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. 2021 Dec 15:12:800968.
doi: 10.3389/fimmu.2021.800968. eCollection 2021.

Computational Prediction of Biomarkers, Pathways, and New Target Drugs in the Pathogenesis of Immune-Based Diseases Regarding Kidney Transplantation Rejection

Rafael Alfaro  1 , Helios Martínez-Banaclocha  1 , Santiago Llorente  2 , Victor Jimenez-Coll  1 , José Antonio Galián  1 , Carmen Botella  1 , María Rosa Moya-Quiles  1 , Antonio Parrado  1 , Manuel Muro-Perez  1 , Alfredo Minguela  1 , Isabel Legaz  3 , Manuel Muro  1
Affiliations

Computational Prediction of Biomarkers, Pathways, and New Target Drugs in the Pathogenesis of Immune-Based Diseases Regarding Kidney Transplantation Rejection

Rafael Alfaro et al. Front Immunol. .

Abstract

Background: The diagnosis of graft rejection in kidney transplantation (KT) patients is made by evaluating the histological characteristics of biopsy samples. The evolution of omics sciences and bioinformatics techniques has contributed to the advancement in searching and predicting biomarkers, pathways, and new target drugs that allow a more precise and less invasive diagnosis. The aim was to search for differentially expressed genes (DEGs) in patients with/without antibody-mediated rejection (AMR) and find essential cells involved in AMR, new target drugs, protein-protein interactions (PPI), and know their functional and biological analysis.

Material and methods: Four GEO databases of kidney biopsies of kidney transplantation with/without AMR were analyzed. The infiltrating leukocyte populations in the graft, new target drugs, protein-protein interactions (PPI), functional and biological analysis were studied by different bioinformatics tools.

Results: Our results show DEGs and the infiltrating leukocyte populations in the graft. There is an increase in the expression of genes related to different stages of the activation of the immune system, antigenic presentation such as antibody-mediated cytotoxicity, or leukocyte migration during AMR. The importance of the IRF/STAT1 pathways of response to IFN in controlling the expression of genes related to humoral rejection. The genes of this biological pathway were postulated as potential therapeutic targets and biomarkers of AMR. These biological processes correlated showed the infiltration of NK cells and monocytes towards the allograft. Besides the increase in dendritic cell maturation, it plays a central role in mediating the damage suffered by the graft during AMR. Computational approaches to the search for new therapeutic uses of approved target drugs also showed that imatinib might theoretically be helpful in KT for the prevention and/or treatment of AMR.

Conclusion: Our results suggest the importance of the IRF/STAT1 pathways in humoral kidney rejection. NK cells and monocytes in graft damage have an essential role during rejection, and imatinib improves KT outcomes. Our results will have to be validated for the potential use of overexpressed genes as rejection biomarkers that can be used as diagnostic and prognostic markers and as therapeutic targets to avoid graft rejection in patients undergoing kidney transplantation.

Keywords: acute rejection; bioinformatics tool; biomarkers; kidney transplant; new target drugs.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Design of the bioinformatics study applied to antibody-mediated rejection. NAR, No Acute Rejection; AMR, Antibody-Mediated Rejection; GO, Gene Ontology, KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, Protein-Protein Interaction, GEO; Gene Expression Omnibus, DEG, Differentially Expressed Genes.
Figure 2
Figure 2
Venn diagrams with the number of differentially expressed genes in four different cohorts analyses. (A) The number of genes overexpressed in the antibody-mediated rejection (AMR) group. (B) The number of under-expressed genes in the AMR group in each study and the overlap between studies.
Figure 3
Figure 3
Protein-protein interaction (PPI) network. The size of the nodes represents the degree of interconnection. The color grading represents the centrality of intermediation (BC). Darker colors reflect a high degree of BC. The numbered boxes indicate the functional modules obtained from the analysis of the PPI network.
Figure 4
Figure 4
Detail of the modules obtained from the PPI network. The figures represent module 0 (A), module 1 (B), module 2 (C), module 3 (D), module 4 (E), module 5 (F), and module 6 (G). Nodes represent the degree of interconnection. The color grading represents the centrality of intermediation (BC). Darker colors reflect a high degree of BC.
Figure 5
Figure 5
Cytolytic activity index in AMR. (A) The cytolytic index (CYT) was compared between the NR group without rejection (NR) and AMR. Comparisons were made using the Mann-Whitney U test. Values expressed as mean ± SEM. (B) ROC curve for the diagnosis of AMR using the CYT. As a reference, the non-discrimination diagonal (black line) is represented. The area under the curve (AUC) and statistical significance are indicated on the graph. Values of p<0.05 were considered significant. ***p<0.001. AMR, Antibody-Mediated Rejection. NR, No rejection; AMR, Antibody-Mediated Rejection.
Figure 6
Figure 6
Correlation of the relative abundance of immune subpopulations with the cytolytic index (CYT). The Spearman correlation coefficient is indicated in each cell. The red color indicates a positive correlation, and the blue color indicates a negative correlation.
Figure 7
Figure 7
New drug search strategy. During AMR events, an alteration of gene expression profiles occurs. In the search for new drugs, we look for those that induce a transcriptional profile opposite to that observed in AMR to counteract the altered genes and restore normal gene expression levels. AMR, Antibody-Mediated Rejection.
Figure 8
Figure 8
Interactions of imatinib and PDGFRA and PDGFRB proteins. Imatinib interactions were obtained from the STITCH database (Top). PDGFRA and PDGFRB interactions with important genes in the AMR were obtained from the STRING database (below). AMR, Antibody-Mediated Rejection.
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