Physiological and genetic convergence supports hypoxia resistance in high-altitude songbirds
Autoři:
Ying Xiong aff001; Liqing Fan aff003; Yan Hao aff001; Yalin Cheng aff001; Yongbin Chang aff001; Jing Wang aff001; Haiyan Lin aff001; Gang Song aff001; Yanhua Qu aff001; Fumin Lei aff001
Působiště autorů:
Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
aff001; University of Chinese Academy of Sciences, Beijing, China
aff002; National Forest Ecosystem Observation & Research Station of Nyingchi Tibet, Institute of Plateau Ecology, Tibet Agriculture & Animal Husbandry University, Linzhi City, China
aff003; Key Laboratory of Forest Ecology in Tibet Plateau (Tibet Agriculture & Animal Husbandry University), Ministry of Education, Linzhi City, China
aff004; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
aff005
Vyšlo v časopise:
Physiological and genetic convergence supports hypoxia resistance in high-altitude songbirds. PLoS Genet 16(12): e1009270. doi:10.1371/journal.pgen.1009270
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009270
Souhrn
Skeletal muscle plays a central role in regulating glucose uptake and body metabolism; however, highland hypoxia is a severe challenge to aerobic metabolism in small endotherms. Therefore, understanding the physiological and genetic convergence of muscle hypoxia tolerance has a potential broad range of medical implications. Here we report and experimentally validate a common physiological mechanism across multiple high-altitude songbirds that improvement in insulin sensitivity contributes to glucose homeostasis, low oxygen consumption, and relative activity, and thus increases body weight. By contrast, low-altitude songbirds exhibit muscle loss, glucose intolerance, and increase energy expenditures under hypoxia. This adaptive mechanism is attributable to convergent missense mutations in the BNIP3L gene, and METTL8 gene that activates MEF2C expression in highlanders, which in turn increases hypoxia tolerance. Together, our findings from wild high-altitude songbirds suggest convergent physiological and genetic mechanisms of skeletal muscle in hypoxia resistance, which highlights the potentially medical implications of hypoxia-related metabolic diseases.
Klíčová slova:
Insulin – Mitochondria – Bird genetics – Bird physiology – Birds – Glucose – Hypoxia – Skeletal muscles
Zdroje
1. West JB. Alexander M. Kellas and the physiological challenge of Mt. Everest. J Appl Physiol. 1987;63(1):3–11. doi: 10.1152/jappl.1987.63.1.3 3305469
2. Semenza GL. Oxygen sensing, hypoxia-inducible factors, and disease pathophysiology. Annu Rev Pathol-Mech. 2014;9:47–71.
3. Tsoli M, Robertson G. Cancer cachexia: malignant inflammation, tumorkines, and metabolic mayhem. Trends Endocrin Met. 2013;24(4):174–83.
4. Fearon KC, Glass DJ, Guttridge DC. Cancer cachexia: mediators, signaling, and metabolic pathways. Cell Metab. 2012;16(2):153–66. doi: 10.1016/j.cmet.2012.06.011 22795476
5. Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KC. Cancer-associated cachexia. Nat Rev Dis Primers. 2018;4:17105. doi: 10.1038/nrdp.2017.105 29345251
6. Scott GR, Elogio TS, Lui MA, Storz JF, Cheviron ZA: Adaptive modifications of muscle phenotype in high-altitude deer mice are associated with evolved changes in gene regulation. Mol Biol Evol 2015; 32(8):1962–1976. doi: 10.1093/molbev/msv076 25851956
7. Amoasii L, Sanchez-Ortiz E, Fujikawa T, Elmquist JK, Bassel-Duby R, Olson EN. NURR1 activation in skeletal muscle controls systemic energy homeostasis. Proc Natl Acad Sci USA. 2019;116(23):11299–308. doi: 10.1073/pnas.1902490116 31110021
8. Leonvelarde F, Sanchez J, Bigard AX, Brunet A, Lesty C, Monge C. High-Altitude Tissue Adaptation in Andean Coots—Capillarity, Fiber Area, Fiber Type and Enzymatic-Activities of Skeletal-Muscle. J Comp Physiol B. 1993; 163:52–58. doi: 10.1007/BF00309665 8459054
9. Scott GR, Egginton S, Richards JG, Milsom WK: Evolution of muscle phenotype for extreme high altitude flight in the bar-headed goose. P R Soc B. 2009; 276(1673):3645–3653. doi: 10.1098/rspb.2009.0947 19640884
10. Schippers M-P, Ramirez O, Arana M, Pinedo-Bernal P, McClelland GB. Increase in carbohydrate utilization in high-altitude Andean mice. Curr Biol. 2012;22(24):2350–4. doi: 10.1016/j.cub.2012.10.043 23219722
11. Wu D-D, Yang C-P, Wang M-S, Dong K-Z, Yan D-W, Hao Z-Q, et al. Convergent genomic signatures of high altitude adaptation among domestic mammals. bioRxiv. 2019:743955.
12. Hao Y, Xiong Y, Cheng Y, Song G, Jia C, Qu Y, et al. Comparative transcriptomics of 3 high-altitude passerine birds and their low-altitude relatives. Proc Natl Acad Sci USA. 2019;116(24):11851–6. doi: 10.1073/pnas.1819657116 31127049
13. Li J-T, Gao Y-D, Xie L, Deng C, Shi P, Guan M-L, et al. Comparative genomic investigation of high-elevation adaptation in ectothermic snakes. Proc Natl Acad Sci USA. 2018;115(33):8406–11. doi: 10.1073/pnas.1805348115 30065117
14. Sun Y-B, Fu T-T, Jin J-Q, Murphy RW, Hillis DM, Zhang Y-P, et al. Species groups distributed across elevational gradients reveal convergent and continuous genetic adaptation to high elevations. Proc Natl Acad Sci USA. 2018;115(45):E10634–E41. doi: 10.1073/pnas.1813593115 30348757
15. del Hoyo J, Elliot A, Sartagal J, Christie D, De Juana E. Handbook of the birds of the world alive. Barcelona: Lynx Edicions. 2014. 2018.
16. Qu Y, Chen C, Xiong Y, She H, Zhang YE, Cheng Y, et al. Rapid phenotypic evolution with shallow genomic differentiation during early stages of high elevation adaptation in Eurasian Tree Sparrows. Natl Sci Rev. 2019.
17. Chaillou T. Skeletal muscle fiber type in hypoxia: adaptation to high-altitude exposure and under conditions of pathological hypoxia. Front Physiol. 2018;9:1450. doi: 10.3389/fphys.2018.01450 30369887
18. Lui MA, Mahalingam S, Patel P, Connaty AD, Ivy CM, Cheviron ZA, et al. High-altitude ancestry and hypoxia acclimation have distinct effects on exercise capacity and muscle phenotype in deer mice. Am J Physiol-Reg I. 2015;308(9):R779–R91. doi: 10.1152/ajpregu.00362.2014 25695288
19. Hoppeler H, Kleinert E, Schlegel C, Claassen H, Howald H, Kayar S, et al. II. Morphological adaptations of human skeletal muscle to chronic hypoxia. Int J Sports Med. 1990;11(S 1):S3–S9.
20. Mahalingam S, McClelland GB, Scott GR. Evolved changes in the intracellular distribution and physiology of muscle mitochondria in high-altitude native deer mice. J Physiol-London. 2017;595(14):4785–801. doi: 10.1113/JP274130 28418073
21. Hoppeler H, Vogt M. Muscle tissue adaptations to hypoxia. J Exp Biol. 2001;204(18):3133–9. 11581327
22. Zurlo F, Larson K, Bogardus C, Ravussin E. Skeletal muscle metabolism is a major determinant of resting energy expenditure. J Clin Invest. 1990;86(5):1423–7. doi: 10.1172/JCI114857 2243122
23. Kennedy JW, Hirshman MF, Gervino EV, Ocel JV, Forse RA, Hoenig SJ, et al. Acute exercise induces GLUT4 translocation in skeletal muscle of normal human subjects and subjects with type 2 diabetes. Diabetes. 1999;48(5):1192–7. doi: 10.2337/diabetes.48.5.1192 10331428
24. Zhang H, Bosch-Marce M, Shimoda LA, Tan YS, Baek JH, Wesley JB, et al. Mitochondrial autophagy is an HIF-1-dependent adaptive metabolic response to hypoxia. J Biol Chem. 2008;283(16):10892–903. doi: 10.1074/jbc.M800102200 18281291
25. Galen SC, Natarajan C, Moriyama H, Weber RE, Fago A, Benham PM, et al. Contribution of a mutational hot spot to hemoglobin adaptation in high-altitude Andean house wrens. Proc Natl Acad Sci USA. 2015;112(45):13958–63. doi: 10.1073/pnas.1507300112 26460028
26. Kumar A, Natarajan C, Moriyama H, Witt CC, Weber RE, Fago A, et al. Stability-mediated epistasis restricts accessible mutational pathways in the functional evolution of avian hemoglobin. Mol Biol Evol. 2017;34(5):1240–51. doi: 10.1093/molbev/msx085 28201714
27. Tobi EW, Slieker RC, Luijk R, Dekkers KF, Stein AD, Xu KM, et al. DNA methylation as a mediator of the association between prenatal adversity and risk factors for metabolic disease in adulthood. Sci Adv. 2018;4(1):eaao4364. doi: 10.1126/sciadv.aao4364 29399631
28. Wang W, Wang J, Zhang T, Wang Y, Zhang Y, Han K. Genome-wide association study of growth traits in Jinghai Yellow chicken hens using SLAF-seq technology. Anim Genet. 2019;50(2):175–6. doi: 10.1111/age.12346 26365057
29. Jakkaraju S, Zhe X, Pan D, Choudhury R, Schuger L. TIPs are tension-responsive proteins involved in myogenic versus adipogenic differentiation. Dev Cell. 2005;9(1):39–49. doi: 10.1016/j.devcel.2005.04.015 15992539
30. Fei P, Wang W, Kim S-h, Wang S, Burns TF, Sax JK, et al. Bnip3L is induced by p53 under hypoxia, and its knockdown promotes tumor growth. Cancer cell. 2004;6(6):597–609. doi: 10.1016/j.ccr.2004.10.012 15607964
31. Gan Z, Fu T, Kelly DP, Vega RB. Skeletal muscle mitochondrial remodeling in exercise and diseases. Cell Res. 2018;28(10):969–80. doi: 10.1038/s41422-018-0078-7 30108290
32. Liu L, Sakakibara K, Chen Q, Okamoto K. Receptor-mediated mitophagy in yeast and mammalian systems. Cell Res. 2014;24(7):787. doi: 10.1038/cr.2014.75 24903109
33. Sun YF, Ren ZP, Wu YF, Lei FM, Dudley R, Li DM: Flying high: limits to flight performance by sparrows on the Qinghai-Tibet Plateau. J Exp Biol. 2016; 219(22):3642–3648. doi: 10.1242/jeb.142216 27609759
34. Ruf T, Geiser F. Daily torpor and hibernation in birds and mammals. Biol Rev. 2015;90(3):891–926. doi: 10.1111/brv.12137 25123049
35. Sweazea KL, Tsosie K, Beckman EJ, Benham PM, Witt CC. Seasonal and elevational variation in glucose and glycogen in two songbird species. Comp Biochem Phys A. 2020:110703. doi: 10.1016/j.cbpa.2020.110703 32283178
36. Scortegagna M, Ding K, Oktay Y, Gaur A, Thurmond F, Yan L-J, et al. Multiple organ pathology, metabolic abnormalities and impaired homeostasis of reactive oxygen species in Epas1−/− mice. Nat Genet. 2003;35(4):331. doi: 10.1038/ng1266 14608355
37. Befani C, Liakos P. The role of hypoxia-inducible factor-2 alpha in angiogenesis. J Cellular Physiol. 2018;233(12):9087–98. doi: 10.1002/jcp.26805 29968905
38. Lin Q, Schwarz J, Bucana C, Olson EN. Control of mouse cardiac morphogenesis and myogenesis by transcription factor MEF2C. Science. 1997;276(5317):1404–7. doi: 10.1126/science.276.5317.1404 9162005
39. Barbosa AC, Kim M-S, Ertunc M, Adachi M, Nelson ED, McAnally J, et al. MEF2C, a transcription factor that facilitates learning and memory by negative regulation of synapse numbers and function. Proc Natl Acad Sci USA. 2008;105(27):9391–6. doi: 10.1073/pnas.0802679105 18599438
40. Weibel E. Profile size and particle size Weibel ER eds. Stereological Methods: Practical Methods for Biological Morphometry, Vol. 1: 51–60. Academic Press, Inc. New York; 1979.
41. Nielsen J, Gejl KD, Hey-Mogensen M, Holmberg HC, Suetta C, Krustrup P, et al. Plasticity in mitochondrial cristae density allows metabolic capacity modulation in human skeletal muscle. J Physiol-London. 2017;595(9):2839–47. doi: 10.1113/JP273040 WOS:000400357800012. 27696420
42. Shi YL, Chi QS, Liu W, Fu HP, Wang DH. Environmental metabolomics reveal geographic variation in aerobic metabolism and metabolic substrates in Mongolian gerbils (Meriones unguiculatus). Comp Biochem Phys D. 2015;14:42–52. doi: 10.1016/j.cbd.2015.03.001 WOS:000355027900005. 25817427
43. Chappell MA, Bech C, Buttemer WA. The relationship of central and peripheral organ masses to aerobic performance variation in house sparrows. J Exp Biol. 1999;202(17):2269–79. 10441080
44. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20. doi: 10.1093/bioinformatics/btu170 WOS:000340049100004. 24695404
45. Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010. 2016.
46. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. doi: 10.1093/bioinformatics/bts635 23104886
47. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC bioinformatics. 2011;12(1):323.
48. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11(10). ARTN R106 doi: 10.1186/gb-2010-11-10-r106 WOS:000287378900008. 20979621
49. Maza E. In papyro comparison of TMM (edgeR), RLE (DESeq2), and MRN normalization methods for a simple two-conditions-without-replicates RNA-seq experimental design. Front Genet. 2016;7:164. doi: 10.3389/fgene.2016.00164 27695478
50. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12). ARTN 550 doi: 10.1186/s13059-014-0550-8 WOS:000346609500022. 25516281
51. Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics. 2008;24(5):719–20. doi: 10.1093/bioinformatics/btm563 WOS:000253746400020. 18024473
52. Han JDJ, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature. 2004;430(6995):88–93. doi: 10.1038/nature02555 WOS:000222356800049. 15190252
53. Reimand J, Kull M, Peterson H, Hansen J, Vilo J. g: Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 2007;35:W193–W200. doi: 10.1093/nar/gkm226 WOS:000255311500037. 17478515
54. Li H, Durbin R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics. 2010;26(5):589–95. doi: 10.1093/bioinformatics/btp698 20080505
55. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303. doi: 10.1101/gr.107524.110 20644199
56. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27(15):2156–8. doi: 10.1093/bioinformatics/btr330 21653522
57. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:14065823. 2014. doi: 10.1016/j.addbeh.2014.05.011 24935795
58. Frolova A, Flessner L, Chi M, Kim ST, Foyouzi-Yousefi N, Moley KH. Facilitative glucose transporter type 1 is differentially regulated by progesterone and estrogen in murine and human endometrial stromal cells. Endocrinology. 2008;150(3):1512–20. doi: 10.1210/en.2008-1081 18948400
Článek vyšel v časopise
PLOS Genetics
2020 Číslo 12
- I mozek má svou krizi středního věku. Jak tyto změny souvisejí s rizikem demence ve stáří?
- Přerušovaný půst může mít významná zdravotní rizika
- Mikroplasty a jejich riziko pro zdraví: Co všechno víme?
- Čokoláda podávaná v malých dávkách neškodí. Vědecky prokázáno!
- Nepřítel mého nepřítele je můj přítel aneb vyřeší fágové terapie antibiotické rezistence?
Nejčtenější v tomto čísle
- Exploiting codon usage identifies intensity-specific modifiers of Ras/MAPK signaling in vivo
- Common maternal and fetal genetic variants show expected polygenic effects on risk of small- or large-for-gestational-age (SGA or LGA), except in the smallest 3% of babies
- PEA15 loss of function and defective cerebral development in the domestic cat
- Precision medicine in cats—The right biomedical model may not be the mouse!