HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Supplemental Material All Versions of this Article: 38/3/362    most recent 00058.2009v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Pappas, D. J. Articles by Baranzini, S. E. PubMed PubMed Citation Articles by Pappas, D. J. Articles by Baranzini, S. E.

Longitudinal system-based analysis of transcriptional responses to type I interferons

¹ Department of Neurology, University of California, San Francisco
² Program in Neurogenetics, Department of Neurology, University of California, Los Angeles, California

Type I interferons (IFNs) are pleiotropic cytokines that modulate both innate and adaptive immune responses. They have been used to treat autoimmune disorders, cancers, and viral infection and have been demonstrated to elicit differential responses within cells, despite sharing a single receptor. The molecular basis for such differential responses has remained elusive. To identify the mechanisms underlying differential type I IFN signaling, we used whole genome microarrays to measure longitudinal transcriptional events within human CD4⁺ T cells treated with IFN-_2b or IFN-β_1a. We identified differentially regulated genes, analyzed them for the enrichment of known promoter elements and pathways, and constructed a network module based on weighted gene coexpression network analysis (WGCNA). WGCNA uses advanced statistical measures to find interconnected modules of correlated genes. Overall, differential responses to IFN in CD4⁺ T cells related to three dominant themes: migration, antigen presentation, and the cytotoxic response. For migration, WGCNA identified subtype-specific regulation of pre-mRNA processing factor 4 homolog B and eukaryotic translation initiation factor 4A2, which work at various levels within the cell to affect the expression of the chemokine CCL5. WGCNA also identified sterile -motif domain-containing 9-like (SAMD9L) as critical in subtype-independent effects of IFN treatment. RNA interference of SAMD9L expression enhanced the migratory phenotype of activated T cells treated with IFN-β compared with controls. Through the analysis of the dynamic transcriptional events after differential IFN treatment, we were able to identify specific signatures and to uncover novel genes that may underpin the type I IFN response.

human; T cell; cytokines; gene regulation