Brief. Bioinformatics - A quantitative model of transcriptional differentiation driving host-pathogen interactions.

Tópicos

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Resumo

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 mixture model-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.

Resumo Limpo

despit expand knowledg biochemistri gene regul involv hostpathogen interact quantit understand process transcript level still limit devis assess comput framework can address question framework found mixtur modelbas likelihood equip function cluster gene per dynam function chang gene express within interconnect system compos host pathogen gene host pathogen cluster group due similar pattern dynam profil like reciproc coevolv gene two organ cluster differ group mean experi strong hostpathogen interact framework can test rate chang individu gene cluster pathogen infect quantifi impact hostpathogen interact framework valid patholog studi poplar leav infect fungal marssonina brunnea coevolv interact gene determin poplarfungus interact identifi new framework find wide applic studi hostpathogen interact interconnect system

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