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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #413969
Research Project: Increasing Accuracy of Genomic Prediction, Developing Algorithms, Selecting Markers, and Evaluating New Traits to Improve Dairy Cattle

Location: Animal Genomics and Improvement Laboratory

Title: Comprehensive reporting of the resistome utilizing real-time sequencing technology

Author
item SEIBEL, SAMANTHA - Pennsylvania State University
item KENNEY, SOPHIA - Pennsylvania State University
item CHUNG, TAEJUNG - Pennsylvania State University
item BIERLY, STEPHANIE - Pennsylvania State University
item VAN SYOC, EMILY - Pennsylvania State University
item SAPRE, ANJALI - Walter Reed Army Institute
item Miles, Asha
item KOVAC, JASNA - Pennsylvania State University
item GANDA, ERIKA - Pennsylvania State University

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 3/15/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: The global burden of antimicrobial resistance (AMR) is steadily increasing. Current gaps in AMR surveillance include the inability to properly profile the resistome, all acquired and intrinsic genes linked to AMR phenotypes. Surveillance may be restricted to culture-dependent techniques, noncomprehensive amplification of all known resistance genes, or limited access to high performance computing. To overcome these limitations, we designed a comprehensive resistome-profiling method coined “rhAMR”. By combining rhPCR technology with primers targeting genes from the MEGARes v2.0 database, rhAMR expands on traditional sequencing technologies to maximize detection of antimicrobial, biocide, and heavy metal resistance genes. We provide evidence for feasibility of using rhAMR beyond Illumina-based sequencing and the application of Oxford Nanopore technology for real-time data acquisition. Mock microbial communities containing 29 enteric bacteria with varied resistome targets were used to evaluate rhAMR. After PCR amplification, samples were subset for library preparation for both Illumina MiSeq and ONT Mk1C endpoints. Post real-time basecalling and demultiplexing, sequencing reads were analyzed via the AMR++ v3.0 pipeline. AMR++ integrates various steps of resistome analysis including quality control, adapter trimming, nonbacterial DNA filtering, and amplicon mapping. When necessary, specific tools capable of handling ONT data were supplemented. Sample alignment count files were combined into a total count matrix from which RPM thresholds were determined. Using the known control resistome profile, rhAMR’s sensitivity and specificity was determined. Through the ONT method, 96 unique genes were detected with 67.0% sensitivity and 96.9% specificity (as compared to 83.5% sensitivity and 96.7% specificity of Illumina method). Future goals include expanding targets to encompass entire MEGARes v3.0 database and improving true positive detection through protocol optimization.