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Background: Juvenile idiopathic arthritis (JIA) is an autoimmune joint disease that frequently co-occurs with other complex phenotypes, including cancers and other autoimmune diseases. Despite the identification of numerous risk variants through genome-wide association studies (GWAS), the affected genes, their connection to JIA pathogenesis, and their role in the development of associated traits remain unclear. This study aims to address these gaps by elucidating the gene-regulatory mechanisms underlying JIA pathogenesis and exploring its potential role in the emergence of associated traits. Methods: A two-sample Mendelian Randomization (MR) analysis was conducted to identify blood-expressed genes causally linked to JIA. A curated protein interaction network was subsequently used to identify sets of singlenucleotide polymorphisms (i.e., spatial eQTL SNPs) that regulate the expression of JIA causal genes and their protein interaction partners. These SNPs were cross-referenced against the GWAS catalog to identify statistically enriched traits associated with JIA. Results: The two-sample MR analysis identified 52 genes whose expression changes in the blood are putatively causal for JIA. These genes (e.g., HLA, LTA, LTB, IL6ST) participate in a range of immune-related pathways (e.g., antigen presentation, cytokine signalling) and demonstrate cell type-specific regulatory patterns across different immune cell types (e.g., PPP1R11 in CD4+ T cells). The spatial eQTLs that regulate JIA causal genes and their interaction partners were statistically enriched for GWAS SNPs linked with 95 other traits, including both known and novel JIA-associated traits. This integrative analysis identified genes whose dysregulation may explain the links between JIA and associated traits, such as autoimmune/inflammatory diseases (genes at 6p22.1 locus), Hodgkin lymphoma (genes at 6p21.3 [FKBPL, PBX2, AGER]), and chronic lymphocytic leukemia (BAK1). Conclusion: Our approach provides a significant advance in understanding the genetic architecture of JIA and associated traits. The results suggest that the burden of associated traits may differ among JIA patients, influenced by their combined genetic risk across different clusters of traits. Future experimental validation of the identified connections could pave the way for refined patient stratification, the discovery of new biomarkers, and shared therapeutic targets. [ABSTRACT FROM AUTHOR]

The ubiquitin-conjugating E2 enzymes play a central role in ubiquitin transfer. Disruptions to the ubiquitin system are implicated in multiple diseases, and as a result, molecules that modulate the activity of the ubiquitin system are of interest. E2 enzyme function relies on interactions with partner proteins, and the disruption of these is an effective way to modulate activity. Here, we report the discovery of ubiquitin variants (UbVs) that inhibit the E2 enzyme, Ube2d2 (UbcH5b). The six UbVs identified inhibit ubiquitin chain building, and the structural and biophysical characterisation of two of these demonstrate they bind to Ube2d2 with low micromolar affinity and high specificity. Both characterised UbVs bind at a site that overlaps with E1 binding, while the more inhibitory UbV has an additional binding site that blocks a critical non-covalent ubiquitin-binding site on the E2 enzyme. The discovery of novel protein-based ubiquitin derivatives that inhibit protein-protein interactions is an important step towards discovering small molecules that inhibit the activity of E2 enzymes. Furthermore, the specificity of the UbVs within the Ube2d family suggests that it may be possible to develop tools to selectively inhibit highly related E2 enzymes., (© 2024 Federation of European Biochemical Societies.)

Background: To develop targeted treatments, it is crucial to identify the full spectrum of genetic drivers in melanoma, including those in non-coding regions. However, recent efforts to explore non-coding regions have primarily focused on gene-adjacent elements such as promoters and non-coding RNAs, leaving intergenic distal regulatory elements largely unexplored., Methods: We used Hi-C chromatin contact data from melanoma cells to map distal, non-coding, promoter-interacting regulatory elements genome-wide in melanoma. Using this "promoter-interaction network", alongside whole-genome sequence and gene expression data from the Pan Cancer Analysis of Whole Genomes, we developed multivariate linear regression models to identify distal somatic mutation hotspots that affect promoter activity., Results: We identified eight recurrently mutated hotspots that are novel, melanoma-specific, located in promoter-interacting distal regulatory elements, alter transcription factor binding motifs, and affect the expression of genes (e.g., HSPB7, CLDN1, ADCY9 and FDXR) previously implicated as tumour suppressors/oncogenes in various cancers., Conclusions: Our study suggests additional non-coding drivers beyond the well-characterised TERT promoter in melanoma, offering new insights into the disruption of complex regulatory networks by non-coding mutations that may contribute to melanoma development. Furthermore, our study provides a framework for integrating multiple levels of biological data to uncover cancer-specific non-coding drivers., Competing Interests: Competing interests The authors declare no competing interests. Ethics approval and consent to participate Ethical approvals and informed consent were obtained by the original data providers for the collection and use of the datasets utilised in this study., (© 2024. The Author(s).)

Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. One of the promising applications of machine learning is in precision medicine, where disease risk is predicted using patient genetic data. However, creating an accurate prediction model based on genotype data remains challenging due to the so-called "curse of dimensionality" (i.e., extensively larger number of features compared to the number of samples). Therefore, the generalizability of machine learning models benefits from feature selection, which aims to extract only the most "informative" features and remove noisy "non-informative," irrelevant and redundant features. In this article, we provide a general overview of the different feature selection methods, their advantages, disadvantages, and use cases, focusing on the detection of relevant features (i.e., SNPs) for disease risk prediction., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Pudjihartono, Fadason, Kempa-Liehr and O'Sullivan.)