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After the deluge: mining Staphylococcus aureus genomic data for clinical associations and host-pathogen interactions.

ABSTRACT: The genome of Staphylococcus aureus has rapidly become one the most frequently sequenced among bacteria, with more than 40000 genome sequences uploaded to public databases. Computational resources required for analysis and quality assessment have lagged behind accumulation of sequence data. Improved analytic pipelines, in combination with the development of customized S. aureus reference databases, can be used to inform S. aureus biology and potentially predict clinical outcome. Here, we review the currently available data about S. aureus genome in public databases, and discuss their potential utility for understanding S. aureus evolution. Also discussed are ways to overcome challenges to the application of whole-genome sequencing data for prevention and management of S. aureus disease.

SUBMITTER: Copin R

PROVIDER: S-EPMC5862737 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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After the deluge: mining Staphylococcus aureus genomic data for clinical associations and host-pathogen interactions.

Copin Richard R Shopsin Bo B Torres Victor J VJ

Current opinion in microbiology 20171219

The genome of Staphylococcus aureus has rapidly become one the most frequently sequenced among bacteria, with more than 40000 genome sequences uploaded to public databases. Computational resources required for analysis and quality assessment have lagged behind accumulation of sequence data. Improved analytic pipelines, in combination with the development of customized S. aureus reference databases, can be used to inform S. aureus biology and potentially predict clinical outcome. Here, we review ...[more]

PMID: 29197673

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Project description:Burkholderia pathogenicity relies on protein virulence factors to control and promote bacterial internalization, survival, and replication within eukaryotic host cells. We recently used yeast two-hybrid (Y2H) screening to identify a small set of novel Burkholderia proteins that were shown to attenuate disease progression in an aerosol infection animal model using the virulent Burkholderia mallei ATCC 23344 strain. Here, we performed an extended analysis of primarily nine B. mallei virulence factors and their interactions with human proteins to map out how the bacteria can influence and alter host processes and pathways. Specifically, we employed topological analyses to assess the connectivity patterns of targeted host proteins, identify modules of pathogen-interacting host proteins linked to processes promoting infectivity, and evaluate the effect of crosstalk among the identified host protein modules. Overall, our analysis showed that the targeted host proteins generally had a large number of interacting partners and interacted with other host proteins that were also targeted by B. mallei proteins. We also introduced a novel Host-Pathogen Interaction Alignment (HPIA) algorithm and used it to explore similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica. We inferred putative roles of B. mallei proteins based on the roles of their aligned Y. pestis and S. enterica partners and showed that up to 73% of the predicted roles matched existing annotations. A key insight into Burkholderia pathogenicity derived from these analyses of Y2H host-pathogen interactions is the identification of eukaryotic-specific targeted cellular mechanisms, including the ubiquitination degradation system and the use of the focal adhesion pathway as a fulcrum for transmitting mechanical forces and regulatory signals. This provides the mechanisms to modulate and adapt the host-cell environment for the successful establishment of host infections and intracellular spread.

Dataset Information

After the deluge: mining Staphylococcus aureus genomic data for clinical associations and host-pathogen interactions.

Publications

After the deluge: mining Staphylococcus aureus genomic data for clinical associations and host-pathogen interactions.

Similar Datasets

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