Ask about this productRelated genes to: SLC22A8 Blocking Peptide
- Gene:
- SLC22A8 NIH gene
- Name:
- solute carrier family 22 member 8
- Previous symbol:
- -
- Synonyms:
- OAT3
- Chromosome:
- 11q12.3
- Locus Type:
- gene with protein product
- Date approved:
- 1999-07-30
- Date modifiied:
- 2016-02-17
Related products to: SLC22A8 Blocking Peptide
Related articles to: SLC22A8 Blocking Peptide
- The arachnoid mater-forming cells, which exhibit expressions of organic anion and cation transporters, as well as tight junction proteins, form the blood-arachnoid barrier. The purpose of the present study was to clarify the roles of the arachnoid mater transporters in the cerebrospinal fluid (CSF) clearance of prostaglandin D (PGD) and metformin by using an intracisternal administration method for evaluating arachnoid mater-mediated transport in rats. Global proteomics of rat leptomeninges showed the transporter expression of Slc22a6/Oat1, Slc22a8/Oat3, Slco2a1/Oatp2a1, Slc22a2/Oct2, and Slco2b1/Oatp2b1. The CSF clearance of PGD and metformin was greater than that of FITC-inulin, which is a marker of CSF bulk flow and parenchymal diffusion. The CSF elimination of PGD was significantly inhibited by p-aminohippuric acid, diclofenac (substrates/inhibitors of Slc22a6/Oat1 and Slc22a8/Oat3), and taurocholate (a broad substrate/inhibitor of Slco family transporters), whereas that of metformin was blocked by cimetidine, tetraethylammonium, and thiamine (substrates/inhibitors of Slc22a2/Oct2). The CSF-to-circulating blood transfer of metformin was inhibited by tetraethylammonium. These results suggested that the PGD clearance from the CSF most likely occurred via Slc22a6/Oat1, Slc22a8/Oat3, Slco2a1/Oatp2a1, and Slco2b1/Oatp2b1, while the metformin clearance was primarily mediated by Slc22a2/Oct2, in the arachnoid mater. These transporters would regulate the CSF concentrations of pharmacologically active substances and drugs. - Source: PubMed
Publication date: 2026/04/17
Yaguchi YukaSasaki KazunariInagaki MaiTerasaki TetsuyaTachikawa Masanori - Short-chain fatty acids (SCFAs) and medium-chain fatty acids (MCFAs) include small organic anions derived from the gut microbiome that interact with organic anion transporters of the SLC22 family, many of which are expressed in the kidney proximal tubule. According to the Remote Sensing and Signaling Theory (RSST), crosstalk between organs (e.g., gut-liver-kidney axis, gut-brain axis) and the gut microbiome is mediated by metabolites and signaling molecules transported by multi-specific "drug" transporters. The renal drug transporter OAT1 (SLC22A6) is also a major transporter of gut-microbiome products and uremic toxins (e.g., indoxyl sulfate); it has been shown to act as part of a regulatory feedback loop involving the gut microbiome. SCFAs, especially propionate and butyrate, have been shown to play a central role in the transcriptional regulation of OAT1 through HDAC inhibition. By fecal metagenomics analyses of knockout mice, we now find that propionate synthesis is among the most altered pathways in the gut microbiome. In contrast, these pathways were only minimally altered in the Oat3 (Slc22a8) knockout. Metabolomics analyses indicate that serum propionate derivatives (e.g., propionyl glycine) and 3-hydroxybutyrate are dependent on OAT1 in the knockout mice and in humans treated with probenecid, an OAT1 inhibitor. The gut microbiome of the knockout mice also exhibited greater fatty acid synthesis, which generates odd-chain-length fatty acids (e.g. heptanoate) when propionate is available. Overall, the data, especially when considered in light of in vitro experiments of others, indicates the in vivo existence of a feedback loop connecting gut-microbiome-derived SCFAs and MCFAs to kidney proximal tubule uptake via OAT1. This bidirectional feedback loop in turn regulates OAT1 expression through HDAC inhibition. The feedback loop is clearly consistent with the Remote Sensing and Signaling Theory-in particular, the centrality of multi-specific "drug" transporters in organ crosstalk and host-microbiome interactions via small molecules with "high information content." The key role of OAT1 function in maintaining tubular secretion in CKD supports the importance of this RSST loop in renal pathophysiology. Modulating this RSST loop could have therapeutic value in chronic kidney disease and other contexts. - Source: PubMed
Publication date: 2026/05/29
Ermakov Vladimir SFalah KianNigam Sanjay K - Organic anion transporters (OATs, SLC22) in the kidney and organic anion-transporting polypeptides (OATPs, SLCO) in the liver play crucial roles in the disposition of small molecule drugs that are organic anions. According to the Remote Sensing and Signaling Theory, these multispecific "drug" transporters are also central to crosstalk between the liver, kidney, and other organs via endogenous small molecules (e.g., metabolites, signaling molecules, gut microbiome products). These multispecific drug transporters govern access of small molecules with high informational content across multiple scales (organism to organelle). Previous chemoinformatic and machine learning methods have proven useful for identifying molecular properties of organic anion drugs that predispose them to handling by the OAT (renal) and the OATP (hepatic) transporters. This is important for understanding pharmacokinetics (ADME) in the context of chronic kidney disease (CKD) and liver disease. Given that OATs and OATPs are involved in many metabolic diseases, we sought to determine whether molecular properties could be identified for distinguishing OAT- versus OATP-interacting endogenous metabolites . This is essential for understanding endogenous small molecule communication between the kidney proximal tubule and hepatocytes in a larger Remote Sensing and Signaling System. We analyzed metabolomics data from OAT and OATP knockout mice, focusing on endogenous metabolites selective for OATs (e.g., OAT1 or SLC22A6; OAT3 or SLC22A8) vs OATPs (including the locus containing Oatp1b2, the closest homologue of human OATP1B1 or SLCO1B1 and OATP1B3 or SLCO1B3). Applying chemoinformatic methods to a data set of 210 metabolites based on knockout mouse metabolomics (92 OAT-selective, 118 OATP-selective), we identified a set of distinguishing molecular properties (e.g., MolLogP, RingCount, NumRotatableBonds). We then used machine learning approaches (e.g., Random Forest, Naive Bayes, Logistic Regression) to classify OAT vs OATP metabolites, achieving over 75% accuracy. These results support the view that transporter knockout mouse metabolomics can help define selectivity of SLC drug transporters for endogenous metabolites, signaling molecules, antioxidants, nutrients, and gut microbiome products. In the context of the Remote Sensing and Signaling Theory, we discuss the implications for understanding organ crosstalk and interorganismal communication as well as drug disposition, drug-metabolite interactions, and metabolite-based drug design. - Source: PubMed
Publication date: 2026/04/16
Nigam Anisha KFalah KianMomper Jeremiah DNigam Sanjay K - Ochratoxin A (OTA), a prevalent food contaminant, is closely linked to the development of various cancers, including clear cell renal cell carcinoma (ccRCC). However, the potential mechanisms remain to be explored. In this study, we employed network toxicology, machine learning, and molecular docking techniques to systematically investigate the potential molecular mechanisms underlying OTA-associated ccRCC. We normalized transcriptional data from two Gene Expression Omnibus (GEO) datasets and analyzed it using differential expression analysis and weighted gene co-expression network analysis (WGCNA), identifying 3224 ccRCC-associated target genes. These were intersected with 232 predicted OTA target genes, yielding a total of 56 overlapping targets. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that these targets were primarily enriched in critical biological processes, including extracellular matrix remodeling, immune microenvironment regulation, signaling pathway transduction, cellular metabolism, and protein homeostasis. Machine learning analysis identified "glmBoost + RF" (a sequential combination of feature selection and classifier) as the optimal model, from which nine key genes were extracted. SHapley Additive exPlanations (SHAP) analysis revealed five core genes (, , , , ), with and serving as the principal driver genes of the model. Validation of the model's diagnostic efficacy and single-cell transcriptome analysis indicated that the core genes exhibited significant differential expression patterns, cell-type-specific expression characteristics, and high independent diagnostic efficacy. Molecular docking analyses predicted stable interactions between OTA and the core target proteins. These findings suggest potential molecular links between OTA exposure and ccRCC, providing a foundation for hypothesis generation and future experimental validation. - Source: PubMed
Publication date: 2026/03/25
Huang ChenjieWei LuluYuan WenqiLu YaohongYan ZiyouZhang Gedi - Ampicillin (AMP) is an organic anion drug widely used in clinical setting as a β-lactam antibiotic. However, the specific transporter involved in mediating AMP transport remains unidentified. Thus, we investigated whether organic anion transporters1/3 (OAT1/3) mediate the renal transport of AMP in this study. Both rOAT1/OAT3 (Slc22a6/Slc22a8) double-knockout and wild-type (WT) rats were administered AMP via intraperitoneal injection simultaneously. Following the knockout, a significant increase in AMP plasma concentration and the area under the plasma concentration-time curve (AUC) was observed, accompanied by a marked reduction in cumulative urinary excretion. OAT1/3-overexpressing cell uptake experiments demonstrated that AMP is a substrate of OAT3, with a Michaelis-Menten constant (K) of 138.6 μM and a maximum transport velocity (V) of 80.43 pmol/mg protein/min. In conclusion, AMP was identified as a substrate of OAT3, rather than OAT1. - Source: PubMed
Publication date: 2026/02/19
Liu Yu-TingGou Xue-YanGan LuMaLiu Yi-MaiMa Yan-RongWu Xin-An