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Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens.
Chasman, Deborah; Walters, Kevin B; Lopes, Tiago J S; Eisfeld, Amie J; Kawaoka, Yoshihiro; Roy, Sushmita.
Afiliação
  • Chasman D; Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
  • Walters KB; Influenza Research Institute, Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
  • Lopes TJ; Influenza Research Institute, Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
  • Eisfeld AJ; Influenza Research Institute, Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
  • Kawaoka Y; Influenza Research Institute, Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
  • Roy S; Division of Virology, Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo, Tokyo, Japan.
PLoS Comput Biol ; 12(7): e1005013, 2016 07.
Article em En | MEDLINE | ID: mdl-27403523
Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteoma / Perfilação da Expressão Gênica / Proteômica / Redes Reguladoras de Genes / Interações Hospedeiro-Patógeno / Transcriptoma Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals / Humans Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2016 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteoma / Perfilação da Expressão Gênica / Proteômica / Redes Reguladoras de Genes / Interações Hospedeiro-Patógeno / Transcriptoma Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals / Humans Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2016 Tipo de documento: Article País de afiliação: Estados Unidos