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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Alloimmunity and Transplantation
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1469500
This article is part of the Research Topic Novel Therapeutic Targets for Alloimmune Injury in Solid Organ Transplantation View all 4 articles

Molecular Landscape of Kidney Allograft Tissues Data Integration Portal (NephroDIP): A Curated Database to Improve Integration of High-throughput Kidney Transplant Datasets

Provisionally accepted
Alex Boshart Alex Boshart 1,2,3Stefan Petrovic Stefan Petrovic 1,2Mark Abovsky Mark Abovsky 4,5*Chiara Pastrello Chiara Pastrello 4,5Sofia Farkona Sofia Farkona 1,2*Kieran P. Manion Kieran P. Manion 1,2Slaghaniya Neupane Slaghaniya Neupane 1,2,3*Maya Allen Maya Allen 1,2,6*I Jurisica I Jurisica 4,5,7,8*Ana Konvalinka Ana Konvalinka 1,2,3,6,9*
  • 1 Toronto General Hospital Research Institute, University Health Network, Toronto, Canada
  • 2 Ajmera Transplant Centre, University Health Network, Toronto, Canada
  • 3 Institute of Medical Science, University of Toronto, Toronto, Canada
  • 4 Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, Canada
  • 5 Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network, Toronto, Canada
  • 6 Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
  • 7 Departments of Medical Biophysics and Computer Science, and Faculty of Dentistry, University of Toronto, Toronto, Canada
  • 8 Institute of Neuroimmunology, Slovak Academy of Sciences,, Bratislava, Slovakia
  • 9 Department of Medicine, Division of Nephrology, University Health Network, Toronto, Canada

The final, formatted version of the article will be published soon.

    Kidney transplantation is the optimal treatment for end-stage kidney disease; however, premature allograft loss remains a serious issue. While many high-throughput omics studies have analyzed patient allograft biospecimens, integration of these datasets is challenging, which represents a considerable barrier to advancing our understanding of the mechanisms of allograft loss. To facilitate integration, we have created a curated database containing all open-access high-throughput datasets from human kidney transplant studies, termed NephroDIP (Nephrology Data Integration Portal). PubMed was searched for high-throughput transcriptomic, proteomic, single nucleotide variant, metabolomic, and epigenomic studies in kidney transplantation, which yielded 9964 studies. From these, 134 studies with available data detailing 260 comparisons and 83262 molecules were included in NephroDIP v1.0. To illustrate the capabilities of NephroDIP, we have used the database to identify common gene, protein, and microRNA networks that are disrupted in patients with chronic antibody-mediated rejection, the most important cause of late allograft loss. We have also explored the role of an immunomodulatory protein galectin-1 (LGALS1), along with its interactors and transcriptional regulators, in kidney allograft injury. We highlight the pathways enriched among LGALS1 interactors and transcriptional regulators in kidney fibrosis and during immunosuppression. Overall, NephroDIP is an open access data portal that facilitates data visualization and will help provide new insights into existing kidney transplant data through integration of distinct studies and modules (https://ophid.utoronto.ca/NephroDIP).

    Keywords: data integration, integrative computational biology, Kidney Transplantation, highthroughput data, Antibody-mediated Rejection, Interstitial fibrosis and tubular atrophy, Transplant immunosuppression, LGALS1

    Received: 23 Jul 2024; Accepted: 03 Sep 2024.

    Copyright: © 2024 Boshart, Petrovic, Abovsky, Pastrello, Farkona, Manion, Neupane, Allen, Jurisica and Konvalinka. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Mark Abovsky, Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, Canada
    Sofia Farkona, Toronto General Hospital Research Institute, University Health Network, Toronto, Canada
    Slaghaniya Neupane, Toronto General Hospital Research Institute, University Health Network, Toronto, Canada
    Maya Allen, Toronto General Hospital Research Institute, University Health Network, Toronto, Canada
    I Jurisica, Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network, Toronto, Canada
    Ana Konvalinka, Toronto General Hospital Research Institute, University Health Network, Toronto, Canada

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.