Direct Evidence on the Contribution of a Missense Mutation in GDF9 to Variation in Ovulation Rate of Finnsheep

peer-reviewedThe Finnish Landrace (Finnsheep) is a well known high-prolificacy sheep breed and has been used in many countries as a source of genetic material to increase fecundity of local breeds. Analyses to date have indicated that mutations with a large effect on ovulation rate are not responsible for the exceptional prolificacy of Finnsheep. The objectives of this study were to ascertain if: 1) any of 12 known mutations with large effects on ovulation rate in sheep, or 2) any other DNA sequence variants within the candidate genes GDF9 and BMP15 are implicated in the high prolificacy of the Finnish Landrace breed; using material from lines developed by divergent selection on ovulation rate. Genotyping results showed that none of 12 known mutations (FecBB, FecXB, FecXG, FecXGR, FecXH, FecXI, FecXL, FecXO, FecXR, FecGE, FecGH, or FecGT) were present in a sample of 108 Finnsheep and, thus, do not contribute to the exceptional prolificacy of the breed. However, DNA sequence analysis of GDF9 identified a previously known mutation, V371M, whose frequency differed significantly (P<0.001) between High and Low ovulation rate lines. While analysis of ovulation rate data for Finnsheep failed to establish a significant association between this trait and V371M, analysis of data on Belclare sheep revealed a significant association between V371M and ovulation rate (P<0.01). Ewes that were heterozygous for V371M exhibited increased ovulation rate (+0.17, s.e. 0.080; P<0.05) compared to wild type and the effect was non-additive (ovulation rate of heterozygotes was significantly lower (P<0.01) than the mean of the homozygotes). This finding brings to 13 the number of mutations that have large effects on ovulation rate in sheep and to 5, including FecBB, FecGE, FecXO and FecXGR, the number of mutations within the TGFβ superfamily with a positive effect on prolificacy in the homozygous state

Systems approaches to drug repositioning

PhD ThesisDrug discovery has overall become less fruitful and more costly, despite vastly increased biomedical knowledge and evolving approaches to Research and Development (R&D). One complementary approach to drug discovery is that of drug repositioning which focusses on identifying novel uses for existing drugs. By focussing on existing drugs that have already reached the market, drug repositioning has the potential to both reduce the timeframe and cost of getting a disease treatment to those that need it. Many marketed examples of repositioned drugs have been found via serendipitous or rational observations, highlighting the need for more systematic methodologies. Systems approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but require an integrative approach to biological data. Integrated networks can facilitate systems-level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person can identify portions of the graph that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated procedures are required to mine integrated networks systematically for these subgraphs and bring them to the attention of the user. The aim of this project was the development of novel computational methods to identify new therapeutic uses for existing drugs (with particular focus on active small molecules) using data integration. A framework for integrating disparate data relevant to drug repositioning, Drug Repositioning Network Integration Framework (DReNInF) was developed as part of this work. This framework includes a high-level ontology, Drug Repositioning Network Integration Ontology (DReNInO), to aid integration and subsequent mining; a suite of parsers; and a generic semantic graph integration platform. This framework enables the production of integrated networks maintaining strict semantics that are important in, but not exclusive to, drug repositioning. The DReNInF is then used to create Drug Repositioning Network Integration (DReNIn), a semantically-rich Resource Description Framework (RDF) dataset. A Web-based front end was developed, which includes a SPARQL Protocol and RDF Query Language (SPARQL) endpoint for querying this dataset. To automate the mining of drug repositioning datasets, a formal framework for the definition of semantic subgraphs was established and a method for Drug Repositioning Semantic Mining (DReSMin) was developed. DReSMin is an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. The ability of DReSMin to identify novel Drug-Target (D-T) associations was investigated. 9,643,061 putative D-T interactions were identified and ranked, with a strong correlation between highly scored associations and those supported by literature observed. The 20 top ranked associations were analysed in more detail with 14 found to be novel and six found to be supported by the literature. It was also shown that this approach better prioritises known D-T interactions, than other state-of-the-art methodologies. The ability of DReSMin to identify novel Drug-Disease (Dr-D) indications was also investigated. As target-based approaches are utilised heavily in the field of drug discovery, it is necessary to have a systematic method to rank Gene-Disease (G-D) associations. Although methods already exist to collect, integrate and score these associations, these scores are often not a reliable re flection of expert knowledge. Therefore, an integrated data-driven approach to drug repositioning was developed using a Bayesian statistics approach and applied to rank 309,885 G-D associations using existing knowledge. Ranked associations were then integrated with other biological data to produce a semantically-rich drug discovery network. Using this network it was shown that diseases of the central nervous system (CNS) provide an area of interest. The network was then systematically mined for semantic subgraphs that capture novel Dr-D relations. 275,934 Dr-D associations were identified and ranked, with those more likely to be side-effects filtered. Work presented here includes novel tools and algorithms to enable research within the field of drug repositioning. DReNIn, for example, includes data that previous comparable datasets relevant to drug repositioning have neglected, such as clinical trial data and drug indications. Furthermore, the dataset may be easily extended using DReNInF to include future data as and when it becomes available, such as G-D association directionality (i.e. is the mutation a loss-of-function or gain-of-function). Unlike other algorithms and approaches developed for drug repositioning, DReSMin can be used to infer any types of associations captured in the target semantic network. Moreover, the approaches presented here should be more generically applicable to other fields that require algorithms for the integration and mining of semantically rich networks.European and Physical Sciences Research Council (EPSRC) and GS

A note on the use of FTA™ technology for storage of blood samples for DNA analysis and removal of PCR inhibitors

peer-reviewedFTA™ technology is widely used across many molecular disciplines for sample capture, storage and analysis. The use of this technology for the long-term storage of blood samples for DNA analysis was examined as well as its potential to remove inhibitors from DNA samples previously extracted from blood with PCR inhibitors remaining. It was found that blood spots stored on FTA™ cards for 8 years at room temperature gave successful PCR products and that FTA™ cards are a useful tool for removing substances in samples which interfere with or inhibit, the PCR reaction

Environmental Mechanism Designs in a New Order of Regulatory Capitalism

Complexity of environmental programs is most apparent with information asymmetries, making the design of efficient mechanisms particularly challenging. As developed theoretically in this paper, a new regulatory capitalism paradigm mating voluntary agreements with environmental education can produce outcomes at least as efficient as voluntary agreements alone. Such a design exploits a key difference between voluntary agreements versus educational programs in terms of their impact on agents' incentive compatibilities. Specifically, in a principal-agent model, voluntary agreements are associated with an incentive-compatibility constraint, whereas educational programs are not. The efficient bundle will likely consist of a set of education programs and voluntary agreements. With the new order of regulatory capitalism, it is time to concentrate on removing barriers yielding inefficient mono-mechanism design and start constructing multidimensional incentives to efficiently allocate effort toward environmental and economic goals.Command and control, environmental education, environmental policy, voluntary agreements, Environmental Economics and Policy,

Investigation of Prolific Sheep from UK and Ireland for Evidence on Origin of the Mutations in BMP15 (FecXG, FecXB) and GDF9 (FecGH) in Belclare and Cambridge Sheep

peer-reviewedThis paper concerns the likely origin of three mutations with large effects on ovulation rate identified in the Belclare and Cambridge sheep breeds; two in the BMP15 gene (FecXG and FecXB) and the third (FecGH) in GDF9. All three mutations segregate in Belclare sheep while one, FecXB, has not been found in the Cambridge. Both Belclare and Cambridge breeds are relatively recently developed composites that have common ancestry through the use of genetic material from the Finnish Landrace and Lleyn breeds. The development of both composites also involved major contributions from exceptionally prolific ewes screened from flocks in Ireland (Belclare) and Britain (Cambridge) during the 1960s. The objective of the current study was to establish the likely origin of the mutations (FecXG, FecXB and FecGH) through analysis of DNA from Finnish Landrace and Lleyn sheep, and Galway and Texel breeds which contributed to the development of the Belclare breed. Ewes with exceptionally high prolificacy (hyper-prolific ewes) in current flocks on Irish farms were identified to simulate the screening of ewes from Irish flocks in the 1960s. DNA was obtained from: prolific ewes in extant flocks of Lleyn sheep (n = 44) on the Lleyn peninsula in Wales; hyper-prolific ewes (n = 41); prolific Galway (n = 41) ewes; Finnish Landrace (n = 124) and Texel (n = 19) ewes. The FecXG mutation was identified in Lleyn but not in Finnish Landrace, Galway or Texel sheep; FecXB was only found among the hyper-prolific ewes. The FecGH mutation was identified in the sample of Lleyn sheep. It was concluded from these findings that the Lleyn breed was the most likely source of the FecXG and FecGH mutations in Belclare and Cambridge sheep and that the FecXB mutation came from the High Fertility line that was developed using prolific ewes selected from commercial flocks in Ireland in the 1960′s and subsequently used in the genesis of the Belclare.Financial support through the Teagasc Walsh Fellowship Scheme, Genesis Faraday SPARK award (Lleyn survey) and Science Foundation Ireland (07/SRC/B1156) is gratefully acknowledged