Dawn Cotter

LMSD: LIPID MAPS structure database

The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. Structures of lipids in the database come from four sources: (i) LIPID MAPS Consortiums core laboratories and partners; (ii) lipids identified by LIPID MAPS experiments; (iii) computationally generated structures for appropriate lipid classes; (iv) biologically relevant lipids manually curated from LIPID BANK, LIPIDAT and other public sources. All the lipid structures in LMSD are drawn in a consistent fashion. In addition to a classification-based retrieval of lipids, users can search LMSD using either text-based or structure-based search options. The text-based search implementation supports data retrieval by any combination of these data fields: LIPID MAPS ID, systematic or common name, mass, formula, category, main class, and subclass data fields. The structure-based search, in conjunction with optional data fields, provides the capability to perform a substructure search or exact match for the structure drawn by the user. Search results, in addition to structure and annotations, also include relevant links to external databases. The LMSD is publicly available at

LIPID MAPS online tools for lipid research

The LIPID MAPS consortium has developed a number of online tools for performing tasks such as drawing lipid structures and predicting possible structures from mass spectrometry (MS) data. A simple online interface has been developed to enable an end-user to rapidly generate a variety of lipid chemical structures, along with corresponding systematic names and ontological information. The structure-drawing tools are available for six categories of lipids: (i) fatty acyls, (ii) glycerolipids, (iii) glycerophospholipids, (iv) cardiolipins, (v) sphingolipids and (vi) sterols. Within each category, the structure-drawing tools support the specification of various parameters such as chain lengths at a specific sn position, head groups, double bond positions and stereochemistry to generate a specific lipid structure. The structure-drawing tools have also been integrated with a second set of online tools which predict possible lipid structures from precursor-ion and product-ion MS experimental data. The MS prediction tools are available for three categories of lipids: (i) mono/di/triacylglycerols, (ii) glycerophospholipids and (iii) cardiolipins. The LIPID MAPS online tools are publicly available at www.lipidmaps.org/tools/.

MitoProteome: mitochondrial protein sequence database and annotation system

MitoProteome is an object-relational mitochondrial protein sequence database and annotation system. The initial release contains 847 human mitochondrial protein sequences, derived from public sequence databases and mass spectrometric analysis of highly purified human heart mitochondria. Each sequence is manually annotated with primary function, subfunction and subcellular location, and extensively annotated in an automated process with data extracted from external databases, including gene information from LocusLink and Ensembl; disease information from OMIM; protein-protein interaction data from MINT and DIP; functional domain information from Pfam; protein fingerprints from PRINTS; protein family and family-specific signatures from InterPro; structure data from PDB; mutation data from PMD; BLAST homology data from NCBI NR; and proteins found to be related based on LocusLink and SWISS-PROT references and sequence and taxonomy data. By highly automating the processes of maintaining the MitoProteome Protein List and extracting relevant data from external databases, we are able to present a dynamic database, updated frequently to reflect changes in public resources. The MitoProteome database is publicly available at http://www. mitoproteome.org/. Users may browse and search MitoProteome, and access a complete compilation of data relevant to each protein of interest, cross-linked to external databases.

Lipid classification, structures and tools.

The study of lipids has developed into a research field of increasing importance as their multiple biological roles in cell biology, physiology and pathology are becoming better understood. The Lipid Metabolites and Pathways Strategy (LIPID MAPS) consortium is actively involved in an integrated approach for the detection, quantitation and pathway reconstruction of lipids and related genes and proteins at a systems-biology level. A key component of this approach is a bioinformatics infrastructure involving a clearly defined classification of lipids, a state-of-the-art database system for molecular species and experimental data and a suite of user-friendly tools to assist lipidomics researchers. Herein, we discuss a number of recent developments by the LIPID MAPS bioinformatics core in pursuit of these objectives.

Bioinformatics and Systems Biology of the Lipidome

Lipids play an important role in physiology and pathophysiology of living systems. Until a few decades ago, the number of lipid molecules that were chemically characterized was a few hundred at most and were catalogued in monographs and compendia.1 Since the advent of the era of the genome and the proteome, there has been increasing recognition that other macromolecules like lipids and polysaccharides in living systems display considerable structural diversity and systematic efforts are underway to identify, characterize and catalog these molecules. With mass spectrometric techniques coming of age, several thousand distinct molecular species have been identified from living species and the roles of several of these are beginning to be characterized.2 Unlike genes and proteins, whose defined alphabets provide the framework for ontologies and classification at the sequence level, lipids and polysaccharides have been characterized for the large part by popular names, with no foundations for systematic classification. The past two decades have witnessed two major advances in lipid biology. In the first, mass spectrometry has enabled the identification of thousands of lipid molecular species from cells and tissues and this has pointed to the important need for developing a systematic ontology that can rationally name and catalog the molecules. Second, the ability to investigate the functional roles of lipid molecules through systematic phenotypic studies has led to the identification of lipids as extremely important players in physiology and pathophysiology of living species.3 In combination with proteins and nucleic acids, lipids are integrally involved in biochemical networks that lead to phenotypes such as homeostasis, differentiation, and death of cells and tissues. Any approach to systems characterization of living systems, of necessity, has to include lipids along with other macromolecules and all complex cellular pathways involving lipid molecular species. Systems biology now extends in its scope to identify biosynthetic and metabolic lipid networks, cellular signaling networks that explicitly include lipid molecules and transcriptional and epigenetic networks where lipids play an integral role.4 Several large scale projects to characterize lipids and their functional roles have been initiated as exemplified by the LIPID MAPS5 effort. The LIPID MAPS is an exemplar systems biology project that measures cell-wide lipid changes in an attempt to reconstruct biochemical pathways associated with lipid processing and signaling. The cell-wide measurements of components of these pathways include mass spectrometric measurements of lipid changes in response to stimulus in mammalian cells, changes in transcription profiles in response to stimulus and in select cases proteomic changes in response to stimulus. Figure 1 shows a schematic of the LIPID MAPS experiments related to different lipid categories/pathways and the subsequent processing of the experimental data generated. Network reconstruction efforts rely on organization, analysis and integration of these data and this requires a strong bioinformatics and systems biology effort. The former has to include development of a systematic and universal classification and nomenclature system, design and development of lipid and lipid-gene, lipid-protein databases with appropriate functional annotations, and efficient query and analysis systems that can be broadly useful to the biology research community. The latter has to include methods for analysis of large scale lipid measurements in cells, reconstruction of lipid metabolic and biosynthetic pathways, and quantitative models of lipid fluxes in cells under varied perturbations. In this review, we will provide a comprehensive summary of extant developments in lipid bioinformatics and systems biology and discuss the outlook for the future integration of lipidomics into cellular and organismic biology. The sections that follow are delineated into the informatics approaches specific to lipid biology followed by an overview and exemplar approach to analysis of large scale lipidomic data towards a systems description of mammalian cells. Figure 1 Overview of the process of performing a quantitative lipid analysis of macrophage cell sample (in this example, a time-course experiment using bone marrow derived macrophages). Extraction methods, LC/GC purification methods, MS acquisition strategies ... 2. Classification, Ontology, Nomenclature and Structure Representation of Lipid Molecules The first step towards classification of lipids is the establishment of an ontology that is extensible, flexible and scalable. One must be able to classify, name and represent these molecules in a logical manner which is amenable to data basing and computational manipulation. Lipids have been loosely defined as biological substances that are generally hydrophobic in nature and in many cases soluble in organic solvents.6 These chemical features are present in a broad range of molecules such as fatty acids, phospholipids, sterols, sphingolipids, terpenes and others. In view of the fact that lipids comprise an extremely heterogeneous collection of molecules from a structural and functional standpoint, it is not surprising that there are significant differences with regard to the scope and organization of current classification schemes. 2.1. Classification, Ontology and Nomenclature In order to address the lack of a consistent classification and nomenclature methodology for lipids, LIPID MAPS consortium members have developed a comprehensive classification system for lipids.7 The consortium has taken a more chemistry-based approach and defines lipids as hydrophobic or amphipathic small molecules that may originate entirely or in part by carbanion based condensations of thioesters (such as fatty acids and polyketides) and/or by carbocation based condensations of isoprene units (such as prenols and sterols). Figure 2 shows the mechanisms of lipid biosynthesis.8 Based on this classification system, lipids have been divided into eight categories: Fatty acyls, Glycerolipids, Glycerophospholipids, Sphingolipids, Sterol lipids, Prenol lipids, Saccharolipids, and Polyketides. Each category is further divided into classes and subclasses. Additionally, following the existing rules and recommendations proposed by the International Union of Biochemistry and Applied Chemists and the International Union of Biochemistry and Molecular Biology (IUPAC-IUBMB) commission on Biochemical Nomenclature, a consistent nomenclature scheme has also been developed to provide systematic names for various classes and subclasses of lipids.7 Figure 2 Mechanisms of lipid biosynthesis. Biosynthesis of ketoacyl- and isoprene-containing lipids proceeds by carbanion and carbocation-mediated chain extension, respectively.8 All lipids in the LIPID MAPS Structure Database (LMSD) are classified and annotated using this comprehensive classification and nomenclature system developed by the LIPID MAPS consortium.

Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools

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 Funds Metabolomics Program, provides data from the Common Funds Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world.

Bioinformatics for Lipidomics

Lipids are recognized as key participants in the regulation and control of cellular function, having important roles in signal transduction processes. The diversity in lipid chemical structure presents a challenge for establishing practical methods to generate and manage high volumes of complex data that translate into a snapshot of cellular lipid changes. The need for high-quality bioinformatics to manage and integrate experimental data becomes imperative at several levels: (1) definition of lipid classification and ontologies, (2) relational database design, (3) capture and automated pipelining of experimental data, (4) efficient management of metadata, (5) development of lipid-centric search tools, (6) analysis and visual display of results, and (7) integration of the lipid knowledge base into biochemical pathways and interactive maps. This chapter describes the recent contributions of the bioinformatics core of the LIPID MAPS consortium toward achieving these objectives.

LMPD: LIPID MAPS proteome database

The LIPID MAPS Proteome Database (LMPD) is an object-relational database of lipid-associated protein sequences and annotations. The initial release contains 2959 records, representing human and mouse proteins involved in lipid metabolism. UniProt IDs were obtained based on keyword search of KEGG and GO databases, and this LMPD protein list was then enhanced with annotations from UniProt, EntrezGene, ENZYME, GO, KEGG and other public resources. We also assigned associations with general lipid categories, based on GO and KEGG annotations. Users may search LMPD by database ID or keyword, and filter by species and/or lipid class associations; from the search results, one can then access a compilation of data relevant to each protein of interest, cross-linked to external databases. The LIPID MAPS Proteome Database (LMPD) is publicly available from the LIPID MAPS Consortium website (). The direct URL is .

An editor for pathway drawing and data visualization in the Biopathways Workbench

BackgroundPathway models serve as the basis for much of systems biology. They are often built using programs designed for the purpose. Constructing new models generally requires simultaneous access to experimental data of diverse types, to databases of well-characterized biological compounds and molecular intermediates, and to reference model pathways. However, few if any software applications provide all such capabilities within a single user interface.ResultsThe Pathway Editor is a program written in the Java programming language that allows de-novo pathway creation and downloading of LIPID MAPS (Lipid Metabolites and Pathways Strategy) and KEGG lipid metabolic pathways, and of measured time-dependent changes to lipid components of metabolism. Accessed through Java Web Start, the program downloads pathways from the LIPID MAPS Pathway database (Pathway) as well as from the LIPID MAPS web server http://www.lipidmaps.org. Data arises from metabolomic (lipidomic), microarray, and protein array experiments performed by the LIPID MAPS consortium of laboratories and is arranged by experiment. Facility is provided to create, connect, and annotate nodes and processes on a drawing panel with reference to database objects and time course data. Node and interaction layout as well as data display may be configured in pathway diagrams as desired. Users may extend diagrams, and may also read and write data and non-lipidomic KEGG pathways to and from files. Pathway diagrams in XML format, containing database identifiers referencing specific compounds and experiments, can be saved to a local file for subsequent use. The program is built upon a library of classes, referred to as the Biopathways Workbench, that convert between different file formats and database objects. An example of this feature is provided in the form of read/construct/write access to models in SBML (Systems Biology Markup Language) contained in the local file system.ConclusionInclusion of access to multiple experimental data types and of pathway diagrams within a single interface, automatic updating through connectivity to an online database, and a focus on annotation, including reference to standardized lipid nomenclature as well as common lipid names, supports the view that the Pathway Editor represents a significant, practicable contribution to current pathway modeling tools.

Tenth Annual LIPID Metabolites and Pathways Strategy (LIPID MAPS) Meeting

Abstract The LIPID Metabolites and Pathways Strategy (LIPID MAPS) Annual Meeting 2013: Impact of Lipidomics on Oxidized Lipids and Non-Alcoholic Fatty Liver Disease, La Jolla, CA, USA, 7–8 May 2013 The LIPID Metabolites and Pathways Strategy (LIPID MAPS) Annual Meeting 2013: Impact of Lipidomics on Oxidized Lipids and Non-Alcoholic Fatty Liver Disease – the tenth annual meeting of the LIPID MAPS Consortium – was once again marked by beautiful Southern California weather, as top scientists from around the globe gathered in La Jolla, CA, 7–8 May 2013.