Longitudinal metabolomic analysis of plasma enables modeling disease progression in Duchenne muscular dystrophy mouse models

Duchenne muscular dystrophy is a severe pediatric neuromuscular disorder caused by the lack of dystrophin. Identification of biomarkers is needed to support and accelerate drug development. Alterations of metabolites levels in muscle and plasma have been reported in pre-clinical and clinical cross-sectional comparisons. We present here a 7-month longitudinal study comparing plasma metabolomic data in wild-type and mdx mice. A mass spectrometry approach was used to study metabolites in up to five time points per mouse at 6, 12, 18, 24 and 30 weeks of age, providing an unprecedented in depth view of disease trajectories. A total of 106 metabolites were studied. We report a signature of 31 metabolites able to discriminate between healthy and disease at various stages of the disease, covering the acute phase of muscle degeneration and regeneration up to the deteriorating phase. We show how metabolites related to energy production and chachexia (e.g. glutamine) are affected in mdx mice plasma over time. We further show how the signature is connected to molecular targets of nutraceuticals and pharmaceutical compounds currently in development as well as to the nitric oxide synthase pathway (e.g. arginine and citrulline). Finally, we evaluate the signature in a second longitudinal study in three independent mouse models carrying 0, 1 or 2 functional copies of the dystrophin paralog utrophin. In conclusion, we report an in-depth metabolomic signature covering previously identified associations and new associations, which enables drug developers to peripherally assess the effect of drugs on the metabolic status of dystrophic mice.Development and application of statistical models for medical scientific researc

Comparing regional organizations in global multilateral institutions:ASEAN, the EU and the UN

Structural change brought about by the end of the Cold War and accelerated globalisation have transformed the global environment. A global governance complex is emerging, characterised by an ever-greater functional and regulatory role for multilateral organisations such as the United Nations (UN) and its associated agencies. The evolving global governance framework has created opportunities for regional organisations to participate as actors within the UN (and other multilateral institutions). This article compares the European Union (EU) and Association of Southeast Asian Nations (ASEAN) as actors within the UN network. It begins by extrapolating framework conditions for the emergence of EU and ASEAN actorness from the literature. The core argument of this article is that EU and ASEAN actorness is evolving in two succinct stages: Changes in the global environment create opportunities for the participation of regional organisations in global governance institutions, exposing representation and cohesion problems at the regional level. In response, ASEAN and the EU have initiated processes of institutional adaptation

Regional actorness and interregional relations:ASEAN, the EU and Mercosur

The European Union (EU) has a long tradition of interregional dialogue mechanisms with other regional organisations and is using these relations to project its own model of institutionalised actorness. This is partly motivated by the emerging actorness of the EU itself, which benefits from fostering capable regional counterparts in other parts of the world. This article advances the argument that actorness, which we conceptualise in terms of institutions, recognition and identity, is a relational concept, dependent on context and perception. Taking the Association of Southeast Asian Nations (ASEAN) and the Common Market of the South (Mercosur) and their relations with the EU as case studies, this article demonstrates that the actorness capabilities of all three organisations have been enhanced as result of ASEAN-EU and Mercosur-EU relations. However, there are clear limits to the development of the three components of regional actorness and to the interregional relations themselves. These limits stem both from the type of interregionalism at play and from the different regional models the actors incorporate. While there is evidence of institutional enhancement in ASEAN and Mercosur, these formal changes have been grafted on top of firmly entrenched normative underpinnings. Within the regional organisations, interactions with the EU generate centrifugal forces concerning the model to pursue, thus limiting their institutional cohesion and capacity. In addition, group-to-group relations have reinforced ASEAN and Mercosur identities in contrast to the EU. The formation of such differences has narrowed the scope of EU interregionalism despite the initial success of improved regional actorness

Venezuela e ALBA: regionalismo contra-hegemônico e ensino superior para todos

Partindo de um quadro teórico neo-gramsciano crítico à globalização, este artigo aplica a nova teoria do regionalismo (NTR) e a teoria do regionalismo regulatório (TRR) à sua análise e teorização dos tratados de comércio da Aliança Bolivariana para os Povos da Nossa América (ALBA-TCP) como regionalismo contra-hegemônico na América Latina e Caribe (ALC). A ALBA está centrada na ideia de um Socialismo do Século XXI, que, como (inicialmente) também a Revolução Bolivariana da Venezuela, substitui a 'vantagem competitiva' pela 'vantagem cooperativa'. Em seu caráter de conjunto de processos multidimensionais e transnacionais a ALBA-TCP opera dentro de/transversalmente a um número de setores e escalas, ao mesmo passo que as transformações estruturais são movidas pela interação de agentes do Estado e agentes não estatais. A política de Educação Superior para Todos (ESPT) do governo venezuelano rejeita a agenda neoliberal globalizada de mercadorização, privatização e elitismo e reinvindica educação pública gratuita em todos os níveis como um direito humano fundamental. A ESPT está sendo regionalizado em um espaço educacional emergente da ALBA e assume um papel-chave nos processos de democracia direta e participatória, dos quais a construção popular (bottom-up) da contra-hegemonia e a redefinição política e econômica da ALC dependem. Antes de produzir sujeitos empreendedores conformes ao capitalismo global, a ESPT procura formar subjetividades ao longo de valores morais de solidariedade e cooperação. Isso será ilustrado com referência a um estudo etnográfico de caso da Universidade Bolivariana da Venezuela (UBV).This paper employs new regionalism theory and regulatory regionalism theory in its analysis and theorisation of the Bolivarian Alliance for the Peoples of Our America (ALBA) as a counter-hegemonic Latin American and Caribbean (LAC) regionalism. As (initially) the regionalisation of Venezuela's Bolivarian Revolution, ALBA is centred around the idea of a 21st Century Socialism that replaces the 'competitive advantage' with the 'cooperative advantage'. ALBA, as a set of multi-dimensional inter- and transnational processes, operates within and across a range of sectors and scales whilst the structural transformations are driven by the interplay of state and non-state actors. The Venezuelan government's Higher Education For All (HEFA) policy, which is being regionalised within an emergent ALBA education space, assumes a key role in the direct democratic and participatory democratic processes upon which a bottom-up construction of counter-hegemony depends. HEFA challenges the globalised neoliberal higher education agenda of commoditisation, privatisation and elitism. Rather than producing enterprising subjects fashioned for global capitalism, HEFA seeks to form subjectivities along the moral values of solidarity and cooperation

Using Workflows to Explore and Optimise Named Entity Recognition for Chemistry

Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR

Cross-sectional serum metabolomic study of multiple forms of muscular dystrophy

Development and application of statistical models for medical scientific researc

The new scalar politics of evaluation: An emerging governance role for evaluation

In this article we analyze how roles for evaluation are described and argued for in key texts produced and/or promoted by three influential international networks: the High Level Forum on Aid Effectiveness; the Organisation for Economic Cooperation and Development Assistance Committee’s Network on Development Evaluation; and the Network of Networks for Impact Evaluation. We contend that these complex multilateral networks are working supranationally through soft power to promote: common standards of evaluation practice; a dominant model of evaluation (impact evaluation); and new evaluation roles, relationships and practices for the field of development. Moreover, we argue that this emerging complex multilateral agenda for evaluation may position evaluation and evaluators within a global governance strategy allowing greater influence to international development organizations. We conclude with a discussion of the implications of the analysis for evaluators working in the field of international development

Peripheral blood transcriptome profiling enables monitoring disease progression in dystrophic mice and patients

DMD is a rare disorder characterized by progressive muscle degeneration and premature death. Therapy development is delayed by difficulties to monitor efficacy non-invasively in clinical trials. In this study, we used RNA-sequencing to describe the pathophysiological changes in skeletal muscle of 3 dystrophic mouse models. We show how dystrophic changes in muscle are reflected in blood by analyzing paired muscle and blood samples. Analysis of repeated blood measurements followed the dystrophic signature at five equally spaced time points over a period of seven months. Treatment with two antisense drugs harboring different levels of dystrophin recovery identified genes associated with safety and efficacy. Evaluation of the blood gene expression in a cohort of DMD patients enabled the comparison between preclinical models and patients, and the identification of genes associated with physical performance, treatment with corticosteroids and body measures. The presented results provide evidence that blood RNA-sequencing can serve as a tool to evaluate disease progression in dystrophic mice and patients, as well as to monitor response to (dystrophin-restoring) therapies in preclinical drug development and in clinical trials.Development and application of statistical models for medical scientific researc

Mining metabolites: extracting the yeast metabolome from the literature

Text mining methods have added considerably to our capacity to extract biological knowledge from the literature. Recently the field of systems biology has begun to model and simulate metabolic networks, requiring knowledge of the set of molecules involved. While genomics and proteomics technologies are able to supply the macromolecular parts list, the metabolites are less easily assembled. Most metabolites are known and reported through the scientific literature, rather than through large-scale experimental surveys. Thus it is important to recover them from the literature. Here we present a novel tool to automatically identify metabolite names in the literature, and associate structures where possible, to define the reported yeast metabolome. With ten-fold cross validation on a manually annotated corpus, our recognition tool generates an f-score of 78.49 (precision of 83.02) and demonstrates greater suitability in identifying metabolite names than other existing recognition tools for general chemical molecules. The metabolite recognition tool has been applied to the literature covering an important model organism, the yeast Saccharomyces cerevisiae, to define its reported metabolome. By coupling to ChemSpider, a major chemical database, we have identified structures for much of the reported metabolome and, where structure identification fails, been able to suggest extensions to ChemSpider. Our manually annotated gold-standard data on 296 abstracts are available as supplementary materials. Metabolite names and, where appropriate, structures are also available as supplementary materials

Automated workflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles

Background: Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) that associate with clinical phenotypes, but these SNPs usually explain just a small part of the heritability and have relatively modest effect sizes. In contrast, SNPs that associate with metabolite levels generally explain a higher percentage of the genetic variation and demonstrate larger effect sizes. Still, the discovery of SNPs associated with metabolite levels is challenging since testing all metabolites measured in typical metabolomics studies with all SNPs comes with a severe multiple testing penalty. We have developed an automated workflow approach that utilizes prior knowledge of biochemical pathways present in databases like KEGG and BioCyc to generate a smaller SNP set relevant to the metabolite. This paper explores the opportunities and challenges in the analysis of GWAS of metabolomic phenotypes and provides novel insights into the genetic basis of metabolic variation through the re-analysis of published GWAS datasets. Results: Re-analysis of the published GWAS dataset from Illig et al. (Nature Genetics, 2010) using a pathway-based workflow (http://www.myexperiment.org/packs/319.html), confirmed previously identified hits and identified a new locus of human metabolic individuality, associating Aldehyde dehydrogenase family1 L1 (ALDH1L1) with serine/glycine ratios in blood. Replication in an independent GWAS dataset of phospholipids (Demirkan et al., PLoS Genetics, 2012) identified two novel loci supported by additional literature evidence: GPAM (Glycerol-3 phosphate acyltransferase) and CBS (Cystathionine beta-synthase). In addition, the workflow approach provided novel insight into the affected pathways and relevance of some of these gene-metabolite pairs in disease development and progression. Conclusions: We demonstrate the utility of automated exploitation of background knowledge present in pathway databases for the analysis of GWAS datasets of metabolomic phenotypes. We report novel loci and potential biochemical mechanisms that contribute to our understanding of the genetic basis of metabolic variation and its relationship to disease development and progression