Ask about this productRelated genes to: ARHGEF19 Blocking Peptide
- Gene:
- ARHGEF19 NIH gene
- Name:
- Rho guanine nucleotide exchange factor 19
- Previous symbol:
- -
- Synonyms:
- FLJ33962, WGEF
- Chromosome:
- 1p36.13
- Locus Type:
- gene with protein product
- Date approved:
- 2004-03-05
- Date modifiied:
- 2018-05-15
Related products to: ARHGEF19 Blocking Peptide
Related articles to: ARHGEF19 Blocking Peptide
- Several genome wide association studies (GWASs) of orofacial cleft have been conducted. However only a few such studies to date have combined all cleft cases, focused on subtypes other than non-syndromic cleft lip with/without cleft palate, or investigated subtype heterogeneity. We conducted a GWAS of orofacial clefts within 2268 cases from the Cleft Collective and 7913 population-based controls; we performed analyses of all orofacial clefts, plus 7 subgroups. We replicated our findings in a meta-analysis of independent samples and investigated patterns of correlation across subgroups. We identified 27 regions at genome-wide significance, 8 of which were novel. We also conducted the first GWAS of Pierre Robin Sequence, despite the small sample size (n cases = 237), we found one genome wide significant SNP (P < 5 × 10-8), and another 21 suggestive associations (P < 10-5). Novel loci include those mapping to LHX8 and TSBP1 (combined clefts), ARHGEF18 and ARHGEF19 (cleft lip with/without palate), FBN2 (cleft lip only), SLC35B3 (cleft palate only), CASC20 (Pierre Robin Sequence) and CHRM2 (non-syndromic cleft palate only). Several novel hits were in regions previously associated with facial morphology in GWAS or were in regions involved in key developmental processes, including neural crest cell migration and craniofacial development. We identified genetic loci with similar effects across all subgroups and some loci which were subtype specific, we also identified 3 loci with opposing effects on cleft lip and Pierre Robin sequence. Our findings highlight the merit of including all orofacial cleft subtypes in GWAS studies and investigating heterogeneity of effects across subtypes. - Source: PubMed
Dack KyleLudwig Kerstin UStergiakouli EvieSandy JonathanAryee SethlinaDavey Smith GeorgeDavies AmyWren YvonneSharp Gemma CHumphries KerryMangold ElisabethGoudswaard LucyHo KarenDudding TomLewis Sarah J - The cave morphs of have evolved a suite of distinct adaptations to life in perpetual darkness, including the loss of eyes and pigmentation loss, as well as profound metabolic changes such as hyperphagia and starvation resilience, traits that sharply contrast with those of their river-dwelling surface counterparts. While changed gene expression is a primary driver of these adaptations, the underlying role of 3D genome organization - a key regulator of gene expression - remains unexplored. Here, we investigate the 3D genome architecture of the livers of surface fish and two cavefish morphs (Pachón and Tinaja) using Hi-C, performing the first comparative 3D genomic analysis in this species. We analyzed and identified cave-specific 3D genomic features, such as genomic compartments and loops, which were conserved in both the cave populations but absent in surface fish. Integrating the 3D genome data with transcriptomic and epigenetic datasets, linked these changes to differential expression of metabolically relevant genes, such as and . Additionally, our study also uncovered genomic inversions unique to cavefish, potentially tied to cave adaptation. Our findings suggest that 3D genome organization contributes to transcriptomic shifts underlying cavefish phenotypes, providing a novel intra-species and morph specific perspective on 3D chromatin evolution. This study establishes a foundation for exploring how genome architecture potentially facilitates adaptation to new environments. Comparison of morphs within the same species also establishes a foundation for better understanding of how 3D genome reorganization may drive speciation and phenotypic diversity. - Source: PubMed
Publication date: 2025/05/13
Biswas TathagataLi HuaRohner Nicolas - Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer and a leading cause of cancer-related deaths worldwide. Using an integrative approach, we analyzed a publicly available merged NSCLC transcriptome dataset using machine learning, protein-protein interaction (PPI) networks and bayesian modeling to pinpoint key cellular factors and pathways likely to be involved with the onset and progression of NSCLC. First, we generated multiple prediction models using various machine learning classifiers to classify NSCLC and healthy cohorts. Our models achieved prediction accuracies ranging from 0.83 to 1.0, with XGBoost emerging as the best performer. Next, using functional enrichment analysis (and gene co-expression network analysis with WGCNA) of the machine learning feature-selected genes, we determined that genes involved in Rho GTPase signaling that modulate actin stability and cytoskeleton were likely to be crucial in NSCLC. We further assembled a PPI network for the feature-selected genes that was partitioned using Markov clustering to detect protein complexes functionally relevant to NSCLC. Finally, we modeled the perturbations in RhoGDI signaling using a bayesian network; our simulations suggest that aberrations in ARHGEF19 and/or RAC2 gene activities contributed to impaired MAPK signaling and disrupted actin and cytoskeleton organization and were arguably key contributors to the onset of tumorigenesis in NSCLC. We hypothesize that targeted measures to restore aberrant ARHGEF19 and/or RAC2 functions could conceivably rescue the cancerous phenotype in NSCLC. Our findings offer promising avenues for early predictive biomarker discovery, targeted therapeutic intervention and improved clinical outcomes in NSCLC. - Source: PubMed
Publication date: 2022/09/09
Gupta SaranshVundavilli HaswanthOsorio Rodolfo S AllendesItoh Mari NMohsen AttayebDatta AniruddhaMizuguchi KenjiTripathi Lokesh P - To assess the expression of ARHGEF19 in human breast cancer, investigate its role in breast cancer, and clarify the mechanism. - Source: PubMed
Publication date: 2021/11/23
Niu LigangZhou YuhuiZhang WeiYan YuRen Yu - The coexistence of coronary artery disease (CAD) and chronic kidney disease (CKD) implies overlapped genetic foundation. However, the common genetic determination between the two diseases remains largely unknown. Relying on summary statistics publicly available from large scale genome-wide association studies ( = 184,305 for CAD and = 567,460 for CKD), we observed significant positive genetic correlation between CAD and CKD ( = 0.173, = 0.024) via the linkage disequilibrium score regression. Next, we implemented gene-based association analysis for each disease through MAGMA (Multi-marker Analysis of GenoMic Annotation) and detected 763 and 827 genes associated with CAD or CKD (FDR < 0.05). Among those 72 genes were shared between the two diseases. Furthermore, by integrating the overlapped genetic information between CAD and CKD, we implemented two pleiotropy-informed informatics approaches including cFDR (conditional false discovery rate) and GPA (Genetic analysis incorporating Pleiotropy and Annotation), and identified 169 and 504 shared genes (FDR < 0.05), of which 121 genes were simultaneously discovered by cFDR and GPA. Importantly, we found 11 potentially new pleiotropic genes related to both CAD and CKD (i.e., , and ). Five of the newly identified pleiotropic genes were further repeated via an additional dataset CAD available from UK Biobank. Our functional enrichment analysis showed that those pleiotropic genes were enriched in diverse relevant pathway processes including quaternary ammonium group transmembrane transporter, dopamine transport. Overall, this study identifies common genetic architectures overlapped between CAD and CKD and will help to advance understanding of the molecular mechanisms underlying the comorbidity of the two diseases. - Source: PubMed
Publication date: 2020/12/03
Chen HaimiaoWang TingYang JinnaHuang ShuipingZeng Ping