Physiological Genomics of Adaptation to High-Altitude Hypoxia

Abstract

Population genomic studies of humans and other animals at high altitude have generated many hypotheses about the genes and pathways that may have contributed to hypoxia adaptation. Future advances require experimental tests of such hypotheses to identify causal mechanisms. Studies to date illustrate the challenge of moving from lists of candidate genes to the identification of phenotypic targets of selection, as it can be difficult to determine whether observed genotype-phenotype associations reflect causal effects or secondary consequences of changes in other traits that are linked via homeostatic regulation. Recent work on high-altitude models such as deer mice has revealed both plastic and evolved changes in respiratory, cardiovascular, and metabolic traits that contribute to aerobic performance capacity in hypoxia, and analyses of tissue-specific transcriptomes have identified changes in regulatory networks that mediate adaptive changes in physiological phenotype. Here we synthesize recent results and discuss lessons learned from studies of high-altitude adaptation that lie at the intersection of genomics and physiology.

Keywords: adaptation; altitude; hypoxia; maladaptive plasticity; oxygen; phenotypic plasticity.

Figure 1

Effects of acute hypoxia on aerobic exercise capacity, as measured by maximal rates of O₂ consumption ($\stackrel{.}{\mathrm{V}}{\mathrm{O}}_2\max$), in humans and rats. The lower the partial pressure of O₂ of inspired air (PIO₂), the greater the degree of hypoxia. Data points represent average values from studies compiled by Gonzalez & Kuwahira (69). Figure adapted with permission from Gonzalez & Kuwahira (69); copyright 2018 John Wiley and Sons.

Figure 2

Deer mice with highland ancestry have higher constitutive peroxisome proliferator-activated receptor γ (PPARγ) expression in the gastrocnemius muscle, as revealed by common-garden experiments involving descendants of high- and low-altitude natives. (a) In the case of PPARγ (Pparg) transcript levels, native altitude had a significant main effect (*F_1,20 = 14.48; P < 0.001), but acclimation treatment did not (F_1,20 = 0.11; P = 0.748). (b) In the case of PPARγ protein abundance, there were significant main effects of both native altitude (*F_1,24 = 5.36; P = 0.030) and acclimation treatment (^†F_1,24 = 6.25; P = 0.020). There were no significant interaction effects in either case. Inset shows representative immunoreactive bands for a highlander (left) and a lowlander (right), each acclimated to hypoxia. Figure adapted with permission from Lui et al. (36); copyright 2015 American Physiological Society.

Figure 3

Large-scale deletion in the β-globin gene cluster of Tibetan antelope is revealed by analysis of pairwise sequence matches with homologous chromosomal regions in other bovids. Purple, green, and blue colored boxes represent members of triplicated gene blocks containing the genes that encode the β-type subunits of juvenile (β^C), adult (β^A), and fetal (β^F) Hb isoforms, respectively. (a) Gray shading denotes percent sequence identity between homologous β-globin gene clusters. (b) An ~45-kb chromosomal deletion in the β-globin gene cluster of Tibetan antelope resulted in secondary loss of the β^A-containing gene block. Figure adapted with permission from Signore & Storz (60); copyright 2020 American Association for the Advancement of Science.

Figure 4

Optimal hematocrit (Hct) is the level that maximizes aerobic exercise performance (measured by $\stackrel{.}{\mathrm{V}}{\mathrm{O}}_2\max$). To estimate optimal Hct for $\stackrel{.}{\mathrm{V}}{\mathrm{O}}_2\max$ and other measures of endurance exercise performance, Schuler et al. (130) experimentally generated a broad range of Hcts by using four sets of mice: wild-type mice (wt), wild-type mice that had their Hcts increased by means of an erythropoiesis-stimulating treatment (wtNESP), transgenic mice that exhibit excessive erythrocytosis owing to overexpression of human Epo (tg6), and the same transgenic mice that had their Hcts reduced by means of a hemolysis-inducing treatment (tg6PHZ). The estimated relationship between Hct and $\stackrel{.}{\mathrm{V}}{\mathrm{O}}_2\max$ revealed that exercise performance is maximized at higher Hcts in polycythemic mice that had their Hcts experimentally reduced (tg6PHZ) than in wild-type mice that had their Hcts experimentally increased (wtNESP). This likely reflects compensatory cardiovascular adjustments in the mice that experience chronic erythrocytosis. In both groups, optimal Hcts for exercise performance were substantially higher than the normal resting levels (represented by values for wild-type controls). Regression plots for the wtNESP and tg6PHZ mice are shown in the bottom panel. For both groups of mice, the relationship between $\stackrel{.}{\mathrm{V}}{\mathrm{O}}_2\max$ and Hct could be described as a second-degree polynomial function. In both upper and lower panels, vertical dotted lines denote the Hct values at which $\stackrel{.}{\mathrm{V}}{\mathrm{O}}_2\max$ is maximized (the optimal Hcts) in both wtNESP and tg6PHZ mice. Figure adapted with permission from Schuler et al. (130); copyright 2010 National Academy of Science.

Figure 5

Maladaptive plasticity in the pulmonary vasoconstrictive response to hypoxia is blunted in highland deer mice (Peromyscus maniculatus). (a) Relative right-ventricle mass, expressed as a percentage of total body mass, increases with hypobaric hypoxia (60-kPa) acclimation in lowland white-footed mice (Peromyscus leucopus) but not in highland P. maniculatus. Plotted values are mean relative mass ± standard error of the mean (SEM). (b,c) Relative right-ventricle mass in both species is associated with the expression of two transcriptional modules (M7 and M8) that are significantly enriched for genes that participate in inflammatory responses and the interferon regulatory factor pathway. Overall module expressions are summarized using principal component analysis and expressed as PC1 scores. Plotted values are mean PC1 values ± SEM. *P < 0.05, **P < 0.01 effect of species within treatment (ANOVA). ^†P < 0.05 effect of treatment within species (ANOVA). Figure modified with permission from Velotta et al. (151); copyright 2018 John Wiley and Sons.