Scientists have long been driven by the desire to describe, organize, classify, and compare objects using taxonomies and/or ontologies. In contrast to biology, geology, and many other scientific disciplines, the world of chemistry still lacks a standardized chemical ontology or taxonomy. Several attempts at chemical classification have been made; but they have mostly been limited to either manual, or semi-automated proof-of-principle applications. This is regrettable as comprehensive chemical classification and description tools could not only improve our understanding of chemistry but also improve the linkage between chemistry and many other fields. For instance, the chemical classification of a compound could help predict its metabolic fate in humans, its druggability or potential hazards associated with it, among others. However, the sheer number (tens of millions of compounds) and complexity of chemical structures is such that any manual classification effort would prove to be near impossible. We have developed a comprehensive, flexible, and computable, purely structure-based chemical taxonomy (ChemOnt), along with a computer program (ClassyFire) that uses only chemical structures and structural features to automatically assign all known chemical compounds to a taxonomy consisting of >4800 different categories. This new chemical taxonomy consists of up to 11 different levels (Kingdom, SuperClass, Class, SubClass, etc.) with each of the categories defined by unambiguous, computable structural rules. Furthermore each category is named using a consensus-based nomenclature and described (in English) based on the characteristic common structural properties of the compounds it contains. The ClassyFire webserver is freely accessible at http://classyfire.wishartlab.com/ . Moreover, a Ruby API version is available at https://bitbucket.org/wishartlab/classyfire_api , which provides programmatic access to the ClassyFire server and database. ClassyFire has been used to annotate over 77 million compounds and has already been integrated into other software packages to automatically generate textual descriptions for, and/or infer biological properties of over 100,000 compounds. Additional examples and applications are provided in this paper. ClassyFire, in combination with ChemOnt (ClassyFire’s comprehensive chemical taxonomy), now allows chemists and cheminformaticians to perform large-scale, rapid and automated chemical classification. Moreover, a freely accessible API allows easy access to more than 77 million “ClassyFire” classified compounds. The results can be used to help annotate well studied, as well as lesser-known compounds. In addition, these chemical classifications can be used as input for data integration, and many other cheminformatics-related tasks.

The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Fund's Metabolomics Program, provides data from the Common Fund's Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world.

Gerhard Liebisch, Eoin Fahy, Junken Aoki, Edward A. Dennis, Thierry Durand, Christer S. Ejsing, Maria Fedorova, Ivo Feussner, William J. Griffiths, Harald Köfeler, Alfred H. Merrill Jr., Robert C. Murphy, Valerie B. O’Donnell, Olga Oskolkova, Shankar Subramaniam, Michael J. O. Wakelam, Friedrich Spener

Decades of research in skeletal muscle physiology have provided multiscale insights into the structural and functional complexity of this important anatomical tissue, designed to accomplish the task of generating contraction, force and movement. Skeletal muscle can be viewed as a biomechanical device with various interacting components including the autonomic nerves for impulse transmission, vasculature for efficient oxygenation, and embedded regulatory and metabolic machinery for maintaining cellular homeostasis. The “omics” revolution has propelled a new era in muscle research, allowing us to discern minute details of molecular cross‐talk required for effective coordination between the myriad interacting components for efficient muscle function. The objective of this review is to provide a systems‐level, comprehensive mapping the molecular mechanisms underlying skeletal muscle structure and function, in health and disease. We begin this review with a focus on molecular mechanisms underlying muscle tissue development (myogenesis), with an emphasis on satellite cells and muscle regeneration. We next review the molecular structure and mechanisms underlying the many structural components of the muscle: neuromuscular junction, sarcomere, cytoskeleton, extracellular matrix, and vasculature surrounding muscle. We highlight aberrant molecular mechanisms and their possible clinical or pathophysiological relevance. We particularly emphasize the impact of environmental stressors (inflammation and oxidative stress) in contributing to muscle pathophysiology including atrophy, hypertrophy, and fibrosis.This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Developmental Biology > Developmental Processes in Health and Disease Models of Systems Properties and Processes > Cellular Models

Retinal hyperpermeability and subsequent macular edema is a cardinal feature of early diabetic retinopathy (DR). Here, we investigated the role of bioactive lipid metabolites, in particular 12/15-lipoxygenase (12/15-LOX) derived metabolites, in this process. Liquid chromatography coupled to mass spectrometry (LC/MS) lipidomic screen of human retinal endothelial cells (HRECs) demonstrated that 15-HETE was the only significantly increased metabolite (2.4 fold, p = 0.0004) by high glucose (30mM) treatment. In the presence of arachidonic acid (AA), additional eicosanoids generated by 12/15-LOX, including 12- and 11-HETEs, were significantly increased. Fluorescein angiography (FA) and retinal albumin leakage showed a significant decrease in retinal hyperpermeability in streptozotocin-induced diabetic mice lacking 12/15-LOX compared to diabetic wild type (WT). Our previous studies demonstrated the potential role of NADPH in mediating the permeability effect of 12- and 15-HETEs, therefore we tested the impact of intraocular injection of 12-HETE in mice lacking the catalytic subunit of NADPH oxidase (NOX2). The permeability effect of 12-HETE was significantly reduced in NOX2-/- mice compared to the WT. In vitro experiments also showed that 15-HETE induced HREC migration and tube formation in NADPH oxidase dependent manner. Taken together our data suggest that 12/15-LOX is implicated in DR via NADPH oxidase dependent mechanism.