Additional file 1 of Multi-omics gut microbiome signatures in obese women: role of diet and uncontrolled eating behavior
Creators: name: Barone, Monica
name: Garelli, Silvia
name: Rampelli, Simone
name: Agostini, Alessandro
name: Matysik, Silke
name: D’Amico, Federica
name: Krautbauer, Sabrina
name: Mazza, Roberta
name: Salituro, Nicola
name: Fanelli, Flaminia
name: Iozzo, Patricia
name: Sanz, Yolanda
name: Candela, Marco
name: Brigidi, Patrizia
name: Pagotto, Uberto
name: Turroni, Silvia
Publisher: name: figshare
Year: 2023
Subjects: subject: Microbiology
subject: FOS: Biological sciences
subject: Genetics
subject: Physiology
subject: Ecology
subject: Cancer

description: Additional file 1: Fig. S1. Assignment of bacterial co-abundance groups (CAGs). Fig. S2. Associations of the microbiota profiles with host metadata. Fig. S3. Distribution of eating behavior-based study groups across the four microbiome clusters (C1-C4). Fig. S4. The transcriptionally active fraction of the gut microbiome differs according to obesity and uncontrolled eating behavior. a Bubble charts showing the transcriptionally active microbial species within the four microbiome configurations (clusters C1–C4). Circle sizes and colors indicate the transcriptional over-abundance of a given species compared to its average normalized abundance in the whole cohort using log2 transformation. b Spider plots showing the number of active species for each metabolism (i.e., carbohydrate, lipid, amino acid and xenobiotic metabolism). Species were counted as “active” when present with log2 normalized abundance &gt; 1 in at least 1/3 of the pathways comprised in the specific metabolic category. Fig. S5. The metabolic activities of the gut microbiome are broadly taxonomically distributed. Fig. S6. Metabolic activities of the four microbiota clusters according to metatranscriptomic analysis. Fig. S7. Fecal lipidomic profiles of the four microbiota clusters (C1-C4). Fig. S8. Associations between the transcriptionally active fraction of the gut microbiome and lipidomics measures. Hierarchical clustering obtained with complete linkage method and Pearson correlation as distance, performed on the sparse partial least square (sPLS) regression of transcriptionally active pathways and species of the gut microbiome and host lipids. CA, cholic acid; CDCA, chenodeoxycholic acid; DCA, deoxycholic acid; GCA, glycocholic acid; GDCA, glycodeoxycholic acid; HDCA, hyodeoxycholic acid; LCA, lithocholic acid; UDCA, ursodeoxycholic acid. Fig. S9. Transcriptional activities of microbiome configurations specifically related to food intake, energy expenditure, and neuroendocrine signaling. Average RNA:DNA ratio for each cluster (C1–C4) with respect to the whole cohort, of a selection of key genes involved in ClpB (orange), bile acid (brown), tryptophan (green), opioid (cyan), eCB (violet), and GABA (navy) production, divided by the assigned microbial species. Phylogenetic tree on the top of the heatmap was built using hierarchical Ward linkage clustering based on the Spearman correlation coefficients of the average DNA-normalized transcript abundance profiles for the species with detectable transcriptomes in the whole cohortTable S1. Genus-level distribution and differences in the gut microbiota profiles among the four clusters (C1-C4). Table S2. Food description and average frequency of daily consumption for each microbiota cluster (C1 to C4). Table S3. Summary of dietary groups and microbiota clusters for each of the 100 enrolled women. Table S4. Loading scores obtained from sPLS correlation analysis between metatranscriptomics and lipidomics datasets. Table S5. Summary of the main distinctive features of the four microbiota clusters in terms of clinical, dietary and lipidomic profiles.