Chromatin landscapes and genetic risk in systemic lupus

A systematic strategy for identifying causal single nucleotide polymorphisms and their target genes on Juvenile arthritis risk haplotypes

BACKGROUND

Although genome-wide association studies (GWAS) have identified multiple regions conferring genetic risk for juvenile idiopathic arthritis (JIA), we are still faced with the task of identifying the single nucleotide polymorphisms (SNPs) on the disease haplotypes that exert the biological effects that confer risk. Until we identify the risk-driving variants, identifying the genes influenced by these variants, and therefore translating genetic information to improved clinical care, will remain an insurmountable task. We used a function-based approach for identifying causal variant candidates and the target genes on JIA risk haplotypes.

METHODS

We used a massively parallel reporter assay (MPRA) in myeloid K562 cells to query the effects of 5,226 SNPs in non-coding regions on JIA risk haplotypes for their ability to alter gene expression when compared to the common allele. The assay relies on 180 bp oligonucleotide reporters ("oligos") in which the allele of interest is flanked by its cognate genomic sequence. Barcodes were added randomly by PCR to each oligo to achieve > 20 barcodes per oligo to provide a quantitative read-out of gene expression for each allele. Assays were performed in both unstimulated K562 cells and cells stimulated overnight with interferon gamma (IFNg). As proof of concept, we then used CRISPRi to demonstrate the feasibility of identifying the genes regulated by enhancers harboring expression-altering SNPs.

RESULTS

We identified 553 expression-altering SNPs in unstimulated K562 cells and an additional 490 in cells stimulated with IFNg. We further filtered the SNPs to identify those plausibly situated within functional chromatin, using open chromatin and H3K27ac ChIPseq peaks in unstimulated cells and open chromatin plus H3K4me1 in stimulated cells. These procedures yielded 42 unique SNPs (total = 84) for each set. Using CRISPRi, we demonstrated that enhancers harboring MPRA-screened variants in the TRAF1 and LNPEP/ERAP2 loci regulated multiple genes, suggesting complex influences of disease-driving variants.

CONCLUSION

Using MPRA and CRISPRi, JIA risk haplotypes can be queried to identify plausible candidates for disease-driving variants. Once these candidate variants are identified, target genes can be identified using CRISPRi informed by the 3D chromatin structures that encompass the risk haplotypes.

Comparison of the three-dimensional chromatin structures of adolescent and adult peripheral blood B cells: implications for the study of pediatric autoimmune diseases

Background/Purpose

Knowledge of the 3D genome is essential to elucidate genetic mechanisms driving autoimmune diseases. The 3D genome is distinct for each cell type, and it is uncertain whether cell lines faithfully recapitulate the 3D architecture of primary human cells or whether developmental aspects of the pediatric immune system require use of pediatric samples. We undertook a systematic analysis of B cells and B cell lines to compare 3D genomic features encompassing risk loci for juvenile idiopathic arthritis (JIA), systemic lupus (SLE), and type 1 diabetes (T1D).

Methods

We isolated B cells from healthy individuals, ages 9-17. HiChIP was performed using CTCF antibody, and CTCF peaks were identified. CTCF loops within the pediatric were compared to three datasets: 1) self-called CTCF consensus peaks called within the pediatric samples, 2) ENCODE's publicly available GM12878 CTCF ChIP-seq peaks, and 3) ENCODE's primary B cell CTCF ChIPseq peaks from two adult females. Differential looping was assessed within the pediatric samples and each of the three peak datasets.

Results

The number of consensus peaks called in the pediatric samples was similar to that identified in ENCODE's GM12878 and primary B cell datasets. We observed <1% of loops that demonstrated significantly differential looping between peaks called within the pediatric samples themselves and when called using ENCODE GM12878 peaks . Significant looping differences were even less when comparing loops of the pediatric called peaks to those of the ENCODE primary B cell peaks. When querying loops found in juvenile idiopathic arthritis, type 1 diabetes, or systemic lupus erythematosus risk haplotypes, we observed significant differences in only 2.2%, 1.0%, and 1.3% loops, respectively, when comparing peaks called within the pediatric samples and ENCODE GM12878 dataset. The differences were even less apparent when comparing loops called with the pediatric vs ENCODE adult primary B cell peak datasets.The 3D chromatin architecture in B cells is similar across pediatric, adult, and EBVtransformed cell lines. This conservation of 3D structure includes regions encompassing autoimmune risk haplotypes.

Conclusion

Thus, even for pediatric autoimmune diseases, publicly available adult B cell and cell line datasets may be sufficient for assessing effects exerted in the 3D genomic space.

3D genome organization and epigenetic regulation in autoimmune diseases

Three-dimensional (3D) genomics is an emerging field of research that investigates the relationship between gene regulatory function and the spatial structure of chromatin. Chromatin folding can be studied using chromosome conformation capture (3C) technology and 3C-based derivative sequencing technologies, including chromosome conformation capture-on-chip (4C), chromosome conformation capture carbon copy (5C), and high-throughput chromosome conformation capture (Hi-C), which allow scientists to capture 3D conformations from a single site to the entire genome. A comprehensive analysis of the relationships between various regulatory components and gene function also requires the integration of multi-omics data such as genomics, transcriptomics, and epigenomics. 3D genome folding is involved in immune cell differentiation, activation, and dysfunction and participates in a wide range of diseases, including autoimmune diseases. We describe hierarchical 3D chromatin organization in this review and conclude with characteristics of C-techniques and multi-omics applications of the 3D genome. In addition, we describe the relationship between 3D genome structure and the differentiation and maturation of immune cells and address how changes in chromosome folding contribute to autoimmune diseases.

B lymphocytes in treatment-naive paediatric patients with lupus are epigenetically distinct from healthy children

BACKGROUND

SLE is likely triggered by gene-environment interactions. We have shown that most SLE-associated haplotypes encompass genomic regions enriched for epigenetic marks associated with enhancer function in lymphocytes, suggesting genetic risk is exerted through altered gene regulation. Data remain scarce on how epigenetic variance contributes to disease risk in paediatric SLE (pSLE). We aim to identify differences in epigenetically regulated chromatin architecture in treatment-naive patients with pSLE compared with healthy children.

METHODS

Using the assay for transposase-accessible chromatin with sequencing (ATACseq), we surveyed open chromatin in 10 treatment-naive patients with pSLE, with at least moderate disease severity, and 5 healthy children. We investigated whether regions of open chromatin unique to patients with pSLE demonstrate enrichment for specific transcriptional regulators, using standard computational approaches to identify unique peaks and a false discovery rate of <0.05. Further analyses for histone modification enrichment and variant calling were performed using bioinformatics packages in R and Linux.

RESULTS

We identified 30 139 differentially accessible regions (DAR) unique to pSLE B cells; 64.3% are more accessible in pSLE than healthy children. Many DAR are found in distal, intergenic regions and enriched for enhancer histone marks (p=0.027). B cells from adult patients with SLE contain more regions of inaccessible chromatin than those in pSLE. In pSLE B cells, 65.2% of the DAR are located within or near known SLE haplotypes. Further analysis revealed enrichment of transcription factor binding motifs within these DAR that may regulate genes involved in pro-inflammatory responses and cellular adhesion.

CONCLUSIONS

We demonstrate an epigenetically distinct profile in pSLE B cells when compared with healthy children and adults with lupus, indicating that pSLE B cells are predisposed for disease onset/development. Increased chromatin accessibility in non-coding genomic regions controlling activation of inflammation suggest that transcriptional dysregulation by regulatory elements controlling B cell activation plays an important role in pSLE pathogenesis.

Polymorphisms of ATG5 Gene Are Associated with Autoimmune Thyroid Diseases, Especially Thyroid Eye Disease

Objective

To explore the association of ATG5 gene polymorphisms with autoimmune thyroid diseases (AITDs) including Hashimoto's thyroiditis (HT) and Graves' illness (GD) as well as their clinical features.

Methods

rs6568431, rs548234, and rs6937876 were selected to investigate the correlation of single-nucleotide polymorphisms of ATG5 gene with AITDs. Their frequencies in 824 AITD patients, including 271 HT patients and 553 GD patients, and 764 healthy controls were tested using both ligase detection reaction and multiplex polymerase chain reaction.

Results

Allele A frequency of rs6568431 in AITDs patients (p = 0.016, OR = 1.201, 95% CI = 1.034 - 1.394) and allele G frequency of rs6937876 in AITDs patients (p = 0.009, OR = 1.223, 95% CI = 1.052 - 1.422) and in GD patients (p = 0.009, OR = 1.247, 95% CI = 1.056 - 1.473) were significantly higher than those in the healthy controls. The frequency of G allele (p = 5.42E - 18, OR = 0.242, 95% CI = 0.173 - 0.339) of rs6937876 was significantly higher in GD patients with ophthalmopathy. However, no relationship was found between family history, age onset, and the three SNPs.

Conclusion

The study is the first to reveal the association between AITDs and ATG5 polymorphisms, and ATG5 gene is considered as a predisposing gene to AITDs, especially GDs.

The Chromatin Accessibility Landscape of Peripheral Blood Mononuclear Cells in Patients With Systemic Lupus Erythematosus at Single-Cell Resolution

Objective

Systemic lupus erythematosus (SLE) is a complex autoimmune disease, and various immune cells are involved in the initiation, progression, and regulation of SLE. Our goal was to reveal the chromatin accessibility landscape of peripheral blood mononuclear cells (PBMCs) in SLE patients at single-cell resolution and identify the transcription factors (TFs) that may drive abnormal immune responses.

Methods

The assay for transposase accessible chromatin in single-cell sequencing (scATAC-seq) method was applied to map the landscape of active regulatory DNA in immune cells from SLE patients at single-cell resolution, followed by clustering, peak annotation and motif analysis of PBMCs in SLE.

Results

Peripheral blood mononuclear cells were robustly clustered based on their types without using antibodies. We identified twenty patterns of TF activation that drive abnormal immune responses in SLE patients. Then, we observed ten genes that were highly associated with SLE pathogenesis by altering T cell activity. Finally, we found 12 key TFs regulating the above six genes (CD83, ELF4, ITPKB, RAB27A, RUNX3, and ZMIZ1) that may be related to SLE disease pathogenesis and were significantly enriched in SLE patients (p <0.05, FC >2). With qPCR experiments on CD83, ELF4, RUNX3, and ZMIZ1 in B cells, we observed a significant difference in the expression of genes (ELF4, RUNX3, and ZMIZ1), which were regulated by seven TFs (EWSR1-FLI1, MAF, MAFA, NFIB, NR2C2 (var. 2), TBX4, and TBX5). Meanwhile, the seven TFs showed highly accessible binding sites in SLE patients.

Conclusions

These results confirm the importance of using single-cell sequencing to uncover the real features of immune cells in SLE patients, reveal key TFs in SLE-PBMCs, and provide foundational insights relevant for epigenetic therapy.

Broadening our understanding of genetic risk for scleroderma/systemic sclerosis by querying the chromatin architecture surrounding the risk haplotypes

BACKGROUND

Genetic variants in the human leukocyte antigen (HLA) locus contribute to the risk for developing scleroderma/systemic sclerosis (SSc). However, there are other replicated loci that also contribute to genetic risk for SSc, and it is unknown whether genetic risk in these non-HLA loci acts primarily on the vasculature, immune system, fibroblasts, or other relevant cell types. We used the Cistrome database to investigate the epigenetic landscapes surrounding 11 replicated SSc associated loci to determine whether SNPs in these loci may affect regulatory elements and whether they are likely to impact a specific cell type.

METHODS

We mapped 11 replicated SNPs to haplotypes and sought to determine whether there was significant enrichment for H3K27ac and H3K4me1 marks, epigenetic signatures of enhancer function, on these haplotypes. We queried pathologically relevant cell types: B cells, endothelial cells, fibroblasts, monocytes, and T cells. We then identified the topologically associated domains (TADs) that encompass the SSc risk haplotypes in primary T cells to identify the full range of genes that may be influenced by SSc causal SNPs. We used gene ontology analyses of the genes within the TADs to gain insight into immunologic functions that might be affected by SSc causal SNPs.

RESULTS

The SSc-associated haplotypes were enriched (p value < 0.01) for H3K4me1/H3K27ac marks in monocytes. Enrichment of one of the two histone marks was found in B cells, fibroblasts, and T cells. No enrichment was identified in endothelial cells. Ontological analyses of genes within the TADs encompassing the risk haplotypes showed enrichment for regulation of transcription, protein binding, activation of T lymphocytes, and proliferation of immune cells.

CONCLUSIONS

The 11 non-HLA SSc risk haplotypes queried are highly enriched for H3K4me1/H3K27ac-marked regulatory elements in a broad range of immune cells and fibroblasts. Furthermore, in immune cells, the risk haplotypes belong to larger chromatin structures encompassing genes that regulate a wide array of immune processes associated with SSc pathogenesis. Though importance of the vasculature in the pathobiology of SSc is widely accepted, we were unable to find evidence for genetic influences on endothelial cell function in these regions.

Variant to Gene Mapping to Discover New Targets for Immune Tolerance

The breakdown of immunological tolerance leads to autoimmune disease, and the mechanisms that maintain self-tolerance, especially in humans, are not fully understood. Genome-wide association studies (GWAS) have identified hundreds of human genetic loci statistically linked to autoimmune disease risk, and epigenetic modifications of DNA and chromatin at these loci have been associated with autoimmune disease risk. Because the vast majority of these signals are located far from genes, identifying causal variants, and their functional consequences on the correct effector genes, has been challenging. These limitations have hampered the translation of GWAS findings into novel drug targets and clinical interventions, but recent advances in understanding the spatial organization of the genome in the nucleus have offered mechanistic insights into gene regulation and answers to questions left open by GWAS. Here we discuss the potential for 'variant-to-gene mapping' approaches that integrate GWAS with 3D functional genomic data to identify human genes involved in the maintenance of tolerance.

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.

Personalized therapy design for systemic lupus erythematosus based on the analysis of protein-protein interaction networks

We analyzed protein expression data for Lupus patients, which have been obtained from publicly available databases. A combination of systems biology and statistical thermodynamics approaches was used to extract topological properties of the associated protein-protein interaction networks for each of the 291 patients whose samples were used to provide the molecular data. We have concluded that among the many proteins that appear to play critical roles in this pathology, most of them are either ribosomal proteins, ubiquitination pathway proteins or heat shock proteins. We propose some of the proteins identified in this study to be considered for drug targeting.

Using Chromatin Architecture to Understand the Genetics and Transcriptomics of Juvenile Idiopathic Arthritis

The presence of abnormal gene expression signatures is a well-described feature of the oligoarticular and polyarticular forms of juvenile idiopathic arthritis. In this review, we discuss how new insights into genetic risk for JIA and the three dimensional architecture of the genome may be used to develop a better understanding of the mechanisms driving these gene expression patterns.

Overall Downregulation of mRNAs and Enrichment of H3K4me3 Change Near Genome-Wide Association Study Signals in Systemic Lupus Erythematosus: Cell-Specific Effects

This study was designed to define gene expression and H3K4me3 histone modifications in T cells, B cells, and monocytes in systemic lupus erythematosus (SLE). Array studies of total peripheral blood mononuclear cells have demonstrated gene expression signatures related to neutrophils, interferon, and other inflammatory pathways. It is not clear how consistent these effects are across different cell types. In this study, RNA-seq and chromatin immunoprecipitation-seq were utilized to identify gene expression patterns and H3K4me3 histone modifications related to promoter activation in SLE. Across the three cell types, there was 55% concordance for gene expression changes related to SLE. Key conserved pathways were ribosome biogenesis among upregulated genes and heat shock response among downregulated genes. ETS family transcription factors (TFs) and STAT1 were revealed as common regulators by position weight matrices. When epigenetic changes were leveraged with gene expression, the pivotal TFs ATF3 and FOS were defined with ATF3 also cross-referencing with gene expression-identified TFs. Genome-wide association study (GWAS) single nucleotide polymorphisms associated with SLE were cross-referenced with both mRNA and H3K4me3 changes in SLE. Baseline mRNA expression and H3K4me3 peak height was higher at sites that cross-referenced with GWAS signals, however, all three cell types exhibited an overall decrease in expression of GWAS-associated RNAs differentially expressed in SLE. H3K4me3 changes in SLE were also enriched in GWAS-associated sites. In summary, the SLE disease process is associated with both shared and cell-specific changes in gene expression and epigenetics. Surprisingly, GWAS-associated RNAs were overall markedly decreased across all three cell types. TF analysis identified ATF3, FOS, STAT1, and ETS family members as critical, all pathways with a recognized relationship to the SLE disease process. GWAS signals clearly mark both cell-type specific changes in SLE as well as concordant changes across all three cell types. Interpretation of single nucleotide polymorphism effects in SLE will require tissue-specific mechanistic studies and therapeutics will require mechanistic studies in multiple cell types.

Thinking BIG rheumatology: how to make functional genomics data work for you

High-throughput sequencing assays have become an increasingly common part of biological research across multiple fields. Even as the resulting sequences pile up in public databases, it is not always obvious how to make use of these data sets. Functional genomics offers approaches to integrate these "big" data into our understanding of rheumatic diseases. This review aims to provide a primer on thinking about big data from functional genomics in the context of rheumatology, using examples from the field's literature as well as the author's own work to illustrate the execution of functional genomics research. Study design is crucial to ensure the right samples are used to address the question of interest. In addition, sequencing assays produce a variety of data types, from gene expression to 3D chromatin structure and single-cell technologies, that can be integrated into a model of the underlying gene regulatory networks. The best approach for this analysis uses the scientific process: bioinformatic methods should be used in an iterative, hypothesis-driven manner to uncover the disease mechanism. Finally, the future of functional genomics will see big data fully integrated into rheumatology, leading to computationally trained researchers and interactive databases. The goal of this review is not to provide a manual, but to enhance the familiarity of readers with functional genomic approaches and provide a better sense of the challenges and possibilities.

Chromatin landscapes and genetic risk for juvenile idiopathic arthritis

Background

The transcriptomes of peripheral blood cells in children with juvenile idiopathic arthritis (JIA) have distinct transcriptional aberrations that suggest impairment of transcriptional regulation. To gain a better understanding of this phenomenon, we studied known JIA genetic risk loci, the majority of which are located in non-coding regions, where transcription is regulated and coordinated on a genome-wide basis. We examined human neutrophils and CD4 primary T cells to identify genes and functional elements located within those risk loci.

Methods

We analyzed RNA sequencing (RNA-Seq) data, H3K27ac and H3K4me1 chromatin immunoprecipitation-sequencing (ChIP-Seq) data, and previously published chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) data to characterize the chromatin landscapes within the known JIA-associated risk loci.

Results

In both neutrophils and primary CD4+ T cells, the majority of the JIA-associated linkage disequilibrium (LD) blocks contained H3K27ac and/or H3K4me1 marks. These LD blocks were also binding sites for a small group of transcription factors, particularly in neutrophils. Furthermore, these regions showed abundant intronic and intergenic transcription in neutrophils. In neutrophils, none of the genes that were differentially expressed between untreated patients with JIA and healthy children were located within the JIA-risk LD blocks. In CD4+ T cells, multiple genes, including HLA-DQA1, HLA-DQB2, TRAF1, and IRF1 were associated with the long-distance interacting regions within the LD regions as determined from ChIA-PET data.

Conclusions

These findings suggest that genetic risk contributes to the aberrant transcriptional control observed in JIA. Furthermore, these findings demonstrate the challenges of identifying the actual causal variants within complex genomic/chromatin landscapes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-017-1260-x) contains supplementary material, which is available to authorized users.