BackgroundM. catarrhalis is a gram-negative, gamma-proteobacterium and an opportunistic human pathogen associated with otitis media (OM) and exacerbations of chronic obstructive pulmonary disease (COPD). With direct and indirect costs for treating these conditions annually exceeding $33 billion in the United States alone, and nearly ubiquitous resistance to beta-lactam antibiotics among M. catarrhalis clinical isolates, a greater understanding of this pathogen's genome and its variability among isolates is needed.ResultsThe genomic sequences of ten geographically and phenotypically diverse clinical isolates of M. catarrhalis were determined and analyzed together with two publicly available genomes. These twelve genomes were subjected to detailed comparative and predictive analyses aimed at characterizing the supragenome and understanding the metabolic and pathogenic potential of this species. A total of 2383 gene clusters were identified, of which 1755 are core with the remaining 628 clusters unevenly distributed among the twelve isolates. These findings are consistent with the distributed genome hypothesis (DGH), which posits that the species genome possesses a far greater number of genes than any single isolate. Multiple and pair-wise whole genome alignments highlight limited chromosomal re-arrangement.ConclusionsM. catarrhalis gene content and chromosomal organization data, although supportive of the DGH, show modest overall genic diversity. These findings are in stark contrast with the reported heterogeneity of the species as a whole, as wells as to other bacterial pathogens mediating OM and COPD, providing important insight into M. catarrhalis pathogenesis that will aid in the development of novel therapeutic regimens.
Currently, time-consuming serial in vitro experimentation involving immunocytochemistry or radiolabeled materials is required to identify which of the numerous Rab-GTPases (Rab) and Rab-GTPase activating proteins (RabGAP) are capable of functional interactions. These interactions are essential for numerous cellular functions, and in silico methods of reducing in vitro trial and error would accelerate the pace of research in cell biology. We have utilized a combination of three-dimensional protein modeling and protein bioinformatics to identify domains present in Rab proteins that are predictive of their functional interaction with a specific RabGAP. The RabF2 and RabSF1 domains appear to play functional roles in mediating the interaction between Rabs and RabGAPs. Moreover, the RabSF1 domain can be used to make in silico predictions of functional Rab/RabGAP pairs. This method is expected to be a broadly applicable tool for predicting protein–protein interactions where existing crystal structures for homologs of the proteins of interest are available.
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