ResFinder 4.0 for predictions of phenotypes from genotypes

International audienceObjectives - WGS-based antimicrobial susceptibility testing (AST) is as reliable as phenotypic AST for several antimicrobial/bacterial species combinations. However, routine use of WGS-based AST is hindered by the need for bioinformatics skills and knowledge of antimicrobial resistance (AMR) determinants to operate the vast majority of tools developed to date. By leveraging on ResFinder and PointFinder, two freely accessible tools that can also assist users without bioinformatics skills, we aimed at increasing their speed and providing an easily interpretable antibiogram as output. Methods - The ResFinder code was re-written to process raw reads and use Kmer-based alignment. The existing ResFinder and PointFinder databases were revised and expanded. Additional databases were developed including a genotype-to-phenotype key associating each AMR determinant with a phenotype at the antimicrobial compound level, and species-specific panels for in silico antibiograms. ResFinder 4.0 was validated using Escherichia coli (n = 584), Salmonella spp. (n = 1081), Campylobacter jejuni (n = 239), Enterococcus faecium (n = 106), Enterococcus faecalis (n = 50) and Staphylococcus aureus (n = 163) exhibiting different AST profiles, and from different human and animal sources and geographical origins. Results - Genotype-phenotype concordance was ≥95% for 46/51 and 25/32 of the antimicrobial/species combinations evaluated for Gram-negative and Gram-positive bacteria, respectively. When genotype-phenotype concordance was <95%, discrepancies were mainly linked to criteria for interpretation of phenotypic tests and suboptimal sequence quality, and not to ResFinder 4.0 performance. Conclusions - WGS-based AST using ResFinder 4.0 provides in silico antibiograms as reliable as those obtained by phenotypic AST at least for the bacterial species/antimicrobial agents of major public health relevance considered

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.Therapeutic cell differentiatio

Presenteeism, stress resilience, and physical activity in older manual workers: a person-centred analysis

© 2017 Springer-Verlag Berlin HeidelbergThis study used a person-centred approach to explore typologies of older manual workers based on presenteeism, stress resilience, and physical activity. Older manual workers (n = 217; 69.1% male; age range 50–77; M age = 57.11 years; SD = 5.62) from a range of UK-based organisations, representing different manual job roles, took part in the study. A cross-sectional survey design was used. Based on the three input variables: presenteeism, stress resilience and physical activity, four distinct profiles were identified on using Latent Profile Analysis. One group (‘High sport/exercise and well-functioning’; 5.50%) engaged in high levels of sport/exercise and exhibited low levels of stress resilience and all types of presenteeism. Another profile (‘Physically burdened’; 9.70%) reported high levels of work and leisure-time physical activity, low stress resilience, as well as high levels of presenteeism due to physical and time demands. A ‘Moderately active and functioning’ group (46.50%) exhibited moderate levels on all variables. Finally, the fourth profile (‘Moderately active with high presenteeism’; 38.20%) reported engaging in moderate levels of physical activity and had relatively high levels of stress resilience, yet also high levels of presenteeism. The profiles differed on work affect and health perceptions largely in the expected directions. There were no differences between the profiles in socio-demographics. These results highlight complex within-person interactions between presenteeism, stress resilience, and physical activity in older manual workers. The identification of profiles of older manual workers who are at risk of poor health and functioning may inform targeted interventions to help retain them in the workforce for longer