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Systematic study of subcellular localization of Arabidopsis PPR proteins confirms a massive targeting to organelles.

ABSTRACT: Four hundred and fifty-eight genes coding for PentatricoPeptide Repeat (PPR) proteins are annotated in the Arabidopsis thaliana genome. Over the past 10 years, numerous reports have shown that many of these proteins function in organelles to target specific transcripts and are involved in post-transcriptional regulation. Therefore, they are thought to be important players in the coordination between nuclear and organelle genome expression. Only four of these proteins have been described to be addressed outside organelles, indicating that some PPRs could function in post-transcriptional regulations of nuclear genes. In this work, we updated and improved our current knowledge on the localization of PPR proteins of Arabidopsis within the plant cell. We particularly investigated the subcellular localization of 166 PPR proteins whose targeting predictions were ambiguous, using a combination of high-throughput cloning and microscopy. Through systematic localization experiments and data integration, we confirmed that PPR proteins are largely targeted to organelles and showed that dual targeting to both the mitochondria and plastid occurs more frequently than expected. These results allow us to speculate that dual-targeted PPR proteins could be important for the fine coordination of gene expressions in both organelles.

SUBMITTER: Colcombet J

PROVIDER: S-EPMC3858439 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Systematic study of subcellular localization of Arabidopsis PPR proteins confirms a massive targeting to organelles.

Colcombet Jean J Lopez-Obando Mauricio M Heurtevin Laure L Bernard Clément C Martin Karine K Berthomé Richard R Lurin Claire C

RNA biology 20130815 9

Four hundred and fifty-eight genes coding for PentatricoPeptide Repeat (PPR) proteins are annotated in the Arabidopsis thaliana genome. Over the past 10 years, numerous reports have shown that many of these proteins function in organelles to target specific transcripts and are involved in post-transcriptional regulation. Therefore, they are thought to be important players in the coordination between nuclear and organelle genome expression. Only four of these proteins have been described to be ad ...[more]

PMID: 24037373

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Project description:Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mitochondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.

Project description:BackgroundThe computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins.ResultsA final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out.ConclusionBoth subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model.

Dataset Information

Systematic study of subcellular localization of Arabidopsis PPR proteins confirms a massive targeting to organelles.

Publications

Systematic study of subcellular localization of Arabidopsis PPR proteins confirms a massive targeting to organelles.

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