A quantitative model of transcriptional differentiation driving host-pathogen interactions

Sheng Zhu, Zhong Wang, Jianxin Wang, Yaqun Wang, Ningtao Wang, Zuoheng Wang, Meng Xu, Xiaohua Su, Mingxiu Wang, Shougong Zhang, Minren Huang, Rongling Wu

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Despite our expanding knowledge about the biochemistry of gene regulation involved in host-pathogen interactions, a quantitative understanding of this process at a transcriptional level is still limited.We devise and assess a computational framework that can address this question.This framework is founded on a mixturemodel-based likelihood, equipped with functionality to cluster genes per dynamic and functional changes of gene expression within an interconnected system composed of the host and pathogen. If genes from the host and pathogen are clustered in the same group due to a similar pattern of dynamic profiles, they are likely to be reciprocally co-evolving. If genes from the two organisms are clustered in different groups, this means that they experience strong host-pathogen interactions. The framework can test the rates of change for individual gene clusters during pathogenic infection and quantify their impacts on host-pathogen interactions.The framework was validated by a pathological study of poplar leaves infected by fungal Marssonina brunnea in which co-evolving and interactive genes that determine poplar-fungus interactions are identified.The new framework should find its wide application to studying host-pathogen interactions for any other interconnected systems.

Original languageEnglish (US)
Article numberbbs047
Pages (from-to)713-723
Number of pages11
JournalBriefings in bioinformatics
Volume14
Issue number6
DOIs
Publication statusPublished - Nov 1 2013

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All Science Journal Classification (ASJC) codes

  • Information Systems
  • Molecular Biology

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