Adaptive Transcriptome Profiling of Subterranean Zokor, Myospalax baileyi, to High- Altitude Stresses in Tibet.


Animals living at high altitudes have evolved distinct phenotypic and genotypic adaptations against stressful environments. We studied the adaptive patterns of altitudinal stresses on transcriptome turnover in subterranean plateau zokors (Myospalax baileyi) in the high-altitude Qinghai-Tibetan Plateau. Transcriptomes of zokors from three populations with distinct altitudes and ecologies (Low: 2846 m, Middle: 3282 m, High: 3,714 m) were sequenced and compared. Phylogenetic and principal component analyses classified them into three divergent altitudinal population clusters. Genetic polymorphisms showed that the population at H, approaching the uppermost species boundary, harbors the highest genetic polymorphism. Moreover, 1056 highly up-regulated UniGenes were identified from M to H. Gene ontologies reveal genes like EPAS1 and COX1 were overexpressed under hypoxia conditions. EPAS1, EGLN1, and COX1 were convergent in high-altitude adaptation against stresses in other species. The fixation indices (F ST and G ST )-based outlier analysis identified 191 and 211 genes, highly differentiated among L, M, and H. We observed adaptive transcriptome changes in Myospalax baileyi, across a few hundred meters, near the uppermost species boundary, regardless of their relatively stable underground burrows' microclimate. The highly variant genes identified in Myospalax were involved in hypoxia tolerance, hypercapnia tolerance, ATP-pathway energetics, and temperature changes.





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Publication Info

Cai, Zhenyuan, Liuyang Wang, Xiaoying Song, Somnath Tagore, Xiangfeng Li, Huihua Wang, Jiarui Chen, Kexin Li, et al. (2018). Adaptive Transcriptome Profiling of Subterranean Zokor, Myospalax baileyi, to High- Altitude Stresses in Tibet. Scientific reports, 8(1). p. 4671. 10.1038/s41598-018-22483-7 Retrieved from

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Liuyang Wang

Assistant Research Professor of Molecular Genetics and Microbiology

Leveraging bioinformatics and big data to understand the intricacies of human diseases.

My overall research goals are centered on unraveling the molecular mechanism underpinning human disease susceptibility and harnessing these findings to innovative diagnostic and therapeutic strategies. I have adopted a multidisciplinary approach that integrates genomics, transcriptomics, and computational biology. Leveraging high-throughput cellular screening and genome-wide association study (GWAS), we have successfully identified hundreds of genomic loci associated with 8 different pathogens (Wang et al. 2018). Utilizing single-cell RNA-seq, we developed scHi-HOST to rapidly identify host genes associated with the influenza virus (Schott and Wang, et al. 2022). I also have developed several novel statistical tools, CPAG and iCPAGdb, that estimate genetic associations among human diseases and traits (Wang et al. 2015, 2021). Combining experimental and computational approaches, I expect to gain a deeper understanding of the genetic architecture of human susceptibility to infection and inflammatory disorders.

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