In addition to changes at the mRNA level, master transcription fa

In addition to changes at the mRNA level, master transcription factors drive epigenetic modifications of many Th effector genes that reinforce the dominant phenotype [61, 62]. These epigenetic patterns are passed on to the cell’s progeny, creating a single Th

clone with similar epigenetic imprinting, that is, a Th-cell phenotype. Cytokine production by Th cells typically requires a few days of differentiation following the initial activation [41, 42], but phenotype induction at the transcriptional level already occurs within a few hours [63-65]. Over the last decade, Th-cell feedback mechanisms have been studied extensively using mathematical modelling. Whereas older studies focused on Th1/Th2 differentiation [66-68], more recent studies have included Treg and the novel Th-cell phenotypes [69-73]. Most of these mathematical models incorporate positive feedback and cross-inhibition. These models are typically parameterized in such manner that only single master transcription factors can be expressed, but co-expression can occur with other parameter regimes [71]. Interestingly, models have been formulated both at the inter- and intracellular level, and models at either level are capable of explaining Th differentiation

in response to outside signals, showing that there is redundancy in the system. Some studies have attempted to incorporate feedbacks at the genetic and epigenetic levels into models [56, 73], although only a single feedback loop is sufficient to explain Th-cell phenotypes. Modelling has also illustrated that selleck screening library master regulator heterodimer formation Baricitinib is sufficient for explaining mutual inhibition [71]. In addition to make the inducible phenotypes mathematically tractable as

alternative ‘steady states’ or ‘attractors’ of a dynamical system, these models provide insight into the development of Th-cell phenotypes over time, that is, the time series of changes that these cells undergo. These models show that early skewing leads to progressive differentiation into Th-cell phenotype as seen by experimental studies [43, 65]. In addition to traditional approaches, Th-cell differentiation has been studied intensively using high-throughput techniques. The targets of many important Th transcription factors have been mapped [9, 13, 14, 63, 74], and expression profiling has been performed by a number of groups [8, 63, 65, 75]. We and others have advocated a time series approach to Th-cell differentiation, because the Th-cell transcriptome is very dynamic in time. Indeed, we have shown that the mRNA signature of Th cells changes rapidly after the cognate priming and that genes can be classified into a ‘core’ and ‘turnover’ groups, and these also differ when different phenotypes are induced [65].

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