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Transcriptome and Network Changes in Climbers at Extreme Altitudes
Published: Wednesday, February 29, 2012
Author: Fang Chen et al.

by Fang Chen, Wei Zhang, Yu Liang, Jialiang Huang, Kui Li, Christopher D. Green, Jiancheng Liu, Guojie Zhang, Bing Zhou, Xin Yi, Wei Wang, Hang Liu, Xiaohong Xu, Feng Shen, Ning Qu, Yading Wang, Guoyi Gao, A. San, LuoSang JiangBai, Hua Sang, Xiangdong Fang, Karsten Kristiansen, Huanming Yang, Jun Wang, Jing-Dong J. Han, Jian Wang

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.