PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting.
Plant-pathogenic Xanthomonas bacteria secrete transcription activator-like effectors (TALEs) into host cells, where they act as transcriptional activators on plant target genes to support bacterial virulence. TALEs have a unique modular DNA-binding domain composed of tandem repeats. Two amino acids...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2019-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007206 |
| _version_ | 1797351510571483136 |
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| author | Annett Erkes Stefanie Mücke Maik Reschke Jens Boch Jan Grau |
| author_facet | Annett Erkes Stefanie Mücke Maik Reschke Jens Boch Jan Grau |
| author_sort | Annett Erkes |
| collection | DOAJ |
| description | Plant-pathogenic Xanthomonas bacteria secrete transcription activator-like effectors (TALEs) into host cells, where they act as transcriptional activators on plant target genes to support bacterial virulence. TALEs have a unique modular DNA-binding domain composed of tandem repeats. Two amino acids within each tandem repeat, termed repeat-variable diresidues, bind to contiguous nucleotides on the DNA sequence and determine target specificity. In this paper, we propose a novel approach for TALE target prediction to identify potential virulence targets. Our approach accounts for recent findings concerning TALE targeting, including frame-shift binding by repeats of aberrant lengths, and the flexible strand orientation of target boxes relative to the transcription start of the downstream target gene. The computational model can account for dependencies between adjacent RVD positions. Model parameters are learned from the wealth of quantitative data that have been generated over the last years. We benchmark the novel approach, termed PrediTALE, using RNA-seq data after Xanthomonas infection in rice, and find an overall improvement of prediction performance compared with previous approaches. Using PrediTALE, we are able to predict several novel putative virulence targets. However, we also observe that no target genes are predicted by any prediction tool for several TALEs, which we term orphan TALEs for this reason. We postulate that one explanation for orphan TALEs are incomplete gene annotations and, hence, propose to replace promoterome-wide by genome-wide scans for target boxes. We demonstrate that known targets from promoterome-wide scans may be recovered by genome-wide scans, whereas the latter, combined with RNA-seq data, are able to detect putative targets independent of existing gene annotations. |
| first_indexed | 2024-03-08T13:01:30Z |
| format | Article |
| id | doaj.art-b38f4973729743ada5c52e693cb8cd0d |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-08T13:01:30Z |
| publishDate | 2019-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-b38f4973729743ada5c52e693cb8cd0d2024-01-19T05:36:43ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-07-01157e100720610.1371/journal.pcbi.1007206PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting.Annett ErkesStefanie MückeMaik ReschkeJens BochJan GrauPlant-pathogenic Xanthomonas bacteria secrete transcription activator-like effectors (TALEs) into host cells, where they act as transcriptional activators on plant target genes to support bacterial virulence. TALEs have a unique modular DNA-binding domain composed of tandem repeats. Two amino acids within each tandem repeat, termed repeat-variable diresidues, bind to contiguous nucleotides on the DNA sequence and determine target specificity. In this paper, we propose a novel approach for TALE target prediction to identify potential virulence targets. Our approach accounts for recent findings concerning TALE targeting, including frame-shift binding by repeats of aberrant lengths, and the flexible strand orientation of target boxes relative to the transcription start of the downstream target gene. The computational model can account for dependencies between adjacent RVD positions. Model parameters are learned from the wealth of quantitative data that have been generated over the last years. We benchmark the novel approach, termed PrediTALE, using RNA-seq data after Xanthomonas infection in rice, and find an overall improvement of prediction performance compared with previous approaches. Using PrediTALE, we are able to predict several novel putative virulence targets. However, we also observe that no target genes are predicted by any prediction tool for several TALEs, which we term orphan TALEs for this reason. We postulate that one explanation for orphan TALEs are incomplete gene annotations and, hence, propose to replace promoterome-wide by genome-wide scans for target boxes. We demonstrate that known targets from promoterome-wide scans may be recovered by genome-wide scans, whereas the latter, combined with RNA-seq data, are able to detect putative targets independent of existing gene annotations.https://doi.org/10.1371/journal.pcbi.1007206 |
| spellingShingle | Annett Erkes Stefanie Mücke Maik Reschke Jens Boch Jan Grau PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. PLoS Computational Biology |
| title | PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. |
| title_full | PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. |
| title_fullStr | PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. |
| title_full_unstemmed | PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. |
| title_short | PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. |
| title_sort | preditale a novel model learned from quantitative data allows for new perspectives on tale targeting |
| url | https://doi.org/10.1371/journal.pcbi.1007206 |
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