Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:2.7.10.2 (focal adhesion kinase)
 44,029 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Genome-wide association studies (GWAS) underscore the genetic basis of multiple sclerosis (MS); however, only few of the newly reported genetic variations relevant in MS have been replicated or correlated for clinical/paraclinical phenotypes such as spinal cord atrophy in independent patient cohorts. We genotyped 141 MS patients for 58 variations reported to reach significance in GWAS. Expanded disability status scale (EDSS) and disease duration (DD) are available from regular clinical examinations. MRI included sagittal high-resolution 3D T1-weighted magnetization-prepared rapid acquisition gradient echo of the cervical cord region used for volumetry. Due dependency of mean upper cervical cord area (MUCCA) with EDSS and/or DD, correction operations were performed compensating for EDSS/DD. We assessed each MS risk locus for possible MUCCA association. We identified twelve risk loci that significantly correlated with MUCCA. For nine loci-BATF, CYP27B1, IL12B, NFKB1, IL7, PLEK, EVI5, TAGAP and nrs669607-patients revealed significantly higher degree of atrophy; TYK2, RGS1 and CLEC16A revealed inverse effects. The weighted genetic risk score over the twelve loci showed significant correlation with MUCCA. Our data reveal a risk gene depending paraclinical/clinical phenotype. Since MUCCA clearly correlates with disability, the candidates identified here may serve as prognostic markers for disability progression.
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PMID:Multiple sclerosis risk loci correlate with cervical cord atrophy and may explain the course of disability. 2562 May 46

Celiac disease (CD) is developed after gluten ingestion in genetically susceptible individuals. It can appear at any time in life, but some differences are commonly observed between individuals with onset early in life or in adulthood. We aimed to investigate the molecular basis underlying those differences. We collected 19 duodenal biopsies of children and adults with CD and compared the expression of 38 selected genes between each other and with the observed in 13 non-CD controls matched by age. A Bayesian methodology was used to analyze the differences of gene expression between groups. We found seven genes with a similarly altered expression in children and adults with CD when compared to controls (C2orf74, CCR6, FASLG, JAK2, IL23A, TAGAP and UBE2L3). Differences were observed in 13 genes: six genes being altered only in adults (IL1RL1, CD28, STAT3, TMEM187, VAMP3 and ZFP36L1) and two only in children (TNFSF18 and ICOSLG); and four genes showing a significantly higher alteration in adults (CCR4, IL6, IL18RAP and PLEK) and one in children (C1orf106). This is the first extensive study comparing gene expression in children and adults with CD. Differences in the expression level of several genes were found between groups, being notorious the higher alteration observed in adults. Further research is needed to evaluate the possible genetic influence underlying these changes and the specific functional consequences of the reported differences.
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PMID:Different Gene Expression Signatures in Children and Adults with Celiac Disease. 2685 34

Approximately half of the unexplained recurrent spontaneous abortions remain unexplained (URSAs). We aimed to provide novel insights into the biological characteristics and related pathways of differentially expressed genes (DE-genes), DE-methylated genes, and DE-miRNAs in URSA, and construct a molecular miRNAs-mRNAs network. Four data sets (GSE22490, GSE121950, GSE73025, and GSE43256) were gained from GEO data sets. We identified the DE-genes, DE-methylated genes, and DE-miRNAs using the LIMMA package in R software. Function and enrichment analyses were conducted using DAVID. A protein-protein network was performed by STRING. We predicted the target genes of DE-miRNA using DIANA-microT-CDS. Then, we constructed miRNAs-mRNAs network. There were 137 genes that overlapped in two expression profile data sets (GSE121950 and GSE22490). We found 10 overlapping DE-methylated genes and DE-genes with opposite expression alteration trends. All those 10 genes were hypermethylated lowly expressed genes. Pathway analysis illustrated that DE-genes were enriched in osteoclast differentiation, leishmaniasis, NF-kappa B signaling pathway, Toll-like receptor signaling pathway, and tuberculosis. Based on protein-protein interaction analysis, TLR8, TLR2, CD86, TLR4, IL10, CD163, FCGR1A, CXCL8, FCGR3A, HCK, PLEK, and MNDA were identified as hub genes for DE-genes. We screened out 47 DE-miRNAs and 42 overlapping DE-genes between predicted target genes of DE-miRNAs and the 137 DE-genes. We then constructed miRNAs-mRNAs network. This study identified several genes and miRNAs involved in the development and progression of URSA, including FCGR1A, FCGR3A, CXCL8, HCK, PLEK, IL10, hsa-miR-498, and hsa-miR-4530. Although further in vivo and in vitro validations are required, our results may provide a theoretical basis for future studies.
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PMID:Bioinformatics Analysis of Differentially Expressed Genes, Methylated Genes, and miRNAs in Unexplained Recurrent Spontaneous Abortion. 3130 34

Ovarian Cancer (OVCA) is the most occurring gynecological cancer worldwide, often diagnosed at a later stage and ultimate results in a high death rate. To overcome this serious health concern, it is important to understand the molecular mechanisms and equally significant to identify the putative biomarkers as well as the therapeutic drug targets for the early diagnosis and treatment of OVCA. In doing so, a strategy is designed to study the most frequently diagnosed cases of OVCA called as High-Grade Serous Ovarian Carcinoma (HGSOC) cell lines with the combination of computational biology, biostatistics and cancer informatics approaches. This study is directed to investigate the global gene expression profiling, and to perform the analyses of identified global Differently Expressed Genes (DEGs) of OVCA. The microarray dataset (GSE71524) is comprised of tumor and cell line samples of OVCA and it was used for the identification of DEGs in the current study. The STRING database was used to construct Protein-Protein Interaction (PPI) network of DEGs, and hub genes were identified by the CytoHubba. In addition, a functional enrichment analysis of up- and down-regulated DEGs was performed by a bioinformatics database called as DAVID. The microRNAs (miRNAs) and transcription factors (TFs) analyses were conducted with the aid of biological tools, MAGIA and GenCOdis3, respectively. As a result, the genes comprised of CSF1R, TYROBP, PLEK, FGR, ACLY, ACACA, LAPTM5, C1 or f162, IL10RA and CD163 were identified as hub genes. Additionally, miRNA analysis resulted in finding an association of zinc finger protein with OVCA comes out after implementing different algorithms. On the other hand, in the TFs analysis resulted in various DEGs that were enriched by NFAT, NF1 and GABP TFs. In this study, it was observed that ACACA, ACLY and CSF1R DEGs showed significant occurrence in different steps, and therefore, these genes were studied, precisely. Nevertheless, the results may help to discover the potential biomarkers with deep understanding of molecular mechanisms. However, further validation is required to explain the OVCA pathogenesis.
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PMID:Computational-based identification and analysis of globally expressed differential genes in high-grade serous ovarian carcinoma cell lines. 3273 84