Tags

Type your tag names separated by a space and hit enter

PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting.
PLoS Comput Biol. 2019 07; 15(7):e1007206.PC

Abstract

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.

Authors+Show Affiliations

Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.Department of Plant Biotechnology, Leibniz Universität Hannover, Hannover, Germany.Department of Plant Biotechnology, Leibniz Universität Hannover, Hannover, Germany.Department of Plant Biotechnology, Leibniz Universität Hannover, Hannover, Germany.Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

31295249

Citation

Erkes, Annett, et al. "PrediTALE: a Novel Model Learned From Quantitative Data Allows for New Perspectives On TALE Targeting." PLoS Computational Biology, vol. 15, no. 7, 2019, pp. e1007206.
Erkes A, Mücke S, Reschke M, et al. PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. PLoS Comput Biol. 2019;15(7):e1007206.
Erkes, A., Mücke, S., Reschke, M., Boch, J., & Grau, J. (2019). PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. PLoS Computational Biology, 15(7), e1007206. https://doi.org/10.1371/journal.pcbi.1007206
Erkes A, et al. PrediTALE: a Novel Model Learned From Quantitative Data Allows for New Perspectives On TALE Targeting. PLoS Comput Biol. 2019;15(7):e1007206. PubMed PMID: 31295249.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. AU - Erkes,Annett, AU - Mücke,Stefanie, AU - Reschke,Maik, AU - Boch,Jens, AU - Grau,Jan, Y1 - 2019/07/11/ PY - 2019/01/26/received PY - 2019/06/20/accepted PY - 2019/07/23/revised PY - 2019/7/12/pubmed PY - 2019/12/20/medline PY - 2019/7/12/entrez SP - e1007206 EP - e1007206 JF - PLoS computational biology JO - PLoS Comput Biol VL - 15 IS - 7 N2 - 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. SN - 1553-7358 UR - https://www.unboundmedicine.com/medline/citation/31295249/PrediTALE:_A_novel_model_learned_from_quantitative_data_allows_for_new_perspectives_on_TALE_targeting_ DB - PRIME DP - Unbound Medicine ER -