ASPASIA: A toolkit for evaluating the effects of biological interventions on SBML model behaviour.

A calibrated computational model reflects behaviours that are expected or observed in a complex system, providing a baseline upon which sensitivity analysis techniques can be used to analyse pathways that may impact model responses. However, calibration of a model where a behaviour depends on an intervention introduced after a defined time point is difficult, as model responses may be dependent on the conditions at the time the intervention is applied. We present ASPASIA (Automated Simulation Parameter Alteration and SensItivity Analysis), a cross-platform, open-source Java toolkit that addresses a key deficiency in software tools for understanding the impact an intervention has on system behaviour for models specified in Systems Biology Markup Language (SBML). ASPASIA can generate and modify models using SBML solver output as an initial parameter set, allowing interventions to be applied once a steady state has been reached. Additionally, multiple SBML models can be generated where a subset of parameter values are perturbed using local and global sensitivity analysis techniques, revealing the model's sensitivity to the intervention. To illustrate the capabilities of ASPASIA, we demonstrate how this tool has generated novel hypotheses regarding the mechanisms by which Th17-cell plasticity may be controlled in vivo. By using ASPASIA in conjunction with an SBML model of Th17-cell polarisation, we predict that promotion of the Th1-associated transcription factor T-bet, rather than inhibition of the Th17-associated transcription factor RORγt, is sufficient to drive switching of Th17 cells towards an IFN-γ-producing phenotype. Our approach can be applied to all SBML-encoded models to predict the effect that intervention strategies have on system behaviour. ASPASIA, released under the Artistic License (2.0), can be downloaded from http://www.york.ac.uk/ycil/software.

Literature mining, gene-set enrichment and pathway analysis for target identification in Behçet's disease.

OBJECTIVES: To use literature mining to catalogue Behçet's associated genes, and advanced computational methods to improve the understanding of the pathways and signalling mechanisms that lead to the typical clinical characteristics of Behçet's patients. To extend this technique to identify potential treatment targets for further experimental validation. METHODS: Text mining methods combined with gene enrichment tools, pathway analysis and causal analysis algorithms. RESULTS: This approach identified 247 human genes associated with Behçet's disease and the resulting disease map, comprising 644 nodes and 19220 edges, captured important details of the relationships between these genes and their associated pathways, as described in diverse data repositories. Pathway analysis has identified how Behçet's associated genes are likely to participate in innate and adaptive immune responses. Causal analysis algorithms have identified a number of potential therapeutic strategies for further investigation. CONCLUSIONS: Computational methods have captured pertinent features of the prominent disease characteristics presented in Behçet's disease and have highlighted NOD2, ICOS and IL18 signalling as potential therapeutic strategies.

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines.

Naïve CD4+ T cells coordinate the immune response by acquiring an effector phenotype in response to cytokines. However, the cytokine responses in memory T cells remain largely understudied. Here we use quantitative proteomics, bulk RNA-seq, and single-cell RNA-seq of over 40,000 human naïve and memory CD4+ T cells to show that responses to cytokines differ substantially between these cell types. Memory T cells are unable to differentiate into the Th2 phenotype, and acquire a Th17-like phenotype in response to iTreg polarization. Single-cell analyses show that T cells constitute a transcriptional continuum that progresses from naïve to central and effector memory T cells, forming an effectorness gradient accompanied by an increase in the expression of chemokines and cytokines. Finally, we show that T cell activation and cytokine responses are influenced by the effectorness gradient. Our results illustrate the heterogeneity of T cell responses, furthering our understanding of inflammation.

Chromatin activity at GWAS loci identifies T cell states driving complex immune diseases.

Immune-disease-associated variants are enriched in active chromatin regions of T cells and macrophages. However, whether these variants function in specific cell states is unknown. Here we stimulated T cells and macrophages in the presence of 13 cytokines and profiled active and open chromatin regions. T cell activation induced major chromatin remodeling, while the presence of cytokines fine-tuned the magnitude of changes. We developed a statistical method that accounts for subtle changes in the chromatin landscape to identify SNP enrichment across cell states. Our results point towards the role of immune-disease-associated variants in early rather than late activation of memory CD4+ T cells, with modest differences across cytokines. Furthermore, variants associated with inflammatory bowel disease are enriched in type 1 T helper (TH1) cells, whereas variants associated with Alzheimer's disease are enriched in different macrophage cell states. Our results represent an in-depth analysis of immune-disease-associated variants across a comprehensive panel of activation states of T cells and macrophages.

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

Gene expression changes caused by the p38 MAPK inhibitor dilmapimod in COPD patients: analysis of blood and sputum samples from a randomized, placebo-controlled clinical trial.

The p38 mitogen-activated protein kinase (MAPK) intracellular signaling pathway responds to a variety of extracellular stimuli, including cytokines, Toll-like receptor agonists, and components of cigarette smoke to influence the expression of proinflammatory mediators. Activation of p38 MAPK is increased within the lungs of chronic obstructive pulmonary disease (COPD) patients. In clinical trials, treatment of COPD patients with p38 MAPK inhibitors has been shown to reduce systemic inflammation plasma biomarkers C-reactive protein (CRP) and fibrinogen. As CRP and fibrinogen have been associated with poor clinical outcomes in COPD patients, such as mortality, exacerbation, and hospitalization, we analyzed gene expression data from COPD subjects treated with dilmapimod with the aim of understanding the effects of p38 MAPK inhibition on the inflammatory genome of immune cells within the systemic circulation. Whole blood and induced sputum samples were used to measure mRNA levels by gene array and PCR. Pathway and network analysis showed STAT1, MMP-9, CAV1, and IL-1ß as genes regulated by dilmapimod that could also influence fibrinogen levels, while only IL-1ß was identified as a gene regulated by dilmapimod that could influence CRP levels. This suggests that p38 MAPK inhibits specific inflammatory pathways, leading to to differential effects on CRP and fibrinogen levels in COPD patients.

Selective expression of regulators of G-protein signaling (RGS) in the human central nervous system.

The human tissue distribution of the nineteen known human regulators of G-protein signaling (RGS) is described. Measurement of RGS mRNA levels in human brain and in nine peripheral tissues revealed striking tissue preferences in gene expression. Five RGS members were identified with enriched expression in brain. RGS4, RGS7, RGS8, RGS11 and RGS17 were all significantly expressed in striatal regions including the nucleus accumbens and putamen. RGS4 had the highest measured levels of mRNA expression and was highly enriched in the gyrus of the cortex and in the parahippocampus. RGS7 and RGS17 had overlapping distribution profiles and were both noticeably enriched in the cerebellum. Several RGS family members showed high expression in peripheral tissues. RGS5 was preferentially expressed in heart, and RGS1, RGS13, RGS18 and GAIP were predominately expressed in lymphocytes. RGS1 was also highly enriched in the lung, as was RGS2 and RGS16. Five family members, RGS3, RGS9, RGS10, RGS 12 and RGS14 had a broad and overlapping mRNA distribution. These results suggest roles of the individual RGS members in a diversity of functions in humans and support a role of several RGS members in the regulation of central nervous system function via modulation of signaling by G-protein coupled receptors.

Cloning and pharmacological characterisation of the guinea pig 5-ht5A receptor.

The guinea pig 5-hydroxytryptamine(5A) (gp5-ht(5A)) receptor was cloned from guinea pig brain using degenerate polymerase chain reaction (PCR) and shows 88%, 85% and 84% amino acid sequence identity versus the human, rat and mouse 5-ht(5A) receptors, respectively. The receptor was transiently expressed in human embryonic kidney (HEK) 293 cells. [(3)H]-Lysergic acid diethylamide (LSD) bound saturably to gp5-ht(5A)/HEK293 membranes with a K(d) of 2.3+/-0.1 nM and B(max) of 15.7+/-3.4 pmol/mg protein. The receptor binding profile, determined by competition with [(3)H]LSD, correlated well with that for the human 5-ht(5A) receptor. 5-HT stimulated [(35)S]GTPgammaS binding to gp5-ht(5A)/HEK293 membranes (pEC(50) 8.1+/-0.2), and the response was surmountably antagonised by methiothepin and ritanserin, giving apparent pK(B) values of 8.0 and 7.2, respectively. The 5-HT response was absent using membranes prepared from gp5-ht(5A)/HEK293 cells pretreated with pertussis toxin (PTX). These data suggest that the gp5-ht(5A) receptor couples to G(i)-proteins in this expression system and shows a similar pharmacological profile to that for the human 5-ht(5A) receptor.

Empowering industrial research with shared biomedical vocabularies.

The life science industries (including pharmaceuticals, agrochemicals and consumer goods) are exploring new business models for research and development that focus on external partnerships. In parallel, there is a desire to make better use of data obtained from sources such as human clinical samples to inform and support early research programmes. Success in both areas depends upon the successful integration of heterogeneous data from multiple providers and scientific domains, something that is already a major challenge within the industry. This issue is exacerbated by the absence of agreed standards that unambiguously identify the entities, processes and observations within experimental results. In this article we highlight the risks to future productivity that are associated with incomplete biological and chemical vocabularies and suggest a new model to address this long-standing issue.

Minimum information about a bioactive entity (MIABE).

Bioactive molecules such as drugs, pesticides and food additives are produced in large numbers by many commercial and academic groups around the world. Enormous quantities of data are generated on the biological properties and quality of these molecules. Access to such data - both on licensed and commercially available compounds, and also on those that fail during development - is crucial for understanding how improved molecules could be developed. For example, computational analysis of aggregated data on molecules that are investigated in drug discovery programmes has led to a greater understanding of the properties of successful drugs. However, the information required to perform these analyses is rarely published, and when it is made available it is often missing crucial data or is in a format that is inappropriate for efficient data-mining. Here, we propose a solution: the definition of reporting guidelines for bioactive entities - the Minimum Information About a Bioactive Entity (MIABE) - which has been developed by representatives of pharmaceutical companies, data resource providers and academic groups.