Multi-omics profiling predicts allograft function after lung transplantation

The Lung Microbiome Predicts Mortality and Response to Azithromycin in Lung Transplant Recipients with Chronic Rejection

Rationale: Chronic lung allograft dysfunction (CLAD) is the leading cause of death after lung transplant, and azithromycin has variable efficacy in CLAD. The lung microbiome is a risk factor for developing CLAD, but the relationship between lung dysbiosis, pulmonary inflammation, and allograft dysfunction remains poorly understood. Whether lung microbiota predict outcomes or modify treatment response after CLAD is unknown. Objectives: To determine whether lung microbiota predict post-CLAD outcomes and clinical response to azithromycin. Methods: Retrospective cohort study using acellular BAL fluid prospectively collected from recipients of lung transplant within 90 days of CLAD onset. Lung microbiota were characterized using 16S rRNA gene sequencing and droplet digital PCR. In two additional cohorts, causal relationships of dysbiosis and inflammation were evaluated by comparing lung microbiota with CLAD-associated cytokines and measuring ex vivo P. aeruginosa growth in sterilized BAL fluid. Measurements and Main Results: Patients with higher bacterial burden had shorter post-CLAD survival, independent of CLAD phenotype, azithromycin treatment, and relevant covariates. Azithromycin treatment improved survival in patients with high bacterial burden but had negligible impact on patients with low or moderate burden. Lung bacterial burden was positively associated with CLAD-associated cytokines, and ex vivo growth of P. aeruginosa was augmented in BAL fluid from transplant recipients with CLAD. Conclusions: In recipients of lung transplants with chronic rejection, increased lung bacterial burden is an independent risk factor for mortality and predicts clinical response to azithromycin. Lung bacterial dysbiosis is associated with alveolar inflammation and may be promoted by underlying lung allograft dysfunction.

Creating superior lungs for transplantation with next-generation gene therapy during ex vivo lung perfusion

Engineering donor organs to better tolerate the harmful non-immunological and immunological responses inherently related to solid organ transplantation would improve transplant outcomes. Our enhanced knowledge of ischemia-reperfusion injury, alloimmune responses and pathological fibroproliferation after organ transplantation, and the advanced toolkit available for gene therapies, have brought this goal closer to clinical reality. Ex vivo organ perfusion has evolved rapidly especially in the field of lung transplantation, where clinicians routinely use ex vivo lung perfusion (EVLP) to confirm the quality of marginal donor lungs before transplantation, enabling safe transplantation of organs originally considered unusable. EVLP would also be an attractive platform to deliver gene therapies, as treatments could be administered to an isolated organ before transplantation, thereby providing a window for sophisticated organ engineering while minimizing off-target effects to the recipient. Here, we review the status of lung transplant first-generation gene therapies that focus on inducing transgene expression in the target cells. We also highlight recent advances in next-generation gene therapies, that enable gene editing and epigenetic engineering, that could be used to permanently change the donor organ genome and to induce widespread transcriptional gene expression modulation in the donor lung. In a future vision, dedicated organ repair and engineering centers will use gene editing and epigenetic engineering, to not only increase the donor organ pool, but to create superior organs that will function better and longer in the recipient.

The microbiome: an integral player in immune homeostasis and inflammation in the respiratory tract

The last decade of microbiome research has highlighted its fundamental role in systemic immune and metabolic homeostasis. The microbiome plays a prominent role during gestation and into early life, when maternal lifestyle factors shape immune development of the newborn. Breast milk further shapes gut colonization, supporting the development of tolerance to commensal bacteria and harmless antigens while preventing outgrowth of pathogens. Environmental microbial and lifestyle factors that disrupt this process can dysregulate immune homeostasis, predisposing infants to atopic disease and childhood asthma. In health, the low-biomass lung microbiome, together with inhaled environmental microbial constituents, establishes the immunological set point that is necessary to maintain pulmonary immune defense. However, in disease perturbations to immunological and physiological processes allow the upper respiratory tract to act as a reservoir of pathogenic bacteria, which can colonize the diseased lung and cause severe inflammation. Studying these host-microbe interactions in respiratory diseases holds great promise to stratify patients for suitable treatment regimens and biomarker discovery to predict disease progression. Preclinical studies show that commensal gut microbes are in a constant flux of cell division and death, releasing microbial constituents, metabolic by-products, and vesicles that shape the immune system and can protect against respiratory diseases. The next major advances may come from testing and utilizing these microbial factors for clinical benefit and exploiting the predictive power of the microbiome by employing multiomics analysis approaches.

Multi-omics integration reveals a nonlinear signature that precedes progression of lung fibrosis

Objectives

Idiopathic pulmonary fibrosis (IPF) is a devastating progressive interstitial lung disease with poor outcomes. While decades of research have shed light on pathophysiological mechanisms associated with the disease, our understanding of the early molecular events driving IPF and its progression is limited. With this study, we aimed to model the leading edge of fibrosis using a data-driven approach.

Methods

Multiple omics modalities (transcriptomics, metabolomics and lipidomics) of healthy and IPF lung explants representing different stages of fibrosis were combined using an unbiased approach. Multi-Omics Factor Analysis of datasets revealed latent factors specifically linked with established fibrotic disease (Factor1) and disease progression (Factor2).

Results

Features characterising Factor1 comprised well-established hallmarks of fibrotic disease such as defects in surfactant, epithelial-mesenchymal transition, extracellular matrix deposition, mitochondrial dysfunction and purine metabolism. Comparatively, Factor2 identified a signature revealing a nonlinear trajectory towards disease progression. Molecular features characterising Factor2 included genes related to transcriptional regulation of cell differentiation, ciliogenesis and a subset of lipids from the endocannabinoid class. Machine learning models, trained upon the top transcriptomics features of each factor, accurately predicted disease status and progression when tested on two independent datasets.

Conclusion

This multi-omics integrative approach has revealed a unique signature which may represent the inflection point in disease progression, representing a promising avenue for the identification of therapeutic targets aimed at addressing the progressive nature of the disease.

Lung microbiome: new insights into the pathogenesis of respiratory diseases

The lungs were long thought to be sterile until technical advances uncovered the presence of the lung microbial community. The microbiome of healthy lungs is mainly derived from the upper respiratory tract (URT) microbiome but also has its own characteristic flora. The selection mechanisms in the lung, including clearance by coughing, pulmonary macrophages, the oscillation of respiratory cilia, and bacterial inhibition by alveolar surfactant, keep the microbiome transient and mobile, which is different from the microbiome in other organs. The pulmonary bacteriome has been intensively studied recently, but relatively little research has focused on the mycobiome and virome. This up-to-date review retrospectively summarizes the lung microbiome's history, composition, and function. We focus on the interaction of the lung microbiome with the oropharynx and gut microbiome and emphasize the role it plays in the innate and adaptive immune responses. More importantly, we focus on multiple respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), fibrosis, bronchiectasis, and pneumonia. The impact of the lung microbiome on coronavirus disease 2019 (COVID-19) and lung cancer has also been comprehensively studied. Furthermore, by summarizing the therapeutic potential of the lung microbiome in lung diseases and examining the shortcomings of the field, we propose an outlook of the direction of lung microbiome research.

The application of multi-omics in the respiratory microbiome: Progresses, challenges and promises

The study of the respiratory microbiome has entered a multi-omic era. Through integrating different omic data types such as metagenome, metatranscriptome, metaproteome, metabolome, culturome and radiome surveyed from respiratory specimens, holistic insights can be gained on the lung microbiome and its interaction with host immunity and inflammation in respiratory diseases. The power of multi-omics have moved the field forward from associative assessment of microbiome alterations to causative understanding of the lung microbiome in the pathogenesis of chronic, acute and other types of respiratory diseases. However, the application of multi-omics in respiratory microbiome remains with unique challenges from sample processing, data integration, and downstream validation. In this review, we first introduce the respiratory sample types and omic data types applicable to studying the respiratory microbiome. We next describe approaches for multi-omic integration, focusing on dimensionality reduction, multi-omic association and prediction. We then summarize progresses in the application of multi-omics to studying the microbiome in respiratory diseases. We finally discuss current challenges and share our thoughts on future promises in the field.

Intestinal microbiota links to allograft stability after lung transplantation: a prospective cohort study

Whether the alternated microbiota in the gut contribute to the risk of allograft rejection (AR) and pulmonary infection (PI) in the setting of lung transplant recipients (LTRs) remains unexplored. A prospective multicenter cohort of LTRs was identified in the four lung transplant centers. Paired fecal and serum specimens were collected and divided into AR, PI, and event-free (EF) groups according to the diagnosis at sampling. Fecal samples were determined by metagenomic sequencing. And metabolites and cytokines were detected in the paired serum to analyze the potential effect of the altered microbiota community. In total, we analyzed 146 paired samples (AR = 25, PI = 43, and EF = 78). Notably, we found that the gut microbiome of AR followed a major depletion pattern with decreased 487 species and compositional diversity. Further multi-omics analysis showed depleted serum metabolites and increased inflammatory cytokines in AR and PI. Bacteroides uniformis, which declined in AR (2.4% vs 0.6%) and was negatively associated with serum IL-1β and IL-12, was identified as a driven specie in the network of gut microbiome of EF. Functionally, the EF specimens were abundant in probiotics related to mannose and cationic antimicrobial peptide metabolism. Furthermore, a support-vector machine classifier based on microbiome, metabolome, and clinical parameters highly predicted AR (AUPRC = 0.801) and PI (AUPRC = 0.855), whereby the microbiome dataset showed a particularly high diagnostic power. In conclusion, a disruptive gut microbiota showed a significant association with allograft rejection and infection and with systemic cytokines and metabolites in LTRs.

Multi-Omics Profiling for Health

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Machine Learning-Based Prognostic Model for Patients After Lung Transplantation

Importance

Although numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable.

Objective

To develop and validate a prognostic model for predicting overall survival in patients after LTx using random survival forests (RSF), a machine learning algorithm.

Design, Setting, and Participants

This retrospective prognostic study included patients who underwent LTx between January 2017 and December 2020. The LTx recipients were randomly assigned to training and test sets in accordance with a ratio of 7:3. Feature selection was performed using variable importance with bootstrapping resampling. The prognostic model was fitted using the RSF algorithm, and a Cox regression model was set as a benchmark. The integrated area under the curve (iAUC) and integrated Brier score (iBS) were applied to assess model performance in the test set. Data were analyzed from January 2017 to December 2019.

Main Outcomes And Measures

Overall survival in patients after LTx.

Results

A total of 504 patients were eligible for this study, consisting of 353 patients in the training set (mean [SD] age, 55.03 [12.78] years; 235 [66.6%] male patients) and 151 patients in the test set (mean [SD] age, 56.79 [10.95] years; 99 [65.6%] male patients). According to the variable importance of each factor, 16 were selected for the final RSF model, and postoperative extracorporeal membrane oxygenation time was identified as the most valuable factor. The RSF model had excellent performance with an iAUC of 0.879 (95% CI, 0.832-0.921) and an iBS of 0.130 (95% CI, 0.106-0.154). The Cox regression model fitted by the same modeling factors to the RSF model was significantly inferior to the RSF model with an iAUC of 0.658 (95% CI, 0.572-0.747; P < .001) and an iBS of 0.205 (95% CI, 0.176-0.233; P < .001). According to the RSF model predictions, the patients after LTx were stratified into 2 prognostic groups displaying significant difference, with mean overall survival of 52.91 months (95% CI, 48.51-57.32) and 14.83 months (95% CI, 9.44-20.22; log-rank P < .001), respectively.

Conclusions and relevance

In this prognostic study, the findings first demonstrated that RSF could provide more accurate overall survival prediction and remarkable prognostic stratification than the Cox regression model for patients after LTx.

Systems-level multi-omics characterization provides novel molecular insights into indomethacin toxicity

The mechanism of indomethacin toxicity at the systemic level is largely unknown. In this study, multi-specimen molecular characterization was conducted in rats treated with three doses of indomethacin (2.5, 5, and 10 mg/kg) for 1 week. Kidney, liver, urine, and serum samples were collected and analyzed using untargeted metabolomics. The kidney and liver transcriptomics data (10 mg indomethacin/kg and control) were subjected to a comprehensive omics-based analysis. Indomethacin exposure at 2.5 and 5 mg/kg doses did not cause significant metabolome changes, whereas considerable alterations in the metabolic profile compared to the control were induced by a dose of 10 mg/kg. Decreased levels of metabolites and an increased creatine level in the urine metabolome indicated injury to the kidney. The integrated omics analysis in both liver and kidney revealed an oxidant-antioxidant imbalance due to an excess of reactive oxygen species, likely originating from dysfunctional mitochondria. Specifically, indomethacin exposure induced changes in metabolites related to the citrate cycle, cell membrane composition, and DNA synthesis in the kidney. The dysregulation of genes related to ferroptosis and suppression of amino acid and fatty acid metabolism were evidence of indomethacin-induced nephrotoxicity. In conclusion, a multi-specimen omics investigation provided important insights into the mechanism of indomethacin toxicity. The identification of targets that ameliorate indomethacin toxicity will enhance the therapeutic utility of this drug.

Crosstalk between the lung microbiome and lung cancer

The human microbiome is a complex ecosystem that mediates interaction between the human host and the environment. All of the human body is colonized by microorganisms. The lung as an organ used to be considered sterile. Recently, however, there has been a growing number of reports with evidence that the lungs are also in a state of carrying bacteria. The pulmonary microbiome is associated with many lung diseases and is increasingly reported in current studies. These include; chronic obstructive pulmonary disease (COPD), asthma, acute chronic respiratory infections, and cancers. These lung diseases are associated with reduced diversity and dysbiosis. It directly or indirectly affects the occurrence and development of lung cancer. Very few microbes directly cause cancer, while many are complicit in cancer growth, usually working through the host's immune system. This review focuses on the correlation between lung microbiota and lung cancer, and investigates the mechanism of action of lung microorganisms on lung cancer, which will provide new and reliable treatments and diagnosis of lung cancer in the future.

Biomarkers for Chronic Lung Allograft Dysfunction: Ready for Prime Time?

Chronic lung allograft dysfunction (CLAD) remains a major hurdle impairing lung transplant outcome. Parallel to the better clinical identification and characterization of CLAD and CLAD phenotypes, there is an increasing urge to find adequate biomarkers that could assist in the earlier detection and differential diagnosis of CLAD phenotypes, as well as disease prognostication. The current status and state-of-the-art of biomarker research in CLAD will be discussed with a particular focus on radiological biomarkers or biomarkers found in peripheral tissue, bronchoalveolar lavage' and circulating blood' in which significant progress has been made over the last years. Ultimately, although a growing number of biomarkers are currently being embedded in the follow-up of lung transplant patients, it is clear that one size does not fit all. The future of biomarker research probably lies in the rigorous combination of clinical information with findings in tissue, bronchoalveolar lavage' or blood. Only by doing so, the ultimate goal of biomarker research can be achieved, which is the earlier identification of CLAD before its clinical manifestation. This is desperately needed to improve the prognosis of patients with CLAD after lung transplantation.

Microbiosis in lung allotransplantation and xenotransplantation: State of the art and future perspective

The respiratory tract is known to harbor a microbial community including bacteria, viruses, and fungi. New techniques contribute enormously to the identification of unknown or culture-independent species and reveal the interaction of the community with the host immune system. The existing respiratory microbiome and substantial equilibrium of the transplanted microbiome from donor lung grafts provide an extreme bloom of dynamic changes in the microenvironment in lung transplantation (LT) recipients. Dysbiosis in grafts are not only related to the modified microbial components but also involve the kinetics of the host-graft "talk," which signifies the destination of graft allograft injury, acute rejection, infection, and chronic allograft dysfunction development in short- and long-term survival. Microbiome-derived factors may contribute to lung xenograft survival when using genetically multimodified pig-derived organs. Here, we review the most advanced knowledge of the dynamics and resilience of microbial communities in transplanted lungs with various pretransplant indications. Conceptual and analytical points of view have been illustrated along the time series, gaining insight into the microbiome and lung grafts. Future endeavors on precise tools, sophisticated models, and novel targeted regimens are needed to improve the long-term survival in these patients.

The human lung microbiome-A hidden link between microbes and human health and diseases

Once thought to be sterile, the human lung is now well recognized to harbor a consortium of microorganisms collectively known as the lung microbiome. The lung microbiome is altered in an array of lung diseases, including chronic lung diseases such as chronic obstructive pulmonary disease, asthma, and bronchiectasis, acute lung diseases caused by pneumonia, sepsis, and COVID-19, and other lung complications such as those related to lung transplantation, lung cancer, and human immunodeficiency virus. The effects of lung microbiome in modulating host immunity and inflammation in the lung and distal organs are being elucidated. However, the precise mechanism by which members of microbiota produce structural ligands that interact with host genes and pathways remains largely uncharacterized. Multiple unique challenges, both technically and biologically, exist in the field of lung microbiome, necessitating the development of tailored experimental and analytical approaches to overcome the bottlenecks. In this review, we first provide an overview of the principles and methodologies in studying the lung microbiome. We next review current knowledge of the roles of lung microbiome in human diseases, highlighting mechanistic insights. We finally discuss critical challenges in the field and share our thoughts on broad topics for future investigation.

Novel biomarkers of chronic lung allograft dysfunction: is there anything reliable?

PURPOSE OF REVIEW

Chronic lung allograft dysfunction (CLAD) remains a major barrier preventing long-term survival following lung transplantation. As our clinical knowledge regarding its definition and presentation has significantly improved over the last years, adequate biomarkers to predict development of CLAD, phenotype of CLAD or prognosis post-CLAD diagnosis are definitely needed.

RECENT FINDINGS

Radiological and physiological markers are gradually entering routine clinical practice. In-depth investigation of biological samples including broncho-alveolar lavage, biopsy and serum has generated potential biomarkers involved in fibrogenesis, airway injury and inflammation but none of these are universally accepted or implemented although progress has been made, specifically regarding donor-derived cell-free DNA and donor-specific antibodies.

SUMMARY

Although a lot of promising biomarkers have been put forward, a very limited number has made it to routine clinical practice. Nevertheless, a biomarker that leads to earlier detection or more adequate disease phenotyping would advance the field enormously.