Comprehensive Analysis of Ubiquitously Expressed Genes in Humans from A Data-driven Perspective

Comprehensive characterization of spatial and temporal gene expression patterns in humans is critical for uncovering the regulatory codes of the human genome and understanding the molecular mechanisms of human diseases.Ubiquitously expressed genes(UEGs)refer to the genes expressed across a majority of,if not all,phenotypic and physiological conditions of an organism.It is known that many human genes are broadly expressed across tissues.However,most previous UEG studies have only focused on providing a list of UEGs without capturing their global expression patterns,thus limiting the potential use of UEG information.In this study,we proposed a novel data-driven framework to leverage the extensive collection of40,000 human transcriptomes to derive a list of UEGs and their corresponding global expression patterns,which offers a valuable resource to further characterize human transcriptome.Our results suggest that about half(12,234;49.01%)of the human genes are expressed in at least 80%of human transcriptomes,and the median size of the human transcriptome is 16,342 genes(65.44%).Through gene clustering,we identified a set of UEGs,named LoVarUEGs,which have stable expression across human transcriptomes and can be used as internal reference genes for expression measurement.To further demonstrate the usefulness of this resource,we evaluated the global expression patterns for 16 previously predicted disallowed genes in islet beta cells and found that seven of these genes showed relatively more varied expression patterns,suggesting that the repression of these genes may not be unique to islet beta cells.

Treatment outcomes of HIV patients with hepatitis B and C virus co-infections in Southwest China:an observational cohort study

Background:Antiretroviral therapy(ART)has reduced mortality among people living with HIV(PLWH)in China,but co-infections of hepatitis B virus(HBV)and hepatitis C virus(HCV)may individually or jointly reduce the effect of ART.This study aimed to evaluate the impacts of HBV/HCV coinfections on treatment drop-out and mortality among PLWH on ART.Methods:A retrospective cohort study analysis of 58,239 people living with HIV(PLWH)who initiated antiretroviral therapy(ART)during 2010-2018 was conducted in Guangxi Province,China.Data were from the observational database of the National Free Antiretroviral Treatment Program.Cox proportional hazard models were fitted to evaluate the effects of baseline infection of HBV or HCV or both on death and treatment attrition among PLWH.Results:Our study showed high prevalence of HBV(11.5%),HCV(6.6%)and HBV-HCV(1.5%)co-infections.The overall mortality rate and treatment attrition rate was 2.95[95%confidence interval(CI):2.88-3.02]and 5.92(95%CI:5.82-6.01)per 100 person-years,respectively.Compared with HIV-only patients,HBV-co-infected patients had 42%higher mortality[adjusted hazard ratio(a HR)=1.42;95%CI 1.32-1.54],HCV-co-infected patients had 65%higher mortality(a HR=1.65;95%CI:1.47-1.86),and patients with both HCV and HBV co-infections had 123%higher mortality(a HR=2.23;95%CI:1.87-2.66).Conclusions:HBV and HCV coinfection may have an additive effect on increasing the risk of all-cause death among PLWH who are on ART.It is suggested that there is need for primary prevention and access to effective hepatitis treatment for PLWH.

RePhine: An Integrative Method for Identification of Drug Response-related Transcriptional Regulators

Transcriptional regulators(TRs)participate in essential processes in cancer pathogenesis and are critical therapeutic targets.Identification of drug response-related TRs from cell line-based compound screening data is often challenging due to low m RNA abundance of TRs,protein modifications,and other confounders(CFs).In this study,we developed a regression-based pharmacogenomic and Ch IP-seq data integration method(Re Phine)to infer the impact of TRs on drug response through integrative analyses of pharmacogenomic and Ch IP-seq data.Re Phine was evaluated in simulation and pharmacogenomic data and was applied to pan-cancer datasets with the goal of biological discovery.In simulation data with added noises or CFs and in pharmacogenomic data,Re Phine demonstrated an improved performance in comparison with three commonly used methods(including Pearson correlation analysis,logistic regression model,and gene set enrichment analysis).Utilizing Re Phine and Cancer Cell Line Encyclopedia data,we observed that Re Phinederived TR signatures could effectively cluster drugs with different mechanisms of action.Re Phine predicted that loss-offunction of EZH2/PRC2 reduces cancer cell sensitivity toward the BRAF inhibitor PLX4720.Experimental validation confirmed that pharmacological EZH2 inhibition increases the resistance of cancer cells to PLX4720 treatment.Our results support that Re Phine is a useful tool for inferring drug response-related TRs and for potential therapeutic applications.The source code for Re Phine is freely available at https://github.com/coexps/Re Phine.