Seminars in Immunology 25 (2013) 282–290

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Review

Early T helper cell programming of gene expression in human夽 Soile Tuomela, Riitta Lahesmaa ∗ Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland

a r t i c l e Keywords: Human T helper cell Transcription Epigenetics High-throughput Genome-wide

i n f o

a b s t r a c t 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. © 2013 The Authors. Published by Elsevier Ltd. All rights reserved.

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: ChIA-PET, chromatin interaction analysis by paired-end tag sequencing; ChIP, chromatin immunoprecipitation; ChIP-seq, chromatin immunoprecipitation 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, highthroughput 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. 夽 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 Åbo Akademi University, P.O. Box 123, FIN-20521 Turku, Finland. Tel.: +358 2 333 8601; fax: +358 2 251 8808. E-mail address: riitta.lahesmaa@btk.fi (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 IFN␥ [8,9], and defects in Th1 cell signaling are associated with susceptibility to Salmonella and mycobacterial infections [10]. Th2 cells express IL4, 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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S. Tuomela, R. Lahesmaa / Seminars in Immunology 25 (2013) 282–290

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 TGF␤1-inducible FOXP3 expression have suppressive function [16]. However, addition of IL2 and retinoic acid to the culturing medium and more recently inhibition of anaphylatoxin 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]. IL9-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. In human also a Th22 subset that secretes IL22 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 IL22 but not IL17, 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, IL17 and IL17F 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 bloodderived 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. In 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 Integration 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

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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 with TCR-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 IL4, 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]. Implementation 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. It 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 TGF␤-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], PIM kinases [55], PRELI [56], SATB1 [57] and most intensively STAT6 [41,49]. Combined to gene regulation studies chromatin immunoprecipitation (ChIP) analyses are indispensable for discrimination of direct and secondary target genes. In our study on the role of STAT6 in human Th2 cell priming, we observed that over 80% of the IL4regulated genes were controlled by STAT6 [41]. The magnitude of the effect of STAT6-siRNA was the strongest among the IL4inducible genes, suggesting that STAT6 is primarily needed for driving the subtype-specific transcriptional changes. In addition by using ChIP-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,

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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 Marker

Methods

Ref.

Marker

Cytokine stimulus

Methods

Ref.

IL12 IL12 + IL2 IL12 + IL2 IL12 + IL2

ChIP-seqa ChIP-seq + RNA-seqb ChIP-seq + RNA-seq ChIP-seq + RNA-seq

[125] [80] [76] [85]

STAT1 STAT4 TBX21, GATA3 TBX21, GATA3

IFNg IL12 IL12 IL12

ChIP-seq ChIP-seq ChIP-chiph + Exp. array ChIP-seq

[125] [125] [64] [63]

IL12

ChIP-seq, DHS-seqc

[84]

H3K4me1, H3K4me3, H3K27ac, CTCF

IL12

ChIP-seq + RNA-seq

[75]

d

IL12

ChIP-seq + Exp. array

[60]

IL12 + IL2 IL12

MAP-seqe + ChIP-seq + Exp. array ChIP-seq + RNA-seq

[126] [79]

IL2 IL4 IL4

ChIP-seq ChIP-seq ChIP-seq + Exp. array

[125] [127] [60]

STAT5A, STAT5B STAT6 GATA3, TBX21

IL2 IL4 IL4

ChIP-seq ChIP-seq + Exp. array ChIP-chip + Exp. array

[125] [41] [64]

IL4 + IL2

ChIP-seq + RNA-seq

[80]

GATA3

IL4

ChIP-seq

[63]

IL4 + IL2 IL4

MAP-seq + Exp. array ChIP-seq + RNA-seq

[126] [79]

H3K4me1, H3K4me3, H3K27ac, CTCF

IL4

ChIP-seq + RNA-seq

[75]

TGFb + IL6 + IL1b TGFb + IL6 + IL1b TGFb + IL6 TGFb + IL6 TGFb + IL6

ChIP-seq + RNA-seq ChIP-seq + RNA-seq ChIP-seq + Exp. array ChIP-seq + Exp. array ChIP-seq + RNA-seq + FAIRE-seqg

[80] [76] [128] [78] [62]

TGFb + IL2 TGFb + IL2

ChIP-seq + RNA-seq ChIP-seq + RNA-seq

[80] [76]

IL6 + IL21

ChIP-seq + Exp. array

[77]

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Th1 STAT4, STAT5A, STAT5B GATA3 H3K4me3, H3K27me3 TBX21; H3K4me1, H3K27me3 (WT and Tbx21−/−) TBX21; H3K4me3, H3K36me3 (WT and Tbx21−/−) STAT4; H3K4me3, H3K27me3, H3K36me3 (WT and Stat4−/−) DNA methylation, H3K4me3, H2K27me3 STAT1; p300 (WT, Stat1−/−, Stat4−/−, Tbx21−/−, Tbx21 oef ; Stat4−/− and Tbx21 oe); H3K4me1 (WT, Stat1−/−, Stat4−/−, Tbx21−/−) Th2 STAT5A, STAT5B STAT5A, STAT5B STAT6; H3K4me3, H3K27me3, H3K36me3 (WT and Stat6−/−) GATA3, FLI1, ETS1; H3K4me1, H3K4me2, H3K4me3, H3K27me3; H3K4me2 and H3K27me3 (WT and Gata3−/−) DNA methylation p300 (WT, Stat6−/−, Gata3 oe; Stat6−/− and Gata3 oe); H3K4me1 (WT and Stat6−/−) Th17 GATA3 H3K4me3, H3K27me3 STAT3, STAT5 pSTAT3; H3K4me3 (WT and Stat3−/−) STAT3, IRF4, BATF, c-Maf, RORg, FOSL2, ETV6, HIF1a, CTCF; p300 (WT, Irf4−/−, Batf−/− and Stat3−/−); p300, H3K4me2, H3K4me3 (WT and RORg−/−) iTreg GATA3 H3K4me3, H3K27me3 Tfh H3K4me3, H3K27me3

Human Cytokine stimulus

a

ChIP-seq, chromatin immunoprecipitation coupled to high-throughput sequencing. 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 ChIP-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. b

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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 DNA binding 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 of Th cell priming in human Biochemical modifications of the DNA and histones, and chromatin conformation form the genetic landscape for transcriptional regulators. These heritable changes are collectively called epigenetic 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 histones, 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 typespecific 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 in Th1, Th2, Th17, iTreg, nTreg and Tfh cells [76,77]. In addition, analysis of the role of master regulators of Th 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 of TBX21, 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 epigenetic 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 of Th cell differentiation process, at 3 days after the initiation of polarization, Th cell lineagespecific 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 Il4, Il5 and Il13 within the Th2 cytokine locus [86,87]. In addition, Ifn 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 of the 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].

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1.3. Regulation of Th 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 HIF1˛ [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 activationinduced 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 ROR␣ 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, The Journal 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 downregulated 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

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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 of lncRNA sequences 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 Tmevpg1 lncRNA is selectively expressed in Th1 cells and participates in the induction of IFN␥ expression [117]. Recently, NeST aka Tmevpg1 was shown to interact with WDR5, a core subunit of an enzyme complex catalyzing the activating histone modification, and increase the presence of H3K4me3 mark in Ifn locus in mouse CD8+ cells [118]. The role of Tmevpg1 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 speciesspecific 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

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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 pairedend 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 Lönnberg 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 of T cell activation, the field of Th 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.

Acknowledgements The work was supported by the Seventh Framework Programme of the European Commission (FP7-SYBILLA-201106, FP7NANOMMUNE-214281, FP7-DIABIMMUNE-202063, FP7-PEVNET261441, and FP7-NANOSOLUTIONS-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.

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