Encyclopedia of bacterial gene circuits whose presence or absence correlate with pathogenicity--a large-scale system analysis of decoded bacterial genomes

Effects of exercise intensity on gut microbiome composition and function in people with type 2 diabetes

Exercise is positively associated with higher microbial diversity, but there is limited information on exercise intensity's effect on gut microbiome composition and function in clinical populations. This study examines whether different intensities of exercise exert differential effects on gut microbiome composition and function in low active people with type 2 diabetes.This is a sub-study of the Exercise for Type 2 Diabetes Study, a single centre, prospective, randomised controlled trial. Participants (n=12) completed 8-weeks of combined aerobic and resistance moderate intensity continuous training (C-MICT) or combined aerobic and resistance high-intensity interval training (C-HIIT). Faecal samples were collected before and after intervention to measure gut microbiome composition and metabolic pathways (metagenome shotgun sequencing) and short-chain fatty acids.Post-exercise α-diversity was different between groups as was the relative abundance of specific taxa was (p<0.05). Post-exercise relative abundance of Bifidobacterium, A. municiphila, and butyrate-producers Lachnospira eligens, Enterococcus spp., and Clostridium Cluster IV were higher at lower exercise intensity. Other butyrate-producers (from Eryspelothrichales and Oscillospirales), and methane producer Methanobrevibacter smithii were higher at higher exercise intensity. Pyruvate metabolism (ko00620),COG 'Cell wall membrane envelope biogenesis' and 'Unknown function' pathways were significantly different between groups and higher in C-MICT post-exercise. Differential abundance analysis on KO showed higher expression of Two-component system in C-HIIT. Transcription factors and 'unknown metabolism' related pathways decreased in both groups. There were no significant between group changes in faecal short chain fatty acids.Exercise intensity had a distinct effect on gut microbiome abundance and metabolic function, without impacting short-chain fatty acid outputTrial registration: Australian New Zealand Clinical Trials Registry identifier: ACTRN12615000475549..

Structure and Mechanism of d-Glucosaminate-6-phosphate Ammonia-lyase: A Novel Octameric Assembly for a Pyridoxal 5'-Phosphate-Dependent Enzyme, and Unprecedented Stereochemical Inversion in the Elimination Reaction of a d-Amino Acid

d-Glucosaminate-6-phosphate ammonia-lyase (DGL) is a pyridoxal 5'-phosphate (PLP)-dependent enzyme that produces 2-keto-3-deoxygluconate 6-phosphate (KDG-6-P) in the metabolism of d-glucosaminic acid by Salmonella enterica serovar typhimurium. We have determined the crystal structure of DGL by SAD phasing with selenomethionine to a resolution of 2.58 Å. The sequence has very low identity with most other members of the aminotransferase (AT) superfamily. The structure forms an octameric assembly as a tetramer of dimers that has not been observed previously in the AT superfamily. PLP is covalently bound as a Schiff base to Lys-213 in the catalytic dimer at the interface of two monomers. The structure lacks the conserved arginine that binds the α-carboxylate of the substrate in most members of the AT superfamily. However, there is a cluster of arginines in the small domain that likely serves as a binding site for the phosphate of the substrate. The deamination reaction performed in D2O gives a KDG-6-P product stereospecifically deuterated at C3; thus, the mechanism must involve an enamine intermediate that is protonated by the enzyme before product release. Nuclear magnetic resonance (NMR) analysis demonstrates that the deuterium is located in the pro-R position in the product, showing that the elimination of water takes place with inversion of configuration at C3, which is unprecedented for a PLP-dependent dehydratase/deaminase. On the basis of the crystal structure and the NMR data, a reaction mechanism for DGL is proposed.

Supply of Methionine During Late-Pregnancy Alters Fecal Microbiota and Metabolome in Neonatal Dairy Calves Without Changes in Daily Feed Intake

To our knowledge, most studies demonstrating the role of manipulating maternal nutrition on hindgut (i.e., large intestine) microbiota in the offspring have been performed in non-ruminants. Whether this phenomenon exists in cattle is largely unknown. Therefore, the objectives of the current study were to evaluate the impact of maternal post-ruminal supply of methionine during late-pregnancy in dairy cows on fecal microbiota and metabolome in neonatal calves, and their association with body development and growth performance during the preweaning period. To achieve this, heifer calves, i.e., neonatal female offspring, born to Holstein cows receiving either a control (CON) diet (n = 13) or CON plus rumen-protected methionine (MET; Evonik Nutrition & Care GmbH) during the last 28 days of pregnancy were used. Fecal samples from heifers were collected from birth until 6 weeks of age, i.e., the preweaning period. Fecal microbiota was analyzed with QIIME 2 whereas fecal metabolites were measured using an untargeted LC-MS approach. At birth, MET heifers had greater (P ≤ 0.05) BW, HH, and WH. During the preweaning period, no differences between groups were detected for starter intake (P = 0.77). However, MET heifers maintained greater (P ≤ 0.05) BW, HH and tended (P = 0.06) to have greater WH and average daily gain (ADG) (P = 0.10). Fecal microbiota and metabolome profiles through 42 days of age in MET heifers indicated greater capacity for hindgut production of endogenous antibiotics and enhanced hindgut functionality and health. Enhancing maternal post-ruminal supply of methionine during late-gestation in dairy cows has a positive effect on hindgut functionality and health in their offspring through alterations in the fecal microbiota and metabolome without affecting feed intake. Those alterations could limit pathogen colonization of the hindgut while providing essential nutrients to the neonate. Together, such responses contribute to the ability of young calves to achieve better rates of nutrient utilization for growth.

LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe-Disease Association prediction

An increasing number of evidences indicate microbes are implicated in human physiological mechanisms, including complicated disease pathology. Some microbes have been demonstrated to be associated with diverse important human diseases or disorders. Through investigating these disease-related microbes, we can obtain a better understanding of human disease mechanisms for advancing medical scientific progress in terms of disease diagnosis, treatment, prevention, prognosis and drug discovery. Based on the known microbe-disease association network, we developed a semi-supervised computational model of Laplacian Regularized Least Squares for Human Microbe-Disease Association (LRLSHMDA) by introducing Gaussian interaction profile kernel similarity calculation and Laplacian regularized least squares classifier. LRLSHMDA reached the reliable AUCs of 0.8909 and 0.7657 based on the global and local leave-one-out cross validations, respectively. In the framework of 5-fold cross validation, average AUC value of 0.8794 +/-0.0029 further demonstrated its promising prediction ability. In case studies, 9, 9 and 8 of top-10 predicted microbes have been manually certified to be associated with asthma, colorectal carcinoma and chronic obstructive pulmonary disease by published literature evidence. Our proposed model achieves better prediction performance relative to the previous model. We expect that LRLSHMDA could offer insights into identifying more promising human microbe-disease associations in the future.

Virulence factor activity relationships (VFARs): a bioinformatics perspective

Virulence factor activity relationships (VFARs) - a concept loosely based on quantitative structure-activity relationships (QSARs) for chemicals was proposed as a predictive tool for ranking risks due to microorganisms relevant to water safety. A rapid increase in sequencing capabilities and bioinformatics tools has significantly increased the potential for VFAR-based analyses. This review summarizes more than 20 bioinformatics databases and tools, developed over the last decade, along with their virulence and antimicrobial resistance prediction capabilities. With the number of bacterial whole genome sequences exceeding 241 000 and metagenomic analysis projects exceeding 13 000 and the ability to add additional genome sequences for few hundred dollars, it is evident that further development of VFARs is not limited by the availability of information at least at the genomic level. However, additional information related to co-occurrence, treatment response, modulation of virulence due to environmental and other factors, and economic impact must be gathered and incorporated in a manner that also addresses the associated uncertainties. Of the bioinformatics tools, a majority are either designed exclusively for virulence/resistance determination or equipped with a dedicated module. The remaining have the potential to be employed for evaluating virulence. This review focusing broadly on omics technologies and tools supports the notion that these tools are now sufficiently developed to allow the application of VFAR approaches combined with additional engineering and economic analyses to rank and prioritize organisms important to a given niche. Knowledge gaps do exist but can be filled with focused experimental and theoretical analyses that were unimaginable a decade ago. Further developments should consider the integration of the measurement of activity, risk, and uncertainty to improve the current capabilities.

Small Molecules That Sabotage Bacterial Virulence

The continued rise of antibiotic-resistant bacterial infections has motivated alternative strategies for target discovery and treatment of infections. Antivirulence therapies function through inhibition of in vivo required virulence factors to disarm the pathogen instead of directly targeting viability or growth. This approach to treating bacteria-mediated diseases may have advantages over traditional antibiotics because it targets factors specific for pathogenesis, potentially reducing selection for resistance and limiting collateral damage to the resident microbiota. This review examines vulnerable molecular mechanisms used by bacteria to cause disease and the antivirulence compounds that sabotage these virulence pathways. By expanding the study of antimicrobial targets beyond those that are essential for growth, antivirulence strategies offer new and innovative opportunities to combat infectious diseases.

Recent "omics" advances in Helicobacter pylori

The development of high-throughput whole genome sequencing (WGS) technologies is changing the face of microbiology, facilitating the comparison of large numbers of genomes from different lineages of a same organism. Our aim was to review the main advances on Helicobacter pylori "omics" and to understand how this is improving our knowledge of the biology, diversity and pathogenesis of H. pylori. Since the first H. pylori isolate was sequenced in 1997, 510 genomes have been deposited in the NCBI archive, providing a basis for improved understanding of the epidemiology and evolution of this important pathogen. This review focuses on works published between April 2015 and March 2016. Helicobacter "omics" is already making an impact and is a growing research field. Ultimately these advances will be translated into a routine clinical laboratory setting in order to improve public health.

An analysis of human microbe-disease associations

The microbiota living in the human body has critical impacts on our health and disease, but a systems understanding of its relationships with disease remains limited. Here, we use a large-scale text mining-based manually curated microbe-disease association data set to construct a microbe-based human disease network and investigate the relationships between microbes and disease genes, symptoms, chemical fragments and drugs. We reveal that microbe-based disease loops are significantly coherent. Microbe-based disease connections have strong overlaps with those constructed by disease genes, symptoms, chemical fragments and drugs. Moreover, we confirm that the microbe-based disease analysis is able to predict novel connections and mechanisms for disease, microbes, genes and drugs. The presented network, methods and findings can be a resource helpful for addressing some issues in medicine, for example, the discovery of bench knowledge and bedside clinical solutions for disease mechanism understanding, diagnosis and therapy.