Publication Title: Reproducible Changes In The Anorexia Nervosa Gut Microbiota Following Inpatient Therapy Remain Distinct From Non-Eating Disorder Controls
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Farnaz Fouladi, Emily C. Bulik-Sullivan, Elaine M. Glenny, Laura M. Thornton, Kylie K. Reed, Stephanie Thomas, Susan Kleiman, Ashlie Watters, Judy Oakes, Eun-Young Huh, Quyen Tang, Jintong Liu, Zorka Djukic, Lauren Harper, Yesel Trillo-Ordoñez, Shan Sun, Ivory Blakely, Philip S. Mehler, Anthony A. Fodor, Lisa Tarantino, Cynthia M. Bulik, Ian M. Carroll
The composition of the gut microbiota in patients with anorexia nervosa (AN), and the ability of this microbial community to influence the host, remains uncertain. To achieve a broader understanding of the role of the intestinal microbiota in patients with AN, we collected fecal samples before and following clinical therapy at two geographically distinct eating disorder units (Center of Excellence for Eating Disorders [UNC-CH] and ACUTE Center for Eating Disorders [Denver Health]). Microbiotas were characterized in patients with AN, before and after inpatient treatment, and in non-eating disorder (non-ED) controls using whole genome shotgun sequencing. The impact of inpatient treatment on the AN gut microbiota was remarkably consistent between eating disorder units. Although weight in patients with AN showed improvements, AN microbiotas post-treatment remained distinct from non-ED controls. Additionally, AN gut microbiotas prior to treatment exhibited more fermentation pathways and a lower ability to degrade carbohydrates than non-ED controls. As the intestinal microbiota can influence nutrient metabolism, our data highlight the complex microbial communities in patients with AN as an element needing further attention post inpatient treatment. Additionally, this study defines the effects of renourishment on the AN gut microbiota and serves as a platform to develop precision nutrition approaches to potentially mitigate impediments to recovery.
Scripts and related resources documenting how the data was transformed into results. This pipeline can be run as an automated BioLockJ pipeline. See analysis/Readme.md for details.
Rscripts used in this analysis.
The input data for the analysis pipeline. This includes counts tables that have been loged and normalized. Raw sequence data is available separately.
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MIT License
Copyright (c) 2021 Farnaz Fouladi
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