BMC Genomics 2009, 10(Suppl 2):S4 http://www.biomedcentral.com/1471-2164/10/S2/S4 Background Pasteurella multocida is a gram–negative bacterial pathogen that has a unique history of serving as a model species for new discoveries. In his seminal experiments in the late eighteenth century, Louis Pasteur demonstrated both attenuation as well as protective immune response utiliz- ing P. multocida in birds [1,2]. P. multocida is also an excel- lent model species for studying the effects of antibiotics on gram-negative bacteria because its short lipopolysac- charide O side chains make it more permeable, allowing investigations on the effects of antibiotics on bacterial metabolism [3]. Of the different P. multocida subspecies, multocida has a broad host range and causes diseases in poultry, cattle, pigs, and rabbits [4]. Zoonosis with P. multocida is caused by cat and dog bites and scratches and respiratory infec- tion [5]. Fowl cholera (avian pasteurellosis) caused by P. multocida can be either chronic or acute and is a septicemic disease that causes significant economic loss to the poul- try industry. Due to the importance of this pathogen to the avian community, the genome of an isolate recovered from chicken fowl cholera (strain Pm70) was sequenced in 2001 [6]. The genome sequence has enabled subse- quent functional genomics research with this species [7- 9]. It has also enabled investigations on the proteomic and transcriptomic response of P. multocida Pm70 to sub- MICs of antibiotics [10-12]. Much attention has been focused on characterization of specific antibiotic resistance mechanisms; however, recent studies are shedding light on non-target effects of antibi- otics. In the current study, we used P. multocida as a model species to investigate the effects of three antibiotics with disparate mechanisms of action (amoxicillin, chlortetra- cycline, and enrofloxacin) on bacterial metabolism. Because biological systems utilize highly complex, interre- lated metabolic and signaling networks, a thorough understanding of bacterial physiology requires studying the global response to a given stimulus in the context of interacting gene networks. Computational systems biol- ogy facilitates this level of analysis. Systems level analysis can identify key regulatory elements of a molecular inter- action network; dynamic changes in these elements gov- ern a response [13]. Application of systems approaches to P. multocida is still in the initial stages, but we have started using these meth- ods to characterize the P. multocida response to antibiotics at the proteome level [11]. Here we describe the use of sys- tems analysis at the transcriptome level to investigate the P. multocida response to sub-MICs of antibiotics. Results and discussion Differentially expressed genes Statistical analysis of microarray data revealed that 1/4 MIC of AMX, CTC, and ENR resulted in significant changes in expression of approximately 25% of the genome (525 genes, Additional file 1, supplemental table 1). The differences in gene expression that were deter- mined to be statistically significant ranged from as small as a 5% decrease (relative to control) to as high as an 11.5- fold increase in expression (recN, Table 1). Earlier micro- array studies with P. multocida arbitrarily chose a 1.5-fold significant change in gene expression as differentially reg- ulated genes. Instead of choosing a pre-determined threshold for determining the biological relevance of changes in gene expression, we considered every signifi- cant change in expression to be a valid change based on our rigorous statistical testing. We validated changes as small as a 15% decrease or increase (for example, expres- sion of murA in response to AMX and ENR, respectively) (Additional file 1, supplemental table 1) by qPCR. Figure 1 shows the correlation between the trends observed in microarray and qPCR for the subset of genes with qPCR validations. Compared to the untreated control, expression of 413, 392, and 473 genes had significantly altered expression in response to AMX, CTC, and ENR, respectively, and the overlap between treatments is shown in the Venn diagram (Figure 2). The extensive overlap of regulated genes with the three different antibiotics is consistent with the recent transcriptome analysis of ampicillin and ofloxacin effects in E. coli, which showed that these two unrelated antibi- otics had a significant overlap of regulated genes [14]. Functional analysis of DE genes: COGs The three antibiotics used in this study have distinct modes of action; AMX is a cell wall biosynthesis inhibitor, CTC is a protein synthesis inhibitor, and ENR inhibits DNA gyrase and DNA topoisomerase IV activities [15]. At the COG category level, the three antibiotics had different effects on gene expression (Figure 3). The AMX and CTC treatments resulted in an overall decrease in gene expres- sion in all COG categories. The overall suppression of gene expression could indicate that the antibiotics have a marked detrimental effect on the fitness of P. multocida at doses below MIC, or it is also possible that the overall transcriptional shutdown is a compensatory response by slowing metabolism. ENR had varying effects on gene expression in different COG categories. In particular, ENR caused a pronounced increase in expression of genes in categories I (lipid metabolism), J (translation, ribosomal structure and biogenesis), and L (DNA replication, recom- bination and repair). All three antibiotics had reduced expression of genes in COGs V (defense mechanisms), G (carbohydrate transport and metabolism), P (inorganic Page 2 of 10 (page number not for citation purposes)