Congratulations to Kara Tsang for winning the 2018 Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Michael Kamin Hart Memorial Scholarship (MSc), the highest academic honour for graduate students in the IIDR. Awarded during the 2018 IIDR Trainee Day, the award was accompanied by a talk by Kara on her Ph.D. research: (Machine) Learning about antibiotic resistance genotype- phenotype relationships”. Well done Kara!
Congratulations to #TeamVirulence for winning the 2018 McMaster Innovation Showcase People’s Choice Poster Award for their poster entitled, “Examining the relationship between virulence and antimicrobial resistance via expansion of the Comprehensive Antibiotic Resistance Database (CARD)”! Left to right: Anatoly MiroshnichenkoHiu-Ki Rachel Tran, Sally Yue Min, and Rafik El Werfalli.
#TeamVirulence also presented their work at the 2018 Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Trainee Day!
Some invitations are more special than others. Dr. Peixoto da Cruz and I went to graduate school together in British Columbia (a long time ago!) and while we have since lived in different hemispheres, the bond remains strong. It was great to visit PUG Goiás and learn about Peixoto’s impressive training program in genetic screening and counselling, plus talk about our AMR surveillance efforts.
Bioinformatics of antimicrobial resistance in the age of molecular epidemiology. Invited Keynote presentation by A.G. McArthur at Reunião de Citogenética do Brasil Central & XII Workshop de Genética da PUC Goiás, Goiânia, Brazil, October 2018.
Maguire, F., B. Alcock, F.S. Brinkman, A.G. McArthur, & R.G. Beiko. 2018. AMRtime: Rapid Accurate Identification of Antimicrobial Resistance Determinants from Metagenomic Data. Oral presentation at the Third American Society for Microbiology Meeting on Rapid Applied Microbial Next-Generation Sequencing and Bioinformatics Pipelines, Washington, D.C.
Abstract: Metagenomics, the direct sequencing of the mixture of genomes present in a sample, is an increasingly common workflow within the life sciences. It is frequently used to investigate previously intractable problems such as the functional characterisation of entire microbial environments. One such use-case of global and national public-health importance is analysing the nature and transmission dynamics of antimicrobial resistance (AMR) determinants in human, agri-food and environmental samples. Recently some tools have been developed to profile AMR from metagenomes, however, these are generally limited to profiling at the level of AMR genes clustered by % sequence identity, which may or may not be biologically meaningful. By exploiting the expertly curated ontological structure of the Comprehensive Antibiotic Resistance Database (CARD) and new CARD Prevalence datasets, we have developed an approach using a hierarchical set of machine learning classifiers. This allows us to produce gene-specific AMR profiles to 2386 determinants as well as profiles for higher order, biologically informed, AMR gene family groups. Firstly, DIAMOND based heuristically accelerated homology searches are used to filter out non-AMR related metagenomic reads. This filtering has been optimised to prioritise minimisation of false negatives over minimising false positives. Features generated from these homology searches as well as sequence features are then used to train a random forest classifier to classify filtered reads into one of 227 CARD AMR gene families (e.g. MCR phosphoethanolamine transferase). For each gene family an additional random forest classifier is trained to classify reads into one of the specific AMR determinants belonging to that family (e.g. MCR-1, MCR-2, MCR-3 etc.). This process involves very little computational overhead when classifying beyond the initial homology search. On a fully held out test-set of MiSeq reads simulated from the CARD canonical gene sequences this method resulted in an average precision and recall of 0.993 and 0.987 at the AMR gene family level. Within the 227 AMR families, 70% (158) had an average F1-score greater than 0.99 for classification to specific AMR determinants. A further 10% (24) averaged F1-scores between 0.8 and 0.99. In comparative analyses on the same dataset this outperformed homology searches alone, read mapping and variation graph based methods in terms of average overall accuracy and precision. Further work will aim to improve classification within certain families and expand AMRtime to include variant based AMR models as well as meta-models (e.g. multi-component efflux pump systems).
Welcome #TeamVirulence, left to right: Rachel Tran (Biochem 3R06), Sally Min (BiomedDC 4A15), Anatoly Miroshnichencko (BiomedDC 4A15), and Rafik El Werfalli (BiomedDC 4A15), who are collectively working on development of CARD:Virulence, a new branch of the Comprehensive Antibiotic Resistance Database dedicated to the molecular surveillance of bacterial virulence factors.
Welcome Jalees Nasir (left) and Martins Oloni (right), new M.Sc. students in the lab and the McMaster Biochemistry & Biomedical Sciences graduate program. Jalees will be working on molecular epidemiological tools for surveillance of viral infections, while Martins will be collaborating with Agriculture and Agri-Food Canada (AAFC) on making our Comprehensive Antibiotic Resistance Database stronger for mobile elements involved in drug resistance in agricultural settings. Welcome Jalees & Martins!
Vision is a crucial aspect of life for humans and animals. Impaired vision can lead to reduced quality of life along with other complications. Cataracts are a leading cause of impaired vision and blindness worldwide. Cataracts develop as a process of aging, although several environmental and lifestyle factors increase the risk of this disease. The toxic metal cadmium (Cd) has been associated with cataract formation and other ocular diseases such as macular degeneration. Cadmium exposure exper- iments were conducted to investigate potential pathways or mechanisms by which Cd may contribute to cataract formation and ocular disease. Zebrafish larvae (72, 96, and 120 hours post fertilization), adult zebrafish (6-month male, 10-month male, and 10-month female) and the B3 human lens epithelial (HLE) cell line were acutely exposed to varying concentrations of Cd. Transcriptomic changes relative to control (0 μM Cd) were determined using microarray analysis for zebrafish larvae and RNA sequencing (RNA-Seq) for adult zebrafish and HLE cells. Gene Ontology (GO) enrichment analysis for the zebrafish larvae exposure (50 μM Cd for 4 or 8 hours) enriched the “retina development in camera-type eye” term, and genes involved in enrichment (dnmt1, ccna2, fen1, mcm3 and slbp) were down-regulated. Gene set enrichment analysis (GSEA) for the 10-month male zebrafish exposure (50 μM Cd for 4 hours) enriched the “embryonic eye morphogenesis” gene set and significant genes involved in enrichment (tcf7l1a, pitx2, fzd8a, sfrp5, lmx1bb, mfap2, six3b, lum, phactr4b, and foxc1a) were down-regulated. GSEA for the 10-month female zebrafish (50 μM Cd for 4 hours) enriched the “photoreceptor cell differentiation” gene set and significant genes involved in enrichment (odc1, thrb, and ush2a) were up-regulated. GO enrichment analysis for up-regulated genes in the HLE cell exposure (10 μM Cd for 4 hours) enriched the terms “eye development” (22 genes) and “lens development in camera-type eye” (CITED2, SKIL, CRYAB, SLC7A11, TGFB2, EPHA2, BCAR3, WNT5B, and BMP4). These results show cadmium is capable of altering transcription of eye-related genes in both zebrafish and human models, which may contribute to the formation of ocular disease. Many of these genes are involved in lens and retina development, yet they are also associated with diseases in these eye structures. Future studies could assess the consequences of altered transcription of these genes which could help elucidate the mechanisms of these changes and the overall effect of cadmium exposure on ocular disease. Ultimately, our study characterized the regulation of eye-related genes in response to Cd exposure, and provided valuable knowledge setting the foundation for identification of the molecular mechanisms contributing to ocular diseases.
BACKGROUND: Physical inactivity impairs insulin sensitivity, which is exacerbated with aging. We examined the impact of 2 wk of acute inactivity and recovery on glycemic control, and integrated rates of muscle protein synthesis in older men and women.
METHODS: Twenty-two overweight, prediabetic older adults (12 men, 10 women, 69 ± 4 y) undertook 7 d of habitual activity (baseline; BL), step reduction (SR; <1,000 steps.d-1 for 14 d), followed by 14 d of recovery (RC). An oral glucose tolerance test was used to assess glycemic control and deuterated water ingestion to measure integrated rates of muscle protein synthesis.
RESULTS: Daily step count was reduced (all p < .05) from BL at SR (7362 ± 3294 to 991 ± 97) and returned to BL levels at RC (7117 ± 3819). Homeostasis model assessment-insulin resistance increased from BL to SR and Matsuda insulin sensitivity index decreased and did not return to BL in RC. Glucose and insulin area under the curve were elevated from BL to SR and did not recover in RC. Integrated muscle protein synthesis was reduced during SR and did not return to BL in RC.
CONCLUSIONS: Our findings demonstrate that 2 wk of SR leads to lowered rates of muscle protein synthesis and a worsening of glycemic control that unlike younger adults is not recovered during return to normal activity in overweight, prediabetic elderly humans.