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Background Actinobacillus suis disease has been reported in a wide range of vertebrate species, but is most commonly found in swine. A. suis is a commensal of the tonsils of the soft palate of swine, but in the presence of unknown stimuli it can invade the bloodstream, causing septicaemia and sequelae such as meningitis, arthritis, and death. It is genotypically and phenotypically similar to A. pleuropneumoniae, the causative agent of pleuropneumonia, and to other members of the family Pasteurellaceae that colonise tonsils. At present, very little is known about the genes involved in attachment, colonisation, and invasion by A. suis (or related members of the tonsil microbiota). Results Bioinformatic analyses of the A. suis H91-0380 genome were done using BASys and blastx in GenBank. Forty-seven putative adhesin-associated genes predicted to encode 24 putative adhesins were discovered. Among these are 6 autotransporters, 25 fimbriae-associated genes (encoding 3 adhesins), 12 outer membrane proteins, and 4 additional genes (encoding 3 adhesins). With the exception of 2 autotransporter-encoding genes (aidA and ycgV), both with described roles in virulence in other species, all of the putative adhesin-associated genes had homologues in A. pleuropneumoniae. However, the majority of the closest homologues of the A. suis adhesins are found in A. ureae and A. capsulatus—species not known to infect swine, but both of which can cause systemic infections. Conclusions A. suis and A. pleuropneumoniae share many of the same putative adhesins, suggesting that the different diseases, tissue tropism, and host range of these pathogens are due to subtle genetic differences, or perhaps differential expression of virulence factors during infection. However, many of the putative adhesins of A. suis share even greater homology with those of other pathogens within the family Pasteurellaceae. Similar to A. suis, these pathogens (A. capsulatus and A. ureae) cause systemic infections and it is tempting to speculate that they employ similar strategies to invade the host, but more work is needed before that assertion can be made. This work begins to examine adhesin-associated factors that allow some members of the family Pasteurellaceae to invade the bloodstream while others cause a more localised infection. Electronic supplementary material The online version of this article (doi:10.1186/s13104-015-1659-x) contains supplementary material, which is available to authorized users.

Background Quantitative real-time PCR is a valuable tool for evaluating bacterial gene expression. However, in order to make best use of this method, endogenous reference genes for expression data normalisation must first be identified by carefully validating the stability of expression under experimental conditions. Therefore, the objective of this study was to validate eight reference genes of the opportunistic swine pathogen, Actinobacillus suis, grown in aerobic cultures with (Epinephrine) or without (Aerobic) epinephrine in the growth medium and in anoxic static cultures (Anoxic), and sampled during exponential and stationary phases. Results Using the RefFinder tool, expression data were analysed to determine whether comprehensive stability rankings of selected reference genes varied with experimental design. When comparing Aerobic and Epinephrine cultures by growth phase, pyk and rpoB were both among the most stably expressed genes, but when analysing both growth phases together, only pyk remained in the top three rankings. When comparing Aerobic and Anoxic samples, proS ranked among the most stable genes in exponential and stationary phase data sets as well as in combined rankings. When analysing the Aerobic, Epinephrine, and Anoxic samples together, only gyrA ranked consistently among the top three most stably expressed genes during exponential and stationary growth as well as in combined rankings; the rho gene ranked as least stably expressed gene in this data set. Conclusions Reference gene stability should be carefully assessed with the design of the experiment in mind. In this study, even the commonly used reference gene 16S rRNA demonstrated large variability in stability depending on the conditions studied and how the data were analysed. As previously suggested, the best approach may be to use a geometric mean of multiple genes to normalise qPCR results. As researchers continue to validate reference genes for various organisms in multiple growth conditions and sampling time points, it may be possible to make informed predictions as to which genes may be most suitable to validate for a given experimental design, but in the meantime, the reference genes used to normalise qPCR data should be selected with caution.