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.

Systemic lupus erythematosus in American Indian/Alaska natives: Incorporating our new understanding of the biology of trauma.

OBJECTIVE: To review the literature regarding systemic lupus erythematosus (SLE) in American Indian/Alaska Native (AI/AN) people and relate prevalence and/or disease severity to our emerging understanding of the biology of trauma and toxic stress. METHODS: We conducted a search and review of the literature using search terms "lupus and American Indians" "ACEs and disease outcome" "Biology of Adversity" "lupus and ACE scores," " lupus and childhood abuse." These search criteria were entered into Google Scholar and articles retrieved from PubMed, NBCI. This approach yielded a small numbers of papers used throughout this review. We excluded articles that were not published in a peer reviewed journals, as well as editorial commentaries. RESULTS: In the AI/AN population, SLE shows high prevalence rates and severe disease manifestations, comparable to the African American population. AI/AN populations also have high rates of childhood trauma. Toxic stress and trauma such as those catalogued in the Adverse Childhood Experiences (ACE) study have broad-reaching immunologic and epigenetic effects that are likely to be relevant to our understanding of SLE in AI/AN people. CONCLUSIONS: AI/AN people have high rates of SLE. These high rates are likely to be driven by many complex factors, not all of which are genetic. Future research is needed to establish (or refute) a causal connection between the biology of adversity and SLE in socially marginalized and historically traumatized populations.

Association of anti-TPM4 autoantibodies with vasculopathic cutaneous manifestations in juvenile dermatomyositis.

OBJECTIVES: AECAs are detected in multiple forms of vasculitis or vasculopathy, including JDM. High levels of tropomyosin alpha-4 chain (TPM4) gene expression in cutaneous lesions and TPM4 protein expression in some endothelial cells (ECs) have been proven. Furthermore, the presence of autoantibodies to tropomyosin proteins have been discovered in DM. We therefore investigated whether anti-TPM4 autoantibodies are an AECA in JDM and are correlated with clinical features of JDM. METHODS: The expression of TPM4 protein in cultured normal human dermal microvascular ECs was investigated by Western blotting. Plasma samples from 63 children with JDM, 50 children with polyarticular JIA (pJIA) and 40 healthy children (HC) were tested for the presence of anti-TPM4 autoantibodies using an ELISA. Clinical features were compared between JDM patients with and without anti-TPM4 autoantibodies. RESULTS: Autoantibodies to TPM4 were detected in the plasma of 30% of JDM, 2% of pJIA (P < 0.0001) and 0% of HC (P < 0.0001). In JDM, anti-TPM4 autoantibodies were associated with the presence of cutaneous ulcers (53%; P = 0.02), shawl sign rash (47%; P = 0.03), mucous membrane lesions (84%; P = 0.04) and subcutaneous edema (42%; P < 0.05). Anti-TPM4 autoantibodies significantly correlated with the use of intravenous steroids and IVIG therapy in JDM (both P = 0.01). The total number of medications received was higher in patients with anti-TPM4 autoantibodies (P = 0.02). CONCLUSION: Anti-TPM4 autoantibodies are detected frequently in children with JDM and are novel myositis-associated autoantibodies. Their presence correlates with vasculopathic and other cutaneous manifestations of JDM that may be indicative of more refractory disease.

Analysis of chromatin data supports a role for CD14+ monocytes/macrophages in mediating genetic risk for juvenile idiopathic arthritis.

Introduction: Genome wide association studies (GWAS) have identified multiple regions that confer genetic risk for the polyarticular/oligoarticular forms of juvenile idiopathic arthritis (JIA). However, genome-wide scans do not identify the cells impacted by genetic polymorphisms on the risk haplotypes or the genes impacted by those variants. We have shown that genetic variants driving JIA risk are likely to affect both innate and adaptive immune functions. We provide additional evidence that JIA risk variants impact innate immunity. Materials and methods: We queried publicly available H3K4me1/H3K27ac ChIP-seq data in CD14+ monocytes to determine whether the linkage disequilibrium (LD) blocks incorporating the SNPs that tag JIA risk loci showed enrichment for these epigenetic marks. We also queried monocyte/macrophage GROseq data, a functional readout of active enhancers. We defined the topologically associated domains (TADs) encompassing enhancers on the risk haplotypes and identified genes within those TADs expressed in monocytes. We performed ontology analyses of these genes to identify cellular processes that may be impacted by these variants. We also used whole blood RNAseq data from the Genotype-Tissue Expression (GTEx) data base to determine whether SNPs lying within monocyte GROseq peaks influence plausible target genes within the TADs encompassing the JIA risk haplotypes. Results: The LD blocks encompassing the JIA genetic risk regions were enriched for H3K4me1/H3K27ac ChIPseq peaks (p=0.00021 and p=0.022) when compared to genome background. Eleven and sixteen JIA were enriched for resting and activated macrophage GROseq peaks, respectively risk regions (p=0.04385 and p=0.00004). We identified 321 expressed genes within the TADs encompassing the JIA haplotypes in human monocytes. Ontological analysis of these genes showed enrichment for multiple immune functions. Finally, we found that SNPs lying within the GROseq peaks are strongly associated with expression levels of plausible target genes in human whole blood. Conclusions: These findings support the idea that both innate and adaptive immunity are impacted by JIA genetic risk variants.

Health inequities in the rheumatic diseases of childhood.

PURPOSE OF REVIEW: To describe differences in disease manifestations and outcomes in pediatric rheumatic diseases as they occur in non-European-descended populations in North America. RECENT FINDINGS: Differences in disease prevalence, clinical phenotypes, disease course, and outcomes have been described across the spectrum of pediatric-onset rheumatic diseases. Although these differences are commonly explained by differences in genetic risk or access to tertiary healthcare facilities, our emerging understanding of the immunobiology of historical/ongoing trauma suggest a more complex explanation for these observed differences. SUMMARY: Health inequities as observed in pediatric rheumatic diseases are likely to emerge from a complex interplay between social and biological factors. The important contribution of historical and repetitive trauma deserves further exploration.

Chromatin architecture around stroke haplotypes provides evidence that genetic risk is conferred through vascular cells.

Introduction: Genome-wide association studies (GWAS) have identified numerous stroke-associated SNPs. To understand how SNPs affect gene expression related to increased stroke risk, we studied epigenetic landscapes surrounding 26 common, validated stroke-associated loci. Methods: We mapped the SNPs to linkage disequilibrium (LD) blocks and examined H3K27ac, H3K4me1, H3K9ac, and H3K4me3 histone marks and transcription-factor binding-sites in pathologically relevant cell types (hematopoietic and vascular cells). Hi-C data were used to identify topologically associated domains (TADs) encompassing the LD blocks and overlapping genes. Results: Fibroblasts, smooth muscle, and endothelial cells showed significant enrichment for enhancer-associated marks within stroke-associated LD blocks. Genes within encompassing TADs reflected vessel homeostasis, cellular turnover, and enzymatic activity. Conclusions: Stroke-associated genetic variants confer risk predominantly through vascular cells rather than hematopoietic cell types.

Previous studies have found several variations in the DNA sequence (known as single nucleotide polymorphisms) linked to higher stroke risk. But the mechanisms behind how they increase risk is unknown. One hypothesis is that they affect non-coding DNA elements (i.e., epigenetics), which in turn drive abnormal changes in gene expression leading to increased stroke risk. To investigate this potential mechanism, we mined publicly available, cell-type specific databases. We searched for overlap between the regions with polymorphisms and regions where DNA transcription machinery bind (i.e., enhancers, transcription factor binding sites). We found that fibroblasts and smooth muscle cells (cells in vessel walls) had more of these DNA elements in regions associated with stroke risk. Bioinformatics analyses of genes that could be affected by changes in these elements were linked to stroke-related mechanisms.

Association of anti-HSC70 autoantibodies with cutaneous ulceration and severe disease in juvenile dermatomyositis.

OBJECTIVES: JDM is an inflammatory myopathy characterized by prominent vasculopathy. AECAs are frequently detected in inflammatory and autoimmune diseases. We sought to determine whether AECAs correlate with clinical features of JDM, and thus serve as biomarkers to guide therapy or predict outcome. METHODS: Plasma samples from 63 patients with JDM, 49 patients with polyarticular JIA and 40 juvenile healthy controls were used to detect anti-heat shock cognate 71 kDa protein (HSC70) autoantibodies, a newly identified AECA, in ELISA assays. Clinical features were compared between JDM patients with and without anti-HSC70 autoantibodies. RESULTS: Anti-HSC70 autoantibodies were detected in 35% of patients with JDM, in 0% of patients with JIA (P < 0.0001) and in 0% of healthy donors (P < 0.0001). Both the presence of cutaneous ulcers (59% vs 17%, P < 0.002) and the use of wheelchairs and/or assistive devices (64% vs 27%, P < 0.007) were strongly associated with anti-HSC70 autoantibodies in JDM. High scores on the severity of myositis damage measures at the time of measurement of anti-HSC70 autoantibodies and an increased number of hospitalizations were also associated with anti-HSC70 autoantibodies. Intravenous immunoglobulin therapy was used more often in anti-HSC70 autoantibody-positive patients. CONCLUSION: Anti-HCS70 autoantibodies are detected frequently in children with JDM and are novel myositis-associated autoantibodies correlating with disease severity.

Identification of Circulating Gene Expression Signatures of Intracranial Aneurysm in Peripheral Blood Mononuclear Cells.

Peripheral blood mononuclear cells (PBMCs) play an important role in the inflammation that accompanies intracranial aneurysm (IA) pathophysiology. We hypothesized that PBMCs have different transcriptional profiles in patients harboring IAs as compared to IA-free controls, which could be the basis for potential blood-based biomarkers for the disease. To test this, we isolated PBMC RNA from whole blood of 52 subjects (24 with IA, 28 without) and performed next-generation RNA sequencing to obtain their transcriptomes. In a randomly assigned discovery cohort of n = 39 patients, we performed differential expression analysis to define an IA-associated signature of 54 genes (q < 0.05 and an absolute fold-change ≥ 1.3). In the withheld validation dataset, these genes could delineate patients with IAs from controls, as the majority of them still had the same direction of expression difference. Bioinformatics analyses by gene ontology enrichment analysis and Ingenuity Pathway Analysis (IPA) demonstrated enrichment of structural regulation processes, intracellular signaling function, regulation of ion transport, and cell adhesion. IPA analysis showed that these processes were likely coordinated through NF-kB, cytokine signaling, growth factors, and TNF activity. Correlation analysis with aneurysm size and risk assessment metrics showed that 4/54 genes were associated with rupture risk. These findings highlight the potential to develop predictive biomarkers from PBMCs to identify patients harboring IAs.

Epigenetic landscapes of intracranial aneurysm risk haplotypes implicate enhancer function of endothelial cells and fibroblasts in dysregulated gene expression.

BACKGROUND: Genome-wide association studies have identified many single nucleotide polymorphisms (SNPs) associated with increased risk for intracranial aneurysm (IA). However, how such variants affect gene expression within IA is poorly understood. We used publicly-available ChIP-Seq data to study chromatin landscapes surrounding risk loci to determine whether IA-associated SNPs affect functional elements that regulate gene expression in cell types comprising IA tissue. METHODS: We mapped 16 significant IA-associated SNPs to linkage disequilibrium (LD) blocks within human genome. Using ChIP-Seq data, we examined these regions for presence of H3K4me1, H3K27ac, and H3K9ac histone marks (typically associated with latent/active enhancers). This analysis was conducted in several cell types that are present in IA tissue (endothelial cells, smooth muscle cells, fibroblasts, macrophages, monocytes, neutrophils, T cells, B cells, NK cells). In cell types with significant histone enrichment, we used HiC data to investigate topologically associated domains (TADs) encompassing the LD blocks to identify genes that may be affected by IA-associated variants. Bioinformatics were performed to determine the biological significance of these genes. Genes within HiC-defined TADs were also compared to differentially expressed genes from RNA-seq/microarray studies of IA tissues. RESULTS: We found that endothelial cells and fibroblasts, rather than smooth muscle or immune cells, have significant enrichment for enhancer marks on IA risk haplotypes (p < 0.05). Bioinformatics demonstrated that genes within TADs subsuming these regions are associated with structural extracellular matrix components and enzymatic activity. The majority of histone marked TADs (83% fibroblasts [IMR90], 77% HUVEC) encompassed at least one differentially expressed gene from IA tissue studies. CONCLUSIONS: These findings provide evidence that genetic variants associated with IA risk act on endothelial cells and fibroblasts. There is strong circumstantial evidence that this may be mediated through altered enhancer function, as genes in TADs encompassing enhancer marks have also been shown to be differentially expressed in IA tissue. These genes are largely related to organization and regulation of the extracellular matrix. This study builds upon our previous (Poppenberg et al., BMC Med Genomics, 2019) by including a more diverse set of data from additional cell types and by identifying potential affected genes (i.e. those in TADs).

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.

CD4+ T cells from children with active juvenile idiopathic arthritis show altered chromatin features associated with transcriptional abnormalities.

Juvenile idiopathic arthritis (JIA) is one of the most common chronic diseases in children. While clinical outcomes for patients with juvenile JIA have improved, the underlying biology of the disease and mechanisms underlying therapeutic response/non-response are poorly understood. We have shown that active JIA is associated with distinct transcriptional abnormalities, and that the attainment of remission is associated with reorganization of transcriptional networks. In this study, we used a multi-omics approach to identify mechanisms driving the transcriptional abnormalities in peripheral blood CD4+ T cells of children with active JIA. We demonstrate that active JIA is associated with alterations in CD4+ T cell chromatin, as assessed by ATACseq studies. However, 3D chromatin architecture, assessed by HiChIP and simultaneous mapping of CTCF anchors of chromatin loops, reveals that normal 3D chromatin architecture is largely preserved. Overlapping CTCF binding, ATACseq, and RNAseq data with known JIA genetic risk loci demonstrated the presence of genetic influences on the observed transcriptional abnormalities and identified candidate target genes. These studies demonstrate the utility of multi-omics approaches for unraveling important questions regarding the pathobiology of autoimmune diseases.

Whole blood transcriptome biomarkers of unruptured intracranial aneurysm.

BACKGROUND: The rupture of an intracranial aneurysm (IA) causes devastating subarachnoid hemorrhages, yet most IAs remain undiscovered until they rupture. Recently, we found an IA RNA expression signature of circulating neutrophils, and used transcriptome data to build predictive models for unruptured IAs. In this study, we evaluate the feasibility of using whole blood transcriptomes to predict the presence of unruptured IAs. METHODS: We subjected RNA from peripheral whole blood of 67 patients (34 with unruptured IA, 33 without IA) to next-generation RNA sequencing. Model genes were identified using the least absolute shrinkage and selection operator (LASSO) in a random training cohort (n = 47). These genes were used to train a Gaussian Support Vector Machine (gSVM) model to distinguish patients with IA. The model was applied to an independent testing cohort (n = 20) to evaluate performance by receiver operating characteristic (ROC) curve. Gene ontology and pathway analyses investigated the underlying biology of the model genes. RESULTS: We identified 18 genes that could distinguish IA patients in a training cohort with 85% accuracy. This SVM model also had 85% accuracy in the testing cohort, with an area under the ROC curve of 0.91. Bioinformatics reflected activation and recruitment of leukocytes, activation of macrophages, and inflammatory response, suggesting that the biomarker captures important processes in IA pathogenesis. CONCLUSIONS: Circulating whole blood transcriptomes can detect the presence of unruptured IAs. Pending additional testing in larger cohorts, this could serve as a foundation to develop a simple blood-based test to facilitate screening and early detection of IAs.

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm.

BACKGROUND: Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. METHODS: Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. RESULTS: Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. CONCLUSIONS: We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

Characterization of Long Non-coding RNA Signatures of Intracranial Aneurysm in Circulating Whole Blood.

BACKGROUND AND OBJECTIVE: Long non-coding RNAs (lncRNAs) may serve as biomarkers for complex disease states, such as intracranial aneurysms. In this study, we investigated lncRNA expression differences in the whole blood of patients with unruptured aneurysms. METHODS: Whole blood RNA from 67 subjects (34 with aneurysm, 33 without) was used for next-generation RNA sequencing. Differential expression analysis was used to define a signature of intracranial aneurysm-associated lncRNAs. To estimate the signature's ability to classify aneurysms and to identify the most predictive lncRNAs, we implemented a nested cross-validation pipeline to train classifiers using linear discriminant analysis. Ingenuity pathway analysis was used to study potential biological roles of differentially expressed lncRNAs, and lncRNA ontology was used to investigate ontologies enriched in signature lncRNAs. Co-expression correlation analysis was performed to investigate associated differential protein-coding messenger RNA expression. RESULTS: Of 4639 detected lncRNAs, 263 were significantly different (p < 0.05) between the two groups, and 84 of those had an absolute fold-change ≥ 1.5. An eight-lncRNA signature (q < 0.35, fold-change ≥ 1.5) was able to separate patients with and without aneurysms on principal component analysis, and had an estimated accuracy of 70.9% in nested cross-validation. Bioinformatics analyses showed that networks of differentially expressed lncRNAs (p < 0.05) were enriched for cell death and survival, connective tissue disorders, carbohydrate metabolism, and cardiovascular disease. Signature lncRNAs shared ontologies that reflected regulation of gene expression, signaling, ubiquitin processing, and p53 signaling. Co-expression analysis showed correlations with messenger RNAs related to inflammatory responses. CONCLUSIONS: Differential expression in whole blood lncRNAs is detectable in patients harboring aneurysms, and reflects expression/signaling regulation, and ubiquitin and p53 pathways. Following validation in larger cohorts, these lncRNAs may be potential diagnostic targets for aneurysm detection by blood testing.

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.

Double negative T cells, a potential biomarker for systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease that is a challenge to diagnose and treat. There is an urgent need for biomarkers to help define organ involvement, and more effective therapies. A unique population of T cells, the CD3+CD4-CD8- (DNeg) cells, is significantly increased in lupus patients. Twenty-seven cases (53%) of pediatric SLE patients had elevated DNeg cells in their peripheral blood, which correlated with kidney function (R2  = 0.54). Significant infiltration of DNeg cells was observed in both adult and pediatric lupus kidneys by immunofluorescence. For the first time, this study provides direct evidence that DNeg cells facilitate kidney injury in preclinical 8-week-old MRL/lpr lupus mice. In lupus mice, the increase in DNeg cells tracked with worsening disease and correlated with kidney function (R2  = 0.85). Our results show that DNeg cells per se can cause kidney dysfunction, increase in number with increase in disease pathology, and could serve as a potential biomarker.

Epigenetic landscapes suggest that genetic risk for intracranial aneurysm operates on the endothelium.

BACKGROUND: Genetics play an important role in intracranial aneurysm (IA) pathophysiology. Genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) that are linked to IA but how they affect disease pathobiology remains poorly understood. We used Encyclopedia of DNA Elements (ENCODE) data to investigate the epigenetic landscapes surrounding genetic risk loci to determine if IA-associated SNPs affect functional elements that regulate gene expression and if those SNPs are most likely to impact a specific type of cells. METHODS: We mapped 16 highly significant IA-associated SNPs to linkage disequilibrium (LD) blocks within the human genome. Within these regions, we examined the presence of H3K4me1 and H3K27ac histone marks and CCCTC-binding factor (CTCF) and transcription-factor binding sites using chromatin immunoprecipitation-sequencing (ChIP-Seq) data. This analysis was conducted in several cell types relevant to endothelial (human umbilical vein endothelial cells [HUVECs]) and inflammatory (monocytes, neutrophils, and peripheral blood mononuclear cells [PBMCs]) biology. Gene ontology analysis was performed on genes within extended IA-risk regions to understand which biological processes could be affected by IA-risk SNPs. We also evaluated recently published data that showed differential methylation and differential ribonucleic acid (RNA) expression in IA to investigate the correlation between differentially regulated elements and the IA-risk LD blocks. RESULTS: The IA-associated LD blocks were statistically significantly enriched for H3K4me1 and/or H3K27ac marks (markers of enhancer function) in endothelial cells but not in immune cells. The IA-associated LD blocks also contained more binding sites for CTCF in endothelial cells than monocytes, although not statistically significant. Differentially methylated regions of DNA identified in IA tissue were also present in several IA-risk LD blocks, suggesting SNPs could affect this epigenetic machinery. Gene ontology analysis supports that genes affected by IA-risk SNPs are associated with extracellular matrix reorganization and endopeptidase activity. CONCLUSION: These findings suggest that known genetic alterations linked to IA risk act on endothelial cell function. These alterations do not correlate with IA-associated gene expression signatures of circulating blood cells, which suggests that such signatures are a secondary response reflecting the presence of IA rather than indicating risk for IA.

The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches.

BACKGROUND: The response to treatment for juvenile idiopathic arthritis (JIA) can be staged using clinical features. However, objective laboratory biomarkers of remission are still lacking. In this study, we used machine learning to predict JIA activity from transcriptomes from peripheral blood mononuclear cells (PBMCs). We included samples from children with Native American ancestry to determine whether the model maintained validity in an ethnically heterogeneous population. METHODS: Our dataset consisted of 50 samples, 23 from children in remission and 27 from children with an active disease on therapy. Nine of these samples were from children with mixed European/Native American ancestry. We used 4 different machine learning methods to create predictive models in 2 populations: the whole dataset and then the samples from children with exclusively European ancestry. RESULTS: In both populations, models were able to predict JIA status well, with training accuracies > 74% and testing accuracies > 78%. Performance was better in the whole dataset model. We note a high degree of overlap between genes identified in both populations. Using ingenuity pathway analysis, genes from the whole dataset associated with cell-to-cell signaling and interactions, cell morphology, organismal injury and abnormalities, and protein synthesis. CONCLUSIONS: This study demonstrates it is feasible to use machine learning in conjunction with RNA sequencing of PBMCs to predict JIA stage. Thus, developing objective biomarkers from easy to obtain clinical samples remains an achievable goal.