Project description:Background: Although genome-wide association studies (GWAS) have identified hundreds of variants associated with risk of autoimmune and immune-related disorders (AID), our understanding of the diseases mechanisms is limited. In particular, more than 90% of the risk variants lie in non-coding regions, and almost 10% of these map to long non-coding RNA transcripts (lncRNAs). LncRNAs are known to show more cell-type specificity than protein-coding genes. Methods: In this study, we aimed to characterize lncRNAs and protein-coding genes located in loci associated with nine AID which have been well-defined by Immunochip analysis, by transcriptome analysis across seven peripheral blood leukocyte populations (granulocytes, monocytes, NK cells, B-cells, memory-T cells, naive CD4+ and naive CD8+ T-cells) and four cord blood derived T-helper cell populations (precursor, primary, polarized (Th1, Th2) T-helper cells). Results: We show that lncRNAs mapping to loci shared between AIDs are significantly enriched in immune cell types when compared to lncRNAs from the whole genome (M-NM-1<0.005). We were not able to prioritize single cell types relevant for specific diseases, but we observed five cell types enriched (M-NM-1<0.005) in five AID (NK cells for inflammatory bowel disease, juvenile idiopathic arthritis, primary biliary cirrhosis, psoriasis; memory-T and CD8+ T-cells in juvenile idiopathic arthritis, primary biliary cirrhosis, psoriasis, rheumatoid arthritis; Th0 and Th2 cells for inflammatory bowel disease, juvenile idiopathic arthritis, primary biliary cirrhosis, psoriasis, rheumatoid arthritis). Furthermore we show that co-expression analyses of lncRNAs and protein-coding genes can predict the signaling pathways in which these AID-associated lncRNAs are involved. Conclusions: The observed enrichment of lncRNA transcripts in AID loci implies an important role for lncRNAs in AID etiology and suggests that lncRNA genes should be studied in more detail to correctly interpret GWAS findings. The co-expression results strongly support a model in which the lncRNA and protein-coding genes function together in the same pathways. 7 immune cell types

Project description:Baker2013 - Cytokine Mediated Inflammation in Rheumatoid Arthritis This model by Baker M. 2013, describes the interaction between pro and anti-inflammatory cytokine signalling in rheumatoid arthritis. Using two ordinary differential equations, the first model [BIOMD0000000550] analyses bifurcation and describes different pathological states by altering inflammatory regulation parameters. The second model [BIOMD0000000549] includes the effect that ageing has on pro-inflammatory signalling, allowing for time-dependant properties and disease progression to be observed. The author also describes potential dosing for reversal of the disease state. This model is described in the article: Mathematical modelling of cytokine-mediated inflammation in rheumatoid arthritis. Baker M, Denman-Johnson S, Brook BS, Gaywood I, Owen MR. Math Med Biol 2013 Dec; 30(4): 311-337 Abstract: Rheumatoid arthritis (RA) is a chronic inflammatory disease preferentially affecting the joints and leading, if untreated, to progressive joint damage and disability. Cytokines, a group of small inducible proteins, which act as intercellular messengers, are key regulators of the inflammation that characterizes RA. They can be classified into pro-inflammatory and anti-inflammatory groups. Numerous cytokines have been implicated in the regulation of RA with complex up and down regulatory interactions. This paper considers a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrates that mathematical modelling may be used to investigate the involvement of cytokines in the disease process. The model displays a range of possible behaviours, such as bistability and oscillations, which are strongly reminiscent of the behaviour of RA e.g. genetic susceptibility and remitting-relapsing disease. We also show that the dose regimen as well as the dose level are important factors in RA treatments. This model is hosted on BioModels Database and identified by: BIOMD0000000550. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Baker2013 - Cytokine Mediated Inflammation in Rheumatoid Arthritis - Age Dependant This model by Baker M. 2013, describes the interaction between pro and anti-inflammatory cytokine signalling in rheumatoid arthritis. Using two ordinary differential equations, the first model [BIOMD0000000550] analyses bifurcation and describes different pathological states by altering inflammatory regulation parameters. The second model [BIOMD0000000549] includes the effect that ageing has on pro-inflammatory signalling, allowing for time-dependant properties and disease progression to be observed. The author also describes potential dosing for reversal of the disease state. This model is described in the article: Mathematical modelling of cytokine-mediated inflammation in rheumatoid arthritis. Baker M, Denman-Johnson S, Brook BS, Gaywood I, Owen MR. Math Med Biol 2013 Dec; 30(4): 311-337 Abstract: Rheumatoid arthritis (RA) is a chronic inflammatory disease preferentially affecting the joints and leading, if untreated, to progressive joint damage and disability. Cytokines, a group of small inducible proteins, which act as intercellular messengers, are key regulators of the inflammation that characterizes RA. They can be classified into pro-inflammatory and anti-inflammatory groups. Numerous cytokines have been implicated in the regulation of RA with complex up and down regulatory interactions. This paper considers a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrates that mathematical modelling may be used to investigate the involvement of cytokines in the disease process. The model displays a range of possible behaviours, such as bistability and oscillations, which are strongly reminiscent of the behaviour of RA e.g. genetic susceptibility and remitting-relapsing disease. We also show that the dose regimen as well as the dose level are important factors in RA treatments. This model is hosted on BioModels Database and identified by: BIOMD0000000549. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Background: Although genome-wide association studies (GWAS) have identified hundreds of variants associated with risk of autoimmune and immune-related disorders (AID), our understanding of the diseases mechanisms is limited. In particular, more than 90% of the risk variants lie in non-coding regions, and almost 10% of these map to long non-coding RNA transcripts (lncRNAs). LncRNAs are known to show more cell-type specificity than protein-coding genes. Methods: In this study, we aimed to characterize lncRNAs and protein-coding genes located in loci associated with nine AID which have been well-defined by Immunochip analysis, by transcriptome analysis across seven peripheral blood leukocyte populations (granulocytes, monocytes, NK cells, B-cells, memory-T cells, naive CD4+ and naive CD8+ T-cells) and four cord blood derived T-helper cell populations (precursor, primary, polarized (Th1, Th2) T-helper cells). Results: We show that lncRNAs mapping to loci shared between AIDs are significantly enriched in immune cell types when compared to lncRNAs from the whole genome (α<0.005). We were not able to prioritize single cell types relevant for specific diseases, but we observed five cell types enriched (α<0.005) in five AID (NK cells for inflammatory bowel disease, juvenile idiopathic arthritis, primary biliary cirrhosis, psoriasis; memory-T and CD8+ T-cells in juvenile idiopathic arthritis, primary biliary cirrhosis, psoriasis, rheumatoid arthritis; Th0 and Th2 cells for inflammatory bowel disease, juvenile idiopathic arthritis, primary biliary cirrhosis, psoriasis, rheumatoid arthritis). Furthermore we show that co-expression analyses of lncRNAs and protein-coding genes can predict the signaling pathways in which these AID-associated lncRNAs are involved. Conclusions: The observed enrichment of lncRNA transcripts in AID loci implies an important role for lncRNAs in AID etiology and suggests that lncRNA genes should be studied in more detail to correctly interpret GWAS findings. The co-expression results strongly support a model in which the lncRNA and protein-coding genes function together in the same pathways.

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