|SHAH, DEVENDRA - Washington State University|
|SANCHEZ-INGUNZA, ROXANA - Ceva Animal Health|
|MADSEN, MELISSA - Ceva Animal Health|
|EL-ATTRACHE, JOHN - Ceva Animal Health|
|LUNGU, BWALYA - Aviagen|
Submitted to: Research in Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/23/2016
Publication Date: 7/12/2016
Citation: Guard, J.Y., Rothrock Jr, M.J., Shah, D., Jones, D.R., Gast, R.K., Sanchez-Ingunza, R., Madsen, M., El-Attrache, J., Lungu, B. 2016. Metabolic parameters linked by phenotype microArray to acid resistance profiles of poultry-associated Salmonella enterica. Research in Microbiology. 167(9-10):745-756.
Interpretive Summary: Salmonella enterica has been difficult to reduce as a cause of foodborne illness since 2010, thus a greater appreciation for factors that allow the pathogen to survive on-farm and in processing plants is required. We used an approach called phenotype microarray to review how Salmonella associated with poultry grow in the presence of 900 compounds that impact growth of bacteria. Results suggest that different isolates of Salmonella enterica vary in resistance to acidic conditions (pH 4.5), sodium lactate and sodium chloride. Thus, it might be as important for the food industry to understand emerging resistance to commonly used preservatives as it is for the medical professions to understand the basis for bacteria evolving resistance to antibiotics used to treat illness. This information might be useful for managing environments prevalent on farm and in the processing plant in an effort to make them less favorable for supporting the growth of Salmonella enterica.
Technical Abstract: Phenotype microarrays were analyzed for 51 datasets derived from Salmonella enterica. The top 4 serovars associated with poultry products and one associated with turkey, respectively Typhimurium, Enteritidis, Heidelberg, Infantis and Senftenberg, were represented. Datasets were clustered into two groups based on ranking by values at pH 4.5 (PM10 A03). Statistical analyses of ranked data included Student’s t-test and effect size. In addition, data were grouped into unpaired and paired clusters based on acid resistance profiles. Unpaired cluster analysis compared results from 24 isolates that appeared most acid resistant to 27 that appeared most acid sensitive (24 x 27 format). Paired cluster analysis included only the 7 most acid resistant and the 7 most acid sensitive datasets (7 x 7 format). Results suggest that a definable set of chemicals differentiate acid resistant isolates from sensitive ones. Differences were more evident at the extremes of phenotype.