Details
| Original language | English |
|---|---|
| Article number | e1007206 |
| Journal | PLoS Computational Biology |
| Volume | 15 |
| Issue number | 7 |
| Publication status | Published - 11 Jul 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.
Keywords
- Computational Biology, Genes, Plant, Genome, Plant, Host Microbial Interactions/genetics, Models, Biological, Oryza/genetics, Plant Diseases/genetics, Tandem Repeat Sequences, Transcription Activator-Like Effectors/genetics, Transcription Initiation Site, Virulence/genetics, Xanthomonas/genetics
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Genetics
- Agricultural and Biological Sciences(all)
- Ecology, Evolution, Behavior and Systematics
- Neuroscience(all)
- Cellular and Molecular Neuroscience
- Biochemistry, Genetics and Molecular Biology(all)
- Molecular Biology
- Environmental Science(all)
- Ecology
- Computer Science(all)
- Computational Theory and Mathematics
- Mathematics(all)
- Modelling and Simulation
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In: PLoS Computational Biology, Vol. 15, No. 7, e1007206, 11.07.2019.
Research output: Contribution to journal › Article › Research › peer review
}
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.
KW - Computational Biology
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
UR - http://www.scopus.com/inward/record.url?scp=85070485246&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1007206
DO - 10.1371/journal.pcbi.1007206
M3 - Article
C2 - 31295249
AN - SCOPUS:85070485246
VL - 15
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 7
M1 - e1007206
ER -