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 field of antibiotic drug discovery and the monitoring of new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. Despite the fact that the first genomes sequenced of free living organisms were those of bacteria, there have been few specialized bioinformatic tools developed to mine the growing amount of genomic data associated with pathogens. In particular, there are few tools to study the genetics and genomics of antibiotic resistance and how it impacts bacterial populations, ecology, and the clinic. We have initiated development of such tools in the form of the Comprehensive Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca). The CARD integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in new unannotated genome sequences. This unique platform provides an informatic tool that bridges antibiotic resistance concerns in health care, agriculture, and the environment.A ntibiotic resistance is an increasing crisis as both the range of microbial antibiotic resistance in clinical settings expands and the pipeline for development of new antibiotics contracts (1). This problem is compounded by the global genomic scope of the antibiotic resistome, such that antibiotic resistance spans a continuum from genes in pathogens found in the clinic to those of benign environmental microbes along with their proto-resistance gene progenitors (2, 3). The recent emergence of New Delhi metallo-ß-lactamase (NDM-1) in Gram-negative organisms (4), which can hydrolyze all ␤-lactams with the exception of monobactams, illustrates the capacity of new antibiotic resistance genes to emerge rapidly from as-yet-undetermined reservoirs. Surveys of genes originating from both clinical and environmental sources (microbes and metagenomes) will provide increasing insight into these reservoirs and offer predictive capacity for the emergence and epidemiology of antibiotic resistance.The increasing opportunity to prepare a broader and comprehensive antibiotic resistance gene census is facilitated by the power and falling costs of next-generation DNA sequencing. For example, whole-genome sequencing (WGS) is being increasingly used to examine new antibiotic-resistant isolates discovered in clinical settings (5). Additionally, culture-independent metagenomic surveys are adding tremendously to the pool of known genes and their distribution outside clinical settings (6, 7). These approaches have the advantage of providing a rapid survey of the antibiotic resistome of new strains, the discovery of newly emergent antibiotic resistance genes, the epidemiology of antibiotic resistance genes, and the horizontal gene transfer (HGT) of known antibiotic resistance genes through plasmids and transposable elements. However, despite the existence of tools for general annotation of prokaryotic genomes (see, e.g., reference 8), prediction of an antibiotic resista...

Antibiotic resistance is a global challenge that impacts all pharmaceutically used antibiotics. The origin of the genes associated with this resistance is of significant importance to our understanding of the evolution and dissemination of antibiotic resistance in pathogens. A growing body of evidence implicates environmental organisms as reservoirs of these resistance genes; however, the role of anthropogenic use of antibiotics in the emergence of these genes is controversial. We report a screen of a sample of the culturable microbiome of Lechuguilla Cave, New Mexico, in a region of the cave that has been isolated for over 4 million years. We report that, like surface microbes, these bacteria were highly resistant to antibiotics; some strains were resistant to 14 different commercially available antibiotics. Resistance was detected to a wide range of structurally different antibiotics including daptomycin, an antibiotic of last resort in the treatment of drug resistant Gram-positive pathogens. Enzyme-mediated mechanisms of resistance were also discovered for natural and semi-synthetic macrolide antibiotics via glycosylation and through a kinase-mediated phosphorylation mechanism. Sequencing of the genome of one of the resistant bacteria identified a macrolide kinase encoding gene and characterization of its product revealed it to be related to a known family of kinases circulating in modern drug resistant pathogens. The implications of this study are significant to our understanding of the prevalence of resistance, even in microbiomes isolated from human use of antibiotics. This supports a growing understanding that antibiotic resistance is natural, ancient, and hard wired in the microbial pangenome.

Solving the antibiotic resistance crisis requires the discovery of new antimicrobial drugs and the preservation of existing ones. The discovery of inhibitors of antibiotic resistance, antibiotic adjuvants, is a proven example of the latter. A major difficulty in identifying new antibiotics is the frequent rediscovery of known compounds, necessitating laborious "dereplication" to identify novel chemical entities. We have developed an antibiotic resistance platform (ARP) that can be used for both the identification of antibiotic adjuvants and for antibiotic dereplication. The ARP is a cell-based array of mechanistically distinct individual resistance elements in an identical genetic background. In dereplication mode, we demonstrate the rapid identification, and thus discrimination, of common antibiotics. In adjuvant discovery mode, we show that the ARP can be harnessed in screens to identify inhibitors of resistance. The ARP is therefore a powerful tool that has broad application in confronting the resistance crisis.