Multiomics Analyses Reveal Novel Gene-metabolite Associations Contributing to Kidney Function

The Challenge: Unraveling Molecular Pathways Involved in ADME of Kidney Metabolites

ADME is a term often used in the context of drug metabolism. However, ADME processes also have a powerful impact on metabolite concentrations, and our current understanding of the relationship between the enzymes and transporter proteins that regulate ADME remains incomplete. Integration of GWAS and metabolomics allows for surveys of thousands of genes and metabolites and is a powerful approach for revealing novel insights into kidney physiology. Here, researchers combined GWAS and metabolomics to understand the genes and metabolites implicated in kidney function, the pathways mediating the physiological processing of metabolites, and how those pathways may be impacted during different disease states.¹

The Metabolon Insight: Integrating GWAS with Metabolomics

Metabolon’s Global Discovery Panel was used to profile the urine samples of patients selected from the German Chronic Kidney Disease (GKCD) study. The metabolomics data were combined with GWAS data to identify genes associated with altered metabolites and pathways (mQTLs) impacting kidney function.

The Solution: Identification of Gene-metabolite Associations and Pathways Involved in Kidney Physiology

mGWAS on urine concentrations of 1,172 endogenous and exogenous metabolites from 1,672 patients revealed 240 novel mQTLs associated with 90 unique genes. These genes are involved in several ADME pathways, including the metabolism of carboxylic acids, amino acids, and fatty acids that are critical for detoxification reactions. Fine mapping analyses of these newly identified mQTL-gene associations revealed cell types and tissues involved in ADME, including the kidney, liver, small intestine, pancreas, and the left ventricle of the heart—a new role for this mitochondria-rich region. Moreover, these genes were enriched in proximal tubule epithelial cells. Pair-wise metabolite ratios were calculated to capture the activity, affinity, and abundance of enzymes involved in ADME. For example, examination of the alterations in the CYP2D6 gene (critical for the metabolism of metoprolol, a commonly prescribed beta blocker) revealed that the ratio of metoprolol to ɑ-hydroxy-metoprolol was significantly different with different rates of metoprolol metabolism, suggesting that associated genes with metabolite ratios can contribute to our understanding pharmaceutical efficacy. mQTLs also informed disease-associated molecular mechanisms. For example, mGWAS data from the UK Biobank revealed an association between the ALPL gene and urinary phosphoethanolamine that was subsequently linked with urolithiasis and kidney stones. These analyses suggest that these novel biomarkers that modulate, but don’t necessarily cause, disease can be utilized to monitor disease risk.

The Outcome: Providing Improved Biomarker and Pathway Resources for Kidney Physiology and Disease

Researchers have leveraged global metabolomic profiling to generate  a comprehensive list of genetic targets, their corresponding substrates, and molecular pathways that underlie kidney physiology, ADME, and kidney disease. Leveraging data from large cohort studies, their findings have revealed novel insights into gene-metabolite associations for human ADME processes, including previously unknown biomarkers. Their work propels kidney research by providing scientists with a comprehensive database of genes and metabolites. It enables doctors and clinicians to improve kidney disease diagnosis, treatment, and monitoring.