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.

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/.

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.

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

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-$\kappa$B 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.