Towards systems pharmacology models of druggable targets and disease mechanisms

The development of essential medicines is being slowed by a lack of efficiency in drug development as ninety per cent of drugs fail at some stage during clinical evaluation. This attrition in drug development is seen not because of a reduction in pharmaceutical research expenditure nor is it caused by a declining understanding of biology, if anything, these are both increasing. Instead, drugs are failing because we are unable to effectively predict how they will work before they are given to patients. This is due to limitations of the current methods used to evaluate a drug’s toxicity and efficacy prior to its development. Quite simply, these methods do not account for the full complexity of biology in humans. Systems pharmacology models are a likely candidate for increasing the efficiency of drug discovery as they seek to comprehensively model the fundamental biology of disease mechanisms in a quantit- ative manner. They are computational models, designed and hailed as a strategy for making well-informed and cost effective decisions on drug viability and target druggability and therefore attempt to reduce this time-consuming and costly attrition. Using text mining and text classification I present a growing landscape of systems pharmacology models in literature growing from humble roots because of step-wise increases in our understanding of biology. Furthermore, I develop a case for the capability of systems pharmacology models in making predictions by constructing a model of interleukin-6 signalling for rheumatoid arthritis. This model shows that druggable target selection is not necessarily an intuitive task as it results in an emergent but unanswered hypothesis for safety concerns in a monoclonal antibody. Finally, I show that predictive classification models can also be used to explore gene expression data in a novel work flow by attempting to predict patient response classes to an influenza vaccine.Funded by the BBSRC and GlaxoSmithKline as part of an industrial CASE studentship

BioModels: ten-year anniversary

BioModels (http://www.ebi.ac.uk/biomodels/) is a repository of mathematical models of biological processes. A large set of models is curated to verify both correspondence to the biological process that the model seeks to represent, and reproducibility of the simulation results as described in the corresponding peer-reviewed publication. Many models submitted to the database are annotated, cross-referencing its components to external resources such as database records, and terms from controlled vocabularies and ontologies. BioModels comprises two main branches: one is composed of models derived from literature, while the second is generated through automated processes. BioModels currently hosts over 1200 models derived directly from the literature, as well as in excess of 140 000 models automatically generated from pathway resources. This represents an approximate 60-fold growth for literature-based model numbers alone, since BioModels’ first release a decade ago. This article describes updates to the resource over this period, which include changes to the user interface, the annotation profiles of models in the curation pipeline, major infrastructure changes, ability to perform online simulations and the availability of model content in Linked Data form. We also outline planned improvements to cope with a diverse array of new challenges

BioModels—15 years of sharing computational models in life science

Computational modelling has become increasingly common in life science research. To provide a platform to support universal sharing, easy accessibility and model reproducibility, BioModels (https://www.ebi.ac.uk/biomodels/), a repository for mathematical models, was established in 2005. The current BioModels platform allows submission of models encoded in diverse modelling formats, including SBML, CellML, PharmML, COMBINE archive, MATLAB, Mathematica, R, Python or C++. The models submitted to BioModels are curated to verify the computational representation of the biological process and the reproducibility of the simulation results in the reference publication. The curation also involves encoding models in standard formats and annotation with controlled vocabularies following MIRIAM (minimal information required in the annotation of biochemical models) guidelines. BioModels now accepts large-scale submission of auto-generated computational models. With gradual growth in content over 15 years, BioModels currently hosts about 2000 models from the published literature. With about 800 curated models, BioModels has become the world’s largest repository of curated models and emerged as the third most used data resource after PubMed and Google Scholar among the scientists who use modelling in their research. Thus, BioModels benefits modellers by providing access to reliable and semantically enriched curated models in standard formats that are easy to share, reproduce and reuse

The Science of Sungrazers, Sunskirters, and Other Near-Sun Comets

This review addresses our current understanding of comets that venture close to the Sun, and are hence exposed to much more extreme conditions than comets that are typically studied from Earth. The extreme solar heating and plasma environments that these objects encounter change many aspects of their behaviour, thus yielding valuable information on both the comets themselves that complements other data we have on primitive solar system bodies, as well as on the near-solar environment which they traverse. We propose clear definitions for these comets: We use the term near-Sun comets to encompass all objects that pass sunward of the perihelion distance of planet Mercury (0.307 AU). Sunskirters are defined as objects that pass within 33 solar radii of the Sun’s centre, equal to half of Mercury’s perihelion distance, and the commonly-used phrase sungrazers to be objects that reach perihelion within 3.45 solar radii, i.e. the fluid Roche limit. Finally, comets with orbits that intersect the solar photosphere are termed sundivers. We summarize past studies of these objects, as well as the instruments and facilities used to study them, including space-based platforms that have led to a recent revolution in the quantity and quality of relevant observations. Relevant comet populations are described, including the Kreutz, Marsden, Kracht, and Meyer groups, near-Sun asteroids, and a brief discussion of their origins. The importance of light curves and the clues they provide on cometary composition are emphasized, together with what information has been gleaned about nucleus parameters, including the sizes and masses of objects and their families, and their tensile strengths. The physical processes occurring at these objects are considered in some detail, including the disruption of nuclei, sublimation, and ionisation, and we consider the mass, momentum, and energy loss of comets in the corona and those that venture to lower altitudes. The different components of comae and tails are described, including dust, neutral and ionised gases, their chemical reactions, and their contributions to the near-Sun environment. Comet-solar wind interactions are discussed, including the use of comets as probes of solar wind and coronal conditions in their vicinities. We address the relevance of work on comets near the Sun to similar objects orbiting other stars, and conclude with a discussion of future directions for the field and the planned ground- and space-based facilities that will allow us to address those science topics

Toward community standards and software for whole-cell modeling

Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate, comprehensive models of complex cells. Methods: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in SBML. Results: Our analysis revealed several challenges to representing WC models using the current standards. Conclusion: We, therefore, propose several new WC modeling standards, software, and databases. Significance:We anticipate that these new standards and software will enable more comprehensive models

RBPMS promotes contractile phenotype splicing in human embryonic stem cell derived vascular smooth muscle cells.

Abstract: Aims: Differentiated Vascular Smooth Muscle Cells (VSMCs) express a unique network of mRNA isoforms via smooth muscle specific alternative splicing (SM-AS) in functionally critical genes, including those comprising the contractile machinery. We previously described RNA Binding Protein Multiple Splicing (RBPMS) as a potent driver of differentiated SM-AS in the rat PAC1 VSMC cell line. What is unknown is how RBPMS affects VSMC phenotype and behaviour. Here, we aimed to dissect the role of RBPMS in SM-AS in human cells and determine the impact on VSMC phenotypic properties. Methods and Results: We used human embryonic stem cell-derived VSMCs (hESCVSMCs) as our platform. hESC-VSMCs are inherently immature and we found that they display only partially differentiated SM-AS patterns while RBPMS protein levels are low. We found that RBPMS overexpression induces SM-AS patterns in hESC-VSMCs akin to the contractile tissue VSMC splicing patterns. We present in silico and experimental findings that support RBPMS’ splicing activity as mediated through direct binding and via functional cooperativity with splicing factor RBFOX2 on a significant subset of targets. We also demonstrate that RBPMS can alter the motility and the proliferative properties of hESCVSMCs to mimic a more differentiated state. Conclusions: Overall, this study emphasizes a critical role for RBPMS in establishing the contractile phenotype splicing program of human VSMCs. Translational Perspective: Since vascular smooth muscle cells (VSMC) phenotype switching is a key factor in the pathology of diseases such as atherosclerosis, aneurysmal syndromes, hypertension and even restenosis, it becomes critical to understand how these phenotypic states are achieved and maintained. Currently these states are primarily defined by the transcriptional profiles of these cells. This study shows that the RBPMS-driven mRNA splicing program is an important post-transcriptional driver of mature VSMC phenotype

Proinflammatory cytokines driving cardiotoxicity in Covid-19.

AIMS: Cardiac involvement is common in patients hospitalised with COVID-19 and correlates with an adverse disease trajectory. While cardiac injury has been largely attributed to direct viral cytotoxicity, serum-induced cardiotoxicity secondary to serological hyperinflammation constitutes a potentially amenable mechanism that remains largely unexplored. METHODS AND RESULTS: To investigate serological drivers of cardiotoxicity in COVID-19 we have established a robust bioassay that assessed the effects of serum from COVID-19 confirmed patients on human embryonic stem cell (hESC)-derived cardiomyocytes. We demonstrate that serum from COVID-19 positive patients significantly reduced cardiomyocyte viability independent of viral transduction, an effect that was also seen in acute respiratory distress syndrome (ARDS). Serum from patients with greater disease severity led to worse cardiomyocyte viability and this significantly correlated with levels of key inflammatory cytokines, including IL-6, TNF-α, IL1-β, IL-10, CRP and neutrophil to lymphocyte ratio with a specific reduction of CD4+ and CD8+ cells. Combinatorial blockade of IL-6 and TNF-α partly rescued the phenotype and preserved cardiomyocyte viability and function. Bulk RNA sequencing of serum-treated cardiomyocytes elucidated specific pathways involved in the COVID-19 response impacting cardiomyocyte viability, structure and function. The observed effects of serum-induced cytotoxicity were cell-type selective as serum exposure did not adversely affect microvascular endothelial cell viability but resulted in endothelial activation and a procoagulant state. CONCLUSION: These results provide direct evidence that inflammatory cytokines are at least in part responsible for the cardiovascular damage seen in COVID-19 and characterise the downstream activated pathways in human cardiomyocytes. The serum signature of patients with severe disease indicates possible targets for therapeutic intervention

Epicardially secreted fibronectin drives cardiomyocyte maturation in 3D-engineered heart tissues.

Ischemic heart failure is due to irreversible loss of cardiomyocytes. Preclinical studies showed that human pluripotent stem cell (hPSC)-derived cardiomyocytes could remuscularize infarcted hearts and improve cardiac function. However, these cardiomyocytes remained immature. Incorporating hPSC-derived epicardial cells has been shown to improve cardiomyocyte maturation, but the exact mechanisms are unknown. We posited epicardial fibronectin (FN1) as a mediator of epicardial-cardiomyocyte crosstalk and assessed its role in driving hPSC-derived cardiomyocyte maturation in 3D-engineered heart tissues (3D-EHTs). We found that the loss of FN1 with peptide inhibition F(pUR4), CRISPR-Cas9-mediated FN1 knockout, or tetracycline-inducible FN1 knockdown in 3D-EHTs resulted in immature cardiomyocytes with decreased contractile function, and inefficient Ca2+ handling. Conversely, when we supplemented 3D-EHTs with recombinant human FN1, we could recover hPSC-derived cardiomyocyte maturation. Finally, our RNA-sequencing analyses found FN1 within a wider paracrine network of epicardial-cardiomyocyte crosstalk, thus solidifying FN1 as a key driver of hPSC-derived cardiomyocyte maturation in 3D-EHTs