Streptococcus dysgalactiae: interactions with host cells and characterizations of GapC
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To determine if Streptococcus dysgalactiae GapC plays a role in the pathogenesis of S. dysgalactiae, an attempt was made to inactivate gapC gene expression by insertion of a Ω Kmʳ cassette, resulting in a single-crossover recombination event. However, attempts to obtain a double-crossover recombination were not successful. The failure to obtain double-crossover mutants together with the result of this study showing that there is only a single copy of gapC in S. dysgalactiae, and the impact of iodoacetate, a specific GAPDH inhibitor on viability of bacteria, suggests that gapC is an essential gene required for basic metabolism. The role of GapC in the S. dysgalactiae adherence/penetration on/into MAC-T cells (a bovine mammary epithelial derived cell line) was investigated with a GapC specific polyclonal antibody. The addition of a molar excess antibody (10 µg/mL) did not directly inhibit bacterial adherence/penetration on/into MAC-T cells. This suggested that GapC, presented on the bacterial surface, probably does not directly participate in adherence/penetration during the pathogenesis. The effect of S. dysgalactiae on MAC-T cell gene expression was also investigated using bovine microarrays. The data was analyzed by B-statistic and three genes that showed the highest possibility of differential expression were (i) succinate dehydrogenase complex, subunit A, flavoprotein (accession number BF0460 15), (ii) myosin, light polypeptide kinase (accession number AW465934) and (iii) an open reading frame (ORF) (accession number AW463818), respectively. None of the selected genes showed differential expression between infected and non-infected MACT cells when tested by Northern blot analysis. It is possible that the conditions used did not mimic the pathogenesis of mastitis in vivo since some of the genes we had expected to detect (i.e. inflammation-associated genes) were not on the list of 25 genes with the highest possibility to show differential expression. It should also be noted that the data analysis performed might not be accurate due to the limited number of microarray slides used.