Weighted gene co-expression network analysis and drug-gene interaction bioinformatics uncover key genes associated with various presentations of malaria infection in African children and major drug candidates

Abdominal aortic aneurysm (AAA) is a prevalent aortic disease that causes high mortality due to asymptomatic gradual expansion and sudden rupture. The underlying molecular mechanisms and effective pharmaceutical therapy for preventing AAA progression have not been fully identified. In this study, we identified the key modules and hub genes involved in AAA growth from the GSE17901 dataset in the Gene Expression Omnibus (GEO) database through the weighted gene co-expression network analysis (WGCNA). Key genes were further selected and validated in the mouse dataset (GSE12591) and human datasets (GSE7084, GSE47472, and GSE57691). Finally, we predicted drug candidates targeting key genes using the Drug–Gene Interaction database. Overall, we identified key modules enriched in the mitotic cell cycle, GTPase activity, and several metabolic processes. Seven key genes (CCR5, ADCY5, ADCY3, ACACB, LPIN1, ACSL1, UCP3) related to AAA progression were identified. A total of 35 drugs/compounds targeting the key genes were predicted, which may have the potential to prevent AAA progression.

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Microarray analysis reveals an inflammatory transcriptomic signature in peripheral blood for sciatica

BMC Neurology ◽

10.1186/s12883-021-02078-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yi Wang ◽

Guogang Dai ◽

Ling Jiang ◽

Shichuan Liao ◽

Jiao Xia

Keyword(s):

Functional Analysis ◽

Network Analysis ◽

Peripheral Blood ◽

Differentially Expressed ◽

Healthy Controls ◽

Toll Like Receptor ◽

Qrt Pcr ◽

Transcriptomic Signature ◽

Transcriptional Changes ◽

Key Genes

Abstract Background Although the pathology of sciatica has been studied extensively, the transcriptional changes in the peripheral blood caused by sciatica have not been characterized. This study aimed to characterize the peripheral blood transcriptomic signature for sciatica. Methods We used a microarray to identify differentially expressed genes in the peripheral blood of patients with sciatica compared with that of healthy controls, performed a functional analysis to reveal the peripheral blood transcriptomic signature for sciatica, and conducted a network analysis to identify key genes that contribute to the observed transcriptional changes. The expression levels of these key genes were assessed by qRT-PCR. Results We found that 153 genes were differentially expressed in the peripheral blood of patients with sciatica compared with that of healthy controls, and 131 and 22 of these were upregulated and downregulated, respectively. A functional analysis revealed that these differentially expressed genes (DEGs) were strongly enriched for the inflammatory response or immunity. The network analysis revealed that a group of genes, most of which are related to the inflammatory response, played a key role in the dysregulation of these DEGs. These key genes are Toll-like receptor 4, matrix metallopeptidase 9, myeloperoxidase, cathelicidin antimicrobial peptide, resistin and Toll-like receptor 5, and a qRT-PCR analysis validated the higher transcript levels of these key genes in the peripheral blood of patients with sciatica than in that of healthy controls. Conclusion We revealed inflammatory characteristics that serve as a peripheral blood transcriptomic signature for sciatica and identified genes that are essential for mRNA dysregulation in the peripheral blood of patients with sciatica.

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Application of weighted gene co‐expression network analysis (WGCNA) to identify novel key genes in diabetic nephropathy

Journal of Diabetes Investigation ◽

10.1111/jdi.13628 ◽

2021 ◽

Author(s):

Zheng Wang ◽

Xiaolei Chen ◽

Chao Li ◽

Wanxin Tang

Keyword(s):

Diabetic Nephropathy ◽

Network Analysis ◽

Key Genes

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Identification of key genes associated with secondary metabolites biosynthesis by system network analysis in Valeriana officinalis

Journal of Plant Research ◽

10.1007/s10265-021-01277-5 ◽

2021 ◽

Author(s):

Mohammad Bolhassani ◽

Ali Niazi ◽

Ahmad Tahmasebi ◽

Ali Moghadam

Keyword(s):

Network Analysis ◽

Secondary Metabolites ◽

Valeriana Officinalis ◽

Key Genes

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Genome regulation and gene interaction networks inferred from muscle transcriptome underlying feed efficiency in Pigs

10.1101/2020.03.20.998203 ◽

2020 ◽

Cited By ~ 2

Author(s):

Victor AO. Carmelo ◽

Haja N. Kadarmideen

Keyword(s):

Network Analysis ◽

Feed Efficiency ◽

Differential Expression Analysis ◽

Gene Interaction ◽

Mitochondrial Genes ◽

Pathway Enrichment Analysis ◽

Mitochondrial Translation ◽

Pathway Enrichment ◽

Genome Regulation ◽

Potential Biomarkers

AbstractImprovement of feed efficiency (FE) is key for sustainability and cost reduction in pig production. Our aim was to characterize the muscle transcriptomic profiles in Danbred Duroc (Duroc) and Danbred Landrace (Landrace), in relation to FE for identifying potential biomarkers. RNA-seq data was analyzed employing differential gene expression methods, gene-gene interaction and network analysis, including pathway and functional analysis. We compared the results with genome regulation in human exercise data. In the differential expression analysis, 13 genes were differentially expressed, including: MRPS11, MTRF1, TRIM63, MGAT4A, KLH30. Based on a novel gene selection method, the divergent count, we performed pathway enrichment analysis. We found 5 significantly enriched pathways related to feed conversion ratio (FCR). These pathways were mainly mitochondrial, and summarized in the mitochondrial translation elongation (MTR) pathway. In the gene interaction analysis, highlights include the mitochondrial genes: PPIF, MRPL35, NDUFS4and the fat metabolism and obesity genes: AACS, SMPDL3B, CTNNBL1, NDUFS4 and LIMD2. In the network analysis, we identified two modules significantly correlated with FCR. Pathway enrichment of modules identified MTR, electron transport chain and DNA repair as enriched pathways. In the network analysis, the mitochondrial gene group NDUF was a key hub group, showing potential as biomarkers. Comparing with human transcriptomic exercise studies, genes related to exercise displayed enrichment in our FCR related genes. We conclude that mitochondrial activity is a driver for FCR in muscle tissue, and mitochondrial genes could be potential biomarkers for FCR in pigs. We hypothesize that increased FE mimics processes triggered in exercised muscle.

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Identification of Key Genes Affecting Porcine Fat Deposition Based on Co-Expression Network Analysis of Weighted Genes

10.21203/rs.3.rs-148726/v1 ◽

2021 ◽

Author(s):

Kai Xing ◽

Huatao Liu ◽

Fengxia Zhang ◽

Yibing Liu ◽

Yong Shi ◽

...

Keyword(s):

Network Analysis ◽

Production Efficiency ◽

Fat Deposition ◽

Rna Seq ◽

Genetic Studies ◽

Gene Expression Matrix ◽

Differential Gene ◽

Key Genes ◽

Strong Candidate ◽

Molecular Regulatory Mechanisms

Abstract Background: Fat deposition is an important economic consideration for pig production. The amount of fat deposition in pigs seriously affects production efficiency, quality, and reproductive performance, while also affecting consumers' choice of pork. Weighted gene co-expression network analysis (WGCNA) has been shown to be effective in pig genetic studies. Therefore, this study aimed to identify modules that co-express genes associated with fat deposition in pigs (Songliao black and Landrace breeds) with extreme levels of backfat (high and low), and to identify the central genes in each of these modules. Results: We used RNA-seq of different pig tissues to construct a gene expression matrix consisting of 12 862 genes from 36 samples. Eleven co-expression modules were identified using WGCNA; the number of genes in these modules ranged from 39 to 3363. We found four co-expression modules were significantly correlated with backfat thickness. A total of 14 genes ( RAD9A , IGF2R , SCAP , TCAP , DGAT1 , GPS2 , IGF1 , MAPK8 , FABP , FABP5 , LEPR , UCP3 , APOF , and FASN ) were found to be related to fat deposition. Conclusions: RAD9A , TCAP , GPS2 , and APOF were found to be the key genes in the four modules according to the degree of gene connectivity. Combining the results of differential gene analysis, APOF was proposed as a strong candidate gene for body size traits. This study explores the key genes that regulate porcine fat deposition and lays the foundation for further research into the molecular regulatory mechanisms behind porcine fat deposition.

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Integrated Bioinformatics and Machine Learning Algorithms Analyses Highlight Related Pathways and Genes Associated with Alzheimer's Disease

Current Bioinformatics ◽

10.2174/1574893617666211220154326 ◽

2021 ◽

Vol 17 ◽

Author(s):

Hui Zhang ◽

Qidong Liu ◽

Xiaoru Sun ◽

Yaru Xu ◽

Yiling Fang ◽

...

Keyword(s):

Machine Learning ◽

Network Analysis ◽

Predictive Model ◽

Differentially Expressed Genes ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Differentially Expressed ◽

Diagnosis And Treatment ◽

Ppi Network ◽

Key Genes

Background: The pathophysiology of Alzheimer's disease (AD) is still not fully studied. Objective: This study aimed to explore the differently expressed key genes in AD and build a predictive model of diagnosis and treatment. Methods: Gene expression data of the entorhinal cortex of AD, asymptomatic AD, and control samples from the GEO database were analyzed to explore the relevant pathways and key genes in the progression of AD. Differentially expressed genes between AD and the other two groups in the module were selected to identify biological mechanisms in AD through KEGG and PPI network analysis in Metascape. Furthermore, genes with a high connectivity degree by PPI network analysis were selected to build a predictive model using different machine learning algorithms. Besides, model performance was tested with five-fold cross-validation to select the best fitting model. Results: A total of 20 co-expression gene clusters were identified after the network was constructed. Module 1 (in black) and module 2 (in royal blue) were most positively and negatively correlated with AD, respectively. Total 565 genes in module 1 and 215 genes in module 2, respectively, overlapped in two differentially expressed genes lists. They were enriched in the G protein-coupled receptor signaling pathway, immune-related processes, and so on. 11 genes were screened by using lasso logistic regression, and they were considered to play an important role in predicting AD samples. The model built by the support vector machine algorithm with 11 genes showed the best performance. Conclusion: This result shed light on the diagnosis and treatment of AD.

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