BackgroundAdvances in sequencing technologies and bioinformatics have made the analysis of microbial communities almost routine. Nonetheless, the need remains to improve on the techniques used for gathering such data, including increasing throughput while lowering cost and benchmarking the techniques so that potential sources of bias can be better characterized.MethodsWe present a triple-index amplicon sequencing strategy to sequence large numbers of samples at significantly lower c ost and in a shorter timeframe compared to existing methods. The design employs a two-stage PCR protocol, incorpo rating three barcodes to each sample, with the possibility to add a fourth-index. It also includes heterogeneity spacers to overcome low complexity issues faced when sequencing amplicons on Illumina platforms.ResultsThe library preparation method was extensively benchmarked through analysis of a mock community in order to assess biases introduced by sample indexing, number of PCR cycles, and template concentration. We further evaluated the method through re-sequencing of a standardized environmental sample. Finally, we evaluated our protocol on a set of fecal samples from a small cohort of healthy adults, demonstrating good performance in a realistic experimental setting. Between-sample variation was mainly related to batch effects, such as DNA extraction, while sample indexing was also a significant source of bias. PCR cycle number strongly influenced chimera formation and affected relative abundance estimates of species with high GC content. Libraries were sequenced using the Illumina HiSeq and MiSeq platforms to demonstrate that this protocol is highly scalable to sequence thousands of samples at a very low cost.ConclusionsHere, we provide the most comprehensive study of performance and bias inherent to a 16S rRNA gene amplicon sequencing method to date. Triple-indexing greatly reduces the number of long custom DNA oligos required for library preparation, while the inclusion of variable length heterogeneity spacers minimizes the need for PhiX spike-in. This design results in a significant cost reduction of highly multiplexed amplicon sequencing. The biases we characterize highlight the need for highly standardized protocols. Reassuringly, we find that the biological signal is a far stronger structuring factor than the various sources of bias.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-017-0279-1) contains supplementary material, which is available to authorized users.

BackgroundDetermining ecological roles of community members and the impact of specific taxa on overall biodiversity in the gastrointestinal (GI) microbiota is of fundamental importance. A step towards a systems-level understanding of the GI microbiota is characterization of biotic interactions. Community time series analysis, an approach based on statistical analysis of changing population abundances within a single system over time, is needed in order to say with confidence that one population is affecting the dynamics of another.ResultsHere, we characterize biotic interaction structures and define ecological roles of major bacterial groups in four healthy individuals by analysing high-resolution, long-term (>180 days) GI bacterial community time series. Actinobacteria fit the description of a keystone taxon since they are relatively rare, but have a high degree of ecological connectedness, and are positively correlated with diversity both within and between individuals. Bacteriodetes were found to be a foundation taxon in that they are numerically dominant and interact extensively, in particular through positive interactions, with other taxa. Although community structure, diversity and biotic interaction patterns were specific to each individual, we observed a strong tendency towards more intense competition within than between phyla. This is in agreement with Darwin’s limiting similarity hypothesis as well as a published biotic interaction model of the GI microbiota based on reverse ecology. Finally, we link temporal enterotype switching to a reciprocal positive interaction between two key genera.ConclusionsIn this study, we identified ecological roles of key taxa in the human GI microbiota and compared our time series analysis results with those obtained through a reverse ecology approach, providing further evidence in favour of the limiting similarity hypothesis first put forth by Darwin. Larger longitudinal studies are warranted in order to evaluate the generality of basic ecological concepts as applied to the GI microbiota, but our results provide a starting point for achieving a more profound understanding of the GI microbiota as an ecological system.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-015-0107-4) contains supplementary material, which is available to authorized users.

All multicellular organisms host microbial communities in and on their bodies, and these microbiomes can have major influences on host biology. Most research has focussed on the oral, skin, and gut microbiomes, whereas relatively little is known about the reproductive microbiome. Here, we review empirical evidence to show that reproductive microbiomes can have significant effects on the reproductive function and performance of males and females. We then discuss the likely repercussions of these effects for evolutionary processes related to sexual selection and sexual conflict, as well as mating systems and reproductive isolation. We argue that knowledge of the reproductive microbiome is fundamental to our understanding of the evolutionary ecology of reproductive strategies and sexual dynamics of host organisms. The Microbiome Revolution and the Reproductive MicrobiomeAnimals and plants live and evolve in a world dominated by microbes, and host a diversity of microbial communities in and on their bodies. A recent explosion in research on host-associated microbial communities (i.e., microbiota and/or microbiomes, see Glossary) is revolutionising biology. While earlier research typically considered microorganisms from a pathological perspective, it is now widely accepted that the relationship between host and microbes spans a continuum, from detrimental to beneficial. Through their influence on host health, physiology, development, and behaviour [1,2], microbiomes can be seen as an integral part of the host phenotype, and potentially also the host genome (e.g., the hologenome concept [3], but see [4]). While considerable attention has been paid to the role of oral, skin, and gut microbiomes in host ecology, evolution, and fitness, less is known about the reproductive microbiome (Box 1). This is surprising given longstanding knowledge of microbes in male and female reproductive systems (e.g., [5]), most notably in the context of sexually transmitted infections (STIs) [6], and more recent DNA-sequencing studies demonstrating the presence of dynamic microbial communities in reproductive tissues [7], especially the vagina [8,9]. Thus, the reproductive microbiome represents an outstanding challenge in the study of host-associated microbial communities.

Temporal dynamics of the human gut microbiota is of fundamental importance for the development of proper gut function and maturation of the immune system. Here we present a description of infant gut ecological dynamics using a combination of nonlinear data modeling and simulations of the early infant gut colonization processes. Principal component analysis of infant microbiota 16S rRNA gene microarray data showed that the main directions of variation were defined by three phylum-specific probes targeting Bacteroides, Proteobacteria and Firmicutes. Nonlinear regression analysis identified several dynamic interactions between these three phyla. Simulations of the early phylum-level colonization process showed the relatively rapid establishment of an equilibrium community after an unstable initial phase. In general, varying the initial composition of phyla in the simulations had little bearing on the final equilibrium. The dynamic interaction model was found to maintain its predictive ability for Proteobacteria and Firmicutes well into the simulation, whereas Bacteroides densities tended to be underestimated, possibly due to host top-down selection for Bacteroides. In accordance with our model, initial perturbation of the microbiota by different mode of delivery (vaginal and C-section) did not affect the later phylum composition in the infants investigated. Considering the predictive ability and convergence of our phylum-level model, we now propose that deterministic bacterial-bacterial interactions are more important for shaping the human infant gut microbiota than previously anticipated.