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The role of long noncoding RNAs (lncRNAs) in disease is incompletely understood, but their regulation of inflammation is increasingly appreciated. We addressed the extent of lncRNA involvement in inflammatory bowel disease (IBD) using biopsy-derived RNA-sequencing data from a large cohort of deeply phenotyped patients with IBD. Weighted gene correlation network analysis revealed gene modules of lncRNAs coexpressed with protein-coding genes enriched for biological pathways, correlated with epithelial and immune cell signatures, or correlated with distal colon expression. Correlation of modules with clinical features uncovered a module correlated with disease severity, with an enriched interferon response signature containing the hub lncRNA IRF1-AS1. Connecting genes to IBD-associated single nucleotide polymorphisms (SNPs) revealed an enrichment of SNP-adjacent lncRNAs in biologically relevant modules. Ulcerative colitis-specific SNPs were enriched in distal colon-related modules, suggesting that disease-specific mechanisms may result from altered lncRNA expression. The function of the IBD-associated SNP-adjacent lncRNA IRF1-AS1 was explored in human myeloid cells, and our results suggested IRF1-AS1 promoted optimal production of TNF-α, IL-6, and IL-23. A CRISPR/Cas9-mediated activation screen in THP-1 cells revealed several lncRNAs that modulated LPS-induced TNF-α responses. Overall, this study uncovered the expression patterns of lncRNAs in IBD that identify functional, disease-relevant lncRNAs.

Previous studies have conducted time course characterization of murine colitis models through transcriptional profiling of differential expression. We characterize the transcriptional landscape of acute and chronic models of dextran sodium sulfate (DSS) and adoptive transfer (AT) colitis to derive temporal gene expression and splicing signatures in blood and colonic tissue in order to capture dynamics of colitis remission and relapse. We identify sub networks of patient-derived causal networks that are enriched in these temporal signatures to distinguish acute and chronic disease components within the broader molecular landscape of IBD. The interaction between the DSS phenotype and chronological time-point naturally defines parsimonious temporal gene expression and splicing signatures associated with acute and chronic phases disease (as opposed to ordinary time-specific differential expression/splicing). We show these expression and splicing signatures are largely orthogonal, i.e. affect different genetic bodies, and that using machine learning, signatures are predictive of histopathological measures from both blood and intestinal data in murine colitis models as well as an independent cohort of IBD patients. Through access to longitudinal multi-scale profiling from disease tissue in IBD patient cohorts, we can apply this machine learning pipeline to generation of direct patient temporal multimodal regulatory signatures for prediction of histopathological outcomes. Longitudinal changes in gene expression, splicing patterns and adaptive immune repertoire in murine models of colitis are examined and associated with histologic inflammation in mice and in patients with inflammatory bowel disease.

B cells, which are critical for intestinal homeostasis, remain understudied in ulcerative colitis (UC). In this study, we recruited three cohorts of patients with UC (primary cohort, n  = 145; validation cohort 1, n  = 664; and validation cohort 2, n  = 143) to comprehensively define the landscape of B cells during UC-associated intestinal inflammation. Using single-cell RNA sequencing, single-cell IgH gene sequencing and protein-level validation, we mapped the compositional, transcriptional and clonotypic landscape of mucosal and circulating B cells. We found major perturbations within the mucosal B cell compartment, including an expansion of naive B cells and IgG + plasma cells with curtailed diversity and maturation. Furthermore, we isolated an auto-reactive plasma cell clone targeting integrin αvβ6 from inflamed UC intestines. We also identified a subset of intestinal CXCL13-expressing TFH-like T peripheral helper cells that were associated with the pathogenic B cell response. Finally, across all three cohorts, we confirmed that changes in intestinal humoral immunity are reflected in circulation by the expansion of gut-homing plasmablasts that correlates with disease activity and predicts disease complications. Our data demonstrate a highly dysregulated B cell response in UC and highlight a potential role of B cells in disease pathogenesis. Multi-modal profiling reveals major alterations in colonic B cells in patients with ulcerative colitis, including reduced clonal diversity of plasma cells, and suggests that circulating gut-homing plasmablasts could serve as a biomarker for disease activity.

Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists.

ObjectiveIBD therapies and treatments are evolving to deeper levels of remission. Molecular measures of disease may augment current endpoints including the potential for less invasive assessments.DesignTranscriptome analysis on 712 endoscopically defined inflamed (Inf) and 1778 non-inflamed (Non-Inf) intestinal biopsies (n=498 Crohn’s disease, n=421 UC and 243 controls) in the Mount Sinai Crohn’s and Colitis Registry were used to identify genes differentially expressed between Inf and Non-Inf biopsies and to generate a molecular inflammation score (bMIS) via gene set variance analysis. A circulating MIS (cirMIS) score, reflecting intestinal molecular inflammation, was generated using blood transcriptome data. bMIS/cirMIS was validated as indicators of intestinal inflammation in four independent IBD cohorts.ResultsbMIS/cirMIS was strongly associated with clinical, endoscopic and histological disease activity indices. Patients with the same histologic score of inflammation had variable bMIS scores, indicating that bMIS describes a deeper range of inflammation. In available clinical trial data sets, both scores were responsive to IBD treatment. Despite similar baseline endoscopic and histologic activity, UC patients with lower baseline bMIS levels were more likely treatment responders compared with those with higher levels. Finally, among patients with UC in endoscopic and histologic remission, those with lower bMIS levels were less likely to have a disease flare over time.ConclusionTranscriptionally based scores provide an alternative objective and deeper quantification of intestinal inflammation, which could augment current clinical assessments used for disease monitoring and have potential for predicting therapeutic response and patients at higher risk of disease flares.

Eric Schadt and colleagues present a predictive causal model of the immune component of inflammatory bowel disease through integration of genetic, regulatory and transcriptional data. They prioritize and validate 12 of the top key drivers experimentally in mouse colitis models and human macrophages. 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.

Organizing experimentally determined protein associations as a hierarchy can be a good approach to elucidating the content of protein complexes and the modularity of subcomplexes. Several challenges exist. First, intrinsically sticky proteins, such as chaperones, are often falsely assigned to many functionally unrelated complexes. Second, the reported collections of proteins may not be true \"complexes\" in the sense that they bind together and perform a joint cellular function. Third, due to imperfect sensitivity of protein detection methods, both false positive and false negative assignments of a protein to complexes may occur. We mitigate the first issue by down-weighting sticky proteins by their occurrence frequencies. We approach the other two problems by merging nearly identical complexes and by constructing a directed acyclic graph (DAG) based on the relationship of partial inclusion. The constructed DAG, within which smaller complexes form parts of the larger, can reveal how different complexes are joined. By merging almost identical complexes one can deemphasize the influence of false positives, while allowing false negatives to be rescued by other nearly identical association data. We investigate several protein weighting schemes and compare their corresponding DAGs using yeast and human complexes. We find that the scheme incorporating weights based on information flow in the network of direct protein-protein interactions produces biologically most meaningful DAGs. In either yeast or human, isolated nodes form a large proportion of the final hierarchy. While most connected components encompass very few nodes, the largest one for each species contains a sizable portion of all nodes. By considering examples of subgraphs composed of nodes containing a specified protein, we illustrate that the graphs' topological features can correctly suggest the biological roles of protein complexes. The input data, final results and the source code are available at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbpmn/ProteinComplexDAG/.

Background Cytoscape is a well-developed flexible platform for visualization, integration and analysis of network data. Apart from the sophisticated graph layout and visualization routines, it hosts numerous user-developed plugins that significantly extend its core functionality. Earlier, we developed a network information flow framework and implemented it as a web application, called ITM Probe . Given a context consisting of one or more user-selected nodes, ITM Probe retrieves other network nodes most related to that context. It requires neither user restriction to subnetwork of interest nor additional and possibly noisy information. However, plugins for Cytoscape with these features do not yet exist. To provide the Cytoscape users the possibility of integrating ITM Probe into their workflows, we developed CytoITMprobe , a new Cytoscape plugin. Findings CytoITMprobe maintains all the desirable features of ITM Probe and adds additional flexibility not achievable through its web service version. It provides access to ITM Probe either through a web server or locally. The input, consisting of a Cytoscape network, together with the desired origins and/or destinations of information and a dissipation coefficient, is specified through a query form. The results are shown as a subnetwork of significant nodes and several summary tables. Users can control the composition and appearance of the subnetwork and interchange their ITM Probe results with other software tools through tab-delimited files. Conclusions The main strength of CytoITMprobe is its flexibility. It allows the user to specify as input any Cytoscape network, rather than being restricted to the pre-compiled protein-protein interaction networks available through the ITM Probe web service. Users may supply their own edge weights and directionalities. Consequently, as opposed to ITM Probe web service, CytoITMprobe can be applied to many other domains of network-based research beyond protein-networks. It also enables seamless integration of ITM Probe results with other Cytoscape plugins having complementary functionality for data analysis.

Genome-wide association studies (GWAS) have identified loci reproducibly associated with inflammatory bowel disease (IBD) and other immune-mediated diseases; however, the molecular mechanisms underlying most of genetic susceptibility remain undefined. Expressional quantitative trait loci (eQTL) of disease-relevant tissue can be employed in order to elucidate the genes and pathways affected by disease-specific genetic variance. In this study, we derived eQTLs for human whole blood and intestine tissues of anti-tumor necrosis factor-resistant Crohn's disease (CD) patients. We interpreted these eQTLs in the context of published IBD GWAS hits to inform on the disease process. At 10% false discovery rate, we discovered that 5,174 genes in blood and 2,063 genes in the intestine were controlled by a nearby single-nucleotide polymorphism (SNP) (i.e., cis-eQTL), among which 1,360 were shared between the two tissues. A large fraction of the identified eQTLs were supported by the regulomeDB database, showing that the eQTLs reside in regulatory elements (odds ratio; OR=3.44 and 3.24 for blood and intestine eQTLs, respectively) as opposed to protein-coding regions. Published IBD GWAS hits as a whole were enriched for blood and intestine eQTLs (OR=2.88 and 2.05; and P value=2.51E-9 and 0.013, respectively), thereby linking genetic susceptibility to control of gene expression in these tissues. Through a systematic search, we used eQTL data to inform 109 out of 372 IBD GWAS SNPs documented in National Human Genome Research Institute catalog, and we categorized the genes influenced by eQTLs according to their functions. Many of these genes have experimentally validated roles in specific cell types contributing to intestinal inflammation. The blood and intestine eQTLs described in this study represent a powerful tool to link GWAS loci to a regulatory function and thus elucidate the mechanisms underlying the genetic loci associated with IBD and related conditions. Overall, our eQTL discovery approach empirically identifies the disease-associated variants including their impact on the direction and extent of expression changes in the context of disease-relevant cellular pathways in order to infer the functional outcome of this aspect of genetic susceptibility.