NR1I3 CMV Expression Vector
- Known as:
- NR1I3 cytomegalovirus Expression Vector
- Catalog number:
- ME0025
- Product Quantity:
- 15 ug
- Category:
- -
- Supplier:
- Panomics
- Gene target:
- NR1I3 CMV Expression Vector
Related genes to: NR1I3 CMV Expression Vector
- Gene:
- NR1I3 NIH gene
- Name:
- nuclear receptor subfamily 1 group I member 3
- Previous symbol:
- -
- Synonyms:
- MB67, CAR1, CAR
- Chromosome:
- 1q23.3
- Locus Type:
- gene with protein product
- Date approved:
- 1999-09-23
- Date modifiied:
- 2015-11-18
Related products to: NR1I3 CMV Expression Vector
Related articles to: NR1I3 CMV Expression Vector
- To explore the mechanism of Bushen Yijing Formula (BSYJF) in the treatment of Alzheimer's disease (AD) through an integrated approach combining transcriptomics, network pharmacology, and molecular docking. - Source: PubMed
Zhang ZhezuoLi DeyuLiu JingLu LeiCai WenhuiYu Guruan - Environmental pollutant mixtures are potential risk factors for metabolic dysfunction-associated steatotic liver disease (MASLD), yet their joint effects in complex co-exposure scenarios and toxicological mechanisms remain incompletely elucidated. This cross-sectional study quantified 54 urinary environmental pollutants via mass spectrometry among 1036 participants from the Yinchuan elderly cohort. Logistic regression and mixed-exposure models evaluated epidemiological associations and joint effects. Biological knowledge-driven machine learning algorithms extracted mechanistic features. Bayesian weighted quantile sum regression showed that neonicotinoid and metal(loid) mixtures were significantly associated with elevated MASLD odds (OR = 1.73 (1.37, 2.20) and OR = 1.51 (1.22, 1.84), respectively, per quartile increase), with acetamiprid and zinc as the leading contributors. Interaction analysis identified significant synergistic effects between thiamethoxam and lead, and between nitenpyram and selenium. Machine learning models incorporating biological knowledge-graph features retained ITGAM (integrin alpha-M, CD11b) and LIF (leukemia inhibitory factor) as key target proteins and GO:0006558 and GO:0032125 as core pathways related to inflammatory signaling, DNA damage, and apoptosis. An adverse outcome pathway framework was constructed for six pollutants that were significantly positively associated with MASLD in logistic regression and retained by machine learning models (thallium, 3-hydroxycarbofuran, acetamiprid, zinc, dinotefuran, and thiamethoxam), linking estrogen receptor agonism and NR1I3 (constitutive androstane receptor, CAR) suppression to downstream DNA damage, oxidative stress, and apoptosis as key mechanistic routes. This study provides an integrative approach connecting population-level mixture exposures with mechanistic hypotheses, offering a scientific basis for the health risk assessment of environmental chemicals. SYNOPSIS: Epidemiological models combined with biological knowledge-driven machine learning identified key pollutant-protein-pathway associations, and an adverse outcome pathway framework was constructed to link these findings to mechanistic hypotheses. - Source: PubMed
Publication date: 2026/07/01
Fu JiamingQiao GuojieMa KerongJia JinhaoLi HonghuiBai ZeyangXiong LimengZhang YuhanGeng XiaozheDuan HongjuWang YuxiGao ChunqingLi XiaoyuZhao YiHu HaoWang GuangjunZhang RuiDi YihongYang HuifangSun Jian - Diethylene glycol dibenzoate (DEGDB), an emerging eco-friendly plasticizer, remains critically understudied with mechanistic and molecular-level evidence linking it to clear cell renal cell carcinoma (ccRCC) progression, representing a major knowledge gap. To fill this gap, we conducted a cross-scale investigation using network toxicology, WGCNA, machine learning, SHAP explainable modeling, and molecular docking as methodological tools to elucidate DEGDB‑induced ccRCC progression. By jointly mining the ChEMBL, PubChem, SwissADME, STRING, and GEO repositories, we rigorously distilled a high-confidence set of 42 target genes, among which TSHR, ADORA2B, ANPEP, CA9, CYP3A4, JUN, NR1I3, and PHGDH were highlighted. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that DEGDB may propel ccRCC progression by disrupting neuro-endocrine-immune network regulation, activating chemical carcinogenesis-related receptors, and perturbing metabolic-degradation pathways such as nitrogen metabolism and lysosomal signaling. Subsequently, 113 machine-learning algorithms were leveraged to construct predictive models, and SHAP-based interpretation pinpointed five core genes-CA9, NR1I3, PHGDH, GABRA2, and ANPEP. Validation against The Cancer Genome Atlas (TCGA) datasets demonstrated that CA9 exhibits marked expression divergence in ccRCC (box-plot analysis) and is strongly associated with unfavorable prognosis (Kaplan-Meier survival curves). Molecular-docking simulations further confirmed robust binding affinities between DEGDB and all five core target proteins (binding energies < -5 kcal/mol). In vitro assays additionally revealed that DEGDB significantly promotes 786-O and A498 proliferation and up-regulates CA9 protein expression. Collectively, our findings indicate that DEGDB accelerates ccRCC progression via the orchestrated modulation of cellular proliferation, disruption of neuro-endocrine-immune homeostasis, and activation of oncogenic receptors. This study provides a theoretical framework for assessing the environmentally relevant health risks posed by emerging plasticizers and for devising preventive strategies against DEGDB‑induced ccRCC under real‑world exposure scenarios. - Source: PubMed
Publication date: 2026/05/22
Yanping MaGuolin TianTao YangAngyang DuJiaxin LiBo TaoYajie LiYaodong JiaYuelong FengHao YangLihong NieRuining Zhao - Risk assessment and management of endogenous potentially toxic components are critical for promoting the rational clinical use of herbal medicines. However, most herbal medicines lack sufficient safety data in clinical, especially for those with multiple botanical sources. As a commonly used multi-botanical source herbal medicine, Uncariae Ramulus Cum Uncis-derived indole alkaloids (IA-URCU) are primarily responsible for its potential hepatotoxicity in clinical practice. Nevertheless, the underlying mechanism of URCU-induced hepatocyte injury remains unclear. - Source: PubMed
Publication date: 2026/04/06
Yang BinSun XueqianZhang XinyueWang ShuoWang YeWang YumingYang ShenshenShu LexinLi Yubo - Current in vitro protocols differentiating hepatocytes fail to activate mature metabolic genes, induce zone-specific phenotypes, and suppress fetal liver signatures. In this issue, Taguchi, Magalhães et al. used CRISPR-Cas9 screening in a mouse model of hepatic development to identify Nr1i3 and Nfix as regulators of hepatocyte maturation and zonation. - Source: PubMed
Stephan Tabea LHoodless Pamela A