Microbial community assembly in wild populations of the fruit fly Drosophila melanogaster

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Abstract

Animals are routinely colonized by microorganisms. Despite many studies documenting the microbial taxa associated with animals, the pattern and ecological determinants of among-animal variation in microbial communities are poorly understood. This study quantified the bacterial communities associated with natural populations of Drosophila melanogaster. Across five collections, each fly bore 16-78 OTUs, predominantly of the Acetobacteraceae, Lactobacillaceae, and Enterobacteriaceae. Positive relationships, mostly among related OTUs, dominated both the significant co-occurrences and co-association networks among bacteria, and OTUs with important network positions were generally of intermediate abundance and prevalence. The prevalence of most OTUs was well predicted by a neutral model suggesting that ecological drift and passive dispersal contribute significantly to microbiome composition. However, some Acetobacteraceae and Lactobacillaceae were present in more flies than predicted, indicative of superior among-fly dispersal. These taxa may be well-adapted to the Drosophila habitat from the perspective of dispersal as the principal benefit of the association to the microbial partners. Taken together, these patterns indicate that both stochastic processes and deterministic processes relating to the differential capacity for persistence in the host habitat and transmission between hosts contribute to bacterial community assembly in Drosophila melanogaster.

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Metabolic dependencies drive species co-occurrence in diverse microbial communities.

Aleksej Zelezniak, Sergej Andrejev, Olga Ponomarova … (2015)

Microbial communities populate most environments on earth and play a critical role in ecology and human health. Their composition is thought to be largely shaped by interspecies competition for the available resources, but cooperative interactions, such as metabolite exchanges, have also been implicated in community assembly. The prevalence of metabolic interactions in microbial communities, however, has remained largely unknown. Here, we systematically survey, by using a genome-scale metabolic modeling approach, the extent of resource competition and metabolic exchanges in over 800 communities. We find that, despite marked resource competition at the level of whole assemblies, microbial communities harbor metabolically interdependent groups that recur across diverse habitats. By enumerating flux-balanced metabolic exchanges in these co-occurring subcommunities we also predict the likely exchanged metabolites, such as amino acids and sugars, that can promote group survival under nutritionally challenging conditions. Our results highlight metabolic dependencies as a major driver of species co-occurrence and hint at cooperative groups as recurring modules of microbial community architecture.

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Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences

Jai Rideout, Yan-Yan He, Jose A. Navas-Molina … (2014)

We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to “classic” open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, “classic” open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of “classic” open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by “classic” open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME’s uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME’s OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.

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High-dimensional graphs and variable selection with the Lasso

Nicolai Meinshausen, Peter Bühlmann (2006)

The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a computationally attractive alternative to standard covariance selection for sparse high-dimensional graphs. Neighborhood selection estimates the conditional independence restrictions separately for each node in the graph and is hence equivalent to variable selection for Gaussian linear models. We show that the proposed neighborhood selection scheme is consistent for sparse high-dimensional graphs. Consistency hinges on the choice of the penalty parameter. The oracle value for optimal prediction does not lead to a consistent neighborhood estimate. Controlling instead the probability of falsely joining some distinct connectivity components of the graph, consistent estimation for sparse graphs is achieved (with exponential rates), even when the number of variables grows as the number of observations raised to an arbitrary power.