Fig. 1

Outline of experimental design. (A) 16S rRNA microbiome data, previously reported by David et al. [49], followed the microbiome community structures of two human donors over the course of a year at nearly daily intervals. Microbiome samples can be grouped into dysbiotic states and non-dysbiotic states from observed shifts in microbiome community structures, and knows changes in donors’ health and activities. Using collected sequences and annotated bacterial genomes (B), metagenomic enzyme profiles were predicted from reported 16S rRNA community structures (C). Using the predicted relative metabolic turnover (PRMT) method (D), metabolic models were generated from enzyme function profiles (E). All three data types (A, C, and E) were divided into training and validation subsets (F). Two approaches were used to divide data into training and validation subsets. The first combined data from donors and selected training and validation subsets to contain an approximately equal number of samples from each donor. In the second approach, training data were selected from a subset of one donor, and all data from the alternate donor were used for the validation set. (G) Support vector machines (SVMs) were used to build predictive models from training data sets for each data type. Models predicted whether samples were collected from a donor with a non-dysbiotic or dysbiotic state. (H) SVM models were validated on data subsets selected in (F). Using features identified as highly predictive for dysbiosis in validated SVM from (G), the molecular mechanisms underlying dysbiosis can be proposed (I)