Gene expression meta-analysis reveals the up-regulation of CREB1 and CREBBP in Brodmann Area 10 of patients with schizophrenia

2020 ◽  
Vol 292 ◽  
pp. 113311
Author(s):  
Shay Ohayon ◽  
Assif Yitzhaky ◽  
Libi Hertzberg
Keyword(s):  
Gene Expression ◽  
Meta Analysis ◽  
Brodmann Area

scholarly journals Expression of DDX11 and DNM1L at the 12p11 Locus Modulates Systemic Lupus Erythematosus Susceptibility

2021 ◽  
Vol 22 (14) ◽  
pp. 7624
Author(s):  
Mohammad Saeed ◽  
Alejandro Ibáñez-Costa ◽  
Alejandra María Patiño-Trives ◽  
Laura Muñoz-Barrera ◽  
Eduardo Collantes Estévez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lupus Erythematosus ◽  
Genome Stability ◽  
Meta Analysis ◽  
Susceptibility Gene ◽  
Significant Snps ◽  
Differentially Expressed ◽  
Systemic Lupus Erythematosus Susceptibility ◽  
Functional Pathways ◽  
Functional Analyses

Objectives: This study employed genetic and functional analyses using OASIS meta-analysis of multiple existing GWAS and gene-expression datasets to identify novel SLE genes. Methods: Four hundred and ten genes were mapped using SNIPPER to 30 SLE GWAS loci and investigated for expression in three SLE GEO-datasets and the Cordoba GSE50395-dataset. Blood eQTL for significant SNPs in SLE loci and STRING for functional pathways of differentially expressed genes were used. Confirmatory qPCR on SLE monocytes was performed. The entire 12p11 locus was investigated for genetic association using two additional GWAS. Expression of 150 genes at this locus was assessed. Based on this significance, qPCRs for DNM1L and KRAS were performed. Results: Fifty genes were differentially expressed in at least two SLE GEO-datasets, with all probes directionally aligned. DDX11, an RNA helicase involved in genome stability, was downregulated in both GEO and Cordoba datasets. The most significant SNP, rs3741869 in OASIS locus 12p11.21, containing DDX11, was a cis-eQTL regulating DDX11 expression. DDX11 was found repressed. The entire 12p11 locus showed three association peaks. Gene expression in GEO datasets identified DNM1L and KRAS, besides DDX11. Confirmatory qPCR validated DNM1L as an SLE susceptibility gene. DDX11, DNM1L and KRAS interact with each other and multiple known SLE genes including STAT1/STAT4 and major components of IFN-dependent gene expression, and are responsible for signal transduction of cytokines, hormones, and growth-factors, deregulation of which is involved in SLE-development. Conclusion: A genomic convergence approach with OASIS analysis of multiple GWAS and expression datasets identified DDX11 and DNM1L as novel SLE-genes, the expression of which is altered in monocytes from SLE patients. This study lays the foundation for understanding the pathogenic involvement of DDX11 and DNM1L in SLE by identifying them using a systems-biology approach, while the 12p11 locus harboring these genes was previously missed by four independent GWAS.

scholarly journals A Comprehensive Gene Expression Meta-analysis Identifies Novel Immune Signatures in Rheumatoid Arthritis Patients

2017 ◽  
Vol 8 ◽  
Author(s):  
Sumbul Afroz ◽  
Jeevan Giddaluru ◽  
Sandeep Vishwakarma ◽  
Saima Naz ◽  
Aleem Ahmed Khan ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Meta Analysis ◽  
Immune Signatures

Gene Expression Profiles as Predictors of Poor Outcomes in Stage II Colorectal Cancer: A Systematic Review and Meta-analysis

2009 ◽  
Vol 8 (4) ◽  
pp. 207-214 ◽  
Author(s):  
An-Ting T. Lu ◽  
Shelley R. Salpeter ◽  
Anthony E. Reeve ◽  
Steven Eschrich ◽  
Patrick G. Johnston ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Stage Ii ◽  
Stage Ii Colorectal Cancer ◽  
Poor Outcomes

scholarly journals HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 244 ◽  
Author(s):  
Antonio Victor Campos Coelho ◽  
Rossella Gratton ◽  
João Paulo Britto de Melo ◽  
José Leandro Andrade-Santos ◽  
Rafael Lima Guimarães ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Differentially Expressed Genes ◽  
Cd4 T Cells ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Infected Cells ◽  
Hiv 1

HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently differentially expressed genes in the context of HIV-1 infection. We selected two studies (22 samples: 15 experimentally infected and 7 mock-infected). We found 208 differentially expressed genes in infected cells when compared to uninfected/mock-infected cells. This result had moderate overlap when compared to previous studies of HIV-1 infection transcriptomics, but we identified 64 genes already known to interact with HIV-1 according to the HIV-1 Human Interaction Database. A gene ontology (GO) analysis revealed enrichment of several pathways involved in immune response, cell adhesion, cell migration, inflammation, apoptosis, Wnt, Notch and ERK/MAPK signaling.

scholarly journals Multi-species transcriptome meta-analysis of the response to retinoic acid in vertebrates and comparative analysis of the effects of retinol and retinoic acid on gene expression in LMH cells

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Clemens Falker-Gieske ◽  
Andrea Mott ◽  
Sören Franzenburg ◽  
Jens Tetens
Keyword(s):  
Gene Expression ◽  
Retinoic Acid ◽  
Cellular Response ◽  
Homo Sapiens ◽  
Meta Analysis ◽  
Early Response ◽  
Rna Seq ◽  
Transcriptomic Response ◽  
Time Points ◽  
Lmh Cells

Abstract Background Retinol (RO) and its active metabolite retinoic acid (RA) are major regulators of gene expression in vertebrates and influence various processes like organ development, cell differentiation, and immune response. To characterize a general transcriptomic response to RA-exposure in vertebrates, independent of species- and tissue-specific effects, four publicly available RNA-Seq datasets from Homo sapiens, Mus musculus, and Xenopus laevis were analyzed. To increase species and cell-type diversity we generated RNA-seq data with chicken hepatocellular carcinoma (LMH) cells. Additionally, we compared the response of LMH cells to RA and RO at different time points. Results By conducting a transcriptome meta-analysis, we identified three retinoic acid response core clusters (RARCCs) consisting of 27 interacting proteins, seven of which have not been associated with retinoids yet. Comparison of the transcriptional response of LMH cells to RO and RA exposure at different time points led to the identification of non-coding RNAs (ncRNAs) that are only differentially expressed (DE) during the early response. Conclusions We propose that these RARCCs stand on top of a common regulatory RA hierarchy among vertebrates. Based on the protein sets included in these clusters we were able to identify an RA-response cluster, a control center type cluster, and a cluster that directs cell proliferation. Concerning the comparison of the cellular response to RA and RO we conclude that ncRNAs play an underestimated role in retinoid-mediated gene regulation.

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