Dataset Information

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

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.

SUBMITTER: Alfaro R

PROVIDER: S-EPMC8714745 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background and objectivesDefinition of individual risk profile is the first step to implement strategies to keep the delicate balance between under- and overimmunosuppression after kidney transplantation.Design, setting, participants, & measurementsWe used data from the Efficacy Limiting Toxicity Elimination Symphony Study (1190 patients between 2002 and 2004) to model risk of rejection and infection in the first year after kidney transplantation. External validation was performed in a study population from the Fixed-Dose Concentration-Controlled Trial (630 patients between 2003 and 2006).ResultsDespite different temporal dynamics, rejections and severe infections had similar overall incidences in the first year after transplantation (23.4% and 25.5%, respectively), and infections were the principal cause of death (43.2% of all deaths). Recipient older age, deceased donor, higher number of HLA mismatches, and high risk for cytomegalovirus disease were associated with infection; deceased donor, higher number of HLA mismatches, and immunosuppressive therapy including cyclosporin A (compared with tacrolimus), with rejection. These factors were integrated into a two-dimensional risk stratification model, which defined four risk groups: low risk for infection and rejection (30.8%), isolated risk for rejection (36.1%), isolated risk for infection (7.0%), and high risk for infection and rejection (26.1%). In internal validation, this model significantly discriminated the subgroups in terms of composite end point (low risk for infection/rejection, 24.4%; isolated risk for rejection and isolated risk for infection, 31.3%; high risk for infection/rejection, 54.4%; P<0.001), rejection episodes (isolated risk for infection and low risk for infection/rejection, 13.0%; isolated risk for rejection and high risk for infection/rejection, 24.2%; P=0.001), and infection episodes (low risk for infection/rejection and isolated risk for rejection, 12.0%; isolated risk for infection and high risk for infection/rejection, 37.6%; P<0.001). External validation confirmed the applicability of the model to an independent cohort.ConclusionsWe propose a two-dimensional risk stratification model able to disentangle the individual risk for rejection and infection in the first year after kidney transplantation. This concept can be applied to implement a personalized immunosuppressive and antimicrobial treatment approach.

Project description:Background: Antibody-mediated rejection (AMR) is the major cause of kidney transplant rejection. The donor-specific human leukocyte antigen (HLA) antibody (DSA) response to a renal allograft is not fully understood yet. mTOR complex has been described in the accommodation or rejection of transplants and integrates responses from a wide variety of signals. The aim of this study was to analyze the expression of the mTOR pathway genes in a large cohort of kidney transplant patients to determine its possible influence on the transplant outcome. Methods: A total of 269 kidney transplant patients monitored for DSA were studied. The patients were divided into two groups, one with recipients that had transplant rejection (+DSA/+AMR) and a second group of recipients without rejection (+DSA/-AMR and -DSA/-AMR, controls). Total RNA was extracted from kidney biopsies and reverse transcribed to cDNA. Human mTOR-PCR array technology was used to determine the expression of 84 mTOR pathway genes. STRING and REVIGO software were used to simulate gene to gene interaction and to assign a molecular function. Results: The studied groups showed a different expression of the mTOR pathway related genes. Recipients that had transplant rejection showed an over-expressed transcript (≥5-fold) of AKT1S1, DDIT4, EIF4E, HRAS, IGF1, INS, IRS1, PIK3CD, PIK3CG, PRKAG3, PRKCB (>12-fold), PRKCG, RPS6KA2, TELO2, ULK1, and VEGFC, compared with patients that did not have rejection. AKT1S1 transcripts were more expressed in +DSA/-AMR biopsies compared with +DSA/+AMR. The main molecular functions of up-regulated gene products were phosphotransferase activity, insulin-like grown factor receptor and ribonucleoside phosphate binding. The group of patients with transplant rejection also showed an under-expressed transcript (≥5-fold) of VEGFA (>15-fold), RPS6, and RHOA compared with the group without rejection. The molecular function of down-regulated gene products such as protein kinase activity and carbohydrate derivative binding proteins was also analyzed. Conclusions: We have found a higher number of over-expressed mTOR pathway genes than under-expressed ones in biopsies from rejected kidney transplants (+DSA/+AMR) with respect to controls. In addition to this, the molecular function of both types of transcripts (over/under expressed) is different. Therefore, further studies are needed to determine if variations in gene expression profiles can act as predictors of graft loss, and a better understanding of the mechanisms of action of the involved proteins would be necessary.