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Abstract
Clinical and environmental meta-omics studies are accumulating an ever-growing amount of microbial abundance data over a wide range of ecosystems. With a sufficiently large sample number, these microbial communities can be explored by constructing and analyzing co-occurrence networks, which detect taxon associations from abundance data and can give insights into community structure. Here, we investigate how co-occurrence networks differ across biomes and which other factors influence their properties. For this, we inferred microbial association networks from 20 different 16S rDNA sequencing data sets and observed that soil microbial networks harbor proportionally fewer positive associations and are less densely interconnected than host-associated networks. After excluding sample number, sequencing depth and beta-diversity as possible drivers, we found a negative correlation between community evenness and positive edge percentage. This correlation likely results from a skewed distribution of negative interactions, which take place preferentially between less prevalent taxa. Overall, our results suggest an under-appreciated role of evenness in shaping microbial association networks.
Highlights
Microorganisms engage in a multitude of ecological interactions, ranging from mutualism to parasitism and competition (Konopka, 2009)
Processed 16S data sets were gathered from the QIIME database (Caporaso et al, 2010), the Earth Microbiome Project (EMP) database (Gilbert et al, 2010), and the Human Microbiome Project (HMP) (Huttenhower et al, 2012; Methé et al, 2012)
We show that microbial network inference can be applied in various contexts to study how environmental properties drive taxon associations, to explore associations underlying community types, or to identify novel potential ecological interactions
Summary
Microorganisms engage in a multitude of ecological interactions, ranging from mutualism to parasitism and competition (Konopka, 2009). We developed a pipeline based on an ensemble approach (Faust et al, 2012), which we used recently to predict interactions in the oceanic plankton community (Lima-Mendez et al, 2015) This pipeline combines a number of measures of dependency, such as correlation (e.g. Spearman), similarity (e.g. mutual information), and dissimilarity (e.g. Kullback–Leibler). The rationale behind this ensemble approach is that different measures make different errors, but tend to agree on the correct associations. This “wisdom of crowds” metaheuristic approach has been demonstrated to deliver robust and accurate results for gene regulatory networks (Marbach et al, 2012)
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