A simulated metagenomic analysis of the gut microbiota of Anorexia Nervosa patients using PICRUSt

Abstract

Introduction Mack et al. (2016) studied the fecal bacteria and archaea of 55 European normal-weight participants (NW), 55 European patients with anorexia nervosa (ANT1), and 44 ANT1 patients following a body mass index increase (ANT2). Spreadsheets of identified microbes and their relative abundance per patient were uploaded to the EBI Metagenomics web server by Mack et al. We aimed to further study the functions of the identified microbes using the PICUSt algorithm (Langille, 2013) and see if these functions are consistent with published literature. Methods Spreadsheets were downloaded from EBI Metagenomics (Project# ERP012549) in JSON Biom format and uploaded to a Galaxy cloud server hosting PICRUSt. All data transformations can be viewed at http://huttenhower.sph.harvard.edu/galaxy/u/farhaansgroup/h/anorexi-astem-2017 . Transformed datasets were downloaded, appended with a .biom file extension, converted to the SPF format using STAMP v2.1.3 (Parks, 2014), and merged into a single file using Microsoft Excel for analysis with STAMP. Differences in propionate metabolism between ANT1, ANT2, and NW samples was chosen for further study. Results & Discussion The proportion of propionate metabolism genes was not significantly different between ANT1 and NW samples (p=0.08), but was different between ANT2 and NW samples (p=0.01) using a pair-wise Welsh’s t-test (0.95 CI) with a Storey FDR multiple test correction. In comparison, Mack et al, detected no differences in propionate concentration between AN and NW fecal samples using gas chromatography while Morito et al (2015) found lower concentrations of propionate in Japanese AN versus NW fecal samples using liquid chromatography. Our discrepancy with Mack et al could have arisen since PICRUSt cannot analyze the genes of eukaryotes, PICRUSt is limited by the depth and breadth of the gene annotations in the KEGG database, and our experimental setup cannot provide data on gene expression. Moreover, 18% of V4 16S rRNA DNA sequences could not be matched to any bacteria or archaea by EBI Metagenomics. In conclusion, while in silico experiments can be useful to predict microbial functions in a sample, in this case, our PICRUSt-based hypothesis that fecal samples from Mack et al would have different concentrations of propionate between AN and NW samples was not borne out by Mack et al’s chromatography experiments. Nonetheless, the conflicting findings between us, Mack et al, and Morito et al warrants further research on whether microbes mediate carbohydrate metabolism differently in patients with a history of anorexia nervosa versus controls.