Molecular landscape of kidney allograft tissues data integration portal (NephroDIP): a curated database to improve integration of high-throughput kidney transplant datasets

Alex Boshart; Stefan Petrovic; Mark Abovsky; Chiara Pastrello; Sofia Farkona; Kieran Manion; Slaghaniya Neupane; Maya Allen; Igor Jurisica; Ana Konvalinka

doi:10.3389/fimmu.2024.1469500

Molecular landscape of kidney allograft tissues data integration portal (NephroDIP): a curated database to improve integration of high-throughput kidney transplant datasets

Front Immunol. 2024 Sep 27:15:1469500. doi: 10.3389/fimmu.2024.1469500. eCollection 2024.

Authors

Alex Boshart^#^{1

2

3}, Stefan Petrovic^#^{1

2}, Mark Abovsky^#^{4

5}, Chiara Pastrello^{4

5}, Sofia Farkona^{1

2}, Kieran Manion^{1

2}, Slaghaniya Neupane^{1

2

3}, Maya Allen^{1

2

6}, Igor Jurisica^#^{4

5

7

8}, Ana Konvalinka^#^{1

2

3

6

9}

Affiliations

¹ Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
² Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada.
³ Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
⁴ Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
⁵ Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
⁶ Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.
⁷ Departments of Medical Biophysics and Computer Science, and Faculty of Dentistry, University of Toronto, Toronto, ON, Canada.
⁸ Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia.
⁹ Department of Medicine, Division of Nephrology, University Health Network, Toronto, ON, Canada.

^# Contributed equally.

Abstract

Introduction: 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.

Methods: 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 9,964 studies.

Results: From these, 134 studies with available data detailing 260 comparisons and 83,262 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.

Discussion: 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: LGALS1; antibody-mediated rejection; data integration; high-throughput data; integrative computational biology; interstitial fibrosis and tubular atrophy; kidney transplantation; transplant immunosuppression.

MeSH terms

Allografts / immunology
Databases, Factual
Graft Rejection* / genetics
Graft Rejection* / immunology
Humans
Kidney / immunology
Kidney / metabolism
Kidney / pathology
Kidney Transplantation* / adverse effects
Proteomics / methods

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported in part by the Canadian Institutes of Health Research (CIHR) Project grant 469957, University Health Network (UHN) Foundation grants (579068260776, 579067450776, and 579072310776), the University of Toronto Academic Merit Award (2020–2026) and the Ajmera Transplant Centre Di Poce Scholar Award to AK. AB was supported by the Frederick Banting and Charles Best Canada Graduate Scholarship-Master’s (CGS M) (2021–2022). IJ was supported in part by funding from Natural Sciences Research Council (NSERC RGPIN-2024-04314), Canada Foundation for Innovation (CFI #225404, #30865), Ontario Research Fund (RDI #34876, RE010-020), IBM and Ian Lawson van Toch Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.