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Item does not contain fulltext Genome-wide association studies (GWAS) have now correlated hundreds of genetic variants with complex genetic diseases and drug efficacy. Functional characterization of these factors remains challenging, particularly because of the lack of human model systems. Molecular and nanotechnological advances, in particular the ability to generate patient-specific PSC lines, differentiate them into diverse cell types, and seed and combine them on microfluidic chips, have led to the establishment of organ-on-a-chip (OoC) platforms that recapitulate organ biology. OoC technology thus provides unique personalized platforms for studying the effects of host genetics and environmental factors on organ physiology. In this review we describe the technology and provide examples of how OoCs may be used for disease modeling and pharmacogenetic research. 01 april 2023

BACKGROUND: . In a biopsy-proven adult celiac disease (CeD) cohort from the Netherlands, male patients were diagnosed with CeD at significantly older ages than female patients.OBJECTIVES: To identify which factors contribute to diagnosis later in life and whether diagnostic delay influences improvement of symptoms after starting a gluten-free diet (GFD).METHODS: . We performed a questionnaire study in 211 CeD patients (67:144, male:female) with median age at diagnosis of 41.8 years (interquartile range: 25-58) and at least Marsh 2 histology.RESULTS: . Classical symptoms (diarrhea, fatigue, abdominal pain and/or weight loss) were more frequent in women than men, but sex was not significantly associated with age at diagnosis. In a multivariate analysis, a non-classical presentation (without any classical symptoms) and a negative family history of CeD were significant predictors of older age at diagnosis (coefficients of 8 and 12 years, respectively). A delay of >3 years between first symptom and diagnosis was associated with slower improvement of symptoms after start of GFD, but not with sex, presentation of classical symptoms or age at diagnosis.CONCLUSION: . Non-classical CeD presentation is more prevalent in men and is associated with a diagnosis of CeD later in life. Recognizing CeD sooner after onset of symptoms is important because a long diagnostic delay is associated with a slower improvement of symptoms after starting a GFD.

Celiac Disease (CeD) is a complex immune disorder involving villous atrophy in the small intestine that is triggered by gluten intake. Current CeD diagnosis is based on late-stage pathophysiological parameters such as detection of specific antibodies in blood and histochemical detection of villus atrophy and lymphocyte infiltration in intestinal biopsies. To date, no early onset biomarkers are available that would help prevent widespread villous atrophy and severe symptoms and co-morbidities. To search for novel CeD biomarkers, we used single-cell RNA sequencing (scRNAseq) to investigate PBMC samples from 11 children before and after seroconversion for CeD and 10 control individuals matched for age, sex and HLA-genotype. We generated scRNAseq profiles of 9559 cells and identified the expected major cellular lineages. Cell proportions remained stable across the different timepoints and health conditions, but we observed differences in gene expression profiles in specific cell types when comparing patient samples before and after disease development and comparing patients with controls. Based on the time when transcripts were differentially expressed, we could classify the deregulated genes as biomarkers for active CeD or as potential pre-diagnostic markers. Pathway analysis showed that active CeD biomarkers display a transcriptional profile associated with antigen activation in CD4+ T cells, whereas NK cells express a subset of biomarker genes even before CeD diagnosis. Intersection of biomarker genes with CeD-associated genetic risk loci pinpointed genetic factors that might play a role in CeD onset. Investigation of potential cellular interaction pathways of PBMC cell subpopulations highlighted the importance of TNF pathways in CeD. Altogether, our results pinpoint genes and pathways that are altered prior to and during CeD onset, thereby identifying novel potential biomarkers for CeD diagnosis in blood.

Celiac disease (CeD) is triggered by gluten and results in inflammation and villous atrophy of the small intestine. We aimed to explore the role of miRNA-mediated deregulation of the transcriptome in CeD. Duodenal biopsies of CeD patients (n = 33) and control subjects (n = 10) were available for miRNA-sequencing, with RNA-sequencing also available for controls (n = 5) and CeD (n = 6). Differential expression analysis was performed to select CeD-associated miRNAs and genes. MiRNA‒target transcript pairs selected from public databases that also displayed a strong negative expression correlation in the current dataset (R &lt, −0.7) were used to construct a CeD miRNA‒target transcript interaction network. The network includes 2030 miRNA‒target transcript interactions, including 423 experimentally validated pairs. Pathway analysis found that interactions are involved in immune-related pathways (e.g., interferon signaling) and metabolic pathways (e.g., lipid metabolism). The network includes 13 genes previously prioritized to be causally deregulated by CeD-associated genomic variants, including STAT1. CeD-associated miRNAs might play a role in promoting inflammation and decreasing lipid metabolism in the small intestine, thereby contributing unbalanced cell turnover in the intestinal crypt. Some CeD-associated miRNAs deregulate genes that are also affected by genomic CeD-risk variants, adding an additional layer of complexity to the deregulated transcriptome in CeD.

Background Recent studies highlight the role of metabolites in immune diseases, but it remains unknown how much of this effect is driven by genetic and non-genetic host factors. Result We systematically investigate circulating metabolites in a cohort of 500 healthy subjects (500FG) in whom immune function and activity are deeply measured and whose genetics are profiled. Our data reveal that several major metabolic pathways, including the alanine/glutamate pathway and the arachidonic acid pathway, have a strong impact on cytokine production in response to ex vivo stimulation. We also examine the genetic regulation of metabolites associated with immune phenotypes through genome-wide association analysis and identify 29 significant loci, including eight novel independent loci. Of these, one locus (rs174584-FADS2) associated with arachidonic acid metabolism is causally associated with Crohn’s disease, suggesting it is a potential therapeutic target. Conclusion This study provides a comprehensive map of the integration between the blood metabolome and immune phenotypes, reveals novel genetic factors that regulate blood metabolite concentrations, and proposes an integrative approach for identifying new disease treatment targets.

Autoimmune and inflammatory diseases are polygenic disorders of the immune system. Many genomic loci harbor risk alleles for several diseases, but the limited resolution of genetic mapping prevents determining if the same allele is responsible, indicating a shared underlying mechanism. Using a collection of 129,058 cases and controls across six diseases, we show that ∼40% of overlapping associations are due to the same allele. We improve fine-mapping resolution for shared alleles two-fold by combining cases and controls across diseases, allowing us to identify more eQTLs driven by the shared alleles. The patterns of sharing indicate widespread shared mechanisms, but not a single global autoimmune mechanism. Our approach can be applied to any set of traits, and is particularly valuable as sample collections become depleted. One sentence summary Genetic mapping in autoimmune diseases increases genetic mapping resolution and reveals shared mechanisms

Conventional model systems cannot fully recapitulate the multifactorial character of complex diseases like celiac disease (CeD), a common chronic intestinal disorder in which many different genetic risk factors interact with environmental factors such as dietary gluten. However, by combining recently developed human induced pluripotent stem cell (hiPSC) technology and organ-on-chip technology, in vitro intestine-on-chip systems can now be developed that integrate the genetic background of complex diseases, the different interacting cell types involved in disease pathology, and the modulating environmental factors such as gluten and the gut microbiome. The hiPSCs that are the basis of these systems can be generated from both diseased and healthy individuals, which means they can be stratified based on their load of genetic risk factors. A CeD-on-chip model system has great potential to improve our understanding of disease etiology and accelerate the development of novel treatments and preventive therapies in CeD and other complex diseases.

Celiac disease is an auto-immune disease in which an immune response to dietary gluten leads to inflammation and subsequent atrophy of small intestinal villi, causing severe bowel discomfort and malabsorption of nutrients. The major instigating factor for the immune response in celiac disease is the activation of gluten-specific CD4+ T cells expressing T cell receptors that recognize gluten peptides presented in the context of HLA-DQ2 and DQ8. Here we provide an in-depth characterization of 28 gluten-specific T cell clones. We assess their transcriptional and epigenetic response to T cell receptor stimulation and link this to genetic factors associated with celiac disease. Gluten-specific T cells have a distinct transcriptional profile that mostly resembles that of Th1 cells but also express cytokines characteristic of other types of T-helper cells. This transcriptional response appears not to be regulated by changes in chromatin state, but rather by early upregulation of transcription factors and non-coding RNAs that likely orchestrate the subsequent activation of genes that play a role in immune pathways. Finally, integration of chromatin and transcription factor binding profiles suggest that genes activated by T cell receptor stimulation of gluten‑specific T cells may be impacted by genetic variation at several genetic loci associated with celiac disease.

BackgroundCeliac disease (CeD) is a complex T cell–mediated enteropathy induced by gluten. Although genome-wide association studies have identified numerous genomic regions associated with CeD, it is difficult to accurately pinpoint which genes in these loci are most likely to cause CeD.ResultsWe used four different in silico approaches – Mendelian Randomization inverse variance weighting, COLOC, LD overlap and DEPICT – to integrate information gathered from a large transcriptomics dataset. This identified 118 prioritized genes across 50 CeD-associated regions. Co-expression and pathway analysis of these genes indicated an association with adaptive and innate cytokine signalling and T cell activation pathways. 51 of these genes are targets of known drug compounds and likely druggable genes, suggesting that our methods can be used to pinpoint potential therapeutic targets. In addition, we detected 172 gene-combinations that were affected by our CeD-prioritized genes in trans. Notably, 41 of these trans-mediated genes appear to be under control of one master regulator, TRAFD1, and were found to be involved in IFNγ signalling and MHC I antigen processing/presentation. Finally, we performed in vitro experiments that validated the role of TRAFD1 as an immune regulator acting in trans.ConclusionsOur strategy has confirmed the role of adaptive immunity in CeD and revealed a genetic link between CeD and the IFNγ signalling and MHC I antigen processing pathways, both major players of immune activation and CeD pathogenesis.

Contains fulltext : 238435.pdf (Publisher’s version ) (Open Access) Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease and is characterized by different stages varying from benign fat accumulation to non-alcoholic steatohepatitis (NASH) that may progress to cirrhosis and liver cancer. In recent years, a regulatory role of long non-coding RNAs (lncRNAs) in NAFLD has emerged. Therefore, we aimed to characterize the still poorly understood lncRNA contribution to disease progression. Transcriptome analysis in 60 human liver samples with various degrees of NAFLD/NASH was combined with a functional genomics experiment in an in vitro model where we exposed HepG2 cells to free fatty acids (FFA) to induce steatosis, then stimulated them with tumor necrosis factor alpha (TNFα) to mimic inflammation. Bioinformatics analyses provided a functional prediction of novel lncRNAs. We further functionally characterized the involvement of one novel lncRNA in the nuclear-factor-kappa B (NF-κB) signaling pathway by its silencing in Hepatoma G2 (HepG2) cells. We identified 730 protein-coding genes and 18 lncRNAs that responded to FFA/TNFα and associated with human NASH phenotypes with consistent effect direction, with most being linked to inflammation. One novel intergenic lncRNA, designated lncTNF, was 20-fold up-regulated upon TNFα stimulation in HepG2 cells and positively correlated with lobular inflammation in human liver samples. Silencing lncTNF in HepG2 cells reduced NF-κB activity and suppressed expression of the NF-κB target genes A20 and NFKBIA. The lncTNF we identified in the NF-κB signaling pathway may represent a novel target for controlling liver inflammation.

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 1 : Supplementary Figure 1: Prediction performance of Decon-cell within 500FG: The Y-axis represents the 73 immune cell types quantified by FACS in the 500FG cohort. The bar plot on the left panel shows the mean Prediction Performance (Spearman correlation coefficient between predicted and measured cells across 100-fold cross validations). On the right panel, box plots represent the distribution of the Prediction Performance within 100 iterations of the cross validations. A cutoff of mean Prediction Performance ≥0.5 was applied to define predictable cell types (green). Supplementary Figure 2. Signature genes selected for prediction of cell proportions by Decon-cell: (A) Total number of marker genes (genes selected in ≥80% of all models in the 100 iterations) per predictable cell type. Different colors indicate different subpopulations. (B) The number of genes significantly correlated with cell counts (Spearman correlation, adjusted P ≤ 0.05) (y-axis) shows the total number of significantly correlated genes, while the x-axis shows the prediction performance (x-axis). (C) Distributions of the total number of “strongly” correlated genes (absolute Spearman correlation ≥0.3) between predictable and unpredictable cell subpopulations. Supplementary Figure 3. Comparison of prediction performance between Decon-cell and other existing methods. (A) Performance of Decon-cell: the measured (x axis) and predicted cell proportions (y-axis) were compared for neutrophils (given by granulocytes in 500FG), lymphocytes and monocytes CD14+ and granulocytes in three independent cohorts (shown by row, from top to bottom: LLDeep (n = 627); LLS (n = 660); RS (n = 773)). (B) Comparison of prediction performance for Decon-cell, CIBERSORT and xCell in three independent cohorts for a total of 4 major immune subpopulations. Supplementary Figure 4. Prediction performance of xCell and CIBERSORT in three independent Dutch populations (LLDeep, n = 627; LLS, n = 660; RS, n = 773). (A) Scatter plots showing the measured cell proportions of circulating immune cells on the x-axis and the xCell enrichment score on the y-axis. (B) Scatter plots showing the measured cell proportions of circulating immune cells on the x-axis and the predicted cell proportions given by CIBERSORT) on the y-axis. Supplementary Figure 5. Expression of marker genes selected by Decon-cell. Expression levels (scaled, log2(TPM + 1)) of signature genes in the data in three purified cell subpopulations: CD4+ T cells (A), neutrophils/granulocytes (B) and monocytes (C) in the data from BLUEPRINT. Cell subpopulations are indicated in different colors by columns. Correlation of each of the signature genes and the cell subpopulation percentage in the 500FG cohort is shown on by the green bar at the left-hand side of heatmap figure, i.e. darker green corresponds to higher correlations. Supplementary Figure 6. Many of the CTi eQTL are cell type exclusive. Colored bar plot on the left shows the total number of significant CTi eQTLs in whole blood eQTLs (as also shown in Fig. 2a). Gray bar plot shows the total number of eQTLs shared across the possible combinations of the six cell subpopulations under study. Supplementary Figure 7. Variation of gene expression across samples for deconvoluted cell-type eQTLs genes in whole blood. Granulocyte eQTL genes show significantly higher variance across the BIOS samples (F test p-value ≤0.05) compared to those from monocytes, CD4+ T cells, CD8+ T cells, B cells and NK cells. Supplementary Figure 8. Validation of CTi eQTLs using effect sizes of eQTLs from purified cells. CTi eQTLs (FDR ≤ 0.05) from the BIOS cohort show a significantly bigger effect size in purified cell eQTLs [9] from their relevant cell subtype as compared to other whole blood eQTLs (diagonal boxed comparisons). The off-diagonal comparisons show that these eQTL genes are specific to a cell subpopulation because the differences in effect sizes are non-significant in all but one case (CD4+ T cell eQTL genes in monocyte-derived eQTLs). Supplementary Figure 9. Validation of CTi eQTLs using effect sizes of K27AC QTLs from purified cells. CTi eQTLs (FDR ≤ 0.05) show a significantly bigger effect size for K27AC QTLs that have peaks located in the promoter region of the eGenes from their relevant cell subtype compared to the rest of the significant whole blood eQTLs (diagonal boxed comparisons). The off-diagonal comparisons show that these eQTL genes are specific to a cell subtype because the differences in effect sizes are non-significant in all but the comparisons across Neutrophils and Monocytes (CD14+). Supplementary Figure 10. Validation of CTi eQTLs using effect sizes of K4ME1 QTLs from purified cells. CTi eQTLs (FDR ≤ 0.05) show a significantly bigger effect size for K4ME1 QTLs (where the eGenes is the closest gene tagging the K4ME1 QTLs peak) from their relevant cell subtype compared to the rest of the significant whole blood eQTLs (diagonal boxed comparisons). The off-diagonal comparisons show that these eQTL genes are specific to a cell subtype because the differences in effect sizes are non-significant in all but the comparisons between neutrophils and monocytes (CD14+). Supplementary Figure 11. Validation of CTi eQTLs using allelic concordance with eQTLs results from purified cells. CTi eQTLs (FDR ≤ 0.05) show high allelic concordance with their respective purified cell eQTLs. Top row shows allelic concordance of deconvoluted granulocyte eQTLs (all in green) against neutrophils, monocytes and CD4+ T cells. Second row shows deconvoluted monocyte eQTLs against purified cell eQTLs in the same order as the top row. Bottom row shows the same comparisons as for deconvoluted CD4+ eQTLs. Allelic concordance of the off-diagonal (comparing CTi eQLTs with non-relevant cell types) show a consistent decrease in allelic concordance. P-values are Bonferroni-corrected Fisher exact tests between groups. Supplementary Figure 12. Validation of CTi eQTLs using allelic concordance with K27AC results from purified cells. CTi eQTLs (FDR ≤ 0.05) show a high allelic concordance in their respective purified cell K27AC QTLs. Top row shows allelic concordance of deconvoluted granulocyte eQTLs (all in green) against neutrophils, monocytes and CD4+ T cells derived from K27AC QTLs. Second row shows deconvoluted monocyte eQTLs (all in orange) against purified cell K27AC QTLs in the same order as top row. Bottom row shows the same comparisons as for deconvoluted CD4+ eQTLs (all in purple). Allelic concordance of the off-diagonal (comparing deconvoluted eQTLs with non-relevant cell types) show a consistent decrease in allelic concordance when compared to the relevant cell type comparisons. P-values are Bonferroni-corrected Fisher exact tests between groups. Supplementary Figure 13. Allelic concordance between whole blood eQTLs and K27AC QTLs for purified neutrophils, CD14+ monocytes and CD4+ T cells. Supplementary Figure 14. Comparison of whole blood eQTLs with eQTLs from single cell RNA-seq Whole blood eQTLs show 89% allelic concordance for significant eQTLs derived from scRNA-seq data, comprising monocytes CD14+, B cells, CD4+ T cells, CD8+ T cells and NK cells. Supplementary Figure 15 Validation of cell type eQTLs detected in the BIOS cohort using the Westra et al method: (A) Expression of eGenes in purified cell subpopulations from BLUEPRINT (green for granulocyte eQTL genes showing expression for purified neutrophils; orange for monocytes; purple for CD4+ T cells; pink for B cells). (B) CT eQTLs detected by the Westra method show a significantly larger effect size in purified cell eQTLs [11] as compared to the rest of the whole blood eQTLs. Boxed-diagonal shows the comparisons with relevant cell types where the effect differences are stronger. Supplementary Figure 16 Allelic concordance rates of cell type eQTLs detected using the Westra et al method and eQTLs from purified cells. Top row shows allelic concordance of granulocyte CT eQTLs against neutrophils, monocytes and CD4+ T cells. Second row shows CT monocyte eQTLs against purified cell eQTLs in the same order as top row. Bottom row shows the same comparisons for CT CD4+ eQTLs. Supplementary Figure 17 Comparison of Decon-eQTL with Westra et al method. Overlap of CT eQTLs detected with Decon-eQTL and the Westra et al method and those found to be significant in purified cell subpopulations for granulocyte QTLs (A), CD4+ T cells (B), and monocytes (C). Supplementary Figure 18 Distribution and correlation among circulating cell proportions. (A) Scatter plots show the correlations between different cell subpopulations in 89 samples from 500FG. Blue line indicates a fitted linear model. Diagonal plots depict the overall density distribution per cell type. Upper right triangle shows the Pearson correlation coefficient for each pairwise comparison. (B) Correlations between different cell subpopulations in the BIOS cohort obtained by prediction using Decon-cell. Supplementary Figure 19.General overview of the Decon2 method. (A) Gene expression can be used to predict cell count percentages of cell counts that are already trained in the Decon-Cell model. Additionally, the model can be trained on different cell types if expression data and cell count proportions are available. (B) Decon-eQTL models the cell type dependent eQTL effect using expression, genotype, and measured cell count proportions or, if unavailable, predicted cell count proportions.

The respective effects of tissue alarmins interleukin (IL)-15 and interferon beta (IFNβ), and IL-21 produced by T cells on the reprogramming of cytotoxic T lymphocytes (CTLs) that cause tissue destruction in celiac disease is poorly understood. Transcriptomic and epigenetic profiling of primary intestinal CTLs showed massive and distinct temporal transcriptional changes in response to tissue alarmins, while the impact of IL-21 was limited. Only anti-viral pathways were induced in response to all the three stimuli, albeit with differences in dynamics and strength. Moreover, changes in gene expression were primarily independent of changes in H3K27ac, suggesting that other regulatory mechanisms drive the robust transcriptional response. Finally, we found that IL-15/IFNβ/IL-21 transcriptional signatures could be linked to transcriptional alterations in risk loci for complex immune diseases. Together these results provide new insights into molecular mechanisms that fuel the activation of CTLs under conditions that emulate the inflammatory environment in patients with autoimmune diseases., Highlights • We report the epigenetic and transcriptomic response of IE-CTLs to tissue-derived alarmins and adaptive cytokine. • IL-15 and IFNβ induce massive and distinct transcriptional changes in IE-CTLs, while the impact of IL-21 was limited. • Genes associated with immune-mediated intestinal diseases are enriched in genes responding to IL-15, IFNβ and IL-21 in IE-CTLs.

Hepatocyte apoptosis in nonalcoholic steatohepatitis (NASH) can lead to fibrosis and cirrhosis, which permanently damage the liver. Understanding the regulation of hepatocyte apoptosis is therefore important to identify therapeutic targets that may prevent the progression of NASH to fibrosis. Recently, increasing evidence has shown that long noncoding (lnc) RNAs are involved in various biological processes and that their dysregulation underlies a number of complex human diseases. By performing gene expression profiling of 4,383 lncRNAs in 82 liver samples from individuals with NASH (n = 48), simple steatosis but no NASH (n = 11), and healthy controls (n = 23), we discovered a liver-specific lncRNA (RP11-484N16.1) on chromosome 18 that showed significantly elevated expression in the liver tissue of NASH patients. This lncRNA, which we named lnc18q22.2 based on its chromosomal location, correlated with NASH grade (r = 0.51, P = 8.11 × 10-7 ), lobular inflammation (r = 0.49, P = 2.35 × 10-6 ), and nonalcoholic fatty liver disease activity score (r = 0.48, P = 4.69 × 10-6 ). The association of lnc18q22.2 to liver steatosis and steatohepatitis was replicated in 44 independent liver biopsies (r = 0.47, P = 0.0013). We provided a genetic structure of lnc18q22.2 showing an extended exon 2 in liver. Knockdown of lnc18q22.2 in four different hepatocyte cell lines resulted in severe phenotypes ranging from reduced cell growth to lethality. This observation was consistent with pathway analyses of genes coexpressed with lnc18q22.2 in human liver or affected by lnc18q22.2 knockdown. Conclusion We identified an lncRNA that can play an important regulatory role in liver function and provide new insights into the regulation of hepatocyte viability in NASH. (Hepatology 2017;66:794-808).

BackgroundCandidemia, one of the most common causes of fungal bloodstream infection, leads to mortality rates up to 40% in affected patients. Understanding genetic mechanisms for differential susceptibility to candidemia may aid in designing host-directed therapies.MethodsWe performed the first genome-wide association study on candidemia, and we integrated these data with variants that affect cytokines in different cellular systems stimulated with Candida albicans.ResultsWe observed strong association between candidemia and a variant, rs8028958, that significantly affects the expression levels of PLA2G4B in blood. We found that up to 35% of the susceptibility loci affect in vitro cytokine production in response to Candida. Furthermore, potential causal genes located within these loci are enriched for lipid and arachidonic acid metabolism. Using an independent cohort, we also showed that the numbers of risk alleles at these loci are negatively correlated with reactive oxygen species and interleukin-6 levels in response to Candida. Finally, there was a significant correlation between susceptibility and allelic scores based on 16 independent candidemia-associated single-nucleotide polymorphisms that affect monocyte-derived cytokines, but not with T cell-derived cytokines.ConclusionsOur results prioritize the disturbed lipid homeostasis and oxidative stress as potential mechanisms that affect monocyte-derived cytokines to influence susceptibility to candidemia.

In sepsis, dysregulated immune responses to infections cause damage to the host. Previous studies have attempted to capture pathogen-induced leukocyte responses. However, the impact of mediators released after pathogen-leukocyte interaction on endothelial cells, and how endothelial cell responses vary depending on the pathogen-type is lacking. Here, we comprehensively characterized the transcriptomic responses of human leukocytes and endothelial cells to Gram negative-bacteria, Gram positive-bacteria, and fungi. We showed that whole pathogen lysates induced strong activation of leukocytes but not endothelial cells. Interestingly, the common response of leukocytes to various pathogens converges on endothelial activation. By exposing endothelial cells to leukocyte-released mediators, we observed a strong activation of endothelial cells at both transcription and protein levels. By adding IL-1RA and TNF-alpha antibody in leukocyte-released mediators before exposing to endothelial cells, we identified specific roles for IL-1 and TNF-alpha in driving the most, but not all, endothelial activation. We also showed for the first time, activation of interferon response by endothelial cells in response to leukocyte-released mediators, independently from IL-1 and TNF-alpha pathways. Our study therefore, not only provides pathogen-dependent transcriptional changes in leukocytes and endothelial cells during infections, but also reveals a role for IFN, together with IL1 and TNF alpha signaling, in mediating leukocyte-endothelial interaction in infections.

Celiac disease (CeD) is a complex immune-mediated disorder that is triggered by dietary gluten in genetically predisposed individuals. CeD is characterized by inflammation and villous atrophy of the small intestine, which can lead to gastrointestinal complaints, malnutrition, and malignancies. Currently, diagnosis of CeD relies on serology (antibodies against transglutaminase and endomysium) and small-intestinal biopsies. Since small-intestinal biopsies require invasive upper-endoscopy, and serology cannot predict CeD in an early stage or be used for monitoring disease after initiation of a gluten-free diet, the search for non-invasive biomarkers is ongoing. Here, we summarize current and up-and-coming non-invasive biomarkers that may be able to predict, diagnose, and monitor the progression of CeD. We further discuss how current and emerging techniques, such as (single-cell) transcriptomics and genomics, can be used to uncover the pathophysiology of CeD and identify non-invasive biomarkers.

Background and aims: Celiac disease (CeD) is a gluten triggered, immune-mediated disease of the small intestine. Few clinical cohort descriptions are available, despite the diverse clinical picture. This study provides an overview of a large Dutch CeD cohort focusing on presenting symptoms, co-occurrence of immune mediated diseases (IMD) and malignancies.Methods: We performed a retrospective study in a Dutch university and a non-university medical hospital and included only biopsy proven (>= Marsh type 2 classification) CeD patients.Results: 412 patients were included, selected from 9468 small-bowel biopsy pathology reports and financial codes.Classical symptoms were present in approximately one third of the cohort (diarrhea (37.4%), fatigue (35.0%), weight loss (31.6%), abdominal pain (33.3%)). Atypical symptoms as constipation (10.4%) and reflux (12.4%) were reported as well. 11.7% was diagnosed without reported symptoms.In 25.2% concomitant IMD occurred (most prevalent: type 1 diabetes mellitus (4.9%), microscopic colitis (4.9%), immune mediated-thyroid disease (4.1%)). CeD patients with a concomitant IMD were diagnosed at a significantly higher age compared to those without (P = 0.002). Malignancies occurred in 53 cases (12.9%), including eight Enteropathy Associated T-cell Lymphomas.Conclusion: This is the first study describing a CeD cohort in such detail in the Netherlands and highlights the clinical heterogeneity and importance of screening for concomitant diseases in CeD. (C) 2016 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Contains fulltext : 165674.pdf (Publisher’s version ) (Closed access) As part of the Human Functional Genomics Project, which aims to understand the factors that determine the variability of immune responses, we investigated genetic variants affecting cytokine production in response to ex vivo stimulation in two independent cohorts of 500 and 200 healthy individuals. We demonstrate a strong impact of genetic heritability on cytokine production capacity after challenge with bacterial, fungal, viral, and non-microbial stimuli. In addition to 17 novel genome-wide significant cytokine QTLs (cQTLs), our study provides a comprehensive picture of the genetic variants that influence six different cytokines in whole blood, blood mononuclear cells, and macrophages. Important biological pathways that contain cytokine QTLs map to pattern recognition receptors (TLR1-6-10 cluster), cytokine and complement inhibitors, and the kallikrein system. The cytokine QTLs show enrichment for monocyte-specific enhancers, are more often located in regions under positive selection, and are significantly enriched among SNPs associated with infections and immune-mediated diseases. PAPERCLIP.

Contains fulltext : 171367.pdf (Publisher’s version ) (Open Access) Genome-wide association and fine-mapping studies in 14 autoimmune diseases (AID) have implicated more than 250 loci in one or more of these diseases. As more than 90% of AID-associated SNPs are intergenic or intronic, pinpointing the causal genes is challenging. We performed a systematic analysis to link 460 SNPs that are associated with 14 AID to causal genes using transcriptomic data from 629 blood samples. We were able to link 71 (39%) of the AID-SNPs to two or more nearby genes, providing evidence that for part of the AID loci multiple causal genes exist. While 54 of the AID loci are shared by one or more AID, 17% of them do not share candidate causal genes. In addition to finding novel genes such as ULK3, we also implicate novel disease mechanisms and pathways like autophagy in celiac disease pathogenesis. Furthermore, 42 of the AID SNPs specifically affected the expression of 53 non-coding RNA genes. To further understand how the non-coding genome contributes to AID, the SNPs were linked to functional regulatory elements, which suggest a model where AID genes are regulated by network of chromatin looping/non-coding RNAs interactions. The looping model also explains how a causal candidate gene is not necessarily the gene closest to the AID SNP, which was the case in nearly 50% of cases.

Contains fulltext : 193382.pdf (Publisher’s version ) (Closed access) The immune response to pathogens varies substantially among people. Whereas both genetic and nongenetic factors contribute to interperson variation, their relative contributions and potential predictive power have remained largely unknown. By systematically correlating host factors in 534 healthy volunteers, including baseline immunological parameters and molecular profiles (genome, metabolome and gut microbiome), with cytokine production after stimulation with 20 pathogens, we identified distinct patterns of co-regulation. Among the 91 different cytokine-stimulus pairs, 11 categories of host factors together explained up to 67% of interindividual variation in cytokine production induced by stimulation. A computational model based on genetic data predicted the genetic component of stimulus-induced cytokine production (correlation 0.28-0.89), and nongenetic factors influenced cytokine production as well.

Although genome-wide association studies and fine mapping have identified 39 non-HLA loci associated with celiac disease (CD), it is difficult to pinpoint the functional variants and susceptibility genes in these loci. We applied integrative approaches to annotate and prioritize functional single nucleotide polymorphisms (SNPs), genes and pathways affected in CD. CD-associated SNPs were intersected with regulatory elements categorized by the ENCODE project to prioritize functional variants, while results from cis-expression quantitative trait loci (eQTL) mapping in 1469 blood samples were combined with co-expression analyses to prioritize causative genes. To identify the key cell types involved in CD, we performed pathway analysis on RNA-sequencing data from different immune cell populations and on publicly available expression data on non-immune tissues. We discovered that CD SNPs are significantly enriched in B-cell-specific enhancer regions, suggesting that, besides T-cell processes, B-cell responses play a major role in CD. By combining eQTL and co-expression analyses, we prioritized 43 susceptibility genes in 36 loci. Pathway and tissue-specific expression analyses on these genes suggested enrichment of CD genes in the Th1, Th2 and Th17 pathways, but also predicted a role for four genes in the intestinal barrier function. We also discovered an intricate transcriptional connectivity between CD susceptibility genes and interferon-gamma, a key effector in CD, despite the absence of CD-associated SNPs in the IFNG locus. Using systems biology, we prioritized the CD-associated functional SNPs and genes. By highlighting a role for B cells in CD, which classically has been described as a T-cell-driven disease, we offer new insights into the mechanisms and pathways underlying CD.

Contains fulltext : 177448.pdf (Publisher’s version ) (Open Access) Candidaemia is a bloodstream infection caused by Candida species that primarily affects specific groups of at-risk patients. Because only small candidaemia patient cohorts are available, classical genome wide association cannot be used to identify Candida susceptibility genes. Therefore, we have applied an integrative genomics approach to identify novel susceptibility genes and pathways for candidaemia. Candida-induced transcriptome changes in human primary leukocytes were assessed by RNA sequencing. Genetic susceptibility to candidaemia was assessed using the Illumina immunochip platform for genotyping of a cohort of 217 patients. We then integrated genetics data with gene-expression profiles, Candida-induced cytokine production capacity, and circulating concentrations of cytokines. Based on the intersection of transcriptome pathways and genomic data, we prioritized 31 candidate genes for candidaemia susceptibility. This group of genes was enriched with genes involved in inflammation, innate immunity, complement, and hemostasis. We then validated the role of MAP3K8 in cytokine regulation in response to Candida stimulation. Here, we present a new framework for the identification of susceptibility genes for infectious diseases that uses an unbiased, hypothesis-free, systems genetics approach. By applying this approach to candidaemia, we identified novel susceptibility genes and pathways for candidaemia, and future studies should assess their potential as therapeutic targets.

Many endocrine diseases, including type 1 diabetes mellitus, Graves disease, Addison disease and Hashimoto disease, originate as an autoimmune reaction that affects disease-specific target organs. These autoimmune diseases are characterized by the development of specific autoantibodies and by the presence of autoreactive T cells. They are caused by a complex genetic predisposition that is attributable to multiple genetic variants, each with a moderate-to-low effect size. Most of the genetic variants associated with a particular autoimmune endocrine disease are shared between other systemic and organ-specific autoimmune and inflammatory diseases, such as rheumatoid arthritis, coeliac disease, systemic lupus erythematosus and psoriasis. Here, we review the shared and specific genetic background of autoimmune diseases, summarize their treatment options and discuss how identifying the genetic and environmental factors that predispose patients to an autoimmune disease can help in the diagnosis and monitoring of patients, as well as the design of new treatments.

AimsBRCA1 mutation carriers are at increased risk of developing high-grade serous ovarian cancer (HGSOC), a malignancy that originates from fallopian tube epithelium. We aimed to identify differentially expressed known and novel miRNAs in BRCA1-associated HGSOC.Methods Small RNA sequencing was performed on eight normal tubal and five HGSOC samples of BRCA1 carriers. Differential expression of a subset of known and novel miRNAs was validated by qRT-PCR on the samples used for small RNA sequencing and a second sample cohort comprising normal and HGSOC tissue of matched BRCA1 and non-BRCA carriers. Data from The Cancer Genome Atlas were used to determine the clinical relevance of the validated differentially expressed miRNAs.Results 59 known and 20 novel miRNAs showed a significant >fourfold expression difference between normal tubal tissue and HGSOC. qRT-PCR validation confirmed a significant difference in expression levels for 10 out of 11 known miRNAs. Upregulation of two novel miRNAs could not be confirmed. Interestingly, for seven miRNAs a significant increase in expression was observed when comparing normal tubal tissue of postmenopausal women with premenopausal women. Expression levels of miR-145-5p significantly increased with International Federation of Gynecology and Obstetrics stage, while the expression levels of the other nine validated miRNAs were not associated with clinical characteristics.Conclusions We report a comprehensive expression signature including both known and novel miRNAs of BRCA1-associated HGSOC. Comparison with previous profiling studies showed a good overlap and a large number of miRNAs not reported to be differentially expressed in HGSOC before underscoring the importance of this study.

Summary Effective immunity requires a complex network of cellular and humoral components that interact with each other and are influenced by different environmental and host factors. We used a systems biology approach to comprehensively assess the impact of environmental and genetic factors on immune cell populations in peripheral blood, including associations with immunoglobulin concentrations, from ∼500 healthy volunteers from the Human Functional Genomics Project. Genetic heritability estimation showed that variations in T cell numbers are more strongly driven by genetic factors, while B cell counts are more environmentally influenced. Quantitative trait loci (QTL) mapping identified eight independent genomic loci associated with leukocyte count variation, including four associations with T and B cell subtypes. The QTLs identified were enriched among genome-wide association study (GWAS) SNPs reported to increase susceptibility to immune-mediated diseases. Our systems approach provides insights into cellular and humoral immune trait variability in humans., Graphical Abstract, Highlights • Understanding inter-individual variation of immune cells and immunoglobulin levels • Season and gender influence B cell subpopulation abundance • Identification of genetic loci that might regulate B cell levels in blood • Cell count QTLs overlap with risk SNPs for (auto)immune/inflammatory disease, As part of the Human Functional Genomics Project, this study by Aguirre-Gamboa et al. maps the contribution of genetics and non-heritable factors onto immune-cell counts and immunoglobulin levels. They find that season and gender influence the abundance of most of B cell subpopulations.

Celiac disease (CeD) is a complex immune-mediated disease. Genetic studies have implicated 43 predisposing loci that collectively explain some 50% of the genetic variance in CeD. More than similar to 90% of CeD-associated single nucleotide polymorphisms (SNPs) localize to the non-coding genome, which we need to better understand to translate genetic knowledge into clinical practice. New genomic technologies and resources are permitting a systematic analysis of the functional elements in the non-coding part of the genome. Here we explain how investigating the regulatory and epigenomic landscape will help to pinpoint the cell types involved in CeD, and the driver genes and gene regulatory networks that are affected by CeD-associated SNPs.

Phagocytosis and autophagy are two ancient, highly conserved processes involved, respectively, in the removal of extracellular organisms and the destruction of organisms in the cytosol. Autophagy, for either metabolic regulation or defence, involves the formation of a double membrane called the autophagosome, which then fuses with lysosomes to degrade the contents, a process that has similarities with phagosome maturation. Toll-like-receptor (TLR) engagement activates a variety of defence mechanisms within phagocytes, including facilitation of phagosome maturation, and also engages autophagy. Therefore we speculated that TLR signalling might link these processes to enhance the function of conventional phagosomes. Here we show that a particle that engages TLRs on a murine macrophage while it is phagocytosed triggers the autophagosome marker LC3 to be rapidly recruited to the phagosome in a manner that depends on the autophagy pathway proteins ATG5 and ATG7; this process is preceded by recruitment of beclin 1 and phosphoinositide-3-OH kinase activity. Translocation of beclin 1 and LC3 to the phagosome was not associated with observable double-membrane structures characteristic of conventional autophagosomes, but was associated with phagosome fusion with lysosomes, leading to rapid acidification and enhanced killing of the ingested organism.

To redirect the lytic activity of cytotoxic T lymphocytes (CTL) toward tumor vascular endothelial cells, we prepared bifunctional proteins with specificity for both alphavbeta3 and CD3. Monocyclic RGD peptides (cRGDfK) were covalently coupled to an anti-CD3 monoclonal antibody at different peptide:protein ratios. The resulting RGDpep-anti-CD3 conjugates bound specifically to alphavbeta3-expressing endothelial cells. Displacement studies with radiolabeled alphavbeta3 ligand demonstrated that the alphavbeta3 binding affinity of RGDpep-anti-CD3 conjugates was elevated as compared to the non-conjugated RGDpep. IC50 values ranged from 150-1.1 nM, depending on the amount of coupled RGDpep molecules per antibody molecule. RGD modification did not affect the ability of anti-CD3 antibodies to bind to CTL. Furthermore, RGDpep-anti-CD3 was fully capable of activating T cells upon CD3 binding as was shown in a Jurkat/NFAT reporter-gene activation assay. All RGDpep-anti-CD3 conjugates were able to induce RGDpep, CD3-dependent lysis of human primary endothelial cells by anti-CD3/IL-2 activated human peripheral blood mononuclear cells (PBMC), with a significant induction of cytotoxicity observed at an E/T ratio as low as 10. Redirecting cytolytic activity reached up to 50% cytotoxicity using the conjugate with the highest RGD peptide load. Combining the good accessibility of tumor blood vessel endothelium for CTL with the efficiency of target cell killing warrants further investigations on anti-tumor effects of this type of conjugates in vivo.

Cellular differentiation is regulated on the level of gene expression and it is known that dysregulation of gene expression can lead to deficiencies in differentiation that contribute to a variety of diseases, particularly of the immune system. Until recently, it was thought that the dysregulation was governed by changes in the binding or activity of a class of proteins called transcription factors. However, the discovery of micro-RNAs and recent descriptions of long noncoding RNAs have given enormous momentum to a whole new field of biology: the regulatory RNAs. In this review, we describe these two classes of regulatory RNAs and summarize what is known about how they regulate aspects of the adaptive and innate immune systems. Finally, we describe what is known about the involvement of micro-RNAs and long noncoding RNAs in three different autoimmune diseases (celiac disease, inflammatory bowel disease, and multiple sclerosis).