Proteomics and Metabolomics in Kidney Disease, including Insights into Etiology, Treatment, and Prevention

Abstract

In this review of the application of proteomics and metabolomics to kidney disease research, we review key concepts, highlight illustrative examples, and outline future directions. The proteome and metabolome reflect the influence of environmental exposures in addition to genetic coding. Circulating levels of proteins and metabolites are dynamic and modifiable, and thus amenable to therapeutic targeting. Design and analytic considerations in proteomics and metabolomics studies should be tailored to the investigator's goals. For the identification of clinical biomarkers, adjustment for all potential confounding variables, particularly GFR, and strict significance thresholds are warranted. However, this approach has the potential to obscure biologic signals and can be overly conservative given the high degree of intercorrelation within the proteome and metabolome. Mass spectrometry, often coupled to up-front chromatographic separation techniques, is a major workhorse in both proteomics and metabolomics. High-throughput antibody- and aptamer-based proteomic platforms have emerged as additional, powerful approaches to assay the proteome. As the breadth of coverage for these methodologies continues to expand, machine learning tools and pathway analyses can help select the molecules of greatest interest and categorize them in distinct biologic themes. Studies to date have already made a substantial effect, for example elucidating target antigens in membranous nephropathy, identifying a signature of urinary peptides that adds prognostic information to urinary albumin in CKD, implicating circulating inflammatory proteins as potential mediators of diabetic nephropathy, demonstrating the key role of the microbiome in the uremic milieu, and highlighting kidney bioenergetics as a modifiable factor in AKI. Additional studies are required to replicate and expand on these findings in independent cohorts. Further, more work is needed to understand the longitudinal trajectory of select protein and metabolite markers, perform transomics analyses within merged datasets, and incorporate more kidney tissue-based investigation.

Keywords: Kidney Genomics Series; Metabolomics; albumins; biological products; biomarkers; chronic renal insufficiency; confounding factors (epidemiology); diabetic nephropathies; diabetic nephropathy; energy metabolism; environmental exposure; goals; kidney; machine learning; mass spectrometry; membranous glomerulonephritis; metabolome; microbiota; peptides; prognosis; proteome; proteomics; research personnel.

Figure 1.

The conceptual relationship between the proteome and metabolome and their upstream counterparts. DNA transcription and subsequent RNA translation yield proteins with a broad range of structure and function. A subset of proteins, particularly enzymes and transporters, plays a prominent role in modulating metabolites (black dots in Figure), which are derived from endogenous metabolism and exogenous sources such as diet and the microbiome.

Figure 2.

Kidney arteriovenous gradients for the circulating metabolome and proteome. Panels show median venous-to-arterial (V/A) ratio for approximately 300 nonlipid metabolites measured by liquid chromatography–mass spectrometry and approximately 1000 proteins measured by aptamer in samples obtained from ten individuals. PTH, parathyroid hormone.

Figure 3.

Outline of common analytic approaches in human proteomics and metabolomics studies. First (A), proteomic or metabolomic analysis is performed on samples obtained from individuals with or without a phenotype, e.g., CKD progression (an alternative is to examine the association of proteins and metabolites with a continuous phenotype, such as eGFR slope). A “volcano plot” (B) can be used to visualize the fold-change and associated P value across all analytes in the case versus control comparison. The strengths of associations are often significantly stronger in unadjusted analysis. Bonferroni adjustment or false discovery rate are often used to account for multiple hypothesis testing. For the identification of clinical biomarkers, (C) it is important to adjust for all potential confounding variables (particularly eGFR and proteinuria), (D) followed by replication in an independent cohort and (E) assay development and validation, i.e., using an orthogonal assay to confirm specificity and permit absolute quantitation. A focus on biologic discovery may warrant less stringent significant thresholds, consideration of unadjusted results, and the use of (F) pathway analysis and machine learning. Ultimately, (G) mechanistic interrogation at the bench is required to test the causal role for select proteins and metabolites in disease.