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.