Leard, L. E. et al. Consensus document for the selection of lung transplant candidates: an update from the International Society for Heart and Lung Transplantation. J. Heart Lung Transplant. 40, 1349–1379 (2021).
Swaminathan, A. C., Todd, J. L. & Palmer, S. M. Advances in human lung transplantation. Annu. Rev. Med. 72, 135–149 (2021).
Bos, S., Vos, R., Van Raemdonck, D. E. & Verleden, G. M. Survival in adult lung transplantation: where are we in 2020? Curr. Opin. Organ Transplant. 25, 268–273 (2020).
Chambers, D. C. et al. The International Thoracic Organ Transplant Registry of the International Society for Heart and Lung Transplantation: Thirty-sixth adult lung and heart-lung transplantation Report-2019; focus theme: donor and recipient size match. J. Heart Lung Transplant. 38, 1042–1055 (2019).
Verleden, G. M. et al. Chronic lung allograft dysfunction: definition, diagnostic criteria, and approaches to treatment-a consensus report from the Pulmonary Council of the ISHLT. J. Heart Lung Transplant. 38, 493–503 (2019).
Tague, L. K. et al. Lung transplant outcomes are influenced by severity of neutropenia and granulocyte colony-stimulating factor treatment. Am. J. Transplant. 20, 250–261 (2020).
Halloran, K. et al. Molecular T-cell‒mediated rejection in transbronchial and mucosal lung transplant biopsies is associated with future risk of graft loss. J. Heart Lung Transplant. 39, 1327–1337 (2020).
Burguete, S. R., Maselli, D. J., Fernandez, J. F. & Levine, S. M. Lung transplant infection. Respirology 18, 22–38 (2013).
Whiteside, S. A., McGinniss, J. E. & Collman, R. G. The lung microbiome: progress and promise. J. Clin. Investig. 131, e150473 (2021).
Budden, K. F. et al. Functional effects of the microbiota in chronic respiratory disease. Lancet Respir. Med. 7, 907–920 (2019).
Gabarre, P. et al. Immunosuppressive therapy after solid organ transplantation and the gut microbiota: bidirectional interactions with clinical consequences. Am. J. Transplant. 22, 1014–1030 (2022).
Swarte, J. C. et al. Gut microbiome dysbiosis is associated with increased mortality after solid organ transplantation. Sci. Transl. Med. 14, eabn7566 (2022).
Lei, Y. M. et al. The composition of the microbiota modulates allograft rejection. J. Clin. Investig. 126, 2736–2744 (2016).
McIntosh, C. M., Chen, L., Shaiber, A., Eren, A. M. & Alegre, M. L. Gut microbes contribute to variation in solid organ transplant outcomes in mice. Microbiome 6, 96 (2018).
Watzenbock, M. L. et al. Multi-omics profiling predicts allograft function after lung transplantation. Eur. Respir. J. 59, 2003292 (2021).
Combs, M. P. et al. Lung microbiota predict chronic rejection in healthy lung transplant recipients: a prospective cohort study. Lancet Respir. Med. 9, 601–612 (2021).
McGinniss, J. E. et al. The lung microbiome in lung transplantation. J. Heart Lung Transplant. 40, 733–744 (2021).
Barcik, W., Boutin, R. C. T., Sokolowska, M. & Finlay, B. B. The role of lung and gut microbiota in the pathology of asthma. Immunity 52, 241–255 (2020).
Wu, Q. et al. Gut microbiota can impact chronic murine lung allograft rejection. Am. J. Respir. Cell Mol. Biol. 60, 131–134 (2019).
Zafar, H. & Saier, M. H. Jr Gut Bacteroides species in health and disease. Gut Microbes 13, 1–20 (2021).
Sun, D. et al. Angiogenin maintains gut microbe homeostasis by balancing α-Proteobacteria and Lachnospiraceae. Gut 70, 666–676 (2021).
Fabersani, E. et al. Bacteroides uniformis CECT 7771 alleviates inflammation within the gut-adipose tissue axis involving TLR5 signaling in obese mice. Sci. Rep. 11, 11788 (2021).
Xiao, P. et al. Mannose metabolism normalizes gut homeostasis by blocking the TNF-α-mediated proinflammatory circuit. Cell. Mol. Immunol. 20, 119–130 (2023).
Meloni, F. et al. Bronchoalveolar lavage cytokine profile in a cohort of lung transplant recipients: a predictive role of interleukin-12 with respect to onset of bronchiolitis obliterans syndrome. J. Heart Lung Transplant. 23, 1053–1060 (2004).
Yang, W. et al. IL-1β-dependent extravasation of preexisting lung-restricted autoantibodies during lung transplantation activates complement and mediates primary graft dysfunction. J. Clin. Investig. 132, e15975 (2022).
Nadeem, A. et al. Glucose-6-phosphate dehydrogenase inhibition attenuates acute lung injury through reduction in NADPH oxidase-derived reactive oxygen species. Clin. Exp. Immunol. 191, 279–287 (2018).
Wang, L. et al. Abscisic acid inhibited reactive oxygen species-mediated endoplasmic reticulum stress by regulating the PPAR-γ signaling pathway in ARDS mice. Phytother. Res. 35, 7027–7038 (2021).
Budden, K. F. et al. Emerging pathogenic links between microbiota and the gut-lung axis. Nat. Rev. Microbiol. 15, 55–63 (2017).
Haak, B. W. et al. Impact of gut colonization with butyrate-producing microbiota on respiratory viral infection following allo-HCT. Blood 131, 2978–2986 (2018).
Kato, K. et al. Longitudinal analysis of the intestinal microbiota in liver transplantation. Transplant. Direct 3, e144 (2017).
Oh, P. L. et al. Characterization of the ileal microbiota in rejecting and nonrejecting recipients of small bowel transplants. Am. J. Transplant. 12, 753–762 (2012).
Dickson, R. P. et al. Enrichment of the lung microbiome with gut bacteria in sepsis and the acute respiratory distress syndrome. Nat. Microbiol. 1, 16113 (2016).
Panzer, A. R. et al. Lung microbiota is related to smoking status and to development of acute respiratory distress syndrome in critically ill trauma patients. Am. J. Respir. Crit. Care Med. 197, 621–631 (2018).
Szychowiak, P., Villageois-Tran, K., Patrier, J., Timsit, J. F. & Ruppé, É. The role of the microbiota in the management of intensive care patients. Ann. Intensive Care 12, 3 (2022).
Young, R. P., Hopkins, R. J. & Marsland, B. The gut-liver-lung axis. modulation of the innate immune response and its possible role in chronic obstructive pulmonary disease. Am. J. Respir. Cell Mol. Biol. 54, 161–169 (2016).
Dickson, R. P. et al. Lung microbiota predict clinical outcomes in critically ill patients. Am. J. Respir. Crit. Care Med. 201, 555–563 (2020).
Luyt, C. E. et al. Ventilator-associated pneumonia in patients with SARS-CoV-2-associated acute respiratory distress syndrome requiring ECMO: a retrospective cohort study. Ann. Intensive Care 10, 158 (2020).
Keller, M. B. et al. Comparison of donor-derived cell-free DNA between single versus double lung transplant recipients. Am. J. Transplant. 22, 2451–2457 (2022).
Xu, Z. et al. Role of circulating MicroRNAs in the immunopathogenesis of rejection after pediatric lung transplantation. Transplantation 101, 2461–2468 (2017).
Stewart, S. et al. Revision of the 1990 working formulation for the standardization of nomenclature in the diagnosis of heart rejection. J. Heart Lung Transplant. 24, 1710–1720 (2005).
Zhao, L. et al. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science 359, 1151–1156 (2018).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).
Li, R. et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 1966–1967 (2009).
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
Kuntal, B. K., Chandrakar, P., Sadhu, S. & Mande, S. S. ‘NetShift’: a methodology for understanding ‘driver microbes’ from healthy and disease microbiome datasets. ISME J. 13, 442–454 (2019).
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).
Navarro-Reig, M., Jaumot, J., García-Reiriz, A. & Tauler, R. Evaluation of changes induced in rice metabolome by Cd and Cu exposure using LC-MS with XCMS and MCR-ALS data analysis strategies. Anal. Bioanal. Chem. 407, 8835–8847 (2015).
Xia, J. & Wishart, D. S. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat. Protoc. 6, 743–760 (2011).
Bac, J., Mirkes, E. M., Gorban, A. N., Tyukin, I. & Zinovyev, A. Scikit-Dimension: a Python package for intrinsic dimension estimation. Entropy 23, 1368 (2021).