Humans are not programmed to be inactive. The combination of both accelerated sedentary lifestyle and constant food availability disturbs ancient metabolic processes leading to excessive storage of energy in tissue, dyslipidaemia and insulin resistance. As a consequence, the prevalence of Type 2 diabetes, obesity and the metabolic syndrome has increased significantly over the last 30 years. A low level of physical activity and decreased daily energy expenditure contribute to the increased risk of cardiovascular morbidity and mortality following atherosclerotic vascular damage. Physical inactivity leads to the accumulation of visceral fat and consequently the activation of the oxidative stress/inflammation cascade, which promotes the development of atherosclerosis. Considering physical activity as a 'natural' programmed state, it is assumed that it possesses atheroprotective properties. Exercise prevents plaque development and induces the regression of coronary stenosis. Furthermore, experimental studies have revealed that exercise prevents the conversion of plaques into a vulnerable phenotype, thus preventing the appearance of fatal lesions. Exercise promotes atheroprotection possibly by reducing or preventing oxidative stress and inflammation through at least two distinct pathways. Exercise, through laminar shear stress activation, down-regulates endothelial AT1R (angiotensin II type 1 receptor) expression, leading to decreases in NADPH oxidase activity and superoxide anion production, which in turn decreases ROS (reactive oxygen species) generation, and preserves endothelial NO bioavailability and its protective anti-atherogenic effects. Contracting skeletal muscle now emerges as a new organ that releases anti-inflammatory cytokines, such as IL-6 (interleukin-6). IL-6 inhibits TNF-α (tumour necrosis factor-α) production in adipose tissue and macrophages. The down-regulation of TNF-α induced by skeletal-muscle-derived IL-6 may also participate in mediating the atheroprotective effect of physical activity.

Cardiovascular BDNF is mainly localized within endothelial cells in which its expression is dependent on endothelial function. These results open new perspectives on the role of endothelial BDNF in cardiovascular health.

No abstract

Analysis of key urinary oxidative stress markers and proinflammatory cytokines showed an absence of oxidative stress and inflammation in the animals exposed to E-vapor aerosols. Conversely, animals exposed to conventional cigarette smoke had high urinary levels of these markers. When compared with conventional cigarette smoke, E-vapor aerosols induced smaller atherosclerotic plaque surface area and volume. Systolic and diastolic cardiac function, as well as endothelial function, were further significantly less affected by electronic cigarette aerosols than conventional cigarette smoke. Molecular analysis demonstrated that E-vapor aerosols induce significantly smaller transcriptomic dysregulation in the heart and aorta compared with conventional cigarette smoke.

Success in extracting biological relationships is mainly dependent on the complexity of the task as well as the availability of high-quality training data. Here, we describe the new corpora in the systems biology modeling language BEL for training and testing biological relationship extraction systems that we prepared for the BioCreative V BEL track. BEL was designed to capture relationships not only between proteins or chemicals, but also complex events such as biological processes or disease states. A BEL nanopub is the smallest unit of information and represents a biological relationship with its provenance. In BEL relationships (called BEL statements), the entities are normalized to defined namespaces mainly derived from public repositories, such as sequence databases, MeSH or publicly available ontologies. In the BEL nanopubs, the BEL statements are associated with citation information and supportive evidence such as a text excerpt. To enable the training of extraction tools, we prepared BEL resources and made them available to the community. We selected a subset of these resources focusing on a reduced set of namespaces, namely, human and mouse genes, ChEBI chemicals, MeSH diseases and GO biological processes, as well as relationship types ‘increases’ and ‘decreases’. The published training corpus contains 11 000 BEL statements from over 6000 supportive text excerpts. For method evaluation, we selected and re-annotated two smaller subcorpora containing 100 text excerpts. For this re-annotation, the inter-annotator agreement was measured by the BEL track evaluation environment and resulted in a maximal F-score of 91.18% for full statement agreement. In addition, for a set of 100 BEL statements, we do not only provide the gold standard expert annotations, but also text excerpts pre-selected by two automated systems. Those text excerpts were evaluated and manually annotated as true or false supportive in the course of the BioCreative V BEL track task.Database URL: http://wiki.openbel.org/display/BIOC/Datasets

Capture and representation of scientific knowledge in a structured format are essential to improve the understanding of biological mechanisms involved in complex diseases. Biological knowledge and knowledge about standardized terminologies are difficult to capture from literature in a usable form. A semi-automated knowledge extraction workflow is presented that was developed to allow users to extract causal and correlative relationships from scientific literature and to transcribe them into the computable and human readable Biological Expression Language (BEL). The workflow combines state-of-the-art linguistic tools for recognition of various entities and extraction of knowledge from literature sources. Unlike most other approaches, the workflow outputs the results to a curation interface for manual curation and converts them into BEL documents that can be compiled to form biological networks. We developed a new semi-automated knowledge extraction workflow that was designed to capture and organize scientific knowledge and reduce the required curation skills and effort for this task. The workflow was used to build a network that represents the cellular and molecular mechanisms implicated in atherosclerotic plaque destabilization in an apolipoprotein-E-deficient (ApoE −/− ) mouse model. The network was generated using knowledge extracted from the primary literature. The resultant atherosclerotic plaque destabilization network contains 304 nodes and 743 edges supported by 33 PubMed referenced articles. A comparison between the semi-automated and conventional curation processes showed similar results, but significantly reduced curation effort for the semi-automated process. Creating structured knowledge from unstructured text is an important step for the mechanistic interpretation and reusability of knowledge. Our new semi-automated knowledge extraction workflow reduced the curation skills and effort required to capture and organize scientific knowledge. The atherosclerotic plaque destabilization network that was generated is a causal network model for vascular disease demonstrating the usefulness of the workflow for knowledge extraction and construction of mechanistically meaningful biological networks.

Cigarette smoking is the major cause of chronic obstructive pulmonary disease. Considerable attention has been paid to the reduced harm potential of nicotine-containing inhalable products such as electronic cigarettes (e-cigarettes). We investigated the effects of mainstream cigarette smoke (CS) and e-vapor aerosols (containing nicotine and flavor) generated by a capillary aerosol generator on emphysematous changes, lung function, and molecular alterations in the respiratory system of female Apoe−/− mice. Mice were exposed daily (3 h/day, 5 days/week) for 6 months to aerosols from three different e-vapor formulations—(1) carrier (propylene glycol and vegetable glycerol), (2) base (carrier and nicotine), or (3) test (base and flavor)—or to CS from 3R4F reference cigarettes. The CS and base/test aerosol concentrations were matched at 35 µg nicotine/L. CS exposure, but not e-vapor exposure, led to impairment of lung function (pressure–volume loop area, A and K parameters, quasi-static elastance and compliance) and caused marked lung inflammation and emphysematous changes, which were confirmed histopathologically and morphometrically. CS exposure caused lung transcriptome (activation of oxidative stress and inflammatory responses), lipidome, and proteome dysregulation and changes in DNA methylation; in contrast, these effects were substantially reduced in response to the e-vapor aerosol exposure. Compared with sham, aerosol exposure (carrier, base, and test) caused a slight impact on lung inflammation and epithelia irritation. Our results demonstrated that, in comparison with CS, e-vapor aerosols induced substantially lower biological and pathological changes in the respiratory tract associated with chronic inflammation and emphysema.

Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language (BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users—learning BEL, working with a completely new interface, and performing complex curation—a score so close to the overall SUS average highlights the usability of BELIEF.Database URL: BELIEF is available at http://www.scaiview.com/belief/