title: Computational predictions provide insights into the biology of TAL effector target sites.
abstract: Transcription activator-like (TAL) effectors are injected into host plant cells by Xanthomonas bacteria to function as transcriptional activators for the benefit of the pathogen. The DNA binding domain of TAL effectors is composed of conserved amino acid repeat structures containing repeat-variable diresidues (RVDs) that determine DNA binding specificity. In this paper, we present TALgetter, a new approach for predicting TAL effector target sites based on a statistical model. In contrast to previous approaches, the parameters of TALgetter are estimated from training data computationally. We demonstrate that TALgetter successfully predicts known TAL effector target sites and often yields a greater number of predictions that are consistent with up-regulation in gene expression microarrays than an existing approach, Target Finder of the TALE-NT suite. We study the binding specificities estimated by TALgetter and approve that different RVDs are differently important for transcriptional activation. In subsequent studies, the predictions of TALgetter indicate a previously unreported positional preference of TAL effector target sites relative to the transcription start site. In addition, several TAL effectors are predicted to bind to the TATA-box, which might constitute one general mode of transcriptional activation by TAL effectors. Scrutinizing the predicted target sites of TALgetter, we propose several novel TAL effector virulence targets in rice and sweet orange. TAL-mediated induction of the candidates is supported by gene expression microarrays. Validity of these targets is also supported by functional analogy to known TAL effector targets, by an over-representation of TAL effector targets with similar function, or by a biological function related to pathogen infection. Hence, these predicted TAL effector virulence targets are promising candidates for studying the virulence function of TAL effectors. TALgetter is implemented as part of the open-source Java library Jstacs, and is freely available as a web-application and a command line program.
authors.affiliation: Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany. grau@informatik.uni-halle.de
authors.forename: Jan
authors.initials: J
authors.lastname: Grau
authors.forename: Annett
authors.initials: A
authors.lastname: Wolf
authors.forename: Maik
authors.initials: M
authors.lastname: Reschke
authors.forename: Ulla
authors.initials: U
authors.lastname: Bonas
authors.forename: Stefan
authors.initials: S
authors.lastname: Posch
authors.forename: Jens
authors.initials: J
authors.lastname: Boch
mesh_terms: D000595:Amino Acid Sequence; D001426:Bacterial Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D004268:DNA-Binding Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling; D018506:Gene Expression Regulation, Plant; D020411:Oligonucleotide Array Sequence Analysis; D010935:Plant Diseases / Q000235:genetics / Q000382:microbiology; D011485:Protein Binding; D015203:Reproducibility of Results; D014157:Transcription Factors / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D014974:Xanthomonas / Q000235:genetics / Q000472:pathogenicity
chemical_list: D001426:Bacterial Proteins; D004268:DNA-Binding Proteins; D014157:Transcription Factors