Author response: A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes

Field: Biochemistry, Genetics and Molecular Biology (Life Sciences)

Article Figures and data Abstract Editor's evaluation eLife digest Introduction Materials and methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: The large inter-individual variability in immune-cell composition and function determines immune responses in general and susceptibility o immune-mediated diseases in particular. While much has been learned about the genetic variants relevant for type 1 diabetes (T1D), the pathophysiological mechanisms through which these variations exert their effects remain unknown. Methods: Blood samples were collected from 243 patients with T1D of Dutch descent. We applied genetic association analysis on >200 immune-cell traits and >100 cytokine production profiles in response to stimuli measured to identify genetic determinants of immune function, and compared the results obtained in T1D to healthy controls. Results: Genetic variants that determine susceptibility to T1D significantly affect T cell composition. Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation. Genome-wide quantitative trait loci (QTLs) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D, including 12 that have never been reported in healthy population studies, implying a disease-specific genetic regulation. Conclusions: This study provides new insights into the genetic factors that affect immunological responses in T1D. Funding: This work was supported by an ERC starting grant (no. 948207) and a Radboud University Medical Centre Hypatia grant (2018) to YL and an ERC advanced grant (no. 833247) and a Spinoza grant of the Netherlands Association for Scientific Research to MGN CT received funding from the Perspectief Biomarker Development Center Research Programme, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). AJ was funded by a grant from the European Foundation for the Study of Diabetes (EFSD/AZ Macrovascular Programme 2015). XC was supported by the China Scholarship Council (201706040081). Editor's evaluation This study examines genetic and non-genetic factors influencing immune responses in type 1 diabetes Key findings are: 1) age and season affect immune cell traits and cytokine production upon stimulation; 2) certain genetic variants that determine susceptibility to T1D significantly affect T cell composition, notably the CCR region that is associated with CCR5+ regulatory T cells; and 3) 15 genetic loci that influence immune responses in T1D, most of which have not been seen previously in healthy populations. The results suggest mechanisms of T1D-specific genetic regulation. https://doi.org/10.7554/eLife.73709.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest Every year around the world, over 100,000 people are diagnosed with type 1 diabetes. This disease develops when the immune system mistakenly destroys the cells that produce a hormone called insulin, leaving affected individuals unable to regulate their blood sugar levels. Type 1 diabetes patients must rely on regular injections of manufactured insulin to survive. The composition and activity of the human immune system is under genetic control, and people with certain changes in their genes are more susceptible than others to develop type 1 diabetes. Previous studies have identified around 60 locations in the human DNA (known as loci) associated with the condition, but it remains unclear how these loci influence the immune system and whether diabetes will emerge. Chu, Janssen, Koenen et al. explored how variations in genetic information can influence the composition of the immune system, and the type of molecules it releases to perform its role. To do so, blood samples from 243 individuals of Dutch descent with type 1 diabetes were collected, and genetic associations were investigated. The results revealed that a major type of immune actors known as T cells are under the control of genetic factors associated with type 1 diabetes susceptibility. For instance, a specific type of T cells showed shared genetic control with type 1 diabetes. In addition, 15 loci were identified that influenced immune responses in the patients. Among those, 12 have never been reported to be involved in immune responses in healthy people, implying that these regions might only regulate the immune system of individuals with type 1 diabetes and other similar disorders. Finally, Chu, Janssen, Koenen et al. propose 11 genes within the identified loci as potential targets for new diabetes medication. These results represent an important resource for researchers exploring the genetic and immune basis of type 1 diabetes, and they could open new avenues for drug development. Introduction Type 1 diabetes (T1D) is a common, chronic, autoimmune disease, characterized by destruction of insulin-producing beta-cells in the pancreas that results in lifelong dependence on exogenous insulin and is associated with a high morbidity and mortality (Atkinson et al., 2014). The causes and immunological pathways responsible for T1D development are still incompletely understood, which hampers the efforts to identify an etiopathogenetic treatment. Many studies have highlighted the role of environmental, genetical, and immunological factors in the pathogenesis of T1D (Pociot and Lernmark, 2016; Rewers and Ludvigsson, 2016). Environmental factors such as being overweight, infections, microbiome composition, and dietary deficiencies have been reported as risk factors for T1D (Rewers and Ludvigsson, 2016). In turn, the immunological pathogenesis (Cabrera et al., 2016) of T1D includes innate inflammation and adaptive immunity, such as enhanced T cell responses (Hundhausen et al., 2016). In the last two decades, large genome-wide association studies (GWAS) performed have underscored the contribution of genetic polymorphisms to T1D for the susceptibility, with ~60 genomic loci associated with T1D risk identified (Barrett et al., 2009; Bradfield et al., 2011; Cooper et al., 2008; Grant et al., 2009; Huang et al., 2012; Onengut-Gumuscu et al., 2015; Ram et al., 2016). While these loci show significant enrichment in specific immune-related biological pathways, such as cytokine signaling and T cell activation (Barrett et al., 2009; Cooper et al., 2008), the functional consequences of many of these loci and genetic variants are still unknown. We thus lack information that could link the genetic susceptibility factors to the immunological pathways potentially important for T1D pathogenesis. The genetically regulated inflammatory response signature in T1D may also be relevant for the inflammatory response in general and may become modified by the chronic hyperglycemic state. In the present study we aimed to comprehensively describe the immunopathological consequences of the genetic variants linked to T1D susceptibility, using a high-throughput functional genomics approach. As a part of the Human Functional Genomics Project (HFGP) (Netea et al., 2016), we carried out deep immunophenotyping in peripheral blood samples from a cohort of 243 T1D patients (300DM) using cell subpopulation composition and cytokine production upon stimulations as proxies of immunological function. Part of the results were then compared to those obtained in a population-based cohort of 500 healthy individuals (500FG) that successfully characterized the impact of genetic factors (Aguirre-Gamboa et al., 2016; Li et al., 2016) on immune responses in healthy individuals. Here, we systematically evaluate the genetic regulation of the immune phenotypes in T1D and show how genetic variations affect immune-cell traits and cytokine production in response to stimulations. In total, we identify 15 genome-wide significant genomic loci (p-value < 5 × 10–8) associated with immune phenotypes in the 300DM cohort, including 12 novel loci that have never been reported in any healthy population study. These data provide a deeper understanding of the immune mechanisms involved in the pathophysiology of T1D and affecting the general inflammatory response and may open avenues toward the development of novel diagnostics and potentially immunotherapies. Materials and methods Study cohort This study mainly focuses on a 300DM cohort, and involves a 500FG cohort (part of HFGP; Netea et al., 2016). In the 300DM cohort, we collected blood samples from 243 T1D patients (132 males and 111 females of Caucasian origin), following a previously described methodology (Aguirre-Gamboa et al., 2016; Ter Horst et al., 2016; Li et al., 2016). Participant’s ages ranged from 20 to 84 years. Detailed information about the 500FG cohort can be found in the previous publications (Aguirre-Gamboa et al., 2016; Ter Horst et al., 2016; Li et al., 2016). Measurement of immune-cell composition Myeloid and lymphoid immune-cell levels were measured by 10-color flow cytometry, and we calculated the parental and grandparental proportion of 73 manually annotated immune cells and the proportion of CD4+ T cells, CD8+ T cells, memory Tregs and monocytes carrying the chemokine receptors: CCR6 (CD196), CXCR3 (CD183), CCR4 (CD194), CCR5 (CD195), and CCR7 (CD197). In total, we ended up with 269 immune-cell traits for the 300DM cohort. We then used the same gating strategy of measuring cell subpopulations as 500FG (see Aguirre-Gamboa et al., 2016 and Figure 1—figure supplement 1). Stimulation of PBMCs and measurement of cytokine production capacity Isolated peripheral blood mononuclear cells (PBMCs) were washed twice with cold phosphate-buffered saline and suspended in Roswell Park Memorial Institute (RPMI) 1640 Dutch-modified culture medium (Gibco/Invitrogen, Breda, the Netherlands) supplemented with 50 mg/l gentamycin (Centraform), 1 mM pyruvate (Gibco/Invitrogen), and 2 mM L-glutamine (Gibco/Invitrogen). Cells were counted on a Sysmex XN-450 Hematology Analyzer (Sysmex Corporation, Kobe, Japan). For the in vitro stimulation experiments, 5 × 105 cells/well were cultured for 24 hr or 7 days at 37°C and 5% CO2 in 96-well round-bottom plates (Greiner). For the 7-day cultures, the medium was supplemented with human pooled serum (pooled from healthy blood donors, end concentration 10%). Supernatants were collected and stored in –20°C until used for ELISA. The following stimulations were used: LPS (100 ng/ml, 1 ng/ml), Pam3cys, Borrelia mix, Candida albicans conidia, C. albicans hyphae, imiquimod (IMQ), Staphylococcus aureus, Streptococcus pneumoniae, palmitic acid (C16), C16+ monosodium urate crystals, Escherichia coli, Mycobacterium tuberculosis, Borrelia burgdorferi, Coxiella burnetii, Cryptococcus neoformans, oxidized low-density lipoprotein (OxLDL), OxLDL+ LPS, polyinosinic:polycytidylic acid, and Rhizopus microspores, Rhizopus oryzae (Li et al., 2016). Concentrations of cytokines in response to various stimulations were measured in PBMCs by ELISA kits, following the manufacturer’s protocol. Following our previously reported definition (Li et al., 2016), we considered IL-1β, IL-6, TNF-α, IL-8, IL-10, IL-1Ra, and MCP-1 to be monocyte-derived cytokines and measured them 24 hr after stimulation. Likewise, we considered INF-γ, IL-17, and IL-22 to be T cell-derived cytokines and measured them at 7 days after stimulation. For all cytokines commercially available kits were used (R&D Systems, Minneapolis, MN, or Sanquin, Amsterdam, the Netherlands). Genotyping, quality control, and imputation DNA samples of 224 Dutch T1D patients were collected and genotyped using the Infinium Global Screening Array. Genotype calling was performed using Opticall 0.7.0 (Shah et al., 2012) with default settings (call rate > 0.99). We then excluded two samples due to contamination or mislabeling, one sample from a related individual (identity be descent > 0.185) and six genetic outliers identified by either heterozygosity rate check (individuals with heterozygosity rate heterozygosity rate ±3 SD from the mean were excluded) or multidimensional scaling (MDS) plots of samples merging with 1000 Genomes data. This left 215 samples for further analysis, and these samples show good consistency with European samples from 1000 Genomes data (Figure 1—figure supplement 2) according to standard protocol (Anderson et al., 2010). DNA samples from the 500FG cohort were genotyped by Illumina Human OmniExpress Exome-8 v1.0 single-nucleotide polymorphism (SNP) chip. Outliers were excluded according to population relationship, medication, and disease information. Details can be found in our previous studies in 500FG (Aguirre-Gamboa et al., 2016; Li et al., 2016). SNPs with a minor allele frequency (MAF) <0.001 were removed from each cohort, and samples with a Hardy-Weinburg equilibrium (HWE) p-value < 1 × 10–5 were removed for the healthy cohort (500FG). We merged the 300DM cohort and 500FG cohort by taking shared genetic variants. We then imputed their genotypes using an online genotype imputation service provided by Michigan Imputation Server (https://imputationserver.sph.umich.edu)(Das et al., 2016) with HRC Panel 1.1 as reference. We excluded SNPs with low imputation quality (R2 < 0.3) and/or a MAF < 0.01 in all imputed samples and/or HWE p-values < 1 × 10–5 in healthy individuals, leaving 4,304,387 SNPs and 666 individuals in the merged cohort (N300DM = 215 and N500FG = 451). Preprocessing the data All statistical analyses were performed using the statistical programming language R. Immune-cell proportion was calculated by dividing counts of their parental by grandparental cell types (Supplementary file 1A). An inverse rank transformation (Aguirre-Gamboa et al., 2016; Orrù et al., 2013) was applied on the proportion values for genetic association analysis. Cytokine levels were log2-transformed. Immune parameter quantitative trait locus mapping After intersecting with available genotype data and excluding volunteers with a mixed background or other genetic background, 214 samples were left for immune parameter quantitative trait locus (QTL) mapping. We next evaluated covariates influencing immune function. Associations with age, gender, and seasonal effects were calculated following previously described methods (Aguirre-Gamboa et al., 2016; Ter Horst et al., 2016; Li et al., 2016), in short, using Spearman’s correlation analysis and a linear regression model. Significance was declared after multiple-testing correction (FDR < 0.05). Age significantly associated with 129/269 immune-cell traits and 12/55 cytokine traits, gender associated with 59/269 immune-cell traits and 5/55 cytokine traits, and seasonal effects associated with 121/269 immune-cell traits and 38/55 cytokine traits (Figure 4—figure supplement 1). We therefore took age, gender, and seasonal effects as covariates in the linear regression for both immune-cell proportion QTL mapping and cytokine QTL mapping. In addition, considering the effect of immune-cell proportion on cytokine production, we took major cell types, including monocyte, lymphocyte, T cell, B cell, and NK cell proportion in PBMC as covariates in a linear model for cytokine QTL mapping, as we have done previously (Li et al., 2016). Linear regression was applied using the R package Matrix-eQTL (Shabalin, 2012) for immune parameter QTL mapping, and the software METAL (Willer et al., 2010) was applied to summary statistics for both cohorts for a meta-analysis, in which the model based on effect size and standard error with default settings were used. p-Values <5 × 10–8 were considered to be genome-wide significant. Calculated lambda values indicated no obvious inflations (0.977–1.026). Extraction of the T1D GWAS SNP list We downloaded a summary of T1D GWAS results from the GWAS-catalog (https://www.ebi.ac.uk/gwas/) (Buniello et al., 2019) in November 2019, and removed data from studies performed in non-European-ancestry populations. Top SNPs from different studies that were in LD (r2 > 0.1) were considered as the same locus, and the SNPs with the lowest p-value were included in the analysis. We noticed that the effect directions of some SNPs were unclear or inconsistent between different studies. In this case, we assigned the direction from the most-recent GWAS (Onengut-Gumuscu et al., 2015). GWAS analysis of the 300DM cohort for the known T1D loci We extracted all proxies in strong LD with the top SNPs from published T1D GWAS (case-control) studies (r2 > 0.8) and performed a chi-square test on clinical status by using PLINK 1.9. Samples in 300DM were taken as cases and samples in 500FG as controls. Impact of T1D GWAS loci on immune phenotypes To detect the impact of T1D GWAS loci on immune-cell populations, we grouped all traits into four categories (B cells, T cells, monocytes, and NK cells), and counted the number of suggestive associations (p-value < 0.05) between the 63 top SNPs from T1D GWAS loci and immune-cell traits. 1000 permuted sets of the 63 SNPs were randomly selected from independent SNPs (r2 > 0.2) pruned from all genotyped SNPs. We then compared the associations of the 63 top GWAS SNPs with the associations between the 1000 permutated sets and the same category of immune traits. We further applied a multivariate linear model to estimate the proportion of variance of each immune phenotype explained by the top SNPs from T1D GWAS loci. We repeated this analysis on 1000 permuted sets of the 63 independent SNPs, which were used as reference set. We then compared the null distribution with the variance explained by 63 top SNPs from T1D GWAS loci. The p-value was calculated by the percent of explained variance from permuted sets greater than the variance explained by the 63 T1D GWAS SNPs. Gene expression analysis on CCR5 and its corresponding ligands genes Normalized gene expression data in PBMCs and pancreas from T1D and controls were acquired from https://www.ebi.ac.uk/gxa (Papatheodorou et al., 2020). A Student’s t-test was applied to compare gene expression between groups. Post-QTL analysis An R package ggplot2 was used to generate Manhattan plots and boxplots. Locus zoom plots were made using an online tool (http://locuszoom.org; Pruim et al., 2010). We used R package coloc (Giambartolomei et al., 2014) to perform colocalization analysis with T1D GWAS summary statistics and immune parameter QTL profiles. For pathway analysis, genes located within ±10 kb of genome-wide significant SNPs (p-value < 5 × 10–8) were extracted and analyzed using the FUMA online tool (Watanabe et al., 2017) (https://fuma.ctglab.nl). ANNOVAR was used to annotate genetic variants (Wang et al., 2010). Other packages used in this paper Pheatmap was used to make heatmaps. plots and plots were using Results between immune-cell counts and cytokine production in T1D We collected blood samples from 243 T1D patients following a previously described methodology (Aguirre-Gamboa et al., 2016; Ter Horst et al., 2016; Li et al., 2016). The of the 300DM and a cohort of healthy individuals (500FG) are in file age was and they a diabetes of the cohort of people with T1D. We measured types of immune cells both and and different cytokines in response to stimulation with four types of human in both cohorts (Figure 1A). Figure 1 with all of data and study and by multidimensional analysis in traits from T cell and traits and cytokines with the same In the proportion of subpopulations and large counts of their parental cell represent the calculated of the grouped cell and cytokine types at of correlation between immune-cell counts and cytokine production in response to stimulations in T1D patients. (FDR < 0.05) are by with correlation are between traits and monocyte-derived cytokines and between adaptive immune-cell counts and T cell-derived cytokines are between adaptive immune-cell counts and monocyte-derived plots the immune-cell (Figure and cytokine production levels (Figure in T1D patients. We a of the B cell subpopulations from the T cell, monocyte, and NK cell population This that T cells, monocytes, and NK cells have more than B cells at which is with our previous in a healthy cohort Aguirre-Gamboa et al., 2016). cytokine are based on their with a between monocyte-derived cytokines and T cell-derived cytokines (Figure This may suggest activation of a of monocyte-derived and T cell-derived cytokine production capacity in T1D. To a between immune cells and we each of the immune-cell counts with each of the cytokine production profiles IL-6, TNF-α, IL-10, IL-8, IL-17, and in response to stimulations. Cytokine levels were based on correlation with immune-cell counts (Figure In with the functional between immune cells and we between and monocyte-derived cytokines IL-6, TNF-α, IL-10, IL-8, and and between T cell and T cell-derived cytokines IL-17, and by in Figure We also found a correlation between monocyte-derived cytokines production in response to four types of stimulations and and counts Figure We similar correlation in 500FG (Figure 1—figure supplement This correlation is in with a previous that a high of adaptive immune cells at is associated with production of cytokines after stimulation et al., and this is not by T1D the between immune cells and cytokines in response to stimulations are similar between T1D and healthy individuals. Impact of T1D GWAS SNPs on immune phenotypes in T1D patients that T1D is a disease with a genetic we whether the known risk variants of T1D affect immune phenotypes and function. We SNPs within the locus in our association studies on cell proportion and cytokine production with our previous findings in we not any significant associations of variants in We then acquired genetic loci from published GWAS of European background were acquired from the GWAS-catalog et al., Among genetic variants in 63 independent T1D loci were present in our and we found that of these 63 were associated with susceptibility to T1D with (p-value < 0.05) (Supplementary file We next whether these genetic risk loci for T1D affect immune and function. The of the association of the 63 T1D GWAS loci with different cell types and cytokines an from an distribution (Figure Figure supplement 1). We further whether this can be explained by by the association of immune traits with T1D GWAS SNPs with that of 1000 randomly selected independent SNPs (Figure Materials and The p-value that the T1D GWAS SNPs are in association with T cell traits in the T1D cohort (p-value = Figure 2 with 2 all Impact of type 1 diabetes (T1D) genome-wide association studies (GWAS) single-nucleotide polymorphisms on immune plots of quantitative trait locus (QTL) profiles of T1D GWAS loci grouped by cell populations. The distribution of p-values of associations with T cells traits a significant from an distribution number of associations and those in of QTL profiles of cell proportion carrying certain chemokine T1D GWAS by and effect direction of the T1D risk a T1D risk allele A association analysis between T1D GWAS loci and immune phenotypes that out of 269 immune-cell phenotypes and out of are associated with at one T1D GWAS locus (p-value < file and We further applied a to test whether immune phenotypes were significantly influenced by the effects of these 63 GWAS loci and to sets of independent SNPs, the 63 T1D GWAS loci significantly more of the variance in cell and 15 cytokine production traits (p-value < Figure Figure supplement As in the (Figure one T1D risk which is in strong LD with the T1D GWAS SNP (Onengut-Gumuscu et al., is associated with a of many CCR5+ CD4+ T cell traits and a of CD4+ T cell traits. signaling pathways regulate the of cells from the (PBMCs) to the To further the of chemokine signaling by CCR5 in T1D, we the changes on CCR5 and its corresponding genes using available data from analysis in PBMCs and from T1D patients and controls et al., et al., (see also Materials and This identified significant expression changes of and in T1D which the of this chemokine pathway (Figure In addition, top within the CCR gene region has the effects on many CCR5 in T1D. For minor allele T in with a of CCR5+ Tregs (Figure and with the regulation from a GWAS locus within the CCR region on CCR5+ we whether CCR5+ Tregs and T1D the same regions by the cell proportion QTL of CCR5+ Tregs and the T1D GWAS colocalization analysis (Giambartolomei et al., 2014). The that CCR5+ Tregs the same regulatory genomic region with T1D, the SNP might be different (Figure these results a role for the CCR region in function in the pathogenesis of T1D. Figure Genetic impact on chemokine signaling in type 1 diabetes of and in peripheral blood mononuclear cells (PBMCs) and pancreas is in T1D patients and compared to healthy controls Locus zoom that single-nucleotide polymorphisms around located in the CCR region are associated with CCR5+ that CCR5+ proportion in different genotypes and locus zoom plots colocalization between CCR5+ proportion quantitative trait locus (QTL) profiles and T1D genome-wide association studies (GWAS) within CCR we that T1D GWAS loci influence immune-cell proportion and cytokine production the of T cell in genetic regulation of T1D. Genetic regulation of immune phenotypes in T1D To further potential genetic regulation of immune phenotypes on the we performed QTL mapping in This identified genome-wide significant (p-value < 5 × 10–8) associated with immune-cell including four associated with T cell subpopulations specific chemokine and (Figure top and file analysis of the cell proportion showed significant enrichment in chemokine and cytokine biological pathways (FDR < Figure 4—figure