Deletion of a coordinate regulator of type 2 cytokine expression in mice

Regulation of T helper cell differentiation by the interplay between histone modification and chromatin interaction

The development and function of the immune system are controlled by temporospatial gene expression programs, which are regulated by cis-regulatory elements, chromatin structure, and trans-acting factors. In this study, we cataloged the dynamic histone modifications and chromatin interactions at regulatory regions during T helper (Th) cell differentiation. Our data revealed that the H3K4me1 landscape established by MLL4 in naive CD4+ T cells is critical for restructuring the regulatory interaction network and orchestrating gene expression during the early phase of Th differentiation. GATA3 plays a crucial role in further configuring H3K4me1 modification and the chromatin interaction network during Th2 differentiation. Furthermore, we demonstrated that HSS3-anchored chromatin loops function to restrict the activity of the Th2 locus control region (LCR), thus coordinating the expression of Th2 cytokines. Our results provide insights into the mechanisms of how the interplay between histone modifications, chromatin looping, and trans-acting factors contributes to the differentiation of Th cells.

Zfp281 and Zfp148 control CD4+ T cell thymic development and TH2 functions

How CD4+ lineage gene expression is initiated in differentiating thymocytes remains poorly understood. Here, we show that the paralog transcription factors Zfp281 and Zfp148 control both this process and cytokine expression by T helper cell type 2 (TH2) effector cells. Genetic, single-cell, and spatial transcriptomic analyses showed that these factors promote the intrathymic CD4+ T cell differentiation of class II major histocompatibility complex (MHC II)-restricted thymocytes, including expression of the CD4+ lineage-committing factor Thpok. In peripheral T cells, Zfp281 and Zfp148 promoted chromatin opening at and expression of TH2 cytokine genes but not of the TH2 lineage-determining transcription factor Gata3. We found that Zfp281 interacts with Gata3 and is recruited to Gata3 genomic binding sites at loci encoding Thpok and TH2 cytokines. Thus, Zfp148 and Zfp281 collaborate with Gata3 to promote CD4+ T cell development and TH2 cell responses.

Esearch3D: propagating gene expression in chromatin networks to illuminate active enhancers

Most cell type-specific genes are regulated by the interaction of enhancers with their promoters. The identification of enhancers is not trivial as enhancers are diverse in their characteristics and dynamic in their interaction partners. We present Esearch3D, a new method that exploits network theory approaches to identify active enhancers. Our work is based on the fact that enhancers act as a source of regulatory information to increase the rate of transcription of their target genes and that the flow of this information is mediated by the folding of chromatin in the three-dimensional (3D) nuclear space between the enhancer and the target gene promoter. Esearch3D reverse engineers this flow of information to calculate the likelihood of enhancer activity in intergenic regions by propagating the transcription levels of genes across 3D genome networks. Regions predicted to have high enhancer activity are shown to be enriched in annotations indicative of enhancer activity. These include: enhancer-associated histone marks, bidirectional CAGE-seq, STARR-seq, P300, RNA polymerase II and expression quantitative trait loci (eQTLs). Esearch3D leverages the relationship between chromatin architecture and transcription, allowing the prediction of active enhancers and an understanding of the complex underpinnings of regulatory networks. The method is available at: https://github.com/InfOmics/Esearch3D and https://doi.org/10.5281/zenodo.7737123.

Enhancer-promoter interaction maps provide insights into skeletal muscle-related traits in pig genome

Background

Gene expression programs are intimately linked to the interplay of active cis regulatory elements mediated by chromatin contacts and associated RNAs. Genome-wide association studies (GWAS) have identified many variants in these regulatory elements that can contribute to phenotypic diversity. However, the functional interpretation of these variants remains nontrivial due to the lack of chromatin contact information or limited contact resolution. Furthermore, the distribution and role of chromatin-associated RNAs in gene expression and chromatin conformation remain poorly understood. To address this, we first present a comprehensive interaction map of nuclear dynamics of 3D chromatin-chromatin interactions (H3K27ac BL-HiChIP) and RNA-chromatin interactions (GRID-seq) to reveal genomic variants that contribute to complex skeletal muscle traits.

Results

In a genome-wide scan, we provide systematic fine mapping and gene prioritization from GWAS leading signals that underlie phenotypic variability of growth rate, meat quality, and carcass performance. A set of candidate functional variants and 54 target genes previously not detected were identified, with 71% of these candidate functional variants choosing to skip over their nearest gene to regulate the target gene in a long-range manner. The effects of three functional variants regulating KLF6 (related to days to 100 kg), MXRA8 (related to lean meat percentage), and TAF11 (related to loin muscle depth) were observed in two pig populations. Moreover, we find that this multi-omics interaction map consists of functional communities that are enriched in specific biological functions, and GWAS target genes can serve as core genes for exploring peripheral trait-relevant genes.

Conclusions

Our results provide a valuable resource of candidate functional variants for complex skeletal muscle-related traits and establish an integrated approach to complement existing 3D genomics by exploiting RNA-chromatin and chromatin-chromatin interactions for future association studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01322-2.

An explainable artificial intelligence approach for decoding the enhancer histone modifications code and identification of novel enhancers in Drosophila

BACKGROUND

Enhancers are non-coding regions of the genome that control the activity of target genes. Recent efforts to identify active enhancers experimentally and in silico have proven effective. While these tools can predict the locations of enhancers with a high degree of accuracy, the mechanisms underpinning the activity of enhancers are often unclear.

RESULTS

Using machine learning (ML) and a rule-based explainable artificial intelligence (XAI) model, we demonstrate that we can predict the location of known enhancers in Drosophila with a high degree of accuracy. Most importantly, we use the rules of the XAI model to provide insight into the underlying combinatorial histone modifications code of enhancers. In addition, we identified a large set of putative enhancers that display the same epigenetic signature as enhancers identified experimentally. These putative enhancers are enriched in nascent transcription, divergent transcription and have 3D contacts with promoters of transcribed genes. However, they display only intermediary enrichment of mediator and cohesin complexes compared to previously characterised active enhancers. We also found that 10-15% of the predicted enhancers display similar characteristics to super enhancers observed in other species.

CONCLUSIONS

Here, we applied an explainable AI model to predict enhancers with high accuracy. Most importantly, we identified that different combinations of epigenetic marks characterise different groups of enhancers. Finally, we discovered a large set of putative enhancers which display similar characteristics with previously characterised active enhancers.

The Causes and Consequences of Spatial Organization of the Genome in Regulation of Gene Expression

Regulation of gene expression in time, space and quantity is orchestrated by the functional interplay of cis-acting elements and trans-acting factors. Our current view postulates that transcription factors recognize enhancer DNA and read the transcriptional regulatory code by cooperative DNA binding to specific DNA motifs, thus instructing the recruitment of transcriptional regulatory complexes forming a plethora of higher-ordered multi-protein-DNA and protein-protein complexes. Here, we reviewed the formation of multi-dimensional chromatin assemblies implicated in gene expression with emphasis on the regulatory role of enhancer hubs as coordinators of stochastic gene expression. Enhancer hubs contain many interacting regulatory elements and represent a remarkably dynamic and heterogeneous network of multivalent interactions. A functional consequence of such complex interaction networks could be that individual enhancers function synergistically to ensure coordination, tight control and robustness in regulation of expression of spatially connected genes. In this review, we discuss fundamental paradigms of such inter- and intra- chromosomal associations both in the context of immune-related genes and beyond.

Transcriptional enhancers: from prediction to functional assessment on a genome-wide scale

Enhancers are cis-regulatory sequences located distally to target genes. These sequences consolidate developmental and environmental cues to coordinate gene expression in a tissue-specific manner. Enhancer function and tissue specificity depend on the expressed set of transcription factors, which recognize binding sites and recruit cofactors that regulate local chromatin organization and gene transcription. Unlike other genomic elements, enhancers are challenging to identify because they function independently of orientation, are often distant from their promoters, have poorly defined boundaries, and display no reading frame. In addition, there are no defined genetic or epigenetic features that are unambiguously associated with enhancer activity. Over recent years there have been developments in both empirical assays and computational methods for enhancer prediction. We review genome-wide tools, CRISPR advancements, and high-throughput screening approaches that have improved our ability to both observe and manipulate enhancers in vitro at the level of primary genetic sequences, chromatin states, and spatial interactions. We also highlight contemporary animal models and their importance to enhancer validation. Together, these experimental systems and techniques complement one another and broaden our understanding of enhancer function in development, evolution, and disease.

Extracellular Vesicles from Pseudomonas aeruginosa Suppress MHC-Related Molecules in Human Lung Macrophages

Pseudomonas aeruginosa, a Gram-negative bacterium, is one of the most common pathogens colonizing the lungs of cystic fibrosis patients. P. aeruginosa secrete extracellular vesicles (EVs) that contain LPS and other virulence factors that modulate the host's innate immune response, leading to an increased local proinflammatory response and reduced pathogen clearance, resulting in chronic infection and ultimately poor patient outcomes. Lung macrophages are the first line of defense in the airway innate immune response to pathogens. Proper host response to bacterial infection requires communication between APC and T cells, ultimately leading to pathogen clearance. In this study, we investigate whether EVs secreted from P. aeruginosa alter MHC Ag expression in lung macrophages, thereby potentially contributing to decreased pathogen clearance. Primary lung macrophages from human subjects were collected via bronchoalveolar lavage and exposed to EVs isolated from P. aeruginosa in vitro. Gene expression was measured with the NanoString nCounter gene expression assay. DNA methylation was measured with the EPIC array platform to assess changes in methylation. P. aeruginosa EVs suppress the expression of 11 different MHC-associated molecules in lung macrophages. Additionally, we show reduced DNA methylation in a regulatory region of gene complement factor B (CFB) as the possible driving mechanism of widespread MHC gene suppression. Our results demonstrate MHC molecule downregulation by P. aeruginosa-derived EVs in lung macrophages, which is consistent with an immune evasion strategy employed by a prokaryote in a host-pathogen interaction, potentially leading to decreased pulmonary bacterial clearance.

Broadening our understanding of the genetics of Juvenile Idiopathic Arthritis (JIA): Interrogation of three dimensional chromatin structures and genetic regulatory elements within JIA-associated risk loci

Objective

The risk loci for juvenile idiopathic arthritis (JIA) consist of extended haplotypes that include functional elements in addition to canonical coding genes. As with most autoimmune diseases, the risk haplotypes for JIA are highly enriched for H3K4me1/H3K27ac histone marks, epigenetic signatures that typically identify poised or active enhancers. In this study, we test the hypothesis that genetic risk for JIA is exerted through altered enhancer-mediated gene regulation.

Methods

We mined publically available HiC and other chromatin conformation data to determine whether H3K27ac-marked regions in 25 JIA risk loci showed physical evidence of contact with gene promoters. We also used in vitro reporter assays to establish as proof-of-concept the idea that genetic variants in linkage disequilibrium with GWAS-identified tag SNPs alter enhancer function.

Results

All 25 loci examined showed multiple contact sites in the 4 different cell lines that we queried. These regions were characterized by HiC-defined loop structures that included 237 immune-related genes. Using in vitro assays, we found that a 657 bp, H3K4me1/H3K27-marked region within the first intron of IL2RA shows enhancer activity in reporter assays, and this activity is attenuated by SNPs on the IL2RA haplotype that we identified using whole genome sequencing of children with JIA. Similarly, we identified a 1,669 bp sequence in an intergenic region of the IL6R locus where SNPs identified in children with JIA increase enhancer function in reporter assays.

Conclusions

These studies provide evidence that altered enhancer function contributes to genetic risk in JIA. Further studies to identify the specific target genes of genetically altered enhancers are warranted.

Disparate expression specificities coded by a shared Hox-C enhancer

Can a single regulatory sequence be shared by two genes undergoing functional divergence? Here we describe a single promiscuous enhancer within the Drosophila Antennapedia Complex, EO053, that directs aspects of the expression of two adjacent genes, pb (a Hox2 ortholog) and zen2 (a divergent Hox3 paralog), with disparate spatial and temporal expression patterns. We were unable to separate the pb-like and zen2-like specificities within EO053, and we identify sequences affecting both expression patterns. Importantly, genomic deletion experiments demonstrate that EO053 cooperates with additional pb- and zen2-specific enhancers to regulate the mRNA expression of both genes. We examine sequence conservation of EO053 within the Schizophora, and show that patterns of synteny between the Hox2 and Hox3 orthologs in Arthropods are consistent with a shared regulatory relationship extending prior to the Hox3/zen divergence. Thus, EO053 represents an example of two genes having evolved disparate outputs while utilizing this shared regulatory region.

Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

ncRNAs in Type-2 Immunity

Immunological diseases, including asthma, autoimmunity and immunodeficiencies, affect a growing percentage of the population with significant unmet medical needs. As we slowly untangle and better appreciate these complex genetic and environment-influenced diseases, new therapeutically targetable pathways are emerging. Non-coding RNA species, which regulate epigenetic, transcriptional and translational responses are critical regulators of immune cell development, differentiation and effector function, and may represent one such new class of therapeutic targets. In this review we focus on type-2 immune responses, orchestrated by T_H2 cell-derived cytokines, IL-4, IL-5 and IL-13, which stimulate a variety of immune and tissue responses- commonly referred to as type-2 immunity. Evolved to protect us from parasitic helminths, type-2 immune responses are observed in individuals with allergic diseases, including Asthma, atopic dermatitis and food allergy. A growing number of studies have identified the involvement of various RNA species, including microRNAs (miRNA) and long non-coding (lncRNA), in type-2 immune responses and in both clinical and pre-clinical disease settings. We highlight these recent findings, identify gaps in our understanding and provide a perspective on how our current understanding can be harnessed for novel treat opportunities to treat type-2 immune-mediated diseases.

Integrative prediction of gene expression with chromatin accessibility and conformation data

BACKGROUND

Enhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter-enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organization of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability.

RESULTS

We have extended our [Formula: see text] framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We designed a novel machine learning approach that allows the prioritization of TFs binding to distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer-promoter loops involving YY1 in different cell lines.

CONCLUSION

We present a novel approach that can be used to prioritize TFs involved in distal and promoter-proximal regulatory events by integrating chromatin accessibility, conformation, and gene expression data. We show that the integration of chromatin conformation data can improve gene expression prediction and aids model interpretability.

Exploring a Drosophila Transcription Factor Interaction Network to Identify Cis-Regulatory Modules

Multiple transcription factors (TFs) bind to specific sites in the genome and interact among themselves to form the cis-regulatory modules (CRMs). They are essential in modulating the expression of genes, and it is important to study this interplay to understand gene regulation. In the present study, we integrated experimentally identified TF binding sites collected from published studies with computationally predicted TF binding sites to identify Drosophila CRMs. Along with the detection of the previously known CRMs, this approach identified novel protein combinations. We determined high-occupancy target sites, where a large number of TFs bind. Investigating these sites revealed that Giant, Dichaete, and Knirp are highly enriched in these locations. A common TAG team motif was observed at these sites, which might play a role in recruiting other TFs. While comparing the binding sites at distal and proximal promoters, we found that certain regulatory TFs, such as Zelda, were highly enriched in enhancers. Our study has shown that, from the information available concerning the TF binding sites, the real CRMs could be predicted accurately and efficiently. Although we only may claim co-occurrence of these proteins in this study, it may actually point to their interaction (as known interaction proteins typically co-occur together). Such an integrative approach can, therefore, help us to provide a better understanding of the interplay among the factors, even though further experimental verification is required.

Long-range interactions between proximal and distal regulatory regions in maize

Long-range chromatin interactions are important for transcriptional regulation of genes, many of which are related to complex agronomics traits. However, the pattern of three-dimensional chromatin interactions remains unclear in plants. Here we report the generation of chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) data and the construction of extensive H3K4me3- and H3K27ac-centered chromatin interaction maps in maize. Results show that the interacting patterns between proximal and distal regulatory regions of genes are highly complex and dynamic. Genes with chromatin interactions have higher expression levels than those without interactions. Genes with proximal-proximal interactions prefer to be transcriptionally coordinated. Tissue-specific proximal–distal interactions are associated with tissue-specific expression of genes. Interactions between proximal and distal regulatory regions further interweave into organized network communities that are enriched in specific biological functions. The high-resolution chromatin interaction maps will help to understand the transcription regulation of genes associated with complex agronomic traits of maize.

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) can discover specific protein-centered chromatin interactions in high resolution. Here, the authors use ChIA-PET to reveal the complex and dynamic interactions between proximal and distal regulatory regions of genes in maize.

Early-life undernutrition reprograms CD4+ T-cell glycolysis and epigenetics to facilitate asthma

BACKGROUND

Exposure to early-life undernutrition is closely related to higher risks of adverse immunologic outcomes in adulthood. Although it has been suggested that asthma has its origins in early life, its underlying mechanisms remain largely unknown.

OBJECTIVE

We characterized the effects of early-life undernutrition on T lymphocytes, which play a pivotal role in immune diseases, and we investigated whether this contributes to susceptibility to asthma in adulthood.

METHODS

Pregnant mice were fed a protein restriction diet (PRD) to establish an early-life undernutrition model. Naive CD4⁺ T cells (CD4⁺CD62L^hiCD44^-) from offspring were used throughout the study. T_H2 differentiation was examined by using fluorescence-activated cell sorting and ELISA under T_H2-polarized conditions in vitro and through ovalbumin-induced experimental asthma in vivo. T-cell metabolism was measured with a Seahorse XF96 Analyzer. DNA methylation levels were measured by using bisulfite sequencing.

RESULTS

PRD CD4⁺ T cells displayed increased activation and proliferation and were prone to differentiate into T_H2 cells both in vitro and in vivo, leading to susceptibility to experimental asthma. Mechanistically, early-life undernutrition upregulated mechanistic target of rapamycin 1-dependent glycolysis and induced conserved noncoding DNA sequence 1 DNA hypomethylation in the T_H2 cytokine locus of CD4⁺ T cells. Glycolysis blockades undermined increased T_H2 skewing and alleviated experimental asthma in PRD mice.

CONCLUSION

Early-life undernutrition induced mechanistic target of rapamycin 1-dependent glycolysis upregulation and T_H2 cytokine locus hypomethylation in CD4⁺ T cells, resulting in increased T-cell activation, proliferation, and T_H2 skewing and further susceptibility to experimental asthma.

Genome-wide DNA methylation profiling shows a distinct epigenetic signature associated with lung macrophages in cystic fibrosis

Background

Lung macrophages are major participants in the pulmonary innate immune response. In the cystic fibrosis (CF) lung, the inability of lung macrophages to successfully regulate the exaggerated inflammatory response suggests dysfunctional innate immune cell function. In this study, we aim to gain insight into innate immune cell dysfunction in CF by investigating alterations in DNA methylation in bronchoalveolar lavage (BAL) cells, composed primarily of lung macrophages of CF subjects compared with healthy controls. All analyses were performed using primary alveolar macrophages from human subjects collected via bronchoalveolar lavage. Epigenome-wide DNA methylation was examined via Illumina MethylationEPIC (850 K) array. Targeted next-generation bisulfite sequencing was used to validate selected differentially methylated CpGs. Methylation-based sample classification was performed using the recursively partitioned mixture model (RPMM) and was tested against sample case-control status. Differentially methylated loci were identified by fitting linear models with adjustment of age, sex, estimated cell type proportions, and repeat measurement.

Results

RPMM class membership was significantly associated with the CF disease status (P = 0.026). One hundred nine CpG loci were differentially methylated in CF BAL cells (all FDR ≤ 0.1). The majority of differentially methylated loci in CF were hypo-methylated and found within non-promoter CpG islands as well as in putative enhancer regions and DNase hyper-sensitive regions.

Conclusions

These results support a hypothesis that epigenetic changes, specifically DNA methylation at a multitude of gene loci in lung macrophages, may participate, at least in part, in driving dysfunctional innate immune cells in the CF lung.

Electronic supplementary material

The online version of this article (10.1186/s13148-018-0580-2) contains supplementary material, which is available to authorized users.

A conserved enhancer regulates Il9 expression in multiple lineages

Cytokine genes are regulated by multiple regulatory elements that confer tissue-specific and activation-dependent expression. The cis-regulatory elements of the gene encoding IL-9, a cytokine that promotes allergy, autoimmune inflammation and tumor immunity, have not been defined. Here we identify an enhancer (CNS-25) upstream of the Il9 gene that binds most transcription factors (TFs) that promote Il9 gene expression. Deletion of the enhancer in the mouse germline alters transcription factor binding to the remaining Il9 regulatory elements, and results in diminished IL-9 production in multiple cell types including Th9 cells, and attenuates IL-9-dependent immune responses. Moreover, deletion of the homologous enhancer (CNS-18) in primary human Th9 cultures results in significant decrease of IL-9 production. Thus, Il9 CNS-25/IL9 CNS-18 is a critical and conserved regulatory element for IL-9 production.

Interleukin-9 (IL-9) is important for allergy, autoimmunity and tumor immunity, but how its expression is regulated is unclear. Here the authors show the essential function of an enhancer, CNS-25 in mouse and CNS-18 in human, for IL-9 expression, with the deletion of this enhancer severely hampering IL-9 production in mice or human cells.

Microfluidic implementation of functional cytometric microbeads for improved multiplexed cytokine quantification

Functional microbeads have been widely applied in molecular identification and other biochemical applications in the past decade, owing to the compatibility with flow cytometry and the commercially available microbeads for a wide range of molecular identification. Nevertheless, there is still a technical hurdle caused by the significant sample volume required (∼50 μl), limited molecular detection limit (∼20 pg/ml), complicated liquid/microbead handling procedures, and the long reaction time (>2 h). In this work, we optimize the operation of an automated microbead-based microfluidic device for the reagent mixing and the dynamic cytokine detection. In particular, we adopt fluorescence microscopy for quantification of multiple microbeads in each microchamber instead of flow cytometry for a lower detection limit. The operation parameters are then configured for improved measurement performance. As demonstrated, we consider the cytokine secretion of human macrophage-differentiating lymphocytes stimulated by lipopolysaccharides. We examine requirements on the mixing duration, minimal sample volume, and the image analysis scheme for the smaller biosample volume (<5 μl), the lower cytokine detection limit (∼5 pg/ml), and shorter process time (∼30 min). Importantly, this microfluidic strategy can be further extended in the molecular profiling using other functional microbeads for a broad range of biomedical applications.

A high throughput, functional screen of human Body Mass Index GWAS loci using tissue-specific RNAi Drosophila melanogaster crosses

Human GWAS of obesity have been successful in identifying loci associated with adiposity, but for the most part, these are non-coding SNPs whose function, or even whose gene of action, is unknown. To help identify the genes on which these human BMI loci may be operating, we conducted a high throughput screen in Drosophila melanogaster. Starting with 78 BMI loci from two recently published GWAS meta-analyses, we identified fly orthologs of all nearby genes (± 250KB). We crossed RNAi knockdown lines of each gene with flies containing tissue-specific drivers to knock down (KD) the expression of the genes only in the brain and the fat body. We then raised the flies on a control diet and compared the amount of fat/triglyceride in the tissue-specific KD group compared to the driver-only control flies. 16 of the 78 BMI GWAS loci could not be screened with this approach, as no gene in the 500-kb region had a fly ortholog. Of the remaining 62 GWAS loci testable in the fly, we found a significant fat phenotype in the KD flies for at least one gene for 26 loci (42%) even after correcting for multiple comparisons. By contrast, the rate of significant fat phenotypes in RNAi KD found in a recent genome-wide Drosophila screen (Pospisilik et al. (2010) is ~5%. More interestingly, for 10 of the 26 positive regions, we found that the nearest gene was not the one that showed a significant phenotype in the fly. Specifically, our screen suggests that for the 10 human BMI SNPs rs11057405, rs205262, rs9925964, rs9914578, rs2287019, rs11688816, rs13107325, rs7164727, rs17724992, and rs299412, the functional genes may NOT be the nearest ones (CLIP1, C6orf106, KAT8, SMG6, QPCTL, EHBP1, SLC39A8, ADPGK /ADPGK-AS1, PGPEP1, KCTD15, respectively), but instead, the specific nearby cis genes are the functional target (namely: ZCCHC8, VPS33A, RSRC2; SPDEF, NUDT3; PAGR1; SETD1, VKORC1; SGSM2, SRR; VASP, SIX5; OTX1; BANK1; ARIH1; ELL; CHST8, respectively). The study also suggests further functional experiments to elucidate mechanism of action for genes evolutionarily conserved for fat storage.

Human Genome Wide Association Studies have successfully found thousands of novel genetic variants associated with many diseases. While these undoubtedly point to new biology, the field has been slowed in exploiting these new findings to reach a better understanding of exactly how they confer increased risk. Many, if not most, appear to be regulatory not coding variants, so their immediate consequence is not obvious. A real rate limiting step is even identifying which gene these variants might be regulating, and in what tissues they are operating to increase disease risk. In the absence of any other information, a first order assumption is that they may be more likely to be regulating a nearby gene, and such variants are often initially annotated by the “nearest” gene until their function is more definitively validated. Exploiting the idea that many genes may have conserved function across species, we conducted a high-throughput screen of fruit-fly orthologs of human genes nearby 78 well validated GWAS variants for human obesity, in order to more precisely identify the gene(s) of action. We systematically knocked down the function of each of these nearby genes in the brain and fat-body of the flies, raised them on a standard diet, and compared their percent body fat with control flies, in order to validate which genes showed a fat response. 43% of the time when fly orthologs existed in the region, we were able to identify the causal gene. Interestingly, nearly half the time (46%), it was not the nearest gene but another nearby one that regulated fat.

EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm

We present EP-DNN, a protocol for predicting enhancers based on chromatin features, in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem cell type (H1) and a differentiated lung cell type (IMR90). We train EP-DNN using p300 binding sites, as enhancers, and TSS and random non-DHS sites, as non-enhancers. We perform same-cell and cross-cell predictions to quantify the validation rate and compare against two state-of-the-art methods, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy with a validation rate of 91.6%, relative to 85.3% for DEEP-ENCODE and 85.5% for RFECS, for a given number of enhancer predictions and also scales better for a larger number of enhancer predictions. Moreover, our H1 → IMR90 predictions turn out to be more accurate than IMR90 → IMR90, potentially because H1 exhibits a richer signature set and our EP-DNN model is expressive enough to extract these subtleties. Our work shows how to leverage the full expressivity of deep learning models, using multiple hidden layers, while avoiding overfitting on the training data. We also lay the foundation for exploration of cross-cell enhancer predictions, potentially reducing the need for expensive experimentation.

Chromatin landscapes and genetic risk in systemic lupus

BACKGROUND

Systemic lupus erythematosus (SLE) is a multi-system, complex disease in which the environment interacts with inherited genes to produce broad phenotypes with inter-individual variability. Of 46 single nucleotide polymorphisms (SNPs) shown to confer genetic risk for SLE in recent genome-wide association studies, 30 lie within noncoding regions of the human genome. We therefore sought to identify and describe the functional elements (aside from genes) located within these regions of interest.

METHODS

We used chromatin immunoprecipitation followed by sequencing to identify epigenetic marks associated with enhancer function in adult neutrophils to determine whether enhancer-associated histone marks were enriched within the linkage disequilibrium (LD) blocks encompassing the 46 SNPs of interest. We also interrogated available data in Roadmap Epigenomics for CD4+ T cells and CD19+ B cells to identify these same elements in lymphoid cells.

RESULTS

All three cell types demonstrated enrichment of enhancer-associated histone marks compared with genomic background within LD blocks encoded by SLE-associated SNPs. In addition, within the promoter regions of these LD blocks, all three cell types demonstrated enrichment for transcription factor binding sites above genomic background. In CD19+ B cells, all but one of the LD blocks of interest were also enriched for enhancer-associated histone marks.

CONCLUSIONS

Much of the genetic risk for SLE lies within or near genomic regions of disease-relevant cells that are enriched for epigenetic marks associated with enhancer function. Elucidating the specific roles of these noncoding elements within these cell-type-specific genomes will be crucial to our understanding of SLE pathogenesis.

Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters

Long-range interactions between regulatory elements and gene promoters play key roles in transcriptional regulation. The vast majority of interactions are uncharted, constituting a major missing link in understanding genome control. Here, we use promoter capture Hi-C to identify interacting regions of 31,253 promoters in 17 human primary hematopoietic cell types. We show that promoter interactions are highly cell type specific and enriched for links between active promoters and epigenetically marked enhancers. Promoter interactomes reflect lineage relationships of the hematopoietic tree, consistent with dynamic remodeling of nuclear architecture during differentiation. Interacting regions are enriched in genetic variants linked with altered expression of genes they contact, highlighting their functional role. We exploit this rich resource to connect non-coding disease variants to putative target promoters, prioritizing thousands of disease-candidate genes and implicating disease pathways. Our results demonstrate the power of primary cell promoter interactomes to reveal insights into genomic regulatory mechanisms underlying common diseases.

Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions

Background

Gene expression is mediated by specialized cis-regulatory modules (CRMs), the most prominent of which are called enhancers. Early experiments indicated that enhancers located far from the gene promoters are often responsible for mediating gene transcription. Knowing their properties, regulatory activity, and genomic targets is crucial to the functional understanding of cellular events, ranging from cellular homeostasis to differentiation. Recent genome-wide investigation of epigenomic marks has indicated that enhancer elements could be enriched for certain epigenomic marks, such as, combinatorial patterns of histone modifications.

Methods

Our efforts in this paper are motivated by these recent advances in epigenomic profiling methods, which have uncovered enhancer-associated chromatin features in different cell types and organisms. Specifically, in this paper, we use recent state-of-the-art Deep Learning methods and develop a deep neural network (DNN)-based architecture, called EP-DNN, to predict the presence and types of enhancers in the human genome. It uses as features, the expression levels of the histone modifications at the peaks of the functional sites as well as in its adjacent regions. We apply EP-DNN to four different cell types: H1, IMR90, HepG2, and HeLa S3. We train EP-DNN using p300 binding sites as enhancers, and TSS and random non-DHS sites as non-enhancers. We perform EP-DNN predictions to quantify the validation rate for different levels of confidence in the predictions and also perform comparisons against two state-of-the-art computational models for enhancer predictions, DEEP-ENCODE and RFECS.

Results

We find that EP-DNN has superior accuracy and takes less time to make predictions. Next, we develop methods to make EP-DNN interpretable by computing the importance of each input feature in the classification task. This analysis indicates that the important histone modifications were distinct for different cell types, with some overlaps, e.g., H3K27ac was important in cell type H1 but less so in HeLa S3, while H3K4me1 was relatively important in all four cell types. We finally use the feature importance analysis to reduce the number of input features needed to train the DNN, thus reducing training time, which is often the computational bottleneck in the use of a DNN.

Conclusions

In this paper, we developed EP-DNN, which has high accuracy of prediction, with validation rates above 90 % for the operational region of enhancer prediction for all four cell lines that we studied, outperforming DEEP-ENCODE and RFECS. Then, we developed a method to analyze a trained DNN and determine which histone modifications are important, and within that, which features proximal or distal to the enhancer site, are important.

T helper 2 and T follicular helper cells: Regulation and function of interleukin-4

Type 2 immunity is characterized by expression of the cytokines interleukin (IL)-4, IL-5, IL-9 and IL-13, which can function in mediating protective immunity in the host or possess a pathogenic role. T helper (Th) 2 cells have emerged to play a beneficial role in mediating anti-parasitic immunity and are also known to be key players in mediating allergic diseases. In addition to the Th2 cells, recent studies have identified T follicular helper (Tfh) cells as an alternative source of IL-4 to regulate type 2 humoral immune responses, indicating that Th2 and Tfh cells exhibit overlapping phenotypical and functional characteristics. Th2 and Tfh cells appear to utilize distinct mechanisms for regulation of IL-4 expression; however unlike Th2 cells, the regulation and function of Tfh-derived IL-4 is not yet fully understood. Understanding of the molecular mechanisms for IL-4 expression and function in both cell subsets will be beneficial for the development of future therapeutic interventions.

Regulation of IL-4 Expression in Immunity and Diseases

IL-4 was first identified as a T cell-derived growth factor for B cells. Studies over the past several decades have markedly expanded our understanding of its cellular sources and function. In addition to T cells, IL-4 is produced by innate lymphocytes, such as NTK cells, and myeloid cells, such as basophils and mast cells. It is a signature cytokine of type 2 immune response but also has a nonimmune function. Its expression is tightly regulated at several levels, including signaling pathways, transcription factors, epigenetic modifications, microRNA, and long noncoding RNA. This chapter will review in detail the molecular mechanism regulating the cell type-specific expression of IL-4 in physiological and pathological type 2 immune responses.

Disease-Associated Single-Nucleotide Polymorphisms From Noncoding Regions in Juvenile Idiopathic Arthritis Are Located Within or Adjacent to Functional Genomic Elements of Human Neutrophils and CD4+ T Cells

OBJECTIVE

Juvenile idiopathic arthritis (JIA) is considered a complex disease in which the environment interacts with inherited genes to produce a phenotype that shows broad interindividual variability. Twenty-four regions of genetic risk for JIA were identified in a recent genome-wide association study (GWAS); however, as is typical of the results of GWAS, most of the regions of genetic risk (22 of 24) were in noncoding regions of the genome. This study was undertaken to identify functional elements (other than genes) that might be located within the regions of genetic risk.

METHODS

We used paired-end RNA sequencing to identify noncoding RNAs (ncRNAs) located within 5 kb of disease-associated single-nucleotide polymorphisms (SNPs). In addition, we used chromatin immunoprecipitation (ChIP) followed by sequencing to identify epigenetic marks associated with enhancer function (H3K4me1 and H3K27ac) in human neutrophils to determine whether enhancer-associated histone marks were enriched in the linkage disequilibrium blocks that encompassed the 22 SNPs identified in the GWAS.

RESULTS

In human neutrophils, we identified H3K4me1 and/or H3K27ac marks in 15 of the 22 regions previously identified as risk loci for JIA. In CD4+ T cells, 18 regions had H3K4me1 and/or H3K27ac marks. In addition, we identified ncRNA transcripts at the rs4705862 and rs6894249 loci in human neutrophils.

CONCLUSION

Much of the genetic risk for JIA lies within or adjacent to regions of neutrophil and CD4+ T cell genomes that carry epigenetic marks associated with enhancer function and/or ncRNA transcripts. These findings are consistent with the hypothesis that JIA is fundamentally a disorder of gene regulation that includes both the innate and the adaptive immune system. Elucidating the specific roles of these noncoding elements within leukocyte genomes will be critical to our understanding of JIA pathogenesis.

T helper 2 (Th2) cell differentiation, type 2 innate lymphoid cell (ILC2) development and regulation of interleukin-4 (IL-4) and IL-13 production

Interleukin-4 (IL-4), IL-5 and IL-13, the signature cytokines that are produced during type 2 immune responses, are critical for protective immunity against infections of extracellular parasites and are responsible for asthma and many other allergic inflammatory diseases. Although many immune cell types within the myeloid lineage compartment including basophils, eosinophils and mast cells are capable of producing at least one of these cytokines, the production of these "type 2 immune response-related" cytokines by lymphoid lineages, CD4 T helper 2 (Th2) cells and type 2 innate lymphoid cells (ILC2s) in particular, are the central events during type 2 immune responses. In this review, I will focus on the signaling pathways and key molecules that determine the differentiation of naïve CD4 T cells into Th2 cells, and how the expression of Th2 cytokines, especially IL-4 and IL-13, is regulated in Th2 cells. The similarities and differences in the differentiation of Th2 cells, IL-4-producing T follicular helper (Tfh) cells and ILC2s as well as their relationships will also be discussed.

Using the ENCODE Resource for Functional Annotation of Genetic Variants

This article illustrates the use of the Encyclopedia of DNA Elements (ENCODE) resource to generate or refine hypotheses from genomic data on disease and other phenotypic traits. First, the goals and history of ENCODE and related epigenomics projects are reviewed. Second, the rationale for ENCODE and the major data types used by ENCODE are briefly described, as are some standard heuristics for their interpretation. Third, the use of the ENCODE resource is examined. Standard use cases for ENCODE, accessing the ENCODE resource, and accessing data from related projects are discussed. Although the focus of this article is the use of ENCODE data, some of the same approaches can be used with data from other projects.

Transcriptional regulation of T helper type 2 differentiation

Considerable progress has been made in recent years towards our understanding of the molecular mechanisms of transcriptional regulation of T helper type 2 (Th2) cell differentiation. Additional transcription factors and chromatin-modifying factors were identified and shown to promote Th2 cell differentiation and inhibit differentiation into other subsets. Analyses of mice lacking several cis-regulatory elements have yielded more insight into the regulatory mechanism of Th2 cytokine genes. Gene deletion studies of several chromatin modifiers confirmed their impact on CD4 T-cell differentiation. In addition, recent genome-wide analyses of transcription factor binding and chromatin status revealed unexpected roles of these factors in Th2-cell differentiation. In this review, these recent findings and their implication are summarized.

'All things considered': transcriptional regulation of T helper type 2 cell differentiation from precursor to effector activation

T helper type 2 (Th2) cells are critical to host defence against helminth infection and the pathogenesis of allergic diseases. The differentiation of Th2 cells from naive CD4 T cells is controlled by intricate transcriptional mechanisms. At the precursor stage of naive CD4 T cells, transcriptional mechanisms maintain the potential and in the meantime prevent spontaneous differentiation to Th2 fate. In addition, intrachromosomal interactions important for co-ordinated expression of Th2 cytokines pre-exist in naive CD4 T cells. Upon T-cell receptor (TCR) engagement, naive CD4 T cells are induced by polarizing signals of the interleukin-4/Stat6 and Jagged/Notch pathways to up-regulate the expression of GATA-3. Once up-regulated, GATA-3 drives Th2 and suppresses Th1 differentiation in a cell autonomous fashion. In this stage of differentiation, the Th2 cytokine locus, as well as the interferon-γ locus, undergoes chromatin remodelling and epigenetic modifications that contribute to the somatic memory of Th2 cytokine gene expression pattern. Once differentiated, Th2 effector cells promptly produce Th2 cytokines upon TCR stimulation, which is regulated by concerted actions of GATA-3, TCR signalling, enhancers and the Th2 locus control region. This review provides a detailed account of the transcriptional regulatory events at these different stages of Th2 differentiation.

Enhancers: five essential questions

It is estimated that the human genome contains hundreds of thousands of enhancers, so understanding these gene-regulatory elements is a crucial goal. Several fundamental questions need to be addressed about enhancers, such as how do we identify them all, how do they work, and how do they contribute to disease and evolution? Five prominent researchers in this field look at how much we know already and what needs to be done to answer these questions.

Transcription factor YY1 is essential for regulation of the Th2 cytokine locus and for Th2 cell differentiation

The Th2 locus control region (LCR) has been shown to be important in efficient and coordinated cytokine gene regulation during Th2 cell differentiation. However, the molecular mechanism for this is poorly understood. To study the molecular mechanism of the Th2 LCR, we searched for proteins binding to it. We discovered that transcription factor YY1 bound to the LCR and the entire Th2 cytokine locus in a Th2-specific manner. Retroviral overexpression of YY1 induced Th2 cytokine expression. CD4-specific knockdown of YY1 in mice caused marked reduction in Th2 cytokine expression, repressed chromatin remodeling, decreased intrachromosomal interactions, and resistance in an animal model of asthma. YY1 physically associated with GATA-binding protein-3 (GATA3) and is required for GATA3 binding to the locus. YY1 bound to the regulatory elements in the locus before GATA3 binding. Thus, YY1 cooperates with GATA3 and is required for regulation of the Th2 cytokine locus and Th2 cell differentiation.

Differentiation of innate type-2 effector cells

Type-2 immune responses are the underlying cause of many allergic diseases and provide protection against parasitic infection. Effective type-2 immune responses are generated by type-2 helper CD4(+) T cells (Th2) as well as type-2 innate effector cells. While we have learned a great deal about how CD4(+) Th2 cells regulate their Th2 cytokine gene transcription, we still do not know how type-2 innate effector cells acquire their capacity to express Th2 cytokine genes. Furthermore, it remains poorly understood how Th2 cytokines regulate the differentiation of innate type-2 progenitor cells. In this review, we will focus on (1) the long distance interaction between the sites of allergic inflammation and the site of hematopoiesis in the bone marrow, (2) the characteristics of innate type-2 progenitors, and (3) the molecular mechanisms by which innate type-2 effector cells acquire the capacity to produce type-2 cytokines.

Interleukin-4 production by follicular helper T cells requires the conserved Il4 enhancer hypersensitivity site V

Follicular helper T cells (Tfh cells) are the major producers of interleukin-4 (IL-4) in secondary lymphoid organs where humoral immune responses develop. Il4 regulation in Tfh cells appears distinct from the classical T helper 2 (Th2) cell pathway, but the underlying molecular mechanisms remain largely unknown. We found that hypersensitivity site V (HS V; also known as CNS2), a 3' enhancer in the Il4 locus, is essential for IL-4 production by Tfh cells. Mice lacking HS V display marked defects in type 2 humoral immune responses, as evidenced by abrogated IgE and sharply reduced IgG1 production in vivo. In contrast, effector Th2 cells that are involved in tissue responses were far less dependent on HS V. HS V facilitated removal of repressive chromatin marks during Th2 and Tfh cell differentiation and increased accessibility of the Il4 promoter. Thus, Tfh and Th2 cells utilize distinct but overlapping molecular mechanisms to regulate Il4, a finding with important implications for understanding the molecular basis of allergic diseases.

CD4+ T helper 2 cells--microbial triggers, differentiation requirements and effector functions

Over the past 10 years we have made great strides in our understanding of T helper cell differentiation, expansion and effector functions. Within the context of T helper type 2 (Th2) cell development, novel innate-like cells with the capacity to secrete large amounts of interleukin-5 (IL-5), IL-13 and IL-9 as well as IL-4-producing and antigen-processing basophils have (re)-emerged onto the type 2 scene. To what extent these new players influence αβ+  CD4+  Th2 cell differentiation is discussed throughout this appraisal of the current literature. We highlight the unique features of Th2 cell development, highlighting the three necessary signals, T-cell receptor ligation, co-stimulation and cytokine receptor ligation. Finally, putting these into context, microbial and allergenic properties that trigger Th2 cell differentiation and how these influence Th2 effector function are discussed and questioned.

Current understanding of Th2 cell differentiation and function

Helper T cell (Th) has been identified as a critical immune cell for regulating immune response since 1980s. The type 2 helper Tcell (Th2), characterized by the production of interleukin-4 (IL-4), IL-5 and IL-13, plays a critical role in immune response against helminths invading cutaneous or mucosal sites. It also has a functional role in the pathophysiology of allergic diseases such as asthma and allergic diarrhea. Currently, most studies have shed light on Th2 cell function and behavior in specific diseases, such as asthma and helminthes inflammation, but not on Th2 cell itself and its differentiation. Based on different cytokines and specific behavior in recent research, Th2 cell is also regarded as new subtypes of T cell, such as IL-9 secreting T cell (Th9) and CXCR5(+) T follicular helper cells. Here, we will discuss the latest view of Th2 cell towards their function and the involvement of Th2 cell in diseases.

An updated view on transcription factor GATA3-mediated regulation of Th1 and Th2 cell differentiation

CD4 T(h) are critical for orchestrating adaptive immune responses. The expression of the transcription factor GATA3 (GATA-binding protein 3) is up-regulated or down-regulated during T(h)2 or T(h)1 cell differentiation, respectively. Furthermore, GATA3 is responsible for induction of T(h)2 differentiation and represses T(h)1 differentiation. In this review, we present an updated view on the molecular mechanisms through which GATA3 regulates T(h)1/T(h)2 differentiation. During T(h)2 cell differentiation, GATA3 directly binds to the T(h)2 cytokine gene locus at several regions and regulates expression. On the other hand, GATA3 inhibits T(h)1 cell differentiation by preventing up-regulation of IL-12 receptor β2 and STAT4 (signal transducer and activator of transcription 4) and neutralization of Runx3 (runt-related transcription factor 3) function through protein-protein interaction. GATA3 may also directly act on the Ifng gene. In summary, GATA3 serves as a transcriptional activator or repressor through direct action on transcriptional machinery and/or affecting chromatin remodeling at many critical loci encoding cytokines, cytokine receptors, signaling molecules as well as transcription factors that are involved in the regulation of T(h)1 and T(h)2 differentiation.

Peripheral CD4+ T-cell differentiation regulated by networks of cytokines and transcription factors

CD4(+) T cells, also known as T-helper (Th) cells, play an important role in orchestrating adaptive immune responses to various infectious agents. They are also involved in the induction of autoimmune and allergic diseases. Upon T-cell receptor (TCR)-mediated cell activation, naive CD4(+) T cells can differentiate into at least four major lineages, Th1, Th2, Th17, and iTreg cells, that participate in different types of immune responses. Networks of cytokines and transcription factors are critical for determining CD4(+) T-cell fates and effector cytokine production. Here, we review collaboration and cross-regulation between various essential cytokines in the activation/induction of key transcription factors during the process of Th cell differentiation towards these distinct lineages. We also discuss the interactions of key transcription factors at both genetic and protein levels and the function of the resulting network(s) in regulating the expression of effector cytokines.

Induction and maintenance of IL-4 expression are regulated differently by the 3' enhancer in CD4 T cells

IL-4 expression is known to be activated in CD4 T cells when they are differentiated to Th2 but not Th1 cells. However, CD4 T cells selected by MH class II-expressing thymocytes, named thymocyte-selected CD4 T cells (T-CD4 T cells), express IL-4 under both Th1 and Th2 conditions. In this study, we investigated molecular mechanisms by which IL-4 gene expression is regulated in T-CD4 T cells. We found that T-CD4 T cells express IL-4 soon after selection in the thymus. Deficiency of DNase I hypersensitive (HS) sites HS5a and HS5 at the 3'-enhancer region in the IL-4 gene decreased IL-4 production, but T-CD4 T cells were able to make IL-4 under the Th1-inducing condition. Consistent with this, IL-4 was expressed in Th1 differentiated T-CD4 T cells in the absence of recombination signal binding protein-J that interacts with HS5. When HS5 was examined separately from other endogenous regulatory elements using a reporter system, CD4 T cells that are selected by thymic epithelial cells cannot transcribe the IL-4 reporter gene with HS5 alone. However, HS5 was able to induce the expression of the IL-4 reporter gene in T-CD4 T cells. Interestingly, the Th1 differentiating signal led to deacetylation at HS5 of the IL-4 endogenous gene, whereas the Th2-inducing environment had no effect. Therefore, in T-CD4 T cells, HS5 plays an essential role during the induction phase of IL-4 expression, but the maintenance of IL-4 expression in Th1 cells requires additional regulatory elements.

Dynamic BRG1 recruitment during T helper differentiation and activation reveals distal regulatory elements

T helper cell differentiation and activation require specific transcriptional programs accompanied by changes in chromatin structure. However, little is known about the chromatin remodeling enzymes responsible. We performed genome-wide analysis to determine the general principles of BRG1 binding, followed by analysis of specific genes to determine whether these general rules were typical of key T cell genes. We found that binding of the remodeling protein BRG1 was programmed by both lineage and activation signals. BRG1 binding positively correlated with gene activity at protein-coding and microRNA (miRNA) genes. BRG1 binding was found at promoters and distal regions, including both novel and previously validated distal regulatory elements. Distal BRG1 binding correlated with expression, and novel distal sites in the Gata3 locus possessed enhancer-like activity, suggesting a general role for BRG1 in long-distance gene regulation. BRG1 recruitment to distal sites in Gata3 was impaired in cells lacking STAT6, a transcription factor that regulates lineage-specific genes. Together, these findings suggest that BRG1 interprets both differentiation and activation signals and plays a causal role in gene regulation, chromatin structure, and cell fate. Our findings suggest that BRG1 binding is a useful marker for identifying active cis-regulatory regions in protein-coding and miRNA genes.

T helper cell differentiation more than just cytokines

CD4(+) T helper (T(H)) cells play a critical role in orchestrating a pleiotropy of immune activities against a large variety of pathogens. It is generally thought that this is achieved through the acquisition of highly specialized functions after activation followed by the differentiation into various functional subsets. The differentiation process of naive precursor T(H) cells into defined effector subsets is controlled by cells of the innate immune system and their complex array of effector molecules such as secreted cytokines and membrane bound costimulatory molecules. These provide a unique quantitative or qualitative signal initiating T(H) development, which is subsequently reinforced via T cell-mediated feedback signals and selective survival and proliferative cues, ultimately resulting in the predominance of a particular T cell subset. In recent years, the number of defined T(H)cell subsets has expanded and the once rigid division of labor among them has been blurred with reports of plasticity among the subsets. In this chapter, we summarize and speculate on the current knowledge of the differentiation requirements of T(H) cell lineages, with particular focus on the T(H)17 subset.

The enhancer HS2 critically regulates GATA-3-mediated Il4 transcription in T(H)2 cells

GATA-3 is a master regulator of T helper type 2 (T(H)2) differentiation. However, the molecular basis of GATA-3-mediated T(H)2 lineage commitment is poorly understood. Here we identify the DNase I-hypersensitive site 2 (HS2) element located in the second intron of the interleukin 4 locus (Il4) as a critical enhancer strictly controlled by GATA-3 binding. Mice lacking HS2 showed substantial impairment in their asthmatic responses and their production of IL-4 but not of other T(H)2 cytokines. Overexpression of Gata3 in HS2-deficient T cells failed to restore Il4 expression. HS2 deletion impaired the trimethylation of histone H3 at Lys4 and acetylation of histone H3 at Lys9 and Lys14 in the Il4 locus. Our results indicate that HS2 is the target of GATA-3 in regulating chromosomal modification of the Il4 locus and is independent of the Il5 and Il13 loci.

Distal regions of the human IFNG locus direct cell type-specific expression

Genes, such as IFNG, which are expressed in multiple cell lineages of the immune system, may employ a common set of regulatory elements to direct transcription in multiple cell types or individual regulatory elements to direct expression in individual cell lineages. By employing a bacterial artificial chromosome transgenic system, we demonstrate that IFNG employs unique regulatory elements to achieve lineage-specific transcriptional control. Specifically, a one 1-kb element 30 kb upstream of IFNG activates transcription in T cells and NKT cells but not in NK cells. This distal regulatory element is a Runx3 binding site in Th1 cells and is needed for RNA polymerase II recruitment to IFNG, but it is not absolutely required for histone acetylation of the IFNG locus. These results support a model whereby IFNG uses cis-regulatory elements with cell type-restricted function.

Th2 LCR is essential for regulation of Th2 cytokine genes and for pathogenesis of allergic asthma

Previous studies have shown that Th2 cytokine genes on mouse chromosome 11 are coordinately regulated by the Th2 locus control region (LCR). To examine the in vivo function of Th2 LCR, we generated CD4-specific Th2 LCR-deficient (cLCR KO) mice using Cre-LoxP recombination. The number of CD4 T cells in the cLCR KO mouse was comparable to that in wild-type mice. The expression of Th2 cytokines was dramatically reduced in in vitro-stimulated naïve CD4 T cells. Deletion of the LCR led to a loss of general histone H3 acetylation and histone H3-K4 methylation, and demethylation of DNA in the Th2 cytokine locus. Upon ovalbumin challenge in the mouse model of allergic asthma, cLCR KO mice exhibited marked reduction in the recruitment of eosinophils and lymphocytes in the bronchoalveolar lavage fluid, serum IgE level, lung airway inflammation, mucus production in the airway walls, and airway hyperresponsiveness. These results directly demonstrate that the Th2 LCR is critically important in the regulation of Th2 cytokine genes, in chromatin remodeling of the Th2 cytokine locus, and in the pathogenesis of allergic asthma.

Transcriptional regulation of Th2 cell differentiation

CD4 T helper 2 (Th2) cells have critical functions in immune responses against extracellular parasites and are involved in asthma and other allergic diseases. The differentiation of naïve CD4 T cells into Th2 cells is initiated from T-cell receptor and cytokine-mediated signaling followed by upregulation of GATA3 and activation of signal transducer and activator of transcription 5 (STAT5), two indispensable events for this differentiation process. In this review, regulation of GATA3 expression and STAT5 activation and functions of these two transcription factors in inducing the expression of Th2 cytokines, cytokine receptors as well as epigenetic modification at Th2 cytokine locus are summarized. Furthermore, I present positive and negative regulatory networks important for Th2 cell commitment, selective growth of committed Th2 cells and suppression of alternative lineage fates. Finally, the difference between in vitro and in vivo Th2 differentiation is discussed.

Differentiation of effector CD4 T cell populations (*)

CD4 T cells play critical roles in mediating adaptive immunity to a variety of pathogens. They are also involved in autoimmunity, asthma, and allergic responses as well as in tumor immunity. During TCR activation in a particular cytokine milieu, naive CD4 T cells may differentiate into one of several lineages of T helper (Th) cells, including Th1, Th2, Th17, and iTreg, as defined by their pattern of cytokine production and function. In this review, we summarize the discovery, functions, and relationships among Th cells; the cytokine and signaling requirements for their development; the networks of transcription factors involved in their differentiation; the epigenetic regulation of their key cytokines and transcription factors; and human diseases involving defective CD4 T cell differentiation.