Antibiotic-resistance bacteria is a growing concern and challenge for public health. Pathogenic bacteria have developed resistance mechanisms that allow them to survive and grow when antibiotics are present. Specific genes have been identified that are associated with bacteria gaining resistance to antibiotics and they are able to share this harmful genetic information with other species and genera through mobile genetic elements (MGEs). This contributes to the spread of antibiotic resistance. DNA from 28 clinical isolates has been extracted from Enterobacter, Escherichia, Pseudomonas, Staphylococcus, Klebsiella, and Stenotrophomonas species to identify antibiotic-resistance genes (ARGs) and their resistance mechanisms. After sequencing, the resulting raw-read files and the SPAdes (3.10.0) assembled contig files need further analysis to identify the MGEs present in the bacterial genomes and ARGs that are currently at risk of being transferred to other bacteria.
Project Information Subsection
Insertion sequence elements, plasmids, integrons, conjugative elements, and transposons will be identified in the 28 genomes using bioinformatics software. The raw-read files will be analyzed using plasmidSPAdes to identify the plasmid sequences in the genomes. Insertion sequences and transposons will be identified using the contig files and the programs ISEScan and TnFinder. The Comprehensive Antibiotic Resistance Database (CARD) will be used to identify ARGs present on the MGEs. Once the MGEs containing ARGs are identified, a phylogenetic analysis using phaME will be completed using the full genomes, MGEs, and ARGs. This work includes incorporating reference genomes from the National Center for Biotechnology Information (NCBI) and CARD to identify which genes are emerging as new threats to the effectiveness of current antibiotics through horizontal gene transfer.
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Kendra Batchelder, Ph.D. Candidate
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Some hands-on experience
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University of Maine at Presque Isle
181 Main St. Presque Isle, Maine. 04769
NE-University of Maine
08/31/2020
No
Already behind3Start date is flexible
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Analysis of large data sets on research computing infrastructure. Cluster computing.
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Student handling of large bioinformatics data sets.