BackgroundInflammatory bowel disease (IBD) is a chronic complex disease of the gastrointestinal tract. Patients with IBD can experience a wide range of symptoms, but the pathophysiological mechanisms that cause these individual differences in clinical presentation remain largely unknown. In consequence, IBD is currently classified into subtypes using clinical characteristics. If we are to develop a more targeted treatment approach, molecular subtypes of IBD need to be discovered that can be used as new drug targets. To achieve this, we need multiple layers of molecular data generated from the same IBD patients.Construction and contentWe initiated the 1000IBD project (https://1000ibd.org) to prospectively follow more than 1000 IBD patients from the Northern provinces of the Netherlands. For these patients, we have collected a uniquely large number of phenotypes and generated multi-omics profiles. To date, 1215 participants have been enrolled in the project and enrolment is on-going. Phenotype data collected for these participants includes information on dietary and environmental factors, drug responses and adverse drug events. Genome information has been generated using genotyping (ImmunoChip, Global Screening Array and HumanExomeChip) and sequencing (whole exome sequencing and targeted resequencing of IBD susceptibility loci), transcriptome information generated using RNA-sequencing of intestinal biopsies and microbiome information generated using both sequencing of the 16S rRNA gene and whole genome shotgun metagenomic sequencing.Utility and discussionAll molecular data generated within the 1000IBD project will be shared on the European Genome-Phenome Archive (https://ega-archive.org, accession no: EGAS00001002702). The first data release, detailed in this announcement and released simultaneously with this publication, will contain basic phenotypes for 1215 participants, genotypes of 314 participants and gut microbiome data from stool samples (315 participants) and biopsies (107 participants) generated by tag sequencing the 16S gene. Future releases will comprise many more additional phenotypes and -omics data layers. 1000IBD data can be used by other researchers as a replication cohort, a dataset to test new software tools, or a dataset for applying new statistical models.ConclusionsWe report on the establishment and future development of the 1000IBD project: the first comprehensive multi-omics dataset aimed at discovering IBD biomarker profiles and treatment targets.Electronic supplementary materialThe online version of this article (10.1186/s12876-018-0917-5) contains supplementary material, which is available to authorized users.
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More than 240 genetic risk loci have been associated with inflammatory bowel disease (IBD), but little is known about how they contribute to disease development in involved tissue. Here, we hypothesized that host genetic variation affects gene expression in an inflammation-dependent way, and investigated 299 snap-frozen intestinal biopsies from inflamed and non-inflamed mucosa from 171 IBD patients. RNA-sequencing was performed, and genotypes were determined using whole exome sequencing and genome wide genotyping. In total, 28,746 genes and 6,894,979 SNPs were included. Linear mixed models identified 8,881 independent intestinal cis-expression quantitative trait loci (cis-eQTLs) (FDR < 0.05) and interaction analysis revealed 190 inflammation-dependent intestinal cis-eQTLs (FDR < 0.05), including known IBD-risk genes and genes encoding immune-cell receptors and antibodies. The inflammation-dependent cis-eQTL SNPs (eSNPs) mainly interact with prevalence of immune cell types. Inflammation-dependent intestinal cis-eQTLs reveal genetic susceptibility under inflammatory conditions that can help identify the cell types involved in and the pathways underlying inflammation, knowledge that may guide future drug development and profile patients for precision medicine in IBD.
Proteogenomics is a multi-omics research field that has the aim to efficiently integrate genomics, transcriptomics and proteomics. With this approach it is possible to identify new patient-specific proteoforms that may have implications in disease development, specifically in cancer. Understanding the impact of a large number of mutations detected at the genomics level is needed to assess the effects at the proteome level. Proteogenomics data integration would help in identifying molecular changes that are persistent across multiple molecular layers and enable better interpretation of molecular mechanisms of disease, such as the causal relationship between single nucleotide polymorphisms (SNPs) and the expression of transcripts and translation of proteins compared to mainstream proteomics approaches. Identifying patient-specific protein forms and getting a better picture of molecular mechanisms of disease opens the avenue for precision and personalized medicine. Proteogenomics is, however, a challenging interdisciplinary science that requires the understanding of sample preparation, data acquisition and processing for genomics, transcriptomics and proteomics. This chapter aims to guide the reader through the technology and bioinformatics aspects of these multi-omics approaches, illustrated with proteogenomics applications having clinical or biological relevance.
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Background and Aims: Iron deficiency (ID) is a frequent extra-intestinal manifestation in patients with Inflammatory Bowel Disease (IBD), who often do not respond to iron supplementation. Iron is a cofactor for hydroxylases that suppress the hypoxia-inducible factor-1α (HIF1α), a transcription factor regulating iron homeostasis. We hypothesized that iron deficiency affects mucosal HIF1α activity in IBD.Methods: IBD patients (n = 101) were subdivided based on iron status (ferritin levels or transferrin saturation) and systemic inflammation (C-reactive protein levels). 154 corresponding ileal and colonic biopsies were analyzed for differential expression of 20 HIF1α pathway-associated genes and related to iron and inflammation status. In vitro expression of selected HIF1α pathway genes were analyzed in wild-type and HIF1A-null Caco-2 cells.Results: Gene expression of the mucosal HIF1α pathway was most affected by intestinal location and inflammatory status. Especially, ileal mucosal TFRC expression, encoding the transferrin receptor TFR1, was increased in inflamed tissue (p < 0.001), and further enhanced in ID. Accordingly, TFRC expression in inflamed tissue associated negatively with serum iron levels, which was not observed in the non-inflamed mucosa. The HIF1α pathway agonist DMOG increased TFRC expression in Caco-2 cells, which was blunted in HIF1A-null cells.Conclusion: We demonstrate that inflammation and anatomical location primarily determine HIF1α pathway activation and downstream TFRC expression in the intestinal mucosa. IBD patients with ID may benefit from treatment with HIF1α-agonists by 1) increasing TFRC-mediated iron absorption in non-inflamed tissue and 2) decreasing mucosal inflammation, thereby improving their responsiveness to oral iron supplementation.
Activation of intronic polyadenylation signals results in premature cleavage and polyadenylation (PCPA). The majority of mammalian microRNAs (miRNAs) are also located within intronic regions of protein-coding genes and are transcriptionally co-expressed with their host genes. Here we show that U1-dependent PCPA by an RNA surveillance mechanism termed telescripting dysregulates miRNA biogenesis in melanoma. When U1 is reduced, miR-211 levels are decreased as a direct consequence of activation of a newly identified alternative intronic polyadenylation signal located upstream of miR-211 within its host gene, the cation channel TRPM1. Various melanoma cell lines revealed decreased U1 levels and a shift from full-length to truncated TRPM1 isoforms with concomitant decreased miR-211 expression. Modulation of TRPM1 alternative polyadenylation (APA) by antisense morpholino oligonucleotides inhibits and potentially restores miR-211 expression to endogenous levels. This mechanism of intronic PCPA and its effects on miRNA biogenesis represents a previously unrecognized additional layer of gene expression regulation suitable for therapeutic modulation when dysregulated as shown for human melanoma.
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