Abstract
The type III secretion system (TTSS) is a key mechanism for host cell interaction used by a variety of bacterial pathogens and symbionts of plants and animals including humans. The TTSS represents a molecular syringe with which the bacteria deliver effector proteins directly into the host cell cytosol. Despite the importance of the TTSS for bacterial pathogenesis, recognition and targeting of type III secreted proteins has up until now been poorly understood. Several hypotheses are discussed, including an mRNA-based signal, a chaperon-mediated process, or an N-terminal signal peptide. In this study, we systematically analyzed the amino acid composition and secondary structure of N-termini of 100 experimentally verified effector proteins. Based on this, we developed a machine-learning approach for the prediction of TTSS effector proteins, taking into account N-terminal sequence features such as frequencies of amino acids, short peptides, or residues with certain physico-chemical properties. The resulting computational model revealed a strong type III secretion signal in the N-terminus that can be used to detect effectors with sensitivity of ∼71% and selectivity of ∼85%. This signal seems to be taxonomically universal and conserved among animal pathogens and plant symbionts, since we could successfully detect effector proteins if the respective group was excluded from training. The application of our prediction approach to 739 complete bacterial and archaeal genome sequences resulted in the identification of between 0% and 12% putative TTSS effector proteins. Comparison of effector proteins with orthologs that are not secreted by the TTSS showed no clear pattern of signal acquisition by fusion, suggesting convergent evolutionary processes shaping the type III secretion signal. The newly developed program EffectiveT3 (http://www.chlamydiaedb.org) is the first universal in silico prediction program for the identification of novel TTSS effectors. Our findings will facilitate further studies on and improve our understanding of type III secretion and its role in pathogen–host interactions.
Highlights
Many Gram-negative bacteria with symbiotic or parasitic lifestyles modulate their environment, the eukaryotic host cell, by the secretion of bacterial proteins into the host cell through the type III secretion system (TTSS) [1]
The majority of positions, have no predictive power due to Area Under the Curve’’ (AUC) values between 0.4–0.6, and using the 15 C-terminal residues resulted in an AUC value comparable to a random prediction (Table S7). These findings show the existence of a common signal encoded in the N-termini of TTSS effector proteins and are in agreement with the N-terminal signal peptide theory [23,24]
In this study we describe the identification of taxonomically universal features of TTSS effector proteins, which formed the basis of the development of the program EffectiveT3, the first universally applicable in silico prediction method for TTSS transported proteins
Summary
Many Gram-negative bacteria with symbiotic or parasitic lifestyles modulate their environment, the eukaryotic host cell, by the secretion of bacterial proteins into the host cell through the type III secretion system (TTSS) [1]. Experimental identification of novel effectors relies on translocation assays using fusion proteins of a putative effector with a reporter gene [11,12,13,14] or detection of effectors in the culture supernatant [11]. An unusual amino acid composition in the N-termini of effectors has been identified as a characteristic of effector proteins and used for their identification [16,17,18] In all these approaches, the computational analysis successfully limited the amount of candidates which had to be included in experimental analyses in order to find novel effectors.
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