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

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.

scholarly journals Identification of key genes affecting porcine fat deposition based on co-expression network analysis of weighted genes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kai Xing ◽  
Huatao Liu ◽  
Fengxia Zhang ◽  
Yibing Liu ◽  
Yong Shi ◽  
...  
Keyword(s):  
Network Analysis ◽  
Production Efficiency ◽  
Fat Deposition ◽  
Rna Sequences ◽  
Gene Expression Matrix ◽  
Differential Gene ◽  
Key Genes ◽  
Strong Candidate ◽  
Molecular Regulatory Mechanisms ◽  
Core Genes

Abstract Background Fat deposition is an important economic consideration in 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) is 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 core genes in each of these modules. Results We used RNA sequences generated in 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 and the number of genes in these modules ranged from 39 to 3,363. Four co-expression modules were significantly correlated with backfat thickness. A total of 16 genes (RAD9A, IGF2R, SCAP, TCAP, SMYD1, PFKM, DGAT1, GPS2, IGF1, MAPK8, FABP, FABP5, LEPR, UCP3, APOF, and FASN) were associated with fat deposition. Conclusions RAD9A, TCAP, SMYD1, PFKM, GPS2, and APOF were the key genes in the four modules based on the degree of gene connectivity. Combining these results with those from differential gene analysis, SMYD1 and PFKM were proposed as strong candidate genes for body size traits. This study explored the key genes that regulate porcine fat deposition and lays the foundation for further research into the molecular regulatory mechanisms underlying porcine fat deposition.

Key Genes in Stomach Adenocarcinoma Identified via Network Analysis of RNA-Seq Data

2017 ◽  
Vol 23 (4) ◽  
pp. 745-752 ◽  
Author(s):  
Li Shen ◽  
Lizhi Zhao ◽  
Jiquan Tang ◽  
Zhiwei Wang ◽  
Weisong Bai ◽  
...  
Keyword(s):  
Network Analysis ◽  
Rna Seq ◽  
Stomach Adenocarcinoma ◽  
Key Genes

scholarly journals Potential Biomarkers of Diabetic Kidney Disease Based on Weighted Gene Co-expression Network Analysis

2020 ◽  
Author(s):  
Yang Chen ◽  
Kangli Xiao ◽  
Ningjie Shi ◽  
Fengying Yang ◽  
Zhenhai Cui ◽  
...  
Keyword(s):  
Network Analysis ◽  
Kidney Disease ◽  
Diabetic Kidney Disease ◽  
Clear Understanding ◽  
Rna Seq ◽  
Ppi Network ◽  
Early Screening ◽  
Diabetic Kidney ◽  
Protein Protein Interaction ◽  
Key Genes

Abstract Background: Diabetic kidney disease (DKD) is one of the serious complications of diabetes. However, there is no unified and clear understanding of the pathogenesis of DKD at present. The evidence suggests that genetic predispositions play a significant role in an individual’s susceptibility to DKD. WGCNA analysis may provide a new way to search for genes closely related to DKD. Methods: In this study we analysed key genes and pathways involved in DKD from RNA-seq count results using 23 peripheral blood samples from patients with DKD. Weighted gene co-expression network analysis (WGCNA) was performed with the WGCNA package.Results: We found turquoise and purple module were characterized as having the highest correlation with large proteinuria stage in DKD. Furthermore, the key genes, including DCN, F2R, LTBP2, B2M, PSMB8, and NLRC5, were evaluated through a protein-protein interaction (PPI) network combined with a co-expression network, which were expected to be potential diagnostic and therapeutic targets of DKD. Conclusions: These signalling pathways and hub genes will provide effective targets and ideas for further study of the pathogenesis of DKD and are expected to have roles in the prevention of, early screening for, and the treatment of DKD.

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

BMC Neurology ◽  
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.

scholarly journals Best practices on the differential expression analysis of multi-species RNA-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew Chung ◽  
Vincent M. Bruno ◽  
David A. Rasko ◽  
Christina A. Cuomo ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Best Practices ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Species ◽  
Rna Seq ◽  
Species Analysis ◽  
Differential Gene ◽  
Multiple Species ◽  
Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

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