Kidney research is entering an era of ‘big data’ and molecular omics data can provide comprehensive insights into the molecular footprints of cells. In contrast to transcriptomics, proteomics and metabolomics generate data that relate more directly to the pathological symptoms and clinical parameters observed in patients. Owing to its complexity, the proteome still holds many secrets, but has great potential for the identification of drug targets. Proteomics can provide information about protein synthesis, modification and degradation, as well as insight into the physical interactions between proteins, and between proteins and other biomolecules. Thus far, proteomics in nephrology has largely focused on the discovery and validation of biomarkers, but the systematic analysis of the nephroproteome can offer substantial additional insights, including the discovery of mechanisms that trigger and propagate kidney disease. Moreover, proteome acquisition might provide a diagnostic tool that complements the assessment of a kidney biopsy sample by a pathologist. Such applications are becoming increasingly feasible with the development of high-throughput and high-coverage technologies, such as versatile mass spectrometry-based techniques and protein arrays, and encourage further proteomics research in nephrology.
Proteomics analyses all the physical and chemical properties of proteins using a wide variety of technical approaches.
The versatility of proteome capture modes gives deep mechanistic insights into clinical samples and animal models of glomerular disease.
Both biomarker discovery and biopsy interrogation are key applications that have been propelled by increasingly simplified and sensitive sample preparation methods.
Given their diversified landscape, the analysis of post-translational modifications can uncover novel mechanisms of protein regulation.
Computational integration and mathematical modelling of molecular processes — tools largely developed for oncology — can be adapted to kidney proteomics research.
An increasingly clinical focus of proteomics analyses in combination with other omics technologies will aid or even drive novel discoveries of disease mechanisms and biomarkers in kidney disease.
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The authors apologize to all researchers whose work could not be cited due to space limitations. M.M.R. was supported by the DFG (RI2811/1 and RI2811/2), as well as the Young Investigator Award from the Novo Nordisk Foundation, grant number NNF19OC0056043. The authors thank Nicolas Palacio-Escat (Heidelberg University) for help with the initial figure panels and Aurelien Dugourd (Aachen University Hospital) for critically reading the manuscript before submission.
The authors declare no competing interests.
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Connectivity map: https://clue.io/cmap
Proteome central: http://proteomecentral.proteomexchange.org/cgi/GetDataset
Renal genomics portal: https://www.wikipathways.org/index.php/Portal:RenalGenomics
Distinct forms of a protein molecule that arise from the same transcript or gene.
- Intensity-based absolute quantification
An algorithm used for protein copy number estimation in which total protein intensity (that is, the sum of all peptide intensities) is divided by the number of peptides.
- Surface plasmon resonance
A biophysical method used to probe protein–protein (and molecule–molecule) interactions.
- PhoNEMES modelling
A computational tool used to build logic models of signalling networks from discovery mass-spectrometry-based phosphoproteomic data.
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Rinschen, M.M., Saez-Rodriguez, J. The tissue proteome in the multi-omic landscape of kidney disease. Nat Rev Nephrol 17, 205–219 (2021). https://doi.org/10.1038/s41581-020-00348-5
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