Title |
New players in the same old game: a system level in silico study to predict type III secretion system and effector proteins in bacterial genomes reveals common themes in T3SS mediated pathogenesis
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Published in |
BMC Research Notes, July 2013
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DOI | 10.1186/1756-0500-6-297 |
Pubmed ID | |
Authors |
Vineet Sadarangani, Sunando Datta, Manonmani Arunachalam |
Abstract |
Type III secretion system (T3SS) plays an important role in virulence or symbiosis of many pathogenic or symbiotic bacteria [CHM 2:291-294, 2007; Physiology (Bethesda) 20:326-339, 2005]. T3SS acts like a tunnel between a bacterium and its host through which the bacterium injects 'effector' proteins into the latter [Nature 444:567-573, 2006; COSB 18:258-266, 2008]. The effectors spatially and temporally modify the host signalling pathways [FEMS Microbiol Rev 35:1100-1125, 2011; Cell Host Microbe5:571-579, 2009]. In spite its crucial role in host-pathogen interaction, the study of T3SS and the associated effectors has been limited to a few bacteria [Cell Microbiol 13:1858-1869, 2011; Nat Rev Microbiol 6:11-16, 2008; Mol Microbiol 80:1420-1438, 2011]. Before one set out to perform systematic experimental studies on an unknown set of bacteria it would be beneficial to identify the potential candidates by developing an in silico screening algorithm. A system level study would also be advantageous over traditional laboratory methods to extract an overriding theme for host-pathogen interaction, if any, from the vast resources of data generated by sequencing multiple bacterial genomes. |
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Turkey | 1 | 3% |
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Unknown | 27 | 82% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 10 | 30% |
Student > Master | 7 | 21% |
Researcher | 7 | 21% |
Professor > Associate Professor | 3 | 9% |
Student > Doctoral Student | 2 | 6% |
Other | 4 | 12% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 7 | 21% |
Computer Science | 3 | 9% |
Immunology and Microbiology | 1 | 3% |
Medicine and Dentistry | 1 | 3% |
Other | 0 | 0% |
Unknown | 2 | 6% |