A pairwise cytokine code explains the organism-wide response to sepsis.

Sepsis is a systemic response to infection with life-threatening consequences. Our understanding of the molecular and cellular impact of sepsis across organs remains rudimentary. Here, we characterize the pathogenesis of sepsis by measuring dynamic changes in gene expression across organs. To pinpoint molecules controlling organ states in sepsis, we compare the effects of sepsis on organ gene expression to those of 6 singles and 15 pairs of recombinant cytokines. Strikingly, we find that the pairwise effects of tumor necrosis factor plus interleukin (IL)-18, interferon-gamma or IL-1ß suffice to mirror the impact of sepsis across tissues. Mechanistically, we map the cellular effects of sepsis and cytokines by computing changes in the abundance of 195 cell types across 9 organs, which we validate by whole-mouse spatial profiling. Our work decodes the cytokine cacophony in sepsis into a pairwise cytokine message capturing the gene, cell and tissue responses of the host to the disease.

DISSECTING TUMOR TRANSCRIPTIONAL HETEROGENEITY FROM SINGLE-CELL RNA-SEQ DATA BY GENERALIZED BINARY COVARIANCE DECOMPOSITION.

Profiling tumors with single-cell RNA sequencing (scRNA-seq) has the potential to identify recurrent patterns of transcription variation related to cancer progression, and so produce new therapeutically-relevant insights. However, the presence of strong inter-tumor heterogeneity often obscures more subtle patterns that are shared across tumors, some of which may characterize clinically-relevant disease subtypes. Here we introduce a new statistical method to address this problem. We show that this method can help decompose transcriptional heterogeneity into interpretable components - including patient-specific, dataset-specific and shared components relevant to disease subtypes - and that, in the presence of strong inter-tumor heterogeneity, our method can produce more interpretable results than existing widely-used methods. Applied to data from three studies on pancreatic cancer adenocarcinoma (PDAC), our method produces a refined characterization of existing tumor subtypes (e.g. classical vs basal), and identifies a new gene expression program (GEP) that is prognostic of poor survival independent of established prognostic factors such as tumor stage and subtype. The new GEP is enriched for genes involved in a variety of stress responses, and suggests a potentially important role for the integrated stress response in PDAC development and prognosis.

Tumor-Draining Lymph Node Reconstruction Promotes B Cell Activation During E0771 Mouse Breast Cancer Growth.

Lymph node metastasis is associated with tumor aggressiveness and poor prognosis in patients. Despite its significance in cancer progression, how immune cells in the tumor-draining lymph node (TDLN) participate in cancer immune regulation remains poorly understood. It has been reported that both anti-tumor and exhausted tumor-specific T cells can be induced in the TDLNs; however, B cell activation and maturation in the TDLN has received far less attention. In our studies using C57BL/6 mouse syngeneic E0771 breast cancer or B16F10 melanoma cell lines, tumor-associated antigens were found colocalized with the follicular dendritic cells (FDCs) in the germinal centers (GCs), where antigen-specific B cell maturation occurs. LN conduits and the subcapsular sinus (SCS) macrophages are two major routes of antigen trafficking to FDCs. Tumor growth induced LN conduit expansion in the B cell zone and disrupted the SCS macrophage layer, facilitating both the entry of tumor-associated antigens into the B cell zone and access to FDCs located in the GCs. Regional delivery of clodronate liposome specifically depleted SCS macrophages in the TDLN, increasing GC formation, and promoting tumor growth. Our study suggests that TDLN reconstruction creates a niche that favors B cell activation and maturation during tumor growth.

The dietary fibre rhamnogalacturonan improves intestinal epithelial barrier function in a microbiota-independent manner.

BACKGROUND AND PURPOSE: Dietary fibre comprises a complex group of polysaccharides that are indigestible but are fermented by gut microbiota, promoting beneficial effects to the intestinal mucosa indirectly through the production of short chain fatty acids. We found that a polysaccharide, rhamnogalacturonan (RGal), from the plant Acmella oleracea, has direct effects on intestinal epithelial barrier function. Our objective was to determine the mechanism whereby RGal enhances epithelial barrier function. EXPERIMENTAL APPROACH: Monolayers of colonic epithelial cell lines (Caco-2, T84) and of human primary cells from organoids were mounted in Ussing chambers to assess barrier function. The cellular mechanism of RGal effects on barrier function was determined using inhibitors of TLR-4 and PKC isoforms. KEY RESULTS: Apically applied RGal (1000 µg ml-1 ) significantly enhanced barrier function as shown by increased transepithelial electrical resistance (TER) and reduced fluorescein isothiocyanate (FITC)-dextran flux in Caco-2, T84 and human primary cell monolayers, and accelerated tight junction reassembly in Caco-2 cells in a calcium switch assay. RGal also reversed the barrier-damaging effects of inflammatory cytokines on FITC-dextran flux and preserved the tight junction distribution of occludin. RGal activated TLR4 in TLR4-expressing HEK reporter cells, an effect that was inhibited by the TLR4 inhibitor, C34. The effect of RGal was also dependent on PKC, specifically the isoforms PKCδ and PKCζ. CONCLUSION AND IMPLICATIONS: RGal enhances intestinal epithelial barrier function through activation of TLR4 and PKC signalling pathways. Elucidation of RGal mechanisms of action could lead to new, dietary approaches to enhance mucosal healing in inflammatory bowel diseases.

Nr4A1 modulates inflammation-associated intestinal fibrosis and dampens fibrogenic signaling in myofibroblasts.

Intestinal fibrosis is a common complication of the inflammatory bowel diseases (IBDs), contributing to tissue stiffening and luminal narrowing. Human nuclear receptor 4A 1 (NR4A1) was previously reported to regulate mesenchymal cell function and dampen fibrogenic signaling. NR4A1 gene variants are associated with IBD risk, and it has been shown to regulate intestinal inflammation. Here, we tested the hypothesis that NR4A1 acts as a negative regulator of intestinal fibrosis through regulating myofibroblast function. Using the SAMP1/YitFc mouse, we tested whether two pharmacological agents known to enhance NR4A1 signaling, cytosporone B (Csn-B) or 6-mercaptopurine (6-MP), could reduce fibrosis. We also used the dextran sulfate sodium (DSS) model of colitis and assessed the magnitude of colonic fibrosis in mouse nuclear receptor 4A 1 (Nr4a1-/-) and their wild-type littermates (Nr4a1+/+). Lastly, intestinal myofibroblasts isolated from Nr4a1-/- and Nr4a1+/+ mice or primary human intestinal myofibroblasts were stimulated with transforming growth factor-ß1 (TGF-ß1), in the presence or absence of Csn-B or 6-MP, and proliferation and ECM gene expression assessed. Csn-B or 6-MP treatment significantly reduced ileal thickness, collagen, and overall ECM content in SAMP1/YitFc mice. This was associated with a reduction in proliferative markers within the mesenchymal compartment. Nr4a1-/- mice exposed to DSS exhibited increased colonic thickening and ECM content. Nr4a1-/- myofibroblasts displayed enhanced TGF-ß1-induced proliferation. Furthermore, Csn-B or 6-MP treatment was antiproliferative in Nr4a1+/+ but not Nr4a1-/- cells. Lastly, activating NR4A1 in human myofibroblasts reduced TGF-ß1-induced collagen deposition and fibrosis-related gene expression. Our data suggest that NR4A1 can attenuate fibrotic processes in intestinal myofibroblasts and could provide a valuable clinical target to treat inflammation-associated intestinal fibrosis.NEW & NOTEWORTHY Fibrosis and increased muscle thickening contribute to stricture formation and intestinal obstruction, a complication that occurs in 30%-50% of patients with CD within 10 yr of disease onset. More than 50% of those who undergo surgery to remove the obstructed bowel will experience stricture recurrence. To date, there are no drug-based approaches approved to treat intestinal strictures. In the current submission, we identify NR4A1 as a novel target to treat inflammation-associated intestinal fibrosis.

Ultra-purification of Lipopolysaccharides reveals species-specific signalling bias of TLR4: importance in macrophage function.

TLR4 location, and bacterial species-derived lipopolysaccharides, play a significant role in the downstream activation of transcription factors, accessory molecules, and products. Here, this is demonstrated through the use of classically-activated and alternatively-activated macrophages. We show that, when polarized, human macrophages differentially express and localize TLR4, resulting in biased recognition and subsequent signalling of LPS derived from Pseudomonas aeruginosa, Escherichia coli, and Salmonella enterica. Analysis of activation demonstrated that in classically activated macrophages, P. aeruginosa signals from the plasma membrane via TLR4 to p65 dependent on TAK1 and TBK1 signalling. E. coli signals dependent or independent of the endosome, utilizing both TAK1- and TBK1-signalling to induce P65 and IRF3 inducible genes and cytokines. S. enterica however, only induces P65 and IRF3 phosphorylation through signalling via the endosome. This finding outlines clear signalling mechanisms by which innate immune cells, such as macrophages, can distinguish between bacterial species and initiate specialized responses through TLR4.

Genetic analyses support the contribution of mRNA N6-methyladenosine (m6A) modification to human disease heritability.

N6-methyladenosine (m6A) plays important roles in regulating messenger RNA processing. Despite rapid progress in this field, little is known about the genetic determinants of m6A modification and their role in common diseases. In this study, we mapped the quantitative trait loci (QTLs) of m6A peaks in 60 Yoruba (YRI) lymphoblastoid cell lines. We found that m6A QTLs are largely independent of expression and splicing QTLs and are enriched with binding sites of RNA-binding proteins, RNA structure-changing variants and transcriptional features. Joint analysis of the QTLs of m6A and related molecular traits suggests that the downstream effects of m6A are heterogeneous and context dependent. We identified proteins that mediate m6A effects on translation. Through integration with data from genome-wide association studies, we show that m6A QTLs contribute to the heritability of various immune and blood-related traits at levels comparable to splicing QTLs and roughly half of expression QTLs. By leveraging m6A QTLs in a transcriptome-wide association study framework, we identified putative risk genes of these traits.

Lipopolysaccharides modulate intestinal epithelial permeability and inflammation in a species-specific manner.

Patients presenting with Inflammatory bowel disease have been shown to exhibit an altered microbiome in both Crohn's disease and Ulcerative colitis. This shift in the microbial content led us to question whether several of these microbes are important in inflammatory processes present in these diseases and more specifically whether lipopolysaccharides from the gram-negative cell wall differentially stimulates resident cells. We, therefore, investigated the possible contribution of five major species of gram-negative bacteria found to be altered in presence during disease progression and evaluate their pathogenicity through LPS. We demonstrated that LPS from these different species had individual capacities to induce NF-κB and pro-inflammatory IL-8 production from HEK-TLR4 cells in a TLR4 dependent manner. Additional work using human intestinal colonic epithelial cell monolayers (Caco-2) demonstrated that the cells responded to the serotype specific LPS in a distinct manner, inducing many inflammatory mediators such as TNF-α and IL-10 in significantly altered proportions. Furthermore, the permeability of Caco-2 monolayers, as a test for their ability to alter intestinal permeability, was also differentially altered by the serotype specific LPS modulating trans-epithelial electrical resistance, small molecule movement, and tight junction integrity. Our results suggest that specific species of bacteria may be potentiating the pathogenesis of IBD and chronic inflammatory diseases through their serotype specific LPS responses.

Detailed modeling of positive selection improves detection of cancer driver genes.

Identifying driver genes from somatic mutations is a central problem in cancer biology. Existing methods, however, either lack explicit statistical models, or use models based on simplistic assumptions. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a model-based approach to driver gene identification. This method explicitly models positive selection at the single-base level, as well as highly heterogeneous background mutational processes. In particular, the selection model captures elevated mutation rates in functionally important sites using multiple external annotations, and spatial clustering of mutations. Simulations under realistic evolutionary models demonstrate the increased power of driverMAPS over current approaches. Applying driverMAPS to TCGA data of 20 tumor types, we identified 159 new potential driver genes, including the mRNA methyltransferase METTL3-METTL14. We experimentally validated METTL3 as a tumor suppressor gene in bladder cancer, providing support to the important role mRNA modification plays in tumorigenesis.

Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.

PURPOSE: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment. METHODS: Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign). RESULTS: The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures. CONCLUSIONS: Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.

Identification of Nonaggressive Pulmonary Nodules Using an Optimized Scoring System.

PURPOSE: The purpose of this study was to define the optimal scoring method for identifying benign intrapulmonary lymph nodes. MATERIALS AND METHODS: Subjects for this study were selected from the COPDGene study, a large multicenter longitudinal observational cohort study. A retrospective case-control analysis was performed using identified nodules on a subset of 377 patients who demonstrated 765 pulmonary nodules on their baseline computed tomography (CT) study. Nodule characteristics of 636 benign nodules (which resolved or showed <20% growth rate at 5 y follow-up) were compared with 51 nodules that occurred in the same lobe as a reported malignancy. Two radiologists scored each pulmonary nodule on the basis of intrapulmonary lymph node characteristics. A simple scoring strategy weighing all characteristics equally was compared with an optimized scoring strategy that weighed characteristics on the basis of their relative importance in identifying benign pulmonary nodules. RESULTS: A total of 479 of 636 benign pulmonary nodules had the majority of lymph node characteristics, whereas only 1 subpleural nodule with the majority of lymph node characteristics appeared to be malignant. Only 279 of 479 (58%) of benign pulmonary nodules with the majority of lymph node characteristics were intrafissural or subpleural. The optimized scoring strategy showed improved performance compared with the simple scoring strategy with average area under the curve of 0.80 versus 0.55. Optimized cutoff scores showed negative likelihood values for both readers of <0.2. A simulation showed a potential reduction in CT utilization of up to 36% for Fleischner criteria and up to 5% for LUNG-RADS. CONCLUSIONS: Nodules with the majority of lymph node characteristics, regardless of location, are likely benign, and weighing certain lymph node characteristics greater than others can improve overall performance. Given the potential to reduce CT utilization, lymph node characteristics should be considered when recommending appropriate follow-up.

A new sequence logo plot to highlight enrichment and depletion.

BACKGROUND: Sequence logo plots have become a standard graphical tool for visualizing sequence motifs in DNA, RNA or protein sequences. However standard logo plots primarily highlight enrichment of symbols, and may fail to highlight interesting depletions. Current alternatives that try to highlight depletion often produce visually cluttered logos. RESULTS: We introduce a new sequence logo plot, the EDLogo plot, that highlights both enrichment and depletion, while minimizing visual clutter. We provide an easy-to-use and highly customizable R package Logolas to produce a range of logo plots, including EDLogo plots. This software also allows elements in the logo plot to be strings of characters, rather than a single character, extending the range of applications beyond the usual DNA, RNA or protein sequences. And the software includes new Empirical Bayes methods to stabilize estimates of enrichment and depletion, and thus better highlight the most significant patterns in data. We illustrate our methods and software on applications to transcription factor binding site motifs, protein sequence alignments and cancer mutation signature profiles. CONCLUSIONS: Our new EDLogo plots and flexible software implementation can help data analysts visualize both enrichment and depletion of characters (DNA sequence bases, amino acids, etc.) across a wide range of applications.

Genotyping Polyploids from Messy Sequencing Data.

Detecting and quantifying the differences in individual genomes (i.e., genotyping), plays a fundamental role in most modern bioinformatics pipelines. Many scientists now use reduced representation next-generation sequencing (NGS) approaches for genotyping. Genotyping diploid individuals using NGS is a well-studied field, and similar methods for polyploid individuals are just emerging. However, there are many aspects of NGS data, particularly in polyploids, that remain unexplored by most methods. Our contributions in this paper are fourfold: (i) We draw attention to, and then model, common aspects of NGS data: sequencing error, allelic bias, overdispersion, and outlying observations. (ii) Many datasets feature related individuals, and so we use the structure of Mendelian segregation to build an empirical Bayes approach for genotyping polyploid individuals. (iii) We develop novel models to account for preferential pairing of chromosomes, and harness these for genotyping. (iv) We derive oracle genotyping error rates that may be used for read depth suggestions. We assess the accuracy of our method in simulations, and apply it to a dataset of hexaploid sweet potato (Ipomoea batatas). An R package implementing our method is available at https://cran.r-project.org/package=updog.

Oligodeoxynucleotide inhibition of Toll-like receptors 3, 7, 8, and 9 suppresses cytokine production in a human rheumatoid arthritis model.

Toll-like receptors (TLRs) are innate immune receptors that respond to both exogenous and endogenous stimuli and are suggested to contribute to the perpetuation of chronic inflammation associated with rheumatoid arthritis (RA). In particular, the endosomal TLRs 3, 7, 8, and 9 have more recently been postulated to be of importance in RA pathogenesis. In this study, pan inhibition of the endosomal TLRs by a phosphorothioate-modified inhibitory oligodeoxynucleotide (ODN) is demonstrated in primary human B cells, macrophages, and RA fibroblasts. Inhibition of TLR8 was of particular interest as TLR8 has been associated with RA pathogenesis in both human and murine arthritis models. ODN1411 competitively inhibited TLR8 signaling and was observed to directly bind to a purified TLR8 ectodomain, suggesting inhibition was through a direct interaction with the receptor. Addition of ODN1411 to human RA synovial membrane cultures significantly inhibited spontaneous cytokine production from these cultures, suggesting a potential role for one or more of the endosomal TLRs in inflammatory cytokine production in RA and the potential for inhibitory ODNs as novel therapies.

A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures.

Recent advances in sequencing technologies have enabled the production of massive amounts of data on somatic mutations from cancer genomes. These data have led to the detection of characteristic patterns of somatic mutations or "mutation signatures" at an unprecedented resolution, with the potential for new insights into the causes and mechanisms of tumorigenesis. Here we present new methods for modelling, identifying and visualizing such mutation signatures. Our methods greatly simplify mutation signature models compared with existing approaches, reducing the number of parameters by orders of magnitude even while increasing the contextual factors (e.g. the number of flanking bases) that are accounted for. This improves both sensitivity and robustness of inferred signatures. We also provide a new intuitive way to visualize the signatures, analogous to the use of sequence logos to visualize transcription factor binding sites. We illustrate our new method on somatic mutation data from urothelial carcinoma of the upper urinary tract, and a larger dataset from 30 diverse cancer types. The results illustrate several important features of our methods, including the ability of our new visualization tool to clearly highlight the key features of each signature, the improved robustness of signature inferences from small sample sizes, and more detailed inference of signature characteristics such as strand biases and sequence context effects at the base two positions 5' to the mutated site. The overall framework of our work is based on probabilistic models that are closely connected with "mixed-membership models" which are widely used in population genetic admixture analysis, and in machine learning for document clustering. We argue that recognizing these relationships should help improve understanding of mutation signature extraction problems, and suggests ways to further improve the statistical methods. Our methods are implemented in an R package pmsignature (https://github.com/friend1ws/pmsignature) and a web application available at https://friend1ws.shinyapps.io/pmsignature_shiny/.

msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding.

Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework fails to account for the substantial variation in the DNase I cleavage profiles across different binding sites. Neither does it account for variation in the profiles at the same binding site across multiple replicate DNase I experiments, which are increasingly available. In this work, we introduce new methods, based on multi-scale models for inhomogeneous Poisson processes, to account for such variation in DNase I cleavage patterns both within and across binding sites. These models account for the spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-seq peaks for those factors. Finally, we explore the effects of DNase I sequence bias on inference of factor binding using a simple extension to our framework that allows for a more flexible background model. The proposed model can also be easily applied to paired-end ATAC-seq and DNase-seq data. msCentipede, a Python implementation of our algorithm, is available at http://rajanil.github.io/msCentipede.

Aryl amide small-molecule inhibitors of microRNA miR-21 function.

MicroRNAs (miRNAs) are single stranded RNA molecules of ∼22 nucleotides that negatively regulate gene expression. MiRNAs are involved in fundamental cellular processes, such as development, differentiation, proliferation, and survival. MiRNA misregulation has been linked to various human diseases, most notably cancer. MicroRNA-21 (miR-21), a well-established oncomiR, is significantly overexpressed in many types of human cancers, thus rendering miR-21 a potential therapeutic target. Using a luciferase-based reporter assay under the control of miR-21 expression, a high-throughput screen of >300,000 compounds led to the discovery of a new aryl amide class of small-molecule miR-21 inhibitors. Structure-activity relationship (SAR) studies resulted in the development of four aryl amide derivatives as potent and selective miR-21 inhibitors. The intracellular levels of various miRNAs in HeLa cells were analyzed by qRT-PCR revealing specificity for miR-21 inhibition over other miRNAs. Additionally, preliminary mechanism of action studies propose a different mode of action compared to previously reported miR-21 inhibitors, thus affording a new chemical probe for future studies.

Interhospital transfer of blunt multiply injured patients to a level 1 trauma center does not adversely affect outcome.

BACKGROUND: Stops at nontrauma centers for severely injured patients are thought to increase deaths and costs, potentially because of unnecessary imaging and indecisive/delayed care of traumatic brain injuries (TBIs). METHODS: We studied 754 consecutive blunt trauma patients with an Injury Severity Score greater than 20 with an emphasis on 212 patients who received care at other sites en route to our level 1 trauma center. RESULTS: Referred patients were older, more often women, and had more severe TBI (all P < .05). After correction for age, sex, and injury pattern, there was no difference in the type of TBI, Glasgow Coma Scale (GCS) upon arrival at the trauma center, or overall mortality between referred and directly admitted patients. GCS at the outside institution did not influence promptness of transfer. CONCLUSIONS: Interhospital transfer does not affect the outcome of blunt trauma patients. However, the unnecessarily prolonged stay of low GCS patients in hospitals lacking neurosurgical care is inappropriate.

Integrated enrichment analysis of variants and pathways in genome-wide association studies indicates central role for IL-2 signaling genes in type 1 diabetes, and cytokine signaling genes in Crohn's disease.

Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn's disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and "Measles" pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14, and FYN) constitute novel putative T1D loci for further study.