Dataset Information

A Survey of Antimicrobial Resistance Determinants in Category A Select Agents, Exempt Strains, and Near-Neighbor Species.

ABSTRACT: A dramatic increase in global antimicrobial resistance (AMR) has been well documented. Of particular concern is the dearth of information regarding the spectrum and prevalence of AMR within Category A Select Agents. Here, we performed a survey of horizontally and vertically transferred AMR determinants among Category A agents and their near neighbors. Microarrays provided broad spectrum screening of 127 Francisella spp., Yersinia spp., and Bacillus spp. strains for the presence/absence of 500+ AMR genes (or families of genes). Detecting a broad variety of AMR genes in each genus, microarray analysis also picked up the presence of an engineered plasmid in a Y. pestis strain. High resolution melt analysis (HRMA) was also used to assess the presence of quinolone resistance-associated mutations in 100 of these strains. Though HRMA was able to detect resistance-causing point mutations in B. anthracis strains, it was not capable of discriminating these point mutations from other nucleotide substitutions (e.g., arising from sequence differences in near neighbors). Though these technologies are well-established, to our knowledge, this is the largest survey of Category A agents and their near-neighbor species for genes covering multiple mechanisms of AMR.

SUBMITTER: Taitt CR

PROVIDER: S-EPMC7084191 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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A Survey of Antimicrobial Resistance Determinants in Category A Select Agents, Exempt Strains, and Near-Neighbor Species.

Taitt Chris R CR Leski Tomasz A TA Chen Amy A Berk Kimberly L KL Dorsey Robert W RW Gregory Michael J MJ Sozhamannan Shanmuga S Frey Kenneth G KG Dutt Diane L DL Vora Gary J GJ

International journal of molecular sciences 20200229 5

A dramatic increase in global antimicrobial resistance (AMR) has been well documented. Of particular concern is the dearth of information regarding the spectrum and prevalence of AMR within Category A Select Agents. Here, we performed a survey of horizontally and vertically transferred AMR determinants among Category A agents and their near neighbors. Microarrays provided broad spectrum screening of 127 Francisella spp., Yersinia spp., and Bacillus spp. strains for the prese ...[more]

PMID: 32121349

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Project description:MotivationThe microbes in human body play a crucial role in influencing the functions of drugs, as they can regulate the activities and toxicities of drugs. Most recent methods for predicting drug-microbe associations are based on graph learning. However, the relationships among multiple drugs and microbes are complex, diverse, and heterogeneous. Existing methods often fail to fully model the relationships. In addition, the attributes of drug-microbe pairs exhibit long-distance spatial correlations, which previous methods have not integrated effectively.ResultsWe propose a new prediction method named DHDMP which is designed to encode the relationships among multiple drugs and microbes and integrate the attributes of various neighbor nodes along with the pairwise long-distance correlations. First, we construct a hypergraph with dynamic topology, where each hyperedge represents a specific relationship among multiple drug nodes and microbe nodes. Considering the heterogeneity of node attributes across different categories, we developed a node category-sensitive hypergraph convolution network to encode these diverse relationships. Second, we construct homogeneous graphs for drugs and microbes respectively, as well as drug-microbe heterogeneous graph, facilitating the integration of features from both homogeneous and heterogeneous neighbors of each target node. Third, we introduce a graph convolutional network with cross-graph feature propagation ability to transfer node features from homogeneous to heterogeneous graphs for enhanced neighbor feature representation learning. The propagation strategy aids in the deep fusion of features from both types of neighbors. Finally, we design spatial cross-attention to encode the attributes of drug-microbe pairs, revealing long-distance correlations among multiple pairwise attribute patches. The comprehensive comparison experiments showed our method outperformed state-of-the-art methods for drug-microbe association prediction. The ablation studies demonstrated the effectiveness of node category-sensitive hypergraph convolution network, graph convolutional network with cross-graph feature propagation, and spatial cross-attention. Case studies on three drugs further showed DHDMP's potential application in discovering the reliable candidate microbes for the interested drugs.Availability and implementationSource codes and supplementary materials are available at https://github.com/pingxuan-hlju/DHDMP.

Dataset Information

A Survey of Antimicrobial Resistance Determinants in Category A Select Agents, Exempt Strains, and Near-Neighbor Species.

Publications

A Survey of Antimicrobial Resistance Determinants in Category A Select Agents, Exempt Strains, and Near-Neighbor Species.

Similar Datasets

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