scispace - formally typeset
Search or ask a question
Author

Annie A. Cheng

Bio: Annie A. Cheng is an academic researcher from McMaster University. The author has contributed to research in topics: Resistome & Barcode. The author has an hindex of 1, co-authored 1 publications receiving 543 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: A new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes, able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants.
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.

1,526 citations

Posted ContentDOI
31 Jul 2022-bioRxiv
TL;DR: ExBarSeq is shown, an integrated method combining in situ sequencing of RNA barcodes, immunostaining, and Expansion Microscopy coupled with an end-to-end software pipeline that automatically extracts barcode identities from large imaging datasets without data processing bottlenecks.
Abstract: Mapping and molecularly annotating mammalian neural circuits is challenging due to the inability to uniquely label cells while also resolving subcellular features such as synaptic proteins or fine cellular processes. We argue that an ideal technology for connectomics would have the following characteristics: the capacity for robust distance-independent labeling, synaptic resolution, molecular interrogation, and scalable computational methods. The recent development of high-diversity cellular barcoding with RNA has provided a way to overcome the labeling limitations associated with spectral dyes, however performing all-optical circuit mapping has not been demonstrated because no method exists to image barcodes throughout cells at synaptic-resolution. Here we show ExBarSeq, an integrated method combining in situ sequencing of RNA barcodes, immunostaining, and Expansion Microscopy coupled with an end-to-end software pipeline that automatically extracts barcode identities from large imaging datasets without data processing bottlenecks. As a proof of concept, we applied ExBarSeq to thick tissue sections from mice virally infected with MAPseq viral vectors and demonstrated the extraction of 50 barcoded cells in the visual cortex as well as cell morphologies uncovered via immunostaining. The current work demonstrates high resolution multiplexing of exogenous barcodes and endogenous synaptic proteins and outlines a roadmap for molecularly annotated connectomics at a brain-wide scale.

Cited by
More filters
Journal ArticleDOI
TL;DR: In this article, the authors present the current understanding of the roles of the environment, including antibiotic pollution, in resistance evolution, in transmission and as a mere reflection of the regional antibiotic resistance situation in the clinic.
Abstract: Antibiotic resistance is a global health challenge, involving the transfer of bacteria and genes between humans, animals and the environment. Although multiple barriers restrict the flow of both bacteria and genes, pathogens recurrently acquire new resistance factors from other species, thereby reducing our ability to prevent and treat bacterial infections. Evolutionary events that lead to the emergence of new resistance factors in pathogens are rare and challenging to predict, but may be associated with vast ramifications. Transmission events of already widespread resistant strains are, on the other hand, common, quantifiable and more predictable, but the consequences of each event are limited. Quantifying the pathways and identifying the drivers of and bottlenecks for environmental evolution and transmission of antibiotic resistance are key components to understand and manage the resistance crisis as a whole. In this Review, we present our current understanding of the roles of the environment, including antibiotic pollution, in resistance evolution, in transmission and as a mere reflection of the regional antibiotic resistance situation in the clinic. We provide a perspective on current evidence, describe risk scenarios, discuss methods for surveillance and the assessment of potential drivers, and finally identify some actions to mitigate risks.

383 citations

Journal ArticleDOI
TL;DR: The Metagenomic Gut Virus catalogue as discussed by the authors contains 189,680 genomes from 11,810 publicly available human stool metagenomes and identified 54,118 candidate viral species, 92% of which were not found in existing databases.
Abstract: Bacteriophages have important roles in the ecology of the human gut microbiome but are under-represented in reference databases. To address this problem, we assembled the Metagenomic Gut Virus catalogue that comprises 189,680 viral genomes from 11,810 publicly available human stool metagenomes. Over 75% of genomes represent double-stranded DNA phages that infect members of the Bacteroidia and Clostridia classes. Based on sequence clustering we identified 54,118 candidate viral species, 92% of which were not found in existing databases. The Metagenomic Gut Virus catalogue improves detection of viruses in stool metagenomes and accounts for nearly 40% of CRISPR spacers found in human gut Bacteria and Archaea. We also produced a catalogue of 459,375 viral protein clusters to explore the functional potential of the gut virome. This revealed tens of thousands of diversity-generating retroelements, which use error-prone reverse transcription to mutate target genes and may be involved in the molecular arms race between phages and their bacterial hosts.

159 citations

Journal ArticleDOI
11 Dec 2020-Animal
TL;DR: (meta)proteomics analysis of bacterial compartment of raw milk is applied to obtain a method that provides a measurement of circulating AMR involved proteins and gathers information about the whole bacterial composition.
Abstract: The environment, including animals and animal products, is colonized by bacterial species that are typical and specific of every different ecological niche. Natural and human-related ecological pressure promotes the selection and expression of genes related to antimicrobial resistance (AMR). These genes might be present in a bacterial consortium but might not necessarily be expressed. Their expression could be induced by the presence of antimicrobial compounds that could originate from a given ecological niche or from human activity. In this work, we applied (meta)proteomics analysis of bacterial compartment of raw milk in order to obtain a method that provides a measurement of circulating AMR involved proteins and gathers information about the whole bacterial composition. Results from milk analysis revealed the presence of 29 proteins/proteoforms linked to AMR. The detection of mainly β-lactamases suggests the possibility of using the milk microbiome as a bioindicator for the investigation of AMR. Moreover, it was possible to achieve a culture-free qualitative and functional analysis of raw milk bacterial consortia.

121 citations

Journal ArticleDOI
16 Mar 2022-iMeta
TL;DR: In this paper , the authors present a platform consisting of three modules, which are preconfigured bioinformatic pipelines, cloud toolsets, and online omics' courses, which combine analytic tools for metagenomics, genomes, transcriptome, proteomics and metabolomics.
Abstract: The platform consists of three modules, which are pre-configured bioinformatic pipelines, cloud toolsets, and online omics' courses. The pre-configured bioinformatic pipelines not only combine analytic tools for metagenomics, genomes, transcriptome, proteomics and metabolomics, but also provide users with powerful and convenient interactive analysis reports, which allow them to analyze and mine data independently. As a useful supplement to the bioinformatics pipelines, a wide range of cloud toolsets can further meet the needs of users for daily biological data processing, statistics, and visualization. The rich online courses of multi-omics also provide a state-of-art platform to researchers in interactive communication and knowledge sharing.

121 citations