Inflammatory Bowel Disease Dataset Research Articles

Causal association of inflammatory bowel disease with sarcoidosis and the mediating role of primary biliary cholangitis.

Previous observational epidemiological studies have identified a potential association between inflammatory bowel disease (IBD) and sarcoidosis. Nonetheless, the precise biological mechanisms underlying this association remain unclear. Therefore, we adopted a Mendelian randomization (MR) approach to investigate the causal relationship between IBD with genetic susceptibility to sarcoidosis, as well as to explore the potential mediating role. The genetic associations were obtained from publicly available genome-wide association studies (GWASs) of European ancestry. The IBD dataset has 31,665 cases and 33,977 controls, consisting of 13,768 individuals with ulcerative colitis (UC) and 17,897 individuals with Crohn's disease (CD). The genetic associations of sarcoidosis with 4,854 cases and 446,523 controls. A bidirectional causality between IBD and sarcoidosis was implemented to be determined by a two-sample MR approach. The inverse variance weighted (IVW) method was utilized as the main statistical method, and a series of sensitivity analyses were performed to detect heterogeneity and horizontal pleiotropy. A two-step MR approach was used to investigate whether the mediating pathway from IBD to sarcoidosis was mediated by PBC. The forward MR analysis indicated that genetic predisposition to IBD was significantly linked to an increased risk of sarcoidosis (OR = 1.088, 95% CI: 1.023-1.158, pIBD-sar = 7.498e-03). Similar causal associations were observed in CD (OR = 1.082, 95% CI: 1.028-1.138, pCD-sar = 2.397e-03) and UC (OR = 1.079, 95% CI: 1.006-1.158, pUC-sar = 0.034). Reverse MR analysis revealed that genetic susceptibility to sarcoidosis was correlated with an augmented risk of CD (OR = 1.306, 95% CI: 1.110-1.537, psar-CD = 1.290e-03) but not IBD or UC. The mediation analysis via two-step MR showed that the causal influence of IBD and CD on sarcoidosis effects was partly mediated by PBC, and the mediating effect was 0.018 (95% CI: 0.005-0.031, p = 7.596e-03) with a mediated proportion of 21.397% in IBD, and 0.014 (95% CI: 0.004-0.024, p = 7.800e-03) with a mediated proportion of 17.737% in CD. The MR analysis provided evidence substantiating the causal effect of IBD (CD and UC) on an increased risk of sarcoidosis, with PBC playing a mediating role in IBD and CD. However, sarcoidosis only enhances the risk of developing CD, but not IBD or UC. These findings illuminate the etiology of sarcoidosis and contribute to the management of IBD patients.

Novel immune cross-talk between inflammatory bowel disease and IgA nephropathy

The mechanisms underlying the complex correlation between immunoglobulin A nephropathy (IgAN) and inflammatory bowel disease (IBD) remain unclear. This study aimed to identify the optimal cross-talk genes, potential pathways, and mutual immune-infiltrating microenvironments between IBD and IgAN to elucidate the linkage between patients with IBD and IgAN. The IgAN and IBD datasets were obtained from the Gene Expression Omnibus (GEO). Three algorithms, CIBERSORTx, ssGSEA, and xCell, were used to evaluate the similarities in the infiltrating microenvironment between the two diseases. Weighted gene co-expression network analysis (WGCNA) was implemented in the IBD dataset to identify the major immune infiltration modules, and the Boruta algorithm, RFE algorithm, and LASSO regression were applied to filter the cross-talk genes. Next, multiple machine learning models were applied to confirm the optimal cross-talk genes. Finally, the relevant findings were validated using histology and immunohistochemistry analysis of IBD mice. Immune infiltration analysis showed no significant differences between IBD and IgAN samples in most immune cells. The three algorithms identified 10 diagnostic genes, MAPK3, NFKB1, FDX1, EPHX2, SYNPO, KDF1, METTL7A, RIDA, HSDL2, and RIPK2; FDX1 and NFKB1 were enhanced in the kidney of IBD mice. Kyoto Encyclopedia of Genes and Genomes analysis showed 15 mutual pathways between the two diseases, with lipid metabolism playing a vital role in the cross-talk. Our findings offer insights into the shared immune mechanisms of IgAN and IBD. These common pathways, diagnostic cross-talk genes, and cell-mediated abnormal immunity may inform further experimental studies.

COMPARATIVE ASSESSMENT OF BIOBANK DATASETS FOR THE STUDY OF INFLAMMATORY BOWEL DISEASE

Abstract Lack of available multiomics datasets has slowed progress in understanding the relationship between genomic variants, gene expression, and disease progress in inflammatory bowel disease (IBD). Biobanks provide an opportunity to produce population-scale datasets to accelerate understanding of these diseases. Ovation’s IBD Omic Data is a multiomic dataset including whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) from disease and normal tissues, derived from clinical biopsies of patients with IBD. Omic data is supplemented with patient phenotypic data including diagnoses, therapies, procedures, and routine laboratory results. We report initial results of analysis of the first 212 patients from this cohort to identify genes differentially expressed between disease and normal colon tissue in patients with IBD, and to explore variants in these genes in both the Ovation IBD Omic Data and UK Biobank (UKBB) genomic data. To generate Ovation IBD Omic Data, DNA and RNA were extracted from fixed formalin paraffin embedded (FFPE) tissue samples of 212 IBD patients. Sequencing was conducted at a depth of 30X WGS. RNA-seq analysis was performed at a depth of 30M 1x50-bp reads per sample. Subsequent analysis was done via STAR aligner, FeatureCounts, and DESeq2. We curated a list of 54 common genes implicated in IBD from the literature as a benchmark. WGS data in the Variant Call Format (VCF) were ingested into the REVEAL platform along with phenotypic data variables. Values for minor allele frequencies (maf) were calculated and annotations were generated using the Ensembl Variant Effect Predictor (VEP). We examined the number of unique genomic variants in coding regions of the Ovation IBD Omic Data compared to those from the UKBB dataset for the benchmark genes. Despite the difference in sample population size, the Ovation data included a median of ~3,600 more unique variants per gene (~85% had more variants identified in a population of 212). Analysis of the global gene expression levels from Ovation’s IBD dataset showed 18 genes from the benchmark had significantly different expression in disease and normal tissue (p-value <0.001) across all IBD patients. We also analyzed the subset of samples from patients with CD (n=58). Notably, SLC4A4 and IFITM3 did not pass the original threshold in the full analysis (p-values were >0.001), however they were statistically significant when the analysis was limited to CD patients (p-value <0.0001). Comparing datasets of varying complexity shows that, despite a smaller sample size, specialized data derived from clinical samples, such as the Ovation IBD Omic Data, can provide additional insights for complex diseases like IBD. Further analysis will focus on the relationship between unique genomic variants identified, expression profiles and associated metadata to identify candidates of potential clinical relevance.

Intestinal Epithelial Axin1 Deficiency Protects Against Colitis via Altered Gut Microbiota

Intestinal homeostasis is maintained by specialized host cells and the gut microbiota. Wnt/β-catenin signaling is essential for gastrointestinal development and homeostasis, and its dysregulation has been implicated in inflammation and colorectal cancer. Axin1 negatively regulates activated Wnt/β-catenin signaling, but little is known regarding its role in regulating host–microbial interactions in health and disease. Here, we aim to demonstrate that intestinal Axin1 determines gut homeostasis and host response to inflammation. Axin1 expression was analyzed in human inflammatory bowel disease datasets. To explore the effects and mechanism of intestinal Axin1 in regulating intestinal homeostasis and colitis, we generated new mouse models with Axin1 conditional knockout in intestinal epithelial cell (IEC; Axin1ΔIEC) and Paneth cell (PC; Axin1ΔPC) to compare with control (Axin1LoxP; LoxP: locus of X-over, P1) mice. We found increased Axin1 expression in the colonic epithelium of human inflammatory bowel disease (IBD). Axin1ΔIEC mice exhibited altered goblet cell spatial distribution, PC morphology, reduced lysozyme expression, and enriched Akkermansia muciniphila (A. muciniphila). The absence of intestinal epithelial and PC Axin1 decreased susceptibility to dextran sulfate sodium (DSS)-induced colitis in vivo. Axin1ΔIEC and Axin1ΔPC mice became more susceptible to DSS-colitis after cohousing with control mice. Treatment with A. muciniphila reduced DSS-colitis severity. Antibiotic treatment did not change the IEC proliferation in the Axin1Loxp mice. However, the intestinal proliferative cells in Axin1ΔIEC mice with antibiotic treatment were reduced compared with those in Axin1ΔIEC mice without treatment. These data suggest non-colitogenic effects driven by the gut microbiome. In conclusion, we found that the loss of intestinal Axin1 protects against colitis, likely driven by epithelial Axin1 and Axin1-associated A. muciniphila. Our study demonstrates a novel role of Axin1 in mediating intestinal homeostasis and the microbiota. Further mechanistic studies using specific Axin1 mutations elucidating how Axin1 modulates the microbiome and host inflammatory response will provide new therapeutic strategies for human IBD.

Automatic disease prediction from human gut metagenomic data using boosting GraphSAGE

BackgroundThe human microbiome plays a critical role in maintaining human health. Due to the recent advances in high-throughput sequencing technologies, the microbiome profiles present in the human body have become publicly available. Hence, many works have been done to analyze human microbiome profiles. These works have identified that different microbiome profiles are present in healthy and sick individuals for different diseases. Recently, several computational methods have utilized the microbiome profiles to automatically diagnose and classify the host phenotype.ResultsIn this work, a novel deep learning framework based on boosting GraphSAGE is proposed for automatic prediction of diseases from metagenomic data. The proposed framework has two main components, (a). Metagenomic Disease graph (MD-graph) construction module, (b). Disease prediction Network (DP-Net) module. The graph construction module constructs a graph by considering each metagenomic sample as a node in the graph. The graph captures the relationship between the samples using a proximity measure. The DP-Net consists of a boosting GraphSAGE model which predicts the status of a sample as sick or healthy. The effectiveness of the proposed method is verified using real and synthetic datasets corresponding to diseases like inflammatory bowel disease and colorectal cancer. The proposed model achieved a highest AUC of 93%, Accuracy of 95%, F1-score of 95%, AUPRC of 95% for the real inflammatory bowel disease dataset and a best AUC of 90%, Accuracy of 91%, F1-score of 87% and AUPRC of 93% for the real colorectal cancer dataset.ConclusionThe proposed framework outperforms other machine learning and deep learning models in terms of classification accuracy, AUC, F1-score and AUPRC for both synthetic and real metagenomic data.

Insilco prediction of the role of the FriZZled5 gene in colorectal cancer

IntroductionIn this study, we aimed to elucidate the crosstalk between the Wnt/β-catenin signaling pathway and colorectal cancer (CRC) associated with inflammatory bowel disease (IBD) using a bioinformatics analysis of putative common biomarkers and a systems biology approach. Materials and methodsThe following criteria were used to search the GEO and ArrayExpress databases for terms related to CRC and IBD: 1. The dataset containing the transcriptomic data, and 2. Untreated samples by medications or drugs. A total of 42 datasets were selected for additional analysis. The GEO2R identified the differentially expressed genes. The genes involved in the Wnt signaling pathway were extracted from the KEGG database. Enrichment analysis and miRNA target prediction were conducted through the ToppGene online tool. ResultsIn CRC datasets, there were 1168 up- and 998 down-regulated probes, whereas, in IBD datasets, there were 256 up- and 200 down-regulated probes. There were 65 upregulated and 57 downregulated genes shared by CRC and IBD. According to KEGG, there were 166 genes in the Wnt pathway. FriZZled5 (FZD5) was a down-regulated gene in both CRC and IBD, as determined by the intersection of CRC- and IBD-related DEGs with the Wnt pathway. It was also demonstrated that miR-191, miR-885-5p, miR-378a-3p, and miR-396-3p affect the FriZZled5 gene expression. ConclusionIt is possible that increased expression of miR-191 and miR-885-5p, or decreased expression of miR-378a -3p and miR396-3, in IBD and CRC results in decreased expression of the FZD5 gene. Based on the function of this gene, FZD5 may be a potential therapeutic target in IBD that progresses to CRC.

Bioinformatics Analysis of Immune Cell Infiltration and Diagnostic Biomarkers between Ankylosing Spondylitis and Inflammatory Bowel Disease.

Ankylosing spondylitis (AS) and inflammatory bowel disease (IBD) are both autoimmune diseases, and they often occur together in clinical practice, but the pathogenesis is unclear. This study is aimed at identifying the hub genes and explore the related immune molecular mechanisms between AS and IBD by bioinformatics analysis. From the public Gene Expression Omnibus (GEO) database, the AS and IBD datasets (GSE73754, GSE59071, GSE25101, and GSE36807) were obtained. The immune cell infiltration in the peripheral blood tissues of GSE73754 and GSE59071 was assessed using the CIBERSORT algorithm. Then, we used the Weighted Gene Coexpression Network Analysis (WGCNA) to identify the Differentially Expressed Genes (DEGs) related to AS and IBD. Then, the immune genes from the ImmPort database intersected with the DEGs to obtain hub genes. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyzed the functional correlation of hub genes. Then, hub genes were verified in GSE25101 and GSE36807. The clusterProfiler software and Gene Set Enrichment Analysis (GSEA) were used to conduct functional enrichment and pathway enrichment studies. Finally, the diagnostic efficacy was assessed using Receiver Operating Characteristic (ROC) curve analysis. The analysis of immune characteristics showed that both AS and IBD were related to immunity, and neutrophils were positively correlated in both diseases. Nine coexpressed genes, including FCGRT, S100A11, IFNGR1, NFKBIZ, JAK2, LYN, PLAUR, ADM, and IL1RN, were linked to immune cells. The GO and KEGG analyses results showed that enrichment analysis was mainly related to cell transport and migration. Finally, the ROC curve was verified with the validation set, and it was found that PLAUR has clinical diagnostic significance and the most excellent specificity and sensitivity, respectively. PLAUR (uPAR) is a promising biomarker and will be an underlying genetic biomarker for diagnosing AS comorbid IBD. Inflammation and immunological modulation mediated by neutrophil infiltration were important in the development of AS and IBD and may be diagnostic and therapeutic targets.

A broad cuproptosis landscape in inflammatory bowel disease.

Cuproptosis, a genetic process of copper-dependent cell death linked to mitochondria respiration, demonstrates its correlation with inhibiting tumoral angiogenesis and motility. Recent studies have developed systematic bioinformatics frameworks to identify the association of cuproptosis with tumors but any non-neoplastic diseases. Therefore, against the background of an increased incidence of inflammatory bowel disease (IBD), the landscape of cuproptosis regulation in IBD is a critical need to be investigated. The differentially expressed cuproptosis-related genes (DECRGs) were identified with human sequencing profiles for four inflammatory digestive disorders. Another four independent IBD datasets from GEO were used as a validation cohort. And experimental mice model provides another validation method. Using single sample gene set enrichment analysis (ssGSEA), receiver operating characteristic (ROC) curve, CIBERSORT, and consensus clustering algorithms, we explored the association between immune score and cuproptosis-related genes, as well as the diagnostic value of these genes. Molecular docking screened potential interaction of IBD drugs with the structural regulator by Autodock Vina. Cuproptosis-related regulators exhibited extensive differential expression in Crohn's Disease (CD), Ulcerative Colitis (UC), Celiac Disease (CEL), and IBD-induced cancer (IBD-CA) that share common differential genes (PDHA1, DBT, DLAT, LIAS). The differential expression of DECRGs was reverified in the validated cohort and immunohistochemistry assay. Moreover, the cell signaling pathways and ontology mainly focused on the mitochondrial respiratory function, which was highly enriched in Gene set enrichment analysis (GSEA). According to ssGSEA and ROC, when considering the four regulators, which showed robust association with immune infiltration in IBD, the area under the ROC (AUC) was 0.743. In addition, two clusters of consensus clustering based on the four regulators exhibit different immune phenotypes. According to molecular docking results, methotrexate gained the highest binding affinity to the main chain of key cuproptosis-related regulators compared with the remaining ten drugs. Cuproptosis-related regulators were widely linked to risk variants, immune cells, immune function, and drug efficacy in IBD. Regulation of cuproptosis may deeply influence the occurrence and development of patients with IBD.

Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data.

Most of the microbiome studies suggest that using ensemble models such as Random Forest results in best predictive power. In this study, we empirically evaluate a more powerful ensemble learning algorithm, multi-view stacked generalization, on pediatric inflammatory bowel disease and adult colorectal cancer patients’ cohorts. We aim to check whether stacking would lead to better results compared to using a single best machine learning algorithm. Stacking achieves the best test set Average Precision (AP) on inflammatory bowel disease dataset reaching AP = 0.69, outperforming both the best base classifier (AP = 0.61) and the baseline meta learner built on top of base classifiers (AP = 0.63). On colorectal cancer dataset, the stacked classifier also outperforms (AP = 0.81) both the best base classifier (AP = 0.79) and the baseline meta learner (AP = 0.75). Stacking achieves best predictive performance on test set outperforming the best classifiers on both patient cohorts. Application of the stacking solves the issue of choosing the most appropriate machine learning algorithm by automating the model selection procedure. Clinical application of such a model is not limited to diagnosis task only, but it also can be extended to biomarker selection thanks to feature selection procedure.

Targeting uPA-uPAR interaction to improve intestinal epithelial barrier integrity in inflammatory bowel disease.

SummaryBackgroundLoss of intestinal epithelial barrier integrity is a critical component of Inflammatory Bowel Disease (IBD) pathogenesis. Co-expression regulation of ligand-receptor pairs in IBD mucosa has not been systematically studied. Targeting ligand-receptor pairs which are induced in IBD mucosa and function in intestinal epithelial barrier integrity may provide novel therapeutics for IBD.MethodsWe performed transcriptomic meta-analysis on public IBD datasets combined with cell surface protein-protein-interaction (PPI) databases. We explored primary human/mouse intestinal organoids and Caco-2 cells for expression and function studies of uPA-uPAR (prime hits from the meta-analysis). Epithelial barrier integrity was measured by Trans-Epithelial Electrical Resistance (TEER), FITC-Dextran permeability and tight junction assessment. Genetic (CRISPR, siRNA and KO mice) and pharmacological (small molecules, neutralizing antibody and peptide inhibitors) approaches were applied. Mice deficient of uPAR were studied using the Dextran Sulfate Sodium (DSS)-induced colitis model.FindingsThe IBD ligand-receptor meta-analysis led to the discovery of a coordinated upregulation of uPA and uPAR in IBD mucosa. Both genes were significantly upregulated during epithelial barrier breakdown in primary intestinal organoids and decreased during barrier formation. Genetic inhibition of uPAR or uPA, or pharmacologically blocking uPA-uPAR interaction protects against cytokine-induced barrier breakdown. Deficiency of uPAR in epithelial cells leads to enhanced EGF/EGFR signalling, a known regulator of epithelial homeostasis and repair. Mice deficient of uPAR display improved intestinal barrier function in vitro and during DSS-induced colitis in vivo.InterpretationOur findings suggest that blocking uPA-uPAR interaction via pharmacological agents protects the epithelial barrier from inflammation-induced damage, indicating a potential therapeutic target for IBD.FundingThe study was funded by Boehringer Ingelheim.

Linking Inflammatory Bowel Disease Symptoms to Changes in the Gut Microbiome Structure and Function.

Inflammatory bowel disease (IBD) is a chronic disease of the gastrointestinal tract that is often characterized by abdominal pain, rectal bleeding, inflammation, and weight loss. Many studies have posited that the gut microbiome may play an integral role in the onset and exacerbation of IBD. Here, we present a novel computational analysis of a previously published IBD dataset. This dataset consists of shotgun sequence data generated from fecal samples collected from individuals with IBD and an internal control group. Utilizing multiple external controls, together with appropriate techniques to handle the compositionality aspect of sequence data, our computational framework can identify and corroborate differences in the taxonomic profiles, bacterial association networks, and functional capacity within the IBD gut microbiome. Our analysis identified 42 bacterial species that are differentially abundant between IBD and every control group (one internal control and two external controls) with at least a twofold difference. Of the 42 species, 34 were significantly elevated in IBD, relative to every other control. These 34 species were still present in the control groups and appear to play important roles, according to network centrality and degree, in all bacterial association networks. Many of the species elevated in IBD have been implicated in modulating the immune response, mucin degradation, antibiotic resistance, and inflammation. We also identified elevated relative abundances of protein families related to signal transduction, sporulation and germination, and polysaccharide degradation as well as decreased relative abundance of protein families related to menaquinone and ubiquinone biosynthesis. Finally, we identified differences in functional capacities between IBD and healthy controls, and subsequently linked the changes in the functional capacity to previously published clinical research and to symptoms that commonly occur in IBD.

Simultaneous Noise Suppression and Incoherent Artifact Reduction in Ultrafast Ultrasound Vascular Imaging.

Ultrasound vascular imaging based on ultrafast plane wave imaging and singular value decomposition (SVD) clutter filtering has demonstrated superior sensitivity in blood flow detection. However, ultrafast ultrasound vascular imaging is susceptible to electronic noise due to the weak penetration of unfocused waves, leading to a lower signal-to-noise ratio (SNR) at larger depths. In addition, incoherent clutter artifacts originating from strong and moving tissue scatterers that cannot be completely removed create a strong mask on top of the blood signal that obscures the vessels. Herein, a method that can simultaneously suppress the background noise and incoherent artifacts is proposed. The method divides the tilted plane or diverging waves into two subgroups. Coherent spatial compounding is performed within each subgroup, resulting in two compounded data sets. An SVD-based clutter filter is applied to each data set, followed by a correlation between the two data sets to produce a vascular image. Uncorrelated noise and incoherent artifacts can be effectively suppressed with the correlation process, while the coherent blood signal can be preserved. The method was evaluated in wire-target simulations and phantom, in which around 7-10-dB SNR improvement was shown. Consistent results were found in a flow channel phantom with improved SNR by the proposed method (39.9 ± 0.2 dB) against conventional power Doppler (29.1 ± 0.6 dB). Last, we demonstrated the effectiveness of the method combined with block-wise SVD clutter filtering in a human liver, breast tumor, and inflammatory bowel disease data sets. The improved blood flow visualization may facilitate more reliable small vessel imaging for a wide range of clinical applications, such as cancer and inflammatory diseases.

Machine Learning Based Risk Assessment and Early Diagnostic Model of Inflammatory Bowel Disease Associated Arthropathy

Inflammatory bowel disease (IBD) is a persistent idiopathic disorder responsible for intestinal inflammatory conditions. IBD exhibits several extraintestinal manifestations, the most common being arthropathy, that are important triggers and risk factors for adverse progression of the disease. Due to a lack of definite diagnostic criteria and treatment regime, a need arises for early diagnosis and management of IBD associated arthropathy to reduce its morbidity. The present study utilizes a machine learning approach for development and validation of a risk prediction and early diagnostic disease model for IBD associated arthropathy. A publically available IBD cases vs control dataset from University of Massachusetts Medical School’s institutional repository was taken and the data was filtered into 54 IBD individuals along with their demographic and clinical characteristics. The arthropathy characteristics were incorporated from literature in the IBD dataset. Data was randomly split into training (50%; n=27) and testing (50%; n=27) for development and validation of model using logistic regression. Out of 54 IBD cases, 21 had a higher risk of developing arthropathy. The area under the receiver operator curve for the validated model was 0.90 (95% CI 0.80–0.99; accuracy 96%). This disease model can aid in identification of high-risk individuals and for early diagnosis of arthropathy in IBD cases before reaching the imaging and invasive diagnostic stage. This model warrants for prospective case-control trials validation.

Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference.

Inferring the topology of a gene regulatory network (GRN) from gene expression data is a challenging but important undertaking for gaining a better understanding of gene regulation. Key challenges include working with noisy data and dealing with a higher number of genes than samples. Although a number of different methods have been proposed to infer the structure of a GRN, there are large discrepancies among the different inference algorithms they adopt, rendering their meaningful comparison challenging. In this study, we used two methods, namely the MIDER (Mutual Information Distance and Entropy Reduction) and the PLSNET (Partial least square based feature selection) methods, to infer the structure of a GRN directly from data and computationally validated our results. Both methods were applied to different gene expression datasets resulting from inflammatory bowel disease (IBD), pancreatic ductal adenocarcinoma (PDAC), and acute myeloid leukaemia (AML) studies. For each case, gene regulators were successfully identified. For example, for the case of the IBD dataset, the UGT1A family genes were identified as key regulators while upon analysing the PDAC dataset, the SULF1 and THBS2 genes were depicted. We further demonstrate that an ensemble-based approach, that combines the output of the MIDER and PLSNET algorithms, can infer the structure of a GRN from data with higher accuracy. We have also estimated the number of the samples required for potential future validation studies. Here, we presented our proposed analysis framework that caters not only to candidate regulator genes prediction for potential validation experiments but also an estimation of the number of samples required for these experiments.

Exploring thematic structure and predicted functionality of 16S rRNA amplicon data.

Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state). Elucidating such relations is often difficult as microbiome data are compositional, sparse, and have high dimensionality. Also, the configuration of co-occurring taxa may represent overlapping subcommunities that contribute to sample characteristics such as host status. Preserving the configuration of co-occurring microbes rather than detecting specific indicator species is more likely to facilitate biologically meaningful interpretations. Additionally, analyses that use taxonomic relative abundances to predict the abundances of different gene functions aggregate predicted functional profiles across taxa. This precludes straightforward identification of predicted functional components associated with subsets of co-occurring taxa. We provide an approach to explore co-occurring taxa using "topics" generated via a topic model and link these topics to specific sample features (e.g., disease state). Rather than inferring predicted functional content based on overall taxonomic relative abundances, we instead focus on inference of functional content within topics, which we parse by estimating interactions between topics and pathways through a multilevel, fully Bayesian regression model. We apply our methods to three publicly available 16S amplicon sequencing datasets: an inflammatory bowel disease dataset, an oral cancer dataset, and a time-series dataset. Using our topic model approach to uncover latent structure in 16S rRNA amplicon surveys, investigators can (1) capture groups of co-occurring taxa termed topics; (2) uncover within-topic functional potential; (3) link taxa co-occurrence, gene function, and environmental/host features; and (4) explore the way in which sets of co-occurring taxa behave and evolve over time. These methods have been implemented in a freely available R package: https://cran.r-project.org/package=themetagenomics, https://github.com/EESI/themetagenomics.

Bayesian analysis of genome-wide inflammatory bowel disease data sets reveals new risk loci.

Genome-wide association studies (GWAS) have identified and validated 200 risk loci for inflammatory bowel disease (IBD) to date, including risk loci for both Crohn's disease and ulcerative colitis. Previously studies mainly used single SNP testing methods and only reported the most significant association within each locus. Advanced methods are needed to detect additional joint effects of multiple SNPs and fine map causal variants in presence of strong linkage disequilibrium. In this study, we applied a powerful Bayesian method to analyze an existing Immunochip data sets for IBD from the international inflammatory bowel disease genetics consortium. The method jointly tested single and set-based SNPs in a unified framework and filtered indirect associations due to linkage disequilibrium, thereby fine-mapping the most likely IBD variants. Using an independent collection of individuals from 11 IBD GWAS as validation, our approach discovered and validated 9 completely new IBD loci and 5 independent signals (excluding the major histocompatibility complex) near known IBD loci reaching genome-wide significance. Several of the replicated new loci implicated functionally more interpretable genes than previous reports. The epigenetic marks at our detected IBD signals demonstrated significant activation signatures in blood cell types and correspondingly substantial repression in stem cells, suggesting regulatory links between genetic variants and IBD. Our analysis of the currently largest IBD datasets therefore added new insights that will be integral to the ongoing efforts in IBD genetics.

A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease.

SummaryGene-based supervised machine learning classification models have been widely used to differentiate disease states, predict disease progression and determine effective treatment options. However, many of these classifiers are sensitive to noise and frequently do not replicate in external validation sets. For complex, heterogeneous diseases, these classifiers are further limited by being unable to capture varying combinations of genes that lead to the same phenotype. Pathway-based classification can overcome these challenges by using robust, aggregate features to represent biological mechanisms. In this work, we developed a novel pathway-based approach, PRObabilistic Pathway Score, which uses genes to calculate individualized pathway scores for classification. Unlike previous individualized pathway-based classification methods that use gene sets, we incorporate gene interactions using probabilistic graphical models to more accurately represent the underlying biology and achieve better performance. We apply our method to differentiate two similar complex diseases, ulcerative colitis (UC) and Crohn’s disease (CD), which are the two main types of inflammatory bowel disease (IBD). Using five IBD datasets, we compare our method against four gene-based and four alternative pathway-based classifiers in distinguishing CD from UC. We demonstrate superior classification performance and provide biological insight into the top pathways separating CD from UC.Availability and ImplementationPROPS is available as a R package, which can be downloaded at http://simtk.org/home/props or on Bioconductor.Supplementary information Supplementary data are available at Bioinformatics online.

A functional genomics predictive network model identifies regulators of inflammatory bowel disease.

A major challenge in inflammatory bowel disease (IBD) is the integration of diverse IBD data sets to construct predictive models of IBD. We present a predictive model of the immune component of IBD that informs causal relationships among loci previously linked to IBD through genome-wide association studies (GWAS) using functional and regulatory annotations that relate to the cells, tissues, and pathophysiology of IBD. Our model consists of individual networks constructed using molecular data generated from intestinal samples isolated from three populations of patients with IBD at different stages of disease. We performed key driver analysis to identify genes predicted to modulate network regulatory states associated with IBD, prioritizing and prospectively validating 12 of the top key drivers experimentally. This validated key driver set not only introduces new regulators of processes central to IBD but also provides the integrated circuits of genetic, molecular, and clinical traits that can be directly queried to interrogate and refine the regulatory framework defining IBD.

A population genetic signal of polygenic adaptation.

Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We use a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model, we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also significantly outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results.

Comparisons of multi-marker association methods to detect association between a candidate region and disease.

The joint use of information from multiple markers may be more effective to reveal association between a genomic region and a trait than single marker analysis. In this article, we compare the performance of seven multi-marker methods. These methods include (1) single marker analysis (either the best-scoring single nucleotide polymorphism in a candidate region or a combined test based on Fisher's method); (2) fixed effects regression models where the predictors are either the observed genotypes in the region, principal components that explain a proportion of the genetic variation, or predictors based on Fourier transformation for the genotypes; and (3) variance components analysis. In our simulation studies, we consider genetic models where the association is due to one, two, or three markers, and the disease-causing markers have varying allele frequencies. We use information from either all the markers in a region or information only from tagging markers. Our simulation results suggest that when there is one disease-causing variant, the best-scoring marker method is preferred whereas the variance components method and the principal components method work well for more common disease-causing variants. When there is more than one disease-causing variant, the principal components method seems to perform well over all the scenarios studied. When these methods are applied to analyze associations between all the markers in or near a gene and disease status for an inflammatory bowel disease data set, the analysis based on the principal components method leads to biologically more consistent discoveries than other methods.