Browsing publications of working group Integrative Informatics for Infection Biology ([HIRI]IIIB) by Submit Date
Now showing items 1-3 of 3
A global genomic approach uncovers novel components for twitching motility-mediated biofilm expansion in Pseudomonas aeruginosa.(Microbiology Society, 2018-11-01)Pseudomonas aeruginosa is an extremely successful pathogen able to cause both acute and chronic infections in a range of hosts, utilizing a diverse arsenal of cell-associated and secreted virulence factors. A major cell-associated virulence factor, the Type IV pilus (T4P), is required for epithelial cell adherence and mediates a form of surface translocation termed twitching motility, which is necessary to establish a mature biofilm and actively expand these biofilms. P. aeruginosa twitching motility-mediated biofilm expansion is a coordinated, multicellular behaviour, allowing cells to rapidly colonize surfaces, including implanted medical devices. Although at least 44 proteins are known to be involved in the biogenesis, assembly and regulation of the T4P, with additional regulatory components and pathways implicated, it is unclear how these components and pathways interact to control these processes. In the current study, we used a global genomics-based random-mutagenesis technique, transposon directed insertion-site sequencing (TraDIS), coupled with a physical segregation approach, to identify all genes implicated in twitching motility-mediated biofilm expansion in P. aeruginosa. Our approach allowed identification of both known and novel genes, providing new insight into the complex molecular network that regulates this process in P. aeruginosa. Additionally, our data suggest that the flagellum-associated gene products have a differential effect on twitching motility, based on whether components are intra- or extracellular. Overall the success of our TraDIS approach supports the use of this global genomic technique for investigating virulence genes in bacterial pathogens.
Morphological, genomic and transcriptomic responses of Klebsiella pneumoniae to the last-line antibiotic colistin.(2018-06-29)Colistin remains one of the few antibiotics effective against multi-drug resistant (MDR) hospital pathogens, such as Klebsiella pneumoniae. Yet resistance to this last-line drug is rapidly increasing. Characterized mechanisms of col
Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica.(2018-01-01)Emerging pathogens are a major threat to public health, however understanding how pathogens adapt to new niches remains a challenge. New methods are urgently required to provide functional insights into pathogens from the massive genomic data sets now being generated from routine pathogen surveillance for epidemiological purposes. Here, we measure the burden of atypical mutations in protein coding genes across independently evolved Salmonella enterica lineages, and use these as input to train a random forest classifier to identify strains associated with extraintestinal disease. Members of the species fall along a continuum, from pathovars which cause gastrointestinal infection and low mortality, associated with a broad host-range, to those that cause invasive infection and high mortality, associated with a narrowed host range. Our random forest classifier learned to perfectly discriminate long-established gastrointestinal and invasive serovars of Salmonella. Additionally, it was able to discriminate recently emerged Salmonella Enteritidis and Typhimurium lineages associated with invasive disease in immunocompromised populations in sub-Saharan Africa, and within-host adaptation to invasive infection. We dissect the architecture of the model to identify the genes that were most informative of phenotype, revealing a common theme of degradation of metabolic pathways in extraintestinal lineages. This approach accurately identifies patterns of gene degradation and diversifying selection specific to invasive serovars that have been captured by more labour-intensive investigations, but can be readily scaled to larger analyses.