COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.

E.U.A. has received research funding from Renalytix AI and Aurinia Pharmaceuticals; has consulting agreements with Ikena Oncology; has patents and applications owned by Icahn School of Medicine at Mount Sinai, all outside the scope of this manuscript. The A.G.-S. laboratory has received research support from GSK, Pfizer, Senhwa Biosciences, Kenall Manufacturing, Blade Therapeutics, Avimex, Johnson & Johnson, Dynavax, 7Hills Pharma, Pharmamar, ImmunityBio, Accurius, Nanocomposix, Hexamer, N-fold LLC, Model Medicines, Atea Pharma, Applied Biological Laboratories and Merck, outside of the reported work. A.G.-S. has consulting agreements for the following companies involving cash and/or stock: Castlevax, Amovir, Vivaldi Biosciences, Contrafect, 7Hills Pharma, Avimex, Pagoda, Accurius, Esperovax, Applied Biological Laboratories, Pharmamar, CureLab Oncology, CureLab Veterinary, Synairgen, Paratus, Pfizer and Prosetta, outside of the reported work. A.G.-S. has been an invited speaker in meeting events organized by Seqirus, Janssen, Abbott, Astrazeneca and Novavax. A.G.-S. is inventor on patents and patent applications on the use of antivirals and vaccines for the treatment and prevention of virus infections and cancer, owned by the Icahn School of Medicine at Mount Sinai, New York, outside of the reported work.

We acknowledge funding from NIH R01DK118222 and DoD W81XWH -20-1-0837 (E.U.A.). Azeloglu, Coca, Campbell, Schaub, and Kretzler are partially supported by Kidney Precision Medicine Project (KPMP), which maintains the Kidney Tissue Atlas of publicly available urine somascan and human transcriptomic data that were used in this study. The KPMP is funded by the following grants from the NIDDK: U01DK133081, U01DK133091, U01DK133092, U01DK133093, U01DK133095, U01DK133097, U01DK114866, U01DK114908, U01DK133090, U01DK133113, U01DK133766, U01DK133768, U01DK114907, U01DK114920, U01DK114923, U01DK114933, U24DK114886, UH3DK114926, UH3DK114861, UH3DK114915, UH3DK114937. L. Chan is supported in part by a grant from the NIH (K23DK124645 and U01DK137259). The mass spectrometry data were obtained from an Orbitrap mass spectrometer funded in part by the NIH grant S10OD025047, for the support of proteomics research at Rutgers Newark campus. This work was partially supported by CRIPT (Center for Research on Influenza Pathogenesis and Transmission), a NIAID funded Center of Excellence for Influenza Research and Response (CEIRR, contract # 75N93021C00014), and by NIAID grants U19AI142733, U19AI135972 and U19AI168631 to A.G-S., by the JPB and OPP foundations and an anonymous philanthropic donor to A.G-S. J. Haydak was supported by NIH T32HD075735 NICHD-Interdisciplinary Training in Systems and Developmental Biology and Birth Defects; A. Mendoza was supported by NIH R01DK131047 Diversity Supplement.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was approved by the Icahn School of Medicine at Mount Sinai Program Institutional Review Board (IRB) and the University of Michigan Medical School Institutional Review Board.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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All proteomics raw data will be made available on the ProteomeXchange PRIDE repository (https://www.ebi.ac.uk/pride/). The results here are in part based upon data generated by KPMP: the urinary SomaScan proteomics (6696930a-f707-430d-a964-110aefa93c62_Urine Biomarker Data-SomaScan-2022\Urine Biomarker Data-SomaScan-2022\Data\SS-2342467_2023-11-30_Urine.ANMLNormalized.xlsx) and the fully annotated scRNA-seq data (KidneyTissueAtlas/521c5b34-3dd0-4871-8064-61d3e3f1775a_PREMIERE_Alldatasets_08132021.h5Seurat) downloaded from https://atlas.kpmp.org/.