Dataset Information

Data sets of eQTL loci, correlation analysis, and overlapped genes among gene sets that their expression levels are closely related to genes of Vegf family.

ABSTRACT: This data is generated from the analysis of similarities and differences in gene expression levels and pathways among genes (VegfA, VegfB, VegfC, and Pgf) in Vegf family using whole genome expression data generated from normal retina of eighty strains of mice. The results have been published in doi:10.1016/j.exer.2018.06.024 (Cui et al., 2018) [1]. Fig. 1 shows the expression quantitative trait loci (eQTL) that regulate the expression level of each gene in the Vegf family. The other three figure show the overlapped genes among the top 500 genes that their expression levels are most closely correlated to each of the Vegf genes. The four tables contain the information of top 50 genes that their expression levels are most closely correlated to each of the Vegf genes, and the correlation of the top 50 genes from one gene to the other genes in the Vegf family.

SUBMITTER: Cui J

PROVIDER: S-EPMC6169370 | biostudies-other | 2018 Oct

REPOSITORIES: biostudies-other

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Data sets of eQTL loci, correlation analysis, and overlapped genes among gene sets that their expression levels are closely related to genes of Vegf family.

Cui Jinglin J Li Jing J Lu Hang H Yu Hong H Wei Dongmei D Jiao Yan Y Jablonski Monica M MM Gu Weikuan W Chen Hong H

Data in brief 20180908

This data is generated from the analysis of similarities and differences in gene expression levels and pathways among genes (VegfA, VegfB, VegfC, and Pgf) in Vegf family using whole genome expression data generated from normal retina of eighty strains of mice. The results have been published in doi:10.1016/j.exer.2018.06.024 (Cui et al., 2018) [1]. Fig. 1 shows the expression quantitative trait loci (eQTL) that regulate the expression level of each gene in the Vegf family. The other three figure ...[more]

PMID: 30294635

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Project description:Background:The HIN-200 family genes in humans have been linked to several autoimmune diseases-particularly to systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Recently, its human counterpart gene cluster, the Ifi200 family in mice, has been linked to spontaneous arthritis disease (SAD). However, many immune-mediated diseases (including RA and SLE) show gender difference. Understanding whether or not and how these genes play a role in sex difference in immune-mediated diseases is essential for diagnosis/treatment. Methods:This study takes advantage of the whole genome gene expression profiles of recombinant inbred (RI) strain populations from female and male mice to analyze potential sex differences in a variety of genes in disease pathways. Expression levels and regulatory QTL of Ifi200 family genes between female and male mice were first examined in a large mouse population, including RI strains derived from C57BL/6J, DBA/2J (BXD), and classic inbred strains. Sex similarities and differences were then analyzed for correlations with gene expression levels between genes in the Ifi200 family and four selected gene sets: known immune Ifi200 pathway-related genes, lupus-relevant genes, osteoarthritis- (OA-) and RA-relevant genes, and sex hormone-related genes. Results:The expression level of Ifi202b showed the most sex difference in correlation with known immune-related genes (the P value for Ifi202b is 0.0004). Ifi202b also showed gender difference in correlation with selected sex hormone genes, with a P value of 0.0243. When comparing coexpression levels between Ifi200 genes and lupus-relevant genes, Ifi203 and Ifi205 showed significant sex difference (P values: 0.0303 and 0.002, resp.). Furthermore, several key genes (e.g., Csf1r, Ifnb1, IL-20, IL-22, IL-24, Jhdm1d, Csf1r, Ifnb1, IL-20, IL-22, IL-24, and Tgfb2 that regulate sex differences in immune diseases) were discovered. Conclusions:Different genes in the Ifi200 family play different roles in sex difference among dissimilar pathways of these four gene groups.

Project description:BackgroundSalmonella are important human and animal pathogens. Though highly related, the Salmonella lineages may be strictly adapted to different hosts or cause different diseases, from mild local illness like gastroenteritis to fatal systemic infections like typhoid. Therefore, rapid and accurate identification of Salmonella is essential for timely and correct diagnosis of Salmonella infections. The current identification methods such as 16S rRNA sequencing and multilocus sequence typing are expensive and time consuming. Additionally, these methods often do not have sufficient distinguishing resolution among the Salmonella lineages.Methodologies/principal findingsWe compared 27 completely sequenced Salmonella genomes to identify possible genomic features that could be used for differentiation of individual lineages. We concatenated 2372 core genes in each of the 27 genomes and constructed a neighbor-joining tree. On the tree, strains of each serotype were clustered tightly together and different serotypes were unambiguously separated with clear genetic distances, demonstrating systematic genomic divergence among the Salmonella lineages. We made detailed comparisons among the 27 genomes and identified distinct sets of genomic differences, including nucleotide variations and genomic islands (GIs), among the Salmonella lineages. Two core genes STM4261 and entF together could unambiguously distinguish all Salmonella lineages compared in this study. Additionally, strains of a lineage have a common set of GIs and closely related lineages have similar sets of GIs.ConclusionsSalmonella lineages have accumulated distinct sets of mutations and laterally acquired DNA (e.g., GIs) in evolution. Two genes entF and STM4261 have diverged sufficiently among the Salmonella lineages to be used for their differentiation. Further investigation of the distinct sets of mutations and GIs will lead to novel insights into genomic evolution of Salmonella and greatly facilitate the elucidation of pathogeneses of Salmonella infections.

Dataset Information

Data sets of eQTL loci, correlation analysis, and overlapped genes among gene sets that their expression levels are closely related to genes of Vegf family.

Publications

Data sets of eQTL loci, correlation analysis, and overlapped genes among gene sets that their expression levels are closely related to genes of Vegf family.

Similar Datasets

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