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Mutations of STAT3 underlie the autosomal dominant form of hyperimmunoglobulin E syndrome (HIES). STAT3 has critical roles in immune cells and thus, hematopoietic stem cell transplantation (HSCT), might be a reasonable therapeutic strategy in this disease. However, STAT3 also has critical functions in nonhematopoietic cells and dissecting the protean roles of STAT3 is limited by the lethality associated with germline deletion of Stat3. Thus, predicting the efficacy of HSCT for HIES is difficult. To begin to dissect the importance of STAT3 in hematopoietic and nonhematopoietic cells as it relates to HIES, we generated a mouse model of this disease. We found that these transgenic mice recapitulate multiple aspects of HIES, including elevated serum IgE and failure to generate Th17 cells. We found that these mice were susceptible to bacterial infection that was partially corrected by HSCT using wild-type bone marrow, emphasizing the role played by the epithelium in the pathophysiology of HIES.

CD4(+) T follicular helper cells (TFH) are critical for the formation and function of B cell responses to infection or immunization, but also play an important role in autoimmunity. The factors that contribute to the differentiation of this helper cell subset are incompletely understood, although several cytokines including IL-6, IL-21, and IL-12 can promote TFH cell formation. Yet, none of these factors, nor their downstream cognate STATs, have emerged as nonredundant, essential drivers of TFH cells. This suggests a model in which multiple factors can contribute to the phenotypic characteristics of TFH cells. Because type I IFNs are often generated in immune responses, we set out to investigate whether these factors are relevant to TFH cell differentiation. Type I IFNs promote Th1 responses, thus one possibility was these factors antagonized TFH-expressed genes. However, we show that type I IFNs (IFN-α/β) induced B cell lymphoma 6 (Bcl6) expression, the master regulator transcription factor for TFH cells, and CXCR5 and programmed cell death-1 (encoded by Pdcd1), key surface molecules expressed by TFH cells. In contrast, type I IFNs failed to induce IL-21, the signature cytokine for TFH cells. The induction of Bcl6 was regulated directly by STAT1, which bound to the Bcl6, Cxcr5, and Pdcd1 loci. These data suggest that type I IFNs (IFN-α/β) and STAT1 can contribute to some features of TFH cells but are inadequate in inducing complete programming of this subset.

CD4(+) helper T cells are crucial for autoimmune and infectious diseases; however, the recognition of the many, diverse fates available continues unabated. Precisely what controls specification of helper T cells and preserves phenotypic commitment is currently intensively investigated. In this review, we will discuss the major factors that impact helper T cell fate choice, ranging from cytokines and the microbiome to metabolic control and epigenetic regulation. We will also discuss the technological advances along with the attendant challenges presented by "big data," which allow the understanding of these processes on comprehensive scales.

Naive T cells differentiate and become distinct subsets in response to changes in the cytokine milieu. Such specialization arises through a complex and dynamic utilization of cis-regulatory enhancer elements. In this brief essay, we review recent findings on the relative contributions of sensors of the cytokine milieu, especially the signal transducer and activator of transcription family transcription factors, 'master regulators', and other transcription factors in the enhancer architecture of T cells. These findings provide new insights into how signal transduction impinges upon the genome., (Published 2013. This article is a U.S. Government work and is in the public domain in the USA.)

Through their functional diversification, distinct lineages of CD4(+) T cells can act to either drive or constrain immune-mediated pathology. Transcription factors are critical in the generation of cellular diversity, and negative regulators antagonistic to alternate fates often act in conjunction with positive regulators to stabilize lineage commitment. Genetic polymorphisms within a single locus encoding the transcription factor BACH2 are associated with numerous autoimmune and allergic diseases including asthma, Crohn's disease, coeliac disease, vitiligo, multiple sclerosis and type 1 diabetes. Although these associations point to a shared mechanism underlying susceptibility to diverse immune-mediated diseases, a function for BACH2 in the maintenance of immune homeostasis has not been established. Here, by studying mice in which the Bach2 gene is disrupted, we define BACH2 as a broad regulator of immune activation that stabilizes immunoregulatory capacity while repressing the differentiation programs of multiple effector lineages in CD4(+) T cells. BACH2 was required for efficient formation of regulatory (Treg) cells and consequently for suppression of lethal inflammation in a manner that was Treg-cell-dependent. Assessment of the genome-wide function of BACH2, however, revealed that it represses genes associated with effector cell differentiation. Consequently, its absence during Treg polarization resulted in inappropriate diversion to effector lineages. In addition, BACH2 constrained full effector differentiation within TH1, TH2 and TH17 cell lineages. These findings identify BACH2 as a key regulator of CD4(+) T-cell differentiation that prevents inflammatory disease by controlling the balance between tolerance and immunity.

CD4 helper T cells are critical for proper immune cell homeostasis and host defense but are also major contributors to immune and inflammatory disease. Arising from a simple biphasic model of differentiation (ie, TH1 and TH2 cells). A bewildering number of fates seem possible for helper T cells. To what extent different helper cell subsets maintain their characteristic gene expression profiles or exhibit functional plasticity is a hotly debated topic. In this review we will discuss how the expression of "signature cytokines" and "master regulator" transcription factors do not neatly conform to a simple helper T-cell paradigm. Although this might seem confusing, the good news is that the newly recognized complexity fits better with our understanding of immunopathogenesis. Finally, we will discuss factors, including epigenetic regulation and metabolic alterations, that contribute to helper cell specificity and plasticity., (Published by Mosby, Inc.)

CD4(+) T cells are critical for the elimination of an immense array of microbial pathogens. Among the ways they accomplish this task is to generate progeny with specialized, characteristic patterns of gene expression. From this perspective, helper cells can be viewed as pluripotent precursors that adopt distinct cell fates. Although there are aspects of helper cell differentiation that can be modeled as a classic cell fate commitment, CD4(+) T cells also maintain considerable flexibility in their transcriptional program. This makes sense in terms of host defense, but raises the question of how these remarkable cells balance both these requirements, a high degree of specific gene expression and the capacity for plasticity. In this review, we discuss recent advances in our understanding of CD4(+) T-cell specification, focusing on how genomic perspectives have influenced our views of these processes. The relative contributions of sensors of the cytokine milieu, especially the signal transducer and activator of transcription family transcription factors, 'master regulators', and other transcription factors are considered as they relate to the helper cell transcriptome and epigenome., (Published 2013. This article is a U.S. Government work and is in the public domain in the USA.)

CD4(+) T helper (Th) cells differentiate into distinct effector subsets that are critical for host defense, but are also implicated in the pathogenesis of autoimmune disorders. Thelper17 (Th17) cells in particular are emerging as important drivers of multiple diseases including psoriasis, spondyloarthropathy and multiple sclerosis. To gain insight into the function of Th17 cells, we performed transcriptional profiling in hopes of elucidating products not previously recognized as being functionally relevant in these T cells. Herein, we demonstrate that tissue inhibitor of metalloproteinase 1 (TIMP1), a secreted protein with pleiotropic effects on cellular growth, survival and integrity of the extracellular matrix, is preferentially produced by Th17 and Th1 cells. We further show that Th1 and Th17 cell TIMP1 regulation follows separate mechanisms with a requirement for STAT4 in the former and STAT3 in the latter. Finally, we demonstrate that when restricted to T cells, expression of TIMP1 promotes neuropathology in experimental allergic encephalomyelitis.

Interleukin (IL)-22-producing innate lymphoid cells (ILCs; ILC22) comprise a heterogeneous population of cells that are dependent on the transcription factor retinoid-related orphan γt (RORγt) and are critical for barrier function of the intestinal mucosa. A distinct ILC22 subset expresses the natural cytotoxicity receptor NKp46 (NKp46+ ILC22); however, the factors that contribute to the generation of this population versus other subsets are largely unknown. Herein, we show that T-bet (encoded by Tbx21) was highly expressed in NKp46+ ILC22, a feature shared by all NKp46+ cells present in the intestine but not by other IL-22-producing populations. Accordingly, the absence of T-bet resulted in loss of NKp46+ ILC22 in the intestinal lamina propria. The residual NKp46+ ILC22 present in Tbx21(-/-) mice showed a marked reduction of Rorγt expression and impairment in IL-22 production. Generation and functions of gut NK1.1+ cells were also altered. Bone marrow chimera experiments revealed a cell-intrinsic requirement for T-bet in these subsets and competitive reconstitution experiments revealed roles for T-bet in multiple ILC subsets. Thus, T-bet has a general importance for ILC in the gut and plays a selective and critical role in the generation of NKp46+ ILC22.

Signaling pathways are intimately involved in cellular differentiation, allowing cells to respond to their environment by regulating gene expression. Although enhancers are recognized as key elements that regulate selective gene expression, the interplay between signaling pathways and actively used enhancer elements is not clear. Here, we use CD4(+) T cells as a model of differentiation, mapping the activity of cell-type-specific enhancer elements in T helper 1 (Th1) and Th2 cells. Our data establish that STAT proteins have a major impact on the activation of lineage-specific enhancers and the suppression of enhancers associated with alternative cell fates. Transcriptome analysis further supports a functional role for enhancers regulated by STATs. Importantly, expression of lineage-defining master regulators in STAT-deficient cells fails to fully recover the chromatin signature of STAT-dependent enhancers. Thus, these findings point to a critical role of STATs as environmental sensors in dynamically molding the specialized enhancer architecture of differentiating cells., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Interleukin-27 (IL-27) is a key immunosuppressive cytokine that counters T helper 17 (Th17) cell-mediated pathology. To identify mechanisms by which IL-27 might exert its immunosuppressive effect, we analyzed genes in T cells rapidly induced by IL-27. We found that IL-27 priming of naive T cells upregulated expression of programmed death ligand 1 (PD-L1) in a signal transducer and activator of transcription 1 (STAT1)-dependent manner. When cocultured with naive CD4(+) T cells, IL-27-primed T cells inhibited the differentiation of Th17 cells in trans through a PD-1-PD-L1 interaction. In vivo, coadministration of naive TCR transgenic T cells (2D2 T cells) with IL-27-primed T cells expressing PD-L1 inhibited the development of Th17 cells and protected from severe autoimmune encephalomyelitis. Thus, these data identify a suppressive activity of IL-27, by which CD4(+) T cells can restrict differentiation of Th17 cells in trans., (Copyright © 2012 Elsevier Inc. All rights reserved.)

T helper cell differentiation occurs in the context of the extracellular cytokine milieu evoked by diverse microbes and other pathogenic stimuli along with T cell receptor stimulation. The culmination of these signals results in specification of T helper lineages, which occurs through the combinatorial action of multiple transcription factors that establish distinctive transcriptomes. In this manner, inducible, but constitutively active, master regulators work in conjunction with factors such as the signal transducer and activator of transcriptions (STATs) that sense the extracellular environment. The acquisition of a distinctive transcriptome also depends on chromatin modifications that impact key cis elements as well as the changes in global genomic organization. Thus, signal transduction and epigenetics are linked in these processes of differentiation. In this review, recent advances in understanding T helper lineage specification and deciphering the action of transcription factors are summarized with emphasis on comprehensive views of the dynamic T cell epigenome.

Follicular helper T (Tfh) cells comprise an important subset of helper T cells; however, their relationship with other helper lineages is incompletely understood. Herein, we showed interleukin-12 acting via the transcription factor STAT4 induced both Il21 and Bcl6 genes, generating cells with features of both Tfh and Th1 cells. However, STAT4 also induced the transcription factor T-bet. With ChIP-seq, we defined the genome-wide targets of T-bet and found that it repressed Bcl6 and other markers of Tfh cells, thereby attenuating the nascent Tfh cell-like phenotype in the late phase of Th1 cell specification. Tfh-like cells were rapidly generated after Toxoplasma gondii infection in mice, but T-bet constrained Tfh cell expansion and consequent germinal center formation and antibody production. Our data argue that Tfh and Th1 cells share a transitional stage through the signal mediated by STAT4, which promotes both phenotypes. However, T-bet represses Tfh cell functionalities, promoting full Th1 cell differentiation., (Copyright © 2011 Elsevier Inc. All rights reserved.)

CD4(+) T cells have critical roles in orchestrating immune responses to diverse microbial pathogens. This is accomplished through the differentiation of CD4(+) T helper cells to specialized subsets in response to microbial pathogens, which evoke a distinct cytokine milieu. Signal transducer and activator of transcription family transcription factors sense these cytokines and they in turn regulate expression of lineage-defining master regulators that programme selective gene expression, resulting in distinctive phenotypes. However, phenotype and restricted gene expression are determined not only by the action of transcription factors; chromatin accessibility is required for these factors to exert their effect. Technical advances have greatly expanded our understanding of transcription factor action and dynamic changes in the epigenome that accompany cellular differentiation. In this review, we will discuss recent progress in the understanding of how cytokines influence gene expression and epigenetic modifications, and the impact of these findings on our views of helper cell lineage commitment and plasticity., (Published 2011. This article is a U.S. Government work and is in the public domain in the USA.)

Signal transducer and activator of transcription (STAT) proteins are well known for their essential roles in transmitting cytokine-mediated signals and specifying T helper (T(H)) cell differentiation. Recent technological advances have revealed that STAT proteins have broad and complex roles in gene regulation and epigenetic control, including important roles as functional repressors. However, the challenge of how to link signal transduction, nucleosome biology and gene regulation remains. The relevance of tackling this problem is highlighted by genome-wide association studies that link cytokine signalling and STATs to various autoimmune or immune deficiency disorders. Defining exactly how extrinsic signals control the specification and plasticity of T(H) cells will provide important insights and perhaps therapeutic opportunities in these diseases.

Interleukin 2 (IL-2), a cytokine linked to human autoimmune disease, limits IL-17 production. Here we found that deletion of the gene encoding the transcription factor STAT3 in T cells abrogated IL-17 production and attenuated autoimmunity associated with IL-2 deficiency. Whereas STAT3 induced IL-17 and the transcription factor RORγt and inhibited the transcription factor Foxp3, IL-2 inhibited IL-17 independently of Foxp3 and RORγt. STAT3 and STAT5 bound to multiple common sites across the locus encoding IL-17. The induction of STAT5 binding by IL-2 was associated with less binding of STAT3 at these sites and the inhibition of associated active epigenetic marks. 'Titration' of the relative activation of STAT3 and STAT5 modulated the specification of cells to the IL-17-producing helper T cell (T(H)17 cell) subset. Thus, the balance rather than the absolute magnitude of these signals determined the propensity of cells to make a key inflammatory cytokine.

Signal transducer and activator of transcription 4 (STAT4) and STAT6 are key factors in the specification of helper T cells; however, their direct roles in driving differentiation are not well understood. Using chromatin immunoprecipitation and massive parallel sequencing, we quantitated the full complement of STAT-bound genes, concurrently assessing global STAT-dependent epigenetic modifications and gene transcription by using cells from cognate STAT-deficient mice. STAT4 and STAT6 each bound over 4000 genes with distinct binding motifs. Both played critical roles in maintaining chromatin configuration and transcription of a core subset of genes through the combination of different epigenetic patterns. Globally, STAT4 had a more dominant role in promoting active epigenetic marks, whereas STAT6 had a more prominent role in antagonizing repressive marks. Clusters of genes negatively regulated by STATs were also identified, highlighting previously unappreciated repressive roles of STATs. Therefore, STAT4 and STAT6 play wide regulatory roles in T helper cell specification., (Copyright 2010 Elsevier Inc. All rights reserved.)

STAT3, an essential transcription factor with pleiotropic functions, plays critical roles in the pathogenesis of autoimmunity. Despite recent data linking STAT3 with inflammatory bowel disease, exactly how it contributes to chronic intestinal inflammation is not known. Using a T cell transfer model of colitis, we found that STAT3 expression in T cells was essential for the induction of both colitis and systemic inflammation. STAT3 was critical in modulating the balance of T helper 17 (Th17) and regulatory T (Treg) cells, as well as in promoting CD4(+) T cell proliferation. We used chromatin immunoprecipitation and massive parallel sequencing (ChIP-Seq) to define the genome-wide targets of STAT3 in CD4(+) T cells. We found that STAT3 bound to multiple genes involved in Th17 cell differentiation, cell activation, proliferation, and survival, regulating both expression and epigenetic modifications. Thus, STAT3 orchestrates multiple critical aspects of T cell function in inflammation and homeostasis., (Copyright 2010 Elsevier Inc. All rights reserved.)

External control of a genetic regulatory network is used for the purpose of avoiding undesirable states, such as those associated with a disease. To date, intervention has mainly focused on the external control of probabilistic Boolean networks via the associated discrete-time discrete-space Markov processes. Implementation of an intervention policy derived for probabilistic Boolean networks requires nearly continuous observation of the underlying biological system since precise application requires the observation of all transitions. In medical applications, as in many engineering problems, the process is sampled at discrete time intervals and a decision to intervene or not must be made at each sample point. In this work, sampling-rate-dependent probabilistic Boolean network is proposed as an extension of probabilistic Boolean network. The proposed framework is capable of capturing the sampling rate of the underlying system.

Markovian regulatory networks constitute a class of discrete state-space models used to study gene regulatory dynamics and discover methods that beneficially alter those dynamics. Thereby, this class of models provides a framework to discover effective drug targets and design potent therapeutic strategies. The salient translational goal is to design therapeutic strategies that desirably modify network dynamics via external signals that vary the expressions of a control gene. The objective of an intervention strategy is to reduce the likelihood of the pathological cellular function related to a disease. The task of finding an effective intervention strategy can be formulated as a sequential decision making problem for a pre-defined cost of intervention and a cost-per-stage function that discriminates the gene-activity profiles. An effective intervention strategy prescribes the actions associated with an external signal that result in the minimum expected cost. This strategy in turn can be used as a treatment that reduces the long-run likelihood of gene expressions favorable to the disease. In this tutorial, we briefly summarize the first method proposed to design such therapeutic interventions, and then move on to some of the recent refinements that have been proposed. Each of these recent intervention methods is motivated by practical or analytical considerations. The presentation of the key ideas is facilitated with the help of two case studies.

An approximate representation for the state space of a context-sensitive probabilistic Boolean network has previously been proposed and utilized to devise therapeutic intervention strategies. Whereas the full state of a context-sensitive probabilistic Boolean network is specified by an ordered pair composed of a network context and a gene-activity profile, this approximate representation collapses the state space onto the gene-activity profiles alone. This reduction yields an approximate transition probability matrix, absent of context, for the Markov chain associated with the context-sensitive probabilistic Boolean network. As with many approximation methods, a price must be paid for using a reduced model representation, namely, some loss of optimality relative to using the full state space. This paper examines the effects on intervention performance caused by the reduction with respect to various values of the model parameters. This task is performed using a new derivation for the transition probability matrix of the context-sensitive probabilistic Boolean network. This expression of transition probability distributions is in concert with the original definition of context-sensitive probabilistic Boolean network. The performance of optimal and approximate therapeutic strategies is compared for both synthetic networks and a real case study. It is observed that the approximate representation describes the dynamics of the context-sensitive probabilistic Boolean network through the instantaneously random probabilistic Boolean network with similar parameters.

A prime objective of modeling genetic regulatory networks is the identification of potential targets for therapeutic intervention. To date, optimal stochastic intervention has been studied in the context of probabilistic Boolean networks, with the control policy based on the transition probability matrix of the associated Markov chain and dynamic programming used to find optimal control policies. Dynamical programming algorithms are problematic owing to their high computational complexity. Two additional computationally burdensome issues that arise are the potential for controlling the network and identifying the best gene for intervention. This paper proposes an algorithm based on mean first-passage time that assigns a stationary control policy for each gene candidate. It serves as an approximation to an optimal control policy and, owing to its reduced computational complexity, can be used to predict the best control gene. Once the best control gene is identified, one can derive an optimal policy or simply utilize the approximate policy for this gene when the network size precludes a direct application of dynamic programming algorithms. A salient point is that the proposed algorithm can be model-free. It can be directly designed from time-course data without having to infer the transition probability matrix of the network.

A key objective of gene network modeling is to develop intervention strategies to alter regulatory dynamics in such a way as to reduce the likelihood of undesirable phenotypes. Optimal stationary intervention policies have been developed for gene regulation in the framework of probabilistic Boolean networks in a number of settings. To mitigate the possibility of detrimental side effects, for instance, in the treatment of cancer, it may be desirable to limit the expected number of treatments beneath some bound. This paper formulates a general constraint approach for optimal therapeutic intervention by suitably adapting the reward function and then applies this formulation to bound the expected number of treatments. A mutated mammalian cell cycle is considered as a case study.

This work integrates rapid techniques for mutation detection by producing single-stranded DNA and (renatured) double-stranded DNA on-chip, labeling these with fluorescent DNA stains and then performing two complementary methods of mutation detection-single stranded conformation polymorphism (SSCP) analysis and heteroduplex analysis (HA). This involves the denaturation of double-stranded polymerase chain reaction (PCR) product into single-stranded DNA, the mutation analysis of the single-stranded DNA by SSCP and the rehybridized double-stranded DNA by HA. These steps were performed entirely on-chip within several minutes of operation. The combination of these two mutation detection methods on-chip provides a highly sensitive method of mutation detection for either genotyping or screening. Many mutation analysis methods rely upon fluorescently labeled samples from a PCR with fluorescently labeled primers. By labeling on-chip we not only attain improved signal strength, but the method is considerably more versatile. Although we used PCR products in this work, this method could be used to analyze DNA from any source. We believe that this combination of several procedures on a single chip represents a significant step in the development of higher levels of integration upon microfluidic devices.