The Helicobacter pylori Genome Project : insights into H. pylori population structure from analysis of a worldwide collection of complete genomes

Helicobacter pylori, a dominant member of the gastric microbiota, shares co-evolutionary history with humans. This has led to the development of genetically distinct H. pylori subpopulations associated with the geographic origin of the host and with differential gastric disease risk. Here, we provide insights into H. pylori population structure as a part of the Helicobacter pylori Genome Project (HpGP), a multi-disciplinary initiative aimed at elucidating H. pylori pathogenesis and identifying new therapeutic targets. We collected 1011 well-characterized clinical strains from 50 countries and generated high-quality genome sequences. We analysed core genome diversity and population structure of the HpGP dataset and 255 worldwide reference genomes to outline the ancestral contribution to Eurasian, African, and American populations. We found evidence of substantial contribution of population hpNorthAsia and subpopulation hspUral in Northern European H. pylori. The genomes of H. pylori isolated from northern and southern Indigenous Americans differed in that bacteria isolated in northern Indigenous communities were more similar to North Asian H. pylori while the southern had higher relatedness to hpEastAsia. Notably, we also found a highly clonal yet geographically dispersed North American subpopulation, which is negative for the cag pathogenicity island, and present in 7% of sequenced US genomes. We expect the HpGP dataset and the corresponding strains to become a major asset for H. pylori genomics

Adaptive Filtering Techniques Combined with Natural Selection-Based Heuristic Algorithms in the Prediction of Protein-Protein Interactions

Part 19: Computational Intelligence Applications in Bioinformatics (CIAB) WorkshopInternational audienceThe analysis of protein-protein interactions (PPIs) is crucial to the understanding of cellular organizations, processes and functions. The reliability of the current experimental approaches interaction data is prone to error. Thus, a variety of computational methods have been developed to supplement the interactions that have been detected experimentally. The present paper’s main objective is to present a novel classification framework for predicting PPIs combining the advantages of two algorithmic methods’ categories (heuristic methods, adaptive filtering techniques) in order to produce high performance classifiers while maintaining their interpretability. Our goal is to find a simple mathematical equation that governs the best classifier enabling the extraction of biological knowledge. State-of-the-art adaptive filtering techniques were combined with the most contemporary heuristic methods which are based in the natural selection process. To the best of our knowledge, this is the first time that the proposed classification framework is applied and analyzed extensively for the problem of predicting PPIs. The proposed methodology was tested with a commonly used data set using all possible combinations of the selected adaptive filtering and heuristic techniques and comparisons were made. The best algorithmic combinations derived from these procedures were Genetic Algorithms with Extended Kalman Filters and Particle Swarm Optimization with Extended Kalman Filters. Using these algorithmic combinations high accuracy interpretable classifiers were produced

Combined Forced Expiratory Volume in 1 Second and Forced Vital Capacity Bronchodilator Response, Exacerbations, and Mortality in Chronic Obstructive Pulmonary Disease.

Rationale: The American Thoracic Society (ATS)/European Respiratory Society defines a positive bronchodilator response (BDR) by a composite of BDR in either forced expiratory volume in 1 second (FEV1) and/or forced vital capacity (FVC) greater than or equal to 12% and 200 ml (ATS-BDR). We hypothesized that ATS-BDR components would be differentially associated with important chronic obstructive pulmonary disease (COPD) outcomes. Objectives: To examine whether ATS-BDR components are differentially associated with clinical, functional, and radiographic features in COPD. Methods: We included subjects with COPD enrolled in the COPDGene study. In the main analysis, we excluded subjects with self-reported asthma. We categorized BDR into the following: 1) No-BDR, no BDR in either FEV1 or FVC; 2) FEV1-BDR, BDR in FEV1 but no BDR in FVC; 3) FVC-BDR, BDR in FVC but no BDR in FEV1; and 4) Combined-BDR, BDR in both FEV1 and FVC. We constructed multivariable logistic, linear, zero-inflated negative binomial, and Cox hazards models to examine the association of BDR categories with symptoms, computed tomography findings, change in FEV1 over time, respiratory exacerbations, and mortality. We also created models using the ATS BDR definition (ATS-BDR) as the main independent variable. Results: Of 3,340 COPD subjects included in the analysis, 1,083 (32.43%) had ATS-BDR, 182 (5.45%) had FEV1-BDR, 522 (15.63%) had FVC-BDR, and 379 (11.34%) had Combined-BDR. All BDR categories were associated with FEV1 decline compared with No-BDR. Compared with No-BDR, both ATS-BDR and Combined-BDR were associated with higher functional residual capacity %predicted, greater internal perimeter of 10 mm, and greater 6-minute-walk distance. In contrast to ATS-BDR, Combined-BDR was independently associated with less emphysema (adjusted beta regression coefficient, -1.67; 95% confidence interval [CI], -2.68 to -0.65; P = 0.001), more frequent respiratory exacerbations (incidence rate ratio, 1.25; 95% CI, 1.03-1.50; P = 0.02) and severe exacerbations (incidence rate ratio, 1.34; 95% CI, 1.05-1.71; P = 0.02), and lower mortality (adjusted hazards ratio, 0.76; 95% CI, 0.58-0.99; P = 0.046). Sensitivity analysis that included subjects with self-reported history of asthma showed similar findings. Conclusions: BDR in both FEV1 and FVC indicates a COPD phenotype with asthma-like characteristics, and provides clinically more meaningful information than current definitions of BDR

Energy dependence of Lambda and anti-Lambda production at CERN-SPS energies

Rapidity distributions for Lambda and anti-Lambda hyperons in central Pb-Pb collisions at 40, 80 and 158 AGeV and for K 0 s mesons at 158 AGeV are presented. The lambda multiplicities are studied as a function of collision energy together with AGS and RHIC measurements and compared to model predictions. A different energy dependence of the Lambda/pi and anti-Lambda/pi is observed. The anti-Lambda/Lambda ratio shows a steep increase with collision energy. Evidence for a anti-Lambda/anti-p ratio greater than 1 is found at 40 AGeV