期刊
BMC SYSTEMS BIOLOGY
卷 9, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s12918-015-0223-6
关键词
T cell differentiation; T helper 17 cell; Sequencing data; Mathematical modeling; Statistical modeling; Computational statistics
资金
- Academy of Finland [Centre of Excellence in Molecular Systems Immunology and Physiology Research] [275537, 258313]
- EU FP7 grant [EC-FP7-SYBILLA-201106]
- EU ERASysBio ERA NET
- Sigrid Juselius Foundation
- Biotechnology and Biological Sciences Research Council [BB/I004114/1] Funding Source: researchfish
- Medical Research Council [MC_U117512792] Funding Source: researchfish
- The Francis Crick Institute [10159] Funding Source: researchfish
- BBSRC [BB/I004114/1] Funding Source: UKRI
- MRC [MC_U117512792] Funding Source: UKRI
- Academy of Finland (AKA) [275537, 275537, 258313] Funding Source: Academy of Finland (AKA)
Background: The differentiation of naive CD4(+) helper T (Th) cells into effector Th17 cells is steered by extracellular cytokines that activate and control the lineage specific transcriptional program. While the inducing cytokine signals and core transcription factors driving the differentiation towards Th17 lineage are well known, detailed mechanistic interactions between the key components are poorly understood. Results: We develop an integrative modeling framework which combines RNA sequencing data with mathematical modeling and enables us to construct a mechanistic model for the core Th17 regulatory network in a data-driven manner. Conclusions: Our results show significant evidence, for instance, for inhibitory mechanisms between the transcription factors and reveal a previously unknown dependency between the dosage of the inducing cytokine TGFa and the expression of the master regulator of competing (induced) regulatory T cell lineage. Further, our experimental validation approves this dependency in Th17 polarizing conditions.
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