Adults affected by Prader-Willi syndrome exhibit a unique microbiota profile.

Adults with Prader-Willi syndrome (PWS), with controlled access to food, consume less energy to maintain body weight than the general adult population. This, combined with their altered gastrointestinal transit time, may impact microbiota composition. The exploratory aim of the study was to characterize the PWS microbiota composition in comparison with non-affected adults. Fecal microbiota composition from adults with PWS were analyzed using 16S rRNA gene amplicon sequencing. Data were merged with 16S profiles from baseline fecal samples of four other adult cohorts that differed by geographical location and age. QIIME 2 sample-classifier, machine learning algorithms were used to cross-train the samples and predict from which dataset the taxonomic profiles belong. Taxa that most distinguished between all datasets were extracted and a visual inspection of the R library PiratePlot plots was performed to select the taxa that differed in abundance specific to PWS.