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BackgroundSystemic lupus erythematosus (SLE) is a complex disease likely triggered by gene-environment interactions. We have shown that most of the SLE-associated haplotypes encompass genomic regions enriched for epigenetic marks associated with enhancer function in neutrophils, and T and B cells, suggesting that genetic risk is exerted through altered gene regulation. Data remain scarce on how epigenetic variance contributes to disease risk in pediatric SLE (pSLE). We aim to identify differences in epigenetically-regulated chromatin architecture in treatment-naïve pSLE patients compared to healthy children.MethodsUsing the assay for transposase-accessible chromatin with sequencing (ATACseq), we surveyed open chromatin in 8 treatment-naïve pSLE patients, with at least moderate disease severity, and 5 healthy children. We investigated whether regions of open chromatin unique to pSLE patients demonstrate enrichment for specific transcriptional regulators, using standard computational approaches to identify unique peaks and a false discovery rate of ResultsThere were 30,139 differentially accessible regions (DAR) identified unique to pSLE B cells, of which 64.3% are more accessible in pSLE than B cells from healthy children. Many of these DAR are found in distal, intergenic regions, and are enriched for enhancer histone marks (p=0.027). When we compared B cells from pSLE patients to those of untreated adults, we found more regions of inaccessible chromatin, and fewer DAR within 10-100kb of known SLE haplotypes. In pSLE B cells, 65.2% of the DAR are located within or near known SLE haplotypes. Further analysis revealed enrichment of several transcription factor binding motifs within these DAR that may regulate genes involved in the pro-inflammatory responses and cellular adhesion.ConclusionsThis is the first report describing differences in chromatin architecture between pSLE patients and healthy children. We demonstrate an epigenetically-distinct profile in pSLE B cells when compared to those from healthy children and adults with lupus, indicating that pSLE B cells are predisposed for disease onset and development. Increased chromatin accessibility in non-coding genomic regions controlling activation of inflammation and the immune response suggest that transcriptional dysregulation by regulatory elements that control B cell activation plays an important role in the pathogenesis of pSLE.

To describe differences in disease manifestations and outcomes in pediatric rheumatic diseases as they occur in non-European-descended populations in North America.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.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.

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. 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. Of 4639 detected lncRNAs, 263 were significantly different (p

IntroductionAlthough genome-wide association studies (GWAS) 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.MethodsWe 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.ResultsWe 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.ConclusionUsing 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.

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.

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 Conclusion Anti-HCS70 autoantibodies are detected frequently in children with JDM and are novel myositis-associated autoantibodies correlating with disease severity.

Purpose of review One of the most important advances in medical research over the past 20 years has been the emergence of technologies to assess complex biological processes on a global scale. Although a great deal of attention has been given to genome-scale genetics and genomics technologies, the utility of studying the proteome in a comprehensive way is sometimes under-appreciated. In this review, we discuss recent advances in proteomics as applied to dermatomyositis/polymyositis as well as findings from other inflammatory diseases that may enlighten our understanding of dermatomyositis/polymyositis. Recent findings Proteomic approaches have been used to investigate basic mechanisms contributing to lung and skin disease in dermatomyositis/polymyositis as well as to the muscle disease itself. In addition, proteomic approaches have been used to identify autoantibodies targeting the endothelium in juvenile dermatomyositis. Studies from other inflammatory diseases have shown the promise of using proteomics to characterize the composition of immune complexes and the protein cargoes of exosomes. Summary There are many relevant scientific and clinical questions in dermatomyositis/polymyositis that can be addressed using proteomics approaches. Careful attention to both methodology and analytic approaches are required to obtain useful and reproducible data.

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.

BackgroundGenetics 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.MethodsWe 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.ResultsThe 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.ConclusionThese 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.

BACKGROUND: There is no effective way to predict cystic fibrosis (CF) pulmonary exacerbations (CFPE) before they become symptomatic or to assess satisfactory treatment responses. METHODS: RNA sequencing of peripheral blood neutrophils from CF patients before and after therapy for CFPE was used to create transcriptome profiles. Transcripts with an average transcripts per million (TPM) level>1.0 and a false discovery rate (FDR)1.5 fold change, FDR-adjusted P< 0.05). The model was able to successfully separate CF flare samples from those taken from the same patients in convalescence with an accuracy of 0.75 in both the training and testing cohorts. Six differently expressed genes were confirmed by real time PCR using both isolated neutrophils and whole blood from an independent cohort of CF patients before and after therapy, even though levels of myeloid related protein MRP8/14 dimers in plasma of CF patients were essentially unchanged by therapy. CONCLUSIONS: Our findings demonstrate the potential of machine learning approaches for classifying disease states and thus developing sensitive biomarkers that can be used to monitor pulmonary disease activity in CF.

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 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).

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 &lt, 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.

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.

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.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.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.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.

ObjectiveThe 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.MethodsWe 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.ResultsAll 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.ConclusionsThese 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.

Background Intracranial aneurysms (IAs) are dangerous because of their potential to rupture and cause deadly subarachnoid hemorrhages. Previously, we found significant RNA expression differences in circulating neutrophils between patients with unruptured IAs and aneurysm-free controls. Searching for circulating biomarkers for unruptured IAs, we tested the feasibility of developing classification algorithms that use neutrophil RNA expression levels from blood samples to predict the presence of an IA. Methods Neutrophil RNA extracted from blood samples from 40 patients (20 with angiography-confirmed unruptured IA, 20 angiography-confirmed IA-free controls) was subjected to next-generation RNA sequencing to obtain neutrophil transcriptomes. In a randomly-selected training cohort of 30 of the 40 samples (15 with IA, 15 controls), we performed differential expression analysis. Significantly differentially expressed transcripts (false discovery rate

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.

Background The pathology of juvenile dermatomyositis (JDM) is characterized by prominent vessel wall and perivascular inflammation. This feature of the disease has remained unexplained and under-investigated. We have hypothesized that plasma exosomes, which play an important role in inter-cellular communication, may play a role in the vascular injury associated with JDM. Objective To characterize the circulating exosomes of children with JDM and determine whether the small RNA cargoes within those exosomes are capable of altering transcriptional programs within endothelial cells. Design/methods We purified exosomes from plasma samples of children with active, untreated JDM (n = 6) and healthy controls (n = 9). We characterized the small RNA cargoes in JDM and control exosomes by RNA sequencing using the Illumina HiSeq 2500 platform. We then incubated isolated exosomes from healthy controls and children with JDM with cultured human aortic endothelial cells (HAEC) for 24 h. Fluorescence microscopy was used to confirm that both control and JDM exosomes were taken up by HAEC. RNA was then purified from HAEC that had been incubated with either control or JDM exosomes and sequenced on the Illumina platform. Differential expression of mRNAs from HAEC incubated with control or JDM exosomes was ascertained using standard computational methods. Finally, we assessed the degree to which differential gene expression in HAEC could be attributed to the different small RNA cargoes in JDM vs control exosomes using conventional and novel analytic methods. Results We identified 10 small RNA molecules that showed differential abundance when we compared JDM and healthy control exosomes. Fluorescence microscopy of labeled exosomes confirmed that both JDM and control exosomes were taken up by HAEC. Differential gene expression analysis revealed 59 genes that showed differential expression between HAEC incubated with JDM exosomes vs HAEC incubated with exosomes from controls. Statistical analysis of gene expression data demonstrated that multiple miRNAs exerted transcriptional control on multiple genes with HAEC. Conclusions Plasma exosomes from children with active, untreated JDM are taken up by HAEC and are associated with alterations in gene expression in those cells. These findings provide new insight into potential mechanisms leading to the targeting of vascular tissue by the immune system in JDM. Electronic supplementary material The online version of this article (10.1186/s12969-019-0347-0) contains supplementary material, which is available to authorized users.

Like most rheumatic diseases, genetic risk loci for juvenile idiopathic arthritis (JIA) are highly enriched for H3K4me1/H3K27ac histone marks, epigenetic signatures often associated with enhancer function. We sought to determine whether an H3K4me1/H3K27ac region of the JIA risk locus, IL2RA exhibits enhancer function and whether enhancer function is altered by JIA-associated genetic variants. We used a conventional luciferase reporter assay to query enhancer function across an H3K4me1/H3K27ac-marked region within the first intron of the IL2RA gene. We used the same approach to query the effects of 4 single nucleotide polymorphisms (SNPs) on enhancer function. We identified a 657 base pair region within the first intron of IL2RA that demonstrated brisk enhancer function. Enhancer activity was observable in both Jurkat T cells and myeloid HL60 cells. We identified 2 genetic variants, rs117119468 (C->T), and rs12722502 (C->T), that attenuated enhancer activity in this 657 bp region. Conclusions – The JIA-associated risk locus, IL2RA, contains at least one functional enhancer that is active in both lymphoid and myeloid cells. We identified 2 genetic variants that attenuate activity of this enhancer, making them strong candidates as causal variants.

Objective Examine caregiver demand and general parent distress as mediators in the parent illness uncertainty-child depressive symptom association in youth with juvenile rheumatic diseases. Methods Children and adolescents completed the Child Depression Inventory; caregivers completed the Parent Perceptions of Uncertainty Scale, the Care for My Child with Rheumatic Disease Scale, and the Brief Symptom Inventory. The pediatric rheumatologist provided ratings of clinical disease status. Results Analyses revealed significant direct associations between illness uncertainty and caregiver demand, and between caregiver demand and both parent distress and child depressive symptoms. Results also revealed significant parent uncertainty → caregiver demand → parent distress and parent uncertainty → caregiver demand → child depressive symptom indirect paths. Conclusions Results highlight the role of illness appraisals in adjustment to juvenile rheumatic diseases, and provide preliminary evidence that parent appraisals of illness uncertainty impact parent distress and child depressive symptoms indirectly through increased perceptions of caregiver demand.

DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Many studies have mapped DNA methylation changes associated with embryogenesis, cell differentiation, and cancer at a genome-wide scale. Our understanding of genome-wide DNA methylation changes in a developmental or disease-related context has been steadily growing. However, the investigation of which CpGs are variably methylated in different normal cell or tissue types is still limited. Here, we present an in-depth analysis of 54 single-CpG-resolution DNA methylomes of normal human cell types by integrating high-throughput sequencing-based methylation data. We found that the ratio of methylated to unmethylated CpGs is relatively constant regardless of cell type. However, which CpGs made up the unmethylated complement was cell-type specific. We categorized the 26,000,000 human autosomal CpGs based on their methylation levels across multiple cell types to identify variably methylated CpGs and found that 22.6% exhibited variable DNA methylation. These variably methylated CpGs formed 660,000 variably methylated regions (VMRs), encompassing 11% of the genome. By integrating a multitude of genomic data, we found that VMRs enrich for histone modifications indicative of enhancers, suggesting their role as regulatory elements marking cell type specificity. VMRs enriched for transcription factor binding sites in a tissue-dependent manner. Importantly, they enriched for GWAS variants, suggesting that VMRs could potentially be implicated in disease and complex traits. Taken together, our results highlight the link between CpG methylation variation, genetic variation, and disease risk for many human cell types.

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.

Background Bennett et al identified the presence of interferon gene expression signatures in peripheral blood of children with systemic lupus erythematosus (pSLE). Here, we aim to identify how B cells contribute to those signatures. We hypothesize that disordered transcription in pSLE will be prominent in B cells and attributed to disease-specific epigenetic alterations. Thus we will not only identify important disease mechanisms in SLE, we will shed light on the genetics of SLE. Our previously published work demonstrated that most of the genetic risk for SLE located within non-coding regions of the genome appears to also contain higher than baseline epigenetic modifications of DNA and transcription factor binding sites that regulate and coordinate transcription. Methods Our specific aim is to assess regions of open chromatin in untreated pSLE and compare findings with healthy children. We propose to use assays of transposase-accessible chromatin with sequencing (ATACseq) to broadly survey open regions of chromatin and clarify the functional epigenome. In this pilot study, we propose to determine feasibility of performing this assay and developing methods for data analysis using 5 pediatric lupus patients and 5 healthy children. Anticipated results and conclusions Our long-term goal is to gain a mechanistic understanding of the aberrant transcriptional signatures in untreated SLE. The data generated by this pilot study will provide the basis for a rigorous power analysis, and firmly establish the working relationship between the Buffalo and Cohen Children’s Medical Center groups, both of which will be essential for a competitive application to NIH. Our preliminary findings from this pilot study will also allow us to begin the exciting process of linking the genetics and epigenetics of pSLE to the well-established transcriptional aberration. Acknowledgements This study is funded, in part, by an Arthritis Foundation-Childhood Arthritis and Rheumatology Research Alliance Small Pilot Grant.

Objective: Systemic lupus erythematosus (SLE) is a systemic autoimmune disease with varied morbidity and mortality. We assessed clinical presentations, autoantibody specificities and therapeutic interventions in Native American (NA) patients with SLE. Methods: Patients with SLE meeting 1997 American College of Rheumatology classification criteria (n=3148) were enrolled between 1992 and 2010 in the multiethnic, cross-sectional Lupus Family Registry and Repository. Clinical, demographic and therapeutic information were extracted from medical records using a standardised form and formalised training. Autoantibodies were assessed by indirect immunofluorescence (antinuclear antibodies (ANA) and antidouble-stranded DNA), precipitin (ENA) and ELISA (IgG and IgM anticardiolipins). Results: NA patients met SLE classification at a younger age (29.89±12.3 years) than European Americans (EA; 32.02±12.87, P=0.0157) and a similar age to African-Americans (AAs) and Hispanics (HIS). More NA patients had concurrent rheumatic diseases or symptoms, such as Raynaud's phenomenon, interstitial lung disease, Sjögren's syndrome and systemic sclerosis. Compared with EAs, NAs were more likely to have high-titre ANA (≥1:3240; P

Objective Although generally classified within the group of inflammatory myopathies, JDM displays many pathological features of vasculitis. Previous work has shown that AECA are abundant in other forms of vasculitis. We therefore investigated whether such antibodies might also be detected in JDM. Methods We screened plasma from children with JDM for the presence of AECA by western blotting and 2D gel electrophoresis (2DE) using proteins extracted from human aortic endothelial cells as the substrate. We performed mass spectrometry to identify candidate antigens from 2DE gels and used ELISA to confirm the presence of specific antibodies. Results We identified 22 candidate target autoantigens for AECA probed with JDM plasma. Interestingly, 17 of these 22 target antigens were proteins associated with antigen processing and protein trafficking. ELISA confirmed the presence of antibodies to heat shock cognate 71 kDa protein in JDM plasma, particularly in children with active, untreated disease. Conclusion Children with JDM express antibodies to autoantigens in endothelial cells. The clinical and pathological significance of such autoantibodies require further investigation.

Background Unruptured intracranial aneurysms (IAs) are typically asymptomatic and undetected except for incidental discovery on imaging. Blood-based diagnostic biomarkers could lead to improvements in IA management. This exploratory study examined circulating neutrophils to determine whether they carry RNA expression signatures of IAs. Methods Blood samples were collected from patients receiving cerebral angiography. Eleven samples were collected from patients with IAs and 11 from patients without IAs as controls. Samples from the two groups were paired based on demographics and comorbidities. RNA was extracted from isolated neutrophils and subjected to next-generation RNA sequencing to obtain differential expressions for identification of an IA-associated signature. Bioinformatics analyses, including gene set enrichment analysis and Ingenuity Pathway Analysis, were used to investigate the biological function of all differentially expressed transcripts. Results Transcriptome profiling identified 258 differentially expressed transcripts in patients with and without IAs. Expression differences were consistent with peripheral neutrophil activation. An IA-associated RNA expression signature was identified in 82 transcripts (p

Both clinical and experimental evidence supports the hypothesis that juvenile idiopathic arthritis (JIA) is a complex trait mediated by gene-environment interactions. While the past 20 years have been marked by advances in understanding genetic risk using candidate gene approaches (e.g., [(1)], the contribution of any single genetic polymorphism is small, particularly for those genes outside of the major histocompatibility complex (MHC). Thus, the recent publication of a genome-wide association study (GWAS) identifying multiple new genomic regions conferring risk for JIA (2) was appropriately received with enthusiasm from geneticists and pediatric rheumatologists. However, the results of the GWAS left many unanswered questions. Most interestingly, Hinks et al found that most genetic risk for JIA resides not within coding regions of genes, but, rather, in intergenic regions and introns. This finding is not unique to JIA. Indeed, most GWAS for complex traits have revealed genetic risk resides predominantly in non-coding regions of the genome (3). The question must inevitably arise, therefore, “What’s in those regions?” After the initial sequencing of the human genome, there was some surprise at the relatively small numbers of genes and the vast regions of the genome that seemed to be devoid of anything informative. However, NIH projects like ENCODE (the Encyclopedia of Functional DNA Elements) and Roadmap Epigenomics have given us a much clearer picture of the non-coding genome and its functions. For example, we are now learning that multiple classes of RNA transcripts are expressed in regions of the genome that do not encode proteins, and that many of these non-coding transcripts serve specific functions, such as regulating transcript stability (e.g., miRNA (4)) or chromatin accessibility (e.g., some long non-coding RNA (5, 6)). In addition to ncRNA, we have learned that the non-coding regions of the genome contain many other functional sites that serve to regulate and coordinate transcription. These functional sites include transcriptional enhancers. Enhancers are cis-acting DNA regions that promote gene transcription. These DNA elements represent an important component of the non-coding genome; recent studies in mouse and human genomes demonstrated that about half of the highly conserved regions had enhancer activity (7, 8). Enhancers may be considerable distance (in genomic terms) from the promoters they regulate, and may not regulate the genes that are most proximal. In some cases (most notably in the immune system), enhancers may regulate more than one gene (9). Enhancers can’t be predicted with accuracy from DNA sequence. However, both ENCODE and Roadmap Epigenomics have shown that identification of enhancers can be facilitated using chromatin immunoprecipitation-sequencing (ChIP-seq) techniques for specific histone marks (10–12), including histone H3 acetylated at lysine 27 (H3K27ac) and histone H3 monomethylated at lysine 4 (H3K4me1) (13). Over the past 10 years, we have published a series of papers demonstrating the importance of neutrophils in the pathogenesis of JIA (14, 15) and its response to therapy (16). These findings led us to question whether the genomic regions associated with genetic risk in JIA might correspond to functional elements in human neutrophils (17). In this paper, we demonstrate that the majority of the non-coding regions identified by Hinks et al contain such elements.

Juvenile dermatomyositis is a complex illness characterized by vascular/perivascular inflammation, primarily in the skin and muscles. In this review, we discuss how proteomic and genomic technologies have expanded our understanding of the immune pathogenesis of this disease. We will also discuss further directions that the field may take to use existing and developing technologies to further our understanding of this often-perplexing disease.

In this work we apply the Internal Standard-based analytical approach that we described in an earlier communication and here we demonstrate experimental results on functional associations among the hypervariably-expressed genes (HVE-genes). Our working assumption was that those genetic components, which initiate the disease, involve HVE-genes for which the level of expression is undistinguishable among healthy individuals and individuals with pathology. We show that analysis of the functional associations of the HVE-genes is indeed suitable to revealing disease-specific differences. We show also that another possible exploit of HVE-genes for characterization of pathological alterations is by using multivariate classification methods. This in turn offers important clues on naturally occurring dynamic processes in the organism and is further used for dynamic discrimination of groups of compared samples. We conclude that our approach can uncover principally new collective differences that cannot be discerned by individual gene analysis.

Deep whole genome sequencing (WGS) allows for the comprehensive study of genetic landscapes at finer resolution than array based methods. We conducted deep WGS on children with the polyarticular form of juvenile idiopathic arthritis (JIA), using 2 independent cohorts to ascertain the sequencing fidelity. Genome wide SNP density analysis identified 18 SNP hotspots with comparison to the 1000 Genome Projects (1KGP) data. A subset of the genes adjacent to SNP hotspots showed statistically significant enrichment in immunological processes. Genes adjacent to indel hotspots were functionally related to G-protein coupled signaling pathways. Further analyses elucidated significantly more JIA SNPs with regulatory potential compared to 1KGP data. Furthermore, SNPs located within linkage disequibilium (LD) blocks containing previously identified JIA-associated SNPs demonstrated higher regulation potential compared to SNPs outside LD blocks. We also demonstrated enrichment of novel JIA variants in histone modification peaks and DNase hypersensitivity sites in B cells. This study greatly expands the number of genetic variants that may contribute to JIA and give us some clues into what may trigger this disease. To date, this study is the first deep WGS effort on children with JIA and provides useful genetic resources for research communities particularly in understanding JIA etiology.