NDLI: Microbiome Dynamics as Predictors of Lung Transplant Rejection

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Microbiome Dynamics as Predictors of Lung Transplant Rejection

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Microbiome Dynamics as Predictors of Lung Transplant Rejection

Content Provider	ACM Digital Library
Author	Andropolis, Kalista Ranjan, Ravi Ferkol, Thomas Finn, Patricia Metwally, Ahmed Dai, Yang Ascoli, Christian Perkins, David
Abstract	Lung transplantation offers the only treatment for multiple chronic diseases. Transplantation is dependent upon successful resistance to organ rejection. For children, a vulnerable population, the five and ten-year survival for lung transplants is only 52% and 29%, respectively. The reason for this low survival rate is primary due to chronic lung graft rejection in the form of Bronchiolitis Obliterans Syndrome (BOS). Our hypothesis is that the changes in the composition of the pulmonary microbiome are associated with the development and progression of graft rejection which is in turn related to detrimental cardiopulmonary outcomes and poor overall survival in lung transplant recipients. Samples were obtained from 6 pediatric lung transplant patients over multiple time points. Bronchoalveolar lavage (BAL) samples were collected at approximately 7 time points for each subject. The DNA isolated from BAL is sequenced on an Illumina MiSeq machine. The longitudinal taxonomic profiles demonstrate the phylum Proteobacteria to be the most abundant across all samples. This suggests that certain members of this phylum may indicate a core microbiome in the lung graft. The association between Pseudomonas aeruginosa overgrowth and clinically suspected infection requiring antibiotic therapy was evaluated throughout the study period employing a Smoothing Splines ANOVA on the microbial taxonomic time series' profile. Overgrowth of Cellulomonas is associated with infection during the early days after the lung transplantation. Conversely, Bradyrhizobium, Acetobacter, and Coriobacterium are more abundant in the non-infected subjects. We also propose MetaLonDA, an R package that can be used to accurately detect metagenomic features (species, genes) relating to the phenotype or disease status, and accurately detect the starting and ending time points when the differences arise. It is able to handle sampling at different time points, unequal number of time points among the subjects, and long gap between longitudinal time points.
Starting Page	619
Ending Page	619
Page Count	1
File Format	PDF
ISBN	9781450347228
DOI	10.1145/3107411.3108229
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Metagenomics Lung transplant Time series Differential abundance Microbiome
Content Type	Text
Resource Type	Article

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