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ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Egg and Poultry Production Safety Research Unit » Research » Publications at this Location » Publication #405854
Research Project: Reduction of Foodborne Pathogens and Antimicrobial Resistance in Poultry Production Environments

Location: Egg and Poultry Production Safety Research Unit

Title: Towards Optimal Microbiome to Inhibit Multidrug Resistance

Author
item PILLAI, NISHA - Mississippi State University
item NANDURI, BINDU - Mississippi State University
item Rothrock, Michael
item CHEN, ZHIQIAN - Mississippi State University
item RAMKUMAR, MAHALINGAM - Mississippi State University

Submitted to: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Publication Type: Proceedings
Publication Acceptance Date: 6/21/2023
Publication Date: 8/29/2023
Citation: Pillai, N., Nanduri, B., Rothrock Jr, M.J., Chen, Z., Ramkumar, M. 2023. Towards Optimal Microbiome to Inhibit Multidrug Resistance. IEEE/ACM Transactions on Computational Biology and Bioinformatics. https://doi.org/10.1109/CIBCB56990.2023.10264914.
DOI: https://doi.org/10.1109/CIBCB56990.2023.10264914

Interpretive Summary: In this research, we propose an explanatory model that learns combinations of microbiome signatures that help predict reduced multidrug resistance (MDR) of disease-causing pathogens. A twin regression network in our model provides a means for estimating the MDR presence using microbiome samples. Further, we extend our model by using a backtracking approach that determines the effect of specific microbiome signa- tures on the prevalence of MDR, to identify potential beneficial combinations of these signatures. Using high-throughput Illumina sequencing of the 16S rRNA gene data from 41 pastured poultry flocks in southeastern U.S. farms, the regression model identifies the factors influencing MDR prevalence in control foodborne pathogens (Salmonella, Campylobacter, and Listeria) at different stages of poultry growth.

Technical Abstract: In this research, we propose an explanatory model that learns combinations of microbiome signatures that help predict reduced multidrug resistance (MDR) of disease-causing pathogens. A twin regression network in our model provides a means for estimating the MDR presence using microbiome samples. Further, we extend our model by using a backtracking approach that determines the effect of specific microbiome signa- tures on the prevalence of MDR, to identify potential beneficial combinations of these signatures. Using high-throughput Illumina sequencing of the 16S rRNA gene data from 41 pastured poultry flocks in southeastern U.S. farms, the regression model identifies the factors influencing MDR prevalence in control foodborne pathogens (Salmonella, Campylobacter, and Listeria) at different stages of poultry growth.