PURPOSE: Antimicrobial resistance (AMR), especially multidrug resistance, is one of the most serious global threats facing public health. We performed a proof of concept study assessing the suitability of shotgun proteomics as a complementary approach to whole-genome sequencing (WGS) for detecting AMR determinants.
EXPERIMENTAL DESIGN: We used previously published shotgun proteomics and WGS data on four isolates of Campylobacter jejuni to perform AMR detection by searching the Comprehensive Antibiotic Resistance Database, and we assessed their detection ability relative to genomics screening and traditional phenotypic testing measured by minimum inhibitory concentration.
RESULTS: Both genomic and proteomic approaches identified the wild type and variant molecular determinants responsible for resistance to tetracycline and ciprofloxacin, in agreement with phenotypic testing. In contrast, the genomic method identified the presence of the β-lactamase gene, blaOXA-61 , in three isolates. However, its corresponding protein product was detected in only a single isolate, consistent with results obtained from phenotypic testing.
Metformin is the most commonly prescribed medication for type 2 diabetes, owing to its glucose-lowering effects, which are mediated through the suppression of hepatic glucose production. However, in addition to its effects on the liver, metformin reduces appetite and in preclinical models exerts beneficial effects on ageing and a number of diverse diseases (for example, cognitive disorders, cancer, cardiovascular disease) through mechanisms that are not fully understood. Given the high concentration of metformin in the liver and its many beneficial effects beyond glycemic control, we reasoned that metformin may increase the secretion of a hepatocyte-derived endocrine factor that communicates with the central nervous system4. Here we show, using unbiased transcriptomics of mouse hepatocytes and analysis of proteins in human serum, that metformin induces expression and secretion of growth differentiating factor 15 (GDF15). In primary mouse hepatocytes, metformin stimulates the secretion of GDF15 by increasing the expression of activating transcription factor 4 (ATF4) and C/EBP homologous protein (CHOP; also known as DDIT3). In wild-type mice fed a high-fat diet, oral administration of metformin increases serum GDF15 and reduces food intake, body mass, fasting insulin and glucose intolerance; these effects are eliminated in GDF15 null mice. An increase in serum GDF15 is also associated with weight loss in patients with type 2 diabetes who take metformin. Although further studies will be required to determine the tissue source(s) of GDF15 produced in response to metformin in vivo, our data indicate that the therapeutic benefits of metformin on appetite, body mass and serum insulin depend on GDF15.
See the Commentary in SciTechDaily.
Bacteria have evolved sophisticated mechanisms to inhibit the growth of competitors. One such mechanism involves type VI secretion systems, which bacteria can use to inject antibacterial toxins directly into neighbouring cells. Many of these toxins target the integrity of the cell envelope, but the full range of growth inhibitory mechanisms remains unknown. Here we identify a type VI secretion effector, Tas1, in the opportunistic pathogen Pseudomonas aeruginosa. The crystal structure of Tas1 shows that it is similar to enzymes that synthesize (p)ppGpp, a broadly conserved signalling molecule in bacteria that modulates cell growth rate, particularly in response to nutritional stress. However, Tas1 does not synthesize (p)ppGpp; instead, it pyrophosphorylates adenosine nucleotides to produce (p)ppApp at rates of nearly 180,000 molecules per minute. Consequently, the delivery of Tas1 into competitor cells drives rapid accumulation of (p)ppApp, depletion of ATP, and widespread dysregulation of essential metabolic pathways, thereby resulting in target cell death. Our findings reveal a previously undescribed mechanism for interbacterial antagonism and demonstrate a physiological role for the metabolite (p)ppApp in bacteria.
See the Commentary at Nature.
Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A, Huynh W, Nguyen A-LV, Cheng AA, Liu S, Min SY, Miroshnichenko A, Tran H-K, Werfalli RE, Nasir JA, Oloni M, Speicher DJ, Florescu A, Singh B, Faltyn M, Hernandez-Koutoucheva A, Sharma AN, Bordeleau E, Pawlowski AC, Zubyk HL, Dooley D, Griffiths E, Maguire F, Winsor GL, Beiko RG, Brinkman FSL, Hsiao WWL, Van Domselaar G, McArthur AG.
The Comprehensive Antibiotic Resistance Database (CARD; https://card.mcmaster.ca) is a curated resource providing reference DNA and protein sequences, detection models and bioinformatics tools on the molecular basis of bacterial antimicrobial resistance (AMR). CARD focuses on providing high-quality reference data and molecular sequences within a controlled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to integrate with software development efforts for resistome analysis and prediction, such as CARD’s Resistance Gene Identifier (RGI) software. Since 2017, CARD has expanded through extensive curation of reference sequences, revision of the ontological structure, curation of over 500 new AMR detection models, development of a new classification paradigm and expansion of analytical tools. Most notably, a new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes. By adding these resistance variants to CARD, we are able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants. Here, we describe updates and recent expansions to CARD and its biocuration process, including new resources for community biocuration of AMR molecular reference data.
The identification and association of the nucleotide sequences encoding antibiotic resistance elements is critical to improve surveillance and monitor trends in antibiotic resistance. Current methods to study antibiotic resistance in various environments rely on extensive deep sequencing or laborious culturing of fastidious organisms, which are both heavily time-consuming operations. An accurate and sensitive method to identify both rare and common resistance elements in complex metagenomic samples is needed. Referencing the Comprehensive Antibiotic Resistance Database, we designed a set of 37,826 probes to specifically target over 2000 nucleotide sequences associated with antibiotic resistance in clinically relevant bacteria. Testing of this probeset on DNA libraries generated from multi-drug resistant bacteria to selectively capture resistance genes reproducibly produced higher reads on-target at greater length of coverage when compared to shotgun sequencing. We also identified additional resistance gene sequences from human gut microbiome samples that sequencing alone was not able to detect. Our method to capture the resistome enables sensitive gene detection in diverse environments where antibiotic resistance represents less than 0.1% of the metagenome.
Edalatmand, A. & A.G. McArthur. 2019. Classifying and curating publications on antimicrobial resistance using machine learning. Poster presentation at the Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Trainee Day, Hamilton, Ontario, Canada.
Khan, S., K.K. Tsang, M. Science, D. Kaufman, D. Mertz, J. Pernica, L. Thabane, A.G. McArthur, & M. Loeb. 2019. GloveCare: A pilot study to assess non-sterile glove-based care for the prevention of late onset infection in the NICU. Poster presentation at the Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Trainee Day, Hamilton, Ontario, Canada.
Nasir, J.A., R.A. Kozak, S. Mubareka, H. Poinar, & A.G. McArthur. 2019. Development of virus surveillance tools for clinical diagnostics. Poster presentation at the Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Trainee Day, Hamilton, Ontario, Canada.
Oloni, M., K. Besennov, J. Nash, & A.G. McArthur. 2019. Using the Comprehensive Antimicrobial Resistance Database (CARD) to predict plasmid-borne AMR genes from genome assembly data. Poster presentation at the Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Trainee Day, Hamilton, Ontario, Canada.
Speicher, D.J., K. Luinstra, J. Maciejewski, K.K. Tsang, S. Patel, V. Allen, A.G. McArthur, & M. Smieja. 2019. Clostridioides difficile strain divergence and prophages in Southern Ontario, Canada (2010-2018). Poster presentation at the Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Trainee Day, Hamilton, Ontario, Canada.
Tran, H.-K. 2019. In silico prediction of novel type VII polymorphic toxins of the Streptococcus Anginosus Group. Oral presentation at the Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Trainee Day, Hamilton, Ontario, Canada.
The McArthur Lab is very proud to announce Amos Raphenya as 2019 IIDR Michael Kamin Kart Memorial Scholarship Recipient (Staff) and Rachel Tran as 2019 IIDR Michael Kamin Kart Memorial Scholarship Recipient (Undergraduate)! These awards are the highest academic honour for trainees and staff in the Michael G. DeGroote Institute for Infectious Disease Research (IIDR). Awarded during the 2019 IIDR Trainee Day, these awards were accompanied by a talk by Rachel on her summer research: In silico prediction of novel type VII polymorphic toxins in the Streptococcus Anginosus Group.
(left to right) Back row: Amos Raphenya, Arman Edalatmand, Brian Alcock, Andrew McArthur, David Speicher, Martins Oloni, Sohaib Syed. Front row: William Huynh, Anna-Lisa Nguyen, Megane Bouchard, Rachel Tran, Hamna Imtiaz, Jalees Nasir, Corie Niu, Hafsa Omer, Kara Tsang. Missing: Marcel Jansen, Sarah Yaqoob. Who’s who?
Welcome 2019-2020 Undergraduates!
Biochem 4T15, BiomedDC 4A15 Thesis – Rachel Tran, Arman Edalatmand, Marcel Jansen, William Huynh, Sohaib Syed
Biochem 3R06, HthSci 3H06 Project – Corie Niu, Hamna Imtiaz, Megane Bouchard
Science 3RP3, LifeSci 3RP3 Inquiry – Hafsa Omer, Sarah Yaqoob
Data Volunteer – Anna-Lisa Nguyen
Glycopeptide antibiotics are produced by Actinobacteria through biosynthetic gene clusters that include genes supporting their regulation, synthesis, export and resistance. The chemical and biosynthetic diversities of glycopeptides are the product of an intricate evolutionary history. Extracting this history from genome sequences is difficult as conservation of the individual components of these gene clusters is variable and each component can have a different trajectory. We show that glycopeptide biosynthesis and resistance in Actinobacteria maps to approximately 150-400 million years ago. Phylogenetic reconciliation reveals that the precursors of glycopeptide biosynthesis are far older than other components, implying that these clusters arose from a pre-existing pool of genes. We find that resistance appeared contemporaneously with biosynthetic genes, raising the possibility that the mechanism of action of glycopeptides was a driver of diversification in these gene clusters. Our results put antibiotic biosynthesis and resistance into an evolutionary context and can guide the future discovery of compounds possessing new mechanisms of action, which are especially needed as the usefulness of the antibiotics available at present is imperilled by human activity.
More details at McMaster’s Brighter World.