Whole transcriptome profiling of prospective endomyocardial biopsies reveals prognostic and diagnostic signatures of cardiac allograft rejection

Piening, Brian D.; Dowdell, Alexa K.; Zhang, Mengqi; Loza, Bao‐Li; Walls, David O.; Gao, Hui; Mohebnasab, Maede; Li, Yun R.; Elftmann, Eric; Wei, Eric; Gandla, Divya; Lad, Hetal; Chaı̈b, Hassan; Sweitzer, Nancy K.; Deng, Mario C.; Pereira, Alexandre C.; Cadeiras, Martín; Shaked, Abraham; Snyder, M; Keating, Brendan J.

doi:10.1016/j.healun.2022.01.1377

Cited by 12 publications

(9 citation statements)

References 39 publications

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“…To further validate that the MP1 population has a cellular signature of allograft rejection rather than of heart failure, we tested genes that were previously reported as hallmark genes of pan-organ or cardiac allograft rejection. 31 As a result, MP1s specifically expressed allograft rejection hallmark genes, such as CXCL9 , IL15 , CD5 , and CDH4 , at higher levels than did macrophages in failing hearts with DCM (Figure 5H). These results further support that MP1s may be specifically associated with the microenvironment that leads to allograft rejection.…”

Section: Resultsmentioning

confidence: 95%

The Macrophage Landscape Across the Lifespan of a Human Cardiac Allograft

Li,

Turaga,

et al. 2024

Circulation

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BACKGROUND: Much of our knowledge of organ rejection after transplantation is derived from rodent models. METHODS: We used single-nucleus RNA sequencing to investigate the inflammatory myocardial microenvironment in human pediatric cardiac allografts at different stages after transplantation. We distinguished donor- from recipient-derived cells using naturally occurring genetic variants embedded in single-nucleus RNA sequencing data. RESULTS: Donor-derived tissue resident macrophages, which accompany the allograft into the recipient, are lost over time after transplantation. In contrast, monocyte-derived macrophages from the recipient populate the heart within days after transplantation and form 2 macrophage populations: recipient MP1 and recipient MP2. Recipient MP2s have cell signatures similar to donor-derived resident macrophages; however, they lack signatures of pro-reparative phagocytic activity typical of donor-derived resident macrophages and instead express profibrotic genes. In contrast, recipient MP1s express genes consistent with hallmarks of cellular rejection. Our data suggest that recipient MP1s activate a subset of natural killer cells, turning them into a cytotoxic cell population through feed-forward signaling between recipient MP1s and natural killer. CONCLUSIONS: Our findings reveal an imbalance of donor-derived and recipient-derived macrophages in the pediatric cardiac allograft that contributes to allograft failure.

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Section: Resultsmentioning

confidence: 95%

The Macrophage Landscape Across the Lifespan of a Human Cardiac Allograft

Li,

Turaga,

et al. 2024

Circulation

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“…However, controlling acute rejection reactions plays a crucial role in early heart function recovery and prognosis for patients. A prospective transcriptome analysis of endomyocardial biopsies revealed that incorporating molecular feature monitoring can better identify heart transplant rejection 43 . Pham et al found that post-heart transplant gene expression profiles exhibit sensitivity and specificity comparable to pathological biopsies in monitoring transplant rejection reactions 44 .…”

Section: Discussionmentioning

confidence: 99%

Exploring personalized treatment for cardiac graft rejection based on a four-archetype analysis model and bioinformatics analysis

Shi,

Yuan,

Cai

et al. 2024

Sci Rep

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Heart transplantation is the gold standard for treating patients with advanced heart failure. Although improvements in immunosuppressive therapies have significantly reduced the frequency of cardiac graft rejection, the incidences of T cell-mediated rejection (TCMR) and antibody-mediated rejection remain almost unchanged. A four-archetype analysis (4AA) model, developed by Philip F. Halloran, illustrated this problem well. It provided a new dimension to improve the accuracy of diagnoses and an independent system for recalibrating the histology guidelines. However, this model was based on the invasive method of endocardial biopsy, which undoubtedly increased the postoperative risk of heart transplant patients. Currently, little is known regarding the associated genes and specific functions of the different phenotypes. We performed bioinformatics analysis (using machine-learning methods and the WGCNA algorithm) to screen for hub-specific genes related to different phenotypes, based Gene Expression Omnibus accession number GSE124897. More immune cell infiltration was observed with the ABMR, TCMR, and injury phenotypes than with the stable phenotype. Hub-specific genes for each of the four archetypes were verified successfully using an external test set (accession number GSE2596). Logistic-regression models based on TCMR-specific hub genes and common hub genes were constructed with accurate diagnostic utility (area under the curve > 0.95). RELA, NFKB1, and SOX14 were identified as transcription factors important for TCMR/injury phenotypes and common genes, respectively. Additionally, 11 Food and Drug Administration-approved drugs were chosen from the DrugBank Database for each four-archetype model. Tyrosine kinase inhibitors may be a promising new option for transplant rejection treatment. KRAS signaling in cardiac transplant rejection is worth further investigation. Our results showed that heart transplant rejection subtypes can be accurately diagnosed by detecting expression of the corresponding specific genes, thereby enabling precise treatment or medication.

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“…There is also scope for AI such as ChatGPT for patients with heart transplants or on advanced therapies, such as in the setting of post heart transplant management guidance. Prior models have attempted to target questions of detection of graph rejection and provide guidance on immunosuppression dosing [68][69][70]. An ANN-based model developed by Medved et al was used to predict waitlist mortality, post-transplant survival, and similar heart allocation processes, particularly of use to clinicians [71].…”

Section: Advanced Heart Failure and Circulatory Supportmentioning

confidence: 99%

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

Rao,

Iqbal,

Isath

et al. 2024

Hearts

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Artificial intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this review, we evaluate the myriad applications of artificial intelligence in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of artificial intelligence such as ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of artificial intelligence as an information retrieval tool and personalized learning resource for medical students and professionals.

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Whole transcriptome profiling of prospective endomyocardial biopsies reveals prognostic and diagnostic signatures of cardiac allograft rejection

Cited by 12 publications

References 39 publications

The Macrophage Landscape Across the Lifespan of a Human Cardiac Allograft

The Macrophage Landscape Across the Lifespan of a Human Cardiac Allograft

Exploring personalized treatment for cardiac graft rejection based on a four-archetype analysis model and bioinformatics analysis

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

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