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

Prediction of peripheral blood lymphocyte subpopulations after renal transplantation

Immune monitoring is essential for maintaining immune homeostasis after renal transplantation (RT). Peripheral blood lymphocyte subpopulations (PBLSs) are widely used biomarkers for immune monitoring, yet there is no established standard reference for PBLSs during immune reconstitution post-RT. PBLS data from stable recipients at various time points post-RT were collected. Binary and multiple linear regressions, along with a mixed-effect linear model, were used to analyze the correlations between PBLSs and clinical parameters. Predictive models for PBLS reference values were developed using Gradient Boosting Regressor, and the models' performance was also evaluated in infected recipients. A total of 1,736 tests from 494 stable recipients and 98 tests from 82 infected recipients were included. Age, transplant time, induction therapy, dialysis duration, serum creatinine, albumin, hemoglobin, and immunosuppressant drug concentration were identified as major factors influencing PBLSs. CD4+ and CD8+ T cells and NK cells increased rapidly, stabilizing within three months post-RT. In contrast, B cells peaked at around two weeks and gradually plateaued after four months. Both static and dynamic predictive models provided accurate reference values for PBLSs at any time post-RT, with the static model showing superior performance in distinguishing stable, infected and sepsis patients. Key factors influencing PBLS reconstitution after RT were identified. The predictive models accurately reflected PBLS reconstitution patterns and provided practical, personalized reference values for PBLSs, contributing to precision-guided care. The study was registered on Chinese Clinical Trial Registry (ChiCTR2300068666).

Computational identification of novel potential genetic pathogenesis and otherwise biomarkers in acute liver allograft rejection

Acute cellular rejection (ACR) is a prevalent postoperative complication following liver transplantation (LT), exhibiting an increasing incidence of morbidity and mortality. However, the molecular mechanisms of ACR following LT remain unclear. To explore the genetic pathogenesis and identify biomarkers of ACR following LT, three relevant Gene Expression Omnibus (GEO) datasets consisting of data on ACR or non-ACR patients after LT were comprehensively investigated by computational analysis. A total of 349 upregulated and 260 downregulated differentially expressed genes (DEGs) and eight hub genes (ISG15, HELZ2, HNRNPK, TIAL1, SKIV2L2, PABPC1, SIRT1, and PPARA) were identified. Notably, HNRNPK, TIAL1, and PABPC1 exhibited the highest predictive potential for ACR with AUCs of 0.706, 0.798, and 0.801, respectively. KEGG analysis of hub genes revealed that ACR following LT was predominately associated with ferroptosis, protein processing in the endoplasmic reticulum, complement and coagulation pathways, and RIG-I/NOD/Toll-like receptor signaling pathway. According to the immune cell infiltration analysis, γδT cells, NK cells, Tregs, and M1/M2-like macrophages had the highest levels of infiltration. Compared to SIRT1, ISG15 was positively correlated with γδT cells and M1-like macrophages but negatively correlated with NK cells, CD4+ memory T cells, and Tregs. In conclusion, this study identified eight hub genes and their potential pathways, as well as the immune cells involved in ACR following LT with the greatest levels of infiltration. These findings provide a new direction for future research on the underlying mechanism of ACR following LT.

Transcriptomics-based identification of TYROBP and TLR8 as novel macrophage-related biomarkers for the diagnosis of acute rejection after kidney transplantation

Macrophages play an important role in the development and progression of acute rejection after kidney transplantation. The study aims to investigate the biological role and significance of macrophage-associated genes (MAG) in acute rejection after kidney transplantation. We utilized transcriptome sequencing results from public databases related to acute rejection of kidney transplantation for comprehensive analysis and validation in animal experiments. We found that a large number of immune-related signaling pathways are activated in acute rejection. PPI protein interaction networks and machine learning were used to establish a Hub gene consisting of TYROBP and TLR8 for the diagnosis of acute rejection. The single-gene GSEA enrichment analysis and immune cell correlation analysis revealed a close correlation between the expression of Hub genes and immune-related biological pathways as well as the expression of multiple immune cells. In addition, the study of TF, miRNAs, and drugs provided a theoretical basis for regulating and treating the Hub genes in acute rejection. Finally, the animal experiments demonstrated once again that acute rejection can aggravate kidney tissue damage, apoptosis level, and increase the release of inflammatory factors. We established and validated a macrophage-associated diagnostic model for acute rejection after kidney transplantation, which can accurately diagnose the biological alterations in acute rejection after kidney transplantation.

MicroRNAs as Potential Graft Rejection or Tolerance Biomarkers and Their Dilemma in Clinical Routines Behaving like Devilish, Angelic, or Frightening Elements

Allograft rejection is a widespread complication in allograft recipients with chronic kidney disease. Undertreatment of subclinical and clinical rejection and later post-transplant problems are caused by an imperfect understanding of the mechanisms at play and a lack of adequate diagnostic tools. Many different biomarkers have been analyzed and proposed to detect and monitor these crucial events in transplant outcomes. In this sense, microRNAs may help diagnose rejection or tolerance and indicate appropriate treatment, especially in patients with chronic allograft rejection. As key epigenetic regulators of physiological homeostasis, microRNAs have therapeutic potential and may indicate allograft tolerance or rejection. However, more evidence and clinical validation are indispensable before microRNAs are ready for clinical prime time.

Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic

Purpose of Review

Multi-omics approach has advanced our knowledge on transplantation-associated clinical outcomes, such as acute rejection and infection, and emerging omics data are becoming available in kidney transplant and COVID-19. Herein, we discuss updated findings of multi-omics data on kidney transplant outcomes, as well as COVID-19 and kidney transplant.

Recent Findings

Transcriptomics, proteomics, and metabolomics revealed various inflammation pathways associated with kidney transplantation-related outcomes and COVID-19. Although multi-omics data on kidney transplant and COVID-19 is limited, activation of innate immune pathways and suppression of adaptive immune pathways were observed in the active phase of COVID-19 in kidney transplant recipients.

Summary

Multi-omics analysis has led us to a deeper exploration and a more comprehensive understanding of key biological pathways in complex clinical settings, such as kidney transplantation and COVID-19. Future multi-omics analysis leveraging multi-center biobank collaborative will further advance our knowledge on the precise immunological responses to allograft and emerging pathogens.

Identification of Genes Associated with Decreasing Abundance of Monocytes in Long-Term Peritoneal Dialysis Patients

Purpose

Chronic kidney disease (CKD) will become an end-stage renal disease (ESRD) at stage 5. Peritoneal dialysis (PD) is required for renal replacement therapy. This study aims to identify monocytes-related genes in peritoneal cells from long-term PD (LPD) patients and short-term PD (SPD) patients.

Methods

Bulk RNA-seq data (GSE125498 dataset) and ScRNA-seq data (GSE130888) were downloaded to identify differentially expressed genes, monocytes-related genes, and monocytes marker genes in LPD patients. Immune infiltration was analyzed in the GSE125498 dataset. Core genes associated with monocytes changes were screened out, followed by functional analysis and expression validation using RT-PCR.

Results

Monocytes are the most abundant immune cell in PD. The number of monocytes was remarkably decreased in LPD compared with SPD. A total of 16 up-regulated core genes negatively correlated with the abundance of monocytes were obtained in LPD. The expression of 16 core genes was lower in monocyte clusters than that in other cell clusters. In addition, LCK, CD3G, CD3E, CD3D, and LAT were involved in the signaling pathways of Th1 and Th2 cell differentiation, T cell receptor signaling pathway, and Th17 cell differentiation. CD2 was involved in hematopoietic cell lineage signaling pathway.

Conclusion

Identification of monocytes related-genes and related signaling pathways could be helpful in understanding the molecular mechanism of monocytes changes during PD.

All That Glitters in cfDNA Analysis Is Not Gold or Its Utility Is Completely Established Due to Graft Damage: A Critical Review in the Field of Transplantation

In kidney transplantation, a biopsy is currently the gold standard for monitoring the transplanted organ. However, this is far from an ideal screening method given its invasive nature and the discomfort it can cause the patient. Large-scale studies in renal transplantation show that approximately 1% of biopsies generate major complications, with a risk of macroscopic hematuria greater than 3.5%. It would not be until 2011 that a method to detect donor-derived cell-free DNA (dd-cfDNA) employing digital PCR was devised based on analyzing the differences in SNPs between the donor and recipient. In addition, since the initial validation studies were carried out at the specific moments in which rejection was suspected, there is still not a good understanding of how dd-cfDNA levels naturally evolve post-transplant. In addition, various factors, both in the recipient and the donor, can influence dd-cfDNA levels and cause increases in the levels of dd-cfDNA themselves without suspicion of rejection. All that glitters in this technology is not gold; therefore, in this article, we discuss the current state of clinical studies, the benefits, and disadvantages.

Monitoring of Serological, Cellular and Genomic Biomarkers in Transplantation, Computational Prediction Models and Role of Cell-Free DNA in Transplant Outcome

The process and evolution of an organ transplant procedure has evolved in terms of the prevention of immunological rejection with the improvement in the determination of immune response genes. These techniques include considering more important genes, more polymorphism detection, more refinement of the response motifs, as well as the analysis of epitopes and eplets, its capacity to fix complement, the PIRCHE algorithm and post-transplant monitoring with promising new biomarkers that surpass the classic serum markers such as creatine and other similar parameters of renal function. Among these new biomarkers, we analyze new serological, urine, cellular, genomic and transcriptomic biomarkers and computational prediction, with particular attention to the analysis of donor free circulating DNA as an optimal marker of kidney damage.

Clinical Significance of the Pre-Transplant CXCR3 and CCR6 Expression on T Cells In Kidney Graft Recipients

BACKGROUND

T cells play a fundamental role in the processes that mediate graft rejection, tolerance, and defense against infections. The CXCR3 and CCR6 receptors, highly expressed in Th1 (type 1 T helper cells)/Tc1 (T cytotoxic cells, type 1), Th1-Tc1, and Th17-Tc17 lymphocytes, respectively, participate in cell migration toward inflamed tissues. The altered expression level of CXCR3 and CCR6 has been associated with different clinical events after renal transplantation, such as acute rejection (AR) and chronic graft dysfunction, but data are still limited. In this study, we evaluated the expression of the receptor CXCR3 and CCR6 in peripheral blood T lymphocytes from kidney transplant recipients (KTR) and their association with viral infections, AR, and allograft function.

METHODS

Through flow cytometry, the peripheral blood expression of CXCR3 and CCR6 in T cells was evaluated in a pretransplant collection of KTR. The levels of these T subpopulations and their association with the incidence of AR, kidney graft function, viral infections, cytomegalovirus, and BK virus were studied. Adverse clinical events and graft function were monitored during the first year post transplant.

RESULTS

KTRs with low pretransplantation levels of Th17 (CD4+CXCR3-CCR6+) (tertile 1, Th17<16.4%) had a higher risk of suffering AR during the first year post transplantation (P = .033). KTRs with viral infections or reactivations during the first 3 months post transplantation had significantly lower levels of Tc17 (CD8+CXCR3-CCR6+) and higher levels of Th1 (CD4+CXCR3+CCR6-). In patients with cytomegalovirus reactivations, the viral peak correlates negatively with the pretransplant levels of Th1 (r = -0.606, P = .037).

CONCLUSIONS

Pretransplantation assessment of Th1-Th17 and Tc1-Tc17 levels may help predict post-transplant clinical events such as AR and reactivation of viral infections.

Into the multi-omics era: Progress of T cells profiling in the context of solid organ transplantation

T cells are the common type of lymphocyte to mediate allograft rejection, remaining long-term allograft survival impeditive. However, the heterogeneity of T cells, in terms of differentiation and activation status, the effector function, and highly diverse T cell receptors (TCRs) have thus precluded us from tracking these T cells and thereby comprehending their fate in recipients due to the limitations of traditional detection approaches. Recently, with the widespread development of single-cell techniques, the identification and characterization of T cells have been performed at single-cell resolution, which has contributed to a deeper comprehension of T cell heterogeneity by relevant detections in a single cell - such as gene expression, DNA methylation, chromatin accessibility, surface proteins, and TCR. Although these approaches can provide valuable insights into an individual cell independently, a comprehensive understanding can be obtained when applied joint analysis. Multi-omics techniques have been implemented in characterizing T cells in health and disease, including transplantation. This review focuses on the thesis, challenges, and advances in these technologies and highlights their application to the study of alloreactive T cells to improve the understanding of T cell heterogeneity in solid organ transplantation.

Serum uric acid as a risk factor for rejection after deceased donor kidney transplantation: A mono-institutional analysis of paired kidneys

Background

Deceased donor kidney transplantation (DDKT) is a major therapeutic option for patients with end-stage renal diseases. Although medical techniques improved in recent years, acute or chronic rejection after DDKT is not uncommon and often results in poor graft survival. Therefore, the determination of risk factors is very important to stratify patients and to improve outcomes. This study aims to evaluate the risk factors for treated rejection (TR) of patients after DDKT.

Methods

Clinical data of deceased donors and corresponding recipients were retrospectively collected. The primary outcome was TR defined as the treatment for rejection within 24 months after DDKT. Univariate comparisons of baseline characteristics were performed with Chi-square test, t-test, and Mann-Whitney U test. Logistic regression was constructed to analyze potential risk factors. Receiver operating characteristic (ROC) curve and Jordan index were generated to determine the optimal cutoff value. The association between continuous variables and TR was examined and visualized by using restricted cubic spline (RCS) models.

Results

Data of 123 deceased donors and 246 recipients were obtained and analyzed. The median age was 41 (4-62) years for recipients and 39 (1-65) years for donors. The recipients who died or suffered graft loss during the follow-up period were 8 (3.3%) and 12 (4.9%), respectively. After univariate analysis and subsequent multivariate analysis, the preoperative serum uric acid (OR, 2.242; 95% CI, 1.037-4.844; P = 0.040), platelet (OR, 2.163; 95% CI, 1.073-4.361, P = 0.031), absolute neutrophil count (OR, 2.183; 95% CI, 1.025-4.649; P = 0.043), and HLA-DQ mismatch (OR, 2.102; 95% CI, 1.093-4.043; P = 0.026) showed statistical significance. RCS models showed that patients with higher levels of uric acid had increased risk of TR.

Conclusions

Serum uric acid and other three indicators were found to be the independent risk factors for TR, which may contribute to stratify patients and develop personalized regimen in perioperative period.

Dissecting the impact of molecular T-cell HLA mismatches in kidney transplant failure: A retrospective cohort study

Introduction

Kidney transplantation is the optimal treatment in end-stage kidney disease, but de-novo donor specific antibody development continues to negatively impact patients undergoing kidney transplantation. One of the recent advances in solid organ transplantation has been the definition of molecular mismatching between donors and recipients' Human Leukocyte Antigens (HLA). While not fully integrated in standard clinical care, cumulative molecular mismatch at the level of eplets (EMM) as well as the PIRCHE-II score have shown promise in predicting transplant outcomes. In this manuscript, we sought to study whether certain T-cell molecular mismatches (TcEMM) were highly predictive of death-censored graft failure (DCGF).

Methods

We studied a retrospective cohort of kidney donor:recipient pairs from the Scientific Registry of Transplant Recipients (2000-2015). Allele level HLA-A, B, C, DRB1 and DQB1 types were imputed from serologic types using the NMDP algorithm. TcEMMs were then estimated using the PIRCHE-II algorithm. Multivariable Accelerated Failure Time (AFT) models assessed the association between each TcEMM and DCGF. To discriminate between TcEMMs most predictive of DCGF, we fit multivariable Lasso penalized regression models. We identified co-expressed TcEMMs using weighted correlation network analysis (WGCNA). Finally, we conducted sensitivity analyses to address PIRCHE and IMGT/HLA version updates.

Results

A total of 118,309 donor:recipient pairs meeting the eligibility criteria were studied. When applying the PIRCHE-II algorithm, we identified 1,935 distinct TcEMMs at the population level. A total of 218 of the observed TcEMM were independently associated with DCGF by AFT models. The Lasso penalized regression model with post selection inference identified a smaller subset of 86 TcEMMs (56 and 30 TcEMM derived from HLA Class I and II, respectively) to be highly predictive of DCGF. Of the observed TcEMM, 38.14% appeared as profiles of highly co-expressed TcEMMs. In addition, sensitivity analyses identified that the selected TcEMM were congruent across IMGT/HLA versions.

Conclusion

In this study, we identified subsets of TcEMMs highly predictive of DCGF and profiles of co-expressed mismatches. Experimental verification of these TcEMMs determining immune responses and how they may interact with EMM as predictors of transplant outcomes would justify their consideration in organ allocation schemes and for modifying immunosuppression regimens.

A Review of Biomarkers of Cardiac Allograft Rejection: Toward an Integrated Diagnosis of Rejection

Despite major advances in immunosuppression, allograft rejection remains an important complication after heart transplantation, and it is associated with increased morbidity and mortality. The gold standard invasive strategy to monitor and diagnose cardiac allograft rejection, based on the pathologic evaluation of endomyocardial biopsies, suffers from many limitations including the low prevalence of rejection, sample bias, high inter-observer variability, and international working formulations based on arbitrary cut-offs that simplify the landscape of rejection. The development of innovative diagnostic and prognostic strategies—integrating conventional histology, molecular profiling of allograft biopsy, and the discovery of new tissue or circulating biomarkers—is one of the major challenges of translational medicine in solid organ transplantation, and particularly in heart transplantation. Major advances in the field of biomarkers of rejection have paved the way for a paradigm shift in the monitoring and diagnosis of cardiac allograft rejection. We review the recent developments in the field, including non-invasive biomarkers to minimize the number of protocol endomyocardial biopsies and tissue biomarkers as companion tools of pathology to refine the diagnosis of cardiac rejection. Finally, we discuss the potential role of these biomarkers to provide an integrated bio-histomolecular diagnosis of cardiac allograft rejection.

PIRCHE-II scores prove useful as a predictive biomarker among kidney transplant recipients with rejection: An analysis of indication and follow-up biopsies

Background

Indication biopsies for deterioration of kidney allograft function often require follow-up biopsies to assess treatment response or lack of improvement. Immune-mediated injury, namely borderline rejection (BLR), T-cell mediated rejection (TCMR), or antibody-mediated rejection (ABMR), results from preformed or de novo alloreactivity due to donor and recipient HLA-mismatches. The impact of HLA-mismatches on alloreactivity is determined by highly immunogenic HLA-epitopes.

Methods

We analyzed 123 kidney transplant recipients (KTRs) from 2009 to 2019 who underwent a first indication and a follow-up biopsy. KTRs were divided into three groups according to the first biopsy: No rejection (NR)/BLR (n=68); TCMR (n=21); ABMR (n=34). The HLA-derived epitope-mismatches were calculated using the Predicted Indirectly Recognizable HLA-Epitopes (PIRCHE-II) algorithm.

Results

Group NR/BLR: KTRs with higher total PIRCHE-II scores were more likely to develop TCMR in the follow-up biopsy (p=0.031). Interestingly, these differences were significant for both HLA-class I- (p=0.017) and HLA-class II-derived (p=0.017) PIRCHE-II scores. Group TCMR: KTRs with ongoing TCMR in the follow-up biopsy were more likely to show higher total PIRCHE-II scores (median 101.50 vs. 74.00). Group ABMR: KTRs with higher total PIRCHE-II scores were more likely to show an increase in the microvascular inflammation score in the follow-up biopsy. This difference was more pronounced for the HLA-class II-derived PIRCHE-II scores (median 70.00 vs. 31.76; p=0.086).

Conclusions

PIRCHE-II scores may prove useful as a biomarker to predict the histopathological changes of immune-related injury from a first indication to a follow-up biopsy. This immunological risk stratification may contribute to individualized treatment strategies.