Browsing by Author "Wang, Liuyang"

Now showing 1 - 9 of 9
  • Thumbnail Image
    ItemOpen Access
    A Bayesian Approach to Inferring Rates of Selfing and Locus-Specific Mutation.
    (Genetics, 2015-11) Redelings, Benjamin D; Kumagai, Seiji; Tatarenkov, Andrey; Wang, Liuyang; Sakai, Ann K; Weller, Stephen G; Culley, Theresa M; Avise, John C; Uyenoyama, Marcy K
    We present a Bayesian method for characterizing the mating system of populations reproducing through a mixture of self-fertilization and random outcrossing. Our method uses patterns of genetic variation across the genome as a basis for inference about reproduction under pure hermaphroditism, gynodioecy, and a model developed to describe the self-fertilizing killifish Kryptolebias marmoratus. We extend the standard coalescence model to accommodate these mating systems, accounting explicitly for multilocus identity disequilibrium, inbreeding depression, and variation in fertility among mating types. We incorporate the Ewens sampling formula (ESF) under the infinite-alleles model of mutation to obtain a novel expression for the likelihood of mating system parameters. Our Markov chain Monte Carlo (MCMC) algorithm assigns locus-specific mutation rates, drawn from a common mutation rate distribution that is itself estimated from the data using a Dirichlet process prior model. Our sampler is designed to accommodate additional information, including observations pertaining to the sex ratio, the intensity of inbreeding depression, and other aspects of reproduction. It can provide joint posterior distributions for the population-wide proportion of uniparental individuals, locus-specific mutation rates, and the number of generations since the most recent outcrossing event for each sampled individual. Further, estimation of all basic parameters of a given model permits estimation of functions of those parameters, including the proportion of the gene pool contributed by each sex and relative effective numbers.
  • Thumbnail Image
    ItemOpen Access
    Adaptive Transcriptome Profiling of Subterranean Zokor, Myospalax baileyi, to High- Altitude Stresses in Tibet.
    (Scientific reports, 2018-03-16) Cai, Zhenyuan; Wang, Liuyang; Song, Xiaoying; Tagore, Somnath; Li, Xiangfeng; Wang, Huihua; Chen, Jiarui; Li, Kexin; Frenkel, Zeev; Gao, Dahai; Frenkel-Morgenstern, Milana; Zhang, Tongzuo; Nevo, Eviatar
    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.
  • Thumbnail Image
    ItemOpen Access
    An atlas connecting shared genetic architecture of human diseases and molecular phenotypes provides insight into COVID-19 susceptibility.
    (Genome medicine, 2021-05) Wang, Liuyang; Balmat, Thomas J; Antonia, Alejandro L; Constantine, Florica J; Henao, Ricardo; Burke, Thomas W; Ingham, Andy; McClain, Micah T; Tsalik, Ephraim L; Ko, Emily R; Ginsburg, Geoffrey S; DeLong, Mark R; Shen, Xiling; Woods, Christopher W; Hauser, Elizabeth R; Ko, Dennis C

    Background

    While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility.

    Results

    Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb)) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs associated with both severe COVID-19 and other human traits demonstrated colocalization of the GWAS signal at the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN). This finding points to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity.

    Conclusions

    Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches. The iCPAGdb web portal is accessible at http://cpag.oit.duke.edu and the software code at https://github.com/tbalmat/iCPAGdb .
  • Thumbnail Image
    ItemOpen Access
    An Atlas of Genetic Variation Linking Pathogen-Induced Cellular Traits to Human Disease.
    (Cell host & microbe, 2018-08) Wang, Liuyang; Pittman, Kelly J; Barker, Jeffrey R; Salinas, Raul E; Stanaway, Ian B; Williams, Graham D; Carroll, Robert J; Balmat, Tom; Ingham, Andy; Gopalakrishnan, Anusha M; Gibbs, Kyle D; Antonia, Alejandro L; eMERGE Network; Heitman, Joseph; Lee, Soo Chan; Jarvik, Gail P; Denny, Joshua C; Horner, Stacy M; DeLong, Mark R; Valdivia, Raphael H; Crosslin, David R; Ko, Dennis C
    Pathogens have been a strong driving force for natural selection. Therefore, understanding how human genetic differences impact infection-related cellular traits can mechanistically link genetic variation to disease susceptibility. Here we report the Hi-HOST Phenome Project (H2P2): a catalog of cellular genome-wide association studies (GWAS) comprising 79 infection-related phenotypes in response to 8 pathogens in 528 lymphoblastoid cell lines. Seventeen loci surpass genome-wide significance for infection-associated phenotypes ranging from pathogen replication to cytokine production. We combined H2P2 with clinical association data from patients to identify a SNP near CXCL10 as a risk factor for inflammatory bowel disease. A SNP in the transcriptional repressor ZBTB20 demonstrated pleiotropy, likely through suppression of multiple target genes, and was associated with viral hepatitis. These data are available on a web portal to facilitate interpreting human genome variation through the lens of cell biology and should serve as a rich resource for the research community.
  • Thumbnail Image
    ItemOpen Access
    Antagonizing the irreversible thrombomodulin-initiated proteolytic signaling alleviates age-related liver fibrosis via senescent cell killing.
    (Cell research, 2023-07) Pan, Christopher C; Maeso-Díaz, Raquel; Lewis, Tylor R; Xiang, Kun; Tan, Lianmei; Liang, Yaosi; Wang, Liuyang; Yang, Fengrui; Yin, Tao; Wang, Calvin; Du, Kuo; Huang, De; Oh, Seh Hoon; Wang, Ergang; Lim, Bryan Jian Wei; Chong, Mengyang; Alexander, Peter B; Yao, Xuebiao; Arshavsky, Vadim Y; Li, Qi-Jing; Diehl, Anna Mae; Wang, Xiao-Fan
    Cellular senescence is a stress-induced, stable cell cycle arrest phenotype which generates a pro-inflammatory microenvironment, leading to chronic inflammation and age-associated diseases. Determining the fundamental molecular pathways driving senescence instead of apoptosis could enable the identification of senolytic agents to restore tissue homeostasis. Here, we identify thrombomodulin (THBD) signaling as a key molecular determinant of the senescent cell fate. Although normally restricted to endothelial cells, THBD is rapidly upregulated and maintained throughout all phases of the senescence program in aged mammalian tissues and in senescent cell models. Mechanistically, THBD activates a proteolytic feed-forward signaling pathway by stabilizing a multi-protein complex in early endosomes, thus forming a molecular basis for the irreversibility of the senescence program and ensuring senescent cell viability. Therapeutically, THBD signaling depletion or inhibition using vorapaxar, an FDA-approved drug, effectively ablates senescent cells and restores tissue homeostasis in liver fibrosis models. Collectively, these results uncover proteolytic THBD signaling as a conserved pro-survival pathway essential for senescent cell viability, thus providing a pharmacologically exploitable senolytic target for senescence-associated diseases.
  • Thumbnail Image
    ItemOpen Access
    Bayesian co-estimation of selfing rate and locus-specific mutation rates for a partially selfing population
    (2017-07-02) Redelings, Benjamin D; Kumagai, Seiji; Wang, Liuyang; Tatarenkov, Andrey; Sakai, Ann K; Weller, Stephen G; Culley, Theresa M; Avise, John C; Uyenoyama, Marcy K
    We present a Bayesian method for characterizing the mating system of populations reproducing through a mixture of self-fertilization and random outcrossing. Our method uses patterns of genetic variation across the genome as a basis for inference about pure hermaphroditism, androdioecy, and gynodioecy. We extend the standard coalescence model to accommodate these mating systems, accounting explicitly for multilocus identity disequilibrium, inbreeding depression, and variation in fertility among mating types. We incorporate the Ewens Sampling Formula (ESF) under the infinite-alleles model of mutation to obtain a novel expression for the likelihood of mating system parameters. Our Markov chain Monte Carlo (MCMC) algorithm assigns locus-specific mutation rates, drawn from a common mutation rate distribution that is itself estimated from the data using a Dirichlet Process Prior (DPP) model. Among the parameters jointly inferred are the population-wide rate of self-fertilization, locus-specific mutation rates, and the number of generations since the most recent outcrossing event for each sampled individual.
  • Thumbnail Image
    ItemOpen Access
    Comparative analyses of clinical and environmental populations of Cryptococcus neoformans in Botswana.
    (Mol Ecol, 2015-07) Chen, Yuan; Litvintseva, Anastasia P; Frazzitta, Aubrey E; Haverkamp, Miriam R; Wang, Liuyang; Fang, Charles; Muthoga, Charles; Mitchell, Thomas G; Perfect, John R
    Cryptococcus neoformans var. grubii (Cng) is the most common cause of fungal meningitis, and its prevalence is highest in sub-Saharan Africa. Patients become infected by inhaling airborne spores or desiccated yeast cells from the environment, where the fungus thrives in avian droppings, trees and soil. To investigate the prevalence and population structure of Cng in southern Africa, we analysed isolates from 77 environmental samples and 64 patients. We detected significant genetic diversity among isolates and strong evidence of geographic structure at the local level. High proportions of isolates with the rare MATa allele were observed in both clinical and environmental isolates; however, the mating-type alleles were unevenly distributed among different subpopulations. Nearly equal proportions of the MATa and MATα mating types were observed among all clinical isolates and in one environmental subpopulation from the eastern part of Botswana. As previously reported, there was evidence of both clonality and recombination in different geographic areas. These results provide a foundation for subsequent genomewide association studies to identify genes and genotypes linked to pathogenicity in humans.
  • Thumbnail Image
    ItemOpen Access
    Editorial: Bioinformatics Tools (and Web Server) for Cancer Biomarker Development.
    (Frontiers in oncology, 2020-01) Xie, Longxiang; Wang, Liuyang; Zhu, Wan; Zhao, Jing; Guo, Xiangqian
  • Thumbnail Image
    ItemOpen Access
    Modeling of variables in cellular infection reveals CXCL10 levels are regulated by human genetic variation and the Chlamydia-encoded CPAF protease.
    (Scientific reports, 2020-10-26) Schott, Benjamin H; Antonia, Alejandro L; Wang, Liuyang; Pittman, Kelly J; Sixt, Barbara S; Barnes, Alyson B; Valdivia, Raphael H; Ko, Dennis C
    Susceptibility to infectious diseases is determined by a complex interaction between host and pathogen. For infections with the obligate intracellular bacterium Chlamydia trachomatis, variation in immune activation and disease presentation are regulated by both host genetic diversity and pathogen immune evasion. Previously, we discovered a single nucleotide polymorphism (rs2869462) associated with absolute abundance of CXCL10, a pro-inflammatory T-cell chemokine. Here, we report that levels of CXCL10 change during C. trachomatis infection of cultured cells in a manner dependent on both host and pathogen. Linear modeling of cellular traits associated with CXCL10 levels identified a strong, negative correlation with bacterial burden, suggesting that C. trachomatis actively suppresses CXCL10. We identified the pathogen-encoded factor responsible for this suppression as the chlamydial protease- or proteasome-like activity factor, CPAF. Further, we applied our modeling approach to other host cytokines in response to C. trachomatis and found evidence that RANTES, another T-cell chemoattractant, is actively suppressed by Chlamydia. However, this observed suppression of RANTES is not mediated by CPAF. Overall, our results demonstrate that CPAF suppresses CXCL10 to evade the host cytokine response and that modeling of cellular infection parameters can reveal previously unrecognized facets of host-pathogen interactions.