Skip to main content
ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Emerging Pests and Pathogens Research » Research » Publications at this Location » Publication #282957
Title: Functional and computational analysis of amino acid patterns predictive of type III secretion system substrates in Pseudomonas syringae

Author
item SCHECHTER, LISA - University Of Missouri
item VALENTA, JOY - University Of Missouri
item Schneider, David
item COLLMER, ALAN - Cornell University
item SAKK, ERIC - Morgan State University

Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/29/2012
Publication Date: 4/27/2012
Citation: Schechter, L.M., Valenta, J.C., Schneider, D.J., Collmer, A., Sakk, E. 2012. Functional and computational analysis of amino acid patterns predictive of type III secretion system substrates in Pseudomonas syringae. PLoS One. 7(4):1-13.

Interpretive Summary: Many species of pathogenic bacteria carry a highly specialized molecular structure known as a "type III secretion system" -- a molecular syringe capable of delivering bacterial proteins directly into host cells. The proteins delivered into the host cell, often referred to as effectors, are primary virulence factors in the sense that they interfere and disrupt the normal function of host cells. Bacteria often encode 30-50 effectors. Unfortunately, it is often very difficult to identify effectors directly. In this paper we describe a new computational tool for predicting which bacterial proteins are likely to function as effectors in plant pathogenic bacteria. Also, we present evidence that the sorting and eventual secretion of effectors is a complicated process involving the recognition of a localized "tag" as well as structural features in other parts of the protein.

Technical Abstract: Bacterial type III secretion systems (T3SSs) deliver proteins called effectors into eukaryotic cells. Although N-terminal amino acid sequences are required for translocation, the mechanism of substrate recognition by the T3SS is unknown. Almost all actively deployed T3SS substrates in the plant pathogen Pseudomonas syringae pathovar tomato strain DC3000 possess characteristic patterns, including (i) greater than 10% serine within the first 50 amino acids, (ii) an aliphatic residue or proline at position 3 or 4, and (iii) a lack of acidic amino acids within the first 12 residues. Here, the functional significance of the P. syringae T3SS substrate compositional patterns was tested. A mutant AvrPto effector protein lacking all three patterns was secreted into culture and translocated into plant cells, suggesting that the compositional characteristics are not absolutely required for T3SS targeting and that other recognition mechanisms exist. To further analyze the unique properties of T3SS targeting signals, we developed a computational algorithm called TEREE (Type III Effector Relative Entropy Evaluation) that distinguishes DC3000 T3SS substrates from other proteins with a high sensitivity and specificity. Although TEREE did not efficiently identify T3SS substrates in Salmonella enterica, it was effective in another P. syringae strain and Ralstonia solanacearum. Thus, the TEREE algorithm may be a useful tool for identifying new effector genes in plant pathogens. The nature of T3SS targeting signals was additionally investigated by analyzing the N-terminus of FtsX, a putative membrane protein that was classified as a T3SS substrate by TEREE. Although the first 50 amino acids of FtsX were unable to target a reporter protein to the T3SS, an AvrPto protein substituted with the first 12 amino acids of FtsX was translocated into plant cells. These results show that the T3SS targeting signals are highly mutable and that secretion may be directed by multiple features of substrates.