title: Inferring Aggregated Functional Traits from Metagenomic Data Using Constrained Non-negative Matrix Factorization: Application to Fiber Degradation in the Human Gut Microbiota.
abstract: Whole Genome Shotgun (WGS) metagenomics is increasingly used to study the structure and functions of complex microbial ecosystems, both from the taxonomic and functional point of view. Gene inventories of otherwise uncultured microbial communities make the direct functional profiling of microbial communities possible. The concept of community aggregated trait has been adapted from environmental and plant functional ecology to the framework of microbial ecology. Community aggregated traits are quantified from WGS data by computing the abundance of relevant marker genes. They can be used to study key processes at the ecosystem level and correlate environmental factors and ecosystem functions. In this paper we propose a novel model based approach to infer combinations of aggregated traits characterizing specific ecosystemic metabolic processes. We formulate a model of these Combined Aggregated Functional Traits (CAFTs) accounting for a hierarchical structure of genes, which are associated on microbial genomes, further linked at the ecosystem level by complex co-occurrences or interactions. The model is completed with constraints specifically designed to exploit available genomic information, in order to favor biologically relevant CAFTs. The CAFTs structure, as well as their intensity in the ecosystem, is obtained by solving a constrained Non-negative Matrix Factorization (NMF) problem. We developed a multicriteria selection procedure for the number of CAFTs. We illustrated our method on the modelling of ecosystemic functional traits of fiber degradation by the human gut microbiota. We used 1408 samples of gene abundances from several high-throughput sequencing projects and found that four CAFTs only were needed to represent the fiber degradation potential. This data reduction highlighted biologically consistent functional patterns while providing a high quality preservation of the original data. Our method is generic and can be applied to other metabolic processes in the gut or in other ecosystems.
authors.affiliation: Institut Micalis, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.|MaIAGE, INRA, Université Paris-Saclay, Jouy-en-Josas, France.
authors.forename: Sébastien
authors.initials: S
authors.lastname: Raguideau
authors.affiliation: MaIAGE, INRA, Université Paris-Saclay, Jouy-en-Josas, France.
authors.forename: Sandra
authors.initials: S
authors.lastname: Plancade
authors.affiliation: MGP, INRA, Université Paris-Saclay, Jouy-en-Josas, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Pons
authors.affiliation: Institut Micalis, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.
authors.forename: Marion
authors.initials: M
authors.lastname: Leclerc
authors.affiliation: MaIAGE, INRA, Université Paris-Saclay, Jouy-en-Josas, France.
authors.forename: Béatrice
authors.identifier: 0000-0001-7821-332X
authors.initials: B
authors.lastname: Laroche
mesh_terms: D000465:Algorithms; D001419:Bacteria / Q000235:genetics / Q000378:metabolism; D050260:Carbohydrate Metabolism / Q000235:genetics / Q000502:physiology; D004043:Dietary Fiber / Q000378:metabolism; D005243:Feces / Q000382:microbiology; D005285:Fermentation; D000069196:Gastrointestinal Microbiome / Q000235:genetics*; D006801:Humans; D056186:Metagenomics / Q000379:methods*