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Functional Microbiomics in Liver Transplantation

Identifying Novel Targets for Improving Allograft Outcomes

Kriss, Michael, MD1,2; Verna, Elizabeth C., MD, MS3; Rosen, Hugo R., MD4; Lozupone, Catherine A., PhD2,5

doi: 10.1097/TP.0000000000002568
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Gut dysbiosis, defined as a maladaptive gut microbial imbalance, has been demonstrated in patients with end-stage liver disease, defined as a contributor to disease progression, and associated clinically with severity of disease and liver-related morbidity and mortality. Despite this well-recognized phenomena in patients with end-stage liver disease, the impact of gut dysbiosis and its rate of recovery following liver transplantation (LT) remains incompletely understood. The mechanisms by which alterations in the gut microbiota impact allograft metabolism and immunity, both directly and indirectly, are multifactorial and reflect the complexity of the gut-liver axis. Importantly, while research has largely focused on quantitative and qualitative changes in gut microbial composition, changes in microbial functionality (in the presence or absence of compositional changes) are of critical importance. Therefore, to translate functional microbiomics into clinical practice, one must understand not only the compositional but also the functional changes associated with gut dysbiosis and its resolution post-LT. In this review, we will summarize critical advances in functional microbiomics in LT recipients as they apply to immune-mediated allograft injury, posttransplant complications, and disease recurrence, while highlighting potential areas for microbial-based therapeutics in LT recipients.

1 Division of Gastroenterology & Hepatology, Department of Medicine, University of Colorado School of Medicine, Aurora, CO.

2 GI and Liver Innate Immune Program, University of Colorado School of Medicine, Aurora, CO.

3 Center for Liver Disease and Transplantation, Department of Medicine, Columbia University Medical Center, New York, NY.

4 Department of Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA.

5 Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO.

Received 20 July 2018. Revision received 7 November 2018.

Accepted 26 November 2018.

The authors declare no conflicts of interest.

M.K. was supported by NIH/NCATS Colorado CTSA (grant UL1 TR002535). Contents are the authors’ sole responsibility and do not necessarily represent official NIH views. E.C.V. was supported by NIH (grant K23 DK101827). C.A.L. was supported by NIH (grants R01 DK104047, RO1 DK108366, and RO1 HL138639).

M.K. participated in writing of the article. All authors of this study participated in revision and final review of the article.

Correspondence: Michael Kriss, MD, 12700 E 19th Ave, Campus Box B146, Aurora, CO 80045. (michael.kriss@ucdenver.edu).

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