bronikowski A, Cunnane SC, et al. The conundrum of human immune system “senescence”. Mech Ageing Dev. 2020; 192: 111357. doi: 10.1016/j.mad.2020.111357

36. Fulop T, Larbi A, Hirokawa K, Cohen AA, Witkowski JM. Immunosenescence is both functional/adaptive and dysfunctional/maladaptive. Semin Immunopathol. 2020; 42 (5): 521–36. doi: 10.1007/s00281-020-00818-9

37. Franceschi C, Garagnani P, Morsiani C, et al. The continuum of aging and age-related diseases: common mechanisms but different rates. Front Med. 2018; 5: 61. doi: https://doi.org/10.3389/fmed.2018.00061

38. Galkin F, Mamoshina P, Aliper A, et al. Biohorology and biomarkers of aging: current state-of-the-art, challenges and opportunities. Ageing Res. Rev. 2020; 60: 101050. doi: 10.1016/j.arr.2020.101050

39. Zhu J, Gao L, Song J, et al. Label-guided generative adversarial network for realistic image synthesis. IEEE Trans Pattern Anal Mach Intell. 2023; 45 (3): 3311–28. doi: 10.1109/TPAMI.2022.3186752

40. Liu Y, Wang J, Wei Z, et al. Association of phenotypic age and accelerated aging with severity and disability in patients with acute ischemic stroke. J Nutr Health Aging. 2024; 28 (12): 100405. doi: 10.1016/j.jnha.2024.100405

41. Brinkley TE, Justice JN, Basu S, et al. Research priorities for measuring biologic age: summary and future directions from the Research Centers Collaborative Network Workshop. GeroScience. 2022; 44: 2573–83. https://doi.org/10.1007/s11357-022-00661-w

42. Bobrov E, Georgievskaya A, Kiselev K, et al. PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging. Aging. 2018; 10: 3249–59. doi: 10.18632/aging.101629

43. Cole JH, Poudel RPK, Tsagkrasoulis D, et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage. 2017; 163: 115–24. doi: 10.1016/j.neuroimage.2017.07.059

44. Yin C, Imms P, Cheng M, et al. Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment. Proc Natl Acad Sci. USA. 2023; 120: e2214634120. doi: 10.1073/pnas.2214634120

45. Zhang Q, Vallerga CL, Walker RM, et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med. 2019; 11 (1): 54. doi: 10.1186/s13073-019-0667-1

46. Gialluisi A, Tirozzi A, Costanzo S, et al. Blood-based biological ageing and red cell distribution width are associated with prevalent Parkinson’s disease: findings from a large Italian population cohort. Front Endocrinol. (Lausanne). 2024; 15: 1376545. doi: 10.3389/fendo.2024.1376545

47. Gompertz B. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Phil Trans R Soc Lond. 1825; 115: 513–83. https://doi.org/10.1098/rspl.1815.0271

48. Gavrilov LA, Gavrilova NS. The reliability theory of aging and longevity. J Theor Biol. 2001; 213: 527–45. doi: 10.1006/jtbi.2001.2430

49. Masoro EJ. Biochemical and molecular mechanisms of aging: from model systems to human longevity. Preface. Biochim Biophys Acta. 2009; 1790 (10): 949–50. doi: 10.1016/j.bbagen.2009.08.009

50. Zhang W, Qu J, Liu GH, Belmonte JCI. The ageing epigenome and its rejuvenation. Nat Rev. Mol Cell Biol. 2020; 21: 137–50. doi: 10.1038/s41580-019-0204-5

51. Moore LD, Le T, Fan G. DNA methylation and its basic function. Neuropsychopharmacol. 2013; 38: 23–38. doi: 10.1038/npp.2012.112

52. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018; 19 (6): 371–84. doi: 10.1038/s41576-018-0004-3

53. Noroozi R, Ghafouri-Fard S, Pisarek A, et al. DNA methylation-based age clocks: from age prediction to age reversion. Ageing Res Rev. 2021; 68: 101314. doi: 10.1016/j.arr.2021.101314

54. Seale K, Horvath S, Teschendorff A, Eynon N, Voisin S. Making sense of the ageing methylome. Nat Rev Genet. 2022; 23 (10): 585–605. doi: 10.1038/s41576-022-00477-6

55. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017; 18 (1): 83. doi: 10.1186/s13059-017-1215-1

56. Peters MJ, Joehanes R, Pilling LC, et al. The transcriptional landscape of age in human peripheral blood. Nat Commun. 2015; 6: 8570. doi: 10.1038/ncomms9570

57. Buckley MT, Sun ED, George BM, et al. Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain. Nat Aging. 2023; 3: 121–37. doi: 10.1038/s43587-022-00335-4

58. Dong Z, Chen Y. Transcriptomics: advances and approaches. Science China Life Sciences. 2013; 56 (10): 960–7. doi: 10.1007/s11427-013-4557-2

59. Harries LW, Hernandez D, Henley W, et al. Human aging is characterized by focused changes in gene expression and deregulation of alternative splicing. Aging Cell. 2011; 10 (5): 868–78. doi: 10.1111/j.1474-9726.2011.00726.x

60. Holly AC, Melzer D, Pilling LC, et al. Towards a gene expression biomarker set for human biological age. Aging Cell. 2013; 12 (2): 324–6. doi: 10.1111/acel.12044

61. Johnson AA, Shokhirev MN, Wyss-Coray T, Lehallier B. Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age. Ageing Res Rev. 2020; 60: 101070. doi: 10.1016/j.arr.2020.101070

62. Moaddel R, Ubaida-Mohien C, Tanaka T, et al. Proteomics in aging research: a roadmap to clinical, translational research. Aging Cell. 2021; 20: e13325. doi: 10.1111/acel.13325

63. Ларина И.М., Пастушкова Л.Х., Каширина Д.Н., Тюжин М.Г. Вклад протеомики и метаболомики в исследование физиологических механизмов адаптации организма человека к условиям жизнедеятельности. Успехи физиологических наук. 2022; 53 (3): 75–104. doi: 10.31857/S0301179822030079

64. Panyard DJ, Yu B, Snyder MP. The metabolomics of human aging: Advances, challenges, and opportunities. Sci Adv. 2022; 8 (42): eadd6155. doi: 10.1126/sciadv.add6155

65. Menni C, Kastenmüller G, Petersen AK, et al. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int J Epidemiol. 2013; 42 (4): 1111–9. doi: 10.1093/ije/dyt094

66. Shamir L, Long J. Quantitative machine learning analysis of brain MRI morphology throughout aging. Curr Aging Sci. 2016; 9 (4): 310–17. doi: 10.2174/1874609809666160413113711

67. Lin L, Jin C, Fu Z, et al. Predicting healthy older adult’s brain age based on structural connectivity networks using artificial neural networks. Comput Methods Programs Biomed. 2016; 125: 8–17. doi: 10.1016/j.cmpb.2015.11.012

68. Xifra-Porxas A, Ghosh A, Mitsis GD, Boudrias MH. Estimating brain age from structural MRI and MEG data: insights from dimensionality reduction techniques. Neuroimage. 2021; 231: 117822. doi: 10.1016/j.neuroimage.2021.117822

69. Rokicki J, Wolfers T, Nordhøy W, et al. Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders. Hum Brain Mapp. 2021; 42 (6): 1714–26. doi: 10.1002/hbm.25323

70. Christman S, Bermudez C, Hao L, et al. Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression. Trans. Psychiatry. 2020; 10 (1): 317. https://doi.org/10.1038/s41398-020-01004-z

71. Ly M, Yu G.Z., Karim H.T., et al. Improving brain age prediction models: incorporation of amyloid status in Alzheimer’s disease. Neurobiol. Aging. 2020; 87 (Pt12): 44–8. doi: 10.1016/j.neurobiolaging.2019.11.005

72. Gialluisi A, Di Castelnuovo A, Costanzo S, et al. Moli-sani Study Investigators. Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing. Eur J Epidemiol. 2022; 37 (1): 35–48. doi: 10.1007/s10654-021-00797-7

73. Peretz L, Rappoport N. Deviation of physiological from chronological age is associated with health. Stud Health Technol. Inform. 2022; 294: 224–8. doi: 10.3233/SHTI220442

74. Mamoshina P, Kochetov K, Putin E, et al. Population specific biomarkers of human aging: A big data study using South Korean, Canadian, and Eastern European patient populations. J Gerontol A Biol Sci Med Sci. 2018; 73 (11): 1482–90. doi: 10.1093/gerona/gly005

75. Alpert A, Pickman Y, Leipold M, et al. A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring. Nat Med. 2019; 25: 487–95. doi: 10.1038/s41591-019-0381-y

76. Lambert K, Moo KG, Arnett A, et al. Deep immune phenotyping reveals similarities between aging, Down syndrome, and autoimmunity. Sci Transl Med. 2022; 14 (627): eabi4888. doi: 10.1126/scitranslmed.abi4888

77. Yousefzadeh MJ, Flores RR, Zhu Y, et al. An aged immune system drives senescence and ageing of solid organs. Nature. 2021; 594 (7861): 100–5. doi: 10.1038/s41586-021-03547-7

78. Franceschi C, Campisi J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Geronto. A Biol Sci Med Sci. 2014; 69 (suppl 1): S4–S9. doi: 10.1093/gerona/glu057

79. Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol. 2018; 15 (9): 505–22. doi: 10.1038/s41569-018-0064-2

80. Ahmadi-Abhari S, Luben RN, Wareham NJ, Khaw KT. Distribution and determinants of C-reactive protein in the older adult population: European prospective investigation into Cancer-Norfolk study. Eur J Clin Invest. 2013; 43 (9): 899–911. doi: 10.1111/eci.12116

81. Zhao H, Zhang H, Qin X. Age-related differences in serum MFG-E8, TGF-β1 and correlation to the severity of atherosclerosis determined by ultrasound. Mol Med Rep. 2017; 16 (6): 9741–8. doi: 10.3892/mmr.2017.7838

82. Liberale L, Badimon L, Montecucco F, et al. Inflammation, aging, and cardiovascular disease. JACC. 2022; 79 (8): 837–47. doi: 10.1016/j.jacc.2021.12.017

83. Yusipov I, Kondakova E, Kalyakulina A, et al. Accelerated epigenetic aging and inflammatory/immunological profile (ipAGE) in patients with chronic kidney disease. GeroScience. 2022; 44 (2): 817–34. doi: 10.1007/s11357-022-00540-4

84. Sayed N, Huang Y, Nguyen K, et al. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. Nat Aging. 2021; 1: 598–615. doi: 10.1038/s43587-021-00082-y

85. Hastings MH, Maywood ES, Brancaccio M. Generation of circadian rhythms in the suprachiasmatic nucleus. Nat Rev Neurosci. 2018; 19: 453–69. doi: 10.1038/s41583-018-0026-z

86. Подколодная О.А., Подколодная Н.Н., Подколодный Н.Л. Циркадные часы млекопитающих: генная сеть и компьютерный анализ. Вавиловский журнал генетики и селекции. 2014; 18 (4/2): 928–38. https://doi.org/10.1134/S2079059715040115

87. Takahashi JS. Transcriptional architecture of the mammalian circadian clock. Nat Rev Genet. 2017; 18: 164–79. doi: 10.1038/nrg.2016.150

88. Patke A, Young MW, Axelrod S. Molecular mechanisms and physiological importance of circadian rhythms. Nat Rev Mol Cell Biol. 2020; 21: 67–84. doi: 10.1038/s41580-019-0179-2

89. Nakamura TJ, Nakamura W, Yamazaki S, et al. Age-related decline in circadian output. J. Neurosci. 2011; 31: 10201–5. doi: 10.1523/JNEUROSCI.0451-11.2011

90. Sellix MT, Evans JA, Leise TL, et al. Aging differentially affects the reentrainment response of central and peripheral circadian oscillators. J Neurosci. 2012; 32: 16193–202. doi: 10.1523/JNEUROSCI.3559-12.2012

91. Li H, Satinoff E. Fetal tissue containing the suprachiasmatic nucleus restores multiple circadian rhythms in old rats. Am J Physio. Regul Integr Comp Physiol. 1998; 275: R1735–R1744. doi: 10.1152/ajpregu.1998.275.6.R1735

92. Farajnia S, Michel S, Deboer T, et al. Evidence for neuronal desynchrony in the aged suprachiasmatic nucleus clock. J Neurosci. 2012; 32 (17): 5891–9. doi: 10.1523/JNEUROSCI.0469-12.2012

93. Acosta-Rodríguez VA, Rijo-Ferreira F, Green CB, Takahashi JS. Importance of circadian timing for aging and longevity. Nat Commun. 2021; 12: 2862. doi: 10.1038/s41467-021-22922-6

94. Nie C, Li Y, Li R, et al. Distinct biological ages of organs and systems identified from a multi-omics study. Cell Rep. 2022; 38: 110459. doi: 10.1016/j.celrep.2022.110459

95. Ahadi S, Zhou W, Schüssler-Fiorenza Rose SM, et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat Med. 2020; 26: 83–90. doi: 10.1038/s41591-019-0719-5

96. Jansen R, Han LK, Verhoeven JE, et al. An integrative study of five biological clocks in somatic and mental health. eLife. 2021; 10: e59479. doi: 10.7554/eLife.59479

97. Elliott ML, Caspi A, Houts RM, et al. Disparities in the pace of biological aging among midlife adults of the same chronological age have implications for future frailty risk and policy. Nat Aging. 2021; 1 (3): 295-308. doi: 10.1038/s43587-021-00044-4