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 Comprehensive Antibiotic Resistance Database (CARD; http://arpcard.mcmaster.ca) is a manually curated resource containing high quality reference data on the molecular basis of antimicrobial resistance (AMR), with an emphasis on the genes, proteins and mutations involved in AMR. CARD is ontologically structured, model centric, and spans the breadth of AMR drug classes and resistance mechanisms, including intrinsic, mutation-driven and acquired resistance. It is built upon the Antibiotic Resistance Ontology (ARO), a custom built, interconnected and hierarchical controlled vocabulary allowing advanced data sharing and organization. Its design allows the development of novel genome analysis tools, such as the Resistance Gene Identifier (RGI) for resistome prediction from raw genome sequence. Recent improvements include extensive curation of additional reference sequences and mutations, development of a unique Model Ontology and accompanying AMR detection models to power sequence analysis, new visualization tools, and expansion of the RGI for detection of emergent AMR threats. CARD curation is updated monthly based on an interplay of manual literature curation, computational text mining, and genome analysis.

The Comprehensive Antibiotic Resistance Database (CARD; card.mcmaster.ca) combines the Antibiotic Resistance Ontology (ARO) with curated AMR gene (ARG) sequences and resistance-conferring mutations to provide an informatics framework for annotation and interpretation of resistomes. As of version 3.2.4, CARD encompasses 6627 ontology terms, 5010 reference sequences, 1933 mutations, 3004 publications, and 5057 AMR detection models that can be used by the accompanying Resistance Gene Identifier (RGI) software to annotate genomic or metagenomic sequences. Focused curation enhancements since 2020 include expanded β-lactamase curation, incorporation of likelihood-based AMR mutations for Mycobacterium tuberculosis, addition of disinfectants and antiseptics plus their associated ARGs, and systematic curation of resistance-modifying agents. This expanded curation includes 180 new AMR gene families, 15 new drug classes, 1 new resistance mechanism, and two new ontological relationships: evolutionary_variant_of and is_small_molecule_inhibitor. In silico prediction of resistomes and prevalence statistics of ARGs has been expanded to 377 pathogens, 21,079 chromosomes, 2,662 genomic islands, 41,828 plasmids and 155,606 whole-genome shotgun assemblies, resulting in collation of 322,710 unique ARG allele sequences. New features include the CARD:Live collection of community submitted isolate resistome data and the introduction of standardized 15 character CARD Short Names for ARGs to support machine learning efforts.

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 (reviewed in refs. 1-3). 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 1-3. 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 system 4. 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. Metformin is one of the most widely used medications in the world. It is a strong base that exists in its protonated form at physiological pH and therefore does not pass through cellular membranes easily. In rodents, oral administration of metformin (250-300 mg kg-1 body weight) results in clinically relevant plasma concentrations of approximately 10-15 μM; however, concentrations in the

S evere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in December 2019 in Wuhan, China (1). SARS-CoV-2 has since spread to ≈185 countries and infected ≈6 million persons, among whom ≈380,000 have died (2). On January 23, 2020, a case of coronavirus disease (COVID-19) was detected in Toronto, Canada (3); since then, multiple cases have been identified across Canada. As SARS-CoV-2 spreads globally, the virus is likely to adapt and evolve. It is critical to isolate SARS-CoV-2 viruses to characterize their ability to infect and replicate in multiple human cell types and to determine if the virus is evolving in its ability to infect human cells and cause severe disease. Isolating the virus also provides the opportunity to share the virus with other researchers for development and testing of diagnostics, drugs, and vaccines. We isolated SARS-CoV-2 from 2 patients with COVID-19 and determined the genomic sequence of each isolate (SARS-CoV-2/SB2 and SARS-CoV-2/ SB3-TYAGNC). In addition, we studied the replication kinetics of SARS-CoV-2/SB3-TYAGNC in human fibroblast, epithelial, and immune cells. Methods Cells We maintained Vero E6 cells (African green monkey cells; American Type Culture Collection [ATCC], https://www.atcc.org) in Dulbecco's modified Eagle medium (DMEM) supplemented with 10% fetal bovine serum (FBS) (Sigma-Aldrich, https://www.sigmaaldrich.com) and 1× l-glutamine and penicillin/ streptomycin (Pen/Strep; Corning, https://ca.vwr. com). Calu-3 cells (human lung adenocarcinoma derived; ATCC) were cultured as previously mentioned (4), as were THF cells (human telomerase lifeextended cells) (5). THP-1 cells (monocytes; ATCC) were cultured in RPMI medium (Gibco Laboratories, https://www.thermofisher.com) supplemented with 10% FBS, 2mM l-glutamine, 1× penicillin/