Scholarly article on topic 'Early T helper cell programming of gene expression in human'

Early T helper cell programming of gene expression in human Academic research paper on "Biological sciences"

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Seminars in Immunology
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{Human / "T helper cell" / Transcription / Epigenetics / High-throughput / Genome-wide}

Abstract of research paper on Biological sciences, author of scientific article — Soile Tuomela, Riitta Lahesmaa

Abstract Molecular mechanisms guiding naïve T helper cell differentiation into functionally specified effector cells are intensively studied. The rapidly growing knowledge is mainly achieved by using mouse cells or disease models. Comparatively exiguous data is gathered from human primary cells although they provide the “ultimate model” for immunology in man, have been exploited in many original studies paving the way for the field, and can be analyzed more easily than ever with the help of modern technology and methods. As usage of mouse models is unavoidable in translational research, parallel human and mouse studies should be performed to assure the relevancy of the hypothesis created during the basic research. In this review, we give an overview on the status of the studies conducted with human primary cells aiming at elucidating the mechanisms instructing the priming of T helper cell subtypes. The special emphasis is given to the recent high-throughput studies. In addition, by comparing the human and mouse studies we intend to point out the regulatory mechanisms and questions which are lacking examination with human primary cells.

Academic research paper on topic "Early T helper cell programming of gene expression in human"



Early T helper cell programming of gene expression in human^

Soile Tuomela, Riitta Lahesmaa *

Turku Centre for Biotechnology, University of Turku and Abo Akademi University, Tykistokatu 6,20520 Turku, Finland


Molecular mechanisms guiding naive T helper cell differentiation into functionally specified effector cells are intensively studied. The rapidly growing knowledge is mainly achieved by using mouse cells or disease models. Comparatively exiguous data is gathered from human primary cells although they provide the "ultimate model" for immunology in man, have been exploited in many original studies paving the way for the field, and can be analyzed more easily than ever with the help of modern technology and methods. As usage of mouse models is unavoidable in translational research, parallel human and mouse studies should be performed to assure the relevancy of the hypothesis created during the basic research. In this review, we give an overview on the status of the studies conducted with human primary cells aiming at elucidating the mechanisms instructing the priming of T helper cell subtypes. The special emphasis is given to the recent high-throughput studies. In addition, by comparing the human and mouse studies we intend to point out the regulatory mechanisms and questions which are lacking examination with human primary cells.

© 2013 The Authors. Published by Elsevier Ltd. All rights reserved.

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1. Introduction

Characterization of signaling pathways and biomedical research relies heavily on experiments done with mouse models. However, failures in translational research are regrettably common [1,2]. The experimental setups for basic research and clinical trials may differ from each other and the disease models may be oversimplified [1-4]. Moreover, there may be significant biological differences between the experimental animals and human [5,6], which could explain the unsatisfactory success rate in introducing new therapeutic interventions onto the market. To address these questions, the benefits and the limitations of experimental models need to be evaluated by comparing the molecular networks across species. Recently published comparison of immunologic responses

Abbreviations: ChlA-PET, chromatin interaction analysis by paired-end tag sequencing; ChIP, chromatin immunoprecipitation; ChlP-seq, chromatin immuno-precipitation coupled to high-throughput sequencing; iTreg, inducible T regulatory cell, adaptive T regulatory; lincRNA, long intergenic non-coding RNA; lncRNA, long non-coding RNA; miRNA, microRNA; RNAi, RNA interference; RNA-seq, high-throughput RNA sequencing; siRNA, short interfering RNA; SNP, single nucleotide polymorphism; STAT, signal transducer and activator of transcription; TCR, T cell receptor; Tfh, follicular T helper cell; Th, T helper cell; UTR, untranslated region of mRNA.

* This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Corresponding author at: Turku Centre for Biotechnology, University of Turku and Abo Akademi University, P.O. Box 123, FIN-20521 Turku, Finland. Tel.: +358 2 333 8601; fax: +358 2 251 8808.

E-mail address: (R. Lahesmaa).

between human inflammatory disease and the corresponding mouse models revealed both species-specific and model-specific discrepancies [7]. Mouse models have been extensively used also in characterization of signaling driving T helper (Th) cell differentiation. Th cells are orchestrators of immune responses working in between the innate and adaptive immunity. Their momentous role is most dramatically evidenced by fatal susceptibility of subjects with AIDS to opportunistic infections and cancer due to dramatic loss of Th cell pool. Unsuccessful dampening of Th cell activity can on the other hand lead to several inflammatory or autoimmune diseases.

Cells of the innate immune system express different cytokines and co-receptors in response to intrinsic features of a pathogen. The cytokine milieu along with the strength of T cell receptor (TCR) crosslinking and engagement of co-receptors delineate the differentiation path, which naïve Th cell will take. As different Th cell subtypes have varying cytokine expression profiles and homing preferences they trigger a unique immune response needed for specific elimination of the intruding pathogens. Originally, two opposite Th cell phenotypes were described and named as Th1 and Th2 cells either targeting intra- or extracellular pathogens, respectively [8,9]. Th1 cells produce high amounts of IFN7 [8,9], and defects in Th1 cell signaling are associated with susceptibility to Salmonella and mycobacterial infections [10]. Th2 cells express 114, IL5 and IL13 genes [8,9] clustered to chromosome 5 in human. Th2 cells are known to control parasite infections, but are also activated in response to a variety of other stimuli [11]. Although this simplistic Th1/Th2-distinction has been a powerful model, it has been replaced during the recent years with a more extensive and flexible view on Th cell differentiation and function. The family of

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Th cells has been expanded to include iTreg (inducible T regulatory, adaptive T regulatory Th cells), Tfh (follicular Th cells,) Th9, Th17 and Th22 cell subsets, and the plasticity between the phenotypes is intensively studied [12].

iTreg cells, generated in periphery, are needed to complement the suppressive function of thymus-derived natural Treg cells [13,14]. Specific differentiation of this subset was suggested to happen in human [15], but it has remained controversial whether the cells with TGFP1-inducible FOXP3 expression have suppressive function [16]. However, addition of 1L2 and retinoic acid to the culturing medium and more recently inhibition of anaphyla-toxin signaling through complement C3a and C5a receptors have for example been reported to increase the suppressive competence of iTreg cells in human [17,18]. Tfh cells, identified from tonsillar samples and shown to express CXCR5 [19,20], are required for the formation of germinal centers and production of high-affinity antibodies [21]. 1L9-producing Th9 cells were identified in human shortly after corresponding mouse studies [22,23]. In contrast to mouse studies, the in vivo existence of human Th9 cells has been shown both in healthy individuals and associated with inflammation [24]. The precise role of Th9 cells in the immune system is still ill-defined, but it is expected to overlap with the function of Th2 cells. 1n human also a Th22 subset that secretes 1L22 but not cytokines characteristic for other Th cell subsets has been reported [25-27]. Moreover, some studies indicate that there is a similar cellular phenotype, namely cells able to secrete 1L22 but not 1L17, also in mice [28-30]. Skin-infiltrating Th22 cells have been suggested to play a role in maintaining epidermal integrity [25-27]. In contrast to Th9 and Th22 cells, which have not yet been completely approved as separate Th cell lineages, Th17 cells have a strong position in the Th cell family. In human, 1L17 and 1L17F secreting Th17 cells were identified from gut tissue of patients with Crohn's disease and from peripheral blood [31-33] and were the ones initiating the nomenclature based on the archetypical cytokine secreted by the subset. Th17 cells induce an immunological response needed for eradication of extracellular bacterial and fungal infections [34].

Two in vitro models are used to study human Th cell priming; polarization of either umbilical cord blood or peripheral blood-derived naïve CD4+ cells. Cord blood CD4+ cells are mainly naïve by nature [35], but the proportion of naïve cells decreases in peripheral blood as a function of age [36]. Priming of Th cell subtypes from naïve CD4+ cells from both of these sources has similar requirements, but also clear differences in their activation and differentiation have been reported [37-40]. Compared to experiments performed with mouse cells, additional challenge with human primary cells is genetic heterogeneity leading to variation in responses. Depending on the question this can be handled by analyzing cells from several individuals side-by-side or by pooling the cells to detect average response.

The aim of this review is to summarize mechanisms which are known to affect gene expression during Th cell differentiation, and to point out cellular regulatory systems whose contribution has not yet been defined in this field (Fig. 1 ). We focus in particularly on the initiation of human Th cell subtype development. 1n addition, due to a limited space the special emphasis is given to high-throughput large-scale studies. To guide readers to more detailed mechanistic studies and a wider variety of original observations, we aim at citing the most relevant review articles.

1.1. Transcriptional regulation of human Th cell priming

1ntegration of external stimuli from cytokines along with crosslinking of TCR and co-receptors in the immunological synapse upon recognition of cognate antigen leads to activation and initiation of functional maturation of naïve Th cell. One of the criteria used to identify different Th cell subsets has been that

their differentiation is driven by quintessential transcription factors which get activated by a specific cytokine or combination of cytokines. Members of the STAT (signal transducer and activator of transcription) family are expressed already in naïve Th cells and are promptly activated by cytokine stimulus-specific phosphorylation. STATs synergistically withTCR-activated transcription factors direct the transcriptional regulation of inducible master regulators. Consequently, all Th cell subsets can be identified from each other based on their distinct transcriptional profiles [25,41-46]. Compared to the Th cell subtype-delineating transcriptional profiles, TCR crosslinking alone causes much more extensive changes in the gene expression both qualitatively and quantitatively [43,47]. Thus it is important to analyze not only the Th cell subtype-specific expression patterns, but also to interpret these in the context of all factors present in the cell. Current microarray and RNA-seq applications can capture global snapshot of the expression of transcripts within the cell population. Thus our view on putative regulators of Th cell differentiation has expanded from few candidate genes to hundreds of genes working in a combinatorial and competitive way. The evolution of the understanding of the global view on the human Th cell transcriptome is evident for example from our studies characterizing the initiation of human Th1 and Th2 cell polarization. The pilot study on cord blood cells polarized toward Th1 and Th2 phenotype resulted in identification of less than hundred genes specifically expressed in either subtype [43]. The follow up experiments with the improved array platforms increased the number of differentially regulated genes several fold [48,49]. Technological improvements have also made it possible to analyze gene expression more cost-efficiently than ever. This has further broadened our view on the Th cell differentiation by making kinetic analysis more feasible. Through profiling of human Th2 cell priming altogether at nine timepoints between 0.5 and 72 h, we identified more than thousand genes to be differentially regulated in response to 1L4, and illustrated how dynamic the process is [41]. The same approach was also shortly used to characterize human Th1 and Th17 cell priming [42,44]. 1mplementation and development of robust computational methods [42,50,51] facilitate efficient data mining of these impressive datasets to improve understanding of the principles of human Th cell priming. 1t is also worth of applying novel methods to further analyze the older datasets. For instance, reanalysis of our Th2 cell data which was gathered under TGFP-modulated differentiation [43] and interpretation of the results in the light of current knowledge of Th cell subtypes lead to identification of the first human "Th9" cell transcriptome [47], although the polarization conditions might not have been the most optimal [37]. Global profiling methods should also be used to characterize heterogeneity and plasticity of Th cell phenotypes, as it has become obvious that interpretations cannot be based solely on expression of key transcription factors or cytokines [52].

Discovery of RNA interference (RNAi) and harnessing it to the benefit of studies on human Th cells [53] have greatly empowered characterization of transcriptional regulation. Our group has exploited RNAi to identify target genes of several factors involved in human Th cell polarization; ATF3 [54], P1M kinases [55], PREL1 [56], SATB1 [57] and most intensively STAT6 [41,49]. Combined to gene regulation studies chromatin immunoprecipi-tation (Ch1P) analyses are indispensable for discrimination of direct and secondary target genes. 1n our study on the role of STAT6 in human Th2 cell priming, we observed that over 80% of the 1L4-regulated genes were controlled by STAT6 [41]. The magnitude of the effect of STAT6-siRNA was the strongest among the 1L4-inducible genes, suggesting that STAT6 is primarily needed for driving the subtype-specific transcriptional changes. 1n addition by using Ch1P-seq, we further demonstrated that around 30% of the STAT6-regulated genes were under a direct control of this factor and formed the core of STAT6-mediated signaling [41]. However,

Fig. 1. Mechanisms of gene expression regulation discussed in this article. Extracellular signals provided by tissue environment and antigen presenting cells in a form of cytokines, and crosslinking of antigen and co-receptors are integrated into instructions for Th cell differentiation. Polarized Th cells have their specific function and tissue localization depending on their cytokine secretion and homing receptor expression pattern. The differentiation is regulated at all levels of gene expression out of which transcription, RNA processing and epigenetics are covered in this review. The regulatory mechanisms are interconnected, form feedback circuitries and control plasticity of Th cell phenotypes. Key open questions for future studies include for example analysis of extent and mechanisms of collaboration between transcription regulators, causality between transcription regulation and epigenetic control, role of RNA processing and non-coding RNAs, mechanisms involved in long-distance gene regulation, and integration and flexibility of different regulatory mechanisms.

RNAi-mediated downregulation has its limitations. For example, achieving a long-lasting downregulation of highly inducible target genes or downregulation of target genes at a specific time point during primary Th cell differentiation is challenging. In addition, silencing several genes simultaneously might lead to exhaustion of the endogenous RNAi machinery resulting in unspecific effects

[58]. Thus, further development and exploitation of novel technical improvements, such as silicon nanowire-based introduction of siRNAs efficiently used with mouse unstimulated primary Th cells

[59] are extremely important for mechanistic studies.

There are several genome-wide studies on transcriptional profiling of T helper cell subtypes and identification of target genes of various transcription factors in mouse. However, there is a striking lack of such human studies (Table 1). In addition, the differences

in experimental design of the human and mouse studies available rarely allow direct unambiguous comparison of the results. For example, STAT6 target genes have been assessed from polarized Th2 cells after restimulation or at the initiation of polarization process in mouse and human, respectively [41,60]. Beyond the limitations due to the experimental layout, it is of interest that there are genes which are either bound by STAT6 or regulated by STAT6 both in human and mouse. This indicates that STAT6 is needed both for initiation and enhancement of Th2 cell polarization. Especially, factors involved in the control of transcription are often regulated by STAT6 in human and mouse (Laurila et al., unpublished). Multiparametric experimental approaches are needed to further elucidate the transcriptional programs resulting in Th cell specification as factors do not work in isolation, but co-operate through

Table 1

Selected original and combinatorial high-throughput analysis of direct target genes of CD4+ cell differentiation master regulators and histone modifications in mouse, and studies performed with human primary cells.

Mouse Human

Marker Cytokine stimulus Methods Ref. Marker Cytokine stimulus Methods Ref.

STAT4, STAT5A, STAT5B IL12 ChIP-seqa [125] STAT1 IFNg ChIP-seq [125]

GATA3 IL12 + IL2 ChIP-seq + RNA-seqb [80] STAT4 IL12 ChIP-seq [125]

H3K4me3, H3K27me3 IL12 + IL2 ChIP-seq +RNA-seq [76] TBX21, GATA3 IL12 ChIP-chiph + Exp. array [64]

TBX21; H3K4me1, H3K27me3 (WTand IL12 + IL2 ChIP-seq + RNA-seq [85] TBX21, GATA3 IL12 ChIP-seq [63]


TBX21; H3K4me3, H3K36me3 (WTand IL12 ChIP-seq, DHS-seqc [84] H3K4me1, H3K4me3, H3K27ac, CTCF IL12 ChIP-seq + RNA-seq [75]


STAT4; H3K4me3, H3K27me3, H3K36me3 (WT IL12 ChIP-seq + Exp. arrayd [60]

and Stat4-/-)

DNA methylation, H3K4me3, H2K27me3 IL12 + IL2 MAP-seqe + ChIP-seq + Exp. array [126]

STAT1; p300 (WT, Stat1-/-, Stat4-/-, IL12 ChIP-seq + RNA-seq [79]

Tbx21-/-, Tbx21 oef; Stat4-/- and Tbx21

oe); H3K4me1 (WT, Stat1-/-, Stat4-/-,


STAT5A, STAT5B IL2 ChIP-seq [125] STAT5A, STAT5B IL2 ChIP-seq [125]

STAT5A, STAT5B IL4 ChIP-seq [127] STAT6 IL4 ChIP-seq + Exp. array [41]

STAT6; H3K4me3, H3K27me3, H3K36me3 (WT IL4 ChIP-seq + Exp. array [60] GATA3, TBX21 IL4 ChIP-chip + Exp. array [64]

and Stat6-/-)

GATA3, FLI1, ETS1; H3K4me1, H3K4me2, IL4 + IL2 ChIP-seq + RNA-seq [80] GATA3 IL4 ChIP-seq [63]

H3K4me3, H3K27me3; H3K4me2 and

H3K27me3 (WT and Gata3-/-)

DNA methylation IL4 + IL2 MAP-seq + Exp. array [126] H3K4me1, H3K4me3, H3K27ac, CTCF IL4 ChIP-seq + RNA-seq [75]

p300 (WT, Stat6-/-, Gata3 oe; Stat6-/- and IL4 ChIP-seq + RNA-seq [79]

Gata3 oe); H3K4me1 (WT and Stat6-/-)

GATA3 TGFb + IL6 + IL1b ChIP-seq + RNA-seq [80]

H3K4me3, H3K27me3 TGFb + IL6 + IL1b ChIP-seq + RNA-seq [76]

STAT3, STAT5 TGFb + IL6 ChIP-seq + Exp. array [128]

pSTAT3; H3K4me3 (WT and Stat3-/-) TGFb + IL6 ChIP-seq + Exp. array [78]

STAT3, IRF4, BATF, c-Maf, RORg, FOSL2, ETV6, TGFb + IL6 ChIP-seq + RNA-seq + FAIRE-seqg [62]

HIF1a, CTCF; p300 (WT, Irf4-/-, Batf-/- and

Stat3-/-); p300, H3K4me2, H3K4me3 (WT

and RORg-/-)

GATA3 TGFb + IL2 ChIP-seq + RNA-seq [80]

H3K4me3, H3K27me3 TGFb + IL2 ChIP-seq + RNA-seq [76]

H3K4me3, H3K27me3 IL6 + IL21 ChIP-seq + Exp. array [77]

a ChlP-seq, chromatin immunoprecipitation coupled to high-throughput sequencing. b RNA-seq, high-throughput RNA sequencing.

c DHS-seq, DNase hypersensitivity analysis coupled to high-throughput sequencing. d Exp. array, expression array profiling.

e MAP-seq, methyl-binding domain affinity purification coupled to high-throughput sequencing. f oe, over-expression

g FAIRE-seq, formaldehyde-assisted isolation of regulatory elements based on open chromatin structure coupled to high-throughput sequencing. h ChlP-chip, chromatin immunoprecipitation coupled to microarray analysis. Some of the above mentioned studies contain combinatorial analysis of binding sites of several transcription factors, or epigenetic modifications and transcription factor binding data which are not specified in this table due to simplicity. Please refer to the original publications for further experimental details.

several mechanisms. Even so called master regulators can be found expressed together in the same cell [61]. Recent mouse studies have impressively reconstructed the transcription factor hierarchy and interplay during Th17 cell differentiation by integrating data from perturbed expression of various genes to time-series mRNA measurements and DNAbinding data [59,62]. Parallel analysis of GATA3 and TBX21 binding patterns in human Th1 and Th2 polarized cells demonstrated that these factors could bind to distinct as well as same genomic regions, and that TBX21 directs the redistribution of GATA3 binding sites to suppress Th2 cell polarizing signaling in Th1 cells [63,64]. Such integrated analyses with parallel modification of expression of several genes along with identification of nucleic acid and protein binding partners on human cells would greatly increase our understanding on the principles of Th cell polarization.

1.2. Epigenetic regulation ofTh cell priming in human

Biochemical modifications of the DNA and histones, and chro-matin conformation form the genetic landscape for transcriptional regulators. These heritable changes are collectively called epige-netic to discern them from the ones affected by alterations in the DNA sequence. Targeted analysis of cytokine loci demonstrated the role of epigenetic mechanisms in Th cell differentiation [65]. Since these first studies validating the concept, the view on overall magnitude of changes in epigenetic patterns has been expanded with the help of technical improvements, especially the ones enabling fast sequencing [66]. The increased knowledge of epigenetic regulation has also revealed novel functions for intergenic regions of our genome, the sequences once thought to be only "junk" DNA. Characterization of different cell types and responses to a range of environmental signals has uncovered that non-coding DNA stretches serve important roles for example as enhancers, insulators or nuclear organizers. Non-coding regions harbor several single nucleotide polymorphisms (SNPs) associated with human diseases [67]. The linkage between epigenetic regulation and disorders manifesting unbalanced Th cell activity is an exciting area for future research [68,69].

Patterns of methylation and acetylation modifications of his-tones, nucleosome positioning and accessibility of chromatin have been studied and correlated with gene expression in human peripheral blood CD4+ T cells before or after activation [70-73]. Human Treg lineage-specific DNA methylation map indicated cell type-specific enhancer activity [74]. In addition to these studies, there is only one recent report on genome-wide epigenetic patterns in human CD4+ cells. This study demonstrated that linage-specific enhancers become active already at early stages of human Th cell differentiation [75]. The lack of epigenetic data related to human primary CD4+ cell differentiation is in sharp contrasts with the numerous genome-wide datasets gathered from mouse cells (Table 1). In mouse, maps of active (H3K4me3) and repressed sites (H3K27me3) have been reported inTh1,Th2,Th17, iTreg, nTregand Tfh cells [76,77]. In addition, analysis ofthe role of master regulators ofTh cell differentiation has shown that STAT3, STAT4, STAT6 and GATA3 regulate the specificity of histone modifications defining enhancers, active or repressed sites [60,62,76,78-80]. Instead, the studies on enhancers in Th1, Th17 and Treg cells have revealed that the role ofTBX21, ROR^t and FOXP3 is much more limited, respectively [62,79,81]. However, different analysis integrating human and mouse datasets suggested that TBX21, similarly to GATA3, regulates especially its immunologically relevant target genes via distal regulatory sites having enhancer or insulator properties [63]. In conclusion, in the light of current knowledge STATs appear to be the main architects of establishing Th subtype-specific epige-netic landscape and the lineage-delineated transcription factors are responsible for further fine-tuning. However, STATs work in the context of TCR stimulation-activated factors, which are important

pioneering factors creating epigenetic landscape enabling Th cell polarization [61,82].

Already at the very early steps ofTh cell differentiation process, at 3 days after the initiation of polarization, Th cell lineage-specific epigenetic marks can be found in human [75]. While at this time cells have just started to proliferate, they already have their lineage-specific transcriptional profiles. The number of Th1 and Th2 cell-specific enhancers at this is time was at the level of two thousand. Majority of the enhancers were at the poised state, as defined by H3K4me1 without co-localizing H3K27ac mark, indicating that cells are ready to respond to environmental signals corresponding to their specific genomic landscape. Importantly, around 30% of the Th1 or Th2 cell-specific enhancers were already active. DNA motif discovery revealed that many predicted enhancers contained putative binding sites for Th1 or Th2 cell-specific transcription factors such as STAT1, STAT4, STAT6 or GATA3. In addition, several predicted enhancers harbored SNPs associated with immune-mediated diseases [75]. In mouse STAT6 has been reported to regulate a great majority of the active enhancers (p300 and H3K4me1 double positive sites) after restimulation of in vitro polarized Th2 cells, part of this control being also direct as evidenced by chromatin binding [79]. When the STAT6 binding sites reported in human [41] and Th2 cell enhancer map [75] are overlaid, a statistically significant (p <0.001) overlap can be found both when the Th2 cell-specific, or all enhancers found in Th2 cells are compared. Based on this analysis there are tens of enhancers at which STAT6 binding and enhancer marks have colocalization of at least 100 nucleotides (Larjo et al., unpublished). However, as the activity of enhancers is known to be sensitive indicator of cell identity and differentiation process [83], to address the role of STAT6 in enhancer-directed gene expression regulation in human requires experiments where all measurements are performed on samples collected at the same time points. The causal relationship between the DNA binding of key transcription factors for Th cell differentiation and regulation of epigenetic marks should be further investigated in human as already reported with transcription factor-deficient mice (Table 1) [60,62,77-80,84,85].

Looping of the distal regulatory sites into juxtaposed conformation coordinates expression of H4, Il5 and IH3 within the Th2 cytokine locus [86,87]. In addition, Ifny and Th2 cytokine loci form a high-order interchromosomal interaction, which holds the genes in an inactive state until activation of transcription of Th1 or Th2 cell-specific signature cytokine [88]. The regulation of chromatin conformation within the Th2 cytokine loci has been reported to be under the control of STAT6, GATA3 and SATB1 [86,87]. In human SATB1 has been highlighted to be important in repressing the expression of IL5 during the priming of Th2 cells by competing the action of GATA3 [57]. This initial observation was followed by characterization ofthe role of SATB1 comprehensively in a wider variety of CD4+ cell differentiation programs leading to the identification of SATB1 as a gate-keeper of T effector cell transcriptional programs over the FOXP3-induced regulatory Treg phenotype [89]. In Treg cells overexpression of SATB1 resulted in induction of several genes associated with Th1, Th2 and Th17 cells, and these cells lost their suppressive function. Thus SATB1 plays an important role in developmental decision between T regulatory and T effector cells, but also fine-tunes Th cell differentiation programs [57,89]. It has not been studied whether reorganization of chromatin structures is involved in SATB1-mediated conversion of Treg cells into effector cells. Likewise, studies exploiting chromosome conformation assays to elucidate human Th cell differentiation have not been reported. Interestingly, structural analysis of GATA3 and FOXP3 indicate that for example these two proteins important for Th cell differentiation have an ability to bridge DNA sequences together and thus enable intra- or interchromosomal interactions [90,91].

1.3. Regulation ofTh cell differentiation by RNA processing and non-coding RNAs

ENCODE project reported that three-quarters of the human genome is being transcribed [92]. RNA molecules which originally were thought to serve only as messengers or structural components in translation have since then got a range of novel functions and regulatory mechanisms associated with them. Genes are known to produce several different isoforms as a result of alternative splicing [92]. Validation of the alternative splicing events observed in polyclonally activated Jurkat cells confirmed that isoform ratios change also in human primary CD4+ cells upon activation [93]. Interestingly, among the genes validated to have changes in exon inclusion rates were GATA3 and HIFla [93] known to contribute to Th2 and Th17 cell phenotype, respectively [94-96]. Shortly after, RNAi screen revealed that hnRNPLL has a key role in the activation-induced splicing in human T cells [97] and defines memory T cell-specific forms of mRNA in mouse [98]. Although genome-wide publications with the latest technology are yet to be published, there are indications that alternative splicing regulates the development and function of different Th cell subtypes. For example, several alternatively spliced forms of regulatory T cell denoting FOXP3 have been observed in human, and alternative exon 2 has been shown to be essential for FOXP3-mediated inhibitory interaction with RORa and ROR^t, the factors promoting Th17 cell differentiation [99,100]. In addition, FOXP3 lacking exon 7 induces Th17 cell polarization, and the expression level of this isoform correlates with elevated IL17 production in Crohn's disease patients (Mailer et al., meeting abstract, TheJournal of Immunology, 2013:190,191.5).

The cellular protein-coding RNA pool is complemented with a variety of non-coding RNA molecules such as microRNAs (miRNA) and long non-coding RNAs (lncRNA) [101]. In addition to regulating the endogenous gene expression the molecular machinery involved in the biogenesis of these RNA molecules is now commonly exploited in molecular biology providing novel research tools especially important to studies performed with human cells as discussed above. miRNAs are about 21 nt long RNA molecules, which repress gene expression posttranscriptionally by constraining the initiation of protein synthesis or inducing mRNA degradation [102]. Mature miRNAs recognize the untranslated region (UTR) of their target mRNAs via imperfect base pairing. The requirement of only partial complementarity enables several transcripts to be down-regulated by a single type of miRNA. On the other hand, there can be functional synergy of different miRNAs regulating the same target mRNA. Primary miRNA transcripts are processed into their functional forms by Dicer and Drosha RNAse III-like enzymes. The first indication that miRNAs regulate Th cell differentiation came from the studies with Dicer-deficient mice, whose Th2 cells have reduced Gata3 expression and augmented IFNg production [103]. Furthermore, miRNAs have been shown to be notably important for suppressive activity of Treg cells as deletion of either Dicer or Drosha leads to impaired Treg development and function [104-107]. In effector CD4+ cells the role of miRNAs seems to be moderate and related to fine-tuning of Th cell phenotypes. Compared to the changes in Th cell transcriptomes, the miRNomes of different CD4+ effector subtypes are much more similar further suggesting their role in adjusting the response. Nevertheless, human naïve CD4+, Th1, Th2 and Th17 cells have signature miRNAs which distinguish these subtypes from each other. Moreover, miR-125b was experimentally shown to participate in the maintenance of the naïve state of the CD4+ T cells [108]. The results showing that miRNAs are needed for maintaining CD4+ cells undifferentiated is in accordance with the finding that activation of T cells leads to enhanced expression of mRNA isoforms with shorter 3'UTRs and thus reduced number of miRNA target sites [109] and that miRNA

expression in general is reduced upon T cell activation [110]. In mouse, miRNomes characteristic for Tfh and iTreg cells have also been reported [111]. The authors of the human miRNome study concluded that mouse and human miRNA pools were unexpectedly different and highlighted the importance of complementing mouse studies with the ones performed with human cells [108]. However, there are not yet reports comparing miRNomes of mouse and human Th subsets or during the differentiation process in a parallel experimental setup. The studies characterizing the function of miRNAs in human Th cell differentiation are in general scarce compared with mouse studies. To get a full overview on the topic, interested readers are advised to explore an excellent review on miRNAs in T cells [102].

A recent publication by Hu et al. catalogued the expression of long intergenic non-coding RNAs (lincRNA), which are a subgroup of lncRNAs, among mouse Th subtypes [112]. By overlaying the data with the previously published DNA binding patterns of STAT4, STAT6, TBX21 and GATA3, and by analyzing the lincRNA expression in knock-out mice the study described that these master regulators participate in defining the Th cell subtype-specific lincRNomes. Corresponding studies on human T cells have not yet been published. However, the authors of a recent review imply that they have unpublished RNA-seq results indicating that human T cell differentiation is guided by selective expression of lncRNAs [113]. Previously, purified Th subtypes sorted from peripheral blood have been shown to selectively express some lncRNAs in a microarray-based expression analysis [114]. One reason for the obscure expression of lncRNAs is that they are devoid of poly-A tail more often than protein-coding genes [115]. Thus, profiling studies using poly-A-selected RNA as a starting material may lack information of a significant proportion of lncRNAs which are expressed in a given cells. LncRNAs are also generally expressed at a lower level than protein-coding genes [115]. Part of the functions of lncRNAs is thought to be determined by their three-dimensional structure, allowing low conservation oflncRNAsequences between the species even when the function might be retained the same. In addition, the expression of lncRNAs is highly tissue type and developmental stage-specific [116]. These facts highlight the importance of studying the expression and function of lncRNAs relevant to CD4+ cell differentiation with human cells in a kinetic fashion. Collier et al. published the first study highlighting the role of a lncRNA in human Th cell polarization process last year. The study showed that Tmevpgl lncRNA is selectively expressed in Th1 cells and participates in the induction of IFN7 expression [117]. Recently, NeST aka Tmevpgl was shown to interact with WDR5, a core subunit of an enzyme complex catalyzing the activating histone modification, and increase the presence ofH3K4me3 mark in Ifny locus in mouse CD8+ cells [118]. The role of Tmevpgl in regulation of epigenetic modifications in CD4+ cells remains to be investigated.

2. Concluding remarks

RNA-level measurements have been used to globally profile the signaling pathways devoted to controlling Th cell differentiation. Most of the data has been gathered from mouse due to availability of gene manipulation techniques not applicable to human cells. However, as human and mouse have varying natural pathogens and habitat, it can be reasonably expected that there are species-specific adaptations in the immune system. Some of these have already been reported [5-7], but more extensive comparative studies are needed to reveal the extent at which results acquired with animal models can be extrapolated to human. Comparative studies can also be valuable in fine-tuning experimental setups and conditions of animal studies to better match the human biology. In this review we have aimed at giving a view on the status of

high-throughput studies on gene expression regulation during early Th cell differentiation in human and pinpoint some of the mechanisms at which the knowledge is lacking behind compared to the mouse studies.

As all gene regulatory mechanisms are highly intermingled, characterization of connectivity of different control levels will be of great interest in the future. This not only requires that coding and non-coding fractions of the RNA along with histone and DNA modifications will be analyzed together from the same samples, but also exploitation of novel techniques to reveal the underpinning regulatory mechanisms. For example analysis of chromatin looping with ChIA-PET (chromatin interaction analysis by paired-end tag sequencing) offers a method of defining three-dimensional chromatin interactions in an unbiased high-throughput fashion without a need to select the interactions to be studied beforehand [119]. Similarly, RNA-RNA and protein-RNA interactions can be studied with applying crosslinking, complex precipitation and high-throughput sequencing [120,121]. In addition, miniaturizing experiments to a single-cell level will aid mechanistic studies, and reveal the extent of variation in stochastic responses. In vivo an extreme response of only a few cells can be enough to trigger a signaling pathway with a strong impact on a system. These kinds of responses can however be invisible when analyzing a cell population revealing only an average response. Down-scaling of sample material requirements will especially benefit studies with human cells with limited availability.

It is also important to remember that as far-reaching the advancements in sequencing technologies are, similar methodologies are not available for proteins. Although RNA-level measurement can be used to predict the protein expression level, the correlation is not perfect. In addition, proteins are heavily posttranslationally modified, which in many cases is the main determinant of the activity of the molecule. To complement this article, a review by Lonnberg et al. [122] gives a sterling overview on the usage of proteomics approaches in studies of T cell biology. In summary, although protein level measurements have been applied in the studies ofT cell activation, the field ofTh cell differentiation is almost completely unexplored. Our group has done some important openings in "omics" beyond transcriptomics in human Th cell differentiation. For example, our recently published articles on IL4-induced nuclear proteome during the priming of human Th2 cells [123], and changes in the composition of lipidome in response to T cell activation [124], are pioneering articles applying modern mass spectrometry to human Th cell biology. As the analysis methods keep on evolving and experts of different fields combine their efforts it is plausible to expect that Th cell biology, as having a central role in immune responses and as offering an attracting model system for developmental biology, will be used as a playground at which different RNA and protein level measurements will be incorporated into a comprehensive view of an interplay of different regulatory mechanisms. This will be of great importance for translational research for Th cell-mediated diseases.


The work was supported by the Seventh Framework Programme of the European Commission (FP7-SYBILLA-201106, FP7-NANOMMUNE-214281, FP7-DIABIMMUNE-202063, FP7-PEVNET-261441, and FP7-NAN0S0LUTI0NS-309329), the Academy of Finland (the Centre of Excellence in Molecular Systems Immunology and Physiology Research, 2012-2017, grant 250114, and grants 77773,203725,207490,116639,115939,123864, and 126063), the Sigrid Jusélius Foundation, the Juvenile Diabetes Research Foundation (JDRF), Turku University Hospital Research Fund, and the

Emil Aaltonen Foundation. Sanna Edelman and Henna Kallionpää are acknowledged for their comments.


[1] Rice J. Animal models: not close enough. Nature 2012;484:S9.

[2] van der Worp HB, Howells DW, Sena ES, Porritt MJ, Rewell S, O'Collins V, et al. Can animal models of disease reliably inform human studies? PLoS Med 2010;7:e1000245.

[3] KarpCL. Unstressing intemperate models: how cold stress undermines mouse modeling. J Exp Med 2012;209:1069-74.

[4] Chen Z, Cheng K, Walton Z, Wang Y, Ebi H, Shimamura T, et al. A murine lung cancer co-clinical trial identifies genetic modifiers of therapeutic response. Nature 2012;483:613-7.

[5] Mestas J, Hughes CC. Of mice and not men: differences between mouse and human immunology. J Immunol 2004;172:2731-8.

[6] den Braber I, Mugwagwa T, Vrisekoop N, Westera L, Mogling R, de Boer AB, et al. Maintenance of peripheral naive T cells is sustained by thymus output in mice but not humans. Immunity 2012;36:288-97.

[7] SeokJ, Warren HS, Cuenca AG, Mindrinos MN, Baker HV, Xu W, et al. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc Natl Acad Sci U S A 2013;110:3507-12.

[8] Liew FY. T(H)1 and T(H)2 cells: a historical perspective. Nat Rev Immunol 2002;2:55-60.

[9] Mosmann TR, Coffman RL. TH1 and TH2 cells: different patterns of lym-phokine secretion lead to different functional properties. Annu Rev Immunol 1989;7:145-73.

[10] Filipe-SantosO, BustamanteJ, ChapgierA, Vogt G, de Beaucoudrey L, Feinberg J, et al. Inborn errors of 1L-12/23- and IFN-^-mediated immunity: molecular, cellular, and clinical features. Semin Immunol 2006;18:347-61.

[11] Pulendran B, Artis D. New paradigms in type 2 immunity. Science 2012;337:431-5.

[12] Hirahara K, PoholekA, VahediG, Laurence A, Kanno Y, MilnerJD, et al. Mechanisms underlying helperT-cell plasticity: implications for immune-mediated disease. J Allergy Clin Immunol 2013;131:1276-87.

[13] Bilate AM, Lafaille JJ. Induced CD4+Foxp3+ regulatory T cells in immune tolerance. Annu Rev Immunol 2012;30:733-58.

[14] Lehtimäki S, Lahesmaa R. Regulatory T cells control immune responses through their non-redundant tissue specific features. Front 1mmunol 2013;4:294.

[15] Walker MR, Kasprowicz DJ, Gersuk VH, Benard A, Van Landeghen M, Buck-ner JH, et al. Induction of FoxP3 and acquisition ofT regulatory activity by stimulated human CD4+CD25-T cells. J Clin Invest 2003;112:1437-43.

[16] Tran DQ, Ramsey H, Shevach EM. Induction of FOXP3 expression in naive human CD4+FOXP3- T cells by T-cell receptor stimulation is transforming growth factor-ß dependent but does not confer a regulatory phenotype. Blood 2007;110:2983-90.

[17] Strainic MG, Shevach EM, An F, Lin F, Medof ME. Absence of signaling into CD4+ cells via C3aR and C5aR enables autoinductive TGF-ß1 signaling and induction of Foxp3+ regulatory T cells. Nat Immunol 2013;14:162-71.

[18] Lu L, Zhou X, Wang J, Zheng SG, Horwitz DA. Characterization of protective human CD4CD25 FOXP3 regulatory T cells generated with 1L-2, TGF-ß and retinoic acid. PLoS ONE 2010;5:e15150.

[19] Schaerli P, Willimann K, Lang AB, Lipp M, Loetscher P, Moser B. CXC chemokine receptor 5 expression defines follicular homing T cells with B cell helper function. J Exp Med 2000;192:1553-62.

[20] Breitfeld D, Ohl L, Kremmer E, Ellwart J, Sallusto F, Lipp M, et al. Follicular B helper T cells express CXC chemokine receptor 5, localize to B cell follicles, and support immunoglobulin production. J Exp Med 2000;192:1545-52.

[21] Crotty S. Follicular helper CD4 T cells (TFH). Annu Rev Immunol 2011;29:621-63.

[22] Wong MT, Ye JJ, Alonso MN, Landrigan A, Cheung RK, Engleman E, et al. Regulation of human Th9 differentiation by type 1 interferons and 1L-21. Immunol Cell Biol 2010;88:624-31.

[23] Staudt V, Bothur E, Klein M, Lingnau K Reuter S, Grebe N, et al. Interferon-regulatory factor 4 is essential for the developmental program of T helper 9 cells. Immunity 2010;33:192-202.

[24] Kaplan MH. Th9 cells: differentiation and disease. 1mmunol Rev 2013;252:104-15.

[25] Eyerich S, Eyerich K, Pennino D, Carbone T, Nasorri F, Pallotta S, et al. Th22 cells represent a distinct humanTcell subset involved in epidermal immunity and remodeling. J Clin Invest 2009;119:3573-85.

[26] Trifari S, Kaplan CD, Tran EH, Crellin NK, Spits H. 1dentification of a human helperTcell population that has abundant production of interleukin 22 and is distinct from T(H)-17, T(H)1 and T(H)2 cells. Nat Immunol 2009;10:864-71.

[27] DuhenT, Geiger R, Jarrossay D, Lanzavecchia A, Sallusto F. Production of interleukin 22 but not interleukin 17 by a subset of human skin-homing memory T cells. Nat Immunol 2009;10:857-63.

[28] Basu R, O'Quinn DB, Silberger DJ, Schoeb TR, Fouser L, Ouyang W, et al. Th22 cells are an important source of 1L-22 for host protection against enteropathogenic bacteria. Immunity 2012;37:1061-75.

[29] Zheng Y, Danilenko DM, Valdez P, Kasman 1, Eastham-Anderson J, Wu J, et al. 1nterleukin-22, a T(H)17 cytokine, mediates 1L-23-induced dermal inflammation and acanthosis. Nature 2007;445:648-51.

[30] Ghoreschi K, Laurence A, Yang XP, Tato CM, McGeachy MJ, Konkel JE, et al. Generation of pathogenic T(H)17 cells in the absence of TGF-p signalling. Nature 2010;467:967-71.

[31] Wilson NJ, Boniface K, Chan JR, McKenzie BS, Blumenschein WM, Mattson JD, et al. Development, cytokine profile and function of human interleukin 17-producing helperT cells. Nat Immunol 2007;8:950-7.

[32] Acosta-Rodriguez EV, Rivino L, Geginat J, Jarrossay D, Gattorno M, Lanzavec-chia A, et al. Surface phenotype and antigenic specificity of human interleukin 17-producing T helper memory cells. Nat Immunol 2007;8:639-46.

[33] Annunziato F, Cosmi L, Santarlasci V, Maggi L, Liotta F, Mazzinghi B, et al. Phenotypic and functional features of human Th17 cells. J Exp Med 2007;204:1849-61.

[34] Korn T, Bettelli E, Oukka M, Kuchroo VK. IL-17 and Th17 cells. Annu Rev Immunol 2009;27:485-517.

[35] Rautajoki KJ, Marttila EM, Nyman TA, Lahesmaa R. Interleukin-4 inhibits caspase-3 by regulating several proteins in the fas pathway during initial stages of human T helper 2 cell differentiation. Mol Cell Proteomics 2007;6:238-51.

[36] Hannet I, Erkeller-Yuksel F, Lydyard P, Deneys V, DeBruyere M. Developmental and maturational changes in human blood lymphocyte subpopulations. Immunol Today 1992;13(215):218.

[37] Ramming A, Druzd D, Leipe J, Schulze-Koops H, Skapenko A. Maturation-related histone modifications in the PU.1 promoter regulate Th9-cell development. Blood 2012;119:4665-74.

[38] Chen L, Cohen AC, Lewis DB. Impaired allogeneic activation and T-helper 1 differentiation of human cord blood naive CD4 T cells. Biol Blood Marrow Transplant 2006;12:160-71.

[39] Zaghouani H, Hoeman CM, Adkins B. Neonatal immunity: faulty T-helpers and the shortcomings of dendritic cells. Trends Immunol 2009;30: 585-91.

[40] Doganci A, Birkholz J, Gehring S, Puhl AG, Zepp F, Meyer CU. In the presence of IL-21 human cord blood T cells differentiate to IL-10-producing Th1 but not Th17 orTh2 cells. Int Immunol 2013;25:157-69.

[41] Elo LL, Jarvenpaa H, Tuomela S, Raghav S, Ahlfors H, Laurila K, et al. Genome-wide profiling of interleukin-4 and STAT6 transcription factor regulation of humanTh2 cell programming. Immunity 2010;32:852-62.

[42] Aijo T, Edelman SM, Lonnberg T, Larjo A, Kallionpaa H, Tuomela S, et al. An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation. BMC Genomics 2012;13:572.

[43] Lund R, AittokallioT, NevalainenO, Lahesmaa R. Identification of novel genes regulated by IL-12, IL-4, or TGF-p during the early polarization of CD4+ lymphocytes. J Immunol 2003;171:5328-36.

[44] TuomelaS, Salo V, Tripathi SK, Chen Z, Laurila K, Gupta B, et al. Identification of early gene expression changes during human Th17 cell differentiation. Blood 2012;119:e151-60.

[45] Chtanova T, Tangye SG, Newton R, Frank N, Hodge MR, Rolph MS, et al. T follicular helper cells express a distinctive transcriptional profile, reflecting their role as non-Th1/Th2 effectorcells that provide help for B cells. J Immunol 2004;173:68-78.

[46] Prots I, Skapenko A, LipskyPE, Schulze-Koops H. Analysis ofthe transcriptional program of developing induced regulatory T cells. PLoS ONE 2011;6:e16913.

[47] van den Ham HJ, de Waal L, Andeweg AC, de Boer RJ. Identification of helper T cell master regulator candidates using the polar score method. J Immunol Methods 2010;361:98-109.

[48] Lund R, Ahlfors H, Kainonen E, Lahesmaa AM, Dixon C, Lahesmaa R. Identification of genes involved in the initiation of human Th1 orTh2 cell commitment. EurJ Immunol 2005;35:3307-19.

[49] Lund RJ, Loytomaki M, Naumanen T, Dixon C, Chen Z, Ahlfors H, et al. Genome-wide identification of novel genes involved in early Th1 and Th2 cell differentiation. J Immunol 2007;178:3648-60.

[50] Elo LL, Lahti L, Skottman H, Kylaniemi M, Lahesmaa R, Aittokallio T. Integrating probe-level expression changes across generations of Affymetrix arrays. Nucleic Acids Res 2005;33:e193.

[51] Elo LL, Jarvenpaa H, Oresic M, Lahesmaa R, AittokallioT. Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process. Bioinformatics 2007;23:2096-103.

[52] CrottyS. The 1-1-1 fallacy. Immunol Rev 2012;247:133-42.

[53] Tahvanainen J, Pykalainen M, Kallonen T, Lahteenmaki H, Rasool O, Lahesmaa R. Enrichment of nucleofected primary human CD4+ T cells: a novel and efficient method for studying gene function and role in human primary T helper cell differentiation. J Immunol Methods 2006;310:30-9.

[54] FilenS, Ylikoski E, Tripathi S, West A, Bjorkman M, Nystrom J, et al. Activating transcription factor 3 is a positive regulator of human IFNG gene expression. J Immunol 2010;184:4990-9.

[55] Tahvanainen J, Kylaniemi MK, Kanduri K, Gupta B, Lahteenmaki H, Kallo-nen T, et al. Proviral integration site for moloney murine leukemia virus (PIM) kinases promote human T helper 1 cell differentiation. J Biol Chem 2013;288:3048-58.

[56] Tahvanainen J, Kallonen T, Lahteenmaki H, Heiskanen KM, Westermarck J, Rao KV, et al. PRELI is a mitochondrial regulator of human primary T-helper cell apoptosis, STAT6, and Th2-cell differentiation. Blood 2009;113: 1268-77.

[57] Ahlfors H, Limaye A, Elo LL, Tuomela S, Burute M, Gottimukkala KV, et al. SATB1 dictates expression of multiple genes including IL-5 involved in human T helper cell differentiation. Blood 2010;116:1443-53.

[58] Jackson AL, Linsley PS. Recognizing and avoiding siRNA off-target effects for target identification and therapeutic application. Nat Rev Drug Discov 2010;9:57-67.

[59] Yosef N, Shalek AK, Gaublomme JT, Jin H, Lee Y, Awasthi A, et al. Dynamic regulatory network controlling TH17 cell differentiation. Nature 2013;496:461-8.

[60] Wei L, Vahedi G, Sun HW, Watford WT, Takatori H, Ramos HL, et al. Discrete roles of STAT4 and STAT6 transcription factors in tuning epigenetic modifications and transcription during T helper cell differentiation. Immunity 2010;32:840-51.

[61] Evans CM, Jenner RG. Transcription factor interplay in T helper cell differentiation. Brief Funct Genomics 2013. August 29 [Epub ahead of print].

[62] Ciofani M, Madar A, Galan C, Sellars M, Mace K, Pauli F, et al. A validated regulatory network forTh17 cell specification. Cell 2012;151:289-303.

[63] Kanhere A, Hertweck A, Bhatia U, Gokmen MR, Perucha E, Jackson I, et al. T-bet and GATA3 orchestrate Th1 and Th2 differentiation through lineage-specific targeting of distal regulatory elements. Nat Commun 2012;3:1268.

[64] Jenner RG, Townsend MJ, Jackson I, Sun K, Bouwman RD, Young RA, et al. The transcription factors T-bet and GATA-3 control alternative pathways ofT-cell differentiation through a shared set of target genes. Proc Natl Acad Sci U S A 2009;106:17876-81.

[65] Ansel KM, Lee DU, Rao A. An epigenetic view of helper T cell differentiation. Nat Immunol 2003;4:616-23.

[66] Kanno Y, Vahedi G, Hirahara K, Singleton K, O'Shea JJ. Transcriptional and epigenetic control of T helper cell specification: molecular mechanisms underlying commitment and plasticity. Annu Rev Immunol 2012;30: 707-31.

[67] ENCODE Project ConsortiumBernstein BE, Birney E, Dunham I, Green ED, Gunter C, et al. An integrated encyclopedia of DNA elements in the human genome. Nature 2012;489:57-74.

[68] Lovinsky-Desir S, Miller RL. Epigenetics, asthma, and allergic diseases: a review ofthe latest advancements. Curr Allergy Asthma Rep 2012;12:211-20.

[69] Ngalamika O, Zhang Y, Yin H, Zhao M, Gershwin ME, Lu Q. Epigenetics, autoimmunity and hematologic malignancies: a comprehensive review. J Autoimmun 2012;39:451-65.

[70] Barski A, Cuddapah S, Cui K, Roh TY, Schones DE, Wang Z, et al. Highresolution profiling of histone methylations in the human genome. Cell 2007;129:823-37.

[71] Schones DE, Cui K, Cuddapah S, Roh TY, Barski A, Wang Z, et al. Dynamic regulation of nucleosome positioning in the human genome. Cell 2008;132:887-98.

[72] Wang Z, Zang C, Rosenfeld JA, Schones DE, Barski A, Cuddapah S, et al. Combinatorial patterns of histone acetylations and methylations in the human genome. Nat Genet 2008;40:897-903.

[73] Boyle AP, Davis S, Shulha HP, Meltzer P, Margulies EH, Weng Z, et al. High-resolution mapping and characterization of open chromatin across the genome. Cell 2008;132:311-22.

[74] Schmidl C, Klug M, Boeld TJ, Andreesen R, Hoffmann P, Edinger M, et al. Lineage-specific DNA methylation in T cells correlates with histone methyla-tion and enhancer activity. Genome Res 2009;19:1165-74.

[75] Hawkins RD, Larjo A, Tripathi SK, Wagner U, Luu Y, Lönnberg T, et al. Global chromatin state analysis reveals lineage-specific enhancers during the initiation of human T helper 1 and T helper 2 cell polarization. Immunity 2013;38:1271-84.

[76] Wei G, Wei L, Zhu J, Zang C, Hu-Li J, Yao Z, et al. Global mapping of H3K4me3 and H3K27me3 reveals specificity and plasticity in lineage fate determination of differentiating CD4+ T cells. Immunity 2009;30:155-67.

[77] Lu KT, Kanno Y, Cannons JL, Handon R, Bible P, Elkahloun AG, et al. Functional and epigenetic studies reveal multistep differentiation and plasticity of in vitro-generated and in vivo-derived follicular T helper cells. Immunity 2011;35:622-32.

[78] Durant L, Watford WT, Ramos HL, Laurence A, Vahedi G, Wei L, et al. Diverse targets of th transcription factor STAT3 contribute to T cell pathogenicity and homeostasis. Immunity 2010;32:605-15.

[79] Vahedi G, Takahashi H, Nakayamada S, Sun HW, Sartorelli V, Kanno Y, et al. STATs shape the active enhancer landscape of T cell populations. Cell 2012;151:981-93.

[80] Wei G, Abraham BJ, Yagi R, Jothi R, Cui K, Sharma S, et al. Genome-wide analyses of transcription factor GATA3-mediated gene regulation in distinct T cell types. Immunity 2011;35:299-311.

[81] Samstein RM, Arvey A, Josefowicz SZ, PengX, Reynolds A, Sandstrom R, et al. Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification. Cell 2012;151:153-66.

[82] Vahedi G, Poholek AC, Hand TW, Laurence A, Kanno Y, O'Shea JJ, et al. Helper T-cell identity and evolution of differential transcriptomes and epigenomes. Immunol Rev 2013;252:24-40.

[83] Ong CT, Corces VG. Enhancer function: new insights into the regulation of tissue-specific gene expression. Nat Rev Genet 2011;12:283-93.

[84] Nakayamada S, Kanno Y, Takahashi H, Jankovic D, Lu KT, Johnson TA, et al. Early Th1 cell differentiation is marked by aTfh cell-like transition. Immunity 2011;35:919-31.

[85] Zhu J, Jankovic D, OlerAJ, Wei G, Sharma S, Hu G, et al. The transcription factor T-bet is induced by multiple pathways and prevents an endogenous Th2 cell program during Th1 cell responses. Immunity 2012;37:660-73.

[86] Cai S, Lee CC, Kohwi-Shigematsu T. SATB1 packages densely looped, transcrip-tionally active chromatin for coordinated expression of cytokine genes. Nat Genet 2006;38:1278-88.

[87 [88 [89

[92 [93 [94

[95 [96 [97

[101 [102 [103

[104 [105

Spilianakis CG, Flavell RA. Long-range intrachromosomal interactions in the T helpertype 2 cytokine locus. Nat 1mmunol 2004;5:1017-27. Spilianakis CG, Lalioti MD, Town T, Lee GR, Flavell RA. 1nterchromosomal associations between alternatively expressed loci. Nature 2005;435:637-45. Beyer M, Thabet Y, Muller RU, Sadlon T, Classen S, Lahl K, et al. Repression of the genome organizer SATB1 in regulatory T cells is required for suppressive function and inhibition of effector differentiation. Nat 1mmunol 2011;12:898-907.

Bandukwala HS, Wu Y, Feuerer M, Chen Y, Barboza B, Ghosh S, et al. Structure of a domain-swapped FOXP3 dimer on DNA and its function in regulatory T cells. 1mmunity 2011;34:479-91.

Chen Y, Bates DL, Dey R, Chen PH, Machado AC, Laird-Offringa 1A, et al. DNA binding by GATA transcription factor suggests mechanisms of DNA looping and long-range gene regulation. Cell Rep 2012;2:1197-206. Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, et al. Landscape of transcription in human cells. Nature 2012;489:101-8. 1p JY, Tong A, Pan Q, Topp JD, Blencowe BJ, Lynch KW. Global analysis of alternative splicing during T-cell activation. RNA 2007;13:563-72. Dang EV, Barbi J, Yang HY, Jinasena D, Yu H, Zheng Y, et al. Control of T(H)17/T(reg) balance by hypoxia-inducible factor 1. Cell 2011;146: 772-84.

Skapenko A, LeipeJ, NiesnerU, Devriendt K, Beetz R, Radbruch A, et al. GATA-3 in human T cell helpertype 2 development. J Exp Med 2004;199:423-8. Kryczek 1, Zhao E, Liu Y, Wang Y, Vatan L, Szeliga W, et al. Human TH17 cells are long-lived effector memory cells. Sci Transl Med 2011;3:104ra100. Oberdoerffer S, Moita LF, Neems D, Freitas RP, Hacohen N, Rao A. Regulation of CD45 alternative splicing by heterogeneous ribonucleoprotein, hnRNPLL. Science 2008;321:686-91.

Wu Z, Jia X, de la Cruz L, Su XC, Marzolf B, Troisch P, et al. Memory T cell RNA rearrangement programmed by heterogeneous nuclear ribonucleopro-tein hnRNPLL. 1mmunity 2008;29:863-75.

1chiyama K, Yoshida H, Wakabayashi Y, Chinen T, Saeki K, Nakaya M, et al. Foxp3 inhibits RORgammat-mediated 1L-17A mRNA transcription through direct interaction with RORgammat. J Biol Chem 2008;283:17003-8. Du J, Huang C, Zhou B, Bluestone JA, Ziegler SF. 1soform-specific inhibition of ROR a-mediated transcriptional activation by human FOXP3. J 1mmunol 2008;180:4785-92.

Esteller M. Non-coding RNAs in human disease. Nat Rev Genet 2011;12:861-74.

Baumjohann D, Ansel KM. MicroRNA-mediated regulation of T helper cell differentiation and plasticity. Nat Rev 1mmunol 2013;13:666-78. Muljo SA, Ansel KM, Kanellopoulou C, Livingston DM, Rao A, Rajewsky K, et al. Aberrant T cell differentiation in the absence of Dicer. J Exp Med 2005;202:261-9.

Cobb BS, Hertweck A, Smith J, O'Connor E, Graf D, Cook T, et al. A role for Dicer

in immune regulation. J Exp Med 2006;203:2519-27.

Liston A, Lu LF, O'Carroll D, Tarakhovsky A, Rudensky AY. Dicer-dependent

microRNA pathway safeguards regulatory T cell function. J Exp Med


Chong MM, Rasmussen JP, Rudensky AY, Littman DR. The RNAse111 enzyme Drosha is critical in T cells for preventing lethal inflammatory disease. J Exp Med 2008;205:2005-17.

Zhou X, Jeker LT, Fife BT, Zhu S, Anderson MS, McManus MT, et al. Selective miRNA disruption in T reg cells leads to uncontrolled autoimmunity. J Exp Med 2008;205:1983-91.

Rossi RL, Rossetti G, Wenandy L, Curti S, Ripamonti A, Bonnal RJ, et al. Distinct microRNA signatures in human lymphocyte subsets and enforcement of the naive state in CD4+ T cells by the microRNA miR-125b. Nat 1mmunol 2011;12:796-803.

110 111 112

Sandberg R, NeilsonJR,SarmaA,Sharp PA, Burge CB. Proliferating cells express mRNAs with shortened 3' untranslated regions and fewer microRNA target sites. Science 2008;320:1643-7.

Bronevetsky Y, Villarino AV, Eisley CJ, Barbeau R, Barczak AJ, Heinz GA, et al. T cell activation induces proteasomal degradation of Argonaute and rapid remodeling ofthe microRNA repertoire. J Exp Med 2013;210:417-32. Kuchen S, Resch W, Yamane A, Kuo N, Li Z, Chakraborty T, et al. Regulation of microRNA expression and abundance during lymphopoiesis. Immunity 2010;32:828-39.

Hu G, Tang Q, Sharma S, Yu F, Escobar TM, Muljo SA, et al. Expression and regulation of intergenic long noncoding RNAs during T cell development and differentiation. Nat Immunol 2013;14:1190-8.

Pagani M, Rossetti G, Panzeri I, de Candia P, Bonnal RJ, Rossi RL, et al. Role of microRNAs and long-non-coding RNAs in CD4(+) T-cell differentiation. Immunol Rev 2013;253:82-96.

Zhang H, Nestor CE, Zhao S, Lentini A, Bohle B, Benson M, et al. Profiling of human CD4(+) T-cell subsets identifies the TH2-specific noncoding RNA GATA3-AS1. J Allergy Clin Immunol 2013. July 16 [Epub ahead of print]. Derrien T, Johnson R, Bussotti G, Tanzer A, Djebali S, Tilgner H, et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res 2012;22:1775-89. Moran VA, Perera RJ, Khalil AM. Emerging functional and mechanistic paradigms of mammalian long non-coding RNAs. Nucleic Acids Res 2012;40:6391-400.

Collier SP, Collins PL, Williams CL, Boothby MR, Aune TM. Cutting edge: influence ofTmevpg1, a long intergenic noncoding RNA, on the expression of Ifng by Th1 cells. J Immunol 2012;189:2084-8.

Gomez JA, Wapinski OL, Yang YW, Bureau JF, Gopinath S, Monack DM, et al. The NeST long ncRNA controls microbial susceptibility and epigenetic activation ofthe interferon-^ locus. Cell 2013;152:743-54. Fullwood MJ, Liu MH, Pan YF, Liu J, Xu H, Mohamed YB, et al. An oestrogen-receptor-a-bound human chromatin interactome. Nature 2009;462:58-64. Helwak A, Kudla G, Dudnakova T, Tollervey D. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 2013;153:654-65.

König J, Zarnack K, Luscombe NM, Ule J. Protein-RNA interactions: new genomic technologies and perspectives. Nat Rev Genet 2012;13:77-83. Lönnberg T, Chen Z, Lahesmaa R. From a gene-centric to whole-proteome view of differentiation of T helper cell subsets Brief Funct Genomics 2013;7 [Epub ahead of print].

Moulder R, Lönnberg T, Elo LL, Filén JJ, Rainio E, Corthals G, et al. Quantitative proteomics analysis of the nuclear fraction of human CD4+ cells in the early phases of lL-4-induced Th2 differentiation. Mol Cell Proteomics 2010;9:1937-53.

Lönnberg T, Yetukuri L, Seppänen-LaaksoT, Lahesmaa R, Oresic M. T-cell activation induces selective changes of cellular lipidome. Front Biosci (Elite Ed) 2013;5:558-73.

Liao W, Lin JX, Wang L, Li P, Leonard WJ. Modulation of cytokine receptors by lL-2 broadly regulates differentiation into helperT cell lineages. Nat Immunol 2011;12:551-9.

Deaton AM, Webb S, Kerr AR, Illingworth RS, GuyJ, Andrews R, et al. Cell type-specific DNA methylation at intragenic CpG islands in the immune system. Genome Res 2011;21:1074-86.

Liao W, Schones DE, Oh J, Cui Y, Cui K, RohTY, et al. Priming for T helpertype 2 differentiation by interleukin 2-mediated induction of interleukin 4 receptor a-chain expression. Nat Immunol 2008;9:1288-96.

Yang XP, Ghoreschi K, Steward-Tharp SM, Rodriguez-Canales J, Zhu J, Grainger JR, et al. Opposing regulation of the locus encoding lL-17 through direct, reciprocal actions of STAT3 and STAT5. Nat Immunol 2011;12:247-54.