FiberGrowth Pipeline: A Framework Toward Predicting Fiber-Specific Growth From Human Gut Bacteroidetes Genomes - Colnet, Bénédicte; Sieber, Christian M. K.; Perraudeau, Fanny; Leclerc, Marion (Frontiers in Microbiology, 2021)
Dietary fibers impact gut colonic health, including through the production of short chain fatty acids. A low fiber diet has been linked to lower bacterial diversity, obesity, type 2 diabetes and promotion of mucosal pathogens. GlycoDietary fibers impact gut colonic health, including through the production of short chain fatty acids. A low fiber diet has been linked to lower bacterial diversity, obesity, type 2 diabetes and promotion of mucosal pathogens. Glycosyl hydrolases (GH) are important enzymes involved in the bacterial catabolism of fiber into short chain fatty acids. However, the GH involved in glycan breakdown (adhesion, hydrolysis, fermentation) are organized in Polysaccharides Utilization Loci (PUL) with complex modularity. Our goal was to explore how the capacity of strains from the Bacteroidetes phylum to grow on fiber could be predicted from their genome sequences. We designed an in silico pipeline called FiberGrowth and independently validated it for 7 different fibers, on 28 genomes from Bacteroidetes type strains. To do so, we compared the existing GH annotation tools, and built PUL models by using published growth and gene expression data. FiberGrowth’s prediction performance in terms of true positive rate (TPR) and false positive rate (FPR) strongly depended on available data and fiber: arabinoxylan (TPR: 0.89, FPR: 0), inulin (0.95, 0.33), heparin (0.8, 0.22) laminarin (0.38, 0.17), levan (0.3, 0.06), mucus (0.13, 0.38), and starch (0.73, 0.41). Being able to better predict fiber breakdown by bacterial strains would help to understand their impact on human nutrition and health. Assuming further gene expression experiment along with discoveries on structural analysis, we hope computational tools like FiberGrowth will help researchers prioritize and design in vitro experiments.