Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection

  1. Ishan Paranjpe
  2. Pushkala Jayaraman
  3. Chen-Yang Su
  4. Sirui Zhou
  5. Steven Chen
  6. Ryan Thompson
  7. Diane Marie Del Valle
  8. Ephraim Kenigsberg
  9. Shan Zhao
  10. Suraj Jaladanki
  11. Kumardeep Chaudhary
  12. Steven Ascolillo
  13. Akhil Vaid
  14. Edgar Gonzalez-Kozlova
  15. Justin Kauffman
  16. Arvind Kumar
  17. Manish Paranjpe
  18. Ross O. Hagan
  19. Samir Kamat
  20. Faris F. Gulamali
  21. Hui Xie
  22. Joceyln Harris
  23. Manishkumar Patel
  24. Kimberly Argueta
  25. Craig Batchelor
  26. Kai Nie
  27. Sergio Dellepiane
  28. Leisha Scott
  29. Matthew A. Levin
  30. John Cijiang He
  31. Mayte Suarez-Farinas
  32. Steven G. Coca
  33. Lili Chan
  34. Evren U. Azeloglu
  35. Eric Schadt
  36. Noam Beckmann
  37. Sacha Gnjatic
  38. Miram Merad
  39. Seunghee Kim-Schulze
  40. Brent Richards
  41. Benjamin S. Glicksberg
  42. Alexander W. Charney
  43. Girish N. Nadkarni

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Abstract

Background

Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms.

Methods

Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort ( N  = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p  < 0.05). Of these, 62 proteins were validated in an external cohort ( p  < 0.05, N  = 261).

Results

We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p  < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury.

Conclusions

Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

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  1. Version published to 10.1038/s43856-023-00307-8
  2. Version published to 10.21203/rs.3.rs-2379226/v1 on Research Square
  3. Version published to 10.1101/2021.12.09.21267548v2 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2021.12.09.21267548: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIRB: The Mount Sinai Institutional Review Board approved this study under a regulatory approval allowing for access to patient level data and biospecimen collection.
    Field Sample Permit: Serum collection and Processing: Blood samples were collected in Serum Separation Tubes with a polymer gel for serum separation.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Differential expression analysis for prevalent AKI: Using data from the AKI cohort, linear regression models were fit independently for each protein using the Limma package in R using the log2 transformed protein values as the dependent variable.
    Limma
    suggested: (LIMMA, RRID:SCR_010943)
    Protein-protein interaction (PPI) network was constructed using the Network X package in Python to display a Minimum Spanning Tree (MST) using Prim’s algorithm.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Network clustering was conducted using the MCL cluster algorithm and functional enrichment was carried out using the STRING Database in Cytoscape29.
    STRING
    suggested: (STRING, RRID:SCR_005223)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, again this would need to be tested using biopsy/autopsy specimens or other mechanistic studies Our study should be interpreted in the context of certain limitations. First, samples were collected during the hospital course of patients with confirmed COVID-19. However, the timepoints were not systematic due to logistical challenges during the peak of the COVID-19 pandemic and thus are not standardized between patients. Since a subset of patients had AKI at the time of admission, these patients were excluded from our analysis since specimens were collected after admission. Thus, our AKI cases may be biased towards less severe presentations. Second, since kidney injury is usually not an isolated phenomenon in critically ill patients, the protein expression changes observed may have been partially due to damage to other organs, such as the lung, liver, and heart. However, we accounted for non-kidney damage by adjusting for the highest level of ventilatory support and thus our results are likely a reflection of kidney injury. However, our results do show the importance of crosstalk between the cardiac system and the kidneys. Finally, our cohort did not include autopsy or kidney biopsy specimens. Histopathological analysis of kidney specimens is necessary to determine the mechanism of AKI and whether viral particles are present in the kidney. In conclusion, we provide the first comprehensive characterization of the plasma proteome of AKI and long term eGFR decline in hospi...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  5. Version published to 10.1101/2021.12.09.21267548v1 on medRxiv