Ashok Reddy Dinasarapu

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.

MicroRNA 203 Modulates Glioma Cell Migration via Robo1/ERK/MMP-9 Signaling

Glioblastoma (GBM) is the most common and malignant primary adult brain cancer. Allelic deletion on chromosome 14q plays an important role in the pathogenesis of GBM, and this site was thought to harbor multiple tumor suppressor genes associated with GBM, a region that also encodes microRNA-203 (miR-203). In this study, we sought to identify the role of miR-203 as a tumor suppressor in the pathogenesis of GBM. We analyzed the miR-203 expression data of GBM patients in 10 normal and 495 tumor tissue samples derived from The Cancer Genome Atlas data set. Quantitative real-time PCR and in situ hybridization in 10 high-grade GBM and 10 low-grade anaplastic astrocytoma tumor samples showed decreased levels of miR-203 expression in anaplastic astrocytoma and GBM tissues and cell lines. Exogenous expression of miR-203 using a plasmid expressing miR-203 precursor (pmiR-203) suppressed glioma cell proliferation, migration, and invasion. We determined that one relevant target of miR-203 was Robo1, given that miR-203 expression decreased mRNA and protein levels as determined by RT-PCR and Western blot analysis. Moreover, cotransfection experiments using a luciferase-based transcription reporter assay have shown direct regulation of Robo1 by miR-203. We also show that Robo1 mediates miR-203 mediated antimigratory functions as up-regulation of Robo1 abrogates miR-203 mediated antimigratory effects. We also show that miR-203 expression suppressed ERK phosphorylation and MMP-9 expression in glioma cells. Furthermore, we demonstrate that miR-203 inhibits migration of the glioma cells by disrupting the Robo1/ERK/MMP-9 signaling axis. Taken together, these studies demonstrate that up-regulation of Robo1 in response to the decrease in miR-203 in glioma cells is responsible for glioma tumor cell migration and invasion.

Analysis of inflammatory and lipid metabolic networks across RAW264.7 and thioglycolate-elicited macrophages

Studies of macrophage biology have been significantly advanced by the availability of cell lines such as RAW264.7 cells. However, it is unclear how these cell lines differ from primary macrophages such as thioglycolate-elicited peritoneal macrophages (TGEMs). We used the inflammatory stimulus Kdo2-lipid A (KLA) to stimulate RAW264.7 and TGEM cells. Temporal changes of lipid and gene expression levels were concomitantly measured and a systems-level analysis was performed on the fold-change data. Here we present a comprehensive comparison between the two cell types. Upon KLA treatment, both RAW264.7 and TGEM cells show a strong inflammatory response. TGEM (primary) cells show a more rapid and intense inflammatory response relative to RAW264.7 cells. DNA levels (fold-change relative to control) are reduced in RAW264.7 cells, correlating with greater downregulation of cell cycle genes. The transcriptional response suggests that the cholesterol de novo synthesis increases considerably in RAW264.7 cells, but 25-hydroxycholesterol increases considerably in TGEM cells. Overall, while RAW264.7 cells behave similarly to TGEM cells in some ways and can be used as a good model for inflammation- and immune function-related kinetic studies, they behave differently than TGEM cells in other aspects of lipid metabolism and phenotypes used as models for various disorders such as atherosclerosis.

Signaling gateway molecule pages—a data model perspective

SUMMARY The Signaling Gateway Molecule Pages (SGMP) database provides highly structured data on proteins which exist in different functional states participating in signal transduction pathways. A molecule page starts with astate of a native protein, without any modification and/or interactions. New states are formed with every post-translational modification or interaction with one or more proteins, small molecules or class molecules and with each change in cellular location. State transitions are caused by a combination of one or more modifications, interactions and translocations which then might be associated with one or more biological processes. In a characterized biological state, a molecule can function as one of several entities or their combinations, including channel, receptor, enzyme, transcription factor and transporter. We have also exported SGMP data to the Biological Pathway Exchange (BioPAX) and Systems Biology Markup Language (SBML) as well as in our custom XML. AVAILABILITY SGMP is available at www.signaling-gateway.org/molecule.

CMAP: Complement Map Database

SUMMARY The human complement system is increasingly perceived as an intricate protein network of effectors, inhibitors and regulators that drives critical processes in health and disease and extensively communicates with associated physiological pathways ranging from immunity and inflammation to homeostasis and development. A steady stream of experimental data reveals new fascinating connections at a rapid pace; although opening unique opportunities for research discoveries, the comprehensiveness and large diversity of experimental methods, nomenclatures and publication sources renders it highly challenging to keep up with the essential findings. With the Complement Map Database (CMAP), we have created a novel and easily accessible research tool to assist the complement community and scientists from related disciplines in exploring the complement network and discovering new connections. AVAILABILITY http://www.complement.us/cmap. CONTACT lambris@upenn.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Statistical insights into major human muscular diseases

Muscular diseases lead to muscle fiber degeneration, impairment of mobility, and in some cases premature death. Many of these muscular diseases are largely idiopathic. The goal of this study was to identify biomarkers based on their functional role and possible mechanisms of pathogenesis, specific to individual muscular disease. We analyzed the muscle transcriptome from five major muscular diseases: acute quadriplegic myopathy (AQM), amyotrophic lateral sclerosis (ALS), mitochondrial encephalomyopathy, lactic acidosis and stroke-like episodes (MELAS), dermatomyositis (DM) and polymyositis (PM) using pairwise statistical comparison to identify uniquely regulated genes in each muscular disease. The genome-wide information encoded in the transcriptome provided biomarkers and functional insights into dysregulation in each muscular disease. The analysis showed that the dysregulation of genes in forward membrane pathway, responsible for transmitting action potential from neural excitation, is unique to AQM, while the dysregulation of myofibril genes, determinant of the mechanical properties of muscle, is unique to ALS, dysregulation of ER protein processing, responsible for correct protein folding, is unique to DM, and upregulation of immune response genes is unique to PM. We have identified biomarkers specific to each muscular disease which can be used for diagnostic purposes.

Omics Approaches to Macrophage Biology

High-throughput (HTP) technologies enabling the simultaneous measurement of thousands of genes, proteins, and metabolites offer new opportunities for understanding the complex mechanisms underlying physiology, health, and disease. Mining these large “omics” datasets (transcriptomics, proteomics, and metabolomics) has required addressing issues such as high dimensionality of the data, experimental variability, noise, and low sensitivity of the methodologies. Numerous approaches have been developed to handle these issues and to utilize these datasets to generate meaningful biological insights. This chapter describes the types of omics measurements that have been performed on mammalian macrophage cells, methods, and tools for their analysis, and examples of insights gained in macrophage biology.