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Self-Designed Effector Prediction Program: Nine algorithms were trained off 
of E. tarda EIB202’s gene products t...
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Genome wide search for type iii secretion system - V. Porter


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Poster presented at the Thompson Rivers University Undergraduate Conference, 2014.

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Genome wide search for type iii secretion system - V. Porter

  1. 1. Methods Self-Designed Effector Prediction Program: Nine algorithms were trained off of E. tarda EIB202’s gene products to make an effector prediction program specific to our pathogen. Six N-terminal lengths and the whole sequence were used to test 15 putative protein attributes and to see which N-terminal length is truly more accurate. We used a set of positive effectors (n=12) and two sets of non-effectors (n=2600+ and n=40) to train algorithm using the program WEKA. Gram-negative bacteria such as the fish pathogen Edwardsiella tarda EIB202 utilize the type III secretion system (T3SS) to secrete virulent effector proteins into the host1. The effector proteins are the most virulent, yet hardest to predict proteins within the secretion system; each individual effector protein exhibits distinct features and are widely distributed through the genome2. This triggered the hypothesis that the effectors are derived through horizontal gene transfer separate to the rest of the T3SS, and then are modified to fit the pathogen’s target host and mechanisms3. Once the effector proteins are secreted, they can go on to perform specific mechanisms within the host that modulate its normal function4. It is speculated that within its genome of 3700+ genes, E. tarda contains approximately 30 T3SS effector proteins, 12 of which Results Discussion and Future Directions References Acknowledgements Putative Effector Features Feature Selection Classify Algorithm Unclassified Trained Model Genes Predicted non-effectors Predicted effectors Prior Knowledge Experimental validation Introduction © 2006 Nature Publishing Group complex: Translocator Effector proteins Translocation pore b c Yersinia in vivo P. aeruginosa in vitro S. typhimurium invJ Yersinia Pseudomonas . Shigella Needle length cont rol Y. enterocolitica S. typhimurium Y. enterocolitica (TABLE 3) (FIG. 6a) Fig 6a Y. enterocolitica yscP (FIG. 6b) (FIG. 6c) a model of the function of the LcrV tip complex. a | No LcrV forms the complex at the tip of the needle. b | Contact with the tip complex assists with the assembly of the translocation assembly platform. c | Anti-LcrV antibodies are protective because formation of the translocation pore59. REVIEWS MICROBIOLOGY 819 have been identified experimentally. Prediction of the remaining 15+ effector proteins using bioinformatics could narrow down the search and significantly reduce the time and labour it takes to experimentally verify effectors. This study was the first steps towards the creation of a new species-specific effector prediction program. Further progress is being done in the feature selection and algorithm selection. Once all these factors are set, the algorithm will be retrained and hopefully result in more accurate prediction scores. Verification of the unknown effectors can later on build a better understanding of the T3SS and also help create a better multi-effector combating vaccine against E. tarda infection in Asian aquaculture. The authors are grateful for the financial contributions from NSERC, Canada (USRA and Discovery Grant) and the Open Funding Project of State Key Lab of Bioreactor Engineering, ECUST from Shanghai, China. Figure 1: The type III secretion injectisome secreting effectors into the host (Cornelis, 2010). 1. Leung, K. Y., Siame, B. A., Tenkink, B. J., Noort, R. J., & Mok, Y. K. (2012). Edwardsiella tarda – Virulence mechanisms of an emerging gastroenteritis pathogen. Microbes and Infection, 14(1), 26-34. 2. McDermott, J. E., Corrigan, A., Peterson, E., Oehmen, C., Niemann, G., Cambronne, E. D., Sharp, D., Adkins. J. N., Samudrala, R., & Heffron, F. (2011). Computational prediction of type III and IV secreted effectors in gram-negative bacteria. Infection and immunity, 79(1), 23-32. 3. Hajri, A., Brin, C., Hunault, G., Lardeux, F., Lemaire, C., Manceau, C., Bouraeu, T., & Poussier, S. (2009). A repertoire for repertoire hypothesis: Repertoires of type three effectors are candidate determinants of host specificity in Xanthomonas. PLoS One, 4(8), e6632. 4. Dean, P. (2011). Functional domains and motifs of bacterial type III effector proteins and their roles in infection. FEMS microbiology reviews, 35(6), 1100-1125. 5. Cornelis, G. R. (2010). The type III secretion injectisome, a complex nanomachine for intracellular toxin delivery. Biological chemistry, 391(7), 745-751. Figure 2: The flow chart model of creating a new effector prediction program. The process begins with adequate feature selection, then algorithm training, and then finally testing the program on unclassified genes to find new effector genes. Attribute Positive Effectors Negative Effectors Average St.Dev. Average St.Dev. Molecular Weight (kDa) 31.2 ±38.2 38.4 ±23.3 G+C Content 60.0 ±10.7 60.8 ±5.7 pI 6.7 ±1.8 7.2 ±1.8 Instability Index 40.5 ±10.7 39.4 ±10.0 A280 Molar Ext. Coef. 0.56 ±0.41 1.03 ±0.57 CAI 0.66 ±0.1 0.69 ±0.08 GRAVY Score (N20) -0.091 ±0.48 -0.023 ±0.42 Small Peptides (N20) 60.0% ±14.4% 46.4% ±11.7% N-terminal Instability 0.53 ±0.20 0.29 ±0.16 Coiled-Coil Regions 0.25 ±0.45 0.07 ±0.25 Alphiatic Index 91.9 ±18.7 97.3 ±16.5 Table 1: Statistical analysis of the significant attributes of effectors compared to non-effectors • The top scoring algorithm was Baysian Network, which had a ROC area under the curve of 0.833 • The data so far was too discrete to decide which length was best • A statistical analysis of the selected attributes was presented in Table 1. Significant attributes were marked in bold.