Association of Predicted HLA T-Cell Epitope Targets and T-Cell-Mediated Rejection After Kidney Transplantation.

RATIONALE & OBJECTIVE: The relationship between human leukocyte antigen (HLA) molecular mismatches and T-cell-mediated rejection (TCMR) is unknown. We investigated the associations between the different donor HLA-derived T-cell targets and the occurrence of TCMR and borderline histologic changes suggestive of TCMR after kidney transplantation. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: All kidney transplant recipients at a single center between 2004 and 2013 with available biopsy data and a DNA sample for high-resolution HLA donor/recipient typing (N = 893). EXPOSURE: Scores calculated by the HLA matching algorithm PIRCHE-II and HLA eplet mismatches. OUTCOME: TCMR, borderline changes suggestive of TCMR, and allograft failure. ANALYTICAL APPROACH: Multivariable cause-specific hazards models were fit to characterize the association between HLA epitopes targets and study outcomes. RESULTS: We found 277 patients developed TCMR, and 134 developed only borderline changes suggestive of TCMR on at least 1 biopsy. In multivariable analyses, only the PIRCHE-II scores for HLA-DRB1 and HLA-DQB1 were independently associated with the occurrence of TCMR and with allograft failure; this was not the case for HLA class I molecules. If restricted to rejection episodes within the first 3 months after transplantation, only the T-cell epitope targets originating from the donor's HLA-DRB1 and HLA-DQB1, but not class I molecules, were associated with the early acute TCMR. Also, the median PIRCHE-II score for HLA class II was statistically different between the patients with TCMR compared to the patients without TCMR (129 [IQR, 60-240] vs 201 [IQR, 96-298], respectively; P < 0.0001). These differences were not observed for class I PIRCHE-II scores. LIMITATIONS: Observational clinical data and residual confounding. CONCLUSIONS: In the absence of HLA-DSA, HLA class II but not class I mismatches are associated with early episodes of acute TCMR and allograft failure. This suggests that current immunosuppressive therapies are largely able to abort the most deleterious HLA class I-directed alloimmune processes; however, alloresponses against HLA-DRB1 and HLA-DQB1 molecular mismatches remain insufficiently suppressed. PLAIN-LANGUAGE SUMMARY: Genetic differences in the human leukocyte antigen (HLA) complex between kidney transplant donors and recipients play a central role in T-cell-mediated rejection (TCMR), which can lead to failure of the transplanted kidney. Evaluating this genetic disparity (mismatch) in the HLA complex at the molecular (epitope) level could contribute to better prediction of the immune response to the donor organ posttransplantation. We investigated the associations of the different donor HLA-derived T-cell epitope targets and scores obtained from virtual crossmatch algorithms with the occurrence of TCMR, borderline TCMR, and graft failure after kidney transplantation after taking into account the influence of donor-specific anti-HLA antibodies. This study illustrates the greater importance of the molecular mismatches in class II molecules compared to class I HLA molecules.

Eplet Mismatch Load and De Novo Occurrence of Donor-Specific Anti-HLA Antibodies, Rejection, and Graft Failure after Kidney Transplantation: An Observational Cohort Study.

BACKGROUND: In kidney transplantation, evaluating mismatches of HLA eplets-small patches of surface-exposed amino acids of the HLA molecule-instead of antigen mismatches might offer a better approach to assessing donor-recipient HLA incompatibility and improve risk assessment and prediction of transplant outcomes. METHODS: To evaluate the effect of number of eplet mismatches (mismatch load) on de novo formation of donor-specific HLA antibodies (DSAs) and transplant outcomes, we conducted a cohort study that included consecutive adult kidney recipients transplanted at a single center from March 2004 to February 2013. We performed retrospective high-resolution genotyping of HLA loci of 926 transplant pairs and used the HLAMatchmaker computer algorithm to count HLA eplet mismatches. RESULTS: De novo DSAs occurred in 43 (4.6%) patients. Multivariable analysis showed a significant independent association between antibody-verified eplet mismatch load and de novo DSA occurrence and graft failure, mainly explained by DQ antibody-verified eplet effects. The association with DQ antibody-verified eplet mismatches was linear, without a safe threshold at which de novo DSA did not occur. Odds for T cell- or antibody-mediated rejection increased by 5% and 12%, respectively, per antibody-verified DQ eplet mismatch. CONCLUSIONS: Eplet mismatches in HLA-DQ confer substantial risk for de novo DSA formation, graft rejection, and graft failure after kidney transplantation. Mismatches in other loci seem to have less effect. The results suggest that antibody-verified HLA-DQ eplet mismatch load could be used to guide personalized post-transplant immunosuppression. Adoption of molecular matching for DQA1 and DQB1 alleles could also help to minimize de novo DSA formation and potentially improve transplant outcomes.

Sensitive HLA antibody testing and the risk of antibody-mediated rejection and graft failure.

Solid phase detection and identification of HLA antibodies in kidney transplantation currently relies on single antigen bead (Luminex®) assays, which is more sensitive than the previously used enzyme-linked immunosorbent assays (ELISA). To evaluate the impact of more sensitive HLA testing on antibody-mediated rejection (AMR) occurrence and allograft survival, we analysed 1818 renal allograft recipients transplanted between March 2004 and May 2021. In 2008, solid phase testing switched from ELISA to Luminex. We included 393 (21.6%) transplantations before and 1425 (78.4%) transplantations after transition from ELISA- to Luminex-based testing. For this study, bio-banked ELISA era samples were tested retrospectively with Luminex. Significantly less pretransplant DSA were found in patients transplanted with pre-existing HLA antibodies in the Luminex (109/387) versus the ELISA period (43/90) (28% vs. 48%, p < 0.01). Throughout histological follow-up, 169 of 1818 (9.3%) patients developed AMR. After implementing Luminex-based testing, the rate of AMR significantly decreased (p = 0.003). However, incidence of graft failure did not significantly differ between both eras. In conclusion, less patients with pretransplant DSA were transplanted since the implementation of Luminex HLA testing. Transition from ELISA- to Luminex-based HLA testing was associated with a significant decrease in AMR occurrence post-transplantation. Since the decline of AMR did not translate into improved graft survival, Luminex-based testing has the added value of preventing low-risk AMR cases. Therefore, Luminex' high sensitivity must be balanced against waiting time for a suitable organ.

A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma.

An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.

An ECHO of Cartilage: In Silico Prediction of Combinatorial Treatments to Switch Between Transient and Permanent Cartilage Phenotypes With Ex Vivo Validation.

A fundamental question in cartilage biology is: what determines the switch between permanent cartilage found in the articular joints and transient hypertrophic cartilage that functions as a template for bone? This switch is observed both in a subset of OA patients that develop osteophytes, as well as in cell-based tissue engineering strategies for joint repair. A thorough understanding of the mechanisms regulating cell fate provides opportunities for treatment of cartilage disease and tissue engineering strategies. The objective of this study was to understand the mechanisms that regulate the switch between permanent and transient cartilage using a computational model of chondrocytes, ECHO. To investigate large signaling networks that regulate cell fate decisions, we developed the software tool ANIMO, Analysis of Networks with interactive Modeling. In ANIMO, we generated an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 proteins with 200 interactions. We called this model ECHO, for executable chondrocyte. Previously, we showed that ECHO could be used to characterize mechanisms of cell fate decisions. ECHO was first developed based on a Boolean model of growth plate. Here, we show how the growth plate Boolean model was translated to ANIMO and how we adapted the topology and parameters to generate an articular cartilage model. In ANIMO, many combinations of overactivation/knockout were tested that result in a switch between permanent cartilage (SOX9+) and transient, hypertrophic cartilage (RUNX2+). We used model checking to prioritize combination treatments for wet-lab validation. Three combinatorial treatments were chosen and tested on metatarsals from 1-day old rat pups that were treated for 6 days. We found that a combination of IGF1 with inhibition of ERK1/2 had a positive effect on cartilage formation and growth, whereas activation of DLX5 combined with inhibition of PKA had a negative effect on cartilage formation and growth and resulted in increased cartilage hypertrophy. We show that our model describes cartilage formation, and that model checking can aid in choosing and prioritizing combinatorial treatments that interfere with normal cartilage development. Here we show that combinatorial treatments induce changes in the zonal distribution of cartilage, indication possible switches in cell fate. This indicates that simulations in ECHO aid in describing pathologies in which switches between cell fates are observed, such as OA.

ECHO, the executable CHOndrocyte: A computational model to study articular chondrocytes in health and disease.

Computational modeling can be used to investigate complex signaling networks in biology. However, most modeling tools are not suitable for molecular cell biologists with little background in mathematics. We have built a visual-based modeling tool for the investigation of dynamic networks. Here, we describe the development of computational models of cartilage development and osteoarthritis, in which a panel of relevant signaling pathways are integrated. In silico experiments give insight in the role of each of the pathway components and reveal which perturbations may deregulate the basal healthy state of cells and tissues. We used a previously developed computational modeling tool Analysis of Networks with Interactive Modeling (ANIMO) to generate an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 nodes and 200 interactions. We performed in silico experiments to characterize molecular mechanisms of cell fate decisions. The model was used to mimic biological scenarios during cell differentiation using RNA-sequencing data of a variety of stem cell sources as input. In a case-study, we wet-lab-tested the model-derived hypothesis that expression of DKK1 (Dickkopf-1) and FRZB (Frizzled related protein, WNT antagonists) and GREM1 (gremlin 1, BMP antagonist) prevents IL1ß (Interleukin 1 beta)-induced MMP (matrix metalloproteinase) expression, thereby preventing cartilage degeneration, at least in the short term. We found that a combination of DKK1, FRZB and GREM1 may play a role in modulating the effects of IL1ß induced inflammation in human primary chondrocytes.

Computational Modeling and Reverse Engineering to Reveal Dominant Regulatory Interactions Controlling Osteochondral Differentiation: Potential for Regenerative Medicine.

The specialization of cartilage cells, or chondrogenic differentiation, is an intricate and meticulously regulated process that plays a vital role in both bone formation and cartilage regeneration. Understanding the molecular regulation of this process might help to identify key regulatory factors that can serve as potential therapeutic targets, or that might improve the development of qualitative and robust skeletal tissue engineering approaches. However, each gene involved in this process is influenced by a myriad of feedback mechanisms that keep its expression in a desirable range, making the prediction of what will happen if one of these genes defaults or is targeted with drugs, challenging. Computer modeling provides a tool to simulate this intricate interplay from a network perspective. This paper aims to give an overview of the current methodologies employed to analyze cell differentiation in the context of skeletal tissue engineering in general and osteochondral differentiation in particular. In network modeling, a network can either be derived from mechanisms and pathways that have been reported in the literature (knowledge-based approach) or it can be inferred directly from the data (data-driven approach). Combinatory approaches allow further optimization of the network. Once a network is established, several modeling technologies are available to interpret dynamically the relationships that have been put forward in the network graph (implication of the activation or inhibition of certain pathways on the evolution of the system over time) and to simulate the possible outcomes of the established network such as a given cell state. This review provides for each of the aforementioned steps (building, optimizing, and modeling the network) a brief theoretical perspective, followed by a concise overview of published works, focusing solely on applications related to cell fate decisions, cartilage differentiation and growth plate biology. Particular attention is paid to an in-house developed example of gene regulatory network modeling of growth plate chondrocyte differentiation as all the aforementioned steps can be illustrated. In summary, this paper discusses and explores a series of tools that form a first step toward a rigorous and systems-level modeling of osteochondral differentiation in the context of regenerative medicine.

A Qualitative Model of the Differentiation Network in Chondrocyte Maturation: A Holistic View of Chondrocyte Hypertrophy.

Differentiation of chondrocytes towards hypertrophy is a natural process whose control is essential in endochondral bone formation. It is additionally thought to play a role in several pathophysiological processes, with osteoarthritis being a prominent example. We perform a dynamic analysis of a qualitative mathematical model of the regulatory network that directs this phenotypic switch to investigate the influence of the individual factors holistically. To estimate the stability of a SOX9 positive state (associated with resting/proliferation chondrocytes) versus a RUNX2 positive one (associated with hypertrophy) we employ two measures. The robustness of the state in canalisation (size of the attractor basin) is assessed by a Monte Carlo analysis and the sensitivity to perturbations is assessed by a perturbational analysis of the attractor. Through qualitative predictions, these measures allow for an in silico screening of the effect of the modelled factors on chondrocyte maintenance and hypertrophy. We show how discrepancies between experimental data and the model's results can be resolved by evaluating the dynamic plausibility of alternative network topologies. The findings are further supported by a literature study of proposed therapeutic targets in the case of osteoarthritis.

A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks.

Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model's scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour.

Relating the chondrocyte gene network to growth plate morphology: from genes to phenotype.

During endochondral ossification, chondrocyte growth and differentiation is controlled by many local signalling pathways. Due to crosstalks and feedback mechanisms, these interwoven pathways display a network like structure. In this study, a large-scale literature based logical model of the growth plate network was developed. The network is able to capture the different states (resting, proliferating and hypertrophic) that chondrocytes go through as they progress within the growth plate. In a first corroboration step, the effect of mutations in various signalling pathways of the growth plate network was investigated.