scholarly journals Meta-analysis reveals pathway signature of Septic Shock

2016 ◽  
Author(s):  
Samanwoy Mukhopadhyay ◽  
Abhaydeep Pandey ◽  
Pravat K Thatoi ◽  
Bidyut K Das ◽  
Balachandran Ravindran ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Human Subjects ◽  
Major Gene ◽  
Meta Analysis ◽  
Osteoclast Differentiation ◽  
Medical Problem ◽  
High Morbidity ◽  
Multiple Data ◽  
Multiple Data Sets

Septic shock is a major medical problem with high morbidity and mortality and incompletely understood biology. Availability of genome-wide expression data from different studies on septic shock empowers the quest for hitherto unidentified pathways by integration and meta-analysis of multiple data sets. Electronic search was performed on medical literature and gene expression databases. Selection of studies was based on the organism (human subjects), tissue of origin (circulating leukocytes) and the platform technology (gene expression microarray). Gene-level meta-analysis was conducted on the six selected studies to identify the genes consistently differentially expressed in septic shock. These genes were then subjected to pathway analysis. The identified up-regulated pathway hsa04380 (Osteoclast Differentiation) was validated in an independent cohort of patients. A simplified model was generated showing the major gene-modules dysregulated in SS.

scholarly journals Transcriptomic meta-analysis reveals up-regulation of gene expression functional in osteoclast differentiation in human septic shock

PLoS ONE ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. e0171689 ◽  
Author(s):  
Samanwoy Mukhopadhyay ◽  
Pravat K. Thatoi ◽  
Abhay D. Pandey ◽  
Bidyut K. Das ◽  
Balachandran Ravindran ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Meta Analysis ◽  
Osteoclast Differentiation ◽  
Regulation Of Gene Expression ◽  
Human Septic Shock

scholarly journals Malachite: A Gene Enrichment Meta-Analysis (GEM) Tool for ToppGene

2019 ◽  
Author(s):  
Gregory R. Gershkowitz ◽  
Zachary B. Abrams ◽  
Caitlin E. Coombes ◽  
Kevin R. Coombes
Keyword(s):  
Gene Expression ◽  
Meta Analysis ◽  
Nicotinamide Adenine Dinucleotide Phosphate ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Multiple Gene ◽  
Gene Enrichment ◽  
Multiple Data ◽  
Multiple Data Sets ◽  
Approved Drugs

AbstractBackgroundResearchers commonly use online tools such as ToppGene to conduct enrichment analyses on gene expression data. This process does not easily allow multiple gene data sets to be analyzed and compared at once. ToppGene requires the user to manually enter gene symbols or other gene identifiers into a text box and to manually sift through forms with many adjustable parameters in order to obtain a downloadable text file of results. This process makes the analysis of multiple sets of genes tedious, time-consuming, and error prone. To address this problem, we developed Malachite, a Python package that enables researchers to perform gene enrichment analyses on multiple gene lists and concatenate the resulting enrichment statistics. In this way, Malachite enables meta-enrichment analyses across multiple data sets.ResultsTo illustrate its use, we applied Malachite to three data sets from the Gene Expression Omnibus comparing gene expression in the large airways of smokers and non-smokers. Biological processes enriched in all three data sets were related to xenobiotic stimulus; molecular functions typically involved nicotinamide adenine dinucleotide phosphate (NADP) activity.ConclusionMalachite enables researchers to automate gene enrichment metaanalyses using ToppGene. Malachite also enhances ToppGene’s gene set analysis of drug-gene relationships by further filtering for FDA approved drugs.

FYN and CD247: key Genes for Septic Shock Based on Bioinformatics and Meta-Analysis

2021 ◽  
Vol 24 ◽  
Author(s):  
Yue Jiang ◽  
Qian Miao ◽  
Lin Hu ◽  
Tingyan Zhou ◽  
Yingchun Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Survival Analysis ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Key Genes ◽  
Protein Protein Interaction Network ◽  
Geo Database

Background: Septic shock is sepsis accompanied by hemodynamic instability and high clinical mortality. Material and Methods: GSE95233, GSE57065, GSE131761 gene-expression profiles of healthy control subjects and septic shock patients were downloaded from the Gene-Expression Omnibus (GEO) database, and differences of expression profiles and their intersection were analysed using GEO2R. Function and pathway enrichment analysis was performed on common differentially expressed genes (DEG), and key genes for septic shock were screened using a protein-protein interaction network created with STRING. Also, data from the GEO database were used for survival analysis for key genes, and a meta-analysis was used to explore expression trends of core genes. Finally, high-throughput sequencing using the blood of a murine sepsis model was performed to analyse the expression of CD247 and FYN in mice. Results: A total of 539 DEGs were obtained (p < 0.05). Gene ontology analysis showed that key genes were enriched in functions, such as immune response and T cell activity, and DEGs were enriched in signal pathways, such as T cell receptors. FYN and CD247 are in the centre of the protein-protein interaction network, and survival analysis found that they are positively correlated with survival from sepsis. Further, meta-analysis results showed that FYN could be useful for the prognosis of patients, and CD247 might distinguish between sepsis and systemic inflammatory response syndrome patients. Finally, RNA sequencing using a mouse septic shock model showed low expression of CD247 and FYN in this model. Conclusion: FYN and CD247 are expected to become new biomarkers of septic shock.

A Meta-Analysis of Gene Expression Studies in Severe Sepsis and Septic Shock

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2741-2741 ◽  
Author(s):  
Maiara M L Fiusa ◽  
Benilton S Carvalho ◽  
Rodolfo M E Hubert ◽  
Welliton Souza ◽  
Iscia Lopes-Cendes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Severe Sepsis ◽  
Peripheral Blood ◽  
Meta Analysis ◽  
Inclusion Criteria ◽  
Comprehensive List ◽  
Expression Studies ◽  
Gene Expression Studies ◽  
Sepsis And Septic Shock

Abstract Introduction: Sepsis represents a complex inflammatory response to infection. Gene expression studies based on microarrays have shown that this response can affect more than 80% of cellular functions and pathways, in what has been termed a “genomic storm”. For several years, sepsis was regarded as a pro-inflammatory condition, and this concept resulted in several experimental treatment strategies aimed to block inflammation. However, systematic failure of these therapies and recent evidence demonstrating that anti-inflammatory pathways are also activated during sepsis illustrate the complexity and our incomplete knowledge about the pathogenesis of this condition. In the last decade, microarray-based gene expression studies have been used in attempts to improve our understanding about sepsis. Raw data from most of these studies are now collected in public archives, thus offering a unique opportunity to combine the information from different studies by meta-analysis. It has been shown that by analyzing data from multiple experiments, biases and artifacts between datasets can be cancelled out, potentially allowing true relationships to stand out. Accordingly, an increasing number of bioinformatics protocols and guidelines about meta-analysis of gene expression studies have been published in the last years. In the context of sepsis, several high-quality microarray-based gene expression studies are available. However, no systematic meta-analysis of these studies has been performed. In order to identify genes and pathways robustly associated with the pathogenesis of sepsis, we performed a meta-analysis of gene expression studies in human severe sepsis and septic shock. Material and methods: Microarray data were identified by searching two public databases (Gene Expression Omnibus and Array-Express) using the following search criteria: (“sepsis or “septic shock”) AND (“peripheral blood” or “leukocytes”) AND (“homo sapiens”). Inclusion criteria were: studies in humans with severe sepsis or septic shock; RNA obtained from peripheral blood leukocytes; availability of raw data; and matched healthy controls from the same study. To improve consistency, only studies using similar platforms were compared. We used the R/BioConductor environment to preprocess the datasets using the Robust Multi-array Average algorithm (RMA) implemented in the ‘oligo’ package and to perform meta-analysis through the ‘RankProd’ package implementation. This is a non-parametric statistical method that utilizes ranks of the log-ratio statistics for all genes across different studies to generate a list of differentially expressed (DE) genes between two conditions, and considered superior to alternative methodologies. For this study, we selected genes with fold-change of expression above 2 and false discovery rate below 0.01, calculated based on 10,000 permutations. Gene set analysis was initially performed using WebGestalt and confirmed in similar tools (KEGG, Pathway Commons, WikiPathways). Only pathways identified by more than one tool were considered. Results: Forty-five studies were identified, of which five fulfilled inclusion criteria. Our meta-analysis included data from 259 patients and 132 controls. Out of 22,216 probesets, we observed 352 as candidates for DE, 215 of which were up-regulated and 137 down-regulated. Top 5 up-regulated genes were CD177, MMP8, HP, ARG1 and ANXA3. Top 5 down-regulated genes were FCER1A, YME1L1, TRDV3, LRRN3 and MYBL1. The gene ontology term associated with the set of DE genes in both analysis with higher statistical significance was "immune response” (adjP=2.85e-27), and the most significant pathways identified were “Hematopoietic cell lineage” (adjP=8.69e-13), “TCR signaling pathway” (adjP=3.04e-10) and “immune system” (adjP=1.08e-19). Discussion and conclusion: The combined analysis of data generated by high-throughput experiments is an attractive and validated strategy to improve the sensitivity and specificity of genome-wide expression data. This meta-analysis provides a comprehensive list of genes, pathways and expression signatures associated with severe sepsis and septic shock, confirming several results from individual studies. In addition, our meta-analysis potentially provides new biological insights about sepsis, by listing a comprehensive list of new candidate genes with robust associations with this condition. Disclosures No relevant conflicts of interest to declare.

scholarly journals Neuro-transcriptomic signatures for mood disorder morbidity and suicide mortality

2019 ◽  
Author(s):  
Mbemba Jabbi ◽  
Dhivya Arasappan ◽  
Simon B. Eickhoff ◽  
Stephen M. Strakowski ◽  
Charles B. Nemeroff ◽  
...  
Keyword(s):  
Gene Expression ◽  
Factor Analysis ◽  
Mood Disorders ◽  
Rna Sequencing ◽  
Meta Analysis ◽  
Psychiatric Morbidity ◽  
Higher Order ◽  
Suicide Mortality ◽  
High Morbidity ◽  
Order Factor

ABSTRACTSuicidal behaviors are strongly linked with mood disorders, but the specific neurobiological and functional gene-expression correlates for this linkage remain elusive. We performed neuroimaging-guided RNA-sequencing in two studies to test the hypothesis that imaging-localized gray matter volume (GMV) loss in mood disorders, harbors gene-expression changes associated with disease morbidity and related suicide mortality in an independent postmortem cohort. To do so, first, we conducted study 1 using an anatomical likelihood estimation (ALE) MRI meta-analysis including a total of 47 voxel-based morphometry (VBM) publications (i.e. 26 control>major depressive disorder (MDD) studies, and 21 control>bipolar disorder (BD) studies) in 2387 (living) participants. Study 1 meta-analysis identified a selective anterior insula cortex (AIC) GMV loss in mood disorders. We then used this results to guide study 2 postmortem tissue dissection and RNA-Sequencing of 100 independent donor brain samples with a life-time history of MDD (N=30), BD (N=37) and control (N=33). In study 2, exploratory factor-analysis identified a higher-order factor representing number of Axis-1 diagnoses (e.g. substance use disorders/psychosis/anxiety, etc.), referred to here as morbidity and suicide-completion referred to as mortality. Comparisons of case-vs-control, and factor-analysis defined higher-order-factor contrast variables revealed that the imaging-identified AIC GMV loss sub-region harbors differential gene-expression changes in high morbidity-&-mortality versus low morbidity-&-mortality cohorts in immune, inflammasome, and neurodevelopmental pathways. Weighted gene co-expression network analysis further identified co-activated gene modules for psychiatric morbidity and mortality outcomes. These results provide evidence that AIC anatomical signature for mood disorders are possible correlates for gene-expression abnormalities in mood morbidity and suicide mortality.

scholarly journals MicroRNA in Human Acute Kidney Injury: A Systematic Review Protocol

2021 ◽  
Vol 8 ◽  
pp. 205435812110099
Author(s):  
Adrianna Douvris ◽  
Dylan Burger ◽  
Rosendo A. Rodriguez ◽  
Edward G. Clark ◽  
Jose Viñas ◽  
...  
Keyword(s):  
Systematic Review ◽  
Acute Kidney Injury ◽  
Animal Models ◽  
Human Subjects ◽  
Meta Analysis ◽  
Kidney Injury ◽  
Diagnostic Tools ◽  
High Morbidity ◽  
Statistical Heterogeneity

Background: Acute kidney injury (AKI) is a common complication of hospitalization with high morbidity and mortality for which no effective treatments exist and for which current diagnostic tools have limitations for earlier identification. MicroRNAs (miRNAs) are small non-coding RNAs that have been implicated in the pathogenesis of AKI, and some miRNAs have shown promise as therapeutic tools in animal models of AKI. However, less is known about the role of miRNAs in human AKI. Objective: To evaluate the role of miRNAs in human subjects with AKI. Design: Systematic review and meta-analysis Measurements: Quantification of miRNA levels from human blood, urine, or kidney biopsy samples, and measures of renal function as defined in the study protocol. Methods: A comprehensive search strategy for Ovid MEDLINE All, Embase, Web of Science, and CENTRAL will be developed to identify investigational studies that evaluated the relationship between miRNA levels and human AKI. Primary outcomes will include measurements of kidney function and miRNA levels. Study screening, review and data extraction will be performed independently by 2 reviewers. Study quality and certainty of evidence will be assessed with validated tools. A narrative synthesis will be included and the possibility for meta-analysis will be assessed according to characteristics of clinical and statistical heterogeneity between studies. Limitations: These include (1) lack of randomized trials of miRNAs for the prevention or treatment of human AKI, (2) quality of included studies, and (3) sources of clinical and statistical heterogeneity that may affect strength and reproducibility of results. Conclusion: Previous studies of miRNAs in different animal models of AKI have generated strong interest on their use for the prevention and treatment of human AKI. This systematic review will characterize the most promising miRNAs for human research and will identify methodological constraints from miRNA research in human AKI to help inform the design of future studies. Systematic review registration: PROSPERO CRD42020201253

scholarly journals Comprehensive Analysis of RNA-Seq Gene Expression Profiling of Brain Transcriptomes Reveals Novel Genes, Regulators, and Pathways in Autism Spectrum Disorder

2020 ◽  
Vol 10 (10) ◽  
pp. 747 ◽  
Author(s):  
Md Rezanur Rahman ◽  
Maria Cristina Petralia ◽  
Rosella Ciurleo ◽  
Alessia Bramanti ◽  
Paolo Fagone ◽  
...  
Keyword(s):  
Gene Expression ◽  
Autism Spectrum Disorder ◽  
Brain Cortex ◽  
Meta Analysis ◽  
Topological Analysis ◽  
Osteoclast Differentiation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Coagulation Cascade ◽  
Rna Seq

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with deficits in social communication ability and repetitive behavior. The pathophysiological events involved in the brain of this complex disease are still unclear. Methods: In this study, we aimed to profile the gene expression signatures of brain cortex of ASD patients, by using two publicly available RNA-seq studies, in order to discover new ASD-related genes. Results: We detected 1567 differentially expressed genes (DEGs) by meta-analysis, where 1194 were upregulated and 373 were downregulated genes. Several ASD-related genes previously reported were also identified. Our meta-analysis identified 235 new DEGs that were not detected using the individual RNA-seq studies used. Some of those genes, including seven DEGs (PAK1, DNAH17, DOCK8, DAPP1, PCDHAC2, and ERBIN, SLC7A7), have been confirmed in previous reports to be associated with ASD. Gene Ontology (GO) and pathways analysis showed several molecular pathways enriched by the DEGs, namely, osteoclast differentiation, TNF signaling pathway, complement and coagulation cascade. Topological analysis of protein–protein interaction of the ASD brain cortex revealed proteomics hub gene signatures: MYC, TP53, HDAC1, CDK2, BAG3, CDKN1A, GABARAPL1, EZH2, VIM, and TRAF1. We also identified the transcriptional factors (TFs) regulating DEGs, namely, FOXC1, GATA2, YY1, FOXL1, USF2, NFIC, NFKB1, E2F1, TFAP2A, HINFP. Conclusion: Novel core genes and molecular signatures involved with ASD were identified by our meta-analysis.

Identifying psychosocial determinants related to alcohol consumption during pregnancy: a systematic literature review

2018 ◽  
Author(s):  
Sylvia Roozen ◽  
Gjalt - Jorn Ygram Peters ◽  
Gerjo Kok ◽  
Leopold Curfs
Keyword(s):  
Alcohol Consumption ◽  
Pregnant Women ◽  
Perceived Risk ◽  
Human Subjects ◽  
Meta Analysis ◽  
Univariate Analysis ◽  
Childbearing Age ◽  
Psychosocial Determinants ◽  
Maternal Alcohol ◽  
Maternal Alcohol Consumption

BackgroundFetal Alcohol Spectrum Disorders (FASD) is an important global health problem in need of prevention. For FASD prevention it is important to understand why pregnant women engage or do not engage in drinking alcohol. It remains unknown which psychosocial determinants related to maternal alcohol consumption are most in need of prevention. The objective of this study was to identify these.MethodWe searched in PubMed, PsychINFO, PsychARTICLES, ERIC, CINAHL, EMBASE and MEDLINE databases up to May 2018 using an extensive query consisting of keywords related to pregnancy (e.g., maternal, prenatal), alcohol use (e.g., alcohol, drink) and determinants (e.g., attitude, norm). Studies were excluded when not published in English, were reviews, or involved non-human subjects. Substantial heterogeneity precluded aggregation or meta-analysis of the data. Instead, data were qualitatively inspected.ResultsA total of 23 studies including 150 identified items were eligible for data analysis. Studies covered over 15 psychosocial determinants (e.g., attitude, perceived social norm, risk perception). Studies differed in their operationalizations. As a majority of data was based on univariate analysis, little is known about the relationship with specific drinking behaviors. The majority of studies targeted perceived risk and motivation to comply with each social referents' approval or disapproval. A large proportion of studies focused on disadvantages and risks of maternal alcohol consumption. Results from these studies show that women do not continue to drink because the risks are unknown to them. Cautious interpretation is needed while the observed heterogeneity hindered firm conclusions. Conclusion We aimed to identify all relevant psychosocial determinants of maternal alcohol consumption behavior(s). The state of the literature precludes such conclusions. It remains unknown which determinants are most in need of intervention. It is recommended for future studies to (i) identify all possible psychosocial determinants of drinking during pregnancy using both quantitative and qualitative methods; (ii) include different target groups (e.g., women with unplanned pregnancies, pregnant women, women in childbearing age); (iii) identify key environmental agents; (iv) operationalize their measures based on theoretical models; (v) report specific variables such as the study method and association with behavior.

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