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International audience; Protozoa have long been considered undesirable residents of the human gut, but recent findings suggest that some of them may positively affect the gut ecosystem. To better understand the role and ecological dynamics of these commensal and potentially beneficial protozoan symbionts, we need efficient methods to detect them, as well as accurate estimates of their prevalence across human populations. Metagenomics provides such an opportunity, allowing simultaneous detection of multiple symbionts in a single analytical procedure. In this study, we collected fecal samples of 68 individuals from three Cameroonian populations with different subsistence modes and compared metagenomics-based and targeted methods of detection for two common protozoan genera: Blastocystis and Ent-amoeba. In addition, we analyzed our data along with publicly available fecal metagenomes from various worldwide populations to explore the prevalence and association patterns of ten protozoan genera. Regarding the detection method, microscopy was much less sensitive than metagenomics for Entamoeba, whereas qPCR was at least as sensitive as meta-genomics for Blastocystis sp. However, metagenomics was more likely to detect co-colonizations by multiple subtypes. Out of the ten examined genera in 127 individuals from Cameroon, Tanzania, Peru, Italy or USA, only three (Blastocystis, Entamoeba and Entero-monas) had an overall prevalence exceeding 10%. All three genera were more common in less industrialized populations and their prevalence differed between continents and subsistence modes, albeit not in a straightforward manner. The majority (72.5%) of colonized individuals carried at least two protozoan species, indicating that mixed-species colonizations are common. In addition, we detected only positive and no negative association patterns between different protozoa. Despite the pitfalls of the metagenomic approach, ranging from the availability of good-quality sequencing data to the lack of standard analytical procedures, we demonstrated its utility in simultaneous detection of multiple protozoan genera, and especially its ability to efficiently detect mixed-species colonizations. Our study corroborates and expands prevalence results previously obtained for Blastocystis sp. and provides novel data for Entamoeba spp. and several other protozoan genera. Furthermore, it indicates that multiple protozoa are common residents of the healthy human gut worldwide.

Background The biological and clinical consequences of the tight interactions between host and microbiota are rapidly being unraveled by next generation sequencing technologies and sophisticated bioinformatics, also referred to as microbiota metagenomics. The recent success of metagenomics has created a demand to rapidly apply the technology to large case–control cohort studies and to studies of microbiota from various habitats, including habitats relatively poor in microbes. It is therefore of foremost importance to enable a robust and rapid quality assessment of metagenomic data from samples that challenge present technological limits (sample numbers and size). Here we demonstrate that the distribution of overlapping k-mers of metagenome sequence data predicts sequence quality as defined by gene distribution and efficiency of sequence mapping to a reference gene catalogue. Results We used serial dilutions of gut microbiota metagenomic datasets to generate well-defined high to low quality metagenomes. We also analyzed a collection of 52 microbiota-derived metagenomes. We demonstrate that k-mer distributions of metagenomic sequence data identify sequence contaminations, such as sequences derived from “empty” ligation products. Of note, k-mer distributions were also able to predict the frequency of sequences mapping to a reference gene catalogue not only for the well-defined serial dilution datasets, but also for 52 human gut microbiota derived metagenomic datasets. Conclusions We propose that k-mer analysis of raw metagenome sequence reads should be implemented as a first quality assessment prior to more extensive bioinformatics analysis, such as sequence filtering and gene mapping. With the rising demand for metagenomic analysis of microbiota it is crucial to provide tools for rapid and efficient decision making. This will eventually lead to a faster turn-around time, improved analytical quality including sample quality metrics and a significant cost reduction. Finally, improved quality assessment will have a major impact on the robustness of biological and clinical conclusions drawn from metagenomic studies. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1406-7) contains supplementary material, which is available to authorized users.