No abstract

High-quality annotation of microsporidian genomes is essential for understanding the biological processes that govern the development of these parasites. Here we present an improved structural annotation method using transcriptional DnA signals. We apply this method to re-annotate four previously annotated genomes, which allow us to detect annotation errors and identify a significant number of unpredicted genes. We then annotate the newly sequenced genome of Anncaliia algerae. A comparative genomic analysis of A. algerae permits the identification of not only microsporidian core genes, but also potentially highly expressed genes encoding membrane-associated proteins, which represent good candidates involved in the spore architecture, the invasion process and the microsporidianhost relationships. Furthermore, we find that the ten-fold variation in microsporidian genome sizes is not due to gene number, size or complexity, but instead stems from the presence of transposable elements. such elements, along with kinase regulatory pathways and specific transporters, appear to be key factors in microsporidian adaptive processes.

Next-generation sequencing (NGS) allows faster acquisition of metagenomic data, but complete exploration of complex ecosystems is hindered by the extraordinary diversity of microorganisms. To reduce the environmental complexity, we created an innovative solution hybrid selection (SHS) method that is combined with NGS to characterize large DNA fragments harbouring biomarkers of interest. The quality of enrichment was evaluated after fragments containing the methyl coenzyme M reductase subunit A gene (mcrA), the biomarker of methanogenesis, were captured from a Methanosarcina strain and a metagenomic sample from a meromictic lake. The methanogen diversity was compared with direct metagenome and mcrA-based amplicon pyrosequencing strategies. The SHS approach resulted in the capture of DNA fragments up to 2.5 kb with an enrichment efficiency between 41 and 100%, depending on the sample complexity. Compared with direct metagenome and amplicons sequencing, SHS detected broader mcrA diversity, and it allowed efficient sampling of the rare biosphere and unknown sequences. In contrast to amplicon-based strategies, SHS is less biased and GC independent, and it recovered complete biomarker sequences in addition to conserved regions. Because this method can also isolate the regions flanking the target sequences, it could facilitate operon reconstructions.

Evaluating the composition of the human gut microbiota greatly facilitates studies on its role in human pathophysiology, and is heavily reliant on culture-independent molecular methods. A microarray designated the Human Gut Chip (HuGChip) was developed to analyze and compare human gut microbiota samples. The PhylArray software was used to design specific and sensitive probes. The DNA chip was composed of 4,441 probes (2,442 specific and 1,919 explorative probes) targeting 66 bacterial families. A mock community composed of 16S rRNA gene sequences from intestinal species was used to define the threshold criteria to be used to analyze complex samples. This was then experimentally verified with three human faecal samples and results were compared (i) with pyrosequencing of the V4 hypervariable region of the 16S rRNA gene, (ii) metagenomic data, and (iii) qPCR analysis of three phyla. When compared at both the phylum and the family level, high Pearson's correlation coefficients were obtained between data from all methods. The HuGChip development and validation showed that it is not only able to assess the known human gut microbiota but could also detect unknown species with the explorative probes to reveal the large number of bacterial sequences not yet described in the human gut microbiota, overcoming the main inconvenience encountered when developing microarrays.

Summary The bioremediation of chloroethene contaminants in groundwater polluted systems is still a serious environmental challenge. Many previous studies have shown that cooperation of several dechlorinators is crucial for complete dechlorination of trichloroethene to ethene. In the present study, we used an explorative functional DNA microarray (DechloArray) to examine the composition of specific functional genes in groundwater samples in which chloroethene bioremediation was enhanced by delivery of hydrogen‐releasing compounds. Our results demonstrate for the first time that complete biodegradation occurs through spatial and temporal variations of a wide diversity of dehalorespiring populations involving both Sulfurospirillum, Dehalobacter, Desulfitobacterium, Geobacter and Dehalococcoides genera. Sulfurospirillum appears to be the most active in the highly contaminated source zone, while Geobacter was only detected in the slightly contaminated downstream zone. The concomitant detection of both bvcA and vcrA genes suggests that at least two different Dehalococcoides species are probably responsible for the dechlorination of dichloroethenes and vinyl chloride to ethene. These species were not detected on sites where cis‐dichloroethene accumulation was observed. These results support the notion that monitoring dechlorinators by the presence of specific functional biomarkers using a powerful tool such as DechloArray will be useful for surveying the efficiency of bioremediation strategies.

Supplementary data are available at Bioinformatics online.

No abstract

Microbial communities are extremely abundant and diverse on earth surface and play key role in the ecosystem functioning. Thus, although next-generation sequencing (NGS) technologies have greatly improved knowledge on microbial diversity, it is necessary to reduce the biological complexity to better understand the microorganism functions. To achieve this goal, we describe a promising approach, based on the solution hybrid selection (SHS) method for the selective enrichment in a target-specific biomarker from metagenomic and metatranscriptomic samples. The success of this method strongly depends on the determination of sensitive, specific, and explorative probes to assess the complete targeted gene repertoire. Indeed, in this method, RNA probes were used to capture large DNA or RNA fragments harboring biomarkers of interest that potentially allow to link structure and function of communities of interest.