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- Zheng, TH, et al.
(author)
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Genome-wide analysis of 944 133 individuals provides insights into the etiology of haemorrhoidal disease
- 2021
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In: Gut. - : BMJ. - 1468-3288 .- 0017-5749. ; 70:8, s. 1538-1549
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Journal article (peer-reviewed)abstract
- Haemorrhoidal disease (HEM) affects a large and silently suffering fraction of the population but its aetiology, including suspected genetic predisposition, is poorly understood. We report the first genome-wide association study (GWAS) meta-analysis to identify genetic risk factors for HEM to date.DesignWe conducted a GWAS meta-analysis of 218 920 patients with HEM and 725 213 controls of European ancestry. Using GWAS summary statistics, we performed multiple genetic correlation analyses between HEM and other traits as well as calculated HEM polygenic risk scores (PRS) and evaluated their translational potential in independent datasets. Using functional annotation of GWAS results, we identified HEM candidate genes, which differential expression and coexpression in HEM tissues were evaluated employing RNA-seq analyses. The localisation of expressed proteins at selected loci was investigated by immunohistochemistry.ResultsWe demonstrate modest heritability and genetic correlation of HEM with several other diseases from the GI, neuroaffective and cardiovascular domains. HEM PRS validated in 180 435 individuals from independent datasets allowed the identification of those at risk and correlated with younger age of onset and recurrent surgery. We identified 102 independent HEM risk loci harbouring genes whose expression is enriched in blood vessels and GI tissues, and in pathways associated with smooth muscles, epithelial and endothelial development and morphogenesis. Network transcriptomic analyses highlighted HEM gene coexpression modules that are relevant to the development and integrity of the musculoskeletal and epidermal systems, and the organisation of the extracellular matrix.ConclusionHEM has a genetic component that predisposes to smooth muscle, epithelial and connective tissue dysfunction.
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- Aarestrup, FM, et al.
(author)
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Towards a European health research and innovation cloud (HRIC)
- 2020
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In: Genome medicine. - : Springer Science and Business Media LLC. - 1756-994X. ; 12:1, s. 18-
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Journal article (peer-reviewed)abstract
- The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.
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- Juzenas, S., et al.
(author)
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Sequencing-based hematopoietic miRNA landscape reveals common and distinct features of autoimmune inflammatory phenotypes
- 2019
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In: Journal of Crohn's & Colitis. - : Oxford University Press. - 1873-9946 .- 1876-4479. ; 13:Suppl. 1, s. S614-S614
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Journal article (other academic/artistic)abstract
- Background: MiRNAs represent a class of small non-coding RNAs which are involved in regulation of protein-coding gene expression. Being implicated in various processes such as development and regu-latory circuits of cells, miRNAs also play an important role in the etiology of a variety of diseases. Imbalance of the regulatory pro-cesses within immune system development and response may lead to disturbed production of pro-inflammatory cytokines and over-reactivity of the immune cells, thus causing relapsing inflamma-tion, a characteristic feature of inflammatory bowel disease (IBD). Recent studies of colonic miRNAs employed NGS for the distinction between CD, UC and healthy controls, or among different CD sub-types. However, NGS-based profiles of blood-circulating miRNAs have thus far not been investigated in the context of IBD together with other immune-mediated diseases, including ankylosing spon-dylitis, psoriasis, systemic lupus erythematosus, rheumatoid arthritis and sarcoidosis, as well as non-immune hemolytic-uremic syndrome.Methods: Study participants were recruited in Germany and Sweden, where peripheral blood samples (PAXgene) as well as phenotypical and clinical information (such as treatment status, dis-ease activity and location) was collected. Small RNA transcriptomes of 680 individuals (Figure 1) were sequenced using Illumina NGS platform. Small RNA-seq data preprocessing and quantification were performed using cutadapt and miraligner (ref. miRBase v22), respectively. Differential expression analysis (DESeq2) and correla-tion (Spearman) analysis have been performed to identify disease activity-, trait- and treatment-specific miRNA signatures. These sig-natures were then utilized in a machine-learning approach to build classification models for IBD diagnostics.Results: The results of multiple pairwise differential expression anal-yses among different immune-mediated inflammatory conditions and healthy controls revealed inflammation-specific as well and dis-ease-specific deregulation of miRNAs. Correlation analysis identified miRNAs positively and negatively correlated with IBD activity. The preliminary results of machine learning classifiers based on miRNA profiles showed that median Matthews correlation coefficient for all model types showed remarkable predictive performance estimated as being 1.00 (median over main diagnoses), as well as ranging from 0.68 to 0.76 (median over CD location) and from 0.69 to 0.77 (median over UC extent).Conclusions: Immune-mediated inflammatory diseases share com-mon and distinct differentially expressed miRNAs, which have a potential to be used in the diagnostics of IBD, including the evalua-tion of the disease activity.
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