Dataset Information

Host FIH-Mediated Asparaginyl Hydroxylation of Translocated Legionella pneumophila Effectors.

ABSTRACT: FIH-mediated post-translational modification through asparaginyl hydroxylation of eukaryotic proteins impacts regulation of protein-protein interaction. We have identified the FIH recognition motif in 11 Legionella pneumophila translocated effectors, YopM of Yersinia, IpaH4.5 of Shigella and an ankyrin protein of Rickettsia. Mass spectrometry analyses of the AnkB and AnkH effectors of L. pneumophila confirm their asparaginyl hydroxylation. Consistent with localization of the AnkB effector to the Legionella-containing vacuole (LCV) membrane and its modification by FIH, our data show that FIH and its two interacting proteins, Mint3 and MT1-MMP are acquired by the LCV in a Dot/Icm type IV secretion-dependent manner. Chemical inhibition or RNAi-mediated knockdown of FIH promotes LCV-lysosomes fusion, diminishes decoration of the LCV with polyubiquitinated proteins, and abolishes intra-vacuolar replication of L. pneumophila. These data show acquisition of the host FIH by a pathogen-containing vacuole and that asparaginyl-hydroxylation of translocated effectors is indispensable for their function.

SUBMITTER: Price C

PROVIDER: S-EPMC5337513 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Host FIH-Mediated Asparaginyl Hydroxylation of Translocated Legionella pneumophila Effectors.

Price Christopher C Merchant Michael M Jones Snake S Best Ashley A Von Dwingelo Juanita J Lawrenz Matthew B MB Alam Nawsad N Schueler-Furman Ora O Kwaik Yousef A YA

Frontiers in cellular and infection microbiology 20170306

FIH-mediated post-translational modification through asparaginyl hydroxylation of eukaryotic proteins impacts regulation of protein-protein interaction. We have identified the FIH recognition motif in 11 Legionella pneumophila translocated effectors, YopM of Yersinia, IpaH4.5 of Shigella and an ankyrin protein of Rickettsia. Mass spectrometry analyses of the AnkB and AnkH effectors of L. pneumophila confirm their asparaginyl hydroxylation. Consistent with local ...[more]

PMID: 28321389

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Project description:A large number of highly pathogenic bacteria utilize secretion systems to translocate effector proteins into host cells. Using these effectors, the bacteria subvert host cell processes during infection. Legionella pneumophila translocates effectors via the Icm/Dot type-IV secretion system and to date, approximately 100 effectors have been identified by various experimental and computational techniques. Effector identification is a critical first step towards the understanding of the pathogenesis system in L. pneumophila as well as in other bacterial pathogens. Here, we formulate the task of effector identification as a classification problem: each L. pneumophila open reading frame (ORF) was classified as either effector or not. We computationally defined a set of features that best distinguish effectors from non-effectors. These features cover a wide range of characteristics including taxonomical dispersion, regulatory data, genomic organization, similarity to eukaryotic proteomes and more. Machine learning algorithms utilizing these features were then applied to classify all the ORFs within the L. pneumophila genome. Using this approach we were able to predict and experimentally validate 40 new effectors, reaching a success rate of above 90%. Increasing the number of validated effectors to around 140, we were able to gain novel insights into their characteristics. Effectors were found to have low G+C content, supporting the hypothesis that a large number of effectors originate via horizontal gene transfer, probably from their protozoan host. In addition, effectors were found to cluster in specific genomic regions. Finally, we were able to provide a novel description of the C-terminal translocation signal required for effector translocation by the Icm/Dot secretion system. To conclude, we have discovered 40 novel L. pneumophila effectors, predicted over a hundred additional highly probable effectors, and shown the applicability of machine learning algorithms for the identification and characterization of bacterial pathogenesis determinants.

Dataset Information

Host FIH-Mediated Asparaginyl Hydroxylation of Translocated Legionella pneumophila Effectors.

Publications

Host FIH-Mediated Asparaginyl Hydroxylation of Translocated <i>Legionella pneumophila</i> Effectors.

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