Nr1h4
- Known as:
- Nr1h4
- Catalog number:
- 043760A
- Product Quantity:
- 250ul
- Category:
- -
- Supplier:
- ABM
- Gene target:
- Nr1h4
Related genes to: Nr1h4
- Gene:
- NR1H4 NIH gene
- Name:
- nuclear receptor subfamily 1 group H member 4
- Previous symbol:
- -
- Synonyms:
- FXR, RIP14, HRR1, HRR-1
- Chromosome:
- 12q23.1
- Locus Type:
- gene with protein product
- Date approved:
- 1999-09-17
- Date modifiied:
- 2018-03-06
Related products to: Nr1h4
anti-Bile Acid Receptor NR1H4anti-Bile Acid Receptor NR1H4anti-Bile Acid Receptor NR1H4 (1B10)anti-Bile Acid Receptor NR1H4 (1B10)anti-Bile Acid Receptor NR1H4 type: Primary antibodies host: Mouseanti-NR1H4 (1G11)anti-NR1H4 (1G11), Mouse monoclonal to NR1H4, Isotype IgG1, Host Mouseanti-NR1H4(1G11)anti-NR1H4(1G11) type: Primary antibodies host: MouseBovine Bile acid receptor(NR1H4) ELISA kitBovine Bile acid receptor(NR1H4) ELISA kitBovine Bile acid receptor(NR1H4) ELISA kit SpeciesBovineBovine nuclear receptor subfamily 1, group H, member 4 (NR1H4) ELISA kitBovine nuclear receptor subfamily 1, group H, member 4 (NR1H4) ELISA kit, Species Bovine, Sample Type serum, plasmaCanine Bile acid receptor(NR1H4) ELISA kit Related articles to: Nr1h4
- No standardized computational pipeline exists for systematically prioritizing microbial metabolite-associated host genes and protein-ligand complexes from publicly available chemical, genomic, and structural databases. This article describes an eight-stage workflow that accepts a user-defined set of gut microbiota-derived metabolites and produces a ranked shortlist of candidate metabolite-associated host genes, enriched biological pathways, and structurally prioritized protein-ligand complexes for experimental follow-up. The pipeline integrates (i) chemoinformatic metabolite profiling; (ii) multi-database candidate target prediction using protein-chemical interaction and ligand-based target-prediction tool and a molecular docking program; (iii) differential gene expression analysis of publicly available transcriptomic data; (iv) target-differentially expressed gene overlap; (v) protein-protein interaction network construction and pathway enrichment; (vi) molecular docking with a molecular docking program; (vii) 200 ns molecular dynamics simulation using a molecular dynamics engine with a protein force field used for molecular dynamics simulations; and (viii) MM-PBSA binding free-energy estimation. As a worked example, nine gut microbiota-derived or microbiota-modified metabolites representing short-chain fatty acids, bile acids, tryptophan-derived metabolites, and urolithin A were processed using the public IBS-C rectal mucosal transcriptomic dataset GSE36701. The workflow ranked 17 unique predicted metabolite-associated genes that were differentially expressed in this dataset. Docking, molecular dynamics simulation, and MM-PBSA analyses structurally prioritized five metabolite-protein complexes: lithocholic acid-VDR, lithocholic acid-NR1H4/FXR, ursodeoxycholic acid-NR1H4/FXR, tryptamine-HTR2A (simulated in an explicit 1-Palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid bilayer), and urolithin A-CASP3. The protocol is designed to be adaptable to other metabolite sets, disease transcriptomic datasets, and target classes; all outputs are hypothesis-generating computational predictions that require independent transcriptomic replication, protein-level validation, and functional ligand-response assays before causal or therapeutic conclusions can be drawn. - Source: PubMed
Publication date: 2026/08/14
Alshamrani Ayed A - In vitro maturation (IVM) of germinal vesicle oocytes may broaden assisted reproductive technologies; however, developmental competence remains limited by the lack of standardized follicular somatic support. We generated an endogenous FOXL2-P2A-tdTomato reporter human induced pluripotent stem cell (hiPSC) line using CRISPR/Cas9 knock-in and reporter-guided transcription factor (TF) programming to produce fetal granulosa-like cells (FGLCs). A flow cytometric TF screen identified TCF21, WT1-KTS, and NR1H4 as the strongest FOXL2 inducers, and combinatorial optimization showed that removing TOX3/ETV5 and adding NR2F2 substantially increased the FOXL2-positive fraction. Bulk RNA-seq, principal component analysis, and TF activity inference positioned the induced cells close to early-gestational human fetal granulosa cell profiles. In a mouse IVM, in vitro fertilization (IVF), blastocyst culture, and embryo transfer pipeline, FGLC supplementation did not change nuclear maturation rates but improved downstream development, most clearly increasing offspring production from cumulus-oocyte complexes. A comprehensive safety assessment of F0 offspring derived from FGLC-treated oocytes revealed a normal sex ratio, postnatal growth, gross anatomy, gonadal histology, and modified SHIRPA neurobehavioral profiles. Next-generation natural mating and germ cell assays demonstrated preserved fertility, normal IVF outcomes, and normal sperm parameters in the F1 generation. These results establish a scalable strategy for generating fetal granulosa-like supporting cells from hiPSCs and provide an effective and multi-generational safety framework for oocyte-contact IVM interventions. - Source: PubMed
Publication date: 2026/07/24
Imura-Kishi KasaneYamaga KatsumaSoeda ShouMasuda KeisukeMizoue YukiKoga ReiriTorigoe DaisukeMiyazaki HirokiSato IoriHikabe OrieKojima KazuakiTakeo ToruHamazaki NobuhikoSeita Yasunari - N-(1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6PPD-Q), a tire rubber antioxidant derivative, accumulates in air, soil, and water and has been found in urine, blood, and cerebrospinal fluid, posing significant health risks. Although 6PPD-Q exhibits intestinal toxicity, its role in inflammatory bowel disease (IBD) remains unclear. The objective of this study was to identify key molecular targets of 6PPD-Q in IBD and to validate their involvement in 6PPD-Q-induced intestinal epithelial cell injury. Using network toxicology, machine learning, molecular docking, and in vitro experiments in human intestinal epithelial cells, we identified 60 overlapping 6PPD-Q-IBD targets, enriched in lipid metabolism, oxidative stress, and inflammation. Multi-model machine learning screened six core genes (NR1H4, ANXA5, SPARC, PCK1, PDK2, and CFB), with NR1H4 as a key mediator. Molecular docking showed strong binding of 6PPD-Q to NR1H4, exceeding that of its parent compound. In vitro experiments confirmed that 6PPD-Q caused lipid droplet and cholesterol accumulation, mitochondrial dysfunction (manifested as ATP synthesis inhibition, mitochondrial ROS burst, decreased membrane potential, and mitochondrial fragmentation), and significantly upregulated the levels of pro-inflammatory cytokines IL-6, TNF-α, and IL-8, thereby triggering inflammatory responses. Moreover, 6PPD-Q exposure significantly downregulated NR1H4 expression. These findings reveal that 6PPD-Q increases IBD risk by interfering with lipid metabolism, disrupting mitochondrial function, upregulating inflammatory cytokines, and downregulating NR1H4, providing important evidence for understanding the risk posed by this emerging environmental pollutant to IBD and for developing preventive strategies. - Source: PubMed
Publication date: 2026/07/28
Wu PeiwenHu WeibinChen GangZhao XuZhang Xiaozhi - - Source: PubMed
Publication date: 2026/07/28
Hsairi ManelFeki ManelGuirat ManelLoukil MounaCharfi SlimSafi FaizaGargouri LamiaKhabou Boudour - The genetic dissection of complex traits in livestock continues to pose a significant challenge in the field of animal genetics and breeding. Although traditional genome-wide association studies (GWAS) are capable of localizing genetic variants associated with specific traits, they are insufficient to elucidate the underlying physiological mechanisms. An integrated analysis of multi-trait GWAS and multi-transcriptomic data systematically identifies key tissues and cell types influencing complex traits in beef cattle and elucidates their genetic regulatory basis. - Source: PubMed
Publication date: 2026/07/28
Zhang BoyuQiu ShiyuanDu ZhenweiXiao HaoSu YingxiaoTang AnyiBao BinwuZhu BoChen YanGao XueXu LingyangWang ZezhaoZhang LupeiGao HuijiangLi JunyaZheng Caihong