PhylArray: phylogenetic probe design algorithm for microarray

International audienceMOTIVATION: Microbial diversity is still largely unknown in most environments, such as soils. In order to get access to this microbial 'black-box', the development of powerful tools such as microarrays are necessary. However, the reliability of this approach relies on probe efficiency, in particular sensitivity, specificity and explorative power, in order to obtain an image of the microbial communities that is close to reality. RESULTS: We propose a new probe design algorithm that is able to select microarray probes targeting SSU rRNA at any phylogenetic level. This original approach, implemented in a program called 'PhylArray', designs a combination of degenerate and non-degenerate probes for each target taxon. Comparative experimental evaluations indicate that probes designed with PhylArray yield a higher sensitivity and specificity than those designed by conventional approaches. Applying the combined PhyArray/GoArrays strategy helps to optimize the hybridization performance of short probes. Finally, hybridizations with environmental targets have shown that the use of the PhylArray strategy can draw attention to even previously unknown bacteria

The effect of primer choice and short read sequences on the outcome of 16S rRNA gene based diversity studies

Different regions of the bacterial 16S rRNA gene evolve at different evolutionary rates. The scientific outcome of short read sequencing studies therefore alters with the gene region sequenced. We wanted to gain insight in the impact of primer choice on the outcome of short read sequencing efforts. All the unknowns associated with sequencing data, i.e. primer coverage rate, phylogeny, OTU-richness and taxonomic assignment, were therefore implemented in one study for ten well established universal primers (338f/r, 518f/r, 799f/r, 926f/r and 1062f/r) targeting dispersed regions of the bacterial 16S rRNA gene. All analyses were performed on nearly full length and in silico generated short read sequence libraries containing 1175 sequences that were carefully chosen as to present a representative substitute of the SILVA SSU database. The 518f and 799r primers, targeting the V4 region of the 16S rRNA gene, were found to be particularly suited for short read sequencing studies, while the primer 1062r, targeting V6, seemed to be least reliable. Our results will assist scientists in considering whether the best option for their study is to select the most informative primer, or the primer that excludes interferences by host-organelle DNA. The methodology followed can be extrapolated to other primers, allowing their evaluation prior to the experiment

Transcriptomic responses of the olive fruit fly Bactrocera oleae and its symbiont Candidatus Erwinia dacicola to olive feeding

The olive fruit fly, Bactrocera oleae, is the most destructive pest of olive orchards worldwide. The monophagous larva has the unique capability of feeding on olive mesocarp, coping with high levels of phenolic compounds and utilizing non-hydrolyzed proteins present, particularly in the unripe, green olives. On the molecular level, the interaction between B. oleae and olives has not been investigated as yet. Nevertheless, it has been associated with the gut obligate symbiotic bacterium Candidatus Erwinia dacicola. Here, we used a B. oleae microarray to analyze the gene expression of larvae during their development in artificial diet, unripe (green) and ripe (black) olives. The expression profiles of Ca. E. dacicola were analyzed in parallel, using the Illumina platform. Several genes were found overexpressed in the olive fly larvae when feeding in green olives. Among these, a number of genes encoding detoxification and digestive enzymes, indicating a potential association with the ability of B. oleae to cope with green olives. In addition, a number of biological processes seem to be activated in Ca. E. dacicola during the development of larvae in olives, with the most notable being the activation of amino-acid metabolism

Non-syntrophic methanogenic hydrocarbon degradation by an archaeal species

The methanogenic degradation of oil hydrocarbons can proceed through syntrophic partnerships of hydrocarbon-degrading bacteria and methanogenic archaea1,2,3. However, recent culture-independent studies have suggested that the archaeon ‘Candidatus Methanoliparum’ alone can combine the degradation of long-chain alkanes with methanogenesis4,5. Here we cultured Ca. Methanoliparum from a subsurface oil reservoir. Molecular analyses revealed that Ca. Methanoliparum contains and overexpresses genes encoding alkyl-coenzyme M reductases and methyl-coenzyme M reductases, the marker genes for archaeal multicarbon alkane and methane metabolism. Incubation experiments with different substrates and mass spectrometric detection of coenzyme-M-bound intermediates confirm that Ca. Methanoliparum thrives not only on a variety of long-chain alkanes, but also on n-alkylcyclohexanes and n-alkylbenzenes with long n-alkyl (C≥13) moieties. By contrast, short-chain alkanes (such as ethane to octane) or aromatics with short alkyl chains (C≤12) were not consumed. The wide distribution of Ca. Methanoliparum4,5,6 in oil-rich environments indicates that this alkylotrophic methanogen may have a crucial role in the transformation of hydrocarbons into methane

Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era

Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective.

Standardisation and optimisation techniques in gut microbiome community analysis

With the emergence of high throughput next-generation sequencing the importance of the human gut microbiota as regulators, modulators and maintainers of human health and disease became more and more imminent. Advances in sequencing in the last two decades enabled the analysis of the composition and dynamics of the gut microbiome in unprecedented resolution and complexity. Investigations of this complex community by marker gene studies allowed assertions on presence, absence and ecological dynamics of gut bacteria. Several studies discovered strong relationships between the gut microbiota and human health. Some of these bacteria are shown to be essential for daily life processes like digestion, nutrition uptake, pathogen resistance and immune maturation. Likewise, disturbances of this close relationship, called dysbiosis, have been found to be associated with diseases like diabetes, obesity, colon cancer and inflammatory bowel disease. All this renders the gut microbiome as a highly relevant target of research in medical diagnostics and microbiome community analysis a valid hypothesis building tool. Nevertheless, the vast amount of different methodologies and lack of broadly accepted standards to create and handle gut microbiome abundance data complicates reproducible or replicable findings across studies. Especially in settings, where samples diverge significantly in their total biomass or microbial load, the analysis of the microbiome is hampered. Several efforts to allow accurate inter sample comparisons have been undertaken, including the use of relative abundances or random feature sub-sampling (rarefaction). While these methodologies are the most frequently used, they are not fully capable to correct for these sample-wide differences. To increase comparability between samples the use of exogenous spike-in bacteria is proposed to correct for sample specific differences in microbial load. The methodology is tested on a dilution experiment with known differences between samples and successfully applied on a clinical microbiome data set. These experiments suggest that current analysis methods lack a pivotal angle on the data, that is comparability between samples differing in microbial load. Meanwhile, the proposed spike-in based calibration to microbial load (SCML) allows for accurate estimation of ratios of absolute endogenous bacteria abundances. Furthermore, microbiome community analysis is heavily dependent on the resolution of the underlying read count data. While resolutions such as operational taxonomic units (OTUs) generally overestimate diversity and create highly redundant and sparse datasets, agglomerations to common taxonomy can obfuscate distinct read count patterns of possible sub-populations inside the given taxonomy. Even though the ladder agglomeration strategy might be valid for taxonomy with low phenotypical divergence, plenty taxonomic lineages in fact contain highly diverse sub-species. Thus, a more appropriate taxonomic unit would adapt its resolution for those densely populated branches, allowing for different count resolutions inside the same community. Here the concept of adaptive taxonomic units (ATUs) is introduced and applied on a perturbation experiment including mice receiving antibiotics. For this data set the different classical count resolutions (i.e. collapsed to order, family or genus etc.) produce highly contradictory results. Meanwhile, adaptive taxonomic units (ATUs) derived by hierarchical affinity merging (HAM) adapt the granularity of taxonomy to the underlying sequencing data. Branches of bacterial phylogeny that are highly covered in the data set receive a higher resolution than those that were infrequently observed. The algorithm hereby merges operational taxonomic units (OTUs) guided not only by sequence dissimilarity, but also by count distribution and OTU size. Due to the agglomeration the number of features is reduced significantly, lowering the complexity of the data, while preserving distributional patterns only observable at OTU level. Consequently, the sparsity of the count data is reduced significantly such that every ATU accumulates reasonable count number and can thus be reliably analysed. The algorithm is provided in the form of the R-Package dOTUClust