scholarly journals Comprehensive Assessment of Smoking and Sex Related Effects in Publicly Available Gene Expression Data

2021 ◽  
Author(s):  
Emily Flynn ◽  
Annie Chang ◽  
Bridget M. Nugent ◽  
Russ Altman
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Gene Expression Data ◽  
Airway Epithelium ◽  
Molecular Mechanisms ◽  
Smoking History ◽  
Expression Data ◽  
Initial Examination ◽  
Tissue Samples ◽  
Additional Group

ABSTRACTSmoking greatly reduces life expectancy in both men and women, but with different patterns of morbidity. After adjusting for smoking history, women have higher risk of respiratory effects and diabetes from smoking, while men show greater mortality from smoking-related cancers. While many smoking-related sex differences have been documented, the underlying molecular mechanisms are not well understood. To date, identification of sex differences in response to smoking has been limited to a small number of studies and the resulting smoking-related effects require further validation. Publicly available gene expression data present a unique opportunity to examine molecular-level sex and smoking effects across many tissues and studies. We performed a systematic search to identify smoking-related studies from healthy tissue samples and found 31 separate studies as well as an additional group of overlapping studies that in total span 2,177 samples and 12 tissues. These samples and studies were overall male-biased. In smoking, while effects appeared to be somewhat tissue-specific and largely autosomal, we identified a small number of genes that were consistently differentially expressed across tissues, including AHRR and GZMH. We also identified one gene, AKR1C3, encoding an aldo-keto reductase, which showed strong opposite direction, smoking-related effects in blood and airway epithelium, with higher expression in airway epithelium and lower expression in blood of smokers versus non-smokers. By contrast, at similar significance thresholds, sex-related effects were entirely sex chromosomal and consistent across tissues, providing evidence of stronger effects of smoking than sex on autosomal expression. Due to sample size limitations, we only examined interaction effects in the largest study, where we identified 30 genes with sex differential effects in response to smoking, only one of which, CAPN9, replicated in a held-out analysis. Overall these results present a comprehensive analysis of smoking-related effects across tissues and an initial examination of sex differential smoking effects in public gene expression data.

Insights into Responses to Extreme Environmental Conditions in Humans from Studies on Saccharomyces cerevisiae

2015 ◽  
Vol 65 (6) ◽  
pp. 444
Author(s):  
Ramesh C. Meena ◽  
Amitabha Chakrabarti
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Stress Response ◽  
Gene Expression Data ◽  
Molecular Mechanisms ◽  
Multiple Stressors ◽  
Expression Data ◽  
Environmental Stress Response ◽  
Yeast Saccharomyces Cerevisiae ◽  
Pathways Analysis

<p>The versatility of the yeast experimental model has aided in innumerable ways in the understanding of fundamental cellular functions and has also contributed towards the elucidation of molecular mechanisms underlying several pathological conditions in humans. Genome-wide expression, functional, localization and interaction studies on the yeast Saccharomyces cerevisiae exposed to various stressors have made profound contributions towards the understanding of stress response pathways. Analysis of gene expression data from S. cerevisiae cells indicate that the expression of a common set of genes is altered upon exposure to all the stress conditions examined. This common response to multiple stressors is known as the Environmental stress response. Knowledge gained from studies on the yeast model has now become helpful in understanding stress response pathways and associated disease conditions in humans. Cross-species microarray experiments and analysis of data with ever improving computational methods has led to a better comparison of gene expression data between diverse organisms that include yeast and humans.</p>

scholarly journals Chip-seq and gene expression data for the identification of functional sub-pathways: a proof of concept in lung cancer

2020 ◽  
Author(s):  
Xanthoula Atsalaki ◽  
Lefteris Koumakis ◽  
George Potamias ◽  
Manolis Tsiknakis
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Systems Biology ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Integrative Approach ◽  
Expression Data ◽  
Gene Regulatory

AbstractHigh-throughput technologies, such as chromatin immunoprecipitation (ChIP) with massively parallel sequencing (ChIP-seq) have enabled cost and time efficient generation of immense amount of genome data. The advent of advanced sequencing techniques allowed biologists and bioinformaticians to investigate biological aspects of cell function and understand or reveal unexplored disease etiologies. Systems biology attempts to formulate the molecular mechanisms in mathematical models and one of the most important areas is the gene regulatory networks (GRNs), a collection of DNA segments that somehow interact with each other. GRNs incorporate valuable information about molecular targets that can be corellated to specific phenotype.In our study we highlight the need to develop new explorative tools and approaches for the integration of different types of -omics data such as ChIP-seq and GRNs using pathway analysis methodologies. We present an integrative approach for ChIP-seq and gene expression data on GRNs. Using public microarray expression samples for lung cancer and healthy subjects along with the KEGG human gene regulatory networks, we identified ways to disrupt functional sub-pathways on lung cancer with the aid of CTCF ChIP-seq data, as a proof of concept.We expect that such a systems biology pipeline could assist researchers to identify corellations and causality of transcription factors over functional or disrupted biological sub-pathways.

scholarly journals A functional landscape of chronic kidney disease entities from public transcriptomic data

2018 ◽  
Author(s):  
Ferenc Tajti ◽  
Christoph Kuppe ◽  
Asier Antoranz ◽  
Mahmoud M. Ibrahim ◽  
Hyojin Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Gene Expression Data ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Kidney Tissue ◽  
Therapeutic Targets ◽  
Expression Data ◽  
Disease Entities

AbstractTo develop efficient therapies and identify novel early biomarkers for chronic kidney disease an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in CKD origin are reflected in gene expression. To this end, we integrated publicly available human glomerular microarray gene expression data for nine kidney disease entities that account for a majority of CKD worldwide. We included data from five distinct studies and compared glomerular gene expression profiles to that of non-tumor parts of kidney cancer nephrectomy tissues. A major challenge was the integration of the data from different sources, platforms and conditions, that we mitigated with a bespoke stringent procedure. This allowed us to perform a global transcriptome-based delineation of different kidney disease entities, obtaining a landscape of their similarities and differences based on the genes that acquire a consistent differential expression between each kidney disease entity and nephrectomy tissue. Furthermore, we derived functional insights by inferring activity of signaling pathways and transcription factors from the collected gene expression data, and identified potential drug candidates based on expression signature matching. We validated representative findings by immunostaining in human kidney biopsies indicating e.g. that the transcription factor FOXM1 is significantly and specifically expressed in parietal epithelial cells in RPGN whereas not expressed in control kidney tissue. These results provide a foundation to comprehend the specific molecular mechanisms underlying different kidney disease entities, that can pave the way to identify biomarkers and potential therapeutic targets. To facilitate this, we provide our results as a free interactive web application: https://saezlab.shinyapps.io/ckd_landscape/.Translational StatementChronic kidney disease is a combination of entities with different etiologies. We integrate and analyse transcriptomics analysis of glomerular from different entities to dissect their different pathophysiology, what might help to identify novel entity-specific therapeutic targets.

scholarly journals Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Bing Jiang ◽  
Shuwen Li ◽  
Zhi Jiang ◽  
Ping Shao
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Gene Expression Data ◽  
Molecular Mechanisms ◽  
Integrative Analysis ◽  
Literature Mining ◽  
High Morbidity ◽  
Expression Data ◽  
Cancer Heterogeneity ◽  
Chemokine Ligand

Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world. The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors. With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer. In the present work, we screened the published gene expression data and analyzed them with integrative tool, combined with pathway and gene ontology enrichment investigation. We identified several consensus differentially expressed genes and these genes were further confirmed with literature mining; at last, two genes, that is, immunoglobulin J chain and C-X-C motif chemokine ligand 17, were screened as novel gastric cancer associated genes. Experimental validation is proposed to further confirm this finding.

scholarly journals The Gene Expression Deconvolution Interactive Tool (GEDIT): accurate cell type quantification from gene expression data

GigaScience ◽  
2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Brian B Nadel ◽  
David Lopez ◽  
Dennis J Montoya ◽  
Feiyang Ma ◽  
Hannah Waddel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Reference Data ◽  
Expression Data ◽  
Cell Type ◽  
Tissue Samples ◽  
Interactive Tool ◽  
Cell Type Composition ◽  
Type Composition ◽  
Human And Mouse

Abstract Background The cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g., cell flow cytometry) are costly, time consuming and have potential to introduce bias. Computational approaches that use expression data to infer cell type abundance offer an alternative solution. While these methods have gained popularity, most fail to produce accurate predictions for the full range of platforms currently used by researchers or for the wide variety of tissue types often studied. Results We present the Gene Expression Deconvolution Interactive Tool (GEDIT), a flexible tool that utilizes gene expression data to accurately predict cell type abundances. Using both simulated and experimental data, we extensively evaluate the performance of GEDIT and demonstrate that it returns robust results under a wide variety of conditions. These conditions include multiple platforms (microarray and RNA-seq), tissue types (blood and stromal), and species (human and mouse). Finally, we provide reference data from 8 sources spanning a broad range of stromal and hematopoietic types in both human and mouse. GEDIT also accepts user-submitted reference data, thus allowing the estimation of any cell type or subtype, provided that reference data are available. Conclusions GEDIT is a powerful method for evaluating the cell type composition of tissue samples and provides excellent accuracy and versatility compared to similar tools. The reference database provided here also allows users to obtain estimates for a wide variety of tissue samples without having to provide their own data.

scholarly journals To Predict Human Biomarker for the Obesity Using Mouse Homologous Expression Data at Different Theiler Stages

2015 ◽  
Vol 45 ◽  
pp. 9-17
Author(s):  
Ashok Kumar ◽  
Ruchika Puri ◽  
Kanika Gupta ◽  
Amit Pal
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Molecular Mechanisms ◽  
Expression Data ◽  
Low Fat Diet ◽  
Homologous Expression ◽  
Melanocortin 4 Receptor ◽  
Human Genes ◽  
Genomics And Proteomics ◽  
Mechanisms Of Development

There are numerous genetic factors like MC4R (Melanocortin-4 receptor), POMC (Pro-opiomelanocortin), SIM1 (Single Minded Gene) etc. important in obesity, which can be used as biomarker. But more reliable diagnostic markers are the need for today, along with new therapeutic strategies that target specific molecules in the disease pathways. As in mouse and human genes, where mutations in one or both species are associated with some phenotypic characteristics as observed in human disease. In molecular mechanisms of development, differentiation, and disease gene expression data provide crucial insights. Up-regulation and down-regulation of selective genes can have major effects on diet-induced obesity, but there is little or no effect when animals are fed a low-fat diet. In present study we have studied the gene expression data of mouse at different theiler stages using GXD BioMart. The interacting partners and pathway of the genes that are already used as biomarker in mouse as well as in humans have been studied. A gene NPY1R (Neuropeptide Y1 receptor) was taken as common after STRING and KEGG results on the basis of biochemical pathways and interactions similar to MC4R. Our present work focuses on comparative genomics and proteomics analysis of NPY1R, which has led to identification of biomarker by comparing it with already known MC4R human and mouse biomarker. It has been concluded that both the proteins are structurally and functionally similar.

scholarly journals The Transcriptomic Toolbox: Resources for Interpreting Large Gene Expression Data within a Precision Medicine Context for Metabolic Disease Atherosclerosis

2019 ◽  
Vol 9 (2) ◽  
pp. 21 ◽  
Author(s):  
Caralina Marín de Evsikova ◽  
Isaac D. Raplee ◽  
John Lockhart ◽  
Gilberto Jaimes ◽  
Alexei V. Evsikov
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Transcriptome Analysis ◽  
Molecular Mechanisms ◽  
Metabolic Diseases ◽  
Expression Data ◽  
Individual Genome ◽  
Promising Alternative ◽  
Large Gene ◽  
Time Required

As one of the most widespread metabolic diseases, atherosclerosis affects nearly everyone as they age; arteries gradually narrow from plaque accumulation over time reducing oxygenated blood flow to central and periphery causing heart disease, stroke, kidney problems, and even pulmonary disease. Personalized medicine promises to bring treatments based on individual genome sequencing that precisely target the molecular pathways underlying atherosclerosis and its symptoms, but to date only a few genotypes have been identified. A promising alternative to this genetic approach is the identification of pathways altered in atherosclerosis by transcriptome analysis of atherosclerotic tissues to target specific aspects of disease. Transcriptomics is a potentially useful tool for both diagnostics and discovery science, exposing novel cellular and molecular mechanisms in clinical and translational models, and depending on experimental design to identify and test novel therapeutics. The cost and time required for transcriptome analysis has been greatly reduced by the development of next generation sequencing. The goal of this resource article is to provide background and a guide to appropriate technologies and downstream analyses in transcriptomics experiments generating ever-increasing amounts of gene expression data.

scholarly journals The impact of sex on gene expression across human tissues

Science ◽  
2020 ◽  
Vol 369 (6509) ◽  
pp. eaba3066 ◽  
Author(s):  
Meritxell Oliva ◽  
Manuel Muñoz-Aguirre ◽  
Sarah Kim-Hellmuth ◽  
Valentin Wucher ◽  
Ariel D. H. Gewirtz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Molecular Mechanisms ◽  
Genome Wide Association Study ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Tissue Expression ◽  
Study Data ◽  
Tissue Samples ◽  
The Impact

Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation.

scholarly journals Identification of Hub Genes and Key Pathways Associated with Anti-VEGF Resistant Glioblastoma Using Gene Expression Data Analysis

Biomolecules ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 403
Author(s):  
Kesavan R. Arya ◽  
Ramachandran P. Bharath Chand ◽  
Chandran S. Abhinand ◽  
Achuthsankar S. Nair ◽  
Oommen V. Oommen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathway ◽  
Gene Expression Data ◽  
Molecular Mechanisms ◽  
Therapeutic Potential ◽  
Interaction Network ◽  
Expression Data ◽  
Hub Genes ◽  
Cancer Pathways ◽  
Anti Vegf

Anti-VEGF therapy is considered to be a useful therapeutic approach in many tumors, but the low efficacy and drug resistance limit its therapeutic potential and promote tumor growth through alternative mechanisms. We reanalyzed the gene expression data of xenografts of tumors of bevacizumab-resistant glioblastoma multiforme (GBM) patients, using bioinformatics tools, to understand the molecular mechanisms of this resistance. An analysis of the gene set data from three generations of xenografts, identified as 646, 873 and 1220, differentially expressed genes (DEGs) in the first, fourth and ninth generations, respectively, of the anti-VEGF-resistant GBM cells. Gene Ontology (GO) and pathway enrichment analyses demonstrated that the DEGs were significantly enriched in biological processes such as angiogenesis, cell proliferation, cell migration, and apoptosis. The protein–protein interaction network and module analysis revealed 21 hub genes, which were enriched in cancer pathways, the cell cycle, the HIF1 signaling pathway, and microRNAs in cancer. The VEGF pathway analysis revealed nine upregulated (IL6, EGFR, VEGFA, SRC, CXCL8, PTGS2, IDH1, APP, and SQSTM1) and five downregulated hub genes (POLR2H, RPS3, UBA52, CCNB1, and UBE2C) linked with several of the VEGF signaling pathway components. The survival analysis showed that three upregulated hub genes (CXCL8, VEGFA, and IDH1) were associated with poor survival. The results predict that these hub genes associated with the GBM resistance to bevacizumab may be potential therapeutic targets or can be biomarkers of the anti-VEGF resistance of GBM.

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