Kamil Slowikowski

Common genetic variants modulate pathogen-sensing responses in human dendritic cells.

Introduction Variation in an individual’s response to environmental factors is likely to influence susceptibility to complex human diseases. The genetic basis of such variation is poorly understood. Here, we identify natural genetic variants that underlie variation in the host innate immune response to infection and analyze the mechanisms by which such variants alter these responses. Identifying the genetic basis of variability in the host response to pathogens. A cohort of 534 individuals donated blood for (a) genotyping of common DNA variants and (b) isolation of immune DCs. DCs were stimulated with viral and bacterial components, and the variability in individuals’ gene expression responses was mapped to specific DNA variants, which were then shown to affect binding of particular transcription factors. Methods We derived dendritic cells (DCs) from peripheral blood monocytes of healthy individuals (295 Caucasians, 122 African Americans, 117 East Asians) and stimulated them with Escherichia coli lipopolysaccharide (LPS), influenza virus, or the cytokine interferon-β (IFN-β) to generate 1598 transcriptional profiles. We genotyped each of these individuals at sites of common genetic variation and identified the genetic variants that best explain variation in gene expression and gene induction between individuals. We then tested mechanistic predictions from these associations using synthetic promoter constructs and genome engineering. Results We identified 264 loci containing genetic variants associated with variation in absolute gene expression in human DCs, of which 121 loci were associated with variation in the induction of gene expression by one or more stimuli. Fine-mapping identified candidate causal single-nucleotide polymorphisms (SNPs) associated with expression variance, and deeper functional experiments localized three of these SNPs to the binding sites of stimulus-activated transcription factors. We also identified a cis variant in the transcription factor, IRF7, associated in trans with the induction of a module of antiviral genes in response to influenza infection. Of the identified genetic variants, 35 were also associated with autoimmune or infectious disease loci found by genome-wide association studies. Discussion The genetic variants we uncover and the molecular basis for their action provide mechanistic explanations and principles for how the innate immune response to pathogens and cytokines varies across individuals. Our results also link disease-associated variants to specific immune pathways in DCs, which provides greater insight into mechanisms underlying complex human phenotypes. Extending our approach to many immune cell types and pathways will provide a global map linking human genetic variants to specific immunological processes. Immune Variation It is difficult to determine the mechanistic consequences of context-dependent genetic variants, some of which may be related to disease (see the Perspective by Gregersen). Two studies now report on the effects of stimulating immunological monocytes and dendritic cells with proteins that can elicit a response to bacterial or viral infection and assess the functional links between genetic variants and profiles of gene expression. M. N. Lee et al. (10.1126/science.1246980) analyzed the expression of more than 400 genes, in dendritic cells from 534 healthy subjects, which revealed how expression quantitative trait loci (eQTLs) affect gene expression within the interferon-β and the Toll-like receptor 3 and 4 pathways. Fairfax et al. (10.1126/science.1246949) performed a genome-wide analysis to show that many eQTLs affected monocyte gene expression in a stimulus- or time-specific manner. Mapping of human host-pathogen gene-by-environment interactions identifies pathogen-specific loci. [Also see Perspective by Gregersen] Little is known about how human genetic variation affects the responses to environmental stimuli in the context of complex diseases. Experimental and computational approaches were applied to determine the effects of genetic variation on the induction of pathogen-responsive genes in human dendritic cells. We identified 121 common genetic variants associated in cis with variation in expression responses to Escherichia coli lipopolysaccharide, influenza, or interferon-β (IFN-β). We localized and validated causal variants to binding sites of pathogen-activated STAT (signal transducer and activator of transcription) and IRF (IFN-regulatory factor) transcription factors. We also identified a common variant in IRF7 that is associated in trans with type I IFN induction in response to influenza infection. Our results reveal common alleles that explain interindividual variation in pathogen sensing and provide functional annotation for genetic variants that alter susceptibility to inflammatory diseases.

Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis

CD4+ T cells are central mediators of autoimmune pathology; however, defining their key effector functions in specific autoimmune diseases remains challenging. Pathogenic CD4+ T cells within affected tissues may be identified by expression of markers of recent activation. Here we use mass cytometry to analyse activated T cells in joint tissue from patients with rheumatoid arthritis, a chronic immune-mediated arthritis that affects up to 1% of the population. This approach revealed a markedly expanded population of PD-1hiCXCR5−CD4+ T cells in synovium of patients with rheumatoid arthritis. However, these cells are not exhausted, despite high PD-1 expression. Rather, using multidimensional cytometry, transcriptomics, and functional assays, we define a population of PD-1hiCXCR5− ‘peripheral helper’ T (TPH) cells that express factors enabling B-cell help, including IL-21, CXCL13, ICOS, and MAF. Like PD-1hiCXCR5+ T follicular helper cells, TPH cells induce plasma cell differentiation in vitro through IL-21 secretion and SLAMF5 interaction (refs 3, 4). However, global transcriptomics highlight differences between TPH cells and T follicular helper cells, including altered expression of BCL6 and BLIMP1 and unique expression of chemokine receptors that direct migration to inflamed sites, such as CCR2, CX3CR1, and CCR5, in TPH cells. TPH cells appear to be uniquely poised to promote B-cell responses and antibody production within pathologically inflamed non-lymphoid tissues.

Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples

Recent research has uncovered an important role for de novo variation in neurodevelopmental disorders. Using aggregated data from 9,246 families with autism spectrum disorder, intellectual disability, or developmental delay, we found that ∼1/3 of de novo variants are independently present as standing variation in the Exome Aggregation Consortiums cohort of 60,706 adults, and these de novo variants do not contribute to neurodevelopmental risk. We further used a loss-of-function (LoF)-intolerance metric, pLI, to identify a subset of LoF-intolerant genes containing the observed signal of associated de novo protein-truncating variants (PTVs) in neurodevelopmental disorders. LoF-intolerant genes also carry a modest excess of inherited PTVs, although the strongest de novo–affected genes contribute little to this excess, thus suggesting that the excess of inherited risk resides in lower-penetrant genes. These findings illustrate the importance of population-based reference cohorts for the interpretation of candidate pathogenic variants, even for analyses of complex diseases and de novo variation.

Disentangling the Effects of Colocalizing Genomic Annotations to Functionally Prioritize Non-coding Variants within Complex-Trait Loci

Identifying genomic annotations that differentiate causal from trait-associated variants is essential to fine mapping disease loci. Although many studies have identified non-coding functional annotations that overlap disease-associated variants, these annotations often colocalize, complicating the ability to use these annotations for fine mapping causal variation. We developed a statistical approach (Genomic Annotation Shifter [GoShifter]) to assess whether enriched annotations are able to prioritize causal variation. GoShifter defines the null distribution of an annotation overlapping an allele by locally shifting annotations; this approach is less sensitive to biases arising from local genomic structure than commonly used enrichment methods that depend on SNP matching. Local shifting also allows GoShifter to identify independent causal effects from colocalizing annotations. Using GoShifter, we confirmed that variants in expression quantitative trail loci drive gene-expression changes though DNase-I hypersensitive sites (DHSs) near transcription start sites and independently through 3′ UTR regulation. We also showed that (1) 15%–36% of trait-associated loci map to DHSs independently of other annotations; (2) loci associated with breast cancer and rheumatoid arthritis harbor potentially causal variants near the summits of histone marks rather than full peak bodies; (3) variants associated with height are highly enriched in embryonic stem cell DHSs; and (4) we can effectively prioritize causal variation at specific loci.

Regulation of gene expression in autoimmune disease loci and the genetic basis of proliferation in CD4+ effector memory T cells.

Genome-wide association studies (GWAS) and subsequent dense-genotyping of associated loci identified over a hundred single-nucleotide polymorphism (SNP) variants associated with the risk of rheumatoid arthritis (RA), type 1 diabetes (T1D), and celiac disease (CeD). Immunological and genetic studies suggest a role for CD4-positive effector memory T (CD+ TEM) cells in the pathogenesis of these diseases. To elucidate mechanisms of autoimmune disease alleles, we investigated molecular phenotypes in CD4+ effector memory T cells potentially affected by these variants. In a cohort of genotyped healthy individuals, we isolated high purity CD4+ TEM cells from peripheral blood, then assayed relative abundance, proliferation upon T cell receptor (TCR) stimulation, and the transcription of 215 genes within disease loci before and after stimulation. We identified 46 genes regulated by cis-acting expression quantitative trait loci (eQTL), the majority of which we detected in stimulated cells. Eleven of the 46 genes with eQTLs were previously undetected in peripheral blood mononuclear cells. Of 96 risk alleles of RA, T1D, and/or CeD in densely genotyped loci, eleven overlapped cis-eQTLs, of which five alleles completely explained the respective signals. A non-coding variant, rs389862A, increased proliferative response (p = 4.75×10−8). In addition, baseline expression of seventeen genes in resting cells reliably predicted proliferative response after TCR stimulation. Strikingly, however, there was no evidence that risk alleles modulated CD4+ TEM abundance or proliferation. Our study underscores the power of examining molecular phenotypes in relevant cells and conditions for understanding pathogenic mechanisms of disease variants.

A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases

There is growing evidence of shared risk alleles for complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing among all individuals (whole-group pleiotropy) or a subset of individuals in a genetically heterogeneous cohort (subgroup heterogeneity). Here we describe the use of a well-powered statistic, BUHMBOX, to distinguish between those two situations using genotype data. We observed a shared genetic basis for 11 autoimmune diseases and type 1 diabetes (T1D; P < 1 × 10−4) and for 11 autoimmune diseases and rheumatoid arthritis (RA; P < 1 × 10−3). This sharing was not explained by subgroup heterogeneity (corrected PBUHMBOX > 0.2; 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (P < 1 × 10−9) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (PBUHMBOX = 0.008; 2,406 seronegative RA cases). We also observed a shared genetic basis for major depressive disorder (MDD) and schizophrenia (P < 1 × 10−4) that was not explained by subgroup heterogeneity (PBUHMBOX = 0.28; 9,238 MDD cases).

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types

We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.A new method tests whether disease heritability is enriched near genes with high tissue-specific expression. The authors use gene expression data together with GWAS summary statistics for 48 diseases and traits to identify disease-relevant tissues.

SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci

Summary: We created a fast, robust and general C++ implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm to identify cell types, tissues and pathways affected by risk loci. It tests trait-associated genomic loci for enrichment of specificity to conditions (cell types, tissues and pathways). We use a non-parametric statistical approach to compute empirical P-values by comparison with null SNP sets. As a proof of concept, we present novel applications of our method to four sets of genome-wide significant SNPs associated with red blood cell count, multiple sclerosis, celiac disease and HDL cholesterol. Availability and implementation: http://broadinstitute.org/mpg/snpsea Contact: soumya@broadinstitute.org Supplementary information: Supplementary data are available at Bioinformatics online.

Functionally distinct disease-associated fibroblast subsets in rheumatoid arthritis

Fibroblasts regulate tissue homeostasis, coordinate inflammatory responses, and mediate tissue damage. In rheumatoid arthritis (RA), synovial fibroblasts maintain chronic inflammation which leads to joint destruction. Little is known about fibroblast heterogeneity or if aberrations in fibroblast subsets relate to pathology. Here, we show functional and transcriptional differences between fibroblast subsets from human synovial tissues using bulk transcriptomics of targeted subpopulations and single-cell transcriptomics. We identify seven fibroblast subsets with distinct surface protein phenotypes, and collapse them into three subsets by integrating transcriptomic data. One fibroblast subset, characterized by the expression of proteins podoplanin, THY1 membrane glycoprotein and cadherin-11, but lacking CD34, is threefold expanded in patients with RA relative to patients with osteoarthritis. These fibroblasts localize to the perivascular zone in inflamed synovium, secrete proinflammatory cytokines, are proliferative, and have an in vitro phenotype characteristic of invasive cells. Our strategy may be used as a template to identify pathogenic stromal cellular subsets in other complex diseases.Synovial fibroblasts are thought to be central mediators of joint destruction in rheumatoid arthritis (RA). Here the authors use single-cell transcriptomics and flow cytometry to identify synovial fibroblast subsets that are expanded and display distinct tissue distribution and function in patients with RA.

Reverse Association Of Single Cells To Rheumatoid Arthritis Accounting For Mixed Effects Identifies An Expanded CD27- HLA-DR+ Effector Memory CD4+ T Cell Population

High dimensional single-cell analyses have dramatically improved the ability to resolve complex mixtures of cells from human disease samples; however, identifying disease-associated cell types or cell states in patient samples remains challenging due to technical and inter-individual variation. Here we present Mixed effects modeling of Associations of Single Cells (MASC), a novel reverse single cell association strategy for testing whether case-control status influences the membership of single cells in any of multiple cellular subsets while accounting for technical confounds and biological variation. Applying MASC to mass cytometry analyses of CD4+ T cells from blood of rheumatoid arthritis (RA) patients and controls revealed a significantly expanded population of CD4+ T cells, identified as CD27- HLA-DR+ effector memory cells, in RA patients (OR = 1.7; p = 1.1 × 10−3). The frequency of CD27- HLA-DR+ cells was similarly elevated in blood samples from a second RA patient cohort, and CD27- HLA-DR+ cell frequency decreased in RA patients who respond to immunosuppressive therapy. Compared to peripheral blood, synovial fluid and synovial tissue samples from RA patients contained ∼5-fold higher frequencies of CD27- HLA-DR+ cells, which comprised ∼10% of synovial CD4+ T cells. We find that CD27- HLA-DR+ cells are abundant producers of IFN-γ and also express perforin and granzyme A at elevated levels. Thus MASC identified the expansion of a unique Th1 skewed effector T cell population with cytotoxic capacity in RA. We propose that MASC is a broadly applicable method to identify disease-associated cell populations in high-dimensional single cell data. One Sentence Summary Mixed-effects regression of single cells identifies a cytotoxic Th1-like CD4+ T cell subset while accounting for inter-individual and technical variation.Defining disease associated cellular subsets from single-cell assays can be confounded by technical variation and inter-individual differences in subset frequencies. We present Mixed Effects Modeling of Associations of Single Cell Populations (MASC), a novel reverse single cell association strategy that tests whether case-control status influences the membership of single cells in any of multiple cellular subsets. MASC is able to account for random technical artifacts that confound clustering and person-to-person biological variation. After demonstrating appropriate type I error, we applied this method to mass cytometry data collected from 52 cases and controls to identify CD4+ memory T cells altered in the circulation of rheumatoid arthritis (RA) patients. MASC identified a population of effector memory T cells expanded in RA patients compared to non-inflammatory controls (OR = 1.7; p = 1.1 x 10^-3). This population was characterized by absence of CD27 and expression of HLA-DR and constituted 1.7% of the total resting CD4+ memory T cells in controls and 3.1% in cases. We replicated the expansion of CD27- HLA-DR+ T cells in an independent cohort of 39 RA patients (p = 0.011), and find that effective RA treatment reduces the frequency of these cells in circulation. Further, we find that CD27- HLA-DR+ cells infiltrate the joints of RA patients and produce abundant effector cytokines upon stimulation. We propose that MASC is a broadly applicable method to identify disease-associated cell populations in high-dimensional single cell data.