A molecular classifier for predicting future graft loss in late kidney transplant biopsies

The Clinical Relevance of the Infiltrating Immune Cell Composition in Kidney Transplant Rejection

Key Points

Background

The link between the histology of kidney transplant rejection, especially antibody-mediated rejection, T-cell–mediated rejection, and mixed rejection, and the types of infiltrating immune cells is currently not well charted. Cost and technical complexity of single-cell analysis hinder large-scale studies of the relationship between cell infiltrate profiles and histological heterogeneity.

Methods

In this cross-sectional study, we assessed the composition of nine intragraft immune cell types by using a validated kidney transplant–specific signature matrix for deconvolution of bulk transcriptomics in three different kidney transplant biopsy datasets (N=403, N=224, N=282). The association and discrimination of the immune cell types with the Banff histology and the association with graft failure were assessed individually and with multivariable models. Unsupervised clustering algorithms were applied on the overall immune cell composition and compared with the Banff phenotypes.

Results

Banff-defined rejection was related to high presence of CD8+ effector T cells, natural killer cells, monocytes/macrophages, and, to a lesser extent, B cells, whereas CD4+ memory T cells were lower in rejection compared with no rejection. Estimated intragraft effector memory–expressing CD45RA (TEMRA) CD8+ T cells were strongly and consistently associated with graft failure. The large heterogeneity in immune cell composition across rejection types prevented supervised and unsupervised methods to accurately recover the Banff phenotypes solely on the basis of immune cell estimates. The lack of correlation between immune cell composition and Banff-defined rejection types was validated using multiplex immunohistochemistry.

Conclusions

Although some specific cell types (FCGR3A + myeloid cells, CD14 + monocytes/macrophages, and NK cells) partly discriminated between rejection phenotypes, the overall estimated immune cell composition of kidney transplants was ill-related to main Banff-defined rejection categories and added to the Banff lesion scoring and evaluation of rejection severity. The estimated intragraft CD8temra cells bore strong and consistent association with graft failure and were independent of Banff-grade rejection.

Blood Gene Signature as a Biomarker for Subclinical Kidney Allograft Rejection: Where Are We?

The observation decades ago that inflammatory injuries because of an alloimmune response might be present even in the absence of concomitant clinical impairment in allograft function conduced to the later definition of subclinical rejection. Many studies have investigated the different subclinical rejections defined according to the Banff classification (subclinical T cell-mediated rejection and antibody-mediated rejection), overall concluding that these episodes worsened long-term allograft function and survival. These observations led several transplant teams to perform systematic protocolar biopsies to anticipate treatment of rejection episodes and possibly prevent allograft loss. Paradoxically, the invasive characteristics and associated logistics of such procedures paved the way to investigate noninvasive biomarkers (urine and blood) of subclinical rejection. Among them, several research teams proposed a blood gene signature developed from cohort studies, most of which achieved excellent predictive values for the occurrence of subclinical rejection, mainly antibody-mediated rejection. Interestingly, although all identified genes relate to immune subsets and pathways involved in rejection pathophysiology, very few transcripts are shared among these sets of genes, highlighting the heterogenicity of such episodes and the difficult but mandatory need for external validation of such tools. Beyond this, their application and value in clinical practice remain to be definitively demonstrated in both biopsy avoidance and prevention of clinical rejection episodes. Their combination with other biomarkers, either epidemiological or biological, could contribute to a more accurate picture of a patient's risk of rejection and guide clinicians in the follow-up of kidney transplant recipients.

Single-cell and bulk RNA sequencing highlights the role of M1-like infiltrating macrophages in antibody-mediated rejection after kidney transplantation

Background

Antibody-mediated rejection (ABMR) significantly affects transplanted kidney survival, yet the macrophage phenotype, ontogeny, and mechanisms in ABMR remain unclear.

Method

We analyzed post-transplant sequencing and clinical data from GEO and ArrayExpress. Using dimensionality reduction and clustering on scRNA-seq data, we identified macrophage subpopulations and compared their infiltration in ABMR and non-rejection cases. Cibersort quantified these subpopulations in bulk samples. Cellchat, SCENIC, monocle2, and monocle3 helped explore intercellular interactions, predict transcription factors, and simulate differentiation of cell subsets. The Scissor method linked macrophage subgroups with transplant prognosis. Furthermore, hdWGCNA, nichnet, and lasso regression identified key genes associated with core transcription factors in selected macrophages, validated by external datasets.

Results

Six macrophage subgroups were identified in five post-transplant kidney biopsies. M1-like infiltrating macrophages, prevalent in ABMR, correlated with pathological injury severity. MIF acted as a primary intercellular signal in these macrophages. STAT1 regulated monocyte-to-M1-like phenotype transformation, impacting transplant prognosis via the IFNγ pathway. The prognostic models built on the upstream and downstream genes of STAT1 effectively predicted transplant survival. The TLR4-STAT1-PARP9 axis may regulate the pro-inflammatory phenotype of M1-like infiltrating macrophages, identifying PARP9 as a potential target for mitigating ABMR inflammation.

Conclusion

Our study delineates the macrophage landscape in ABMR post-kidney transplantation, underscoring the detrimental impact of M1-like infiltrating macrophages on ABMR pathology and prognosis.

Taurine Deficiency Is a Hallmark of Injured Kidney Allografts

BACKGROUND

Taurine is one of the most abundant amino acids in humans. Low taurine levels are associated with cellular senescence, mitochondrial dysfunction, DNA damage, and inflammation in mouse, all of which can be reversed by supplementation. It is unknown whether taurine metabolism is associated with kidney allograft function and survival.

METHODS

We performed urine metabolomic profiling of kidney transplant recipients in the early and late phases after transplantation combined with transcriptomic analysis of human kidney allografts. Single-nucleus RNA sequencing data sets of mouse kidneys after ischemia-reperfusion injury were analyzed. We analyzed the association of urinary taurine levels and taurine metabolism genes with kidney function, histology, and graft survival.

RESULTS

Urine taurine concentrations were significantly lower in kidney transplant recipients who experienced delayed graft function. In a mouse model of ischemia-reperfusion injury, the taurine biosynthesis gene, CSAD, but not the taurine transporter SLC6A6, was repressed. In the late stage of transplantation, low level of taurine in urine was associated with impaired kidney function and chronic structural changes. Urine taurine level in the lowest tertile was predictive of graft loss. Expression of the taurine transporter SLC6A6 in the upper median, but not CSAD, was associated with chronic kidney injury and was predictive of graft loss.

CONCLUSIONS

Low urine taurine level is a marker of injury in the kidney allograft, is associated with poor kidney function, is associated with chronic histological changes, and is predictive of graft survival. The differential expression of CSAD and SLC6A6, depending on the time after transplantation and marks of injury, highlights different mechanisms affecting taurine metabolism.

Comprehensive molecular and cellular characterization of endoplasmic reticulum stress-related key genes in renal ischemia/reperfusion injury

Background

Renal ischemia-reperfusion injury (RIRI) is an inevitable complication in the process of kidney transplantation and lacks specific therapy. The study aims to determine the underlying mechanisms of RIRI to uncover a promising target for efficient renoprotection.

Method

Four bulk RNA-seq datasets including 495 renal samples of pre- and post-reperfusion were collected from the GEO database. The machine learning algorithms were utilized to ascertain pivotal endoplasmic reticulum stress genes. Then, we incorporated correlation analysis and determined the interaction pathways of these key genes. Considering the heterogeneous nature of bulk-RNA analysis, the single-cell RNA-seq analysis was performed to investigate the mechanisms of key genes at the single-cell level. Besides, 4-PBA was applied to inhibit endoplasmic reticulum stress and hence validate the pathological role of these key genes in RIRI. Finally, three clinical datasets with transcriptomic profiles were used to assess the prognostic role of these key genes in renal allograft outcomes after RIRI.

Results

In the bulk-RNA analysis, endoplasmic reticulum stress was identified as the top enriched pathway and three endoplasmic reticulum stress-related genes (PPP1R15A, JUN, and ATF3) were ranked as top performers in both LASSO and Boruta analyses. The three genes were found to significantly interact with kidney injury-related pathways, including apoptosis, inflammatory response, oxidative stress, and pyroptosis. For oxidative stress, these genes were more strongly related to oxidative markers compared with antioxidant markers. In single-cell transcriptome, the three genes were primarily upregulated in endothelium, distal convoluted tubule cells, and collecting duct principal cells among 12 cell types of renal tissues in RIRI. Furthermore, distal convoluted tubule cells and collecting duct principal cells exhibited pro-inflammatory status and the highest pyroptosis levels, suggesting their potential as main effectors of three key genes for mediating RIRI-associated injuries. Importantly, inhibition of these key genes using 4-phenyl butyric acid alleviated functional and histological damage in a mouse RIRI model. Finally, the three genes demonstrated highly prognostic value in predicting graft survival outcomes.

Conclusion

The study identified three key endoplasmic reticulum stress-related genes and demonstrated their prognostic value for graft survival, providing references for individualized clinical prevention and treatment of postoperative complications after renal transplantation.

Interferon-γ and its response are determinants of antibody-mediated rejection and clinical outcomes in patients after renal transplantation

Interferon-γ (IFN-γ) is an important cytokine in tissue homeostasis and immune response, while studies about it in antibody-mediated rejection (ABMR) are very limited. This study aims to comprehensively elucidate the role of IFN-γ in ABMR after renal transplantation. In six renal transplantation cohorts, the IFN-γ responses (IFNGR) biological process was consistently top up-regulated in ABMR compared to stable renal function or even T cell-mediated rejection in both allografts and peripheral blood. According to single-cell analysis, IFNGR levels were found to be broadly elevated in most cell types in allografts and peripheral blood with ABMR. In allografts with ABMR, M1 macrophages had the highest IFNGR levels and were heavily infiltrated, while kidney resident M2 macrophages were nearly absent. In peripheral blood, CD14+ monocytes had the top IFNGR level and were significantly increased in ABMR. Immunofluorescence assay showed that levels of IFN-γ and M1 macrophages were sharply elevated in allografts with ABMR than non-rejection. Importantly, the IFNGR level in allografts was identified as a strong risk factor for long-term renal graft survival. Together, this study systematically analyzed multi-omics from thirteen independent cohorts and identified IFN-γ and IFNGR as determinants of ABMR and clinical outcomes in patients after renal transplantation.

Multiomics Data Reveal the Important Role of ANXA2R in T Cell-mediated Rejection After Renal Transplantation

BACKGROUND

T cell-mediated rejection (TCMR) is a severe issue after renal transplantation, but research on its T cell-receptor (TCR) repertoire is lacking. This study intended to elucidate the TCR repertoire landscape in TCMR and hence identify novel potential targets.

METHODS

A total of 12 multiomics data sets were collected. The TRUST4 algorithm was used to construct and analyze the TCR repertoire in renal allografts with TCMR and stable renal function. Then, novel TCR-related key genes were identified through various criteria and literature research. In bulk transcriptome, cell line, single-cell transcriptome data sets, multiple immune cell infiltration algorithms, and gene set enrichment analysis were used to analyze potential mechanisms of the identified key gene. Twenty-three pathological sections were collected for immunofluorescence staining in the clinical cohort. Finally, the diagnostic and prognostic values of ANXA2R were evaluated in multiple renal transplant data sets.

RESULTS

Allografts with TCMR showed significantly increased clonotype and specific clonal expansion. ANXA2R was found to be a novel key gene for TCMR and showed strong positive connections with the TCR complex and lymphocyte cells, especially CD8 + T cells. Immunofluorescence staining confirmed the existence of ANXA2R + CD8 + T cells, with their percentage significantly elevated in TCMR compared with stable renal function. Finally, both mRNA and protein levels of ANXA2R showed promising diagnostic and prognostic value for renal transplant recipients.

CONCLUSIONS

ANXA2R , identified as a novel TCR-related gene, had critical roles in clinicopathology, diagnosis, and prognosis in renal transplantation, which offered promising potential therapeutic targets.

Identification of potential necroinflammation-associated necroptosis-related biomarkers for delayed graft function and renal allograft failure: a machine learning-based exploration in the framework of predictive, preventive, and personalized medicine

Delayed graft function (DGF) is one of the key post-operative challenges for a subset of kidney transplantation (KTx) patients. Graft survival is significantly lower in recipients who have experienced DGF than in those who have not. Assessing the risk of chronic graft injury, predicting graft rejection, providing personalized treatment, and improving graft survival are major strategies for predictive, preventive, and personalized medicine (PPPM/3PM) to promote the development of transplant medicine. However, since PPPM aims to accurately identify disease by integrating multiple omics, current methods to predict DGF and graft survival can still be improved. Renal ischemia/reperfusion injury (IRI) is a pathological process experienced by all KTx recipients that can result in varying occurrences of DGF, chronic rejection, and allograft failure depending on its severity. During this process, a necroinflammation-mediated necroptosis-dependent secondary wave of cell death significantly contributes to post-IRI tubular cell loss. In this article, we obtained the expression matrices and corresponding clinical data from the GEO database. Subsequently, nine differentially expressed necroinflammation-associated necroptosis-related genes (NiNRGs) were identified by correlation and differential expression analysis. The subtyping of post-KTx IRI samples relied on consensus clustering; the grouping of prognostic risks and the construction of predictive models for DGF (the area under the receiver operating characteristic curve (AUC) of the internal validation set and the external validation set were 0.730 and 0.773, respectively) and expected graft survival after a biopsy (the internal validation set's 1-year AUC: 0.770; 2-year AUC: 0.702; and 3-year AUC: 0.735) were based on the least absolute shrinkage and selection operator regression algorithms. The results of the immune infiltration analysis showed a higher infiltration abundance of myeloid immune cells, especially neutrophils, macrophages, and dendritic cells, in the cluster A subtype and prognostic high-risk groups. Therefore, in the framework of PPPM, this work provides a comprehensive exploration of the early expression landscape, related pathways, immune features, and prognostic impact of NiNRGs in post-KTx patients and assesses their capabilities as.predictors of post-KTx DGF and graft loss,targets of the vicious loop between regulated tubular cell necrosis and necroinflammation for targeted secondary and tertiary prevention, andreferences for personalized immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00320-w.

Utility of Banff Human Organ Transplant Gene Panel in Human Kidney Transplant Biopsies

BACKGROUND

Microarray transcript analysis of human renal transplantation biopsies has successfully identified the many patterns of graft rejection. To evaluate an alternative, this report tests whether gene expression from the Banff Human Organ Transplant (B-HOT) probe set panel, derived from validated microarrays, can identify the relevant allograft diagnoses directly from archival human renal transplant formalin-fixed paraffin-embedded biopsies. To test this hypothesis, principal components (PCs) of gene expressions were used to identify allograft diagnoses, to classify diagnoses, and to determine whether the PC data were rich enough to identify diagnostic subtypes by clustering, which are all needed if the B-HOT panel can substitute for microarrays.

METHODS

RNA was isolated from routine, archival formalin-fixed paraffin-embedded tissue renal biopsy cores with both rejection and nonrejection diagnoses. The B-HOT panel expression of 770 genes was analyzed by PCs, which were then tested to determine their ability to identify diagnoses.

RESULTS

PCs of microarray gene sets identified the Banff categories of renal allograft diagnoses, modeled well the aggregate diagnoses, showing a similar correspondence with the pathologic diagnoses as microarrays. Clustering of the PCs identified diagnostic subtypes including non-chronic antibody-mediated rejection with high endothelial expression. PCs of cell types and pathways identified new mechanistic patterns including differential expression of B and plasma cells.

CONCLUSIONS

Using PCs of gene expression from the B-Hot panel confirms the utility of the B-HOT panel to identify allograft diagnoses and is similar to microarrays. The B-HOT panel will accelerate and expand transcript analysis and will be useful for longitudinal and outcome studies.

The impact of the cytoplasmic ubiquitin ligase TNFAIP3 gene variation on transcription factor NF-κB activation in acute kidney injury

Nuclear factor κB (NF-κB) activation is a deleterious molecular mechanism that drives acute kidney injury (AKI) and manifests in transplanted kidneys as delayed graft function. The TNFAIP3 gene encodes A20, a cytoplasmic ubiquitin ligase and a master negative regulator of the NF- κB signaling pathway. Common population-specific TNFAIP3 coding variants that reduce A20's enzyme function and increase NF- κB activation have been linked to heightened protective immunity and autoimmune disease, but have not been investigated in AKI. Here, we functionally identified a series of unique human TNFAIP3 coding variants linked to the autoimmune genome-wide association studies single nucleotide polymorphisms of F127C; namely F127C;R22Q, F127C;G281E, F127C;W448C and F127C;N449K that reduce A20's anti-inflammatory function in an NF- κB reporter assay. To investigate the impact of TNFAIP3 hypomorphic coding variants in AKI we tested a mouse Tnfaip3 hypomorph in a model of ischemia reperfusion injury (IRI). The mouse Tnfaip3 coding variant I325N increases NF- κB activation without overt inflammatory disease, providing an immune boost as I325N mice exhibit enhanced innate immunity to a bacterial challenge. Surprisingly, despite exhibiting increased intra-kidney NF- κB activation with inflammation in IRI, the kidney of I325N mice was protected. The I325N variant influenced the outcome of IRI by changing the dynamic expression of multiple cytoprotective mechanisms, particularly by increasing NF- κB-dependent anti-apoptotic factors BCL-2, BCL-XL, c-FLIP and A20, altering the active redox state of the kidney with a reduction of superoxide levels and the enzyme super oxide dismutase-1, and enhancing cellular protective mechanisms including increased Foxp3⁺ T cells. Thus, TNFAIP3 gene variants represent a kidney and population-specific molecular factor that can dictate the course of IRI.

Gene signature and prediction model of the mitophagy-associated immune microenvironment in renal ischemia-reperfusion injury

Background

Renal ischemia-reperfusion injury (IRI) is an inevitable occurrence during kidney transplantation. Mitophagy, ferroptosis, and the associated immune microenvironment (IME) have been shown to play important roles in renal IRI. However, the role of mitophagy-associated IME genes in IRI remains unclear. In this study, we aimed to construct a prediction model of IRI prognosis based on mitophagy-associated IME genes.

Method

The specific biological characteristics of the mitophagy-associated IME gene signature were comprehensively analyzed using public databases such as GEO, Pathway Unification, and FerrDb. Correlations between the expression of prognostic genes and immune-related genes and IRI prognosis were determined by Cox regression, LASSO analysis, and Pearson’s correlation. Molecular validation was performed using human kidney 2 (HK2) cells and culture supernatant as well as the serum and kidney tissues of mice after renal IRI. Gene expression was measured by PCR, and inflammatory cell infiltration was examined by ELISA and mass cytometry. Renal tissue damage was characterized using renal tissue homogenate and tissue sections.

Results

The expression of the mitophagy-associated IME gene signature was significantly correlated with IRI prognosis. Excessive mitophagy and extensive immune infiltration were the primary factors affecting IRI. In particular, FUNDC1, SQSTM1, UBB, UBC, KLF2, CDKN1A, and GDF15 were the key influencing factors. In addition, B cells, neutrophils, T cells, and M1 macrophages were the key immune cells present in the IME after IRI. A prediction model for IRI prognosis was constructed based on the key factors associated with the mitophagy IME. Validation experiments in cells and mice indicated that the prediction model was reliable and applicable.

Conclusion

We clarified the relationship between the mitophagy-related IME and IRI. The IRI prognostic prediction model based on the mitophagy-associated IME gene signature provides novel insights on the prognosis and treatment of renal IRI.

The Molecular Diagnosis Might Be Clinically Useful in Discrepant Kidney Allograft Biopsy Findings: An Analysis of Clinical Outcomes

BACKGROUND

The Molecular Microscope Diagnostic System (MMDx) may overcome histology shortcomings. Previous studies have simply examined discrepant findings but have not attempted to determine clinical endpoints. To measure performance, clinical outcomes are strongly required.

METHODS

This single-center cohort study described discrepancies between MMDx and histology from 51 kidney transplant recipients (KTRs) and analyzed 72 indication biopsies, including 21 follow-up biopsies. Clinical performance was assessed by a combined endpoint of graft failure, rejection on follow-up biopsy, de novo donor-specific antibody, and improvement of kidney allograft function upon antirejection treatment.

RESULTS

MMDx agreed in 33 (65%) and differed in 18 (35%) of 51 KTRs. Most discrepancies occurred in biopsies called no rejection by MMDx and rejection by histology (15/24, 63%). In contrast, in biopsies called rejection by MMDx, 3 were classified as no rejection by histology (3/27, 11%). Discrepant findings between MMDx and histology occurred following delayed graft function and MMDx from biopsies with a low percentage of cortex. Among 15 biopsies classified as no rejection by MMDx but rejection by histology, the clinical course suggested no rejection in 9 cases. Six KTRs reached the endpoint, showing predominant t ≥ 2 lesions.

CONCLUSIONS

The most often occurring discrepancy is rejection by histology but no rejection by MMDx. As more KTRs do not meet the combined endpoint for rejection, MMDx might be clinically useful in these discrepant cases. Although strong histological findings have priority in indicating the treatment, clinical implementation of MMDx could strengthen treatment strategies.

N6-methyladenosine regulators-related immune genes enable predict graft loss and discriminate T-cell mediate rejection in kidney transplantation biopsies for cause

Objective

The role of m6A modification in kidney transplant-associated immunity, especially in alloimmunity, still remains unknown. This study aims to explore the potential value of m6A-related immune genes in predicting graft loss and diagnosing T cell mediated rejection (TCMR), as well as the possible role they play in renal graft dysfunction.

Methods

Renal transplant-related cohorts and transcript expression data were obtained from the GEO database. First, we conducted correlation analysis in the discovery cohort to identify the m6A-related immune genes. Then, lasso regression and random forest were used respectively to build prediction models in the prognosis and diagnosis cohort, to predict graft loss and discriminate TCMR in dysfunctional renal grafts. Connectivity map (CMap) analysis was applied to identify potential therapeutic compounds for TCMR.

Results

The prognostic prediction model effectively predicts the prognosis and survival of renal grafts with clinical indications (P< 0.001) and applies to both rejection and non-rejection situations. The diagnostic prediction model discriminates TCMR in dysfunctional renal grafts with high accuracy (area under curve = 0.891). Meanwhile, the classifier score of the diagnostic model, as a continuity index, is positively correlated with the severity of main pathological injuries of TCMR. Furthermore, it is found that METTL3, FTO, WATP, and RBM15 are likely to play a pivotal part in the regulation of immune response in TCMR. By CMap analysis, several small molecular compounds are found to be able to reverse TCMR including fenoldopam, dextromethorphan, and so on.

Conclusions

Together, our findings explore the value of m6A-related immune genes in predicting the prognosis of renal grafts and diagnosis of TCMR.

CARARIME: Interactive web server for comprehensive analysis of renal allograft rejection in immune microenvironment

Background

Renal transplantation is a very effective treatment for renal failure patients following kidney transplant. However, the clinical benefit is restricted by the high incidence of organ rejection. Therefore, there exists a wealth of literature regarding the mechanism of renal transplant rejection, including a large library of expression data. In recent years, research has shown the immune microenvironment to play an important role in renal transplant rejection. Nephrology web analysis tools currently exist to address chronic nephropathy, renal tumors and children’s kidneys, but no such tool exists that analyses the impact of immune microenvironment in renal transplantation rejection.

Methods

To fill this gap, we have developed a web page analysis tool called Comprehensive Analysis of Renal Allograft Rerejction in Immune Microenvironment (CARARIME).

Results

CARARIME analyzes the gene expression and immune microenvironment of published renal transplant rejection cohorts, including differential analysis (gene expression and immune cells), prognosis analysis (logistics regression, Univariable Cox Regression and Kaplan Meier), correlation analysis, enrichment analysis (GSEA and ssGSEA), and ROC analysis.

Conclusions

Using this tool, researchers can easily analyze the immune microenvironment in the context of renal transplant rejection by clicking on the available options, helping to further the development of approaches to renal transplant rejection in the immune microenvironment field. CARARIME can be found in http://www.cararime.com.

Shifting Clinical Trial Endpoints in Kidney Transplantation: The Rise of Composite Endpoints and Machine Learning to Refine Prognostication

The measurement of outcomes in kidney transplantation has been more accurately documented than almost any other surgical procedure result in recent decades. With significant improvements in short- and long-term outcomes related to optimized immunosuppression, outcomes have gradually shifted away from conventional clinical endpoints (ie, patient and graft survival) to surrogate and composite endpoints. This article reviews how outcomes measurements have evolved in the past 2 decades in the setting of increased data collection and summarizes recent advances in outcomes measurements pertaining to clinical, histopathological, and immune outcomes. Finally, we discuss the use of composite endpoints and Bayesian concepts, specifically focusing on the integrative box risk prediction score, in conjunction with machine learning to refine prognostication.

Evaluating the Link between BAFF System Gene Expression and Acute Rejection Development in Kidney Transplantation

B-cell activating factor (BAFF) system signaling is critical for B-cell homeostasis, effector functions, and tolerance maintenance in transplants, but it has not been studied in kidney transplant recipients (KTRs). The aim was to analyze the changes in BAFF system expression in KTRs with/without acute rejection (AR/NAR). The BAFF system expression was analyzed by qPCR in 40 KTRs. A meta-analysis of BAFF system expression and histological renal damage was identified by the Chronic Allograft Damage Index (CADI) and performed from the GEO database. Proliferation-inducing ligand (APRIL) expression increased at three- and six-months post-KT (p = 0.014 and p < 0.001). B-cell maturation antigen (BCMA) expression increased at six-months post-KT (p = 0.038). BAFF expression remained stable in NAR-KTRs, but was increased in CADI concerning the No-CADI group at one year (p = 0.008). BCMA expression increased in the CADI group at one- (p = 0.001) and six-years post-KT (p = 0.024). At three months, the transmembrane activator and calcium modulator interactor (TACI) gene significantly elevated KTRs with DSAs (donor-specific antibody; p = 0.034). KTRs with DSAs significantly increase the B-cell activating factor receptor (R-BAFF; p = 0.021) and TACI (p = 0.018) between pre- and three-month post-KT. Changes in the expression of the BAFF system increase during post-KTR in the development of AR and chronic allograft damage, and could be an important pathological tool to detect and prevent kidney graft outcomes.

T Cells With Activated STAT4 Drive the High-Risk Rejection State to Renal Allograft Failure After Kidney Transplantation

In kidney transplantation, deteriorated progression of rejection is considered to be a leading course of postoperative mortality. However, the conventional histologic diagnosis is limited in reading the rejection status at the molecular level, thereby triggering mismatched pathogenesis with clinical phenotypes. Here, by applying uniform manifold approximation and projection and Leiden algorithms to 2,611 publicly available microarray datasets of renal transplantation, we uncovered six rejection states with corresponding signature genes and revealed a high-risk (HR) state that was essential in promoting allograft loss. By identifying cell populations from single-cell RNA sequencing data that were associated with the six rejection states, we identified a T-cell population to be the pathogenesis-triggering cells associated with the HR rejection state. Additionally, by constructing gene regulatory networks, we identified that activated STAT4, as a core transcription factor that was regulated by PTPN6 in T cells, was closely linked to poor allograft function and prognosis. Taken together, our study provides a novel strategy to help with the precise diagnosis of kidney allograft rejection progression, which is powerful in investigating the underlying molecular pathogenesis, and therefore, for further clinical intervention.

Proposed Definitions of Antibody-Mediated Rejection for Use as a Clinical Trial Endpoint in Kidney Transplantation

Antibody-mediated rejection (AMR) is caused by antibodies that recognize donor human leukocyte antigen (HLA) or other targets. As knowledge of AMR pathophysiology has increased, a combination of factors is necessary to confirm the diagnosis and phenotype. However, frequent modifications to the AMR definition have made it difficult to compare data and evaluate associations between AMR and graft outcome. The present paper was developed following a Broad Scientific Advice request from the European Society for Organ Transplantation (ESOT) to the European Medicines Agency (EMA), which explored whether updating guidelines on clinical trial endpoints would encourage innovations in kidney transplantation research. ESOT considers that an AMR diagnosis must be based on a combination of histopathological factors and presence of donor-specific HLA antibodies in the recipient. Evidence for associations between individual features of AMR and impaired graft outcome is noted for microvascular inflammation scores ≥2 and glomerular basement membrane splitting of >10% of the entire tuft in the most severely affected glomerulus. Together, these should form the basis for AMR-related endpoints in clinical trials of kidney transplantation, although modifications and restrictions to the Banff diagnostic definition of AMR are proposed for this purpose. The EMA provided recommendations based on this Broad Scientific Advice request in December 2020; further discussion, and consensus on the restricted definition of the AMR endpoint, is required.

Preliminary Investigation of the Biomarkers of Acute Renal Transplant Rejection Using Integrated Proteomics Studies, Gene Expression Omnibus Datasets, and RNA Sequencing

A kidney transplant is often the best treatment for end-stage renal disease. Although immunosuppressive therapy sharply reduces the occurrence of acute allograft rejection (AR), it remains the main cause of allograft dysfunction. We aimed to identify effective biomarkers for AR instead of invasive kidney transplant biopsy. We integrated the results of several proteomics studies related to AR and utilized public data sources. Gene ontology (GO) and pathway analyses were used to identify important biological processes and pathways. The performance of the identified proteins was validated using several public gene expression omnibus (GEO) datasets. Samples that performed well were selected for further validation through RNA sequencing of peripheral blood mononuclear cells of patients with AR (n = 16) and non-rejection (n = 19) from our medical center. A total of 25 differentially expressed proteins (DEPs) overlapped in proteomic studies of urine and blood samples. GO analysis showed that the DEPs were mainly involved in the immune system and blood coagulation. Pathway analysis showed that the complement and coagulation cascade pathways were well enriched. We found that immunoglobulin heavy constant alpha 1 (IGHA1) and immunoglobulin κ constant (IGKC) showed good performance in distinguishing AR from non-rejection groups validated with several GEO datasets. Through RNA sequencing, the combination of IGHA1, IGKC, glomerular filtration rate, and donor age showed good performance in the diagnosis of AR with ROC AUC 91.4% (95% CI: 82–100%). Our findings may contribute to the discovery of potential biomarkers for AR monitoring.

A functional TGFB1 polymorphism in the donor associates with long-term graft survival after kidney transplantation

Background

Improvement of long-term outcomes in kidney transplantation remains one of the most pressing challenges, yet drug development is stagnating. Human genetics offers an opportunity for much-needed target validation in transplantation. Conflicting data exist about the effect of transforming growth factor-beta 1 (TGF-β1) on kidney transplant survival, since TGF-β1 has pro-fibrotic and protective effects. We investigated the impact of a recently discovered functional TGFB1 polymorphism on kidney graft survival.

Methods

We performed an observational cohort study analysing recipient and donor DNA in 1271 kidney transplant pairs from the University Medical Centre Groningen in The Netherlands, and associated a low-producing TGFB1 polymorphism (rs1800472-C > T) with 5-, 10- and 15-year death-censored kidney graft survival.

Results

Donor genotype frequencies of rs1800472 in TGFB1 differed significantly between patients with and without graft loss (P = 0.014). Additionally, the low-producing TGFB1 polymorphism in the donor was associated with an increased risk of graft loss following kidney transplantation (hazard ratio = 2.12 for the T-allele; 95% confidence interval 1.18–3.79; P = 0.012). The incidence of graft loss within 15 years of follow-up was 16.4% in the CC-genotype group and 31.6% in the CT-genotype group. After adjustment for transplant-related covariates, the association between the TGFB1 polymorphism in the donor and graft loss remained significant. In contrast, there was no association between the TGFB1 polymorphism in the recipient and graft loss.

Conclusions

Kidney allografts possessing a low-producing TGFB1 polymorphism have a higher risk of late graft loss. Our study adds to a growing body of evidence that TGF-β1 is beneficial, rather than harmful, for kidney transplant survival.

The Biology and Molecular Basis of Organ Transplant Rejection

Allograft rejection is defined as tissue injury in a transplanted allogeneic organ produced by the effector mechanisms of the adaptive alloimmune response. Effector T lymphocytes and IgG alloantibodies cause two different types of rejection that can occur either individually or simultaneously: T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR). In TCMR, cognate effector T cells infiltrate the graft and orchestrate an interstitial inflammatory response in the kidney interstitium in which effector T cells engage antigen-presenting myeloid cells, activating the T cells, antigen-presenting cells, and macrophages. The result is intense expression of IFNG and IFNG-induced molecules, expression of effector T cell molecules and macrophage molecules and checkpoints, and deterioration of parenchymal function. The diagnostic lesions of TCMR follow, i.e. interstitial inflammation, parenchymal deterioration, and intimal arteritis. In ABMR, HLA IgG alloantibodies produced by plasma cells bind to the donor antigens on graft microcirculation, leading to complement activation, margination, and activation of NK cells and neutrophils and monocytes, and endothelial injury, sometimes with intimal arteritis. TCMR becomes infrequent after 5-10 years post-transplant, probably reflecting adaptive mechanisms such as checkpoints, but ABMR can present even decades post-transplant. Some rejection is triggered by inadequate immunosuppression and non-adherence, challenging the clinician to target effective immunosuppression even decades post-transplant.

Progressive decline of function in renal allografts with normal 1-year biopsies: Gene expression studies fail to identify a classifier

Histologic findings on 1-year biopsies such as inflammation with fibrosis and transplant glomerulopathy predict renal allograft loss by 5 years. However, almost half of the patients with graft loss have a 1-year biopsy that is either normal or has only interstitial fibrosis. The goal of this study was to determine if there was a gene expression profile in these relatively normal 1-year biopsies that predicted subsequent decline in renal function. Using transcriptome microarrays we measured intragraft mRNA levels in a retrospective Discovery cohort (170 patients with a normal/minimal fibrosis 1-year biopsy, 54 with progressive decline in function/graft loss and 116 with stable function) and developed a nested 10-fold cross-validated gene classifier that predicted progressive decline in renal function (positive predictive value = 38 ± 34%%; negative predictive value = 73 ± 30%, c-statistic = .59). In a prospective, multicenter Validation cohort (270 patients with Normal/Interstitial Fibrosis [IF]), the classifier had a 20% positive predictive value, 85% negative predictive value and .58 c-statistic. Importantly, the majority of patients with graft loss in the prospective study had 1-year biopsies scored as Normal or IF. We conclude predicting graft loss in many renal allograft recipients (i.e., those with a relatively normal 1-year biopsy and eGFR > 40) remains difficult.

Integrated Analysis of Prognostic Genes Associated With Ischemia-Reperfusion Injury in Renal Transplantation

Background

Ischemia–reperfusion injury (IRI) remains an inevitable and major challenge in renal transplantation. The current study aims to obtain deep insights into underlying mechanisms and seek prognostic genes as potential therapeutic targets for renal IRI (RIRI).

Methods

After systematically screening the Gene Expression Omnibus (GEO) database, we collected gene expression profiles of over 1,000 specimens from 11 independent cohorts. Differentially expressed genes (DEGs) were identified by comparing allograft kidney biopsies taken before and after reperfusion in the discovery cohort and further validated in another two independent transplant cohorts. Then, graft survival analysis and immune cell analysis of DEGs were performed in another independent renal transplant cohort with long-term follow-ups to further screen out prognostic genes. Cell type and time course analyses were performed for investigating the expression pattern of prognostic genes in more dimensions utilizing a mouse RIRI model. Finally, two novel genes firstly identified in RIRI were verified in the mouse model and comprehensively analyzed to investigate potential mechanisms.

Results

Twenty DEGs upregulated in the process of RIRI throughout different donor types (living donors, cardiac and brain death donors) were successfully identified and validated. Among them, upregulation of 10 genes was associated with poor long-term allograft outcomes and exhibited strong correlations with prognostic immune cells, like macrophages. Furthermore, certain genes were found to be only differentially expressed in specific cell types and remained with high expression levels even months after RIRI in the mouse model, which processed the potential to serve as therapeutic targets. Importantly, two newly identified genes in RIRI, Btg2 and Rhob, were successfully confirmed in the mouse model and found to have strong connections with NF-κB signaling.

Conclusions

We successfully identified and validated 10 IRI-associated prognostic genes in renal transplantation across different donor types, and two novel genes with crucial roles in RIRI were recognized for the first time. Our findings offered promising potential therapeutic targets for RIRI in renal transplantation.

Data-Driven Kidney Transplant Phenotyping as a Histology-Independent Framework for Biomarker Discovery

BACKGROUND

In transplant medicine, clinical decision making largely relies on histology of biopsy specimens. However, histology suffers from low specificity, sensitivity, and reproducibility, leading to suboptimal stratification of patients. We developed a histology-independent immune framework of kidney graft homeostasis and rejection.

METHODS

We applied tailored RNA deconvolution for leukocyte enumeration and coregulated gene network analysis to published bulk human kidney transplant RNA transcriptomes as input for unsupervised, high-dimensional phenotype clustering. We used framework-based graft survival analysis to identify a biomarker that was subsequently characterized in independent transplant biopsy specimens.

RESULTS

We found seven immune phenotypes that confirm known rejection types and uncovered novel signatures. The molecular phenotypes allow for improved graft survival analysis compared with histology, and identify a high-risk group in nonrejecting transplants. Two fibrosis-related phenotypes with distinct immune features emerged with reduced graft survival. We identified lysyl oxidase-like 2 (LOXL2)-expressing peritubular CD68+ macrophages as a framework-derived biomarker of impaired allograft function. These cells precede graft fibrosis, as demonstrated in longitudinal biopsy specimens, and may be clinically useful as a biomarker for early fibrogenesis.

CONCLUSIONS

This study provides a comprehensive, data-driven atlas of human kidney transplant phenotypes and demonstrates its utility to identify novel clinical biomarkers.

Integrative Analysis of Prognostic Biomarkers for Acute Rejection in Kidney Transplant Recipients

BACKGROUND

Noninvasive biomarkers may predict adverse events such as acute rejection after kidney transplantation and may be preferable to existing methods because of superior accuracy and convenience. It is uncertain how these biomarkers, often derived from a single study, perform across different cohorts of recipients.

METHODS

Using a cross-validation framework that evaluates the performance of biomarkers, the aim of this study was to devise an integrated gene signature set that predicts acute rejection in kidney transplant recipients. Inclusion criteria were publicly available datasets of gene signatures that reported acute rejection episodes after kidney transplantation. We tested the predictive probability for acute rejection using gene signatures within individual datasets and validated the set using other datasets. Eight eligible studies of 1454 participants, with a total of 512 acute rejections episodes were included.

RESULTS

All sets of gene signatures had good positive and negative predictive values (79%-96%) for acute rejection within their own cohorts, but the predictability reduced to <50% when tested in other independent datasets. By integrating signature sets with high specificity scores across all studies, a set of 150 genes (included CXCL6, CXCL11, OLFM4, and PSG9) which are known to be associated with immune responses, had reasonable predictive values (varied between 69% and 90%).

CONCLUSIONS

A set of gene signatures for acute rejection derived from a specific cohort of kidney transplant recipients do not appear to provide adequate prediction in an independent cohort of transplant recipients. However, the integration of gene signature sets with high specificity scores may improve the prediction performance of these markers.

An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation

Efforts at finding potential biomarkers of tolerance after kidney transplantation have been hindered by limited sample size, as well as the complicated mechanisms underlying tolerance and the potential risk of rejection after immunosuppressant withdrawal. In this work, three different publicly available genome-wide expression data sets of peripheral blood lymphocyte (PBL) from 63 tolerant patients were used to compare 14 different machine learning models for their ability to predict spontaneous kidney graft tolerance. We found that the Best Subset Selection (BSS) regression approach was the most powerful with a sensitivity of 91.7% and a specificity of 93.8% in the test group, and a specificity of 86.1% and a sensitivity of 80% in the validation group. A feature set with five genes (HLA-DOA, TCL1A, EBF1, CD79B, and PNOC) was identified using the BSS model. EBF1 downregulation was also an independent factor predictive of graft rejection and graft loss. An AUC value of 84.4% was achieved using the two-gene signature (EBF1 and HLA-DOA) as an input to our classifier. Overall, our systematic machine learning exploration suggests novel biological targets that might affect tolerance to renal allografts, and provides clinical insights that can potentially guide patient selection for immunosuppressant withdrawal.

Correlation of Donor-Derived Cell-free DNA with Histology and Molecular Diagnoses of Kidney Transplant Biopsies

BACKGROUND

Circulating donor-derived cell free DNA (cfDNA), a minimally invasive diagnostic tool for kidney transplant rejection, was validated using traditional histology. The Molecular Microscope (MMDx) tissue gene expression platform may provide increased precision to traditional histology.

METHODS

In this single-center prospective study of 208 biopsies (median=5.8 months) post-transplant, we report on the calibration of cfDNA with simultaneous biopsy assessments using MMDx and histology by Area under the curve (AUC) analyses for optimal criterion, as well as for, previously published cfDNA cut-offs ≤0.21% to 'rule-out' rejection and ≥1% to 'rule-in' rejection.

RESULTS

There were significant discrepancies between histology and MMDx, with MMDx identifying more antibody-mediated rejection (65; 31%) than histology (43; 21%); the opposite was true for T-cell mediated rejection [TCMR; histology: 27 (13%) vs MMDx: 13 (6%)]. Most of the TCMR discrepancies were seen for histologic borderline/1A TCMR. AUC Curves for cfDNA and prediction of rejection were slightly better with MMDx (AUC=0.80; 95%CI: 0.74-0.86) vs. histology (AUC=0.75; 95%CI: 0.69-0.81). A cfDNA≤0.21% had similar sensitivity (~91%) to 'rule-out' rejection by histology and MMDx. Specificity was slightly higher with MMDx (92%) compared with histology (85%) to 'rule-in' rejection using cfDNA criterion≥1%. Strong positive quantitative correlations were observed between cfDNA scores and molecular acute kidney injury (AKI) for both 'rejection' and 'nonrejection' biopsies.

CONCLUSIONS

Molecular diagnostics using tissue gene expression and blood-based donor-derived cell-free DNA may add precision to some cases of traditional histology. The positive correlation of cfDNA with molecular AKI suggests a dose-dependent association with tissue injury irrespective of rejection characteristics.

A Three-Gene Peripheral Blood Potential Diagnosis Signature for Acute Rejection in Renal Transplantation

Background: Acute rejection (AR) remains a major issue that negatively impacts long-term allograft survival in renal transplantation. The current study aims to apply machine learning methods to develop a non-invasive diagnostic test for AR based on gene signature in peripheral blood. Methods: We collected blood gene expression profiles of 251 renal transplant patients with biopsy-proven renal status from three independent cohorts in the Gene Expression Omnibus database. After differential expression analysis and machine learning algorithms, selected biomarkers were applied to the least absolute shrinkage and selection operator (LASSO) logistic regression to construct a diagnostic model in the training cohort. The diagnostic ability of the model was further tested in validation cohorts. Gene set enrichment analysis and immune cell assessment were also conducted for further investigation. Results: A novel diagnostic model based on three genes (TSEN15, CAPRIN1 and PRR34-AS1) was constructed in the training cohort (AUC = 0.968) and successfully verified in the validation cohort (AUC = 0.925) with high accuracy. Moreover, the diagnostic model also showed a promising value in discriminating T cell-mediated rejection (TCMR) (AUC = 0.786). Functional enrichment analysis and immune cell evaluation demonstrated that the AR model was significantly correlated with adaptive immunity, especially T cell subsets and dendritic cells. Conclusion: We identified and validated a novel three-gene diagnostic model with high accuracy for AR in renal transplant patients, and the model also performed well in distinguishing TCMR. The current study provided a promising tool to be used as a precise and cost-effective non-invasive test in clinical practice.

Discovering novel injury features in kidney transplant biopsies associated with TCMR and donor aging

We previously characterized the molecular changes in acute kidney injury (AKI) and chronic kidney disease (CKD) in kidney transplant biopsies, but parenchymal changes selective for specific types of injury could be missed by such analyses. The present study searched for injury changes beyond AKI and CKD related to specific scenarios, including correlations with donor age. We defined injury using previously defined gene sets and classifiers and used principal component analysis to discover new injury dimensions. As expected, Dimension 1 distinguished normal vs. injury, and Dimension 2 separated early AKI from late CKD, correlating with time posttransplant. However, Dimension 3 was novel, distinguishing a set of genes related to epithelial polarity (e.g., PARD3) that were increased in early AKI and decreased in T cell-mediated rejection (TCMR) but not in antibody-mediated rejection. Dimension 3 was increased in kidneys from older donors and was particularly important in survival of early kidneys. Thus high Dimension 3 scores emerge as a previously unknown element in the kidney response-to-injury that affects epithelial polarity genes and is increased in AKI but depressed in TCMR, indicating that in addition to general injury elements, certain injury elements are selective for specific pathologic mechanisms. (ClinicalTrials.gov NCT01299168).

Factors associated with kidney graft survival in pure antibody-mediated rejection at the time of indication biopsy: Importance of parenchymal injury but not disease activity

We studied the relative association of clinical, histologic, and molecular variables with risk of kidney transplant failure after an indication biopsy, both in all kidneys and in kidneys with pure antibody-mediated rejection (ABMR). From a prospective study of 1679 biopsies with histologic and molecular testing, we selected one random biopsy per patient (N = 1120), including 321 with pure molecular ABMR. Diagnoses were associated with actuarial survival differences but not good predictions. Therefore we concentrated on clinical (estimated GFR [eGFR], proteinuria, time posttransplant, donor-specific antibody [DSA]) and molecular and histologic features reflecting injury (acute kidney injury [AKI] and atrophy-fibrosis [chronic kidney disease (CKD)] and rejection. For all biopsies, univariate analysis found that failure was strongly associated with low eGFR, AKI, CKD, and glomerular deterioration, but not with rejection activity. In molecular ABMR, the findings were similar: Molecular and histologic activity and DSA were not important compared with injury. Survival in DSA-negative and DSA-positive molecular ABMR was similar. Multivariate survival analysis confirmed the dominance of molecular AKI, CKD, and eGFR. Thus, at indication biopsy, the dominant predictors of failure, both in all kidneys and in ABMR, were related to molecular AKI and CKD and to eGFR, not rejection activity, presumably because rejection confers risk via injury.

Three-month course of intragraft transcriptional changes in kidney allografts with early histological minimal injury - a cohort study

The tubulitis with/without interstitial inflammation not meeting criteria for T-cell-mediated rejection (minimal allograft injury) is the most frequent histological findings in early transplant biopsies. The course of transcriptional changes in sequential kidney graft biopsies has not been studied yet. Molecular phenotypes were analyzed using the Molecular Microscope^® Diagnostic System (MMDx) in 46 indication biopsies (median 13 postoperative days) diagnosed as minimal allograft injury and in corresponding follow-up biopsies at 3 months. All 46 patients with minimal injury in early biopsy received steroid pulses. MMDx interpreted indication biopsies as no-rejection in 34/46 (74%), T-cell-mediated rejection (TCMR) in 4/46 (9%), antibody-mediated rejection in 6/46 (13%), and mixed rejection in 2/46 (4%) cases. Follow-up biopsies were interpreted by MMDx in 37/46 (80%) cases as no-rejection, in 4/46 (9%) as TCMR, and in 5/46 (11%) as mixed rejection. Follow-up biopsies showed a decrease in MMDx-assessed acute kidney injury (P = 0.001) and an increase of atrophy-fibrosis (P = 0.002). The most significant predictor of MMDx rejection scores in follow-up biopsies was the tubulitis classifier score in initial biopsies (AUC = 0.84, P = 0.002), confirmed in multivariate binary regression (OR = 16, P = 0.016). Molecular tubulitis score at initial biopsy has the potential to discriminate patients at risk for molecular rejection score at follow-up biopsy.

HNRNPA1-mediated exosomal sorting of miR-483-5p out of renal tubular epithelial cells promotes the progression of diabetic nephropathy-induced renal interstitial fibrosis

Diabetic nephropathy (DN) is a serious complication in type 1 and type 2 diabetes, and renal interstitial fibrosis plays a key role in DN progression. Here, we aimed to probe into the role and potential mechanism of miR-483-5p in DN-induced renal interstitial fibrosis. In this study, we corroborated that miR-483-5p expression was lessened in type 1 and type 2 diabetic mice kidney tissues and high glucose (HG)-stimulated tubular epithelial cells (TECs), and raised in the exosomes derived from renal tissues in type 1 and type 2 diabetic mice. miR-483-5p restrained the expressions of fibrosis-related genes in vitro and renal interstitial fibrosis in vivo. Mechanistically, miR-483-5p bound both TIMP2 and MAPK1, and TIMP2 and MAPK1 were bound up with the regulation of miR-483-5p on renal TECs under HG conditions. Importantly, HNRNPA1-mediated exosomal sorting transported cellular miR-483-5p out of TECs into the urine. Our results expounded that HNRNPA1-mediated exosomal sorting transported cellular miR-483-5p out of TECs into the urine, thus lessening the restraint of cellular miR-483-5p on MAPK1 and TIMP2 mRNAs, and ultimately boosting extracellular matrix deposition and the progression of DN-induced renal interstitial fibrosis.

Causes of Renal Allograft Injury in Recipients With Normal Donor-derived Cell-free DNA

Background.

Donor-derived cell-free DNA (dd-cfDNA) is a noninvasive biomarker for the early detection of organ transplant rejection and other causes of graft injury. For nonrejection renal injuries, there is little information about the performance characteristics of this biomarker. We highlight some of the possible causes of kidney injury that may arise in patients with normal dd-cfDNA levels.

Methods.

We performed a retrospective analysis of solitary renal transplant cases between January 2017 and November 2019. Those who had an abnormal laboratory or pathological finding within 1 mo of a normal dd-cfDNA test were selected. Subgroups were stratified for those who had normal or abnormal/rising serum creatinine, and differences between the groups were analyzed.

Results.

Of 414 individuals who received a kidney transplant, 24 (7.5%) had a total of 41 normal dd-cfDNA values and 51 abnormal laboratory tests or histologic findings. The most common graft-injuring event was BK virus viremia (24 of 51). Other abnormal findings included urinary traction infections (n = 4), CMV viremia (n = 4), and biopsies demonstrating antibody-mediated rejection (AMR) (n = 2), T cell–mediated rejection (n = 1), focal segmental glomerulosclerosis (n = 2), nondonor-specific antibody chronic AMR (n = 1), and interstitial fibrosis and tubular atrophy (n = 7). Subgroup analysis of those with normal dd-cfDNA and normal/stable versus abnormal/rising creatinine showed that BK virus viremia was the most common abnormal finding in both groups at 53% and 38% respectively. On biopsy, 1 case of acute T cell–mediated rejection (1B and 2B) was seen with normal/stable creatinine, whereas 1 of nonspecific C4d focally positive and 1 of nondonor-specific antibody AMR were seen with abnormal/rising creatinine.

Conclusions.

Low levels of serum dd-cfDNA do not preclude detection of active graft-injuring events and that subclinical injuries may be developing. Context is important in the interpretation of dd-cfDNA, so renal biopsy remains a part of the diagnostic pathway for allograft dysfunction and maintenance of allograft health.

Molecular Analysis of Renal Allograft Biopsies: Where Do We Stand and Where Are We Going?

A renal core biopsy for histological evaluation is the gold standard for diagnosing renal transplant pathology. However, renal biopsy interpretation is subjective and can render insufficient precision, making it difficult to apply a targeted therapeutic regimen for the individual patient. This warrants a need for additional methods assessing disease state in the renal transplant. Significant research activity has been focused on the role of molecular analysis in the diagnosis of renal allograft rejection. The identification of specific molecular expression patterns in allograft biopsies related to different types of allograft injury could provide valuable information about the processes underlying renal transplant dysfunction and can be used for the development of molecular classifier scores, which could improve our diagnostic and prognostic ability and could guide treatment. Molecular profiling has the potential to be more precise and objective than histological evaluation and may identify injury even before it becomes visible on histology, making it possible to start treatment at the earliest time possible. Combining conventional diagnostics (histology, serology, and clinical data) and molecular evaluation will most likely offer the best diagnostic approach. We believe that the use of state-of-the-art molecular analysis will have a significant impact in diagnostics after renal transplantation. In this review, we elaborate on the molecular phenotype of both acute and chronic T cell-mediated rejection and antibody-mediated rejection and discuss the additive value of molecular profiling in the setting of diagnosing renal allograft rejection and how this will improve transplant patient care.

Key driver genes as potential therapeutic targets in renal allograft rejection

Acute rejection (AR) in renal transplantation is an established risk factor for reduced allograft survival. Molecules with regulatory control among immune pathways of AR that are inadequately suppressed, despite standard-of-care immunosuppression, could serve as important targets for therapeutic manipulation to prevent rejection. Here, an integrative, network-based computational strategy incorporating gene expression and genotype data of human renal allograft biopsy tissue was applied, to identify the master regulators - the key driver genes (KDGs) - within dysregulated AR pathways. A 982-meta-gene signature with differential expression in AR versus non-AR was identified from a meta-analysis of microarray data from 735 human kidney allograft biopsy samples across 7 data sets. Fourteen KDGs were derived from this signature. Interrogation of 2 publicly available databases identified compounds with predicted efficacy against individual KDGs or a key driver-based gene set, respectively, which could be repurposed for AR prevention. Minocycline, a tetracycline antibiotic, was chosen for experimental validation in a murine cardiac allograft model of AR. Minocycline attenuated the inflammatory profile of AR compared with controls and when coadministered with immunosuppression prolonged graft survival. This study demonstrates that a network-based strategy, using expression and genotype data to predict KDGs, assists target prioritization for therapeutics in renal allograft rejection.

The molecular diagnosis of rejection in liver transplant biopsies: First results of the INTERLIVER study

Molecular diagnosis of rejection is emerging in kidney, heart, and lung transplant biopsies and could offer insights for liver transplant biopsies. We measured gene expression by microarrays in 235 liver transplant biopsies from 10 centers. Unsupervised archetypal analysis based on expression of previously annotated rejection-related transcripts identified 4 groups: normal "R1_normal " (N = 129), T cell-mediated rejection (TCMR) "R2_TCMR " (N = 37), early injury "R3_injury " (N = 61), and fibrosis "R4_late " (N = 8). Groups differed in median time posttransplant, for example, R3_injury 99 days vs R4_late 3117 days. R2_TCMR biopsies expressed typical TCMR-related transcripts, for example, intense IFNG-induced effects. R3_injury displayed increased expression of parenchymal injury transcripts (eg, hypoxia-inducible factor EGLN1). R4_late biopsies showed immunoglobulin transcripts and injury-related transcripts. R2_TCMR correlated with histologic rejection although with many discrepancies, and R4_late with fibrosis. R2_TCMR , R3_injury , and R4_late correlated with liver function abnormalities. Supervised classifiers trained on histologic rejection showed less agreement with histology than unsupervised R2_TCMR scores. No confirmed cases of clinical antibody-mediated rejection (ABMR) were present in the population, and strategies that previously revealed ABMR in kidney and heart transplants failed to reveal a liver ABMR phenotype. In conclusion, molecular analysis of liver transplant biopsies detects rejection, has the potential to resolve ambiguities, and could assist with immunosuppressive management.

Profiling of mRNA of interstitial fibrosis and tubular atrophy with subclinical inflammation in recipients after kidney transplantation

Interstitial fibrosis and tubular atrophy (IFTA) with inflammation (IFTA-I) is strongly correlated with kidney allograft failure. Diagnosis of IFTA-I accurately and early is critical to prevent graft failure and improve graft survival. In the current study, through analyzing the renal allograft biopsy in patients with stable function after kidney transplantation (STA), IFTA and IFTA-I group with semi-supervised principal components methods, we found that CD2, IL7R, CCL5 based signature could not only distinguish STA and IFTA-I well, but predict IFTA-I with a high degree of accuracy with an area under the curve (AUC) of 0.91 (P = 0.00023). Additionally, IRF8 demonstrated significant differences among STA, IFTA and IFTA-I groups, suggesting that IRF8 had the capacity to discriminate the different classifications of graft biopsies well. Also, with Kaplan-Meier and log-rank methods, we found that IRF8 could serve as the prognostic marker for renal graft failure in those biopsies without rejection (AUC = 0.75) and the recipients expressing high had a higher risk for renal graft loss (P < 0.0001). This research may provide new targets for therapeutic prevention and intervention for post-transplantation IFTA with or with inflammation.

Subclinical Inflammation in Renal Transplantation

The standardization of renal allograft pathology began in 1991 at the first Banff Conference held in Banff, Alberta, Canada. The first task of transplant pathologists, clinicians, and surgeons was to establish diagnostic criteria for T-cell-mediated rejection (TCMR). The histological threshold for this diagnosis was arbitrarily set at "i2t2": a mononuclear interstitial cell infiltrate present in at least 25% of normal parenchyma and >4 mononuclear cells within the tubular basement membrane of nonatrophic tubules. TCMR was usually found in dysfunctional grafts with an elevation in the serum creatinine; however, our group and others found this extent of inflammation in "routine" or "protocol" biopsies of normally functioning grafts: "subclinical" TCMR. The prevalence of TCMR is higher in the early months posttransplant and has decreased with the increased potency of current immunosuppressive agents. However, the pathogenicity of lesser degrees of inflammation under modern immunosuppression and the relation between ongoing inflammation and development of donor-specific antibody has renewed our interest in subclinical alloreactivity. Finally, the advances in our understanding of pretransplant risk assessment, and our increasing ability to monitor patients less invasively posttransplant, promises to usher in the era of precision medicine.

An age-independent gene signature for monitoring acute rejection in kidney transplantation

Acute rejection (AR) remains a significant problem that negatively impacts long-term renal allograft survival. Numerous therapies are used to prevent AR that differ by center and recipient age. This variability confounds diagnostic methods. Methods: To develop an age-independent gene signature for AR effective across a broad array of immunosuppressive regimens, we compiled kidney transplant biopsy (n=1091) and peripheral blood (n=392) gene expression profiles from 12 independent public datasets. After removing genes differentially expressed in pediatric and adult patients, we compared gene expression profiles from biopsy and peripheral blood samples of patients with AR to those who were stable (STA), using Mann-Whitney U Tests with validation in independent testing datasets. We confirmed this signature in pediatric and adult patients (42 AR and 47 STA) from our institutional biorepository. Results: We identified a novel age-independent gene network that identified AR from both kidney and blood samples. We developed a 90-probe set signature targeting 76 genes that differentiated AR from STA and found an 8 gene subset (DIP2C, ENOSF1, FBXO21, KCTD6, PDXDC1, REXO2, HLA-E, and RAB31) that was associated with AR. Conclusion: We used publicly available datasets to create a gene signature of AR that identified AR irrespective of immunosuppression regimen or recipient age. This study highlights a novel model to screen and validate biomarkers across multiple treatment regimens.

Identification of Candidate Biomarkers for Transplant Rejection from Transcriptome Data: A Systematic Review

BACKGROUND

Traditional methods for rejection control in transplanted patients are considered invasive, risky, and prone to sampling errors. Using molecular biomarkers as an alternative protocol to biopsies, for monitoring rejection may help to mitigate some of these problems, increasing the survival rates and well-being of patients. Recent advances in omics technologies provide an opportunity for screening new molecular biomarkers to identify those with clinical utility.

OBJECTIVE

This systematic literature review (SLR) aimed to summarize existing evidence derived from large-scale expression profiling regarding differentially expressed mRNA and miRNA in graft rejection, highlighting potential molecular biomarkers in transplantation.

METHODS

The study was conducted following PRISMA methodology and the BiSLR guide for performing SLR in bioinformatics. PubMed, ScienceDirect, and EMBASE were searched for publications from January 2001 to January 2018, and studies (i) aiming at the identification of transplant rejection biomarkers, (ii) including human subjects, and (iii) applying methodologies for differential expression analysis from large-scale expression profiling were considered eligible. Differential expression patterns reported for genes and miRNAs in rejection were summarized from both cross-organ and organ-specific perspectives, and pathways enrichment analysis was performed for candidate biomarkers to interrogate their functional role in transplant rejection.

RESULTS

A total of 821 references were collected, resulting in 604 studies after removal of duplicates. After application of inclusion and exclusion criteria, 33 studies were included in our analysis. Among the 1517 genes and 174 miRNAs identifed, CXCL9, CXCL10, STAT1, hsa-miR-142-3p, and hsa-miR-155 appeared to be particularly promising as biomarkers in transplantation, with an increased expression associated with transplant rejection in multiple organs. In addition, hsa-miR-28-5p was consistently decreased in samples taken from rejected organs.

CONCLUSION

Despite the need for further research to fill existing knowledge gaps, transcriptomic technologies have a relevant role in the discovery of accurate biomarkers for transplant rejection diagnostics. Studies have reported consistent evidence of differential expression associated with transplant rejection, although issues such as experimental heterogeneity hinder a more systematic characterization of observed molecular changes. Special attention has been giving to large-scale mRNA expression profiling in rejection, whereas there is still room for improvements in the characterization of miRnome in this condition.

PROSPERO REGISTRATION NUMBER

CRD42018083321.

Molecular Assessment of Kidney Allografts: Are We Closer to a Daily Routine?

Kidney allograft pathology assessment has been traditionally based on clinical and histological criteria. Despite improvements in Banff histological classification, the diagnostics in particular cases is problematic reflecting a complex pathogenesis of graft injuries. With the advent of molecular techniques, polymerase-chain reaction, oligo- and microarray technologies allowed to study molecular phenotypes of graft injuries, especially acute and chronic rejections. Moreover, development of the molecular microscope diagnostic system (MMDx) to assess kidney graft biopsies, represents the first clinical application of a microarray-based method in transplantation. Whether MMDx may replace conventional pathology is the subject of ongoing research, however this platform is particularly useful in complex histological findings and may help clinicians to guide the therapy.

Identifying 4 Novel lncRNAs as Potential Biomarkers for Acute Rejection and Graft Loss of Renal Allograft

Acute rejection (AR) after kidney transplant is one of the major obstacles to obtain ideal graft survival. Reliable molecular biomarkers for AR and renal allograft loss are lacking. This study was performed to identify novel long noncoding RNAs (lncRNAs) for diagnosing AR and predicting the risk of graft loss. The several microarray datasets with AR and nonrejection specimens of renal allograft downloaded from Gene Expression Omnibus database were analyzed to screen differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). Univariate and multivariate Cox regression analyses were used to identify optimal prognosis-related DElncRNAs for constructing a risk score model. 39 common DElncRNAs and 185 common DEmRNAs were identified to construct a lncRNA-mRNA regulatory relationship network. DElncRNAs were revealed to regulate immune cell activation and proliferation. Then, 4 optimal DElncRNAs, ATP1A1-AS1, CTD-3080P12.3, EMX2OS, and LINC00645, were selected from 17 prognostic DElncRNAs to establish the 4-lncRNA risk score model. In the training set, the high-risk patients were more inclined to graft loss than the low-risk patients. Time-dependent receiver operating characteristics analysis revealed the model had good sensitivity and specificity in prediction of 1-, 2-, and 3-year graft survival after biopsy (AUC = 0.891, 0.836, and 0.733, respectively). The internal testing set verified the result well. Gene set enrichment analysis which expounded NOD-like receptor, the Toll-like receptor signaling pathways, and other else playing important role in immune response was enriched by the 4 lncRNAs. Allograft-infiltrating immune cells analysis elucidated the expression of 4 lncRNAs correlated with gamma delta T cells and eosinophils, etc. Our study identified 4 novel lncRNAs as potential biomarkers for AR of renal allograft and constructed a lncRNA-based model for predicting the risk of graft loss, which would provide new insights into mechanisms of AR.

Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers

We previously reported a system for assessing rejection in kidney transplant biopsies using microarray-based gene expression data, the Molecular Microscope^® Diagnostic System (MMDx). The present study was designed to optimize the accuracy and stability of MMDx diagnoses by replacing single machine learning classifiers with ensembles of diverse classifier methods. We also examined the use of automated report sign-outs and the agreement between multiple human interpreters of the molecular results. Ensembles generated diagnoses that were both more accurate than the best individual classifiers, and nearly as stable as the best, consistent with expectations from the machine learning literature. Human experts had ≈93% agreement (balanced accuracy) signing out the reports, and random forest-based automated sign-outs showed similar levels of agreement with the human experts (92% and 94% for predicting the expert MMDx sign-outs for T cell-mediated (TCMR) and antibody-mediated rejection (ABMR), respectively). In most cases disagreements, whether between experts or between experts and automated sign-outs, were in biopsies near diagnostic thresholds. Considerable disagreement with histology persisted. The balanced accuracies of MMDx sign-outs for histology diagnoses of TCMR and ABMR were 73% and 78%, respectively. Disagreement with histology is largely due to the known noise in histology assessments (ClinicalTrials.gov NCT01299168).

SUBSTRA: Supervised Bayesian Patient Stratification

Motivation

Patient stratification methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratification methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals.

Results

We introduce a Bayesian method called SUBSTRA that uses regularized biclustering to identify patient subtypes and interpretable subtype-specific transcript clusters. The method iteratively re-weights feature importance to optimize phenotype prediction performance by producing more phenotype-relevant patient subtypes. We investigate the performance of SUBSTRA in finding relevant features using simulated data and successfully benchmark it against state-of-the-art unsupervised stratification methods and supervised alternatives. Moreover, SUBSTRA achieves predictive performance competitive with the supervised benchmark methods and provides interpretable transcriptional features in diverse biological settings, such as drug response prediction, cancer diagnosis, or kidney transplant rejection.

Availability and implementation

The R code of SUBSTRA is available at https://github.com/sahandk/SUBSTRA.

Supplementary information

Supplementary data are available at Bioinformatics online.

Comparative transcriptome analysis of peripheral blood mononuclear cells in renal transplant recipients in everolimus- and tacrolimus-based immunosuppressive therapy

To better define the biological impact of immunosuppression on peripheral blood mononuclear cells (PBMC), we employed RNASeq analysis to compare the whole transcriptomic profile of a group of renal transplant recipients undergoing maintenance treatment with Everolimus (EVE) with those treated with Tacrolimus (TAC). Then, obtained results were validated by classical biomolecular methodologies. The statistical analysis allowed the identification of four genes discriminating the 2 study groups: Sushi Domain Containing 4 (SUSD4, P = 0.02), T Cell Leukemia/Lymphoma 1A (TCL1A, P = 0.02), adhesion G protein-coupled receptor E3 (ADGRE3, P = 0.01), Immunoglobulin Heavy Constant Gamma 3 (IGHG3, P = 0.03). All of them were significantly down-regulated in patients treated with EVE compared to TAC. The Area under Receiver Operating Characteristic (AUROC) of the final model based on these 4 genes was 73.1% demonstrating its good discriminative power. RT-PCR and ELISA validated transcriptomic results. Additionally, an in vitro model confirmed that EVE significantly down-regulates (P<0.001) TCL1A, SUSD4, ADGRE3 and IgHG3 in PBMCs as well as in T cells and monocytes isolated from healthy subjects. Taken together, our data, revealed, for the first time, a new four gene-based transcriptomic fingerprint down-regulated by EVE in PBMCs of renal transplant patients that could improve the available knowledge regarding some of the biological/cellular effects of the mTOR-Is (including their antineoplastic and immune-regulatory properties).

Molecular phenotype of kidney transplant indication biopsies with inflammation in scarred areas

In kidney transplant biopsies, inflammation in areas of atrophy-fibrosis (i-IFTA) is associated with increased risk of failure, presumably because inflammation is evoked by recent parenchymal injury from rejection or other insults, but some cases also have rejection. The present study explored the frequency of rejection in i-IFTA, by using histology Banff 2015 and a microarray-based molecular diagnostic system (MMDx). In unselected indication biopsies (108 i-IFTA, 73 uninflamed IFTA [i0-IFTA], and 53 no IFTA), i-IFTA biopsies occurred later, showed more scarring, and had more antibody-mediated rejection (ABMR) based on histology (28%) and MMDx (45%). T cell-mediated rejection (TCMR) was infrequent in i-IFTA based on histology (8%) and MMDx (16%). Twelve i-IFTA biopsies (11%) had molecular TCMR not diagnosed by histology, although 6 were called borderline and almost all had histologic TCMR lesions. The prominent feature of i-IFTA biopsies was molecular injury (eg, acute kidney injury [AKI] transcripts). In multivariate analysis of biopsies >1 year posttransplant, the strongest associations with graft loss were AKI transcripts and histologic atrophy-scarring; i-IFTA was not significant when molecular AKI was included. We conclude that i-IFTA in indication biopsies reflects recent/ongoing parenchymal injury, often with concomitant ABMR but few with TCMR. Thus, the application of Banff i-IFTA in the population of late biopsies needs to be reconsidered.