scholarly journals Differential expression of COVID-19-related genes in European Americans and African Americans

2020 ◽  
Author(s):  
Urminder Singh ◽  
Eve Syrkin Wurtele
Keyword(s):  
Gene Expression ◽  
African Americans ◽  
Large Scale ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Postal Code ◽  
Socioeconomic Condition ◽  
European Americans ◽  
Expression Of Genes ◽  
Sequencing Studies

ABSTRACTThe Coronavirus disease 2019 (COVID-19) pandemic has affected African American populations disproportionately in regards to both morbidity and mortality. A multitude of factors likely account for this discrepancy. Gene expression represents the interaction of genetics and environment. To elucidate whether levels of expression of genes implicated in COVID-19 vary in African Americans as compared to European Americans, we re-mine The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) RNA-Seq data. Multiple genes integral to infection, inflammation and immunity are differentially regulated across the two populations. Most notably, F8A2 and F8A3, which encode the HAP40 protein that mediates early endosome movement in Huntington’s Disease, are more highly expressed by up to 24-fold in African Americans. Such differences in gene expression can establish prognostic signatures and have critical implications for precision treatment of diseases such as COVID-19. We advocate routine inclusion of information such as postal code, education level, and profession (as a proxies for socioeconomic condition) and race in the metadata about each individual sampled for sequencing studies. This relatively simple change would enable large-scale data-driven approaches to dissect relationships among race, socio-economic factors, and disease.

scholarly journals African Americans and European Americans exhibit distinct gene expression patterns across tissues and tumors that are associated with immunologic and infectious functions and environmental exposures

2020 ◽  
Author(s):  
Urminder Singh ◽  
Kyle Hernandez ◽  
Bruce Aronow ◽  
Eve Wurtele
Keyword(s):  
Gene Expression ◽  
African American ◽  
African Americans ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Severe Infection ◽  
Mitigation Strategies ◽  
European Americans

Abstract The COVID-19 pandemic has affected African American populations disproportionately with respect to prevalence, morbidity, and mortality. Because gene expression profiles represent combined genetic, socioenvironmental, and physiological effects, and could provide therapeutic biomarkers and environmental mitigation strategies, we undertook a large-scale assessment of differential gene expression between African Americans and European Americans. To do this, we mined RNA-Seq datasets from normal and diseased (tumor) conditions whose metadata could be used to evaluate differential patterns. We observed widespread differential expression of genes implicated in COVID-19 and integral to epithelial boundary function, inflammation, infection, and reactive oxygen stress. Notably, expression of the little-studied F8A2 gene is up to 40-fold greater in African Americans. F8A2, like F8A1, encodes HAP40 protein, which mediates early endosome movement. African American gene expression signatures reveal increased number or activity of esophageal glandular cells and lung ACE2-positive basal keratinocytes. These findings have potential to establish prognostic signatures, refine approaches to minimizing risk of severe infection, and improve precision treatment of COVID-19.

scholarly journals Tissue-specific sex differences in human gene expression

2019 ◽  
Vol 28 (17) ◽  
pp. 2976-2986 ◽  
Author(s):  
Irfahan Kassam ◽  
Yang Wu ◽  
Jian Yang ◽  
Peter M Visscher ◽  
Allan F McRae
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Complex Traits ◽  
Tissue Expression ◽  
Regulatory Elements ◽  
Tissue Specific ◽  
Significance Threshold ◽  
Expression Of Genes ◽  
Causal Variants ◽  
Similar Distance

Abstract Despite extensive sex differences in human complex traits and disease, the male and female genomes differ only in the sex chromosomes. This implies that most sex-differentiated traits are the result of differences in the expression of genes that are common to both sexes. While sex differences in gene expression have been observed in a range of different tissues, the biological mechanisms for tissue-specific sex differences (TSSDs) in gene expression are not well understood. A total of 30 640 autosomal and 1021 X-linked transcripts were tested for heterogeneity in sex difference effect sizes in n = 617 individuals across 40 tissue types in Genotype–Tissue Expression (GTEx). This identified 65 autosomal and 66 X-linked TSSD transcripts (corresponding to unique genes) at a stringent significance threshold. Results for X-linked TSSD transcripts showed mainly concordant direction of sex differences across tissues and replicate previous findings. Autosomal TSSD transcripts had mainly discordant direction of sex differences across tissues. The top cis-expression quantitative trait loci (eQTLs) across tissues for autosomal TSSD transcripts are located a similar distance away from the nearest androgen and estrogen binding motifs and the nearest enhancer, as compared to cis-eQTLs for transcripts with stable sex differences in gene expression across tissue types. Enhancer regions that overlap top cis-eQTLs for TSSD transcripts, however, were found to be more dispersed across tissues. These observations suggest that androgen and estrogen regulatory elements in a cis region may play a common role in sex differences in gene expression, but TSSD in gene expression may additionally be due to causal variants located in tissue-specific enhancer regions.

scholarly journals GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins

Cells ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 675 ◽  
Author(s):  
Xia ◽  
Liu ◽  
Zhang ◽  
Guo
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Line ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Protein Levels

High-throughput technologies generate a tremendous amount of expression data on mRNA, miRNA and protein levels. Mining and visualizing the large amount of expression data requires sophisticated computational skills. An easy to use and user-friendly web-server for the visualization of gene expression profiles could greatly facilitate data exploration and hypothesis generation for biologists. Here, we curated and normalized the gene expression data on mRNA, miRNA and protein levels in 23315, 9009 and 9244 samples, respectively, from 40 tissues (The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx)) and 1594 cell lines (Cancer Cell Line Encyclopedia (CCLE) and MD Anderson Cell Lines Project (MCLP)). Then, we constructed the Gene Expression Display Server (GEDS), a web-based tool for quantification, comparison and visualization of gene expression data. GEDS integrates multiscale expression data and provides multiple types of figures and tables to satisfy several kinds of user requirements. The comprehensive expression profiles plotted in the one-stop GEDS platform greatly facilitate experimental biologists utilizing big data for better experimental design and analysis. GEDS is freely available on http://bioinfo.life.hust.edu.cn/web/GEDS/.

scholarly journals iEDGE: integration of Epi-DNA and Gene Expression and applications to the discovery of somatic copy number-associated drivers in cancer

2019 ◽  
Author(s):  
Amy Li ◽  
Bjoern Chapuy ◽  
Xaralabos Varelas ◽  
Paola Sebastiani ◽  
Stefano Monti
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
Regulatory Networks ◽  
Large Scale ◽  
Method Development ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cis And Trans

AbstractThe emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators – such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others – as predictive features of cancer outcome. However, identification of “driver genes” associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively.First, iEDGE identifies the cis and trans genes associated with the presence/absence of a particular epi-DNA alteration across samples. Tests of statistical mediation are then performed to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks.We used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible athttps://montilab.bu.edu/iEDGE.

scholarly journals Unraveling patterns of disrupted gene expression across a complex tissue

2021 ◽  
Author(s):  
Kelsie E Hunnicutt ◽  
Jeffrey M Good ◽  
Erica L Larson
Keyword(s):  
Gene Expression ◽  
Evolutionary Biology ◽  
Large Scale ◽  
Expression Profiles ◽  
Cell Types ◽  
Tissue Expression ◽  
Network Evolution ◽  
F1 Hybrids ◽  
Cellular Composition ◽  
Mus Musculus Musculus

Whole tissue RNASeq is the standard approach for studying gene expression divergence in evolutionary biology and provides a snapshot of the comprehensive transcriptome for a given tissue. However, whole tissues consist of diverse cell types differing in expression profiles, and the cellular composition of these tissues can evolve across species. Here, we investigate the effects of different cellular composition on whole tissue expression profiles. We compared gene expression from whole testes and enriched spermatogenesis populations in two species of house mice, Mus musculus musculus and M. m. domesticus, and their sterile and fertile F1 hybrids, which differ in both cellular composition and regulatory dynamics. We found that cellular composition differences skewed expression profiles and differential gene expression in whole testes samples. Importantly, both approaches were able to detect large-scale patterns such as disrupted X chromosome expression although whole testes sampling resulted in decreased power to detect differentially expressed genes. We encourage researchers to account for histology in RNASeq and consider methods that reduce sample complexity whenever feasible. Ultimately, we show that differences in cellular composition between tissues can modify expression profiles, potentially altering inferred gene ontological processes, insights into gene network evolution, and processes governing gene expression evolution.

scholarly journals Gene expression based inference of drug resistance in cancer

2021 ◽  
Author(s):  
Smriti Chawla ◽  
Anja Rockstroh ◽  
Melanie Lehman ◽  
Ellca Rather ◽  
Atishay Jain ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Resistance ◽  
Cell Lines ◽  
Large Scale ◽  
Activity Patterns ◽  
The Cancer Genome Atlas ◽  
Learning Approaches ◽  
Expression Data ◽  
Sequencing Data ◽  
Pathway Activity

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of large-scale drug screening datasets has provided an opportunity for predicting appropriate patient-tailored therapies by employing machine learning approaches. In this study, we report a predictive modeling approach to infer treatment response in cancers using gene expression data. In particular, we demonstrate the benefits of considering integrated chemogenomics approach, utilizing the molecular drug descriptors and pathway activity information as opposed to gene expression levels. We performed extensive validation of our approach on tissue-derived single-cell and bulk expression data. Further, we constructed several prostate cancer cell lines and xenografts, exposed to differential treatment conditions to assess the predictability of the outcomes. Our approach was further assessed on pan-cancer RNA-sequencing data from The Cancer Genome Atlas (TCGA) archives, as well as an independent clinical trial study describing the treatment journey of three melanoma patients. To summarise, we benchmarked the proposed approach on cancer RNA-seq data, obtained from cell lines, xenografts, as well as humans. We concluded that pathway-activity patterns in cancer cells are reasonably indicative of drug resistance, and therefore can be leveraged in personalized treatment recommendations.

scholarly journals African Americans and European Americans exhibit distinct gene expression patterns across tissues and tumors associated with immunologic functions and environmental exposures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Urminder Singh ◽  
Kyle M. Hernandez ◽  
Bruce J. Aronow ◽  
Eve Syrkin Wurtele
Keyword(s):  
Gene Expression ◽  
African American ◽  
African Americans ◽  
Environmental Factors ◽  
Cellular Response ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Reactive Oxygen ◽  
Mitigation Strategies ◽  
European Americans

AbstractThe COVID-19 pandemic has affected African American populations disproportionately with respect to prevalence, and mortality. Expression profiles represent snapshots of combined genetic, socio-environmental (including socioeconomic and environmental factors), and physiological effects on the molecular phenotype. As such, they have potential to improve biological understanding of differences among populations, and provide therapeutic biomarkers and environmental mitigation strategies. Here, we undertook a large-scale assessment of patterns of gene expression between African Americans and European Americans, mining RNA-Seq data from 25 non-diseased and diseased (tumor) tissue-types. We observed the widespread enrichment of pathways implicated in COVID-19 and integral to inflammation and reactive oxygen stress. Chemokine CCL3L3 expression is up-regulated in African Americans. GSTM1, encoding a glutathione S-transferase that metabolizes reactive oxygen species and xenobiotics, is upregulated. The little-studied F8A2 gene is up to 40-fold more highly expressed in African Americans; F8A2 encodes HAP40 protein, which mediates endosome movement, potentially altering the cellular response to SARS-CoV-2. African American expression signatures, superimposed on single cell-RNA reference data, reveal increased number or activity of esophageal glandular cells and lung ACE2-positive basal keratinocytes. Our findings establish basal prognostic signatures that can be used to refine approaches to minimize risk of severe infection and improve precision treatment of COVID-19 for African Americans. To enable dissection of causes of divergent molecular phenotypes, we advocate routine inclusion of metadata on genomic and socio-environmental factors for human RNA-sequencing studies.

scholarly journals Tissue, age, sex, and disease patterns of matrisome expression in GTEx transcriptome data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tim O. Nieuwenhuis ◽  
Avi Z. Rosenberg ◽  
Matthew N. McCall ◽  
Marc K. Halushka
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Tissue Expression ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Normal Tissues ◽  
Disease Patterns ◽  
Generalized Matrix ◽  
Gene Transcripts ◽  
Matrix Control

AbstractThe extracellular matrix (ECM) has historically been explored through proteomic methods. Whether or not global transcriptomics can yield meaningful information on the human matrisome is unknown. Gene expression data from 17,382 samples across 52 tissues, were obtained from the Genotype-Tissue Expression (GTEx) project. Additional datasets were obtained from The Cancer Genome Atlas (TCGA) program and the Gene Expression Omnibus for comparisons. Gene expression levels generally matched proteome-derived matrisome expression patterns. Further, matrisome gene expression properly clustered tissue types, with some matrisome genes including SERPIN family members having tissue-restricted expression patterns. Deeper analyses revealed 382 gene transcripts varied by age and 315 varied by sex in at least one tissue, with expression correlating with digitally imaged histologic tissue features. A comparison of TCGA tumor, TCGA adjacent normal and GTEx normal tissues demonstrated robustness of the GTEx samples as a generalized matrix control, while also determining a common primary tumor matrisome. Additionally, GTEx tissues served as a useful non-diseased control in a separate study of idiopathic pulmonary fibrosis (IPF) matrix changes, while identifying 22 matrix genes upregulated in IPF. Altogether, these findings indicate that the transcriptome, in general, and GTEx in particular, has value in understanding the state of organ ECM.

scholarly journals Depth-dependent transcriptomic response of diatoms during spring bloom in the western subarctic Pacific Ocean

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Shigekatsu Suzuki ◽  
Takafumi Kataoka ◽  
Tsuyoshi Watanabe ◽  
Haruyo Yamaguchi ◽  
Akira Kuwata ◽  
...  
Keyword(s):  
Gene Expression ◽  
Environmental Conditions ◽  
Large Scale ◽  
Metabolic Response ◽  
Transcriptomic Response ◽  
Subarctic Pacific ◽  
Decline Phase ◽  
Western Subarctic Pacific ◽  
Subsurface Chlorophyll Maximum ◽  
Expression Of Genes

Abstract Diatoms play important roles in primary production and carbon transportation in various environments. Large-scale diatom bloom occurs worldwide; however, metabolic responses of diatoms to environmental conditions have been little studied. Here, we targeted the Oyashio region of the western subarctic Pacific where diatoms bloom every spring and investigated metabolic response of major diatoms to bloom formation by comparing metatranscriptomes between two depths corresponding to different bloom phases. Thalassiosira nordenskioeldii and Chaetoceros debilis are two commonly occurring species at the study site. The gene expression profile was drastically different between the surface (late decline phase of the bloom; 10 m depth) and the subsurface chlorophyll maximum (SCM, initial decline phase of the bloom; 30 m depth); in particular, both species had high expression of genes for nitrate uptake at the surface, but for ammonia uptake at the SCM. Our culture experiments using T. nordenskioeldii imitating the environmental conditions showed that gene expression for nitrate and ammonia transporters was induced by nitrate addition and active cell division, respectively. These results indicate that the requirement for different nitrogen compounds is a major determinant of diatom species responses during bloom maturing.

A systematic survey of HOX and TALE expression profiling in human cancers

2018 ◽  
Vol 62 (11-12) ◽  
pp. 865-876 ◽  
Author(s):  
Yunlong Jia ◽  
Françoise Bleicher ◽  
Samir Merabet
Keyword(s):  
Comparative Analysis ◽  
Expression Profiling ◽  
Large Scale ◽  
Protein Complexes ◽  
Expression Profiles ◽  
Gene Families ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Human Cancers ◽  
Different Tissues

HOX and TALE genes encode homeodomain (HD)-containing transcription factors that act in concert in different tissues to coordinate cell fates and morphogenesis throughout embryonic development. These two evolutionary conserved families contain several members that form different types of protein complexes on DNA. Mutations affecting the expression of HOX or TALE genes have been reported in a number of cancers, but whether and how the two gene families could be perturbed together has never been explored systematically. As a consequence, the putative collaborative role between HOX and TALE members for promoting or inhibiting oncogenesis remains to be established in most cancer contexts. Here, we address this issue by considering HOX and TALE expression profiling in normal and cancer adult tissues, using normalized RNA-sequencing expression data deriving from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) research projects. Information was extracted from 28 cancer types originating from 21 different tissues, constituting a unique comparative analysis of HOX and TALE expression profiles between normal and cancer contexts in human. We present the general and specific rules that could be deduced from this large-scale comparative analysis. Overall this work provides a precious annotated support to better understand the role of specific HOX/TALE combinatorial codes in human cancers.

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