EPIC-TABSAT: analysis tool for targeted bisulfite sequencing experiments and array-based methylation studies.

DNA methylation is one of the major epigenetic modifications and has frequently demonstrated its suitability as diagnostic and prognostic biomarker. In addition to chip and sequencing based epigenome wide methylation profiling methods, targeted bisulfite sequencing (TBS) has been established as a cost-effective approach for routine diagnostics and target validation applications. Yet, an easy-to-use tool for the analysis of TBS data in combination with array-based methylation results has been missing. Consequently, we have developed EPIC-TABSAT, a user-friendly web-based application for the analysis of targeted sequencing data that additionally allows the integration of array-based methylation results. The tool can handle multiple targets as well as multiple sequencing files in parallel and covers the complete data analysis workflow from calculation of quality metrics to methylation calling and interactive result presentation. The graphical user interface offers an unprecedented way to interpret TBS data alone or in combination with array-based methylation studies. Together with the computation of target-specific epialleles it is useful in validation, research, and routine diagnostic environments. EPIC-TABSAT is freely accessible to all users at https://tabsat.ait.ac.at/.

Systemic autoinflammatory diseases.

Systemic autoinflammatory diseases (SAIDs) are a growing group of disorders caused by a dysregulation of the innate immune system leading to episodes of systemic inflammation. In 1997, MEFV was the first gene identified as disease causing for Familial Mediterranean Fever, the most common hereditary SAID. In most cases, autoinflammatory diseases have a strong genetic background with mutations in single genes. Since 1997 more than 30 new genes associated with autoinflammatory diseases have been identified, affecting different parts of the innate immune system. Nevertheless, for at least 40-60% of patients with phenotypes typical for SAIDs, a distinct diagnosis cannot be met, leading to undefined SAIDs (uSAIDs). However, SAIDs can also be of polygenic or multifactorial origin, with environmental influence modulating the phenotype. The implementation of a disease continuum model combining the adaptive and the innate immune system with autoinflammatory and autoimmune diseases shows the complexity of SAIDs and the importance of new methods to elucidate molecular changes and causative factors in SAIDs. Diagnosis is often based on clinical presentation and genetic testing. The timeline from onset to diagnosis takes up to 7.3 years, highlighting the indisputable need to identify new treatment and diagnostic targets. Recently, other factors are under investigation as additional contributors to the pathogenesis of SAIDs. This review gives an overview of pathogenesis and etiology of SAIDs, and summarizes recent diagnosis and treatment options.

Patients with Systemic Juvenile Idiopathic Arthritis (SJIA) Show Differences in Autoantibody Signatures Based on Disease Activity.

Systemic juvenile idiopathic arthritis (SJIA) is a severe rheumatic disease in children. It is a subgroup of juvenile idiopathic arthritis (JIA; MIM #604302), which is the most common rheumatic disease in children. The diagnosis of SJIA often comes with a significant delay, and the classification between autoinflammatory and autoimmune disease is still discussed. In this study, we analyzed the immunological responses of patients with SJIA, using human proteome arrays presenting immobilized recombinantly expressed human proteins, to analyze the involvement of autoantibodies in SJIA. Results from group comparisons show several differentially reactive antigens involved in inflammatory processes. Intriguingly, many of the identified antigens had a high reactivity against proteins involved in the NF-κB pathway, and it is also notable that many of the detected DIRAGs are described as dysregulated in rheumatoid arthritis. Our data highlight novel proteins and pathways potentially dysregulated in SJIA and offer a unique approach to unraveling the underlying disease pathogenesis in this chronic arthropathy.

Aberrant Naive CD4-Positive T Cell Differentiation in Systemic Juvenile Idiopathic Arthritis Committed to B Cell Help.

OBJECTIVE: Systemic juvenile idiopathic arthritis (JIA) features characteristics of autoinflammation and autoimmunity, culminating in chronic arthritis. In this study, we hypothesized that aberrant or incomplete polarization of T helper cells contributes to disease pathology. METHODS: Cells or serum samples were obtained from healthy controls (n = 72) and systemic JIA patients (n = 171). Isolated naive T helper cells were cultured under Th1, Th17, and T follicular helper (Tfh) or T peripheral helper (Tph)-polarizing conditions and were partly cocultured with allogenic memory B cells. Cell samples were then analyzed for surface marker, transcription factor, and cytokine expression, as well as plasmablast generation. Serum samples were subjected to multiplexed bead and self-antigen arrays and enzyme-linked immunosorbent assays, and all data were compared to retrospective RNA profiling analyses. RESULTS: Differentiation of systemic JIA-naive T helper cells toward Th1 cells resulted in low expression levels of interferon-γ (IFNγ) and eomesodermin, which was associated in part with disease duration. In contrast, developing Th1 cells in patients with systemic JIA were found to produce elevated levels of interleukin-21 (IL-21), which negatively correlated with cellular expression of IFNγ and eomesodermin. In both in vitro and ex vivo analyses, IL-21 together with programmed cell death 1 (PD-1), inducible T cell costimulator (ICOS), and CXCR5 expression induced naive T helper cells from systemic JIA patients to polarize toward a Tfh/Tph cell phenotype. Retrospective analysis of whole-blood RNA-sequencing data demonstrated that Bcl-6, a master transcription factor in Tfh/Tph cell differentiation, was overexpressed specifically in patients with systemic JIA. Naive T helper cells from systemic JIA patients which were stimulated in vitro promoted B cellular plasmablast generation, and self-antigen array data indicated that IgG reactivity profiles of patients with systemic JIA differed from those of healthy controls. CONCLUSION: In the pathogenesis of systemic JIA, skewing of naive T helper cell differentiation toward a Tfh/Tph cell phenotype may represent an echo of autoimmunity, which may indicate the mechanisms driving progression toward chronic destructive arthritis.

Differential regulation of LRRC37A2 in gastric cancer by DNA methylation.

Gastric cancer (GC) is one of the leading types of fatal cancer worldwide. Epigenetic manipulation of cancer cells is a useful tool to better understand gene expression regulatory mechanisms and contributes to the discovery of novel biomarkers. Our research group recently reported a list of 83 genes that are potentially modulated by DNA methylation in GC cell lines. Herein, we further explored the regulation of one of these genes, LRRC37A2, in clinical samples. LRRC37A2 expression was evaluated by RT-qPCR, and DNA methylation was studied using next-generation bisulphite sequencing in 36 GC and paired adjacent nonneoplastic tissue samples. We showed that both reduced LRRC37A2 mRNA levels and increased LRRC37A2 exon methylation were associated with undifferentiated and poorly differentiated tumours. Moreover, LRRC37A2 gene expression and methylation levels were inversely correlated at the +45 exon CpG site. We suggest that DNA hypermethylation may contribute to reducing LRRC37A2 expression in undifferentiated and poorly differentiated GC. Therefore, our results show how some genes may be useful to stratify patients who are more likely to benefit from epigenetic therapy.Abbreviations: AR: androgen receptor; 5-AZAdC: 5-aza-2'-deoxycytidine; B2M: beta-2-microglobulin; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GC: gastric cancer; GLM: general linear model; LRRC37A2: leucine-rich repeat containing 37 member A2; SD: standard deviation; TFII-I: general transcription factor II-I; TSS: transcription start site; XBP1: X-box binding protein 1.

The impact of DNA demethylation on the upregulation of the NRN1 and TNFAIP3 genes associated with advanced gastric cancer.

Gastric cancer (GC) is the third leading cause of cancer-related death worldwide. Very few therapeutic options are currently available in this neoplasia. The use of 5-Aza-2'-deoxycytidine (5-AZAdC) was approved for the treatment of myelodysplastic syndromes, and this drug can treat solid tumours at low doses. Epigenetic manipulation of GC cell lines is a useful tool to better understand gene expression regulatory mechanisms for clinical applications. Therefore, we compared the gene expression profile of 5-AZAdC-treated and untreated GC cell lines by a microarray assay. Among the genes identified in this analysis, we selected NRN1 and TNFAIP3 to be evaluated for gene expression by RT-qPCR and DNA methylation by bisulfite DNA next-generation sequencing in 43 and 52 pairs of GC and adjacent non-neoplastic tissue samples, respectively. We identified 83 candidate genes modulated by DNA methylation in GC cell lines. Increased expression of NRN1 and TNFAIP3 was associated with advanced tumours (P < 0.05). We showed that increased NRN1 and TNFAIP3 expression seems to be regulated by DNA demethylation in GC samples: inverse correlations between the mRNA and DNA methylation levels in the promoter of NRN1 (P < 0.05) and the intron of TNFAIP3 (P < 0.05) were detected. Reduced NRN1 promoter methylation was associated with III/IV TNM stage tumours (P = 0.03) and the presence of Helicobacter pylori infection (P = 0.02). The identification of demethylated activated genes in GC may be useful in clinical practice, stratifying patients who are less likely to benefit from 5-AZAdC-based therapies. KEY MESSAGES: Higher expression of NRN1 and TNFAIP3 is associated with advanced gastric cancer (GC). NRN1 promoter hypomethylation contributes to gene upregulation in advanced GC. TNFAIP3 intronic-specific CpG site demethylation contributes to gene upregulation in GC. These findings may be useful to stratify GC patients who are less likely to benefit from DNA demethylating-based therapies.

The Pattern and Distribution of Induced Mutations in J. curcas Using Reduced Representation Sequencing.

Mutagenesis in combination with Genotyping by Sequencing (GBS) is a powerful tool for introducing variation, studying gene function and identifying causal mutations underlying phenotypes of interest in crop plant genomes. About 400 million paired-end reads were obtained from 82 ethylmethane sulfonate (EMS) induced mutants and 14 wild-type accessions of Jatropha curcas for the detection of Single Nucleotide Polymorphisms (SNPs) and Insertion/Deletions (InDels) by two different approaches (nGBS and ddGBS) on an Illumina HiSeq 2000 sequencer. Using bioinformatics analyses, 1,452 induced SNPs and InDels were identified in coding regions, which were distributed across 995 genes. The predominantly observed mutations were G/C to A/T transitions (64%), while transversions were observed at a lower frequency (36%). Regarding the effect of mutations on gene function, 18% of the mutations were located in intergenic regions. In fact, mutants with the highest number of heterozygous SNPs were found in samples treated with 0.8% EMS for 3 h. Reconstruction of the metabolic pathways showed that in total 16 SNPs were located in six KEGG pathways by nGBS and two pathways by ddGBS. The most highly represented pathways were ether-lipid metabolism and glycerophospholipid metabolism, followed by starch and sucrose metabolism by nGBS and triterpenoid biosynthesis as well as steroid biosynthesis by ddGBS. Furthermore, high genome methylation was observed in J. curcas, which might help to understand the plasticity of the Jatropha genome in response to environmental factors. At last, the results showed that continuously vegetatively propagated tissue is a fast, efficient and accurate method to dissolve chimeras, especially for long-lived plants like J. curcas. Obtained data showed that allelic variations and in silico analyses of gene functions (gene function prediction), which control important traits, could be identified in mutant populations using nGBS and ddGBS. However, the handling of GBS data is more difficult and more challenging than the traditional TILLING strategy in mutated plants, since the Jatropha genome sequence is incomplete, which makes alignment and variant analysis of target sequence reads challenging to perform and interpret. Therefore, providing a complete Jatropha reference genome sequence with high quality should be a priority for any breeding program.