Celiac disease:From genetic variation to molecular culprits

Coeliakie (CeD) is een auto-immuunziekte die veroorzaakt door gluten bij genetisch gevoelige individuen.. Het doel van dit proefschrift was om meer inzicht te krijgen in de functie van enkele van de CeD-kandidaat genen én de biologische mechanismen waar deze genen bij betrokken zijn. Hoofdstuk 1 geeft een overzicht van de voornaamste celtypes, cytokines, immuun moleculen en genetische- en omgevingsfactoren die zouden kunnen bijdragen aan CeD. In hoofdstuk 2 bestuderen we de rol die LPP, een slecht gekenmerkt eiwit in darmepitheelcellen. We vonden dat LPP bijdraagt aan de ziekte via zowel non-immuun als immuun functies van darmepitheelcellen. In hoofdstuk 3 hebben we 118 CeD-geassocieerde kandidaat genen geïdentificeerd door middel van expression quantitative trait loci (eQTL) analyse en statistische om eQTL genen aan een genetisch risico op CeD te koppelen. In hoofdstuk 4 hebben we onderzocht hoe cytokines, die verhoogd zijn in weefsels die door auto-immuunziektes zijn aangedaan (IFNb, Il-15 en IL-21), de genexpressie profiel van Intra-epitheliale cytotoxische lymfocyten (IE-CTLs) veranderen. In een poging om de rol van lncRNAs in het aangeboren immuunsysteem beter te begrijpen, hebben we lncRNAs (RP11-291B21.2) gekarakteriseerd in IEL-CTLs (hoofdstuk 6). We vonden dat deze lncRNA een rol zou kunnen spelen in T-cel activatie. Tenslotte heb ik In hoofdstuk 7 de belangrijkste bevindingen van dit proefschrift samengevat. Verder bespreek ik enkele tekortkomingen in het vaststellen van kandidaat genen en biologische mechanismen geassocieerd met CeD. Daarnaast bespreek ik hoe nieuwe technologieën kunnen worden gebruikt om deze tekortkomingen te overkome.Celiac disease (CeD) is an autoimmune disease triggered by gluten in genetically susceptible individuals. The aim of this thesis was to gain more insight into the function of some of the CeD candidate genes and the biological mechanisms in which these genes are involved. Chapter 1 provides an overview of the main cell types, cytokines, immune molecules and genetic and environmental factors that could contribute to CeD. In chapter 2 we study the role of LPP, a poorly characterized protein in intestinal epithelial cells. We found that LPP contributes to the disease through both non-immune and immune functions of intestinal epithelial cells. In chapter 3, we identified 118 CeD-associated candidate genes by expression quantitative trait loci (eQTL) analysis and statistics to link eQTL genes to a genetic risk of CeD. In chapter 4 we investigated how cytokines, which are elevated in tissues affected by autoimmune diseases (IFNb, Il-15 and IL-21), alter the gene expression profile of Intra-epithelial cytotoxic lymphocytes (IE-CTLs). In an effort to better understand the role of lncRNAs in the innate immune system, we characterized lncRNAs (RP11-291B21.2) in IEL-CTLs (Chapter 6). We found that this lncRNA could play a role in T cell activation. Finally, in Chapter 7 I summarized the main findings of this thesis. Furthermore, I discuss some shortcomings in the determination of candidate genes and biological mechanisms associated with CeD. In addition, I discuss how new technologies can be used to overcome these shortcomings

Foxp3-positive macrophages display immunosuppressive properties and promote tumor growth

Identification of a population of Foxp3-expressing suppressive macrophages

Deconvolution of bulk blood eQTL effects into immune cell subpopulations

BACKGROUND: Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL). RESULTS: The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (≥ 96-100%) and chromatin mark QTL (≥87-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect. CONCLUSIONS: Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool (www.molgenis.org/deconvolution)

Additional file 3 of Deconvolution of bulk blood eQTL effects into immune cell subpopulations

Additional file 3 : Supplementary Table 2: Summary statistics from Decon-eQTLs for the 16,362 whole blood eQTLs