Advancing mouse models for transplantation research.

Mouse models have been instrumental in understanding mechanisms of transplant rejection and tolerance, but cross-study reproducibility and translation of experimental findings into effective clinical therapies are issues of concern. The Mouse Models in Transplantation symposium gathered scientists and physician-scientists involved in basic and clinical research in transplantation to discuss the strengths and limitations of mouse transplant models and strategies to enhance their utility. Participants recognized that increased procedure standardization, including the use of prespecified, defined endpoints, and statistical power analyses, would benefit the field. They also discussed the generation of new models that incorporate environmental and genetic variables affecting clinical outcomes as potentially important. If implemented, these strategies are expected to improve the reproducibility of mouse studies and increase their translation to clinical trials and, ideally, new Food and Drug Administration-approved drugs.

Defining the Need for Causal Inference to Understand the Impact of Social Determinants of Health: A Primer on Behalf of the Consortium for the Holistic Assessment of Risk in Transplantation (CHART).

Objective: This study aims to introduce key concepts and methods that inform the design of studies that seek to quantify the causal effect of social determinants of health (SDOH) on access to and outcomes following organ transplant. Background: The causal pathways between SDOH and transplant outcomes are poorly understood. This is partially due to the unstandardized and incomplete capture of the complex interactions between patients, their neighborhood environments, the tertiary care system, and structural factors that impact access and outcomes. Designing studies to quantify the causal impact of these factors on transplant access and outcomes requires an understanding of the fundamental concepts of causal inference. Methods: We present an overview of fundamental concepts in causal inference, including the potential outcomes framework and direct acyclic graphs. We discuss how to conceptualize SDOH in a causal framework and provide applied examples to illustrate how bias is introduced. Results: There is a need for direct measures of SDOH, increased measurement of latent and mediating variables, and multi-level frameworks for research that examine health inequities across multiple health systems to generalize results. We illustrate that biases can arise due to socioeconomic status, race/ethnicity, and incongruencies in language between the patient and clinician. Conclusions: Progress towards an equitable transplant system requires establishing causal pathways between psychosocial risk factors, access, and outcomes. This is predicated on accurate and precise quantification of social risk, best facilitated by improved organization of health system data and multicenter efforts to collect and learn from it in ways relevant to specialties and service lines.

The anti-CD40L monoclonal antibody AT-1501 promotes islet and kidney allograft survival and function in nonhuman primates.

Prior studies of anti-CD40 ligand (CD40L)-based immunosuppression demonstrated effective prevention of islet and kidney allograft rejection in nonhuman primate models; however, clinical development was halted because of thromboembolic complications. An anti-CD40L-specific monoclonal antibody, AT-1501 (Tegoprubart), was engineered to minimize risk of thromboembolic complications by reducing binding to Fcγ receptors expressed on platelets while preserving binding to CD40L. AT-1501 was tested in both a cynomolgus macaque model of intrahepatic islet allotransplantation and a rhesus macaque model of kidney allotransplantation. AT-1501 monotherapy led to long-term graft survival in both islet and kidney transplant models, confirming its immunosuppressive potential. Furthermore, AT-1501-based regimens after islet transplant resulted in higher C-peptide, greater appetite leading to weight gain, and reduced occurrence of cytomegalovirus reactivation compared with conventional immunosuppression. These data support AT-1501 as a safe and effective agent to promote both islet and kidney allograft survival and function in nonhuman primate models, warranting further testing in clinical trials.

Development, Deployment, and Implementation of a Machine Learning Surgical Case Length Prediction Model and Prospective Evaluation.

OBJECTIVE: To implement a machine learning model using only the restricted data available at case creation time to predict surgical case length for multiple services at different locations. BACKGROUND: The operating room is one of the most expensive resources in a health system, estimated to cost $22 to $133 per minute and generate about 40% of hospital revenue. Accurate prediction of surgical case length is necessary for efficient scheduling and cost-effective utilization of the operating room and other resources. METHODS: We introduced a similarity cascade to capture the complexity of cases and surgeon influence on the case length and incorporated that into a gradient-boosting machine learning model. The model loss function was customized to improve the balance between over- and under-prediction of the case length. A production pipeline was created to seamlessly deploy and implement the model across our institution. RESULTS: The prospective analysis showed that the model output was gradually adopted by the schedulers and outperformed the scheduler-predicted case length from August to December 2022. In 33,815 surgical cases across outpatient and inpatient platforms, the operational implementation predicted 11.2% fewer underpredicted cases and 5.9% more cases within 20% of the actual case length compared with the schedulers and only overpredicted 5.3% more. The model assisted schedulers to predict 3.4% more cases within 20% of the actual case length and 4.3% fewer underpredicted cases. CONCLUSIONS: We created a unique framework that is being leveraged every day to predict surgical case length more accurately at case posting time and could be potentially utilized to deploy future machine learning models.

American Society of Transplant Surgeons-American Society of Transplantation report of FDA meeting on regulatory expectations for xenotransplantation products.

In June 2022, the US Food and Drug Administration Center for Biologics Evaluation and Research held the 73rd meeting of the Cellular, Tissue, and Gene Therapies Advisory Committee for public discussion of regulatory expectations for xenotransplantation products. The members of a joint American Society of Transplant Surgeons/American Society of Transplantation committee on xenotransplantation compiled a meeting summary focusing on 7 topics believed to be key by the committee: (1) preclinical evidence supporting progression to a clinical trial, (2) porcine kidney function, (3) ethical aspects, (4) design of initial clinical trials, (5) infectious disease issues, (6) industry perspectives, and (7) regulatory oversight.

Immune Phenotype and Postoperative Complications After Elective Surgery.

OBJECTIVES: To characterize and quantify accumulating immunologic alterations, pre and postoperatively in patients undergoing elective surgical procedures. BACKGROUND: Elective surgery is an anticipatable, controlled human injury. Although the human response to injury is generally stereotyped, individual variability exists. This makes surgical outcomes less predictable, even after standardized procedures, and may provoke complications in patients unable to compensate for their injury. One potential source of variation is found in immune cell maturation, with phenotypic changes dependent on an individual's unique, lifelong response to environmental antigens. METHODS: We enrolled 248 patients in a prospective trial facilitating comprehensive biospecimen and clinical data collection in patients scheduled to undergo elective surgery. Peripheral blood was collected preoperatively, and immediately on return to the postanesthesia care unit. Postoperative complications that occurred within 30 days after surgery were captured. RESULTS: As this was an elective surgical cohort, outcomes were generally favorable. With a median follow-up of 6 months, the overall survival at 30 days was 100%. However, 20.5% of the cohort experienced a postoperative complication (infection, readmission, or system dysfunction). We identified substantial heterogeneity of immune senescence and terminal differentiation phenotypes in surgical patients. More importantly, phenotypes indicating increased T-cell maturation and senescence were associated with postoperative complications and were evident preoperatively. CONCLUSIONS: The baseline immune repertoire may define an immune signature of resilience to surgical injury and help predict risk for surgical complications.

A random forest model using flow cytometry data identifies pulmonary infection after thoracic injury.

BACKGROUND: Thoracic injury can cause impairment of lung function leading to respiratory complications such as pneumonia (PNA). There is increasing evidence that central memory T cells of the adaptive immune system play a key role in pulmonary immunity. We sought to explore whether assessment of cell phenotypes using flow cytometry (FCM) could be used to identify pulmonary infection after thoracic trauma. METHODS: We prospectively studied trauma patients with thoracic injuries who survived >48 hours at a Level 1 trauma center from 2014 to 2020. Clinical and FCM data from serum samples collected within 24 hours of admission were considered as potential variables. Random forest and logistic regression models were developed to estimate the risk of hospital-acquired and ventilator-associated PNA. Variables were selected using backwards elimination, and models were internally validated with leave-one-out. RESULTS: Seventy patients with thoracic injuries were included (median age, 35 years [interquartile range (IQR), 25.25-51 years]; 62.9% [44 of 70] male, 61.4% [42 of 70] blunt trauma). The most common injuries included rib fractures (52 of 70 [74.3%]) and pulmonary contusions (26 of 70 [37%]). The incidence of PNA was 14 of 70 (20%). Median Injury Severity Score was similar for patients with and without PNA (30.5 [IQR, 22.6-39.3] vs. 26.5 [IQR, 21.6-33.3]). The final random forest model selected three variables (Acute Physiology and Chronic Health Evaluation score, highest pulse rate in first 24 hours, and frequency of CD4 + central memory cells) that identified PNA with an area under the curve of 0.93, sensitivity of 0.91, and specificity of 0.88. A logistic regression with the same features had an area under the curve of 0.86, sensitivity of 0.76, and specificity of 0.85. CONCLUSION: Clinical and FCM data have diagnostic utility in the early identification of patients at risk of nosocomial PNA following thoracic injury. Signs of physiologic stress and lower frequency of central memory cells appear to be associated with higher rates of PNA after thoracic trauma. LEVEL OF EVIDENCE: Diagnostic Test/Criteria; Level IV.

Xenorecognition and costimulation of porcine endothelium-derived extracellular vesicles in initiating human porcine-specific T cell immune responses.

Porcine vascular endothelial cells (PECs) form a mechanistic centerpiece of xenograft rejection. Here, we determined that resting PECs release swine leukocyte antigen class I (SLA-I) but not swine leukocyte antigen class-II DR (SLA-DR) expressing extracellular vesicles (EVs) and investigated whether these EVs proficiently initiate xenoreactive T cell responses via direct xenorecognition and costimulation. Human T cells acquired SLA-I+ EVs with or without direct contact to PECs, and these EVs colocalized with T cell receptors. Although interferon gamma-activated PECs released SLA-DR+ EVs, the binding of SLA-DR+ EVs to T cells was sparse. Human T cells demonstrated low levels of proliferation without direct contact to PECs, but marked T cell proliferation was induced following exposure to EVs. EV-induced proliferation proceeded independent of monocytes/macrophages, suggesting that EVs delivered both a T cell receptor signal and costimulation. Costimulation blockade targeting B7, CD40L, or CD11a significantly reduced T cell proliferation to PEC-derived EVs. These findings indicate that endothelial-derived EVs can directly initiate T cell-mediated immune responses, and suggest that inhibiting the release of SLA-I EVs from organ xenografts has the potential to modify the xenograft rejection. We propose a secondary-direct pathway for T cell activation via xenoantigen recognition/costimulation by endothelial-derived EVs.

A comparison of deprivation indices and application to transplant populations.

The choice of deprivation index can influence conclusions drawn regarding the extent of deprivation within a community and the identification of the most deprived communities in the United States. This study aimed to determine the degree of correlation among deprivation indices commonly used to characterize transplant populations. We used a retrospective cohort consisting of adults listed for liver or kidney transplants between 2008 and 2018 to compare 4 deprivation indices: neighborhood deprivation index, social deprivation index (SDI), area deprivation index, and social vulnerability index. Pairwise correlation between deprivation indices by transplant referral regions was measured using Spearman correlations of population-weighted medians and upper quartiles. In total, 52 individual variables were used among the 4 deprivation indices with 25% overlap. For both organs, the correlation between the population-weighted 75th percentile of the deprivation indices by transplant referral region was highest between SDI and social vulnerability index (liver and kidney, 0.93) and lowest between area deprivation index and SDI (liver, 0.19 and kidney, 0.15). The choice of deprivation index affects the applicability of research findings across studies examining the relationship between social risk and clinical outcomes. Appropriate application of these measures to transplant populations requires careful index selection based on the intended use and included variable relevance.

Age and Comorbidities Predict COVID-19 Outcome, Regardless of Innate Immune Response Severity: A Single Institutional Cohort Study.

The COVID-19 pandemic has claimed over eight hundred thousand lives in the United States alone, with older individuals and those with comorbidities being at higher risk of severe disease and death. Although severe acute respiratory syndrome coronavirus 2-induced hyperinflammation is one of the mechanisms underlying the high mortality, the association between age and innate immune responses in COVID-19 mortality remains unclear. DESIGN: Flow cytometry of fresh blood and multiplexed inflammatory chemokine measurements of sera were performed on samples collected longitudinally from our cohort. Aggregate impact of comorbid conditions was calculated with the Charlson Comorbidity Index, and association between patient factors and outcomes was calculated via Cox proportional hazard analysis and repeated measures analysis of variance. SETTING: A cohort of severely ill COVID-19 patients requiring ICU admission was followed prospectively. PATIENTS: In total, 67 patients (46 male, age 59 ± 14 yr) were included in the study. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Mortality in our cohort was 41.8%. We identified older age (hazard ratio [HR] 1.09 [95% CI 1.07-1.11]; p = 0.001), higher comorbidity index (HR 1.24 [95% CI 1.14-1.35]; p = 0.039), and hyponatremia (HR 0.90 [95% CI 0.82-0.99]; p = 0.026) to each independently increase risk for death in COVID-19. We also found that neutrophilia (R = 0.2; p = 0.017), chemokine C-C motif ligand (CCL) 2 (R = 0.3; p = 0.043), and C-X-C motif chemokine ligand 9 (CXCL9) (R = 0.3; p = 0.050) were weakly but significantly correlated with mortality. Older age was associated with lower monocyte (R = -0.2; p = 0.006) and cluster of differentiation (CD) 16+ cell counts (R = -0.2; p = 0.002) and increased CCL11 concentration (R = 0.3; p = 0.050). Similarly, younger patients (< 65 yr) demonstrated a rise in CD4 (b-coefficient = 0.02; p = 0.036) and CD8 (0.01; p = 0.001) counts, as well as CCL20 (b-coefficient = 6.8; p = 0.036) during their ICU stay. This CD8 count rise was also associated with survival (b-coefficient = 0.01; p = 0.023). CONCLUSIONS: Age, comorbidities, and hyponatremia independently predict mortality in severe COVID-19. Neutrophilia and higher CCL2 and CXCL9 levels are also associated with higher mortality, while independent of age.

Multidimensional machine learning models predicting outcomes after trauma.

BACKGROUND: An emerging body of literature supports the role of individualized prognostic tools to guide the management of patients after trauma. The aim of this study was to develop advanced modeling tools from multidimensional data sources, including immunological analytes and clinical and administrative data, to predict outcomes in trauma patients. METHODS: This was a prospective study of trauma patients at Level 1 centers from 2015 to 2019. Clinical, flow cytometry, and serum cytokine data were collected within 48 hours of admission. Sparse logistic regression models were developed, jointly selecting predictors and estimating the risk of ventilator-associated pneumonia, acute kidney injury, complicated disposition (death, rehabilitation, or nursing facility), and return to the operating room. Model parameters (regularization controlling model sparsity) and performance estimation were obtained via nested leave-one-out cross-validation. RESULTS: A total of 179 patients were included. The incidences of ventilator-associated pneumonia, acute kidney injury, complicated disposition, and return to the operating room were 17.7%, 28.8%, 22.5%, and 12.3%, respectively. Regarding extensive resource use, 30.7% of patients had prolonged intensive care unit stay, 73.2% had prolonged length of stay, and 23.5% had need for prolonged ventilatory support. The models were developed and cross-validated for ventilator-associated pneumonia, acute kidney injury, complicated dispositions, and return to the operating room, yielding predictive areas under the curve from 0.70 to 0.91. Each model derived its optimal predictive value by combining clinical, administrative, and immunological analyte data. CONCLUSION: Clinical, immunological, and administrative data can be combined to predict post-traumatic outcomes and resource use. Multidimensional machine learning modeling can identify trauma patients with complicated clinical trajectories and high resource needs.

Malnutrition and immune cell subsets in children undergoing kidney transplantation.

BACKGROUND: Malnutrition, including obesity and undernutrition, among children is increasing in prevalence and is common among children on renal replacement therapy. The effect of malnutrition on the pre-transplant immune system and how the pediatric immune system responds to the insult of both immunosuppression and allotransplantation is unknown. We examined the relationship of nutritional status with post-transplant outcomes and characterized the peripheral immune cell phenotypes of children from the Immune Development of Pediatric Transplant (IMPACT) study. METHODS: Ninety-eight patients from the IMPACT study were classified as having obesity, undernutrition, or normal nutrition-based pre-transplant measurements. Incidence of infectious and alloimmune outcomes at 1-year post-transplantation was compared between nutritional groups using Gray's test and Fine-Gray subdistribution hazards model. Event-free survival was estimated by Kaplan-Meier method and compared between groups. Differences in immune cell subsets between nutritional groups over time were determined using generalized estimating equations accounting for the correlation between repeated measurements. RESULTS: We did not observe that nutritional status was associated with infectious or alloimmune events or event-free survival post-transplant. We demonstrated that children with obesity had distinct T-and B-cell signatures relative to those with undernutrition and normal nutrition, even when controlling for immunosuppression. Children with obesity had a lower frequency of CD8 Tnaive cells 9-month post-transplant (p < .001), a higher frequency of CD4 CD57 + PD1- T cells, and lower frequencies of CD57-PD1+ CD8 and CD57-PD1- CD8 T cells at 12-month transplant (p < .05 for all). CONCLUSIONS: Children with obesity have distinct immunophenotypes that may influence the tailoring of immunosuppression.

Cellular microRNAs correlate with clinical parameters in multiple injury patients.

INTRODUCTION: The pathophysiology of the inflammatory response after major trauma is complex, and the magnitude correlates with severity of tissue injury and outcomes. Study of infection-mediated immune pathways has demonstrated that cellular microRNAs may modulate the inflammatory response. The authors hypothesize that the expression of microRNAs would correlate to complicated recoveries in polytrauma patients (PtPs). METHODS: Polytrauma patients enrolled in the prospective observational Tissue and Data Acquisition Protocol with Injury Severity Score of >15 were selected for this study. Polytrauma patients were divided into complicated recoveries and uncomplicated recovery groups. Polytrauma patients' blood samples were obtained at the time of admission (T0). Established biomarkers of systemic inflammation, including cytokines and chemokines, were measured using multiplexed Luminex-based methods, and novel microRNAs were measured in plasma samples using multiplex RNA hybridization. RESULTS: Polytrauma patients (n = 180) had high Injury Severity Score (26 [20-34]) and complicated recovery rate of 33%. MicroRNAs were lower in PtPs at T0 compared with healthy controls, and bivariate analysis demonstrated that variations of microRNAs correlated with age, race, comorbidities, venous thromboembolism, pulmonary complications, complicated recovery, and mortality. Positive correlations were noted between microRNAs and interleukin 10, vascular endothelial growth factor, Acute Physiology and Chronic Health Evaluation, and Sequential Organ Failure Assessment scores. Multivariable Lasso regression analysis of predictors of complicated recovery based on microRNAs, cytokines, and chemokines revealed that miR-21-3p and monocyte chemoattractant protein-1 were predictive of complicated recovery with an area under the curve of 0.78. CONCLUSION: Systemic microRNAs were associated with poor outcomes in PtPs, and results are consistent with previously described trends in critically ill patients. These early biomarkers of inflammation might provide predictive utility in early complicated recovery diagnosis and prognosis. Because of their potential to regulate immune responses, microRNAs may provide therapeutic targets for immunomodulation. LEVEL OF EVIDENCE: Diagnostic Tests/Criteria; Level II.

Introducing thymus for promoting transplantation tolerance.

Establishing tolerance remains a central, if elusive, goal of transplantation. In solid-organ transplantation, one strategy for inducing tolerance has been cotransplantation of various forms of thymic tissue along with another organ. As one of the biological foundations of central tolerance, thymic tissue carries with it the ability to induce tolerance to any other organ or tissue from the same donor (or another donor tissue-matched to the thymic tissue) if successfully transplanted. In this review, we outline the history of this approach as well as work to date on its application in organ transplantation, concluding with future directions. We also review our experience with allogeneic processed thymus tissue for the treatment of congenital athymia, encompassing complete DiGeorge syndrome and other rare genetic disorders, and consider whether allogeneic processed thymic tissue implantation may offer a novel method for future experimentation with tolerance induction in organ transplantation.

Social determinants of health data in solid organ transplantation: National data sources and future directions.

Health equity research in transplantation has largely relied on national data sources, yet the availability of social determinants of health (SDOH) data varies widely among these sources. We sought to characterize the extent to which national data sources contain SDOH data applicable to end-stage organ disease (ESOD) and transplant patients. We reviewed 10 active national data sources based in the United States. For each data source, we examined patient inclusion criteria and explored strengths and limitations regarding SDOH data, using the National Institutes of Health PhenX toolkit of SDOH as a data collection instrument. Of the 28 SDOH variables reviewed, eight-core demographic variables were included in ≥80% of the data sources, and seven variables that described elements of social status ranged between 30 and 60% inclusion. Variables regarding identity, healthcare access, and social need were poorly represented (≤20%) across the data sources, and five of these variables were included in none of the data sources. The results of our review highlight the need for improved SDOH data collection systems in ESOD and transplant patients via: enhanced inter-registry collaboration, incorporation of standardized SDOH variables into existing data sources, and transplant center and consortium-based investigation and innovation.

IFI16-STING-NF-κB signaling controls exogenous mitochondrion-induced endothelial activation.

Mitochondria released from injured cells activate endothelial cells (ECs), fostering inflammatory processes, including allograft rejection. The stimulator of interferon genes (STING) senses endogenous mitochondrial DNA, triggering innate immune activation via NF-κB signaling. Here, we show that exogenous mitochondria exposure induces EC STING-NF-κB activation, promoting EC/effector memory T cell adhesion, which is abrogated by NF-κB and STING inhibitors. STING activation in mitochondrion-activated ECs is independent of canonical cGMP-AMP synthetase sensing/signaling, but rather is mediated by interferon gamma-inducible factor 16 (IFI16) and can be inhibited by IFI16 inhibition. Internalized mitochondria undergo mitofusion and STING-dependent mitophagy, leading to selective sequestration of internalized mitochondria. The exposure of donor hearts to exogenous mitochondria activates murine heart ECs in vivo. Collectively, our results suggest that IFI16-STING-NF-κB signaling regulates exogenous mitochondrion-induced EC activation and mitophagy, and exogenous mitochondria foster T cell-mediated CoBRR. These data suggest a novel, donor-directed, therapeutic approach toward mitigating perioperative allograft immunogenicity.

A Multidimensional Bioinformatic Platform for the Study of Human Response to Surgery.

OBJECTIVE: To design and establish a prospective biospecimen repository that integrates multi-omics assays with clinical data to study mechanisms of controlled injury and healing. BACKGROUND: Elective surgery is an opportunity to understand both the systemic and focal responses accompanying controlled and well-characterized injury to the human body. The overarching goal of this ongoing project is to define stereotypical responses to surgical injury, with the translational purpose of identifying targetable pathways involved in healing and resilience, and variations indicative of aberrant peri-operative outcomes. METHODS: Clinical data from the electronic medical record combined with large-scale biological data sets derived from blood, urine, fecal matter, and tissue samples are collected prospectively through the peri-operative period on patients undergoing 14 surgeries chosen to represent a range of injury locations and intensities. Specimens are subjected to genomic, transcriptomic, proteomic, and metabolomic assays to describe their genetic, metabolic, immunologic, and microbiome profiles, providing a multidimensional landscape of the human response to injury. RESULTS: The highly multiplexed data generated includes changes in over 28,000 mRNA transcripts, 100 plasma metabolites, 200 urine metabolites, and 400 proteins over the longitudinal course of surgery and recovery. In our initial pilot dataset, we demonstrate the feasibility of collecting high quality multi-omic data at pre- and postoperative time points and are already seeing evidence of physiologic perturbation between timepoints. CONCLUSIONS: This repository allows for longitudinal, state-of-the-art geno-mic, transcriptomic, proteomic, metabolomic, immunologic, and clinical data collection and provides a rich and stable infrastructure on which to fuel further biomedical discovery.

Single-Cell-Based High-Throughput Ig and TCR Repertoire Sequencing Analysis in Rhesus Macaques.

Recent advancements in microfluidics and high-throughput sequencing technologies have enabled recovery of paired H and L chains of Igs and VDJ and VJ chains of TCRs from thousands of single cells simultaneously in humans and mice. Despite rhesus macaques being one of the most well-studied model organisms for the human adaptive immune response, high-throughput single-cell immune repertoire sequencing assays are not yet available due to the complexity of these polyclonal receptors. We used custom primers that capture all known rhesus macaque Ig and TCR isotypes and chains that are fully compatible with a commercial solution for single-cell immune repertoire profiling. Using these rhesus-specific assays, we sequenced Ig and TCR repertoires in >60,000 cells from cryopreserved rhesus PBMCs, splenocytes, and FACS-sorted B and T cells. We were able to recover every Ig isotype and TCR chain, measure clonal expansion in proliferating T cells, and pair Ig and TCR repertoires with gene expression profiles of the same single cells. Our results establish the ability to perform high-throughput immune repertoire analysis in rhesus macaques at the single-cell level.