Scoary2: rapid association of phenotypic multi-omics data with microbial pan-genomes.

Unraveling bacterial gene function drives progress in various areas, such as food production, pharmacology, and ecology. While omics technologies capture high-dimensional phenotypic data, linking them to genomic data is challenging, leaving 40-60% of bacterial genes undescribed. To address this bottleneck, we introduce Scoary2, an ultra-fast microbial genome-wide association studies (mGWAS) software. With its data exploration app and improved performance, Scoary2 is the first tool to enable the study of large phenotypic datasets using mGWAS. As proof of concept, we explore the metabolome of yogurts, each produced with a different Propionibacterium reichii strain and discover two genes affecting carnitine metabolism.

OpenGenomeBrowser: a versatile, dataset-independent and scalable web platform for genome data management and comparative genomics.

BACKGROUND: As the amount of genomic data continues to grow, there is an increasing need for systematic ways to organize, explore, compare, analyze and share this data. Despite this, there is a lack of suitable platforms to meet this need. RESULTS: OpenGenomeBrowser is a self-hostable, open-source platform to manage access to genomic data and drastically simplifying comparative genomics analyses. It enables users to interactively generate phylogenetic trees, compare gene loci, browse biochemical pathways, perform gene trait matching, create dot plots, execute BLAST searches, and access the data. It features a flexible user management system, and its modular folder structure enables the organization of genomic data and metadata, and to automate analyses. We tested OpenGenomeBrowser with bacterial, archaeal and yeast genomes. We provide a docker container to make installation and hosting simple. The source code, documentation, tutorials for OpenGenomeBrowser are available at opengenomebrowser.github.io and a demo server is freely accessible at opengenomebrowser.bioinformatics.unibe.ch . CONCLUSIONS: To our knowledge, OpenGenomeBrowser is the first self-hostable, database-independent comparative genome browser. It drastically simplifies commonly used bioinformatics workflows and enables convenient as well as fast data exploration.

Whole-genome analyses reveal a novel prophage and cgSNPs-derived sublineages of Brachyspira hyodysenteriae ST196.

BACKGROUND: Brachyspira (B.) hyodysenteriae is a fastidious anaerobe spirochete that can cause swine dysentery, a severe mucohaemorragic colitis that affects pig production and animal welfare worldwide. In Switzerland, the population of B. hyodysenteriae is characterized by the predominance of macrolide-lincosamide-resistant B. hyodysenteriae isolates of sequence type (ST) ST196, prompting us to obtain deeper insights into the genomic structure and variability of ST196 using pangenome and whole genome variant analyses. RESULTS: The draft genome of 14 B. hyodysenteriae isolates of ST196, sampled during a 7-year period from geographically distant pig herds, was obtained by whole-genome sequencing (WGS) and compared to the complete genome of the B. hyodysenteriae isolate Bh743-7 of ST196 used as reference. Variability results revealed the existence of 30 to 52 single nucleotide polymorphisms (SNPs), resulting in eight sublineages of ST196. The pangenome analysis led to the identification of a novel prophage, pphBhCH20, of the Siphoviridae family in a single isolate of ST196, which suggests that horizontal gene transfer events may drive changes in genomic structure. CONCLUSIONS: This study contributes to the catalogue of publicly available genomes and provides relevant bioinformatic tools and information for further comparative genomic analyses for B. hyodysenteriae. It reveals that Swiss B. hyodysenteriae isolates of the same ST may have evolved independently over time by point mutations and acquisition of larger genetic elements. In line with this, the third type of mobile genetic element described so far in B. hyodysenteriae, the novel prophage pphBhCH20, has been identified in a single isolate of B. hyodysenteriae of ST196.

A beta-glucosidase of an insect herbivore determines both toxicity and deterrence of a dandelion defense metabolite.

Gut enzymes can metabolize plant defense compounds and thereby affect the growth and fitness of insect herbivores. Whether these enzymes also influence feeding preference is largely unknown. We studied the metabolization of taraxinic acid ß-D-glucopyranosyl ester (TA-G), a sesquiterpene lactone of the common dandelion (Taraxacum officinale) that deters its major root herbivore, the common cockchafer larva (Melolontha melolontha). We have demonstrated that TA-G is rapidly deglucosylated and conjugated to glutathione in the insect gut. A broad-spectrum M. melolontha ß-glucosidase, Mm_bGlc17, is sufficient and necessary for TA-G deglucosylation. Using cross-species RNA interference, we have shown that Mm_bGlc17 reduces TA-G toxicity. Furthermore, Mm_bGlc17 is required for the preference of M. melolontha larvae for TA-G-deficient plants. Thus, herbivore metabolism modulates both the toxicity and deterrence of a plant defense compound. Our work illustrates the multifaceted roles of insect digestive enzymes as mediators of plant-herbivore interactions.

Plants produce certain substances to fend off attackers like plant-feeding insects. To stop these compounds from damaging their own cells, plants often attach sugar molecules to them. When an insect tries to eat the plant, the plant removes the stabilizing sugar, 'activating' the compounds and making them toxic or foul-tasting. Curiously, some insects remove the sugar themselves, but it is unclear what consequences this has, especially for insect behavior. Dandelions, Taraxacum officinale, make high concentrations of a sugar-containing defense compound in their roots called taraxinic acid ß-D-glucopyranosyl ester, or TA-G for short. TA-G deters the larvae of the Maybug ­ a pest also known as the common cockchafer or the doodlebug ­ from eating dandelion roots. When Maybug larvae do eat TA-G, it is found in their systems without its sugar. However, it is unclear whether it is the plant or the larva that removes the sugar. A second open question is how the sugar removal process affects the behavior of the Maybug larvae. Using chemical analysis and genetic manipulation, Huber et al. investigated what happens when Maybug larvae eat TA-G. This revealed that the acidity levels in the larvae's digestive system deactivate the proteins from the dandelion that would normally remove the sugar from TA-G. However, rather than leaving the compound intact, larvae remove the sugar from TA-G themselves. They do this using a digestive enzyme, known as a beta-glucosidase, that cuts through sugar. Removing the sugar from TA-G made the compound less toxic, allowing the larvae to grow bigger, but it also increased TA-G's deterrent effects, making the larvae less likely to eat the roots. Any organism that eats plants, including humans, must deal with chemicals like TA-G in their food. Once inside the body, enzymes can change these chemicals, altering their effects. This happens with many medicines, too. In the future, it might be possible to design compounds that activate only in certain species, or under certain conditions. Further studies in different systems may aid the development of new methods of pest control, or new drug treatments.

In Silico Comparison Shows that the Pan-Genome of a Dairy-Related Bacterial Culture Collection Covers Most Reactions Annotated to Human Microbiomes.

The diversity of the human microbiome is positively associated with human health. However, this diversity is endangered by Westernized dietary patterns that are characterized by a decreased nutrient variety. Diversity might potentially be improved by promoting dietary patterns rich in microbial strains. Various collections of bacterial cultures resulting from a century of dairy research are readily available worldwide, and could be exploited to contribute towards this end. We have conducted a functional in silico analysis of the metagenome of 24 strains, each representing one of the species in a bacterial culture collection composed of 626 sequenced strains, and compared the pathways potentially covered by this metagenome to the intestinal metagenome of four healthy, although overweight, humans. Remarkably, the pan-genome of the 24 strains covers 89% of the human gut microbiome's annotated enzymatic reactions. Furthermore, the dairy microbial collection covers biological pathways, such as methylglyoxal degradation, sulfate reduction, g-aminobutyric (GABA) acid degradation and salicylate degradation, which are differently covered among the four subjects and are involved in a range of cardiometabolic, intestinal, and neurological disorders. We conclude that microbial culture collections derived from dairy research have the genomic potential to complement and restore functional redundancy in human microbiomes.

Drosophila melanogaster in nutrition research-the importance of standardizing experimental diets.

The fruit fly Drosophila melanogaster has been increasingly recognized as an important model organism in nutrition research. In order to conduct nutritional studies in fruit flies, special attention should be given to the composition of the experimental diets. Besides complex diets, which are often based on maize, yeast, sucrose, and agar, Drosophila can be also fed chemically defined diets. These so-called holidic diets are standardized in terms of their macro- and micronutrient composition although the quantitative nutrient requirements of flies have yet not been fully established and warrant further investigations. For instance, only few studies address the fatty acid, vitamin, mineral, and trace element requirements of fruit flies. D. melanogaster may be also of interest in the field of nutritional medicine. Diet-induced diabetes and obesity models have been established, and in this context, often, the so-called high-fat and high-sugar diets are fed. However, the composition of these diets is not sufficiently defined and varies between studies. A consensus within the scientific community needs to be reached to standardize the exact composition of experimental complex and holidic diets for D. melanogaster in nutrition research. Since D. melanogaster is an established valuable model system for numerous human diseases, standardized diets are also a prerequisite to conduct diet-disease interaction studies. We suggest that a comprehensive approach, which combines deep phenotyping with disease-related Drosophila models under defined dietary conditions, might lead to the foundation of a so-called fly clinic.

Rapid determination of γ-value and xanthate group distribution on viscose by liquid-state (1)H NMR spectroscopy.

A method for the determination of the γ-value and more importantly the distribution of xanthate groups on cellulose xanthate produced during the viscose process is presented. The method is based upon stabilization of xanthate groups attached to the cellulose chain by reaction with 4-methylbenzyl bromide and analysis of the resulting product by liquid-state (1)H NMR. Careful analysis of the proton-spectrum using deconvolution gave a very fast method for the measurement of the γ-value which compared well with the data obtained by IR spectroscopy. In addition it could be shown that the distribution of the xanthate groups on the anhydroglucose monomeric unit (xanthation at position 2, 3 or 6) changes significantly during ripening. The method gave useful results even for viscose with low γ-values of about 25.

Evolving chemometric models for predicting dynamic process parameters in viscose production.

In viscose production, it is important to monitor three process parameters in order to assure a high quality of the final product: the concentrations of H(2)SO(4), Na(2)SO(4) and Z(n)SO(4). During on-line production these process parameters usually show a quite high dynamics depending on the fiber type that is produced. Thus, conventional chemometric models, which are trained based on collected calibration spectra from Fourier transform near infrared (FT-NIR) measurements and kept fixed during the whole life-time of the on-line process, show a quite imprecise and unreliable behavior when predicting the concentrations of new on-line data. In this paper, we are demonstrating evolving chemometric models which are able to adapt automatically to varying process dynamics by updating their inner structures and parameters in a single-pass incremental manner. These models exploit the Takagi-Sugeno fuzzy model architecture, being able to model flexibly different degrees of non-linearities implicitly contained in the mapping between near infrared spectra (NIR) and reference values. Updating the inner structures is achieved by moving the position of already existing local regions and by evolving (increasing non-linearity) or merging (decreasing non-linearity) new local linear predictors on demand, which are guided by distance-based and similarity criteria. Gradual forgetting mechanisms may be integrated in order to out-date older learned relations and to account for more flexibility of the models. The results show that our approach is able to overcome the huge prediction errors produced by various state-of-the-art chemometric models. It achieves a high correlation between observed and predicted target values in the range of [0.95,0.98] over a 3 months period while keeping the relative error below the reference error value of 3%. In contrast, the off-line techniques achieved correlations below 0.5, ten times higher error rates and the more deteriorate, the more time passes by.