Austin Yan

Bio: Austin Yan is an academic researcher from McMaster University. The author has contributed to research in topics: Microbiome & Metagenomics. The author has an hindex of 2, co-authored 2 publications receiving 1173 citations.

The Comprehensive Antibiotic Resistance Database

Abstract: 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.

Transformation of the Anticancer Drug Doxorubicin in the Human Gut Microbiome.

Abstract: Bacteria living in the human gut are implicated in the etiology of several diseases. Moreover, dozens of drugs are metabolized by elements of the gut microbiome, which may have further implications for human health. Here, we screened a collection of gut isolates for their ability to inactivate the widely used antineoplastic drug doxorubicin and identified a strain of Raoultella planticola as a potent inactivator under anaerobic conditions. We demonstrate that R. planticola deglycosylates doxorubicin to metabolites 7-deoxydoxorubicinol and 7-deoxydoxorubicinolone via a reductive deglycosylation mechanism. We further show that doxorubicin is degraded anaerobically by Klebsiella pneumoniae and Escherichia coli BW25113 and present evidence that this phenotype is dependent on molybdopterin-dependent enzyme(s). Deglycosylation of doxorubicin by R. planticola under anaerobic conditions is found to reduce toxicity to the model species Caenorhabditis elegans, providing a model to begin understanding the role of do...

Multiomic spatial analysis reveals a distinct mucosa-associated virome

Abstract: ABSTRACT The human gut virome has been increasingly explored in recent years. However, nearly all virome-sequencing efforts rely solely on fecal samples and few studies leverage multiomic approaches to investigate phage–host relationships. Here, we combine metagenomics, metaviromics, and metatranscriptomics to study virome-bacteriome interactions at the colonic mucosal-luminal interface in a cohort of three individuals with inflammatory bowel disease; non-IBD controls were not included in this study. We show that the mucosal viral population is distinct from the stool virome and houses abundant crAss-like phages that are undetectable by fecal sampling. Through viral protein prediction and metatranscriptomic analysis, we explore viral gene transcription, prophage activation, and the relationship between the presence of integrase and temperate phages in IBD subjects. We also show the impact of deep sequencing on virus recovery and offer guidelines for selecting optimal sequencing depths in future metaviromic studies. Systems biology approaches such as those presented in this report will enhance our understanding of the human virome and its interactions with our microbiome and our health.

CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database

Abstract: The Comprehensive Antibiotic Resistance Database (CARD; http://arpcardmcmasterca) 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

RASTtk: A modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes

Abstract: The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.

CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database

Abstract: 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.

Tackling antibiotic resistance: the environmental framework

Abstract: Antibiotic resistance is a threat to human and animal health worldwide, and key measures are required to reduce the risks posed by antibiotic resistance genes that occur in the environment. These measures include the identification of critical points of control, the development of reliable surveillance and risk assessment procedures, and the implementation of technological solutions that can prevent environmental contamination with antibiotic resistant bacteria and genes. In this Opinion article, we discuss the main knowledge gaps, the future research needs and the policy and management options that should be prioritized to tackle antibiotic resistance in the environment.

Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center

Abstract: The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center (https://www.patricbrc.org). Recent changes to PATRIC include a redesign of the web interface and some new services that provide users with a platform that takes them from raw reads to an integrated analysis experience. The redesigned interface allows researchers direct access to tools and data, and the emphasis has changed to user-created genome-groups, with detailed summaries and views of the data that researchers have selected. Perhaps the biggest change has been the enhanced capability for researchers to analyze their private data and compare it to the available public data. Researchers can assemble their raw sequence reads and annotate the contigs using RASTtk. PATRIC also provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace. Private data can be compared by 'virtual integration' to any of PATRIC's public data. The number of genomes available for comparison in PATRIC has expanded to over 80 000, with a special emphasis on genomes with antimicrobial resistance data. PATRIC uses this data to improve both subsystem annotation and k-mer classification, and tags new genomes as having signatures that indicate susceptibility or resistance to specific antibiotics.