Gene co-expression analyses of health(span) across multiple species

Health(span)-related gene clusters/modules were recently identified based on knowledge about the cross-species genetic basis of health, to interpret transcriptomic datasets describing health-related interventions. However, the cross-species comparison of health-related observations reveals a lot of heterogeneity, not least due to widely varying health(span) definitions and study designs, posing a challenge for the exploration of conserved healthspan modules and, specifically, their transfer across species. To improve the identification and exploration of conserved/transferable healthspan modules, here we apply an established workflow based on gene co-expression network analyses employing GEO/ArrayExpress data for human and animal models, and perform a comprehensive meta-analysis of the resulting modules related to health(span), yielding a small set of health(span) candidate genes, backed by the literature. For each experiment, WGCNA (weighted gene correlation network analysis) was thus used to infer modules of genes which correlate in their expression with a "health phenotype score" and to determine the most-connected (hub) genes for each such module, and their interactions. After mapping these hub genes to their human orthologs, 12 health(span) genes were identified in at least two species (ACTN3, ANK1, MRPL18, MYL1, PAXIP1, PPP1CA, SCN3B, SDCBP, SKIV2L, TUBG1, TYROBP, WIPF1), for which enrichment analysis by g:profiler finds an association with actin filament-based movement and associated organelles as well as muscular structures. We conclude that a meta-study of hub genes from co-expression network analyses for the complex phenotype health(span), across multiple species, can yield molecular-mechanistic insights and can direct experimentalists to further investigate the contribution of individual genes and their interactions to health(span).

Download Full-text

Weighted Gene Correlation Network Meta-Analysis Reveals Functional Candidate Genes Associated with High- and Sub-Fertile Reproductive Performance in Beef Cattle

Genes ◽

10.3390/genes11050543 ◽

2020 ◽

Vol 11 (5) ◽

pp. 543 ◽

Cited By ~ 1

Author(s):

Pablo A. S. Fonseca ◽

Aroa Suárez-Vega ◽

Angela Cánovas

Keyword(s):

Candidate Genes ◽

Meta Analysis ◽

Economic Benefits ◽

Correlation Network ◽

Economic Losses ◽

Hormone Regulation ◽

Sequencing Data ◽

Hub Genes ◽

Gene Correlation ◽

Upstream Regulators

Improved reproductive efficiency could lead to economic benefits for the beef industry, once the intensive selection pressure has led to a decreased fertility. However, several factors limit our understanding of fertility traits, including genetic differences between populations and statistical limitations. In the present study, the RNA-sequencing data from uterine samples of high-fertile (HF) and sub-fertile (SF) animals was integrated using co-expression network meta-analysis, weighted gene correlation network analysis, identification of upstream regulators, variant calling, and network topology approaches. Using this pipeline, top hub-genes harboring fixed variants (HF × SF) were identified in differentially co-expressed gene modules (DcoExp). The functional prioritization analysis identified the genes with highest potential to be key-regulators of the DcoExp modules between HF and SF animals. Consequently, 32 functional candidate genes (10 upstream regulators and 22 top hub-genes of DcoExp modules) were identified. These genes were associated with the regulation of relevant biological processes for fertility, such as embryonic development, germ cell proliferation, and ovarian hormone regulation. Additionally, 100 candidate variants (single nucleotide polymorphisms (SNPs) and insertions and deletions (INDELs)) were identified within those genes. In the long-term, the results obtained here may help to reduce the frequency of subfertility in beef herds, reducing the associated economic losses caused by this condition.

Download Full-text

Host–Viral Interactions Revealed among Shared Transcriptomics Signatures of ARDS and Thrombosis: A Clue into COVID-19 Pathogenesis

TH Open ◽

10.1055/s-0040-1721706 ◽

2020 ◽

Vol 04 (04) ◽

pp. e403-e412

Author(s):

Aastha Mishra ◽

Shankar Chanchal ◽

Mohammad Z. Ashraf

Keyword(s):

Gene Expression ◽

Virus Disease ◽

Meta Analysis ◽

Random Effect ◽

Enrichment Analysis ◽

Coagulation Factors ◽

Gene Expression Omnibus ◽

Endothelial Damage ◽

Pathway Enrichment Analysis ◽

Hub Genes

AbstractSevere novel corona virus disease 2019 (COVID-19) infection is associated with a considerable activation of coagulation pathways, endothelial damage, and subsequent thrombotic microvascular injuries. These consistent observations may have serious implications for the treatment and management of this highly pathogenic disease. As a consequence, the anticoagulant therapeutic strategies, such as low molecular weight heparin, have shown some encouraging results. Cytokine burst leading to sepsis which is one of the primary reasons for acute respiratory distress syndrome (ARDS) drive that could be worsened with the accumulation of coagulation factors in the lungs of COVID-19 patients. However, the obscurity of this syndrome remains a hurdle in making decisive treatment choices. Therefore, an attempt to characterize shared biological mechanisms between ARDS and thrombosis using comprehensive transcriptomics meta-analysis is made. We conducted an integrated gene expression meta-analysis of two independently publicly available datasets of ARDS and venous thromboembolism (VTE). Datasets GSE76293 and GSE19151 derived from National Centre for Biotechnology Information–Gene Expression Omnibus (NCBI-GEO) database were used for ARDS and VTE, respectively. Integrative meta-analysis of expression data (INMEX) tool preprocessed the datasets and effect size combination with random effect modeling was used for obtaining differentially expressed genes (DEGs). Network construction was done for hub genes and pathway enrichment analysis. Our meta-analysis identified a total of 1,878 significant DEGs among the datasets, which when subjected to enrichment analysis suggested inflammation–coagulation–hypoxemia convolutions in COVID-19 pathogenesis. The top hub genes of our study such as tumor protein 53 (TP53), lysine acetyltransferase 2B (KAT2B), DExH-box helicase 9 (DHX9), REL-associated protein (RELA), RING-box protein 1 (RBX1), and proteasome 20S subunit beta 2 (PSMB2) gave insights into the genes known to be participating in the host–virus interactions that could pave the way to understand the various strategies deployed by the virus to improve its replication and spreading.

Download Full-text

Integrated WGCNA and PPI Network to Screen Hub Genes Signatures for Infantile Hemangioma

Frontiers in Genetics ◽

10.3389/fgene.2020.614195 ◽

2021 ◽

Vol 11 ◽

Author(s):

Miao Xu ◽

Tianxiang Ouyang ◽

Kaiyang Lv ◽

Xiaorong Ma

Keyword(s):

Differential Expression ◽

Enrichment Analysis ◽

Infantile Hemangioma ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Normal Tissues ◽

Network Analyses ◽

Cycle Arrest ◽

Auc Value

BackgroundInfantile hemangioma (IH) is characterized by proliferation and regression.MethodsBased on the GSE127487 dataset, the differentially expressed genes (DEGs) between 6, 12, or 24 months and normal samples were screened, respectively. STEM software was used to screen the continued up-regulated or down-regulated in common genes. The modules were assessed by weighted gene co-expression network analysis (WGCNA). The enrichment analysis was performed to identified the biological function of important module genes. The area under curve (AUC) value and protein-protein interaction (PPI) network were used to identify hub genes. The differential expression of hub genes in IH and normal tissues was detected by qPCR.ResultsThere were 5,785, 4,712, and 2,149 DEGs between 6, 12, and 24 months and normal tissues. We found 1,218 DEGs were up-regulated or down-regulated expression simultaneously in common genes. They were identified as 10 co-expression modules. Module 3 and module 4 were positively or negatively correlated with the development of IH, respectively. These two module genes were significantly involved in immunity, cell cycle arrest and mTOR signaling pathway. The two module genes with AUC greater than 0.8 at different stages of IH were put into PPI network, and five genes with the highest degree were identified as hub genes. The differential expression of these genes was also verified by qRTPCR.ConclusionFive hub genes may distinguish for proliferative and regressive IH lesions. The WGCNA and PPI network analyses may help to clarify the molecular mechanism of IH at different stages.

Download Full-text

Identification Of Putative Gene Signatures Associated With Diagnosis And Prognosis Of Breast Cancer

Clinical & Investigative Medicine ◽

10.25011/cim.v44i3.37194 ◽

2021 ◽

Vol 44 (3) ◽

pp. E45-54

Author(s):

Chao Tan ◽

Fang Zuo ◽

Mingqian Lu ◽

Sai Chen ◽

Zhenzhen Tian ◽

...

Keyword(s):

Risk Model ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Support Vector ◽

Ppi Network ◽

Hub Genes ◽

Putative Gene ◽

Network Analyses ◽

Cluster 2

Purpose: This study aimed to identify potential diagnostic and therapeutic biomakers for the development ofbreast cancer (BC). Methods: GSE86374 dataset containing 159 samples was acquired from the Gene Expression Omnibus (GEO) database followed by differentially expressed genes (DEGs) identification and cluster analysis. Corresponding functional enrichment and protein-protein interaction (PPI) network analyses were performed to identify hub genes. Prognostic evaluation using clinical information obtained from TCGA database and hub genes was conducted to screen for crucial indicators for BC progression. The risk model was established and validated. Results: In total, 186 DEGs were identified and grouped into four clusters: 96 in cluster 1; 69 in cluster 2; 16 in cluster 3; and 5 in cluster 4. Functional enrichment analysis showed that DEGs, including ADH1B in cluster 1, were dramatically enriched in the tyrosine and drug metabolism pathways, while genes in cluster 2, including SPP1 and RRM2, played crucial roles in PI3K-Akt and p53 signalling pathway. SPP1 and RRM2 served as hub genes in the PPI network, resulting in an support vector machine classifier with good accuracy and specificity.Ad ditionally, the results of prognostic analysis suggest that age, metastasis stage, SPP1 and ADH1B were correlated with risk of BC, which was validated by using the established risk model analysis. Conclusion: SPP1, RRM2 and ADH1B appear to play vital roles in the development of BC. Age and TNM stage were also preferentially associated with risk of developing BC. Evaluation of the risk model based on larger sample size and further experimental validation are required.

Download Full-text

Differentially expressed circulating miRNAs in postmenopausal osteoporosis: a meta-analysis

Bioscience Reports ◽

10.1042/bsr20190667 ◽

2019 ◽

Vol 39 (5) ◽

Cited By ~ 5

Author(s):

Elif Pala ◽

Tuba Denkçeken

Keyword(s):

Postmenopausal Osteoporosis ◽

Signaling Pathway ◽

Target Genes ◽

Meta Analysis ◽

Adherens Junction ◽

Enrichment Analysis ◽

Rank Aggregation ◽

Cochrane Library ◽

Pathway Enrichment Analysis ◽

Hub Genes

AbstractMicroRNAs (miRNAs) have been proven to play a crucial role in postmenopausal osteoporosis (PMO), and studies on their diagnostic value have been increasing. In our study, we aim to identify the key miRNAs in the PMO that might be potential biomarkers. A comprehensive systematic literature search was conducted by searching PubMed, Web of Science, Embase and Cochrane Library databases. In the total of 16 independent miRNA expression studies which contained 327 PMO patients and 328 postmenopausal (PM) healthy control samples, miRNAs were evaluated by using robust rank aggregation (RRA) method. A statistically significant meta-signature of up-regulated hsa-miR-133a-3p (P = 1.38e−03) was determined. Then bioinformatics analysis to recruit putative target genes prediction of hsa-miR-133a-3p and pathway enrichment analysis to reveal what biological processes this miRNA may affect were conducted. It was indicated that pathways were commonly associated with adrenergic signaling in cardiomyocytes, adherens junction, PI3K-Akt signaling pathway and AMPK signaling pathway. Furthermore, STRING and Cytoscape tools were used to visualize the interactions between target genes of hsa-miR-133a-3p. Six genes were detected as hub genes among 576 targets which were CDC42, RHOA, EGFR, VAMP2, PIK3R2 and FN1. After Kyoto Encyclopedia of Genes and Genomes pathway analysis, it was detected that these hub genes were mostly enriched in signaling pathways and cancer. In this meta-analysis, it is stated that circulating hsa-miR-133a-3p may serve as a potential non-invasive biomarker and therapeutic target in PMO.

Download Full-text

Identification of IL-6 as a potential mediator of the myocardial fibrosis that occurs in response to surgery with cardiopulmonary bypass in children with Tetralogy of Fallot

Cardiology in the Young ◽

10.1017/s1047951121001803 ◽

2021 ◽

pp. 1-7

Author(s):

Hongtao Liu ◽

Yun Zhang ◽

Zhenhai Wu ◽

Liangqing Zhang

Keyword(s):

Heart Failure ◽

Network Analysis ◽

Myocardial Fibrosis ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Gene Correlation ◽

Cell Expression

Abstract Background: Tetralogy of Fallot is a common CHD. Studies have shown a close link between heart failure and myocardial fibrosis. Interleukin-6 has been suggested to be a post-independent factor of heart failure. This study aimed to explore the relationship between IL-6 and myocardial fibrosis during cardiopulmonary bypass. Material and Methods: We downloaded the expression profile dataset GSE132176 from Gene Expression Omnibus. After normalising the raw data, Gene Set Enrichment Analysis and differential gene expression analysis were performed using R. Further, a weighted gene correlation network analysis and a protein–protein interaction network analysis were used to identify HUB genes. Finally, we downloaded single-cell expression data for HUB genes using PanglaoDB. Results: There were 119 differentially expressed genes in right atrium tissues comparing the post-CPB group with the pre-CPB group. IL-6 was found to be significantly up-regulated in the post-CPB group. Six genes (JUN, FOS, ATF3, EGR1, IL-6, and PTGS2) were identified as HUB genes by a weighted gene correlation network analysis and a protein–protein interaction network analysis. Gene Set Enrichment Analysis showed that IL-6 affects the myocardium during CPB mainly through the JAK/STAT signalling pathway. Finally, we used PanglaoDB data to analyse the single-cell expression of the HUB genes. Conclusion: Our findings suggest that high expression of IL-6 and the activation of the JAK/STAT signalling pathway during CPB maybe the potential mechanism of myocardial fibrosis. We speculate that the high expression of IL-6 might be an important factor leading to heart failure after ToF surgery. We expect that these findings will provide a basis for the development of targeted drugs.

Download Full-text

Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

Public Health Genomics ◽

10.1159/000517308 ◽

2021 ◽

Vol 24 (5-6) ◽

pp. 267-279

Author(s):

Xianyang Zhu ◽

Wen Guo

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Meta Analysis ◽

Wnt Signaling Pathway ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes

Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. Methods: Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). Results: A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified. Conclusion: In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

Download Full-text

Systems biology approach identifies functional modules and regulatory hubs related to secondary metabolites accumulation after the transition from autotrophic to heterotrophic growth condition in the microalga

10.1101/841411 ◽

2019 ◽

Author(s):

B Panahi ◽

Mohammad Farhadian ◽

Mohammad amin Hejazi

Keyword(s):

Secondary Metabolites ◽

Growth Condition ◽

Molecular Mechanisms ◽

Meta Analysis ◽

Enrichment Analysis ◽

Integrated Approach ◽

Functional Enrichment ◽

Functional Modules ◽

Heterotrophic Growth ◽

Hub Genes

Heterotrophic cultures are among the most promising strategies put forth to overcome the low biomass and secondary metabolites productivity challenge. To shedding light on the underlying molecular mechanisms, weighted gene co-expression network analysis (WGCNA) was integrated with transcriptome meta-analysis, connectivity analysis, functional enrichment, and hub genes identification. Meta-analysis and Functional enrichment analysis demonstrated that with the progress to developmental phases at heterotrophic growth condition, most of the biological processes are up-regulated, which leads to the change of genetic architectures and phenotypic outcomes. WGNCA analysis of meta-genes also created four significant functional modules across logarithmic (LG), transition (TR), and production peak (PR) phases. The expression pattern and connectivity characteristics of the brown module as a non-preserved module vary across LG, TR, and PR phases. Functionally analysis identified “Carotenoid biosynthesis”, “Fatty acid metabolism”, and “Methane metabolism” as enriched pathways in the non-preserved module. Moreover, the integrated approach was applied here, identified some hub genes, such as serine hydroxymethyltransferase (SHMT1) for development of metabolites accumulating strains in microalgae. Our study provided a new insight into underlying mechanisms of metabolite accumulation and opens new avenue for the future applied studies in the microalgae field.

Download Full-text

Co-expression analysis to identify key modules and hub genes associated with COVID19 in Platelets

10.1101/2021.09.01.458644 ◽

2021 ◽

Author(s):

Ahmed B. Alarabi ◽

Attayeb Mohsen ◽

Kenji Mizuguchi ◽

Fatima Z. Alshbool ◽

Fadi T. Khasawneh

Keyword(s):

Virus Disease ◽

Enrichment Analysis ◽

Gene Clusters ◽

Protein Translation ◽

Mitochondrial Activity ◽

Hub Genes ◽

Interesting Insight ◽

Corona Virus ◽

Primary Driver ◽

Poor Outcomes

The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is a highly contagious virus that causes a severe respiratory disease known as Corona virus disease 2019 (COVID19). Indeed, COVID19 increases the risk of cardiovascular occlusive/thrombotic events and is linked to poor outcomes. The pathophysiological processes underlying COVID19-induced thrombosis are complex, and remain poorly understood. To this end, platelets play important roles in regulating our cardiovascular system, including via contributions to coagulation and inflammation. There is an ample of evidence that circulating platelets are activated in COVID19 patients, which is a primary driver of the thrombotic outcome observed in these patients. However, the comprehensive molecular basis of platelet activation in COVID19 disease remains elusive, which warrants more investigation. Hence, we employed gene co-expression network analysis combined with pathways enrichment analysis to further investigate the aforementioned issues. Our study revealed three important gene clusters/modules that were closely related to COVID19. Furthermore, enrichment analysis showed that these three modules were mostly related to platelet metabolism, protein translation, mitochondrial activity, and oxidative phosphorylation, as well as regulation of megakaryocyte differentiation, and apoptosis, suggesting a hyperactivation status of platelets in COVID19. We identified the three hub genes from each of three key modules according to their intramodular connectivity value ranking, namely: COPE, CDC37, CAPNS1, AURKAIP1, LAMTOR2, GABARAP MT-ND1, MT-ND5, and MTRNR2L12. Collectively, our results offer a new and interesting insight into platelet involvement in COVID19 disease at the molecular level, which might aid in defining new targets for treatment of COVID19-induced thrombosis.

Download Full-text

The Key Candidate Genes in Tubulointerstitial Injury of Chronic Kidney Diseases (CKD) Patients as Determined by Bioinformatic Analysis

10.21203/rs.2.12859/v1 ◽

2019 ◽

Author(s):

Wanpeng Wang ◽

Jianxiao Shen ◽

Yan Pan ◽

Chaojun Qi ◽

Senlin Liang ◽

...

Keyword(s):

Kidney Diseases ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Expression Data ◽

Tubulointerstitial Injury ◽

Hub Genes ◽

Online Tool ◽

Differential Expression Genes ◽

Gene Correlation ◽

The Relationship

Abstract Background: Tubulointerstitial injury (TIL) is common in chronic kidney disease (CKD), which in turn, leads to loss of renal function. The aims of present study were to screen critical genes with tubulointerstitial lesion in CKD by weighted gene correlation network analysis (WGCNA). Methods: GSE104954 gene expression data downloaded from GEO database were used for analysis for differential expression genes(DEGs); Meanwhile, 90 expression data of tubulointerstitial samples in GSE47185 combined with clinic information were applied to WGCNA. According to the enrichment analysis and relationship with estimated glomerular filtration rate (eGFR), the eGFR-associated modules were defined, and the hub gene is selected according to the average intramodular connectivity (Kwithin); Clinical data from Nephroseq were obtained to further validate the relationship between CKD and hub genes. Results: Totally 294 DEGs were screened. Using the WGCNA, we identified 15 co-expressed modules, the blue, brown and yellow modules exhibited strongly association with eGFR, and were significantly enriched in several signaling pathways that have been reported involved in pathogenesis of CKD. Furthermore, it was found that the 4 genes (PLG, ITGB2, CTSS and CCL5) was one of the DEGs which also be identified as hub genes according to Kwithin. Finally, the Nephroseq online tool showed that the tubulointerstitial expression levels of PLG significantly positively correlated with the eGFR, while ITGB2, CTSS and CCL5 connected negatively to the eGFR. Conclusion: WGCNA is an efficient approach to system biology. By this procedure, the present study improved the understanding of the transcriptome status of renal tubulointerstitial injury in CKD.

Download Full-text