Preprocurement In Situ Donor Lung Tissue Gene Expression Classifies Primary Graft Dysfunction Risk

Applications of transcriptomics in ischemia reperfusion research in lung transplantation

Ischemia-reperfusion (IR) injury contributes to primary graft dysfunction, a major cause of early mortality after lung transplantation. Transcriptomics uses high-throughput techniques to profile the RNA transcripts within a sample and provides a unique view of the mechanisms underlying various biological processes. This review aims to highlight the applications of transcriptomics in lung IR injury studies, which have thus far revealed inflammatory responses to be the major event activated by IR, identified potential biomarkers and therapeutic targets, and investigated the mechanisms of therapeutic interventions. Ex vivo lung perfusion, together with advanced bioinformatic and transcriptomic techniques, including single-cell RNA-sequencing, microRNA profiling, and multi-omics, continue to expand the capabilities of transcriptomics. In the future, the construction of biospecimen banks and the promotion of international collaborations among clinicians and researchers have the potential to advance our understanding of IR injury and improve the management of lung transplant recipients.

NKG2D receptor activation drives primary graft dysfunction severity and poor lung transplantation outcomes

Clinical outcomes after lung transplantation, a life-saving therapy for patients with end-stage lung diseases, are limited by primary graft dysfunction (PGD). PGD is an early form of acute lung injury with no specific pharmacologic therapies. Here, we present a large multicenter study of plasma and bronchoalveolar lavage (BAL) samples collected on the first posttransplant day, a critical time for investigations of immune pathways related to PGD. We demonstrated that ligands for NKG2D receptors were increased in the BAL from participants who developed severe PGD and were associated with increased time to extubation, prolonged intensive care unit length of stay, and poor peak lung function. Neutrophil extracellular traps (NETs) were increased in PGD and correlated with BAL TNF-α and IFN-γ cytokines. Mechanistically, we found that airway epithelial cell NKG2D ligands were increased following hypoxic challenge. NK cell killing of hypoxic airway epithelial cells was abrogated with NKG2D receptor blockade, and TNF-α and IFN-γ provoked neutrophils to release NETs in culture. These data support an aberrant NK cell/neutrophil axis in human PGD pathogenesis. Early measurement of stress ligands and blockade of the NKG2D receptor hold promise for risk stratification and management of PGD.

The Lung Allograft Microbiome Associates with Pepsin, Inflammation, and Primary Graft Dysfunction

Rationale: Primary graft dysfunction (PGD) is the principal cause of early morbidity and mortality after lung transplantation. The lung microbiome has been implicated in later transplantation outcomes but has not been investigated in PGD. Objectives: To define the peritransplant bacterial lung microbiome and relationship to host response and PGD. Methods: This was a single-center prospective cohort study. Airway lavage samples from donor lungs before organ procurement and recipient allografts immediately after implantation underwent bacterial 16S ribosomal ribonucleic acid gene sequencing. Recipient allograft samples were analyzed for cytokines by multiplex array and pepsin by ELISA. Measurements and Main Results: We enrolled 139 transplant subjects and obtained donor lung (n = 109) and recipient allograft (n = 136) samples. Severe PGD (persistent grade 3) developed in 15 subjects over the first 72 hours, and 40 remained without PGD (persistent grade 0). The microbiome of donor lungs differed from healthy lungs, and recipient allograft microbiomes differed from donor lungs. Development of severe PGD was associated with enrichment in the immediate postimplantation lung of oropharyngeal anaerobic taxa, particularly Prevotella. Elevated pepsin, a gastric biomarker, and a hyperinflammatory cytokine profile were present in recipient allografts in severe PGD and strongly correlated with microbiome composition. Together, immediate postimplantation allograft Prevotella/Streptococcus ratio, pepsin, and indicator cytokines were associated with development of severe PGD during the 72-hour post-transplantation period (area under the curve = 0.81). Conclusions: Lung allografts that develop PGD have a microbiome enriched in anaerobic oropharyngeal taxa, elevated gastric pepsin, and hyperinflammatory phenotype. These findings suggest a possible role for peritransplant aspiration in PGD, a potentially actionable mechanism that warrants further investigation.

Primary graft dysfunction: what we know

Many advances in lung transplant have occurred over the last few decades in the understanding of primary graft dysfunction (PGD) though effective prevention and treatment remain elusive. This review will cover prior understanding of PGD, recent findings, and directions for future research. A consensus statement updating the definition of PGD in 2016 highlights the growing complexity of lung transplant perioperative care taking into account the increasing use of high flow oxygen delivery and pulmonary vasodilators in the current era. PGD, particularly more severe grades, is associated with worse short- and long-term outcomes after transplant such as chronic lung allograft dysfunction. Growing experience have helped identify recipient, donor, and intraoperative risk factors for PGD. Understanding the pathophysiology of PGD has advanced with increasing knowledge of the role of innate immune response, humoral cell immunity, and epithelial cell injury. Supportive care post-transplant with technological advances in extracorporeal membranous oxygenation (ECMO) remain the mainstay of treatment for severe PGD. Future directions include the evolving utility of ex vivo lung perfusion (EVLP) both in PGD research and potential pre-transplant treatment applications. PGD remains an important outcome in lung transplant and the future holds a lot of potential for improvement in understanding its pathophysiology as well as development of preventative therapies and treatment.

Innate immunity in lung transplantation

Innate immune pathways early after pulmonary transplantation have been shown to cause primary graft dysfunction (PGD) and also predispose to late graft failure. Recent studies in animal models have elucidated critical mechanisms governing such innate immune responses. Here, we discuss pathways of inflammatory cell death, triggers for sterile and infectious inflammation, and signaling cascades that mediate lung injury early after transplantation. These studies highlight potential avenues for lung-specific therapies early following lung transplantation to dampen innate immune responses and improve outcomes.

Primary graft dysfunction

PURPOSE OF REVIEW

Primary graft dysfunction (PGD) is a devastating complication in the acute postoperative lung transplant period, associated with high short-term mortality and chronic rejection. We review its definition, pathophysiology, risk factors, prevention, treatment strategies, and future research directions.

RECENT FINDINGS

New analyses suggest donation after circulatory death and donation after brain death donors have similar PGD rates, whereas donors >55 years are not associated with increased PGD risk. Recipient pretransplant diastolic dysfunction and overweight or obese recipients with predominant abdominal subcutaneous adipose tissue have increased PGD risk. Newly identified recipient biomarkers and donor and recipient genes increase PGD risk, but their clinical utility remains unclear. Mixed data still exists regarding cold ischemic time and PGD risk, and increased PGD risk with cardiopulmonary bypass remains confounded by transfusions. Portable ex vivo lung perfusion (EVLP) may prevent PGD, but its use is limited to a handful of centers. Although updates to current PGD treatment are lacking, future therapies are promising with targeted therapy and the use of EVLP to pharmacologically recondition donor lungs.

SUMMARY

There is significant progress in defining PGD and identifying its several risk factors, but effective prevention and treatment strategies are needed.

Peel Learning for Pathway-Related Outcome Prediction

MOTIVATION

Traditional regression models are limited in outcome prediction due to their parametric nature. Current deep learning methods allow for various effects and interactions and have shown improved performance, but they typically need to be trained on a large amount of data to obtain reliable results. Gene expression studies often have small sample sizes but high dimensional correlated predictors so that traditional deep learning methods are not readily applicable.

RESULTS

In this paper, we proposed peel learning, a novel neural network that incorporates the prior relationship among genes. In each layer of learning, overall structure is peeled into multiple local substructures. Within the substructure, dependency among variables is reduced through linear projections. The overall structure is gradually simplified over layers and weight parameters are optimized through a revised backpropagation. We applied PL to a small lung transplantation study to predict recipients' post-surgery primary graft dysfunction using donors' gene expressions within several immunology pathways, where PL showed improved prediction accuracy compared to conventional penalized regression, classification trees, feed-forward neural network, and a neural network assuming prior network structure. Through simulation studies, we also demonstrated the advantage of adding specific structure among predictor variables in neural network, over no or uniform group structure, which is more favorable in smaller studies. The empirical evidence is consistent with our theoretical proof of improved upper bound of PL's complexity over ordinary neural networks.

AVAILABILITY AND IMPLEMENTATION

PL algorithm was implemented in Python and the open-source code and instruction will be available at https://github.com/Likelyt/Peel-Learning.