scholarly journals Systematic evaluation of transcriptomic disease risk and diagnostic biomarker overlap between COVID-19 and tuberculosis: a patient-level meta-analysis

2020 ◽  
Author(s):  
Dylan Sheerin ◽  
Abhimanyu ◽  
Xutao Wang ◽  
W Evan Johnson ◽  
Anna Coussens
Keyword(s):  
Gene Expression ◽  
Disease Risk ◽  
Meta Analysis ◽  
Risk Scores ◽  
Systematic Evaluation ◽  
Rna Seq ◽  
Sequencing Data ◽  
Transcriptional Responses ◽  
Patient Level ◽  
Gene Expression Signatures

AbstractBackgroundThe novel coronavirus, SARS-CoV-2, has increased the burden on healthcare systems already strained by a high incidence of tuberculosis (TB) as co-infection and dual presentation are occurring in syndemic settings. We aimed to understand the interaction between these diseases by profiling COVID-19 gene expression signatures on RNA-sequencing data from TB-infected individuals.MethodsWe performed a systematic review and patient-level meta-analysis by querying PubMed and pre-print servers to derive eligible COVID-19 gene expression signatures from human whole blood (WB), PBMCs or BALF studies. A WB influenza dataset served as a control respiratory disease signature. Three large TB RNA-seq datasets, comprising multiple cohorts from the UK and Africa and consisting of TB patients across the disease spectrum, were chosen to profile these signatures. Putative “COVID-19 risk scores” were generated for each sample in the TB datasets using the TBSignatureProfiler package. Risk was stratified by time to TB diagnosis in progressors and contacts of pulmonary and extra-pulmonary TB. An integrative analysis between TB and COVID-19 single-cell RNA-seq data was performed and a population-level meta-analysis was conducted to identify shared gene ontologies between the diseases and their relative enrichment in COVID-19 disease severity states.Results35 COVID-19 gene signatures from nine eligible studies comprising 98 samples were profiled on TB RNA-seq data from 1181 samples from 853 individuals. 25 signatures had significantly higher COVID-19 risk in active TB (ATB) compared with latent TB infection (p <0·005), 13 of which were validated in two independent datasets. FCN1- and SPP1-expressing macrophages enriched in BALF during severe COVID-19 were identified in circulation during ATB. Shared perturbed ontologies included antigen presentation, epigenetic regulation, platelet activation, and ROS/RNS production were enriched with increasing COVID-19 severity. Finally, we demonstrate that the overlapping transcriptional responses may complicate development of blood-based diagnostic signatures of co-infection.InterpretationOur results identify shared dysregulation of immune responses in COVID-19 and TB as a dual risk posed by co-infection to COVID-19 severity and TB disease progression. These individuals should be followed up for TB in the months subsequent to SARS-CoV-2 diagnosis.

scholarly journals Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Danqing Xu ◽  
Chen Wang ◽  
Atlas Khan ◽  
Ning Shang ◽  
Zihuai He ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Disease Risk ◽  
Association Studies ◽  
Large Datasets ◽  
Risk Scores ◽  
Sequencing Data ◽  
Case Definitions ◽  
Phenotypic Data ◽  
Clinical Risk ◽  
Phenotypic Features

AbstractLabeling clinical data from electronic health records (EHR) in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Furthermore, existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies in case definitions across different algorithms. We describe here quantitative disease risk scores based on almost unsupervised methods that require minimal input from clinicians, can be applied to large datasets, and alleviate some of the main weaknesses of existing phenotyping algorithms. We show applications to phenotypic data on approximately 100,000 individuals in eMERGE, and focus on several complex diseases, including Chronic Kidney Disease, Coronary Artery Disease, Type 2 Diabetes, Heart Failure, and a few others. We demonstrate that relative to existing approaches, the proposed methods have higher prediction accuracy, can better identify phenotypic features relevant to the disease under consideration, can perform better at clinical risk stratification, and can identify undiagnosed cases based on phenotypic features available in the EHR. Using genetic data from the eMERGE-seq panel that includes sequencing data for 109 genes on 21,363 individuals from multiple ethnicities, we also show how the new quantitative disease risk scores help improve the power of genetic association studies relative to the standard use of disease phenotypes. The results demonstrate the effectiveness of quantitative disease risk scores derived from rich phenotypic EHR databases to provide a more meaningful characterization of clinical risk for diseases of interest beyond the prevalent binary (case-control) classification.

scholarly journals HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 244 ◽  
Author(s):  
Antonio Victor Campos Coelho ◽  
Rossella Gratton ◽  
João Paulo Britto de Melo ◽  
José Leandro Andrade-Santos ◽  
Rafael Lima Guimarães ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Differentially Expressed Genes ◽  
Cd4 T Cells ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Infected Cells ◽  
Hiv 1

HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently differentially expressed genes in the context of HIV-1 infection. We selected two studies (22 samples: 15 experimentally infected and 7 mock-infected). We found 208 differentially expressed genes in infected cells when compared to uninfected/mock-infected cells. This result had moderate overlap when compared to previous studies of HIV-1 infection transcriptomics, but we identified 64 genes already known to interact with HIV-1 according to the HIV-1 Human Interaction Database. A gene ontology (GO) analysis revealed enrichment of several pathways involved in immune response, cell adhesion, cell migration, inflammation, apoptosis, Wnt, Notch and ERK/MAPK signaling.

scholarly journals Multi-species transcriptome meta-analysis of the response to retinoic acid in vertebrates and comparative analysis of the effects of retinol and retinoic acid on gene expression in LMH cells

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Clemens Falker-Gieske ◽  
Andrea Mott ◽  
Sören Franzenburg ◽  
Jens Tetens
Keyword(s):  
Gene Expression ◽  
Retinoic Acid ◽  
Cellular Response ◽  
Homo Sapiens ◽  
Meta Analysis ◽  
Early Response ◽  
Rna Seq ◽  
Transcriptomic Response ◽  
Time Points ◽  
Lmh Cells

Abstract Background Retinol (RO) and its active metabolite retinoic acid (RA) are major regulators of gene expression in vertebrates and influence various processes like organ development, cell differentiation, and immune response. To characterize a general transcriptomic response to RA-exposure in vertebrates, independent of species- and tissue-specific effects, four publicly available RNA-Seq datasets from Homo sapiens, Mus musculus, and Xenopus laevis were analyzed. To increase species and cell-type diversity we generated RNA-seq data with chicken hepatocellular carcinoma (LMH) cells. Additionally, we compared the response of LMH cells to RA and RO at different time points. Results By conducting a transcriptome meta-analysis, we identified three retinoic acid response core clusters (RARCCs) consisting of 27 interacting proteins, seven of which have not been associated with retinoids yet. Comparison of the transcriptional response of LMH cells to RO and RA exposure at different time points led to the identification of non-coding RNAs (ncRNAs) that are only differentially expressed (DE) during the early response. Conclusions We propose that these RARCCs stand on top of a common regulatory RA hierarchy among vertebrates. Based on the protein sets included in these clusters we were able to identify an RA-response cluster, a control center type cluster, and a cluster that directs cell proliferation. Concerning the comparison of the cellular response to RA and RO we conclude that ncRNAs play an underestimated role in retinoid-mediated gene regulation.

Impact of therapy on gene expression in high-risk prostate cancer (PCA) treated with neoadjuvant docetaxel and androgen deprivation therapy.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 8-8
Author(s):  
Himisha Beltran ◽  
Alexander Wyatt ◽  
Edmund Chedgy ◽  
Ladan Fazli ◽  
Andrea Sboner ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Hormone Receptors ◽  
Dna Analysis ◽  
Rna Extraction ◽  
Post Treatment ◽  
Rna Seq ◽  
Sequencing Data ◽  
Quantitative Expression ◽  
High Risk Prostate Cancer

8 Background: Molecular analyses of neoadjuvant post-treatment radical prostatectomy (RP) specimens has been challenging as often times only microscopic foci remain present at time of RP precluding RNA-seq. DNA analysis alone in the absence of expression may be suboptimal in elucidating complex mechanisms of resistance and/or prognostic risk stratification. We therefore set out to develop an assay that could quantify mRNA expression in treated and untreated PCA using formalin fixed paraffin embedded (FFPE) tissues. Methods: We evaluated 40 untreated and post-treatment FFPE specimens as well as patient-matched pre-treated needle biopsies and baseline clinical data from patients enrolled on CALGB 90203: a randomized phase 3 trial comparing noeadjuvant docetaxel and ADT followed by RP vs RP alone for men with high risk localized PCA. High-density tumor areas were selected for RNA extraction (min 50ng RNA). We used NanoString nCounter to quantify gene expression of a custom panel of 75 genes including AR and androgen regulated, neural/neuroendocrine (NE), EMT, cell cycle, hormone receptors, TMPRSS-ERG, ARv7 splice variant, and housekeeper genes. mRNA data was integrated with matched whole exome sequencing data. Frozen specimens and RNA-Seq (n = 7) were used for QC and comparative analysis. Results: Quantitative expression using Nanostring showed high correlation with RNA-seq of patient-matched frozen tissue (Spearman coefficient 0.9). There was significant upregulation of AR and the ARv7 expression following treatment, as well as a subset of NE and EMT genes; three high chromogranin A outlier cases were identified in the treatment arm. There was an overall higher AR score in treated cases (based on expression of 30 AR signaling genes) compared to untreated, along the spectrum of CRPC. Conclusions: These data support the feasibility of quantifying gene expression in neoadjuvant-treated PCA cases with limited FFPE tissue requirement. Extensive characterization of AR status and NE/EMT genes identifies molecular outliers that can arise post-treatment and provides new insight into the heterogeneity of treatment response and potential early markers of resistance. Clinical trial information: NCT00430183.

RNA sequencing analysis for profiling activation of cancer-associated molecular pathways.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e13032-e13032 ◽  
Author(s):  
Anton Buzdin ◽  
Andrew Garazha ◽  
Maxim Sorokin ◽  
Alex Glusker ◽  
Alexey Aleshin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Original Data ◽  
Tissue Expression ◽  
Molecular Pathways ◽  
Sequencing Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Healthy Human ◽  
Tissue Samples ◽  
Normal Tissues

e13032 Background: Intracellular molecular pathways (IMPs) control all major events in the living cell. They are considered hotspots in contemporary oncology because knowledge of IMPs activation is essential for understanding mechanisms of molecular pathogenesis in oncology. Profiling IMPs requires RNA-seq data for tumors and for a collection of reference normal tissues. However, there is a shortage now in such profiles for normal tissues from healthy human donors, uniformly profiled in a single series of experiments. Access to the largest dataset of normal profiles GTEx is only partly available through the dbGaP. In TCGA database, norms are adjacent to surgically removed tumors and may be affected by tumor-linked growth factors, inflammation and altered vascularization. ENCODE datasets were for the autopsies of normal tissues, but they can’t form statistically significant reference groups. Methods: Tissue samples representing 20 organs were taken from post-mortal human healthy donors killed in road accidents no later than 36 hours after death, blood samples were taken from healthy volunteers. Gene expression was profiled in RNA-seq experiments using the same reagents, equipment and protocols. Bioinformatic algorithms for IMP analysis were developed and validated using experimental and public gene expression datasets. Results: From original sequencing data we constructed the biggest fully open reference expression database of normal human tissues including 465 profiles termed Oncobox Atlas of Normal Tissue Expression (ANTE, original data: GSE120795). We next developed a method termed Oncobox for interrogating activation of IMPs in human cancers. It includes modules of expression data harmonization and comparison and an algorithm for automatic annotation of molecular pathways. The Oncobox system enables accurate scoring of thousands molecular pathways using RNA-seq data. Oncobox pathway analysis is also applicable for quantitative proteomics and microRNA data in oncology. Conclusions: The Oncobox system can be used for a plethora of applications in cancer research including finding differentially regulated genes and IMPs, and for discovery of new pathway-related diagnostic and prognostic biomarkers.

Microarray meta-analysis defines global angiogenesis-related gene expression signatures in human carcinomas

2011 ◽  
Vol 52 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Mario Anders ◽  
Marion Fehlker ◽  
Qing Wang ◽  
Christoph Wissmann ◽  
Christian Pilarsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Meta Analysis ◽  
Related Gene ◽  
Gene Expression Signatures ◽  
Human Carcinomas ◽  
Angiogenesis Related Gene

scholarly journals Among family variation in survival and gene expression uncovers adaptive genetic variation in a threatened fish

2019 ◽  
Author(s):  
Avril M. Harder ◽  
Janna R. Willoughby ◽  
William R. Ardren ◽  
Mark R. Christie
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Atlantic Salmon ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Adaptive Genetic Variation ◽  
Transcriptional Responses ◽  
Survival Analyses ◽  
Thiamine Treatment ◽  
Family Variation

AbstractVariation in among-family transcriptional responses to different environmental conditions can help to identify adaptive genetic variation, even prior to a selective event. Coupling differential gene expression with formal survival analyses allows for the disentanglement of treatment effects, required for understanding how individuals plastically respond to environmental stressors, from the adaptive genetic variation responsible for among-family variation in survival and gene expression. We applied this experimental design to investigate responses to an emerging conservation issue, thiamine (vitamin B1) deficiency, in a threatened population of Atlantic salmon (Salmo salar). Thiamine is an essential vitamin that is increasingly limited in many ecosystems. In Lake Champlain, Atlantic salmon cannot acquire thiamine in sufficient quantities to support natural reproduction; fertilized eggs must be reared in hatcheries and treated with supplemental thiamine. We evaluated transcriptional responses (RNA-seq) to thiamine treatment across families and found 3,616 genes differentially expressed between control (no supplemental thiamine) and treatment individuals. Fewer genes changed expression additively (i.e., equally among families) than non-additively (i.e., family-by-treatment effects) in response to thiamine. Differentially expressed genes were related to known physiological effects of thiamine deficiency, including oxidative stress, cardiovascular irregularities, and neurological abnormalities. We also identified 1,446 putatively adaptive genes that were strongly associated with among-family survival in the absence of thiamine treatment, many of which related to neurogenesis and visual perception. Our results highlight the utility of coupling RNA-seq with formal survival analyses to identify candidate genes that underlie the among-family variation in survival required for an adaptive response to natural selection.

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