The transcriptional response of Pasteurella multocida to three classes of antibiotics

Bindu Nanduri, Leslie A. Shack, Shane C. Burgess, Mark L. Lawrence

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Background: Pasteurella multocida is a gram-negative bacterial pathogen that has a broad host range. One of the diseases it causes is fowl cholera in poultry. The availability of the genome sequence of avian P. multocida isolate Pm70 enables the application of functional genomics for observing global gene expression in response to a given stimulus. We studied the effects of three classes of antibiotics on the P. multocida transcriptome using custom oligonucleotide microarrays from NimbleGen Systems. Hybridizations were conducted with RNA isolated from three independent cultures of Pm70 grown in the presence or absence of sub-minimum inhibitory concentration (sub-MIC) of antibiotics. Differentially expressed (DE) genes were identified by ANOVA and Dunnett's test. Biological modeling of the differentially expressed genes (DE) was conducted based on Clusters of Orthologous (COG) groups and network analysis in Pathway Studio. Results: The three antibiotics used in this study, amoxicillin, chlortetracycline, and enrofloxacin, collectively influenced transcription of 25% of the P. multocida Pm70 genome. Some DE genes identified were common to more than one antibiotic. The overall transcription signatures of the three antibiotics differed at the COG level of the analysis. Network analysis identified differences in the SOS response of P. multocida in response to the antibiotics. Conclusion: This is the first report of the transcriptional response of an avian strain of P. multocida to sub-lethal concentrations of three different classes of antibiotics. We identified common adaptive responses of P. multocida to antibiotic stress. The observed changes in gene expression of known and putative P. multocida virulence factors establish the molecular basis for the therapeutic efficacy of sub-MICs. Our network analysis demonstrates the feasibility and limitations of applying systems modeling to high throughput datasets in 'non-model' bacteria.

Original languageEnglish (US)
Article numberS4
JournalBMC genomics
Issue numberSUPPL. 2
StatePublished - Jul 14 2009

ASJC Scopus subject areas

  • Biotechnology
  • Genetics


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