| 1. |
- Elo, Laura L., et al.
(author)
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Genome-wide profiling of interleukin-4 and STAT6 transcription factor regulation of human Th2 cell programming
- 2010
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In: Immunity. - : Cell Press. - 1074-7613 .- 1097-4180. ; 32:6, s. 852-862
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Journal article (peer-reviewed)abstract
- Dissecting the molecular mechanisms by which T helper (Th) cells differentiate to effector Th2 cells is important for understanding the pathogenesis of immune-mediated diseases, such as asthma and allergy. Because the STAT6 transcription factor is an upstream mediator required for interleukin-4 (IL-4)-induced Th2 cell differentiation, its targets include genes important for this process. Using primary human CD4(+) T cells, and by blocking STAT6 with RNAi, we identified a number of direct and indirect targets of STAT6 with ChIP sequencing. The integration of these data sets with detailed kinetics of IL-4-driven transcriptional changes showed that STAT6 was predominantly needed for the activation of transcription leading to the Th2 cell phenotype. This integrated genome-wide data on IL-4- and STAT6-mediated transcription provide a unique resource for studies on Th cell differentiation and, in particular, for designing interventions of human Th2 cell responses.
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| 2. |
- Elo, Laura L., et al.
(author)
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Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process
- 2007
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In: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 23:16, s. 2096-2103
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Journal article (peer-reviewed)abstract
- MOTIVATION: Coexpression networks have recently emerged as a novel holistic approach to microarray data analysis and interpretation. Choosing an appropriate cutoff threshold, above which a gene-gene interaction is considered as relevant, is a critical task in most network-centric applications, especially when two or more networks are being compared.RESULTS: We demonstrate that the performance of traditional approaches, which are based on a pre-defined cutoff or significance level, can vary drastically depending on the type of data and application. Therefore, we introduce a systematic procedure for estimating a cutoff threshold of coexpression networks directly from their topological properties. Both synthetic and real datasets show clear benefits of our data-driven approach under various practical circumstances. In particular, the procedure provides a robust estimate of individual degree distributions, even from multiple microarray studies performed with different array platforms or experimental designs, which can be used to discriminate the corresponding phenotypes. Application to human T helper cell differentiation process provides useful insights into the components and interactions controlling this process, many of which would have remained unidentified on the basis of expression change alone. Moreover, several human-mouse orthologs showed conserved topological changes in both systems, suggesting their potential importance in the differentiation process.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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