Background: Organic anion transporting polypeptide (OATP) 1B1, OATP1B3, and OATP2B1 (encoded by SLCO1B1, SLCO1B3, SLCO2B1) mediate the hepatic uptake of endogenous compounds like bile acids and of drugs, for example, the lipid-lowering atorvastatin, thereby influencing hepatobiliary elimination. Here we systematically elucidated the contribution of SLCO variants on expression of the three hepatic OATPs under consideration of additional important covariates. Methods: Expression was quantified by RT-PCR and immunoblotting in 143 Caucasian liver samples. A total of 109 rare and common variants in the SLCO1B3-SLCO1B1 genomic region and the SLCO2B1 gene were genotyped by MALDI-TOF mass spectrometry and genome-wide SNP microarray technology. SLCO1B1 haplotypes affecting hepatic OATP1B1 expression were associated with pharmacokinetic data of the OATP1B1 substrate atorvastatin (n = 82). Results: Expression of OATP1B1, OATP1B3, and OATP2B1 at the mRNA and protein levels showed marked interindividual variability. All three OATPs were expressed in a coordinated fashion. By a multivariate regression analysis adjusted for non-genetic and transcription covariates, increased OATP1B1 expression was associated with the coding SLCO1B1 variant c.388A > G (rs2306283) even after correction for multiple testing (P = 0.00034). This held true for haplotypes harboring c.388A > G but not the functional variant c.521T > C (rs4149056) associated with statin-related myopathy. c.388A > G also significantly affected atorvastatin pharmacokinetics. SLCO variants and non-genetic and regulatory covariates together accounted for 59% of variability of OATP1B1 expression. Conclusions: Our results show that expression of OATP1B1, but not of OATP1B3 and OATP2B1, is significantly affected by genetic variants. The SLCO1B1 variant c.388A > G is the major determinant with additional consequences on atorvastatin plasma levels.

Background: Organic cation transporters (OCTs) determine not only physiological processes but are also involved in the cellular uptake of anticancer agents. Based on microarray analyses in hepatocellular carcinoma (HCC), SLC22A1/OCT1 mRNA seems to be downregulated, but systematic protein expression data are currently missing. Moreover, the underlying molecular mechanisms responsible for altered SLC22A1 expression in HCC are not fully understood. Therefore, we investigated the role of DNA methylation in the transcriptional regulation of the family members SLC22A1/OCT1, SLC22A2/OCT2 and SLC22A3/OCT3 in HCC. Methods: Semiquantitative immunohistochemistry of SLC22A1 protein expression was performed in paired HCC and histological normal adjacent liver tissues (n = 71) using tissue microarray analyses, and the results were correlated with clinicopathological features. DNA methylation, quantified by MALDI-TOF mass spectrometry and gene expression of SLC22A1, SLC22A2 and SLC22A3 were investigated using fresh-frozen HCC (n = 22) and non-tumor adjacent liver tissues as well as histologically normal liver samples (n = 120) from a large-scale liverbank. Results: Based on tissue microarray analyses, we observed a significant downregulation of SLC22A1 protein expression in HCC compared to normal adjacent tissue (P < 0.0001). SLC22A1 expression was significantly inverse correlated with expression of the proliferation marker MIB1/Ki-67 (r_s = -0.464, P < 0.0001). DNA methylation of SLC22A1 was significantly higher in HCC compared with non-tumor adjacent liver tissue and was lowest in histologically normal liver tissue. Methylation levels for SLC22A1 in combination with RASSF1A resulted in a specificity of > 90% and a sensitivity of 82% for discriminating HCC and tumor-free liver tissue. Conclusions: DNA methylation of SLC22A1 is associated with downregulation of SLC22A1 in HCC and might be a new biomarker for HCC diagnosis and prognosis. Moreover, targeting SLC22A1 methylation by demethylating agents may offer a novel strategy for anticancer therapy of HCC.

As metabolic rewiring is crucial for cancer cell proliferation, metabolic phenotyping of patient-derived organoids is desirable to identify drug-induced changes and trace metabolic vulnerabilities of tumor subtypes. We established a novel protocol for metabolomic and lipidomic profiling of colorectal cancer organoids by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) facing the challenge of capturing metabolic information from a minimal sample amount (120% compared to matrix-derived signals present in blank samples. As a proof-of-concept, the method was applied to examine the early metabolic response of colorectal cancer organoids to 5-fluorouracil treatment. Statistical analysis revealed dose-dependent changes in the metabolic profiles of treated organoids including elevated levels of 2′-deoxyuridine, 2′-O-methylcytidine, inosine and 1-methyladenosine and depletion of 2′-deoxyadenosine and specific phospholipids. In accordance with the mechanism of action of 5-fluorouracil, changed metabolites are mainly involved in purine and pyrimidine metabolism. The novel protocol provides a first basis for the assessment of metabolic drug response phenotypes in 3D organoid models.

BACKGROUND: Alterations in circulating metabolites have been described in obese metabolic dysfunction–associated steatotic liver disease (MASLD), but data on lean MASLD are lacking. We investigated serum metabolites, including microbial bile acids and short-chain fatty acids (SCFAs), and their association with lean and obese MASLD. METHODS: Serum samples from 204 people of European descent were allocated to four groups: lean healthy, lean MASLD, obese healthy, and obese MASLD. Liquid chromatography–mass spectrometry–based metabolomics and linear model analysis were performed. MASLD prediction was assessed based on least absolute shrinkage and selection operator regression. Functional effects of altered molecules were verified in organotypic 3D primary human liver cultures. RESULTS: Lean MASLD was characterized by elevated isobutyrate, methionine sulfoxide, propionate, and phosphatidylcholines. Patients with obese MASLD had increased sarcosine and decreased lysine and asymmetric dimethylarginine. Using metabolites, sex, and BMI, MASLD versus healthy could be predicted with a median AUC of 86.5% and 85.6% in the lean and obese subgroups, respectively. Functional experiments in organotypic 3D primary human liver cultures showed propionate and isobutyrate induced lipid accumulation and altered expression of genes involved in lipid and glucose metabolism. CONCLUSION: Lean MASLD is characterized by a distinct metabolite pattern related to amino acid metabolism, lipids, and SCFAs, while metabolic pathways of lipid accumulation are differentially activated by microbial metabolites. We highlight an important role of microbial metabolites in MASLD, with implications for predictive and mechanistic assessment of liver disease across weight categories. FUNDING: Robert Bosch Stiftung, Swedish Research Council (2021-02801, 2023-03015, 2024-03401), ERC Consolidator Grant 3DMASH (101170408), Ruth and Richard Julin Foundation for Gastroenterology (2021-00158), SciLifeLab and Wallenberg National Program for Data-Driven Life Science (WASPDDLS22:006), Novo Nordisk Foundation (NNF23OC0085944, NNF23OC0084420), PMU-FFF (E-18/28/148-FEL).

The hepatic Na⁺-taurocholate cotransporting polypeptide NTCP/SLC10A1 is important for the uptake of bile salts and selected drugs. Its inhibition results in increased systemic bile salt concentrations. NTCP is also the entry receptor for the hepatitis B/D virus. We investigated interindividual hepatic SLC10A1/NTCP expression using various omics technologies. SLC10A1/NTCP mRNA expression/protein abundance was quantified in well-characterized 143 human livers by real-time PCR and LC-MS/MS-based targeted proteomics. Genome-wide SNP arrays and SLC10A1 next-generation sequencing were used for genomic analyses. SLC10A1 DNA methylation was assessed through MALDI-TOF MS. Transcriptomics and untargeted metabolomics (UHPLC-Q-TOF-MS) were correlated to identify NTCP-related metabolic pathways. SLC10A1 mRNA and NTCP protein levels varied 44-fold and 10.4-fold, respectively. Non-genetic factors (e.g., smoking, alcohol consumption) influenced significantly NTCP expression. Genetic variants in SLC10A1 or other genes do not explain expression variability which was validated in livers (n = 50) from The Cancer Genome Atlas. The identified two missense SLC10A1 variants did not impair transport function in transfectants. Specific CpG sites in SLC10A1 as well as single metabolic alterations and pathways (e.g., peroxisomal and bile acid synthesis) were significantly associated with expression. Inter-individual variability of NTCP expression is multifactorial with the contribution of clinical factors, DNA methylation, transcriptional regulation as well as hepatic metabolism, but not genetic variation.

Stratification of cancer patients to identify those with worse prognosis is increasingly important. Through in silico analyses, we recently developed a gene expression-based prognostic score (S3-score) for clear cell renal cell carcinoma (ccRCC), using the cell type-specific expression of 97 genes within the human nephron. Herein, we verified the score using whole-transcriptome data of independent cohorts and extend its application for patients with metastatic disease receiving tyrosine kinase inhibitor treatment. Finally, we sought to improve the signature for clinical application using qRT-PCR. A 97 gene-based S3-score (S397) was evaluated in a set of 52 primary non-metastatic and metastatic ccRCC patients as well as in 53 primary metastatic tumors of sunitinib-treated patients. Gene expression data of The Cancer Genome Atlas (n = 463) was used for platform transfer and development of a simplified qRT-PCR-based 15-gene S3-score (S315). This S315-score was validated in 108 metastatic and non-metastatic ccRCC patients and ccRCC-derived metastases including in part several regions from one metastasis. Univariate and multivariate Cox regression stratified by T, N, M, and G were performed with cancer-specific and progression-free survival as primary endpoints. The S397-score was significantly associated with cancer-specific survival (CSS) in 52 ccRCC patients (HR 2.9, 95% Cl 1.0–8.0, PLog-rank = 3.3 × 10–2) as well as progression-free survival in sunitinib-treated patients (2.1, 1.1–4.2, PLog-rank = 2.2 × 10–2). The qRT-PCR based S315-score performed similarly to the S397-score, and was significantly associated with CSS in our extended cohort of 108 patients (5.0, 2.1–11.7, PLog-rank = 5.1 × 10–5) including metastatic (9.3, 1.8–50.0, PLog-rank = 2.3 × 10–3) and non-metastatic patients (4.4, 1.2–16.3, PLog-rank = 1.6 × 10–2), even in multivariate Cox regression, including clinicopathological parameters (7.3, 2.5–21.5, PWald = 3.3 × 10–4). Matched primary tumors and metastases revealed similar S315-scores, thus allowing prediction of outcome from metastatic tissue. The molecular-based qRT-PCR S315-score significantly improved prediction of CSS by the established clinicopathological-based SSIGN score (P = 1.6 × 10–3). The S3-score offers a new clinical avenue for ccRCC risk stratification in the non-metastatic, metastatic, and sunitinib-treated setting.