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.

Use of an Exposome Approach to Understand the Effects of Exposures From the Natural, Built, and Social Environments on Cardio-Vascular Disease Onset, Progression, and Outcomes

“…Sequencing and alignment of BD2K technologies in concert with large, novel, datasets that are harmonized within the context of multiple and longitudinal OMICS data and exposures from the natural, built, physical, and social environment holds great promise for the robust identification of cardiovascular/cardio-metabolic subclinical risk markers and our ability to discriminate cardio-metabolic trajectories in early stage patients with sub-clinical risk factors ( 201 – 203 ). They also are likely to provide new opportunities for completing exposures pathways, from source of exposure in the external environment to disease outcome, to population-level disparities.…”

Proteomic analysis in lupus mice identifies Coronin-1A as a potential biomarker for lupus nephritis

“…Notably, our results were further supported by transcriptomic data obtained from human kidney biopsies [15] and were validated in additional spontaneous and induced SLE mouse models. To our knowledge these proteins have not been identified previously in proteomics studies in other kidney diseases [39,40].…”

Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches

“…Over the past few years, hybrid approaches in metabolomics have been applied in biological samples that are associated with different kinds of disease, such as cardiovascular disease [ 76 ], neurodegenerative disease [ 77 ], cancer [ 39 , 40 , 41 ], kidney dysfunction [ 78 ], and diabetes mellitus [ 55 ]. The established hybrid methods discussed above also have been used in a variety of biological samples (such as urine, serum/plasma, and tissue samples) to facilitate broader metabolite coverage or to validate metabolite biomarkers for disease.…”