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

Abstract

Background: 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.

Results: We have developed an in silico protocol in which the most conserved set of T3SS proteins was used as the query against the entire bacterial database with increasingly stringent search parameters. It enabled us to identify several uncharacterized T3SS positive bacteria. We adopted a similar strategy to predict the presence of the already known effectors in the newly identified T3SS positive bacteria. The huge resources of biochemical data [FEMS Microbiol Rev 35:1100-1125, 2011; Cell Host Microbe 5:571-579, 2009; BMC Bioinformatics 7(11):S4, 2010] on the T3SS effectors enabled us to search for the common theme in T3SS mediated pathogenesis. We identified few cellular signalling networks in the host, which are manipulated by most of the T3SS containing pathogens. We went on to look for correlation, if any, between the biological quirks of a particular class of bacteria with the effectors they harbour. We could pin point few effectors, which were enriched in certain classes of bacteria.

Conclusion: The current study would open up new avenues to explore many uncharacterized T3SS positive bacteria. The experimental validation of the predictions from this study will unravel a generalized mechanism for T3SS positive bacterial infection into host cell.

Figure 1

Flow chart diagram of the methodology used for identification of T3SS positive bacteria and their effectors. 1a: Prediction of T3SS. 1b: Prediction of their effectors.

Figure 2

Frequency of occurrences of individual T3SS proteins in bacterial genome. The homologues of each of the T3SS proteins have been predicted in bacterial genomes at different ‘e’ values. The numbers are plotted as a bar diagram. The colour codes used for different ‘e’ values are shown in the figure. ‘e’ values of 0.1, 0.01, 0.001, 0.0001, 0.00001 are shown in dark blue, red, green, magenta and light blue respectively.

Figure 3

Prediction of T3SS positive bacteria at different ‘e’ values. The number of bacteria which were predicted to harbour ‘All’: all 10 T3SS proteins, ‘F’: all but YscF, ‘Q’: all but YscQ and ‘F & Q’: all but YscF and YscQ are plotted as a bar diagram. The colour codes used for different ‘e’ values are shown in the figure. ‘e’ values of 0.1, 0.01, 0.001, 0.0001, 0.00001 are shown in dark blue, red, green, magenta and light blue respectively.