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Microarray analysis of antimicrobial resistance genes in Salmonella enterica from preharvest poultry environment
Author(s) -
Zou W.,
Frye J.G.,
Chang C.W.,
Liu J.,
Cerniglia C.E.,
Nayak R.
Publication year - 2009
Publication title -
journal of applied microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.889
H-Index - 156
eISSN - 1365-2672
pISSN - 1364-5072
DOI - 10.1111/j.1365-2672.2009.04270.x
Subject(s) - preharvest , salmonella enterica , salmonella , biology , microbiology and biotechnology , antimicrobial , gene , microarray , food microbiology , antibiotic resistance , genetics , bacteria , gene expression , botany , antibiotics , postharvest
Aims: To detect antimicrobial resistance genes in Salmonella isolates from turkey flocks using the microarray technology. Methods and Results: A 775 gene probe oligonucleotide microarray was used to detect antimicrobial resistance genes in 34 isolates. All tetracycline‐resistant Salmonella harboured tet(A) , tet(C) or tet(R) , with the exception of one Salmonella serotype Heidelberg isolate. The sul1 gene was detected in 11 of 16 sulfisoxazole‐resistant isolates. The aadA , aadA1 , aadA2 , strA or strB genes were found in aminoglycoside‐resistant isolates of Salm. Heidelberg, Salmonella serotype Senftenberg and untypeable Salmonella . The prevalence of mobile genetic elements, such as class I integron and transposon genes, in drug‐resistant Salmonella isolates suggested that these elements may contribute to the dissemination of antimicrobial resistance genes in the preharvest poultry environment. Hierarchical clustering analysis demonstrated a close relationship between drug‐resistant phenotypes and the corresponding antimicrobial resistance gene profiles. Conclusions: Salmonella serotypes isolated from the poultry environment carry multiple genes that can render them resistant to several antimicrobials used in poultry and humans. Significance and Impact of the Study: Multiple antimicrobial resistance genes in environmental Salmonella isolates could be identified efficiently by microarray analysis. Hierarchical clustering analysis of the data was also found to be a useful tool for analysing emerging patterns of drug resistance.