PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Annett Erkes
  • Stefanie Mücke
  • Maik Reschke
  • Jens Boch
  • Jan Grau

Organisationseinheiten

Externe Organisationen

  • Martin-Luther-Universität Halle-Wittenberg
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Details

OriginalspracheEnglisch
Aufsatznummere1007206
FachzeitschriftPLoS Computational Biology
Jahrgang15
Ausgabenummer7
PublikationsstatusVeröffentlicht - 11 Juli 2019

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.

ASJC Scopus Sachgebiete

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PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. / Erkes, Annett; Mücke, Stefanie; Reschke, Maik et al.
in: PLoS Computational Biology, Jahrgang 15, Nr. 7, e1007206, 11.07.2019.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Erkes A, Mücke S, Reschke M, Boch J, Grau J. PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. PLoS Computational Biology. 2019 Jul 11;15(7):e1007206. doi: 10.1371/journal.pcbi.1007206, 10.15488/10460
Erkes, Annett ; Mücke, Stefanie ; Reschke, Maik et al. / PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. in: PLoS Computational Biology. 2019 ; Jahrgang 15, Nr. 7.
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title = "PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting",
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.",
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author = "Annett Erkes and Stefanie M{\"u}cke and Maik Reschke and Jens Boch and Jan Grau",
note = "Funding: This work was supported by grants from the Deutsche Forschungsgemeinschaft (http:// www.dfg.de) (BO 768 1496/8-1 to JB and GR 4587/1-1 to JG) and by the COST actions FA1208",
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Download

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

N1 - Funding: This work was supported by grants from the Deutsche Forschungsgemeinschaft (http:// www.dfg.de) (BO 768 1496/8-1 to JB and GR 4587/1-1 to JG) and by the COST actions FA1208

PY - 2019/7/11

Y1 - 2019/7/11

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.

AB - 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.

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KW - Genes, Plant

KW - Genome, Plant

KW - Host Microbial Interactions/genetics

KW - Models, Biological

KW - Oryza/genetics

KW - Plant Diseases/genetics

KW - Tandem Repeat Sequences

KW - Transcription Activator-Like Effectors/genetics

KW - Transcription Initiation Site

KW - Virulence/genetics

KW - Xanthomonas/genetics

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DO - 10.1371/journal.pcbi.1007206

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VL - 15

JO - PLoS Computational Biology

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