Genetic Elements Orchestrating Lactobacillus crispatus Glycogen Metabolism in the Vagina.

Glycogen in the female lower reproductive tract is a major carbon source for colonization and acidification by common vaginal Lactobacillus species, such as Lactobacillus crispatus. Previously, we identified the amylopullulanase encoding gene pulA of Lactobacillus crispatus to correlate with the ability to autonomously utilize glycogen for growth. Here, we further characterize genetic variation and differential regulation of pulA affecting the presence of its gene product on the outer surface layer. We show that alpha-glucan degrading activity dissipates when Lactobacillus crispatus is grown on glucose, maltose and maltotriose, in agreement with carbon catabolite repression elements flanking the pulA gene. Proteome analysis of the S-layer confirmed that the amylopullulanase protein is highly abundant in an S-layer enriched fraction, but not in a strain with a defective amylopullulanase variant or in an amylopullulanase-sufficient strain grown on glucose. In addition, we provide evidence that Lactobacillus crispatus pulA mutants are relevant in vivo, as they are commonly observed in metagenome datasets of human vaginal microbial communities. Analysis of the largest publicly available dataset of 1507 human vaginal metagenomes indicates that among the 270 samples that contain a Lactobacillus crispatuspulA gene, 62 samples (23%) had a defective variant of this gene. Taken together, these results demonstrate that both environmental, as well as genetic factors explain the variation of Lactobacillus crispatus alpha-glucosidases in the vaginal environment.

metaModules identifies key functional subnetworks in microbiome-related disease.

MOTIVATION: The human microbiome plays a key role in health and disease. Thanks to comparative metatranscriptomics, the cellular functions that are deregulated by the microbiome in disease can now be computationally explored. Unlike gene-centric approaches, pathway-based methods provide a systemic view of such functions; however, they typically consider each pathway in isolation and in its entirety. They can therefore overlook the key differences that (i) span multiple pathways, (ii) contain bidirectionally deregulated components, (iii) are confined to a pathway region. To capture these properties, computational methods that reach beyond the scope of predefined pathways are needed. RESULTS: By integrating an existing module discovery algorithm into comparative metatranscriptomic analysis, we developed metaModules, a novel computational framework for automated identification of the key functional differences between health- and disease-associated communities. Using this framework, we recovered significantly deregulated subnetworks that were indeed recognized to be involved in two well-studied, microbiome-mediated oral diseases, such as butanoate production in periodontal disease and metabolism of sugar alcohols in dental caries. More importantly, our results indicate that our method can be used for hypothesis generation based on automated discovery of novel, disease-related functional subnetworks, which would otherwise require extensive and laborious manual assessment. AVAILABILITY AND IMPLEMENTATION: metaModules is available at https://bitbucket.org/alimay/metamodules/ CONTACT: a.may@vu.nl or s.abeln@vu.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NGS-eval: NGS Error analysis and novel sequence VAriant detection tooL.

Massively parallel sequencing of microbial genetic markers (MGMs) is used to uncover the species composition in a multitude of ecological niches. These sequencing runs often contain a sample with known composition that can be used to evaluate the sequencing quality or to detect novel sequence variants. With NGS-eval, the reads from such (mock) samples can be used to (i) explore the differences between the reads and their references and to (ii) estimate the sequencing error rate. This tool maps these reads to references and calculates as well as visualizes the different types of sequencing errors. Clearly, sequencing errors can only be accurately calculated if the reference sequences are correct. However, even with known strains, it is not straightforward to select the correct references from databases. We previously analysed a pyrosequencing dataset from a mock sample to estimate sequencing error rates and detected sequence variants in our mock community, allowing us to obtain an accurate error estimation. Here, we demonstrate the variant detection and error analysis capability of NGS-eval with Illumina MiSeq reads from the same mock community. While tailored towards the field of metagenomics, this server can be used for any type of MGM-based reads. NGS-eval is available at http://www.ibi.vu.nl/programs/ngsevalwww/.

Unraveling the outcome of 16S rDNA-based taxonomy analysis through mock data and simulations.

MOTIVATION: 16S rDNA pyrosequencing is a powerful approach that requires extensive usage of computational methods for delineating microbial compositions. Previously, it was shown that outcomes of studies relying on this approach vastly depend on the choice of pre-processing and clustering algorithms used. However, obtaining insights into the effects and accuracy of these algorithms is challenging due to difficulties in generating samples of known composition with high enough diversity. Here, we use in silico microbial datasets to better understand how the experimental data are transformed into taxonomic clusters by computational methods. RESULTS: We were able to qualitatively replicate the raw experimental pyrosequencing data after rigorously adjusting existing simulation software. This allowed us to simulate datasets of real-life complexity, which we used to assess the influence and performance of two widely used pre-processing methods along with 11 clustering algorithms. We show that the choice, order and mode of the pre-processing methods have a larger impact on the accuracy of the clustering pipeline than the clustering methods themselves. Without pre-processing, the difference between the performances of clustering methods is large. Depending on the clustering algorithm, the most optimal analysis pipeline resulted in significant underestimations of the expected number of clusters (minimum: 3.4%; maximum: 13.6%), allowing us to make quantitative estimations of the bacterial complexity of real microbiome samples.

Coarse-grained versus atomistic simulations: realistic interaction free energies for real proteins.

MOTIVATION: To assess whether two proteins will interact under physiological conditions, information on the interaction free energy is needed. Statistical learning techniques and docking methods for predicting protein-protein interactions cannot quantitatively estimate binding free energies. Full atomistic molecular simulation methods do have this potential, but are completely unfeasible for large-scale applications in terms of computational cost required. Here we investigate whether applying coarse-grained (CG) molecular dynamics simulations is a viable alternative for complexes of known structure. RESULTS: We calculate the free energy barrier with respect to the bound state based on molecular dynamics simulations using both a full atomistic and a CG force field for the TCR-pMHC complex and the MP1-p14 scaffolding complex. We find that the free energy barriers from the CG simulations are of similar accuracy as those from the full atomistic ones, while achieving a speedup of >500-fold. We also observe that extensive sampling is extremely important to obtain accurate free energy barriers, which is only within reach for the CG models. Finally, we show that the CG model preserves biological relevance of the interactions: (i) we observe a strong correlation between evolutionary likelihood of mutations and the impact on the free energy barrier with respect to the bound state; and (ii) we confirm the dominant role of the interface core in these interactions. Therefore, our results suggest that CG molecular simulations can realistically be used for the accurate prediction of protein-protein interaction strength. AVAILABILITY AND IMPLEMENTATION: The python analysis framework and data files are available for download at http://www.ibi.vu.nl/downloads/bioinformatics-2013-btt675.tgz.

Unveiling microbial biomarkers of ruminant methane emission through machine learning.

Background: Enteric methane from cow burps, which results from microbial fermentation of high-fiber feed in the rumen, is a significant contributor to greenhouse gas emissions. A promising strategy to address this problem is microbiome-based precision feed, which involves identifying key microorganisms for methane production. While machine learning algorithms have shown success in associating human gut microbiome with various human diseases, there have been limited efforts to employ these algorithms to establish microbial biomarkers for methane emissions in ruminants. Methods: In this study, we aim to identify potential methane biomarkers for methane emission from ruminants by employing regression algorithms commonly used in human microbiome studies, coupled with different feature selection methods. To achieve this, we analyzed the microbiome compositions and identified possible confounding metadata variables in two large public datasets of Holstein cows. Using both the microbiome features and identified metadata variables, we trained different regressors to predict methane emission. With the optimized models, permutation tests were used to determine feature importance to find informative microbial features. Results: Among the regression algorithms tested, random forest regression outperformed others and allowed the identification of several crucial microbial taxa for methane emission as members of the native rumen microbiome, including the genera Piromyces, Succinivibrionaceae UCG-002, and Acetobacter. Additionally, our results revealed that certain herd locations and feed composition markers, such as the lipid intake and neutral-detergent fiber intake, are also predictive features for methane emissions. Conclusion: We demonstrated that machine learning, particularly regression algorithms, can effectively predict cow methane emissions and identify relevant rumen microorganisms. Our findings offer valuable insights for the development of microbiome-based precision feed strategies aiming at reducing methane emissions.

Development of a workflow for the selection, identification and optimization of lactic acid bacteria with high γ-aminobutyric acid production.

Lactic acid bacteria produce γ-aminobutyric acid (GABA) as an acid stress response. GABA is a neurotransmitter that may improve sleep and resilience to mental stress. This study focused on the selection, identification and optimization of a bacterial strain with high GABA production, for development as a probiotic supplement. The scientific literature and an industry database were searched for probiotics and potential GABA producers. In silico screening was conducted to identify genes involved in GABA production. Subsequently, 17 candidates were screened for in vitro GABA production using thin layer chromatography, which identified three candidate probiotic strains Levilactobacillus brevis DSM 20054, Lactococcus lactis DS75843and Bifidobacterium adolescentis DSM 24849 as producing GABA. Two biosensors capable of detecting GABA were developed: 1. a transcription factor-based biosensor characterized by the interaction with the transcriptional regulator GabR was developed in Corynebacterium glutamicum; and 2. a growth factor-based biosensor was built in Escherichia coli, which used auxotrophic complementation by expressing 4-aminobutyrate transaminase (GABA-T) that transfers the GABA amino group to pyruvate, hereby forming alanine. Consequently, the feasibility of developing a workflow based on co-culture with producer strains and a biosensor was tested. The three GABA producers were identified and the biosensors were encapsulated in nanoliter reactors (NLRs) as alginate beads in defined gut-like conditions. The E. coli growth factor-based biosensor was able to detect changes in GABA concentrations in liquid culture and under gut-like conditions. L. brevis and L. lactis were successfully encapsulated in the NLRs and showed growth under miniaturized intestinal conditions.

One-step synthesis of magnetic fly ash composites for methylene blue removal: batch and column study.

This study focuses on the synthesis of magnetic fly ash composites and its application for the removal of methylene blue (MB) from wastewater. By-product of oil power plants, oil fly ash, was treated with magnetic nanoparticles after chemical surface modification and dubbed modified fly ash (MFA). MFA was characterized by X-ray fluorescence, diffractogram analysis, scanning electron microscopy, energy-dispersive X-ray (EDX), and Fourier transform infrared (FT-IR) and N2 physisorption. MB (methylene blue) was removed from an aqueous solution using the response surface modelling (RSM) technique, which was used for optimization reasons. All four independent factors were investigated to see how they affected the removal process: adsorbent dosage; contact time; pH; and beginning dye concentration. The rate of MB removal was strongly influenced by the pH of the solution. The Langmuir and Freundlich models were used to examine equilibrium data A for MB adsorption onto the MFA in linear and nonlinear forms. Langmuir gave a better fit. The adsorption kinetics shown by increased kinetic statistics were better characterized by a pseudo-second-order MFA model. As far as thermodynamic characteristics go, adsorption is endothermic and occurs spontaneously. It has been proven that MFA may be used as an adsorbent to remove MB dye with high efficiency, and the quadratic model has been proved to be statistically significant.

On the Role of Bioinformatics and Data Science in Industrial Microbiome Applications.

Advances in sequencing and computational biology have drastically increased our capability to explore the taxonomic and functional compositions of microbial communities that play crucial roles in industrial processes. Correspondingly, commercial interest has risen for applications where microbial communities make important contributions. These include food production, probiotics, cosmetics, and enzyme discovery. Other commercial applications include software that takes the user's gut microbiome data as one of its inputs and outputs evidence-based, automated, and personalized diet recommendations for balanced blood sugar levels. These applications pose several bioinformatic and data science challenges that range from requiring strain-level resolution in community profiles to the integration of large datasets for predictive machine learning purposes. In this perspective, we provide our insights on such challenges by touching upon several industrial areas, and briefly discuss advances and future directions of bioinformatics and data science in microbiome research.

High-Resolution Screening of Viral Communities and Identification of New Pathogens in Fish Using Next-Generation Sequencing.

Discovery of viral genomes in fish has historically been based on viral enrichment, random priming, cloning, and Sanger sequencing. However, the development of next-generation sequencing has enabled the possibility to sequence the entire virome of a tissue sample. This has led to an enormous increase in discovery of new viruses. In this chapter, we describe a simple and rapid method for viral discovery in fish. The method is based on Illumina sequencing of total RNA from diseased tissue or cell culture and in silico removal of host RNA.

Novel sequencing technologies to support industrial biotechnology.

Industrial biotechnology develops and applies microorganisms for the production of bioproducts and enzymes with applications ranging from food and feed ingredients and processing to bio-based chemicals, biofuels and pharmaceutical products. Next generation DNA sequencing technologies play an increasingly important role in improving and accelerating microbial strain development for existing and novel bio-products via screening, gene and pathway discovery, metabolic engineering and additional optimization and understanding of large-scale manufacturing. In this mini-review, we describe novel DNA sequencing and analysis technologies with a focus on applications to industrial strain development, enzyme discovery and microbial community analysis.

Integrating Candida albicans metabolism with biofilm heterogeneity by transcriptome mapping.

Candida albicans biofilm formation is an important virulence factor in the pathogenesis of disease, a characteristic which has been shown to be heterogeneous in clinical isolates. Using an unbiased computational approach we investigated the central metabolic pathways driving biofilm heterogeneity. Transcripts from high (HBF) and low (LBF) biofilm forming isolates were analysed by RNA sequencing, with 6312 genes identified to be expressed in these two phenotypes. With a dedicated computational approach we identified and validated a significantly differentially expressed subnetwork of genes associated with these biofilm phenotypes. Our analysis revealed amino acid metabolism, such as arginine, proline, aspartate and glutamate metabolism, were predominantly upregulated in the HBF phenotype. On the contrary, purine, starch and sucrose metabolism was generally upregulated in the LBF phenotype. The aspartate aminotransferase gene AAT1 was found to be a common member of these amino acid pathways and significantly upregulated in the HBF phenotype. Pharmacological inhibition of AAT1 enzyme activity significantly reduced biofilm formation in a dose-dependent manner. Collectively, these findings provide evidence that biofilm phenotype is associated with differential regulation of metabolic pathways. Understanding and targeting such pathways, such as amino acid metabolism, is potentially useful for developing diagnostics and new antifungals to treat biofilm-based infections.