BIOINFORMATICS REVIEW- APRIL 2017

April 2017 VOL 3 ISSUE4

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Lipidomics: An overview

Lipidomics: An overview

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Contents

April 2017

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Topics Editorial....

03 Lipidomics Lipidomics: An overview

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FOUNDER TARIQ ABDULLAH EDITORIAL EXECUTIVE EDITOR TARIQ ABDULLAH FOUNDING EDITOR MUNIBA FAIZA SECTION EDITORS FOZAIL AHMAD ALTAF ABDUL KALAM MANISH KUMAR MISHRA SANJAY KUMAR NABAJIT DAS REPRINTS AND PERMISSIONS You must have permission before reproducing any material from Bioinformatics Review. Send E-mail requests to info@bioinformaticsreview.com. Please include contact detail in your message.

BACK ISSUE Bioinformatics Review back issues can be downloaded in digital format from bioinformaticsreview.com at $5 per issue. Back issue in print format cost $2 for India delivery and $11 for international delivery, subject to availability. Pre-payment is required CONTACT PHONE +91. 991 1942-428 / 852 7572-667 MAIL Editorial: 101 FF Main Road Zakir Nagar, Okhla New Delhi IN 110025 STAFF ADDRESS To contact any of the Bioinformatics Review staff member, simply format the address as firstname@bioinformaticsreview.com PUBLICATION INFORMATION Volume 1, Number 1, Bioinformatics Reviewâ„¢ is published monthly for one year (12 issues) by Social and Educational Welfare Association (SEWA)trust (Registered under Trust Act 1882). Copyright 2015 Sewa Trust. All rights reserved. Bioinformatics Review is a trademark of Idea Quotient Labs and used under license by SEWA trust. Published in India

EDITORIAL

Bioinformatics Review (BiR): Bridging Between The Two Worlds Informatics and Biology are two sciences which are as different from each other as possible. One runs on the core concept of variation and another on strict reasoning. But still, these two have combined in a most natural way under the realm of “Bioinformatics”. For a biologist today it’s difficult to imagine a world without all biological databases and further no branch to decipher the huge enigma that it brings. Bioinformatics Review (BiR) journal is a platform to discover the latest happenings in this melting pot of two varied fields.

Dr. Roopam Sharma

Honorary Editor

The era of “omics” kick-started with the drafting of Human Genome Project (HGP) in 2003. Since then, a number of technological advancements especially, NGS has been generating mind-boggling data for the knowledge banks. Latest inventions like single-cell transcriptomics or metagenomics of most unusual habitats show how the evolution of technological advancements is directly resulting in breakthroughs in biological sciences.

Among various areas of biology which has benefited from these advancements is Pathology. In fact, deciphering the molecular and genetic basis of diseases in humans was the guiding force behind human genome sequencing Project. Bioinformatics has led to an impressive increase in recognition of possible pathogenic factors in varied systems, so much so that new techniques are being devised to increase the speed to actually test these factors in the wet lab. If we consider computationally, smaller but ever-changing genomes and transcriptomes of these pathogens, make them a much suitable candidate to test out many hypotheses for Bioinformatics studies. Effector Bioinformatics involves building custom pipelines for distinct species based on characteristics of effectors and size of the genome involved. These can be based on

Letters and responses: info@bioinformaticsreview.com

EDITORIAL

Homology or feature extraction or both, e.g. discovery of RXLR motifs in Oomycete effectors allowed many more effectors to be identified. This collaboration of two sciences for plant pathology has led to the development of many general use platforms like Broad-Fungal Genome Initiative, EuPathDB, PhytoPath and so on, but there is much need of developing specified resources like PHIbase for specific areas like effector biology. The use of machinelearning techniques like artificial neural network approach (which is actually based on biological neural networks) really shows how the two branches are so distinct yet so intertwined. All in all, it’s a brave new world where artificial communication is not only stimulating but also helping us understand the communication (between host and pathogen) going within the realm of life.

In this issue, BiR focusses on reviews related to some of the very basic techniques which have been used in computational biology and its applications in various biological studies. We look forward to continued support from our readers and contributors. For suggestions and feedback, do write to us at info@bioinformaticsreview.com

LIPIDOMICS

Lipidomics: An overview

Image Credit: Stock Photos

“Lipidomics is an emerging field in the name of the 'omics' for system-level analysis of lipids and their interacting partners within a cell.�

ipids are the essential metabolites in human body performing different functions such as energy storage, endocrine actions, cell signaling, morphogenesis, and so on.

Using a lipidomics approach, it has become easier to study the lipids species in an organism.

L

Lipidomics is an emerging field in the name of the 'omics' for system-level analysis of lipids and their interacting partners within a cell. Lipidomics aims to define and quantitate all of the molecular lipid species present in a cell. It provides a powerful approach to understanding lipid biology [1]. The lipid molecular species can be described by the 8 known categories of lipids, numerous classes, and subclasses, which were defined by the LIPID MAPS Initiative

in lipidomics [2,3] in collaboration with International Committee for the Classification and Nomenclature of Lipids (ICCNL) [4,5].

Some of the modern technologies such as liquid chromatography (LC), mass spectrometry (MS), nuclear magnetic resonance (NMR) are being used to identify and quantify all the lipid species in order to understand their function in biological systems.

The LIPID MAPS Consortium carried out the comprehensive analysis of lipidome in mammals by developing innovative lipidomics technique based on liquid chromatography coupled with mass spectrometry [2], which is most widely used nowadays.

MS technology has been proved to be highly efficient in the characterization and quantification of lipid molecular species in lipid

extracts. One of the reasons behind this could be the ability of MS to characterize and separate each ionized particle according to their mass-to-charge (m/z) ratio. MS can also provide structural information by fragmenting the lipid ions which can be achieved by using tandem MS, or MS/MS. Basically, there are two different approaches for lipidomics analysis: a. to apply some extraction protocols optimized for each lipid category, and then subject to LC to separate the present lipids molecular species optimally [6-11], then the LC eluate is coupled directly to the mass spectrometer for further analysis such as molecular fragmentation (MS/MS), ion scanning, etc.

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b. another approach, also known as "shotgun lipidomics", involves the offline extraction of lipids followed

by MS analysis without LC separation [12].

Several tools are available for lipidomics and some are emerging concerning the combination of genomics and lipidomics to identify clinically relevant biomarkers [13].

For example, SimLipid is a highthroughput characterization tool for lipids [13]. It analyzes lipid mass spectrometric data to profile them using LC coupled with MALDIMS, MS/MS data, and also remove the overlapping isotopic peaks from multiple spectra in batch mode [14].

References 1. Dennis, E. A. (2009). Lipidomics joins the omics evolution. Proceedings of the National Academy of Sciences, 106(7), 2089-2090.

2. Dennis, E. A., Brown, H. A., Deems, R. A., Glass, C. K., Merrill, A. H., Murphy, R. C., ... & van Nieuwenhze, M. (2005). The LIPID MAPS approach to lipidomics. Functional Lipidomics, 1-15.

3. Schmelzer, K., Fahy, E., Subramaniam, S., & Dennis, E. A. (2007). The lipid maps initiative in lipidomics. Methods in enzymology, 432, 171-183.

4. Fahy, E., Subramaniam, S., Brown, H. A., Glass, C. K., Merrill, A. H., Murphy, R. C., ... & Shimizu, T. (2005). A comprehensive classification system for lipids. Journal of lipid research, 46(5), 839-862.

5. Fahy, E., Subramaniam, S., Murphy, R. C., Nishijima, M., Raetz, C. R., Shimizu, T., ... & Dennis, E. A. (2009). Update of the LIPID MAPS comprehensive classification system for lipids.

Journal of lipid research, 50(Supplement), S9-S14.

6. Krank, J., Murphy, R. C., Barkley, R. M., Duchoslav, E., & McAnoy, A. (2007). Qualitative analysis and quantitative assessment of changes in neutral glycerol lipid molecular species within cells.

Methods in enzymology, 432, 1-20.

7. Ivanova, P. T., Milne, S. B., Byrne, M. O., Xiang, Y., & Brown, H. A. (2007). Glycerophospholipid identification and quantitation by electrospray ionization mass spectrometry. Methods in enzymology, 432, 21-57.

8. Deems, R., Buczynski, M. W., Bowers‐Gentry, R., Harkewicz, R., & Dennis, E. A. (2007). Detection and quantitation of eicosanoids via high performance liquid chromatography‐electrospray ionization‐ mass spectrometry. Methods in enzymology, 432, 59-82.

Analysis of Sphingolipids by Liquid Chromatography–Tandem Mass Spectrometry:“Inside‐Out” Sphingolipidomics.

Methods in enzymology, 432, 83-115.

10. Garrett, T. A., Guan, Z., & Raetz, C. R. (2007).

Analysis of Ubiquinones, Dolichols, and Dolichol Diphosphate‐Oligosaccharides by Liquid Chromatography‐Electrospray Ionization‐Mass Spectrometry. Methods in enzymology, 432, 117143.

11. McDonald, J. G., Thompson, B. M., McCrum, E. C., & Russell, D. W. (2007). Extraction and analysis of sterols in biological matrices by high performance liquid chromatography electrospray ionization mass spectrometry. Methods in enzymology, 432, 145-170.

12. Ejsing, C. S., Sampaio, J. L., Surendranath, V., Duchoslav, E., Ekroos, K., Klemm, R. W., ... & Shevchenko, A. (2009). Global analysis of the yeast lipidome by quantitative shotgun mass spectrometry. Proceedings of the National Academy of Sciences, 106(7), 2136-2141.

13. Wenk, M. R. (2010). Lipidomics: new tools and applications. Cell, 143(6), 888-895.

14. Isaac, G., McDonald, S., & Astarita, G. (2011).

Automated Lipid Identification Using UPLC/HDMSE in Combination with SimLipid. Waters Application Note 720004169en.

9. Sullards, M. C., Allegood, J. C., Kelly, S., Wang, E., Haynes, C. A., Park, H., ... & Merrill, A. H. (2007).

Structure‐Specific, Quantitative Methods for

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