The LUX Score: A Metric for Lipidome Homology

A lipidome is the set of lipids in a given organism, cell or cell compartment and this set reflects the organism’s synthetic pathways and interactions with its environment. Recently, lipidomes of biological model organisms and cell lines were published and the number of functional studies of lipids is increasing. In this study we propose a homology metric that can quantify systematic differences in the composition of a lipidome. Algorithms were developed to 1. consistently convert lipids structure into SMILES, 2. determine structural similarity between molecular species and 3. describe a lipidome in a chemical space model. We tested lipid structure conversion and structure similarity metrics, in detail, using sets of isomeric ceramide molecules and chemically related phosphatidylinositols. Template-based SMILES showed the best properties for representing lipid-specific structural diversity. We also show that sequence analysis algorithms are best suited to calculate distances between such template-based SMILES and we adjudged the Levenshtein distance as best choice for quantifying structural changes. When all lipid molecules of the LIPIDMAPS structure database were mapped in chemical space, they automatically formed clusters corresponding to conventional chemical families. Accordingly, we mapped a pair of lipidomes into the same chemical space and determined the degree of overlap by calculating the Hausdorff distance. We named this metric the ‘Lipidome jUXtaposition (LUX) score’. First, we tested this approach for estimating the lipidome similarity on four yeast strains with known genetic alteration in fatty acid synthesis. We show that the LUX score reflects the genetic relationship and growth temperature better than conventional methods although the score is based solely on lipid structures. Next, we applied this metric to high-throughput data of larval tissue lipidomes of Drosophila. This showed that the LUX score is sufficient to cluster tissues and determine the impact of nutritional changes in an unbiased manner, despite the limited information on the underlying structural diversity of each lipidome. This study is the first effort to define a lipidome homology metric based on structures that will enrich functional association of lipids in a similar manner to measures used in genetics. Finally, we discuss the significance of the LUX score to perform comparative lipidome studies across species borders.

[1]  Fred J. Damerau,et al.  A technique for computer detection and correction of spelling errors , 1964, CACM.

[2]  S. Sweeney,et al.  Invertebrate models of lysosomal storage disease: what have we learned so far? , 2011, Invertebrate Neuroscience.

[3]  Min Han,et al.  Monomethyl Branched-Chain Fatty Acids Play an Essential Role in Caenorhabditis elegans Development , 2004, PLoS biology.

[4]  Evan Bolton,et al.  The PubChem chemical structure sketcher , 2009, J. Cheminformatics.

[5]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[6]  Juan Antonio Vizcaíno,et al.  Shorthand notation for lipid structures derived from mass spectrometry , 2013, Journal of Lipid Research.

[7]  Chris Morley,et al.  Open Babel: An open chemical toolbox , 2011, J. Cheminformatics.

[8]  Christer S. Ejsing,et al.  Global analysis of the yeast lipidome by quantitative shotgun mass spectrometry , 2009, Proceedings of the National Academy of Sciences.

[9]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[10]  David Vidal,et al.  LINGO, an Efficient Holographic Text Based Method To Calculate Biophysical Properties and Intermolecular Similarities , 2005, J. Chem. Inf. Model..

[11]  Ernst Hafen,et al.  Biochemical membrane lipidomics during Drosophila development. , 2013, Developmental cell.

[12]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[13]  M. Frohman,et al.  Mammalian phospholipase D physiological and pathological roles , 2012, Acta physiologica.

[14]  D. Toke,et al.  ELO2 and ELO3, Homologues of theSaccharomyces cerevisiae ELO1 Gene, Function in Fatty Acid Elongation and Are Required for Sphingolipid Formation* , 1997, The Journal of Biological Chemistry.

[15]  Matej Oresic,et al.  Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis , 2007, BMC Systems Biology.

[16]  Robert C. Edgar,et al.  MUSCLE: multiple sequence alignment with high accuracy and high throughput. , 2004, Nucleic acids research.

[17]  Suzanne Eaton,et al.  Effects of diet and development on the Drosophila lipidome , 2012, Molecular systems biology.

[18]  Christer S. Ejsing,et al.  High-content screening of yeast mutant libraries by shotgun lipidomics. , 2014, Molecular bioSystems.

[19]  Sufia Sadaf,et al.  Altered lipid homeostasis in Drosophila InsP3 receptor mutants leads to obesity and hyperphagia , 2013, Disease Models & Mechanisms.

[20]  Andreas Bender,et al.  How Similar Are Similarity Searching Methods? A Principal Component Analysis of Molecular Descriptor Space , 2009, J. Chem. Inf. Model..

[21]  H. Riezman,et al.  Yeast lipid analysis and quantification by mass spectrometry. , 2010, Methods in enzymology.

[22]  V. Ramakrishnan,et al.  Systems Biological Approach of Molecular Descriptors Connectivity: Optimal Descriptors for Oral Bioavailability Prediction , 2012, PloS one.

[23]  Christer S. Ejsing,et al.  Characterization of yeast mutants lacking alkaline ceramidases YPC1 and YDC1. , 2014, FEMS yeast research.

[24]  M. A. Surma,et al.  Flexibility of a Eukaryotic Lipidome – Insights from Yeast Lipidomics , 2012, PloS one.

[25]  A. Mohanapriya,et al.  Comparative QSAR analysis of cyclo-oxygenase2 inhibiting drugs , 2012, Bioinformation.

[26]  Eoin Fahy,et al.  Template-based combinatorial enumeration of virtual compound libraries for lipids , 2012, Journal of Cheminformatics.

[27]  M. Scott,et al.  Genetic dissection of a cell-autonomous neurodegenerative disorder: lessons learned from mouse models of Niemann-Pick disease type C , 2013, Disease Models & Mechanisms.

[28]  Lorenz C. Blum,et al.  Chemical space as a source for new drugs , 2010 .

[29]  Eoin Fahy,et al.  LIPID MAPS online tools for lipid research , 2007, Nucleic Acids Res..

[30]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[31]  R. Kraut.  Roles of sphingolipids in Drosophila development and disease , 2011, Journal of neurochemistry.

[32]  David W. Russell,et al.  LMSD: LIPID MAPS structure database , 2006, Nucleic Acids Res..

[33]  Michael C. Hutter,et al.  Bioisosteric Similarity of Molecules Based on Structural Alignment and Observed Chemical Replacements in Drugs , 2009, J. Chem. Inf. Model..

[34]  G. Shui,et al.  Lipidomics as a principal tool for advancing biomedical research. , 2013, Journal of genetics and genomics = Yi chuan xue bao.

[35]  R. Kühnlein.  Lipid droplet-based storage fat metabolism in Drosophila , 2012, Journal of Lipid Research.