Biology subjects

Kottgen, A.

Publications and source records attributed to Kottgen, A..

5 recordsLinked to original sources

bioRxiv2026

Sexually dimorphic effects of mutations in ccRCC driver genes in renal proximal tubule cells

Clear cell renal cell carcinoma (ccRCC) arises more frequently in men than in women, but it remains unclear why this is the case. Mouse models that mimic the truncal mutation of the two most commonly affected tumor suppressor genes in human ccRCC revealed that the transcriptional effects of Vhl or Vhl/Pbrm1 mutation in male and female proximal tubule cells only partially overlap. Cellular responses to mutations in these genes were also sex-specific. Vhl and Vhl/Pbrm1 mutation in females, but only Vhl/Pbrm1 mutation in males, induced the accumulation of PLIN2-coated lipid droplets. The accumulation of optically-clear lipid droplets, reflecting the hallmark clear-cell histological feature of ccRCC, only arose in Vhl/Pbrm1 mutant male mice. Consistent with lipotoxic tubular damage, markers of tubular repair and ongoing epithelial cell proliferation correlated with the lipid accumulation phenotypes. Vhl/Pbrm1 mutation caused stronger clonal expansion than Vhl mutation in males, but not in females. These findings suggest that there are likely to be sexual dimorphisms at the very earliest stages of ccRCC evolution that result from the interplay of intrinsic and mutation-induced transcriptional differences between male and female proximal tubule cells.

cancer biology↗

bioRxiv2026

Transcriptome-based cell type assignment for kidney cell culture models

BackgroundKidney cell lines are widely used to model kidney physiology and disease; however, their gene expression profiles may differ from primary cells due to immortalization, culture conditions, or experimental treatments. Determining whether a cell line resembles its native cell type is critical for interpreting in vitro findings. We developed a transcriptome-based approach that matches bulk RNA-seq data from kidney cell lines, primary cells, or tissues to reference cell types derived from single-cell RNA-seq (scRNA-seq) datasets. MethodsReference transcriptomic profiles were generated from two human and two murine kidney scRNA-seq datasets by pseudobulk aggregation. Bulk RNA-seq data from microdissected kidney tissue, non-kidney negative controls, and kidney cell lines were matched to these references using three statistical similarity measures (Spearman correlation, Euclidean distance, Poisson distance) and three machine learning classifiers (Random Forest, XGBoost, TabPFN). Each was assessed with global gene expression, curated kidney marker gene lists, and the most variable genes. Matching accuracy was evaluated through a three-step validation strategy: within-dataset matching, cross-reference comparison, and validation against primary kidney tissue and negative controls. ResultsGene expression rank-based Spearman correlation and TabPFN, a foundation model for tabular data, emerged as the most accurate and specific approaches, particularly with curated kidney marker gene lists. Both methods correctly identified microdissected kidney tubule segments and were robust against non-kidney negative controls. Applied to commonly used kidney cell lines, OK cells retained proximal tubule identity, particularly under shear stress, while other proximal tubule lines (HK-2, HKC-8, HKC-11) showed inconsistent matching. Collecting duct-derived mIMCD-3 maintained stable similarity across passages, culture conditions, and genetic modifications. ConclusionWe provide two complementary implementations: CellMatchR, an accessible web-based tool using Spearman correlation for routine use, and comprehensive scripts for TabPFN-based matching (link will be added after peer reviewed publication). Together, these resources enable researchers to make informed decisions about kidney cell culture model selection, interpretation, and stability. Translational StatementKidney cell lines are fundamental tools in nephrology research, yet their transcriptomic similarity to native cell types is rarely validated systematically. We demonstrate that combining bulk RNA-seq data with single-cell reference datasets enables robust assessment of cell line identity using gene expression-rank-based correlation and machine learning approaches. By providing a comprehensive evaluation of matching methods, curated kidney marker gene lists, and reference datasets, our study serves as both a practical resource and a methodological framework for the kidney research community, facilitating informed selection of cell culture models, quality control of experimental conditions, developing new experimental cell culture models, and more reliable translation of in vitro findings to kidney physiology and disease.

bioinformatics↗

bioRxiv2024

SLC17 transporters mediate renal excretion of Lac-Phe in mice and humans

N-lactoyl-phenylalanine (Lac-Phe) is a lactate-derived metabolite that suppresses food intake and body weight. Little is known about the mechanisms that mediate Lac-Phe transport across cell membranes. Here we identify SLC17A1 and SLC17A3, two kidney-restricted plasma membrane-localized solute carriers, as physiologic urine Lac-Phe transporters. In cell culture, SLC17A1/3 exhibit high Lac-Phe efflux activity. In humans, levels of Lac-Phe in urine exhibit a strong genetic association with the SLC17A1-4 locus. Urine Lac-Phe levels are also increased following a Wingate sprint test. In mice, genetic ablation of either SLC17A1 or SLC17A3 reduces urine Lac-Phe levels. Despite these differences, both knockout strains have normal blood Lac-Phe and body weights, demonstrating that urine and plasma Lac-Phe pools are functionally and biochemically de-coupled. Together, these data establish SLC17 family members as the physiologic urine transporters for Lac-Phe and uncover a biochemical pathway for the renal excretion of this signaling metabolite.

biochemistry↗

bioRxiv2020

Polygenic Risk Scores for Kidney Function to the Circulating Proteome, and Incident Kidney Diseases: the Atherosclerosis Risk in Communities Study

Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (estimated glomerular filtration rate, eGFR). The relationship of polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known. We developed a genome-wide polygenic risk score (PRS) using a weighted average of 1.2 million SNPs for eGFR using the LDpred algorithm, summary statistics generated by a European-ancestry (EA) meta-analysis of the CKDGen Consortium (N=558,423) and UK Biobank GWAS for eGFR (90% of the cohort; N=289,432), followed by best parameter selection using data from the remaining 10% of the UK Biobank (N=32,159). We then tested the association of the PRS among 8,886 EA participants in the Atherosclerosis Risk in Communities (ARIC) study (mean age: 54{+/-}6 years, 53% female) with incident chronic kidney disease (CKD), end stage kidney disease (ESKD), kidney failure (KF), and acute kidney injury (AKI). We also examined 4,877 plasma proteins measured at two time points (visit 3 (1993-95) and visit 5 (2011-13)) in relation to the PRS and compared associations between the proteome and eGFR itself. All models were adjusted for age, sex, center, and the first 10 principal components of ancestry. The developed PRS had an R2 for eGFR of 0.07 in ARIC. Over 30 years of follow up, the number of incident CKD, ESKD, KF, and AKI were 2,959, 137, 470, and 1,723, respectively. The PRS showed significant associations with all outcomes: hazard ratios (95% CI) per 1 SD lower PRS were 1.33 (1.28, 1.39), 1.20 (1.00, 1.42), 1.17 (1.06, 1.28), and 1.07 (1.02, 1.12) for incident CKD, ESKD, KF, and AKI respectively. The PRS was significantly associated (Bonferroni threshold P<1.02 x 10-5) with 108 proteins at both time points. The strongest associations were with cystatin-C (a marker of kidney function used in clinical practice), collagen alpha-1 (XV) chain, and desmocollin-2. All significant correlations with the PRS were negative, except those of testican-2 and angiostatin. Correlations of proteins with eGFR were much stronger than those with the PRS. Overall, we demonstrated that the PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases as well as broadly influence the plasma proteome.

genetics↗

bioRxiv2019

Kidney Function and Blood Pressure: A Bi-directional Mendelian Randomisation Study

ObjectiveTo evaluate the bi-directional causal relation between kidney function and blood pressure. DesignMendelian randomisation study. SettingWe performed two-sample Mendelian randomisation analyses. Genetic instruments of kidney function traits were selected from summary statistics of genome-wide association studies (GWAS) of glomerular filtration rate estimated from serum creatinine (eGFRcr) and blood urea nitrogen (BUN) and were required to be associated with both eGFRcr and BUN to ensure that the instruments were more likely to represent the underlying kidney function. Genetic instruments of blood pressure were selected from summary statistics of GWAS of systolic and diastolic blood pressure. We investigated Mendelian randomisation hypothesis using several alternative approaches, including methods that are most robust to the presence of horizontal pleiotropy. ParticipantsThe summary statistics of eGFRcr included 567,460 participants from 54 cohorts, and the summary statistics of BUN included 243,031 participants from 48 cohorts from the Chronic Kidney Disease Genetics (CKDGen) Consortium. The summary statistics of systolic and diastolic blood pressure included 757,601 participants from the UK Biobank and 78 cohorts from the International Consortium for Blood Pressure (ICBP). ResultsSignificant evidence supported the causal effects of higher kidney function on lower blood pressure with multiple methods. Based on the mode-based Mendelian randomisation analysis approach, known for its robustness to the presence of pleiotropic effect, the effect estimate for 1 SD higher in eGFRcr was -0.17 SD unit (95 % CI: -0.09 to -0.24) in systolic blood pressure (SBP) and -0.15 SD unit (95% CI: -0.07 to -0.22) in diastolic blood pressure (DBP). In contrast, the causal effects of blood pressure on kidney function were not statistically significant. ConclusionsMendelian randomisation analyses support causal effects of higher kidney function on lower blood pressure. These results suggest preventing kidney function decline can reduce the public health burden of hypertension.

genetics↗