Microbial Co-occurrence Relationships in the Human Microbiome

By Karoline Faust, Jarupon Fah Sathirapongsasuti, Jacques Izard, Nicola Segata, Dirk Gevers, Jeroen Raes, Curtis Huttenhower. PLoS Comput. Biol., 2012

1,423 citations recorded by the source corpus.

The healthy microbiota show remarkable variability within and among individuals. In addition to external exposures, ecological relationships (both oppositional and symbiotic) between microbial inhabitants are important contributors to this variation. It is thus of interest to assess what relationships might exist among microbes and determine their underlying reasons. The initial Human Microbiome Project (HMP) cohort, comprising 239 individuals and 18 different microbial habitats, provides an unprecedented resource to detect, catalog, and analyze such relationships. Here, we applied an ensemble method based on multiple similarity measures in combination with generalized boosted linear models (GBLMs) to taxonomic marker (16S rRNA gene) profiles of this cohort, resulting in a global network of 3,005 significant co-occurrence and co-exclusion relationships between 197 clades occurring throughout the human microbiome. This network revealed strong niche specialization, with most microbial associations occurring within body sites and a number of accompanying inter-body site relationships. Microbial communities within the oropharynx grouped into three distinct habitats, which themselves showed no direct influence on the composition of the gut microbiota. Conversely, niches such as the vagina demonstrated little to no decomposition into region-specific interactions. Diverse mechanisms underlay individual interactions, with some such as the co-exclusion of Porphyromonaceae family members and Streptococcus in the subgingival plaque supported by known biochemical dependencies. These differences varied among broad phylogenetic groups as well, with the Bacilli and Fusobacteria, for example, both enriched for exclusion of taxa from other clades. Comparing phylogenetic versus functional similarities among bacteria, we show that dominant commensal taxa (such as Prevotellaceae and Bacteroides in the gut) often compete, while potential pathogens (e.g. Treponema and Prevotella in the dental plaque) are more likely to co-occur in complementary niches. This approach thus serves to open new opportunities for future targeted mechanistic studies of the microbial ecology of the human microbiome.

Subfields: Molecular Biology, Periodontics, Physiology

View Paper: https://www.semanticscholar.org/paper/c6cb67b4fcfb9acfd3d55001e536a9826371e689

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Citations in the corpus

Fap2 Mediates Fusobacterium nucleatum Colorectal Adenocarcinoma Enrichment by Binding to Tumor-Expressed Gal-GalNAc (2016, background): “…hypothesized that fusobacteria that colonize CRC originate from the oral cavity, as fusobacteria are core resident members of the human oral microbiome and are found infrequently in the gut (Human Microbiome Project Consortium, 2012; Dewhirst et al., 2010; Faust et al., 2012; Strauss et al., 2011).”
Network analysis methods for studying microbial communities: A mini review (2021, background): “This is the common phenomena observed in microbial network studies [42,97] and is known as assortativity [98].”
Correlation detection strategies in microbial data sets vary widely in sensitivity and precision (2016, methodology): “For example, CoNet (Faust et al., 2012) acknowledges that various techniques have different strengths and weaknesses and/or are designed to optimally detect different functional relationships, and thus uses an ensemble method with the ReBoot procedure for P-value computation to combine information f”
Determinants of community structure in the global plankton interactome (2015, methodology): “Taxon-taxon co-occurrence networks were constructed as in (78), selecting Spearman and Kullback-Leibler dissimilarity measures.”
Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment (2021, methodology): “While these observations were made on data from the Human Microbiome Project, the computational methodology can be transferred to other research questions involving marker gene sequencing (Faust et al., 2012).”
Clumpak: a program for identifying clustering modes and packaging population structure inferences across K (2015, background): “…identifying clusters, and it has previously been adapted for diverse biological problems, including orthology assignments (Enright et al. 2002), detection of operational taxonomic units (Ratnasingham & Hebert 2013), and identiﬁcation of co-occurring associations among microbes (Faust et al. 2012).”
Microbial diversity and ecological networks as indicators of environmental quality (2017, background): “2013), the human body (Faust et al. 2012) and the plant microbiome (Karimi et al.”