Transcriptome and Network Changes in Climbers at Extreme Altitudes

Abstract

Extreme altitude can induce a range of cellular and systemic responses. Although it is known that hypoxia underlies the major changes and that the physiological responses include hemodynamic changes and erythropoiesis, the molecular mechanisms and signaling pathways mediating such changes are largely unknown. To obtain a more complete picture of the transcriptional regulatory landscape and networks involved in extreme altitude response, we followed four climbers on an expedition up Mount Xixiabangma (8,012 m), and collected blood samples at four stages during the climb for mRNA and miRNA expression assays. By analyzing dynamic changes of gene networks in response to extreme altitudes, we uncovered a highly modular network with 7 modules of various functions that changed in response to extreme altitudes. The erythrocyte differentiation module is the most prominently up-regulated, reflecting increased erythrocyte differentiation from hematopoietic stem cells, probably at the expense of differentiation into other cell lineages. These changes are accompanied by coordinated down-regulation of general translation. Network topology and flow analyses also uncovered regulators known to modulate hypoxia responses and erythrocyte development, as well as unknown regulators, such as the OCT4 gene, an important regulator in stem cells and assumed to only function in stem cells. We predicted computationally and validated experimentally that increased OCT4 expression at extreme altitude can directly elevate the expression of hemoglobin genes. Our approach established a new framework for analyzing the transcriptional regulatory network from a very limited number of samples.

Population transcriptomes reveal synergistic responses of DNA polymorphism and RNA expression to extreme environments on the Qinghai–Tibetan Plateau in a predatory bird

“…S1, Supporting information), or because little research has been conducted into study of functional elements (RNA or protein). Intriguingly, two recent studies examining EPAS1 mRNA expression levels found no significant difference in Han Chinese before and during mountain climbing (Chen et al 2012), and lower EPAS1 expression in Tibetan migrants compared with lowland British (Petousi et al 2014). In contrast, our work demonstrates a refined role for EPAS1 in the gene adaptation and expression plasticity of saker falcons to the low-oxygen environment of the QTP.…”

“…Intriguingly, two recent studies examining EPAS1 mRNA expression levels found no significant difference in Han Chinese before and during mountain climbing (Chen et al . ), and lower EPAS1 expression in Tibetan migrants compared with lowland British (Petousi et al . ).…”

Variability in, variability out: best practice recommendations to standardize pre-analytical variables in the detection of circulating and tissue microRNAs

“…Biological and environmental factors are predominantly common to both tissue and circulating miRNAs. Multiple studies have highlighted that both external and patient-intrinsic -Study design -Develop standard procedures for specimen selection and handling -Review miRNA databases to select appropriate miRNAs for study -Avoid blood cell miRNAs -Account for environmental and biological preanalytic variables with appropriate case and control subjects -Document relevant preanalytic variables and deviations from standard procedures -Extraction -Common extraction method for all specimens -Normalization -Use exogenous "spike in" miRNA or validated endogenous miRNA factors such as age, sex, race, body mass index (BMI), diet, fasting state, underlying illnesses/organ dysfunction, blood cell count, exercise, vitamin supplementation, medications, smoking, diurnal variation, chemical exposure and even altitude can influence miRNA concentrations and are often difficult to standardize [2,13,[16][17][18][19][20][21][22][23][24][25][26][27].…”

Understanding human diseases with high-throughput quantitative measurement and analysis of molecular signatures

“…New algorithms have also been developed to specifically address the problem of integrating different layers of data. For example, flow optimization algorithms were used to identify main pathways from protein-protein interaction networks that link genetic screen hits to gene expression changes involved in neurodegeneration [74], microRNA (miRNA) expression changes and mRNA expression changes in response to extremely high altitudes [75], or Genome-Wide Association Study (GWAS) hits to differentially expressed genes between control and disease cohorts [76]. These innovative integration approaches for different types of data from different layers have the potential to identify global regulation patterns, and also to provide detailed biological insights regarding complex biological processes, such as the development of complex phenotypes and diseases (Figure 1).…”