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Predicting Changes in Renal Metabolism after Compound Exposure with a Genome-Scale Metabolic Model

ABSTRACT: The kidneys are metabolically active organs that are important for several physiological tasks such as the secretion of soluble wastes into urine. Other functions of the kidneys include synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to five compounds with varying levels of nephrotoxicity (acetaminophen, carbon tetrachloride, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene for six and twenty-four hours), and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. We observed changes in fatty acid metabolism and amino acid metabolism, consistent with changes in existing markers of kidney function such as Lcn2 (lipocalin), Clu (clusterin), and the carnitine transporter Octn2 (solute carrier family 22, member 5). The iRno metabolic network reconstruction was able to predict alterations in these same pathways after integrating transcriptomics data, and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism to provide a step towards identifying novel biomarkers of kidney function and dysfunction.

ORGANISM(S): Rattus norvegicus

PROVIDER: GSE141628 | GEO | 2021/01/05

REPOSITORIES: GEO

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Project description:The transcriptional regulation of drug-metabolizing enzymes and transporters (here collectively referred to as DMEs) in the developing proximal tubule is not well understood. As in the liver, DME regulation in the PT may be mediated through nuclear receptors which are thought to “sense” deviations from homeostasis by being activated by ligands, some of which are handled by DMEs, including drug transporters. Systems analysis of transcriptomic data during kidney development predicted a set of upstream transcription factors, including Hnf4a and Hnf1a, as well as Nr3c1 (Gr), Nfe2l2 (Nrf2), Ppara, and Tp53. Motif analysis of cis-regulatory further suggested that Hnf4a and Hnf1a are the main transcriptional regulators in the PT. Available expression data from tissue-specific Hnf4a KO tissues revealed that distinct subsets of DMEs were regulated by Hnf4a in a tissue-specific manner. ChIP-seq was performed to characterize the PT-specific binding sites of Hnf4a in rat kidneys at three developmental stages (prenatal, immature, adult), which further supported a major role for Hnf4a in regulating PT gene expression, including DMEs. In ex vivo kidney organ culture, an antagonist of Hnf4a (but not a similar inactive compound) led to predicted changes in DME expression, including among others Fmo1, Cyp2d2, Cyp2d4, Nqo2, as well as organic cation transporters and organic anion transporters Slc22a1(Oct1), Slc22a2 (Oct2), Slc22a6 (Oat1), Slc22a8(Oat3), and Slc47a1(Mate1). Conversely, overexpression of Hnf1a and Hnf4a in primary mouse embryonic fibroblasts (MEFs), sometimes considered a surrogate for mesenchymal stem cells, induced expression of several of these proximal tubule DMEs, as well as epithelial markers and a PT-specific brush border marker Ggt1. These cells had organic anion transporter function. Taken together, the data strongly supports a critical role for HNF4a and Hnf1a in the tissue-specific regulation of drug handling and differentiation toward a PT cellular identity. Hnf4a binding was examined in rat kidneys at three timepoints (E20, P13 and Adult) and p300 binding was examined in adult rat kidney cortex tissue using ChIP-seq. Four corresponding input DNA samples were used as controls for peak calling.

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Predicting Changes in Renal Metabolism after Compound Exposure with a Genome-Scale Metabolic Model

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