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Virulence-related regulatory network of Pseudomonas syringae.
Huang, Jiadai; Yao, Chunyan; Sun, Yue; Ji, Quanjiang; Deng, Xin.
Afiliação
  • Huang J; Department of Biomedical Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong SAR 999077 China.
  • Yao C; Department of Biomedical Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong SAR 999077 China.
  • Sun Y; Department of Biomedical Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong SAR 999077 China.
  • Ji Q; Gene Editing Center, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
  • Deng X; Department of Biomedical Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong SAR 999077 China.
Comput Struct Biotechnol J ; 20: 6259-6270, 2022.
Article em En | MEDLINE | ID: mdl-36420163
Transcription factors (TFs) play important roles in regulating multiple biological processes by binding to promoter regions and regulating the global gene transcription levels. Pseudomonas syringae is a Gram-negative phytopathogenic bacterium harbouring 301 putative TFs in its genome, approximately 50 of which are responsible for virulence-related gene and pathway regulation. Over the past decades, RNA sequencing, chromatin immunoprecipitation sequencing, high-throughput systematic evolution of ligands by exponential enrichment, and other technologies have been applied to identify the functions of master regulators and their interactions in virulence-related pathways. This review summarises the recent advances in the regulatory networks of TFs involved in the type III secretion system (T3SS) and non-T3SS virulence-associated pathways, including motility, biofilm formation, quorum sensing, nucleotide-based secondary messengers, phytotoxins, siderophore production, and oxidative stress. Moreover, this review discusses the future perspectives in terms of TF-mediated pathogenesis mechanisms and provides novel insights that will help combat P. syringae infections based on the regulatory networks of TFs.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Comput Struct Biotechnol J Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Comput Struct Biotechnol J Ano de publicação: 2022 Tipo de documento: Article