Introduction 

It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.

Objectives 

This work simplifies this process.

Methods 

A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.

Results 

The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.

Conclusion 

This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations

Barrett, David A.

Casbas-Pinto, Ferran

Hodgman, Charlie

Ravipati, Srinivarao

Introduction \ud
It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.\ud
Objectives \ud
This work simplifies this process.\ud
Methods \ud
A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.\ud
Results \ud
The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.\ud
Conclusion \ud
This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations

Name not available

A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles

Nottingham ePrints

Casbas-Pinto, Ferran and Ravipati, Srinivarao and Barrett, David A. and Hodgman, Charlie (2017) A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles. Metabolomics, 13 . 81/1-81/4. ISSN 1573-3890 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/42506/1/Casbas-etal-LipidNetworks-Momics-2017.pdfCopyright and reuse: The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions.This article is made available under the Creative Commons Attribution licence and may be reused according to the conditions of the licence.  For more details see: http://creativecommons.org/licenses/by/2.5/A note on versions: The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher’s version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.ukVol.:(0123456789) Metabolomics (2017) 13:81 DOI 10.1007/s11306-017-1214-ySOFTWARE/DATABASEA methodology for elucidating regulatory mechanisms leading to changes in lipid profilesFerran Casbas Pinto1,2 · Srinivarao Ravipati2 · David A. Barrett2 · T. Charles Hodgman1 Received: 6 December 2016 / Accepted: 3 May 2017 / Published online: 29 May 2017 © The Author(s) 2017. This article is an open access publicationInformation Section  2). However, the latter task is made difficult as a result of the complex chemical nature of lipids and their ubiquitous presence in multiple roles in biological tissues. In addition, the identification of many lipid molec-ular species is complicated by the high number of structural isomers corresponding to a given lipid mass. Although some low-throughput mass spectrometry techniques lead to precisely defined molecular structures (Nguyen et  al. 2015), there is still more technical development needed before this can be applied in a routine way to global lipi-domics. Pathways of lipid metabolism are both complex and often not fully understood: some reactions are not well characterized; some metabolites are not endogenously syn-thesised but enter through the diet (Holmes et  al. 2008); and many enzymes have low substrate specificity, allow-ing them to accept a huge range of fatty acids and alcohols as substrates (Gibellini and Smith 2010). In addition, most biological and clinical scientists refer to lipids by com-mon and often inconsistent names, rather than a systematic naming scheme, which can lead to confusion. These fac-tors together make it difficult to automate the construction and analysis of metabolic pathways and networks, and a basic requirement for progress is to define a mechanism for resolving synonyms and one-to-many relationships in lipid identities (Horne et al. 2004).Most recent lipidomics papers conclude their study with a list of the lipids that were found to be significantly dif-ferent between two groups, for example between diseased and healthy tissue or between treated and control samples. However, there is a small number of examples that go one step further. For example, one can create and test a predic-tive model (Floegel et al. 2013), apply advanced statistics to classify profiles into high or low risk of suffering a disease (Rhee et al. 2011), manually construct a network between known disease-associated genes, signal-transduction Abstract Introduction It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.Objectives This work simplifies this process.Methods A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.Results The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycys-tic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.Conclusion This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.1 IntroductionRecent advances in analytical methods, especially mass spectrometry, have shown that complex lipid profiles can be influenced by many factors and have the potential to improve our understanding of the pathophysiology underly-ing disease processes (references listed in Supplementary Electronic supplementary material The online version of this article (doi:10.1007/s11306-017-1214-y) contains supplementary material, which is available to authorized users. * T. Charles Hodgman  Charlie.Hodgman@nottingham.ac.uk1 School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK2 Centre for Analytical Bioscience, School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, UK F. Casbas Pinto et al.1 381 Page 2 of 4pathways and the perturbed levels of specific lipids (Wang-Sattler et  al. 2012), or colour-code pathway or network maps from databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Cottret et  al. 2010; Titz et al. 2015).In this work, we propose a method of systematically con-verting lists of perturbed lipids to integrate them with met-abolic network which permits visualisation of the affected pathways. This process involves four steps, the first two involving the construction of a network (that links enzymes and hence genes to reactions between specific lipids) and a thesaurus (linking generic lipid identifiers to a list of possi-ble specific metabolites). The second two steps involve the implementation of algorithms to generate the subset of the total network that corresponds to the input profile of per-turbed lipids, and to rank enzyme nodes in terms of their significance to the perturbation. As a proof of concept, we apply this method to a published lipidomics dataset .2  Methods2.1  Data sourcesSource data for creating the holistic human lipidomic net-work included LIPID MAPS (Sud et  al. 2007), KEGG (Kanehisa 1996), HMDB (Wishart et  al. 2013) and other papers cited in Supplementary Information Section 3.2.2  Network constructionThe lipid metabolic network was created using Python scripts applied to data from the above sources, plus manual curation, and has been stored in a mySQL database. All scripts can be obtained from https://github.com/Ferran-Casbas/Lipid-Pathway. The network contains a metabo-lite node for each specific lipid, but does not distinguish between the fatty-acyl groups at the sn-1 and sn-2 chain positions to limit the network size and complexity and because analytical resolution of sn-1/sn-2 sidechains is rarely achieved across the whole lipidome. Other nodes may refer to individual reactions, and the enzymes (speci-fied by their Human Genome Identifier) catalysing them. Further details are in the Supplementary Information Section 4.2.3  Subset generationThe whole process is depicted in Fig. 1. The input used is a list of lipids whose concentrations are significantly higher or lower between two groups, determined for example after univariate or multivariate data analysis of a lipidom-ics dataset and the structural confirmation of the chemical identities of the changed lipids. Lipid names should con-form to the standard in LIPID MAPS (http://www.lipid-maps.org/data/classification/lipid_cns.html#N). The pre-ferred level of identification is to include the lipid class and the numbers of carbons and double bonds in each side chain. For example, this approach can include ambiguously identified molecules such as DG(36:2) which indicates a diglyceride with two side chains totalling 36 carbons with 2 double bonds. The fold change or significance score for the metabolites (often the VIP scores from Partial Least Squares analysis) can also be input and used for meta-bolic prioritisation. Alternatively, +1 and −1 can be used to imply the direction of change in lipid level between one group and another.PHP scripts then use the list to produce a subgraph (sub-network). The first phase of the analysis works through the list to identify specific, ambiguous and unrecognisable terms. The software reports on this and recommends what might be done to clarify the unrecognised terms. Once the lipids of interest have been adequately associated with net-work nodes, a sub-network is generated by selecting from the lipidomic network these nodes and their two nearest neighbours, so that the reactions and enzyme/genes link-ing these lipids are also included. If the input lipids have fold change or significance scores, the output sub-network contains Relative Importance scores, which indicate how much influence the enzyme nodes have on producing the observed lipid-profile perturbation in light of the network topology. This can be particularly valuable when larger sub-networks are returned. More information on this is in Supplementary Information Section 6.The above scripts are available through a web inter-face, which can be found at: http://lipidnetwork.notting-ham.ac.uk/. The web tool outputs two files which can be imported into a network visualization software, such as Cytoscape (Doerks et al. 2002), to see the metabolic path-ways involved in the profile changes. Another option is to open the node-info file in a spreadsheet to retrieve the list of enzymes, which can then be used as a query to other network tools, such as the BioGRID (Stark et al. 2006) or genemania, to identify potential regulators.3  Results and discussionThis work describes a novel method for identifying enzymes and potential regulators related to measured changes in the lipid profile obtained from mass spectrom-etry analysis. The method includes the development of a holistic lipidomic network that comprises 127 enzymes, 13,934 reactions and 7561 metabolites. The latter are from 16 lipid subclasses: 3124 glycerolipids, 3575 glycerophos-pholipids, 290 sphingolipids, 126 fatty acyls (including A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles 1 3Page 3 of 4 81eicosansoids, fatty amides, esters and their conjugates), and 446 miscellaneous lipids. An image of the complete network can be seen in Supplementary Fig. 1, and further details of the network’s properties are described in Sup-plementary Tables  1–3. The Supplementary Information Section  9 outlines extensions to this method that can be carried out by suitably experienced bioinformaticians.To show the power and utility of the method, it has been applied to a published dataset that contains lipid profile for women with polycystic ovary syndrome (PCOS) compared Fig. 1  The Lipid network method. a Input list of lipids with OPLS-DA model scores. b Depicts the web server that contains scripts and the lipidomic network, in which node colours green, dark blue, orange, red and white respectively correspond to glycerophospho-lipids, sphingolipids, diglycerides, enzymes and specific reactions. c Derived subset of lipid metabolism linking the input perturbed metabolites; metabolite, reaction and enzyme nodes are respectively represented by rectangles, triangles and ellipses; input metabolite nodes are respectively coloured a brighter shade or green or red depending on the extent to which their levels have been elevated or decreased, or blue if they correspond to nodes introduced during the subset retrieval; the borders of enzyme nodes are shaded blue or red respectively to depict the extent of positive or negative Relative Importance Score (the sign corresponding to the increase or decrease in enzyme activity). d The list of retrieved enzymes ranked by Rela-tive Importance F. Casbas Pinto et al.1 381 Page 4 of 4with matched controls (2). The contribution score of the lipids found in significantly different concentration was assigned from an OPLS-DA analysis (see Fig.  1). The output .sif and node-attributes files were imported into Cytoscape and the nodes manually stacked to aid visual interpretation of the network. This allows us to view the enzymes that have a higher impact on the profile.In this example, 14 enzymes have been retrieved falling into two separate components, with the highest in absolute value of Relative Importance scores being for PLD1 (a phosphatidylcholine-specific phospholipase) and Chpt1 (a choline phosphotransferase). This suggests that the changes in plasma lipid profile associated with PCOS may be domi-nated by a reduction in Chpt1 activity and/or an increase in PLD1 activity. The former is responsible for changes in DG and PC, and PLD1 is responsible for changes in PE and PC. Since there is equilibrium between the PA and DG species, a simple hydrolysis would convert one into the other, the perturbation of Pld1 could by itself explain the majority of observed changes.The list of enzymes provided by this metabolic insight can be used to query other biomolecular network tools to identify potential common regulators of these lipids. 11 of them were present in BIOGRID, 4 linked by ELAVL1 and 2 by ILF3, which both protect target mRNAs from degra-dation (see Supplementary Fig. S7). Querying GeneMania revealed consistent patterns for regulation of Chpt1 and Pisd by microRNAs recognising the target sequence TGC TTG CT, and Dgkz, Ppap2a and Smpd1 by the transcription factors MAZ, NFAT and STAT5B (Supplementary Fig. S8). Unfortunately, both regulators have an enzyme whose activity goes in the opposite direction to the rest, so it is difficult to assert any strong hypotheses at this stage.4  ConclusionsTo conclude, the methodology presented here provides a way to move from tables of perturbed lipid levels to a visu-alization of the metabolic network that could cause these changes and thence to potential regulators. It also provides a metric (Importance) to rank the positive or negative con-tribution of each enzyme to produce the changes seen.Acknowledgements FCP, SR and DB acknowledge funding from EU FP7 Grant 305815 (D-BOARD).Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest.Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institu-tional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.Open Access This article is distributed under the terms of the Crea-tive Commons Attribution 4.0 International License (http://creativecom-mons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.ReferencesCottret, L., Wildridge, D., Vinson, F., Barrett, M. P., Charles, H., Sagot, M. F., et al. (2010). MetExplore: A web server to link metabolomic experiments and genome-scale metabolic networks. Nucleic Acids Research, 38, W132–W137. doi:10.1093/nar/gkq312.Doerks, T., Copley, R. R., Schultz, J., Ponting, C. P., & Bork, P. (2002). Systematic identification of novel protein domain families associ-ated with nuclear functions. Genome Research, 12, 47–56.Floegel, A., Stefan, N., Yu, Z., Mühlenbruch, K., Drogan, D., Joost, H.-G., et al. (2013). Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes, 62, 639–648. doi:10.2337/db12-0495.Gibellini, F., & Smith, T. K. (2010). The Kennedy pathway—de novo synthesis of phosphatidylethanolamine and phosphatidylcholine. IUBMB Life, 62, 414–428. doi:10.1002/iub.337.Holmes, E., Loo, R. L., Stamler, J., Bictash, M., I. K. S. Yap, Chan, Q., et  al. (2008). Human metabolic phenotype diversity and its association with diet and blood pressure. Nature, 453, 396–400. doi:10.1038/nature06882.Horne, A. B., Hodgman, T. C., Spence, H. D., & Dalby, A. R. (2004). Constructing an enzyme-centric view of metabolism. Bioinformat-ics, 20, 2050–2055. doi:10.1093/bioinformatics/bth199.Kanehisa, M. (1996). Toward pathway engineering: A new database of genetic and molecular pathways. Science & Technology Japan, 59, 34–38.Nguyen, T., Aparicio, M., & Saleh, M. A. (2015). Accurate mass GC/LC-quadrupole time of flight mass spectrometry analysis of fatty acids and triacylglycerols of spicy fruits from the Apiaceae family. Molecules, 20, 21421–21432. doi:10.3390/molecules201219779.Rhee, E. P., Cheng, S., Larson, M. G., Walford, G. A., Lewis, G. D., McCabe, E., et al. (2011). Lipid profiling identifies a triacylglyc-erol signature of insulin resistance and improves diabetes predic-tion in humans. The Journal of Clinical Investigation, 121, 1402–1411. doi:10.1172/JCI44442.Stark, C., Breitkreutz, B. J., Reguly, T., Boucher, L., Breitkreutz, A., & Tyers, M. (2006). BioGRID: A general repository for interaction datasets. Nucleic Acids Research, 34, D535–D539. doi:10.1093/nar/gkj109.Sud, M., Fahy, E., Cotter, D., Brown, A., Dennis, E. A., Glass, C. K., et  al. (2007). LMSD: LIPID MAPS structure database. Nucleic Acids Research, 35, D527–D532. doi:10.1093/nar/gkl838.Titz, B., Boué, S., Phillips, B., Talikka, M., Vihervaara, T., Schneider, T., et al. (2015). Effects of cigarette smoke, cessation and switching to two heat-not-burn tobacco products on lung lipid metabolism in C57BL/6 and Apoe-/- mice—An integrative systems toxicol-ogy analysis. Toxicological Sciences, 149, 441–457. doi:10.1093/toxsci/kfv244.Wang-Sattler, R., Yu, Z., Herder, C., Messias, A. C., Floegel, A., He, Y., et al. (2012). Novel biomarkers for pre-diabetes identified by metabolomics. Molecular Systems Biology, 8, 615. doi:10.1038/msb.2012.43.Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., et  al. (2013). HMDB 3.0—The Human Metabolome Data-base in 2013. Nucleic Acids Research, 41(D1), D801–D807.

Nottingham eTheses

Introduction It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.Objectives This work simplifies this process.Methods A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.Results The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.Conclusion This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations

Casbas Pinto, Ferran

Repository@Nottingham

Vol.:(0123456789) Metabolomics (2017) 13:81 DOI 10.1007/s11306-017-1214-ySOFTWARE/DATABASEA methodology for elucidating regulatory mechanisms leading to changes in lipid profilesFerran Casbas Pinto1,2 · Srinivarao Ravipati2 · David A. Barrett2 · T. Charles Hodgman1 Received: 6 December 2016 / Accepted: 3 May 2017 / Published online: 29 May 2017 © The Author(s) 2017. This article is an open access publicationInformation Section  2). However, the latter task is made difficult as a result of the complex chemical nature of lipids and their ubiquitous presence in multiple roles in biological tissues. In addition, the identification of many lipid molec-ular species is complicated by the high number of structural isomers corresponding to a given lipid mass. Although some low-throughput mass spectrometry techniques lead to precisely defined molecular structures (Nguyen et  al. 2015), there is still more technical development needed before this can be applied in a routine way to global lipi-domics. Pathways of lipid metabolism are both complex and often not fully understood: some reactions are not well characterized; some metabolites are not endogenously syn-thesised but enter through the diet (Holmes et  al. 2008); and many enzymes have low substrate specificity, allow-ing them to accept a huge range of fatty acids and alcohols as substrates (Gibellini and Smith 2010). In addition, most biological and clinical scientists refer to lipids by com-mon and often inconsistent names, rather than a systematic naming scheme, which can lead to confusion. These fac-tors together make it difficult to automate the construction and analysis of metabolic pathways and networks, and a basic requirement for progress is to define a mechanism for resolving synonyms and one-to-many relationships in lipid identities (Horne et al. 2004).Most recent lipidomics papers conclude their study with a list of the lipids that were found to be significantly dif-ferent between two groups, for example between diseased and healthy tissue or between treated and control samples. However, there is a small number of examples that go one step further. For example, one can create and test a predic-tive model (Floegel et al. 2013), apply advanced statistics to classify profiles into high or low risk of suffering a disease (Rhee et al. 2011), manually construct a network between known disease-associated genes, signal-transduction Abstract Introduction It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.Objectives This work simplifies this process.Methods A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.Results The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycys-tic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.Conclusion This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.1 IntroductionRecent advances in analytical methods, especially mass spectrometry, have shown that complex lipid profiles can be influenced by many factors and have the potential to improve our understanding of the pathophysiology underly-ing disease processes (references listed in Supplementary Electronic supplementary material The online version of this article (doi:10.1007/s11306-017-1214-y) contains supplementary material, which is available to authorized users. * T. Charles Hodgman  Charlie.Hodgman@nottingham.ac.uk1 School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK2 Centre for Analytical Bioscience, School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, UK F. Casbas Pinto et al.1 381 Page 2 of 4pathways and the perturbed levels of specific lipids (Wang-Sattler et  al. 2012), or colour-code pathway or network maps from databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Cottret et  al. 2010; Titz et al. 2015).In this work, we propose a method of systematically con-verting lists of perturbed lipids to integrate them with met-abolic network which permits visualisation of the affected pathways. This process involves four steps, the first two involving the construction of a network (that links enzymes and hence genes to reactions between specific lipids) and a thesaurus (linking generic lipid identifiers to a list of possi-ble specific metabolites). The second two steps involve the implementation of algorithms to generate the subset of the total network that corresponds to the input profile of per-turbed lipids, and to rank enzyme nodes in terms of their significance to the perturbation. As a proof of concept, we apply this method to a published lipidomics dataset .2  Methods2.1  Data sourcesSource data for creating the holistic human lipidomic net-work included LIPID MAPS (Sud et  al. 2007), KEGG (Kanehisa 1996), HMDB (Wishart et  al. 2013) and other papers cited in Supplementary Information Section 3.2.2  Network constructionThe lipid metabolic network was created using Python scripts applied to data from the above sources, plus manual curation, and has been stored in a mySQL database. All scripts can be obtained from https://github.com/Ferran-Casbas/Lipid-Pathway. The network contains a metabo-lite node for each specific lipid, but does not distinguish between the fatty-acyl groups at the sn-1 and sn-2 chain positions to limit the network size and complexity and because analytical resolution of sn-1/sn-2 sidechains is rarely achieved across the whole lipidome. Other nodes may refer to individual reactions, and the enzymes (speci-fied by their Human Genome Identifier) catalysing them. Further details are in the Supplementary Information Section 4.2.3  Subset generationThe whole process is depicted in Fig. 1. The input used is a list of lipids whose concentrations are significantly higher or lower between two groups, determined for example after univariate or multivariate data analysis of a lipidom-ics dataset and the structural confirmation of the chemical identities of the changed lipids. Lipid names should con-form to the standard in LIPID MAPS (http://www.lipid-maps.org/data/classification/lipid_cns.html#N). The pre-ferred level of identification is to include the lipid class and the numbers of carbons and double bonds in each side chain. For example, this approach can include ambiguously identified molecules such as DG(36:2) which indicates a diglyceride with two side chains totalling 36 carbons with 2 double bonds. The fold change or significance score for the metabolites (often the VIP scores from Partial Least Squares analysis) can also be input and used for meta-bolic prioritisation. Alternatively, +1 and −1 can be used to imply the direction of change in lipid level between one group and another.PHP scripts then use the list to produce a subgraph (sub-network). The first phase of the analysis works through the list to identify specific, ambiguous and unrecognisable terms. The software reports on this and recommends what might be done to clarify the unrecognised terms. Once the lipids of interest have been adequately associated with net-work nodes, a sub-network is generated by selecting from the lipidomic network these nodes and their two nearest neighbours, so that the reactions and enzyme/genes link-ing these lipids are also included. If the input lipids have fold change or significance scores, the output sub-network contains Relative Importance scores, which indicate how much influence the enzyme nodes have on producing the observed lipid-profile perturbation in light of the network topology. This can be particularly valuable when larger sub-networks are returned. More information on this is in Supplementary Information Section 6.The above scripts are available through a web inter-face, which can be found at: http://lipidnetwork.notting-ham.ac.uk/. The web tool outputs two files which can be imported into a network visualization software, such as Cytoscape (Doerks et al. 2002), to see the metabolic path-ways involved in the profile changes. Another option is to open the node-info file in a spreadsheet to retrieve the list of enzymes, which can then be used as a query to other network tools, such as the BioGRID (Stark et al. 2006) or genemania, to identify potential regulators.3  Results and discussionThis work describes a novel method for identifying enzymes and potential regulators related to measured changes in the lipid profile obtained from mass spectrom-etry analysis. The method includes the development of a holistic lipidomic network that comprises 127 enzymes, 13,934 reactions and 7561 metabolites. The latter are from 16 lipid subclasses: 3124 glycerolipids, 3575 glycerophos-pholipids, 290 sphingolipids, 126 fatty acyls (including A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles 1 3Page 3 of 4 81eicosansoids, fatty amides, esters and their conjugates), and 446 miscellaneous lipids. An image of the complete network can be seen in Supplementary Fig. 1, and further details of the network’s properties are described in Sup-plementary Tables  1–3. The Supplementary Information Section  9 outlines extensions to this method that can be carried out by suitably experienced bioinformaticians.To show the power and utility of the method, it has been applied to a published dataset that contains lipid profile for women with polycystic ovary syndrome (PCOS) compared Fig. 1  The Lipid network method. a Input list of lipids with OPLS-DA model scores. b Depicts the web server that contains scripts and the lipidomic network, in which node colours green, dark blue, orange, red and white respectively correspond to glycerophospho-lipids, sphingolipids, diglycerides, enzymes and specific reactions. c Derived subset of lipid metabolism linking the input perturbed metabolites; metabolite, reaction and enzyme nodes are respectively represented by rectangles, triangles and ellipses; input metabolite nodes are respectively coloured a brighter shade or green or red depending on the extent to which their levels have been elevated or decreased, or blue if they correspond to nodes introduced during the subset retrieval; the borders of enzyme nodes are shaded blue or red respectively to depict the extent of positive or negative Relative Importance Score (the sign corresponding to the increase or decrease in enzyme activity). d The list of retrieved enzymes ranked by Rela-tive Importance F. Casbas Pinto et al.1 381 Page 4 of 4with matched controls (2). The contribution score of the lipids found in significantly different concentration was assigned from an OPLS-DA analysis (see Fig.  1). The output .sif and node-attributes files were imported into Cytoscape and the nodes manually stacked to aid visual interpretation of the network. This allows us to view the enzymes that have a higher impact on the profile.In this example, 14 enzymes have been retrieved falling into two separate components, with the highest in absolute value of Relative Importance scores being for PLD1 (a phosphatidylcholine-specific phospholipase) and Chpt1 (a choline phosphotransferase). This suggests that the changes in plasma lipid profile associated with PCOS may be domi-nated by a reduction in Chpt1 activity and/or an increase in PLD1 activity. The former is responsible for changes in DG and PC, and PLD1 is responsible for changes in PE and PC. Since there is equilibrium between the PA and DG species, a simple hydrolysis would convert one into the other, the perturbation of Pld1 could by itself explain the majority of observed changes.The list of enzymes provided by this metabolic insight can be used to query other biomolecular network tools to identify potential common regulators of these lipids. 11 of them were present in BIOGRID, 4 linked by ELAVL1 and 2 by ILF3, which both protect target mRNAs from degra-dation (see Supplementary Fig. S7). Querying GeneMania revealed consistent patterns for regulation of Chpt1 and Pisd by microRNAs recognising the target sequence TGC TTG CT, and Dgkz, Ppap2a and Smpd1 by the transcription factors MAZ, NFAT and STAT5B (Supplementary Fig. S8). Unfortunately, both regulators have an enzyme whose activity goes in the opposite direction to the rest, so it is difficult to assert any strong hypotheses at this stage.4  ConclusionsTo conclude, the methodology presented here provides a way to move from tables of perturbed lipid levels to a visu-alization of the metabolic network that could cause these changes and thence to potential regulators. It also provides a metric (Importance) to rank the positive or negative con-tribution of each enzyme to produce the changes seen.Acknowledgements FCP, SR and DB acknowledge funding from EU FP7 Grant 305815 (D-BOARD).Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest.Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institu-tional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.Open Access This article is distributed under the terms of the Crea-tive Commons Attribution 4.0 International License (http://creativecom-mons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.ReferencesCottret, L., Wildridge, D., Vinson, F., Barrett, M. P., Charles, H., Sagot, M. F., et al. (2010). MetExplore: A web server to link metabolomic experiments and genome-scale metabolic networks. Nucleic Acids Research, 38, W132–W137. doi:10.1093/nar/gkq312.Doerks, T., Copley, R. R., Schultz, J., Ponting, C. P., & Bork, P. (2002). Systematic identification of novel protein domain families associ-ated with nuclear functions. Genome Research, 12, 47–56.Floegel, A., Stefan, N., Yu, Z., Mühlenbruch, K., Drogan, D., Joost, H.-G., et al. (2013). Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes, 62, 639–648. doi:10.2337/db12-0495.Gibellini, F., & Smith, T. K. (2010). The Kennedy pathway—de novo synthesis of phosphatidylethanolamine and phosphatidylcholine. IUBMB Life, 62, 414–428. doi:10.1002/iub.337.Holmes, E., Loo, R. L., Stamler, J., Bictash, M., I. K. S. Yap, Chan, Q., et  al. (2008). Human metabolic phenotype diversity and its association with diet and blood pressure. 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A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles

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