Lee Harland

Transgenes encompassing dual-promoter CpG islands from the human TBP and HNRPA2B1 loci are resistant to heterochromatin-mediated silencing.

The genetic elements that are responsible for establishing a transcriptionally competent, open chromatin structure at a region of the genome that consists only of ubiquitously expressed, housekeeping genes are currently unknown. We demonstrate for the first time through functional analysis in stably transfected tissue culture cells that transgenes containing methylation-free CpG islands spanning the dual divergently transcribed promoters from the human TATA binding protein (TBP)-proteasome component-B1 (PSMB1) and heterogeneous nuclear ribonucleoprotein A2/B1 (HNRPA2B1)-heterochromatin protein 1Hs-gamma (chromobox homolog 3, CBX3) gene loci are sufficient to prevent transcriptional silencing and a variegated expression pattern when integrated within centromeric heterochromatin. In addition, only transgene constructs extending over both the HNRPA2B1 and the CBX3 promoters, and not the HNRPA2B1 promoter alone, were able to confer high and stable long-term EGFP reporter gene expression. These observations suggest that methylation-free CpG islands associated with dual, divergently transcribed promoters possess an independent dominant chromatin opening function and may therefore be major determinants in establishing and maintaining a region of open chromatin at housekeeping gene loci.

Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery

Pharmaceutical research and development is facing substantial challenges that have prompted the industry to shift funding from early- to late-stage projects. Among the effects is a major change in the attitude of many companies to their internal bioinformatics resources: the focus has moved from the vigorous pursuit of intellectual property towards exploration of pre-competitive cross-industry collaborations and engagement with the public domain. High-quality, open and accessible data are the foundation of pre-competitive research, and strong public–private partnerships have considerable potential to enhance public data resources, which would benefit everyone engaged in drug discovery. In this article, we discuss the background to these changes and propose new areas of collaboration in computational biology and chemistry between the public domain and the pharmaceutical industry.

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside

BackgroundTranslational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery.ResultsWe developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records. We demonstrate the use of Semantic Web technologies in the integration of patient and biomedical data, and reveal how such a knowledge base can aid physicians in providing tailored patient care and facilitate the recruitment of patients into active clinical trials. Thus, patients, physicians and researchers may explore the knowledge base to better understand therapeutic options, efficacy, and mechanisms of action.ConclusionsThis work takes an important step in using Semantic Web technologies to facilitate integration of relevant, distributed, external sources and progress towards a computational platform to support personalized medicine.AvailabilityTMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql.

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.

Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research.

Systems chemical biology, the integration of chemistry, biology and computation to generate understanding about the way small molecules affect biological systems as a whole, as well as related fields such as chemogenomics, are central to emerging new paradigms of drug discovery such as drug repurposing and personalized medicine. Recent Semantic Web technologies such as RDF and SPARQL are technical enablers of systems chemical biology, facilitating the deployment of advanced algorithms for searching and mining large integrated datasets. In this paper, we aim to demonstrate how these technologies together can change the way that drug discovery is accomplished.

Applying linked data approaches to pharmacology: Architectural decisions and implementation

The discovery of new medicines requires pharmacologists to interact with a number of information sources ranging from tabular data to scientific papers, and other specialized formats. In this application report, we describe a linked data platform for integrating multiple pharmacology datasets that form the basis for several drug discovery applications. The functionality offered by the platform has been drawn from a collection of prioritised drug discovery business questions created as part of the Open PHACTS project, a collaboration of research institutions and major pharmaceutical companies. We describe the architecture of the platform focusing on seven design decisions that drove its development with the aim of informing others developing similar software in this or other domains. The utility of the platform is demonstrated by the variety of drug discovery applications being built to access the integrated data.An alpha version of the OPS platform is currently available to the Open PHACTS consortium and a first public release will be made in late 2012, see http://www.openphacts.org/ for details.

Scientific competency questions as the basis for semantically enriched open pharmacological space development

Molecular information systems play an important part in modern data-driven drug discovery. They do not only support decision making but also enable new discoveries via association and inference. In this review, we outline the scientific requirements identified by the Innovative Medicines Initiative (IMI) Open PHACTS consortium for the design of an open pharmacological space (OPS) information system. The focus of this work is the integration of compound-target-pathway-disease/phenotype data for public and industrial drug discovery research. Typical scientific competency questions provided by the consortium members will be analyzed based on the underlying data concepts and associations needed to answer the questions. Publicly available data sources used to target these questions as well as the need for and potential of semantic web-based technology will be presented.

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.

LCR-mediated, long-term tissue-specific gene expression within replicating episomal plasmid and cosmid vectors

Locus control regions (LCRs) are transcriptional regulatory elements, which possess a dominant chromatin remodelling and transcriptional activating capability conferring full physiological levels of expression on a gene linked in cis, when integrated into the host cell genome. Using the human β-globin LCR (βLCR) as a model, we show that this class of control element can drive high levels of tissue-specific gene expression in stably transfected cultured cells from within an Epstein–Barr virus-based plasmid REV. Furthermore, a 38-kb βLCR minilocus-REV cosmid vector was efficiently retained and maintained therapeutic levels of β-globin transgene expression in the absence of drug selective pressure over a 2-month period of continuous culture equivalent to at least 60 generations. This demonstrates for the first time the feasibility of using REVs for gene therapy of the haemoglobinopathies. Importantly, our results demonstrate that as in the case of integrated transgenes, expression from within REVs is prone to silencing but that the inclusion of the βLCR prevented this repression of gene function. Therefore, appropriate control elements to provide and maintain tissue-specific gene expression, as well as the episomal status of REVs is a crucial feature in vector design. Our data suggest that LCRs can contribute to this vital function.

Drug discovery FAQs: workflows for answering multidomain drug discovery questions

Modern data-driven drug discovery requires integrated resources to support decision-making and enable new discoveries. The Open PHACTS Discovery Platform (http://dev.openphacts.org) was built to address this requirement by focusing on drug discovery questions that are of high priority to the pharmaceutical industry. Although complex, most of these frequently asked questions (FAQs) revolve around the combination of data concerning compounds, targets, pathways and diseases. Computational drug discovery using workflow tools and the integrated resources of Open PHACTS can deliver answers to most of these questions. Here, we report on a selection of workflows used for solving these use cases and discuss some of the research challenges. The workflows are accessible online from myExperiment (http://www.myexperiment.org) and are available for reuse by the scientific community.