Ask about this productRelated genes to: HSD17B6 Blocking Peptide
- Gene:
- HSD17B6 NIH gene
- Name:
- hydroxysteroid 17-beta dehydrogenase 6
- Previous symbol:
- -
- Synonyms:
- HSE, RODH, SDR9C6
- Chromosome:
- 12q13.3
- Locus Type:
- gene with protein product
- Date approved:
- 2005-02-23
- Date modifiied:
- 2016-06-03
Related products to: HSD17B6 Blocking Peptide
Related articles to: HSD17B6 Blocking Peptide
- Ulcerative colitis (UC) is a chronic inflammatory bowel disease with limited non-invasive biomarkers and variable responses to probiotics. This study investigates the probiotic potential of Enterococcus hirae Y-HS isolated from healthy beef cattle and its mechanisms in alleviating UC. In vitro probiotic properties of Y-HS were assessed. Public transcriptomic datasets (GSE179285, GSE87466, GSE206285) were analysed to identify differentially expressed genes in UC patients. Machine learning integrated with protein-protein interaction network analysis identified core diagnostic genes. A DSS-induced murine colitis model was established to evaluate Y-HS intervention effects. Y-HS exhibited excellent gastrointestinal tolerance, no haemolytic activity and antibiotic susceptibility. Transcriptomic analysis identified 768 DEGs in UC patients. Machine learning yielded four metabolism-associated signature genes-CYP3A4, UGT1A6, HSD17B6 and SRD5A3-with diagnostic accuracy (AUC 0.72-0.84). In DSS-induced colitis, Y-HS dose-dependently attenuated disease activity, remodelled gut microbiota (increasing Lactobacillus, decreasing Escherichia-Shigella), activated PXR/Nrf2 signalling, upregulated detoxification enzymes (CYP3A4, UGT1A6) and tight junction proteins, while downregulating HSD17B6, SRD5A3 and cleaved caspase-3. These changes were accompanied by reduced pro-inflammatory cytokines and elevated IL-10. E. hirae Y-HS alleviates UC through coordinated modulation of gut microbiota, host metabolism, inflammation and barrier function. The identified metabolic gene signature offers potential non-invasive biomarkers for UC. - Source: PubMed
Publication date: 2026/07/07
You FumingBao HanLi WentongZhang HanzhaoLi YangLi JiangSun MingaoYang YuxiaChao Luomeng - With the rapid expansion in the diversity and production of chemical, the associated environmental pollution and health risks have become increasingly prominent, underscoring the urgent need for rapid identification of potential high-risk contaminants. In this study, we propose an integrated computational framework for the rapid hazard assessment of emerging contaminants. N-(1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD), a widely used rubber antioxidant, was selected as a case study. A total of 24 transformation products were obtained, including both previously reported compounds (e.g., 6PPD-quinone) and newly predicted ones. Following structural optimization via Gaussian-based quantum chemical calculations, frontier molecular orbital analysis was conducted to elucidate plausible biotransformation mechanisms of previously unreported metabolites. Quantitative structure-activity relationship models for ten aquatic species were developed to construct species sensitivity distributions, yielding a predicted no-effect concentration (PNEC) of 1550 ng/L. Several metabolites exhibited higher toxicity than the parent compound. Reverse molecular docking was performed against 23,391 human proteins and integrated with physiologically based toxicokinetic modeling. The results revealed a predominance of hepatic bioactivity and high-affinity binding to key metabolic enzymes (CYP4F3, CYP2J2, HSD17B6, and HSD17B2), suggesting potential molecular initiating events. The preliminary zebrafish experiments provided supportive evidence for these potential mechanisms, including oxidative stress, inflammatory responses, and hepatic dysfunction, while no significant effects were observed at PNEC-level concentrations. Overall, this study demonstrates a practical and generalizable framework for early hazard screening of emerging contaminants, providing a valuable tool for environmental risk assessment and management. - Source: PubMed
Publication date: 2026/05/28
Ding TingtingMeng LinghaoZhang YahuiDai ZhuoyaDu ShilinWang XuedongYan JinQian QiuhuiWang ZejunWang Huili - Soy isoflavones are phytoestrogens that exhibit both estrogenic and/or antiestrogenic effects. This research investigated the potential of soy isoflavones as functional feed additives to promote ovarian development in female Chinese mitten crabs (). One hundred ninety-two crabs (101.52 ± 4.57 g) were randomly assigned to four groups (six replicates per group and eight crabs per replicate), and fed diets supplemented with 0.00, 32.51, 70.83, or 369.03 mg/kg soy isoflavones for 11 weeks. Compared to the control group, supplementation with 32.51 mg/kg soy isoflavones significantly increased the gonadosomatic index, hemolymph vitellogenin content, and the mRNA levels in the hepatopancreas and ovary ( < 0.05). Moreover, supplementation with 32.51 and 70.83 mg/kg soy isoflavones significantly promoted yolk granule formation ( < 0.05). At the molecular level, soy isoflavones modulated estradiol levels and activated the estrogen-related receptor signaling. They also upregulated the expression levels of , , and genes ( < 0.05), compared to the control group. Additionally, they increased estradiol synthesis through activating the cyclic adenosine monophosphate/PKA/CREB protein signaling pathway. High dose supplementation (369.03 mg/kg) did not significantly affect ovarian development ( > 0.05). Therefore, soy isoflavones exhibit a U-shaped effect on ovarian development of , with 32.51 mg/kg being an effective dose for promoting ovarian maturation. - Source: PubMed
Publication date: 2026/03/14
He LongLi JinpingCao DexiangLiu ZhijunQin ChuanjieWang XiaodanQin JianguangLi ErchaoChen Liqiao - The scarcity of reliable biomarkers and predictive models for platinum resistance in lung adenocarcinoma (LUAD) poses a significant clinical challenge. This study endeavors to identify molecular subtypes related to platinum resistance and construct a robust predictive model through multi-omics techniques. We performed integrative analysis of public datasets using advanced bioinformatics strategies, including spatial transcriptome deconvolution and consensus clustering. Bulk RNA deconvolution analysis was conducted to characterize tumor microenvironment heterogeneity. Feature selection was performed using the Supervised Principal Component (SuperPC) algorithm, followed by diagnostic model construction validated through receiver operating characteristic (ROC) analysis. Functional validation was performed through cytological experiments measuring cisplatin IC50 alterations following gene manipulation in LUAD cell lines. Consensus clustering revealed distinct LUAD subtypes, with Cluster1 demonstrating significant platinum resistance. We first subtyped the patients in the bulk transcriptome data based on consistency clustering, and then analyzed the differences between different platinum-resistant subtypes (Cluster 1 and Cluster 2), so as to screen 333 isotype-specific differentially expressed genes and 15 platinum resistance-related (PRR) genes were selected through machine learning. A refined 5-gene signature (ANKRD29/CACNA2D2/DSP/HSD17B6/SPP1) achieved exceptional predictive performance (AUC = 0.9639). Spatial transcriptomics demonstrated compartmentalized expression patterns: SPP1/DSP localized to tumor niches, HSD17B6/CACNA2D2 to epithelial regions, and ANKRD29 depletion in stromal areas. Cellular colocalization analysis revealed malignant epithelial PH proximity to myeloid and mast cells. Functional validation confirmed that ANKRD29/CACNA2D2 overexpression sensitized A549/DDP cells to cisplatin, while DSP/SPP1/HSD17B6 overexpression induced resistance. Experiments in nude mice have shown that these genes are closely related to cisplatin resistance in LUAD. This study identifies the Cluster1 subtype and malignant epithelial PH as crucial determinants of platinum resistance in LUAD. Our innovative 5-gene predictive model exhibits clinical-grade diagnostic accuracy, and spatial transcriptomic characterization offers mechanistic insights into the dynamics of the tumor microenvironment. - Source: PubMed
Publication date: 2026/02/07
Chen JieChen YixinLu YiHe YuyuJiang FengHu LijuanWang Yumin - Phalloidin is a lethal toxin found in highly toxic mushrooms of the genus Amanita; it causes severe liver injury, yet its molecular mechanism has not been systematically elucidated. In this study, Male Kunming mice received a single intraperitoneal injection of Phalloidin (0.3 mg/kg and 0.6 mg/kg). we predicted Phalloidin targets by network toxicology and explored its toxicity mechanism by combining metabolomics and proteomics in the livers of Phalloidin-toxicized mice. Metabolomic and proteomic profiling revealed that Phalloidin broadly perturbed hepatic metabolism. Specifically, it down-regulated nucleotide metabolism, coenzyme A synthesis and phospholipid metabolism, and reduced the DNA-replication-related MCM family proteins. Concomitantly, the lipid-metabolism proteins Hsd17b6 and Dhcr24 were aberrantly expressed. These alterations collectively promoted hepatocyte death. Integrative multi-omics analysis further identified abnormal expression of ten core regulatory targets, including the cell-cycle checkpoint protein Cdkn1b, the MAPK signaling proteins Mapk14 and Map2k2, and the insulin signaling components Insr and Igf2r. Functional enrichment indicated that Phalloidin synergistically induces hepatic injury by interfering with three key pathways: the FoxO, phospholipase D and cAMP signaling cascades. Molecular-docking simulations confirmed high-affinity binding between Phalloidin and these core targets, providing direct evidence of their physical interaction. Collectively, this study systematically delineates the molecular network underlying Phalloidin-induced liver injury and proposes these pathways as potential therapeutic targets for Amanita mushroom poisoning. - Source: PubMed
Publication date: 2025/11/20
Lv ShaofangGong YaozhenGuo HuaZheng ChongYe JianfangZou LuZhu KaiLi HaichangLi LeiDong Yongxi