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1306 Mol. Nutr. Food Res. 2013, 57, 1306–1318DOI 10.1002/mnfr.201200759

REVIEW

Lipidomics in nutrition and food research

Tuulia Hyotylainen, Isabel Bondia-Pons and Matej Oresic

VTT Technical Research Centre of Finland, Espoo, Finland

Lipids are a diverse class of metabolites that play several key roles in the maintenance of humanhealth. Lipidomics, which focuses on the global study of molecular lipids in cells, tissues, andbiofluids, has been advancing rapidly over the past decade. Recent developments in MS andcomputational methods enable the lipid analysis with high throughput, resolution, sensitivity,and ability for structural identification of several hundreds of lipids. In nutrition research,lipidomics can be effectively used to elucidate the interactions between diet, nutrients, andhuman metabolism. Lipidomics can also be applied to optimize the effects of food processingon the dietary value, and in the evaluation of food-related health effects.

Keywords:

Lipid metabolism / Lipidomics / MS / Nutrigenomics / Personalized medicine

Received: November 14, 2012Revised: December 7, 2012

Accepted: December 29, 2012

1 Introduction

Lipids are structurally as well as functionally a diverse classof metabolites. Lipids play important functional roles as ma-jor constituents of cellular membranes, signaling molecules,and as energy source (Fig. 1). Due to their structures com-prising multiple common characteristic building blocks, e.g.fatty acids (FAs) or phospholipid headgroups, it has been es-timated that hundreds of thousands distinct lipid moleculesexist [1]. Lipids consist of two distinct types of chemicalsubunits, namely ketoacyl and isoprene groups. The mostwidely used classification divides the lipids into eight classes:fatty acyls, glycerolipids, glycerophospholipids, sphingolipids(SLs), sterol lipids, prenol lipids, saccharolipids, and polyke-tides [2] (Fig. 2).

Fatty acyls are structurally the simplest subclass that in-cludes various types of FAs, eicosanoids, fatty alcohols, fattyaldehydes, fatty esters, fatty amides, fatty nitriles, fatty ethers,and hydrocarbons. Many lipids in this class, especially theeicosanoids derived from n-6 and n–3 polyunsaturated FAs

Correspondence: Dr. Tuulia Hyotylainen, VTT Technical ResearchCentre of Finland, Tietotie 2, P.O. Box 1000, FIN-02044 VTT, Espoo,FinlandE-mail: [email protected]: +358-20-722-7071

Abbreviations: AT, adipose tissue; DGs, diacylglycerols; DHA,docosahexanoic acid; EWAT, epididymal adipose tissue; FAs,fatty acids; GPs, glycerophospholipids; HFD, high-fat diet; IM-

MS, ion mobility-MS; IR, insulin resistance; lysoPC, lysophos-phatidylcholine; MLCL, monolysocardiolipin; PCs, phosphatidyl-cholines; PLs, phospholipids; PUFAs, polyunsaturated FAs; SLs,sphingolipids; T2DM, type 2 diabetes

(PUFAs), have distinct biological activities. FAs are also themajor lipid building blocks of more complex lipids, such asglycerolipids, i.e. monoacylglycerols, diacylglycerols (DGs),and triacylglycerols (commonly referred to as triglycerides;TGs). These neutral lipids have a glycerol backbone withFA chains attached to the glycerol group, most commonlywith an ester bond, but ether bonded FAs can be also foundin GLs in minor amounts. Glycerophospholipids (GPs) arekey components of cellular membranes although they arealso involved in metabolism and signaling. GPs most com-monly found in biological membranes are phosphatidyl-choline (PC), phosphatidylethanolamine, phosphatidylinos-itol, phosphatidylglycerol, and phosphatidylserine. SLs, onthe other hand, are a complex family of compounds thatshare a common structural feature, a sphingoid base back-bone. Among these, sphingomyelins and ceramides (Cer) arealso abundant constituents of cellular membranes. SMs andGPs are commonly referred to as phospholipids (PLs) due toa phosphate group in the headgroup. Sterols are a class oflipids containing a common steroid nucleus of a fused four-ring structure with a hydrocarbon side chain and an alcoholgroup. Cholesterol is the primary sterol lipid in animal fatand an important part of the lipid membrane.

Lipids play diverse and important roles in nutrition andhealth. While many lipids can be endogenously synthesizedin the body, certain essential FAs such as linoleic acid andalpha-linolenic acid, have to be obtained from the diet becausethey cannot be synthesized from simple precursors in thediet [3]. The lipids obtained by diet are mainly in the formof fats, such as TGs and cholesterol, and PLs. Dietary fatsare an important component of the diet, but an excessiveintake of dietary fat is associated with many health problems,including obesity, cardiovascular disease, and some cancers.

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Figure 1. Structural and func-tional diversity of lipids.

Herein we review recent progress of lipidomics and how ithas contributed to nutrition and food research.

2 Lipidomics

Lipidomics is a subfield of metabolomics that focuses onthe global study of molecular lipids within a cell, tissue, andbiofluids [4,5]–including the analysis of lipid species and theirabundance as well as the studies of their biological activi-ties, subcellular localization, and tissue distribution [6]. Rapidprogress of lipidomics has been facilitated by the advances inboth MS [7–9] and computational methods, including data ac-quisition [9–11], bioinformatics [12–14], and systems biologyapproaches [5,15]. Typical lipidomics workflow in lipidomicsis illustrated in Fig. 3, starting from sampling, followed bysample preparation, analysis, data preprocessing, statisticalanalysis, and bioinformatics.

Due to the large number of molecular lipids in biologicalsystems, their wide concentration range (from attomolar tomicromolar) and chemical diversity, it is not possible to coverthe whole lipidome with a single analytical technique. Twotypes of analytics are commonly applied, in lipidomics stud-ies, (i) hypothesis-driven targeted analysis and (ii) a comprehen-sive, hypothesis-generating, nontargeted profiling approach. Inthe targeted analysis, only a defined group of lipids, usuallywithin the same lipid class, is analyzed with a targeted an-alytical protocol. While this approach allows very sensitiveand robust determination of the selected metabolites, it givesrelatively limited information about the global lipidome. The

nontargeted approaches aim to cover molecular lipids across awide range of structural classes in a single analysis. While be-ing more comprehensive than targeted approaches, the non-targeted methods are typically only semiquantitative, and it isnot possible to optimize the method for all compounds.

The most commonly applied analytical platforms forlipidomics are based on MS, often combined with chromato-graphic separation methods such as LC and GC. NMR hasnot been widely used in lipidomics, mainly because it is oftenchallenging to directly identify molecular lipids in complexmixtures with NMR. The similarity of the spectra of lipidswith respect to the limited structural carbon chain informa-tion is another challenge in lipid analysis with NMR.

2.1 Analytical methods

MS can be used for lipidomics without prior chromato-graphic separation, in the so-called shotgun approach bya direct infusion of the sample (extract) into the MS [11].Typically, high-resolution mass spectrometers such ashybrid quadrupole-time-of-flight MS and Fourier transformMS instruments, either the ion cyclotron resonance orthe Orbitrap, are commonly used in shotgun MS. Theadvantages of shotgun methodology are its simplicity andspeed, while its major limitation is the ion suppression,which hampers the sensitivity and quantitative robustness ofthe determination [16]. Typically, labeled standards are usedin shotgun approach to correct the matrix effects; however,standards are not available for all lipids. In addition, it is


1308 T. Hyotylainen et al. Mol. Nutr. Food Res. 2013, 57, 1306–1318

Figure 2. Major classes of lipids, with illustrative examples.

not possible to distinguish isobaric and isomeric species,whose masses are identical, and they often display similarfragmentation profiles. Thus, the applicability of shotgunMS in the search of novel, previously unknown lipids isrelatively restricted. Nevertheless, its application might beconsidered in the future as a screening tool to identify thebiochemical mechanisms underlying the metabolic diseases.

MS is commonly combined with LC for the lipidomicanalyses. The sensitivity of LC-MS approach is typically high,and identification of novel lipids is possible. In recent years,ultra high-performance LC coupled to MS-based methodolo-gies have been widely used for both targeted and nontargetedanalyses, using various types of mass spectrometers, from a

simple single quadrupole to hybrid instruments and to high-resolution instruments [17]. For global profiling, the popularchoices are combinations of UHPLC with QTOF/MS or withtandem ion mobility TOF/MS, both allowing fast analysisand high-resolution MS detection [18,19]. Typically, up to sev-eral hundreds of lipids can be separated with the UPLC-MSmethodologies in the profiling studies of various biologicalmatrices [20]. The matrix effects are an important challengealso in the global profiling with ultra high-performance LCcoupled to MS methods as it is not possible to use labeledstandards for all compounds. The sensitivity is typically notas high as in targeted methods, both because the method-ological conditions cannot be optimized for each compoundseparately as well as because metabolites present at high con-centrations may hinder the analysis of minor metabolitesdue to matrix suppression. For targeted analyses, typicallytriple quadrupole is used for the detection with ultra high-performance LC coupled to MS, such as by using selective ionmonitoring. With the most recent UPHLC-triple quadrupoleinstruments, very high sensitivity can be obtained (at the levelof picomoles). The targeted lipid methods may include meth-ods for eicosanoids, sterol lipids such as steroids and bileacids [21, 22].

Since GC-based methods are only suitable for sufficientlyvolatile compounds, most of the lipids cannot be analyzedby GC. However, for sufficiently volatile lipids, GC-basedmethods are a viable option, and GC-MS (and GC coupled toflame ionization detection) is the most widely used methodfor the analysis of FAs, both for free FAs (FFA) and esterifiedFAs [23]. GC-MS methods are also used for the analysis ofsteroids [24]. For FFA and steroids, derivatization by silylationis the most common methodology, while esterified FAs aretypically analyzed as their methyl esters (FAME).

Recently, novel methodologies including ion mobility-MS(IM-MS) [25] and multidimensional methodologies [26] havebeen utilized in lipidomics. By coupling ion mobility spec-trometry with MS (2D IM-MS), a rapid separation of isomers,conformers, and enantiomers can be obtained in addition to aresolving power similar to that of capillary GC. IM-MS has al-ready shown its strength in lipid characterization, includingPLs [27–29] and in the analysis of complex biological sam-ples such as tissues [30]. The advances in imaging MS havealso played an important role in the development of imagingIM-MS for lipid analysis [31, 32]. The combination of IM-MSexperiments with molecular dynamics computational model-ing [33] might be a useful tool to elucidate the structure andstability of lipid-incorporated complexes in future foodomicstudies. In addition to the drift time ion mobility method, thepotential of the newly introduced traveling wave ion mobilitybased-instruments for the analysis of molecular lipids is ex-pected to impact in the area of food research over the next fewyears. Also comprehensive multidimensional LC-MS, whichinherits the advantages of the existing methodologies andovercomes some of the limitations, is an attractive emerg-ing approach for comprehensive lipidomic characterizationof complex biological or food matrices [26].


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Figure 3. Overview of thelipidomics platform.

2.2 Lipidomics data analysis

As in other "omics" disciplines, data analysis plays a key rolein lipidomics. Particularly in the global profiling approaches,the amount of data is large and it is challenging to interpretthe data without proper bioinformatics. Before any statisticalanalysis can even be performed, data preprocessing is re-quired, including signal processing, data normalization, andtransformation, so that the raw data signals are transformedinto the format that can be used for the statistical data analy-sis [34,35]. Given the large degree of covariation between thelipids, particularly those of the same functional class, the firststep of statistical analysis is commonly data reduction, suchas by principal components analysis. Both unsupervised andsupervised methods can be used, depending on the goal ofthe specific analysis. In unsupervised data analysis, such ashierarchical cluster analysis and principal components anal-ysis, no prior information about sample grouping is used.In the supervised approach, such as principal component re-gression and neural networks, each sample or metabolite isfirst associated to already known class or phenotypic variable,and this prior information is then used in the analysis, e.g.classification or regression [34]. Other techniques utilized inlipidomics include artificial neural networks, self-organizingmaps, and linear discriminant analysis, among manyothers [34, 35].

2.3 Lipid pathway analysis

The enormous functional and structural diversity of lipidsand their complex regulation at multiple spatial and tempo-ral scale adds an additional layer of complexity to the studies

of lipids using a systems biology approach [14]. The mea-sured molecular lipid profiles are studied in the context ofknown or inferred biological networks such as metabolicor signaling pathways. The LIPID MAPS consortium hascomprehensively reconstructed lipid pathways based on bothexperimental data and data from literature by using toolssuch as VANTED [36] and the Pathway Editor [37]. The usesof the VANTED platform in lipidomics have been previ-ously reviewed extensively [38]. The Pathway Editor allowsde novo pathway creation and downloading of LIPID MAPSand KEGG lipid metabolic pathways, as well as retrieval ofmeasured time-dependent changes to lipid components ofmetabolism [37].

Lipid-signaling pathways are complex and the therapeu-tic potential of modulation of intracellular and systemiclipid metabolism is well recognized [39, 40]. Understandingthe integrated lipidomic networks and decoding the coor-dinately regulated pathways will therefore constitute majorgoals for the following years in applied lipidomics research.A quantitative dynamic model of C16- branch of sphingolipidmetabolism developed by Gupta et al. [41] is a clear exampleof integration of lipidomics and transcriptomics data towarda systems biology approach for understanding sphingolipidbiology. Yetukuri et al. developed a lipid pathway recon-struction method based on lipidomics and transcriptomicsdata [42]. Similar studies are expected in the future for otherlipids.

It is important to point out that a better understandingof the lipidome at the physiological level lipids does notonly have to include lipid modeling at the level of biologicalpathways, but also at the level of the biophysical systemsthe lipids are part of, such as cellular membranes andlipoproteins particles [14].



3 Applications of lipidomics in nutritionand food research

Dietary choices may help prevent or promote a disease. Dietis associated with several diseases including obesity, dia-betes, atherosclerosis, hypertension, malignancy, osteoporo-sis, inflammatory disease, and even infectious diseases [43].However, the link between specific diets and health out-comes is generally poorly understood. Humans differ in theirmetabolic regulation, and an optimal diet for one individualis not necessarily optimum for another. A primary goal of nu-trition research is to optimize health and prevent or delay thedisease. While some individuals are easily affected by a dietthat leads to obesity and its metabolic comorbidities, othersubjects with a similar phenotype remain healthy followingthe same diet. Developments in nutritional metabolomics aretherefore needed to facilitate transition of nutritional sciencesfrom population-based to individual-based criteria for nutri-tion research, to support the integrated models for futurepersonalized diet and nutrition forecasting.

Lipidome, or more broadly metabolome, is sensitive tomany pathogenically relevant factors such as host genotype,gut microbiota, and the diet. Lipidomics is therefore consid-ered as a powerful platform to study the interactions betweengenes, diet, nutrients, and human metabolism, and how theytogether contribute to health and disease. Linking the indi-vidual’s “metabotype” to the diet and health may allow es-timation of (i) individual nutritional status, (ii) follow-up ofcompliance, progress, and success of dietary guidance andintervention, (iii) identification of side effects, unexpectedmetabolic responses, or lack of response to specific dietarychanges, (iv) recognition of metabolic shifts in individualsdue to environmental changes, lifestyle modifications, aswell as (v) normal progression of aging and maturation [44].Lipidomics can also be applied in food research, such as in de-velopment of food products, in the evaluation of food quality,functionality, bioactivity, and toxicity.

3.1 Lipidomics to characterize the effects of specific

diets and challenge tests

Characterization of dietary effects in physiological context ischallenging. Early alterations in metabolism due to dietary ef-fects may be masked by biological variation (both within andbetween persons), which is generally very large. Furthermore,the analysis of samples in most metabolomics studies are ob-tained in a fasted state, and only few studies reported thetime-resolved changes of human metabolome in response toa challenge [45–47]. Significant changes in bile acids were forinstance linked to glucose homeostasis by applying an LC-MS/MS metabolic profiling strategy to samples from bothhealthy and impaired-glucose tolerance individuals after anoral glucose tolerance test [45]. Their findings laid the ground-work for using metabolic profiling to define an individual’s

insulin response profile, which might have value in predict-ing diabetes, its complications, and in guiding therapy whenconfirmation with data from larger, prospective clinical stud-ies of prediabetics become available.

In order to extend the knowledge on the dynamics of thehuman metabolome in response to different challenges, arecent study, with a special focus in lipid and amino acidchanges, was performed in healthy men who underwent aprolonged fasting 36-h period, a standard liquid diet, an oralglucose tolerance test, and an oral lipid tolerance test [48]. Thestudy results showed that physiological challenges increasedinterindividual variation even in phenotypically similar volun-teers, revealing specific metabotypes not observable at base-line. Plasma-free carnitine and acylcarnitines were shown tobest define any catabolic and anabolic condition and theirtransitions, and their ratio was suggested by the authors asmarker for the metabolic state.

Specific examples of lipidomics application to character-ize the effects of specific diets in recent human interventionstudies are briefly described here. Lipidomic profiles werefor instance studied in an 8-week dietary intervention includ-ing either fatty fish or lean fish in subjects with myocardialinfarction or unstable ischemic attack [49, 50]. Among over300 lipids identified and quantified, multiple lipid species in-cluding ceramides, lysophosphatidylcholines (lysoPC), DGs,PCs, and lysophosphatidylethanolamines were significantlydecreased in the group on fatty fish diet, while in the leanfish diet group cholesterol esters and specific long-chain TGssignificantly increased after 8 weeks of intervention [49]. Thedecrease in lysoPC in the fatty fish group may be related toanti-inflammatory effects of n-3 FAs as lysoPC is the majorbioactive lipid component of oxidized LDL and may be re-sponsible for many of their inflammatory effects [51]. Theseresults, together with the fact that the prevalence of impairedglucose tolerance and type 2 diabetes (T2DM) is lower in pop-ulations with a high intake of n-3 FAs, support the hypothesisthat DGs and ceramides may be the link between n-3 FAsand insulin resistance (IR). A further evidence of the role ofceramides was observed in a study related to the proinflam-matory cytokines IL-6 and TNF-� that are associated with IRand the metabolic syndrome [52]. Ceramides may thus con-tribute to the induction of inflammation involved in IR statesthat frequently coexist with coronary heart disease.

Lipidomics profiling was also applied to study the effectsof a plant sterol intervention on lipid metabolism [53]. Plantsterols are well known to reduce levels of total cholesteroland LDL cholesterol [54]. Lipidomic analysis in serum sam-ples from a placebo-controlled, parallel intervention studyof 4-week consumption of two plant sterol-enriched yogurtdrinks differing in fat content in healthy mildly hypercholes-terolemic subjects showed significant effects of the plantsterol intake on the serum lipidome [53]. Although bothdrinks showed a similar reduction in total cholesterol andLDL cholesterol [55], the low-fat drink had interestingly themost significant impact on the serum lipidome, by reducingthe levels of several sphingomyelins. This reduction, which


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correlated well with the reduction in LDL cholesterol, was ex-plained by colocalization of SMs and cholesterol on the sur-face of LDL lipoprotein. In addition, the observed significantreductions in serum levels of two LysoPCs (LysoPC(16:1) andLysoPC(20:1)) and cholesteryl-arachidonate, may also suggestreduced inflammation and atherogenic potential.

Lipidomics may also provide powerful tools for identify-ing new biomarkers behind the clinical effects of probioticintervention trials as confirmed in the first study investigat-ing the effects of probiotic intervention on global lipidomicprofiles in humans [56]. In that study, a 3-week interventionwith probiotic lactobacillus rhamnosus GG bacteria showeddecreased levels of LysoPCs, SMs, and several PCs in the pro-biotic group. These changes may contribute to the metabolicevents behind the beneficial effects of lactobacillus rhamno-sus GG on gut barrier function [57].

Lipidomics is also beginning to be applied to determine ifthere are significant lipidome changes in response to caloricrestriction diets. A targeted approach was performed for fast-ing and postprandial plasma samples obtained before andafter a 6-month intervention in which subjects were random-ized to different caloric restriction diets and a weight main-tenance diet as control [58]. The study revealed significantlarger differences in fasting-to-postprandial concentrations ofmedium and long chain acylcarnitines, which are known asbyproducts of FA oxidation, in the 25% caloric restriction dietversus the control diet. In addition, the observed differenceswere related to improvements in insulin sensitivity [58].

3.2 Lipidomics and diet-related diseases

The origin of nutrition-related diseases, such as obesity andrelated lipid disturbances is multifactorial. Environmentalfactors, including nutritional status, dietary patterns, andlifestyle factors, play a key role in the development of obe-sity, in addition to the genetic variation, which also influ-ences body fat accumulation and lipid metabolism. As it wasrecently pointed out by Quehenberger and Dennis [39], theabundance of individual molecular species in plasma may in-deed be indicative of the variety of specific human diseases.

Environmental and lifestyle factors, including dietary fac-tors, play a key role in the development of obesity, but cross-sectional studies comparing lipid profiles in obese versusnonobese humans do not permit unequivocal distinction be-tween genetic versus environmental effects [59]. This canbe best done by studying monozygotic twins discordant forobesity. In a study focused on young and healthy obesitydiscordant monozygotic twins, Pietilainen et al. showed thatobesity, independent of genetic factors, was related to distinctchanges in the global serum lipid profile, as obtained by theUPLC-QTOF/MS platform [46]. In comparison to nonobesecotwins, the obese cotwins had increased levels of lysoPCs,which are lipids found in proinflammatory and proathero-genic conditions [60], as well as decreased levels of etherPLs, which are known to exert antioxidative properties [61].

Importantly, these lipid changes were associated with IR, ametabolic characteristic of acquired obesity in these healthyadult twins. The authors therefore pointed out that propermanagement of obesity, with a new generation of therapies di-rected at several targets in the lipid metabolism pathways, willmost likely correct these abnormalities, and favorably modifythe risk, course, and outcome of diabetes and cardiovasculardiseases. Further lipidomic analyses of adipose tissue (AT)in the same twin population provided more detailed mecha-nistic picture of the acquired obesity [62]. The study revealedthat the obese twin individuals had increased proportions ofpalmitoleic and arachidonic acid in their adipose tissue, in-cluding increased levels of ethanolamine plasmalogens con-taining arachidonic acid, despite lower dietary PUFA intake.Information gathered from these twins and from a separateset of morbidly obese subjects, was used for molecular dy-namics simulations of lipid bilayers, and the conclusionswere further supported by in vitro adipocyte confirmatorystudies. This novel strategy enabled the authors to identifyadaptive mechanisms that may lay behind the characteristicremodeling of the AT lipidome in response to positive-energy-balance-induced adipose tissue expansion during the evolu-tion of obesity. The simulations suggested that the observedlipid remodeling maintains the biophysical properties of lipidmembranes at the price of increasing their vulnerabilityto inflammation [62] (Fig. 4).

Although obesity is associated with T2DM, not all obeseindividuals develop T2DM [63]. Whether T2DM is associatedwith specific changes in the plasma lipidome is thereforean important question. However, it is difficult to investigatemechanisms involved in the progression from obesity to IRand T2DM in humans, as the study setting requires a long-term follow-up, and the use of animal models is a commonand complimentary approach that also provides a mechanisticand systemic insight into the development of specific T2DM-related complications. The chronically high-fat fed mousemodel was recently used in a plasma lipidomics study aimingat gaining insight in specific molecular lipid species associ-ated with both obesity and T2DM [64]. The levels of severalTGs and DGs were elevated along with a number of SLs,but unexpectedly, the 12-week high fat diet (HFD) induceda reduction in LysoPC plasma levels. Nevertheless, only verymodest changes in metabolically active tissue LysoPCs werenoted. In contrast with Han et al. [65], the authors were un-able to show that HFD caused an increase in LysoPC contentin liver and muscle, the two most important tissues in reg-ulating insulin sensitivity. The authors concluded that dietand adiposity, rather than IR and diabetes per se, might playa role in altering the plasma LysoPC profile. Treating cul-tured adipocytes with LysoPC stimulated glucose uptake in adose-dependent manner via an insulin-independent mecha-nism involving activation of proteinkinase C � [66]. WhetherLysoPC may exert beneficial effects on glucose metabolism inhumans will undoubtedly need further research in the field.

An in vivo study combining time-resolved microarray anal-yses of mesenteric, subcutaneous, and epididymal adipose



Figure 4. A model for physiological regulation of lipid membrane composition in obesity. In healthy obesity, lipid membranes adaptas adipocytes expand in size. Given that adaptation seems to involve a relative increase in precursors of pro-inflammatory mediators,adaptation might increase vulnerability to inflammation. Reproduced with permission from Pietilainen et al. [49].

tissue (EWAT) during high-fat feeding of male transgenicapolipoprotein E3 Leiden (ApoE3Leiden) mice with histologyand targeted lipidomics reported that the contents of linoleicacid and alpha-linolenic acid in EWAT were increased com-pared to other depots [67]. The authors suggested that theandrogen receptor, which was overexpressed in EWAT ascompared to other tissues, may mediate depot-dependent dif-ferences of de novo lipogenesis and proposed that the accu-mulation of dietary essential FAs are accumulated in EWATas a result of androgen-mediated suppression of lipogenesis,thus providing an adaptive mechanism to provide precursorsfor epididymal PUFA synthesis. Specific plasma FFA andtheir ratio were also proposed as future predictors of glu-cose intolerance using the same model in a study combininglipidomics and transcriptomics [68].

The adverse sequelae of obesity and type 2 diabetes mayalso result from disruption in the efficiency of transitionsof metabolic flux that occur during changes in substrate uti-lization (such as glucose versus FA) or changes in energydemand. However, the biochemical mechanisms behind theintegration of multiple cell-specific responses are not yet wellcharacterized [69]. Mitochondrial and peroxisomal phospho-lipases are key actors in the regulation of cellular bioener-getics and signaling [70, 71]. Mice deficient in phospholi-pase A2� (iPLA �−/−) are resistant to HFD-induced weightgain, hyperinsulinemia, and IR [72]. Shotgun lipidomics ofAT from wild-type mice demonstrated a twofold increase intriacylglyceride content after high-fat feeding as comparedto iPLA �−/− mice fed either a standard diet or an HFD.In addition, shotgun lipidomics of skeletal muscle revealeda decreased content of cardiolipin with an altered molecu-lar species composition in iPLA �−/− mice, thereby iden-tifying for the first time the mechanism underlying mito-chondrial uncoupling in this experimental model [73]. Tissuemacrophage inflammatory pathways have been also shown tocontribute to obesity-associated IR [74]. Lipidomics analysisrevealed that the treatment with a novel anti-inflammatory

compound HE3286 reduced liver cholesterol and triacylglyc-eride content in Zucker diabetic fat rats, leading to a feedbackelevation of LDL receptor and HMG-CoA reductase expres-sion [75].

The risk of inflammatory disease is also influenced byboth life-stage and lifestyle. Circulating levels of inflamma-tory markers, such as eicosanoids and cytokines, increaseboth postmenopause in women and postovariectomy in ro-dents [76]. Resolvins and protectins are a family of lipid me-diators derived from omega-3 long-chain PUFAs with po-tent anti-inflammatory and resolving activities [77]. Poulsenet al. [78] reported the presence of these lipid mediators inmurine bone marrow and demonstrated by using LC-MS/MSlipidomics approach, that the profile of lipid mediators fromthe lipoxygenase pathway is modified by ovariectomy andby dietary intake of the precursor long-chain PUFAs. Thesupplementation with eicosapentaenoic acid and docosahex-anoic acid (DHA) increased the percentage of both FA in bonemarrow as well as the proportion of lipoxygenase mediatorsbiosynthesized from eicosapentaenoic acid and DHA.

Several studies have suggested preventive or therapeuticactivities of DHA in several neurodegenerative and psychi-atric diseases such as depression [79] and Alzheimer’s dis-ease [80]. A lipidomic approach showed that after 1 month offish oil supplementation with omega-3 FAs, PE in cortex andhippocampus brain areas became enriched with DHA at theexpense of arachidonyl-containing PE species in male Wistarrats in a higher degree than in brain striatum [81]. These datamight in part explain the mixed therapeutic results obtainedin neurological disorders, many of which are likely regionspecific.

Unlike rodents, humans preferentially use dietary DHAfor building up the brain membrane PLs [82]. Losses of DHAcaused by dietary constraints are substituted by generation ofdocosapentaenoic acid. A lipidomics study with pregnant ratsshowed that when alpha-linolenic acid nutritional deficiencyis imposed, docosapentaenoic acid appears to substitute for


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the losses of DHA in tight linkage with specific saturated andmonounsaturated long-chain hydrocarbons at the sn-1 posi-tion, sustaining a highly conserved molecular species com-position [83].

Hypertension is recognized to be related, among otherfactors, to unhealthy dietary habits such as excessive in-take of calories, alcohol, and salt [84]. Large population-based cohort studies have shown that dyslipidemia plays akey role in the development of hypertension [85]. A plasmalipidomics approach based on LC-IT-TOF/MS revealed thatlipid metabolism in hypertensive subjects is clearly differentfrom that in normotensive subjects, with PCs and TGs beinghighly abundant in the plasma of hypertensive patients [86].In this study, the TGs containing three or two SFA chainswere significantly upregulated in hypertensive subjects, sug-gesting potential role of lipotoxicity [86]. In addition, a largenumber of neutral lipid species were significantly elevated inhypertensive subjects but significantly decreased after treat-ment with antihypertensive agents.

The integration of lipidomic data with genetic, proteomic,and metabolomic data is expected to provide a powerful ana-lytical approach for elucidating the mechanisms behind lipid-based diseases [87]. Studies of hepatic lipid metabolism canfor instance provide mechanistic insights into the develop-ment of fatty liver disease, which is a disease associated withchronic alcohol intake. For instance, in a study focusing on FAmetabolism in fatty liver disease, the integration of gene ex-pression data with targeted lipidomics analyses of plasma andliver from control and alcohol-fed C57BL/6 mice led to betterunderstanding of alcohol-induced changes in hepatic lipidlevels [88]. The targeted lipidomics platform applied includedmeasurements of hepatic FFA and FA-CoA, which are essen-tial precursors of many liver lipids, as well as FA ethyl esters,which are produced by the nonoxidative metabolism of alco-hol; and on the metabolism of two lipid-signaling families,SLs, and endocannabinoids. The study results support theconcept that decreased mitochondrial FA oxidation is one ofthe contributing factors in alcoholic fatty liver disease. Alcoholfeeding led to elevated FFA levels, coupled with decreased ex-pression of genes associated with FA oxidation [88]. Clugstonet al. were indeed the first to report that decreases in FA-CoAlevels in the liver of alcohol-fed mice were associated withdecreased expression of FA-CoA-synthesizing genes [88]. Inthat study, there was also an increase of ceramide levels in thealcohol-fed mice, which was associated with increased levelsof the precursor metabolites sphingosine and sphinganine.

A new method based on high-resolution LC-MS and high-energy collisional dissociation fragmentation with a specialfocus on characterization of mitochondrial cardiolipins andmonolysocardiolipins (MLCL) was applied to a lipidomic pro-filing analysis of rat liver mitochondrial samples in a nu-tritional intervention study [89]. The study hypothesis wasthat intraclass shifts of fats and carbohydrates in the diet af-fect the physiological function and biochemical fingerprintof mitochondria. The diets used in the study were isocaloricand comprised six different fat groups with the major con-

stituent of each being either SFA, trans FA, MUFA, or one ofthe three groups of PUFAs varying in the omega-6/omega-3ratio. Among the identified compounds, two MLCL species,MLCL (18:2)3 and MLCL(18:2)2(18:1) were present in the ratliver mitochondrial samples. MLCL is an intermediate in car-diolipin metabolism as well as a potential byproduct of lipidperoxidation damage. The study showed a trend linking theamount of MLCL (18:2)3 present in mitochondria with themajor fat component of the diet. The greatest relative per-centage of this species was found in the liver mitochondriafrom rats maintained on diet containing trans fat as the ma-jor constituent. This result might reflect impaired cardiolipinmaturation or increased steady-state oxidative stress in theliver mitochondria of animals fed these diets [89].

During malignant transformation in cancer, lipidmetabolism of cells changes drastically and therefore alter-ations in lipid metabolism are a prominent feature of solidtumors [90]. Due to this link between lipid metabolism andcancer, metabolomics has been increasingly applied in can-cer research. In a study of 267 human breast tissues, M. Hilvoet al. showed that progression of breast cancer is strongly as-sociated with increase in membrane PLs containing de novosynthesized saturated FAs [91]. In colorectal carcinogenesis,diet may have an important role because of the direct contactof nutrients and their metabolites with the intestinal mu-cosa [92]. This is in fact supported by the epidemiologicaldata indicating a link between the intake of dietary lipids anddevelopment of colorectal cancer [93]. An increased risk forcancer development is found in subjects consuming dietshigh in red and processed meat [94]. Deep-fried/oxidized fatssuch as hydroxyl- and hydroperoxy- FAs have been shownto influence lipid metabolism by activation of PPAR-� [95]. Agrowing number of studies support the findings that bioactivedietary components containing long-chain PUFAs modulateimportant determinants that link inflammation to cancer de-velopment and tumor progression [96], while short-chain FAs(especially butyrate) that are mainly produced by the micro-biome using fermentable dietary polysaccharides, may helpprevent cancer [97].

3.3 Lipidomics to study food quality

Food quality and safety are the two main issues related toboth processed and fresh foods. Lipidomics may also be uti-lized for controlling the quality, as well as to detect fraudin food products. The specificity and sensitivity of MS-basedmethods is officially recognized by international quality sys-tem control agencies and the application of multistage ionanalysis has become mandatory to adhere to worldwide regu-lations regarding the recognition of fraud and bad practices infood manipulation [98]. During the last decade, the search formarkers of authenticity, quality, safety of foods, as well as thediscovery of signature peptides, has improved due to advancesboth in proteomics [99], allergonomics [100], and lipidomics.A novel term “foodomics” has been coined to define studies



in the food and nutrition domains through the application ofadvanced “omics” technologies to improve consumer’s wellbeing and health [101]. Herrero et al. have recently reviewedthe MS-based strategies that have been or can be applied inthis challenging field [102].

TGs present in oils and fats are important constituents ofthe human diet. The nutritional value of fats largely dependson the degree of FA saturation. Using the lipidomicsapproach, the stereospecific composition of TGs can be usedas a tool to characterize different fats of interest in humanhealth such as olive oil. In fact, there are several factorsthat affect the quality and composition of oils. The soil andclimate, and the processing and chemical treatments duringstorage of the vegetable seeds or fruits from which the oilis extracted are crucial factors in the composition of the oil.Oil samples are routinely analyzed by single quadrupole andion trap MS [103]. Multistage MS3 analysis of ions has alsoproved to be a useful approach in the characterization of TGsin complex mixtures [104].

In the evaluation of fish quality, shotgun lipidomics hasbeen applied successfully [105]. Freshness of the fish is afundamental quality feature that is closely linked to the mi-crobiome flora, storage temperature, handling, and physio-logical conditions of the fish [106]. Changes in phospholipidcomposition during storage are one of the most importantpostmortem changes affecting the freshness of fish, with ox-idation and hydrolysis of PLs being the mean reasons forquality deterioration [107]. Recently, shotgun lipidomics wasshown to be an effective method for qualitative and quantita-tive analyses of PLs from the Ctennopharyngodon idellus mus-cle at room temperature storage [108]. Interestingly, some PEmolecular species that were present in low concentrationsin original samples, such as PE (16:0/16:1), have increasedduring the fish storage. The authors suggested that those PEspecies may come from microbiome breeding in the muscle,a phenomenon that had not been identified previously [108],implying its potential relevance as a marker of fish quality.

Also MALDI-TOF/MS has been utilized in the quality con-trol by Calvano et al. [109], who demonstrated the use of anew MALDI matrix based on lumazine for the analysis ofPLs. Lumazine is photochemically stable under UV laser ir-radiation, and it displays very few matrix-related ions in bothionization modes, and thus appears very suitable for study-ing PLs in complex mixtures. The method was applied forthe characterization of crude lipid extracts of several dietaryproducts including cow milk, soymilk, and hen egg. The re-sults showed that PEs, PSs, and PIs could be detected, andindividual PL classes could be detected with high sensitivityin the negative ion mode with a relatively low presence ofmatrix adducts [109].

4 Future perspectives

Lipidomics, together with metabolomics, has been in-creasingly utilized in nutritional studies as well as in the

development of food products and evaluation of food func-tionality, bioactivity, and toxicity. The advanced analyticaltechniques, particularly LC-MS-based methods in combi-nation with bioinformatics tools can give a deep insightof the biological processes and molecular composition infood-related studies. The current analytical methodologiesalready allow lipid analysis with high-throughput, resolution,sensitivity, and ability for structural identification. Furtherdevelopment is still needed in the data processing, datamining, and interpretation of the data. The current lackof nutritional studies in which the lipidomics data can becritically interpreted in a physiological level highlights aneed to deepen into the lipid modeling, both at a biologicalpathway but also at a biophysical systems level.

In nutritional studies, lipidomics allows sensitive mea-surement of reporters of complex pathological states relatedto altered metabolism. Combining the lipidomics data withthe individual phenotype can provide relevant informationon the molecular events initiated by the nutrient intake andthe specific adaptations of the body to altered flux of certainnutrients through specific metabolic pathways. Lipidomicshas already proven to be a useful tool in the identificationof individual variability in responses to nutritional interven-tions. In the future, such approaches will allow developmentof more individualized approach for dietary guidance andallows shifting the focus of clinical nutrition research fromdisease treatment and management to one of disease pre-vention. In food development, lipidomics can be used in theoptimization of the effect of food processing on the dietaryvalue, such as bioactivity and bioavailability of the food prod-ucts, and in the evaluation of their health effects and theirsafety. Lipidomics can also be utilized in the identification ofclinical endpoints of a dietary intervention as well as in theidentification of novel biomarkers. These in turn can be uti-lized in validation of health claims of functional and healthpromoting dietary components.

This work was supported by the EU-funded project ETHER-PATHS (FP7-KBBE-222639, http://www.etherpaths.org/).

The authors have declared no conflict of interest.

5 References

[1] Yetukuri, L., Ekroos, K., Vidal-Puig, A., Oresic, M., Informat-ics and computational strategies for the study of lipids. Mol.BioSyst. 2008, 4, 121–127.

[2] Fahy, E., Subramaniam, S., Brown, H. A., Glass, C. K. et al.,A comprehensive classification system for lipids. J. LipidRes. 2005, 46, 839–862.

[3] Simopoulos, A. P., Human requirement for N-3 polyunsat-urated fatty acids. Poult. Sci. 2000, 79, 961–970.

[4] Wenk, M. R., The emerging field of lipidomics. Nat. Rev.Drug Discov. 2005, 4, 594–610.

[5] Oresic, M., Metabolomics, a novel tool for studies of nutri-tion, metabolism and lipid dysfunction. Nutr. Metab. Car-diovas. Dis. 2009, 19, 816–824.


http://www.etherpaths.org/

Mol. Nutr. Food Res. 2013, 57, 1306–1318 1315

[6] Dennis, E. A., Lipidomics joins the omics evolution. Proc.Nat. Acad. Sci. USA 2009, 106, 2089–2090.

[7] Chait, B. T., Mass spectrometry in the postgenomic era.Annu. Rev. Biochem. 2011, 80, 239–246.

[8] Jung, H. R., Sylvanne, T., Koistinen, K. M., Tarasov, K. et al.,High throughput quantitative molecular lipidomics. BBA–Mol. Cell Biol. Lip. 2011, 1811, 925–934.

[9] Sandra, K., Pereira, S., Vanhoenacker, G., David, F. et al.,Comprehensive blood plasma lipidomics by liquid chro-matography/quadrupole time-of-flight mass spectrometry.J. Chromatogr. A 2010, 1217, 4087–4099.

[10] Han, X., Gross, R. W., Shotgun lipidomics, multidimen-sional MS analysis of cellular lipidomes. Expert Rev. Pro-teomics 2005, 2, 253–264.

[11] Stahlman, M., Ejsing, C. S., Tarasov, K., Perman, J. et al.,High-throughput shotgun lipidomics by quadrupole time-of-flight mass spectrometry. J. Chromatogr. B. 2009, 877,2664–2672.

[12] Niemela, P. S., Castillo, S., Sysi-Aho, M., Oresic, M., Bioin-formatics and computational methods for lipidomics. J.Chromatogr. B. 2009, 877, 2855–2862.

[13] Song, H., Ladenson, J., Turk, J., Algorithms for automaticprocessing of data from mass spectrometric analyses oflipids. J. Chromatogr. B. 2009, 877, 2847–2854.

[14] Oresic, M., Informatics and computational strategies for thestudy of lipids. BBA–Mol. Cell Biol. Lip. 2011, 1811, 991–999.

[15] Gross, R., Han, X., Lipidomics at the interface of struc-ture and function in systems biology. Chem. Biol. 2011, 18,284–291.

[16] Moco, S., Vervoort, J., Moco, S., Bino, R. J. et al.,Metabolomics technologies and metabolite identification.TrAC 2007, 26, 855–866.

[17] Metz, T. O., Zhang, Q., Page, J. S., Shen, Y. et al., The futureof liquid chromatography-mass spectrometry (LC-MS) inmetabolic profiling and metabolomic studies for biomarkerdiscovery. Biomark Med. 2007, 1, 159–185.

[18] Yang, B., Zhang, A., Sun, H., Dong, W. et al.,Metabolomic study of insomnia and intervention effectsof Suanzaoren decoction using ultra-performance liq-uid chromatography/electrospray-ionization synapt high-definition mass spectrometry. J. Pharm. Biomed. Anal.2012, 58, 113–124.

[19] Nygren, H., Seppanen-Laakso, T., Castillo, S., Hyotylainen,T. et al., Liquid chromatography-mass spectrometry LC-MS-based lipidomics for studies of body fluids and tissues.Meth. Mol. Biol. 2011, 708, 247–257.

[20] Ivanova, P. T., Milne, S. B., Myers, D. S., Brown, H. A.,Lipidomics: a mass spectrometry based, systems levelanalysis of cellular lipids. Curr. Opin. Chem. Biol. 2009, 13,525–531.

[21] Balazy, M., Eicosanomics: targeted lipidomics ofeicosanoids in biological systems. Prostaglandins OtherLip. Mediat. 2004, 173–180.

[22] Bobeldijk, I., Hekman, M., de Vries-van der Weij, J., Coulier,L., Quantitative profiling of bile acids in biofluids and tis-sues based on accurate mass high resolution LC-FT-MS:

compound class targeting in a metabolomics workflow. J.Chromatogr. B. Analyt. Technol. Biomed. Life Sci. 2008, 871,306–313.

[23] Roberts, L. D., McCombie, G., Titman, C. M., Griffin, J. L.,A matter of fat: an introduction to lipidomic profiling meth-ods. J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci.2008, 871, 174–181.

[24] McDonald, J. G., Matthew, S., Auchus, R. J., Steroidprofiling by gas chromatography-mass spectrometry andhigh performance liquid chromatography-mass spec-trometry for adrenal diseases. Horm. Cancer 2011, 2,324–332.

[25] Kliman, M., May, J. C., McLean, J. A., Lipid analy-sis and lipidomics by structurally selective ion mobility-mass spectrometry. BBA–Mol. Cell Biol. Lip. 2011 1811,935–945.

[26] Guo, X., Lankmayr, E., Multidimensional approaches in LCand MS for phospholipid bioanalysis. Bioanalysis 2010, 2,1109–1123.

[27] Jackson, S. N., Ugarov, M., Post, J. D., Egan, T. et al., A studyof phospholipids by ion mobility TOFMS. J. Am. Soc. MassSpectr. 2008, 19, 1655–1662.

[28] Kim, H. I., Kim, H., Pang, E. S., Ryu, E. K. et al., Structuralcharacterization of unsaturated phosphatidylcholines us-ing traveling wave ion mobility spectrometry. Anal. Chem.2009, 81, 8289–8297.

[29] Trimpin, S., Tan, B., Bohrer, B. C., O’Dell, D. K. et al., Profilingof phospholipids and related lipid structures using multidi-mensional ion mobility spectrometry-mass spectrometry.Int. J. Mass Spectrom. 2009, 287, 58–69.

[30] Ridenour, W. B., Kliman, M., McLean, J. A., Caprioli, R.M., Structural characterization of phospholipids and pep-tides directly from tissue sections by MALDI traveling-waveion mobility-mass spectrometry. Anal. Chem. 2010, 82,1881–1889.

[31] Woods, A. S., Jackson, S. N., The application and potentialof ion mobility mass spectrometry in imaging MS with afocus on lipids. Meth. Mol. Biol. 2010, 656, 99–111.

[32] Goto-Inoue, N., Hayasaka, T., Zaima, N., Setou, M., Imagingmass spectrometry for lipidomics. BBA–Mol. Cell Biol. Lip.2011, 1811, 961–969.

[33] Van der Spoel, D., Marklund, E. G., Larsson, D. S., Caleman,C., Proteins, lipids, and water in the gas phase. Macromol.Biosci. 2011, 11, 50–59.

[34] Katajamaa, M., Oresic, M., Data processing for massspectrometry-based metabolomics. J. Chromatogr. A.2007, 1158, 318–328.

[35] Sumner, L. W., Urbanczyk-Wochniak, E., Broeckling, C. D.,Metabolomics data analysis, visualization, and integration.Meth. Molec. Biol. 2007, 406, 409–436.

[36] Junker, B. H., Klukas, C., Schreiber, F., VANTED: a systemfor advanced data analysis and visualization in the con-text of biological networks. BMC Bioinformatics 2006, 7,109.

[37] Byrnes, R. W., Cotter, D., Maer, A., Li, J. et al., An editor forpathway drawing and data visualization in the BiopathwaysWorkbench. BMC Syst. Biol. 2009, 3, 99.



[38] Wheelock, C. E., Goto, S., Yetukuri, L., D’Alexandri, F. L. et al.,Bioinformatics strategies for the analysis of lipids. Meth.Mol. Biol. 2009, 580, 339–368.

[39] Quehenberger, O., Dennis, E. A., The human plasmalipidome. N. Engl. J. Med. 2011, 365, 1812–1823.

[40] Oresic, M., Hanninen, V. A., Vidal-Puig, A., Trends Biotech-nol. 2008, 12, 647–652.

[41] Gupta, S., Maurya, M. R., Merrill, A. H., Glass, C. K. et al.,Integration of lipidomics and transcriptomics data towardsa systems biology model of sphingolipid metabolism. BMCSyst. Biol. 2011, 5, 26.

[42] Yetukuri, L., Katajamaa, M., Medina-Gomez, G., Seppanen-Laakso, T. et al., Bioinformatics strategies for lipidomicsanalysis: characterization of obesity related hepatic steato-sis. BMC Syst. Biol. 2007, 1, 12.

[43] Qi, L., Gene-diet interactions in complex disease: currentfindings and relevance for public health. Curr. Nutr. Rep.2012, 1, 222–227.

[44] Stella, C., Beckwith-Hall, B., Cloarec, O., Holmes, E. et al.,Susceptibility of human metabolic phenotypes to dietarymodulation. J. Proteome Res. 2006, 5, 2780–2788.

[45] Shaham, O., Wei, R., Wang, T. J., Ricciardi, C. et al.,Metabolic profiling of the human response to a glucosechallenge reveals distinct axes of insulin sensitivity. Molec.Syst. Biol. 2008, 4, 1–9.

[46] Wopereis, S., Rubingh, C. M., van Erk, M. J., Verheij, E. R.et al., Metabolic profiling of the response to an oral glucosetolerance test detects subtle metabolic changes. PLoS One2009, 4, e4525.

[47] Rubio-Aliaga, I., de Roos, B., Duthie, S. J., Crosley,L. K. et al., Metabolomics of prolonged fasting in humansreveals new catabolic markers. Metabolomics 2011, 7,375–387.

[48] Krug, S., Kastenmuller, G., Stuckler, F., Rist, M. J. et al.,The dynamic range of the human metabolome revealed bychallenges. FASEB J. 2012, 26, 2607–2619.

[49] Lankinen, M., Schwab, U., Erkkila, A., Seppanen-Laakso, T.et al., Fatty fish intake decreases lipids related to inflam-mation and insulin signaling. A lipidomics approach. PLoSOne 2009, 4, e5258.

[50] Schwab, U., Seppanen-Laakso, T., Yetukuri, L., Agren, J.et al., Triacylglycerol fatty acid composition in diet-inducedweight loss in subjects with abnormal glucose metabolism–the GENOBIN Study. PLoS One 2008, 3, e2630.

[51] Aiyar, N., Disa, J., Ao, Z., Ju, H. et al., Lysophosphatidyl-choline induces inflammatory activation of human coro-nary artery smooth muscle cells. Mol. Cell Biochem. 2007,295, 113–120.

[52] de Mello, V., Erkkila, A., Schwab, U., Pulkkinen, L. et al.,The effect of fatty or lean fish intake on inflammatory geneexpression in peripheral blood mononuclear cells of pa-tients with coronary heart disease. Eur. J. Nutr. 2009, 48,447–455.

[53] Szymanska, E., van Dorsten, F. A., Troost, J., Paliukhovich,I. et al., A lipidomic analysis approach to evaluate the re-sponse to cholesterol-lowering food intake. Metabolomics2012, 8, 894–906.

[54] De Smet, E., Mensink, R. P., Plat, J., Effects of plant sterolsand stanols on intestinal cholesterol metabolism: sug-gested mechanisms from past to present. Mol. Nutr. FoodRes. 2012, 56, 1058–1072.

[55] Noakes, M., Clifton, P. M., Doornbos, A. M., Trautwein, E. A.,Plant sterol ester-enriched milk and yoghurt effectively re-duce serum cholesterol in modestly hypercholesterolemicsubjects. Eur. J. Nutr. 2005, 44, 214–222.

[56] Kekkonen, R. A., Sysi-Aho, M., Seppanen-Laakso, T.,Julkunen, I. et al., Effect of probiotic Lactobacillus rham-nosus GG intervention on global serum lipidomic pro-files in healthy adults. World J. Gastroenterol. 2008, 14,3188–3194.

[57] Ng, S. C., Hart, A. L., Kamm, M. A., Stagg, A. J. et al., Mech-anisms of action of probiotics: recent advances. Inflamm.Bowel Dis. 2009, 15, 300–310.

[58] Huffman, K. M., Redman, L. M., Landerman, L. R., Pieper,C. F. et al., Caloric restriction alters the metabolic responseto a mixed-meal: results from a randomized, controlledtrial. PLoS One 2012, 7, e28190.

[59] Pietilainen, K. H., Sysi-Aho, M., Rissanen, A., Seppanen-Laakso, T. et al., Acquired obesity is associated withchanges in the serum lipidomic profile independent of ge-netic effects. A monozygotic twin study. PLoS ONE 2007, 2,e218.

[60] Yan, S., Chai, H., Wang, H., Yang, H. et al., Effects oflysophosphatidylcholine on monolayer cell permeability ofhuman coronary artery endothelial cells. Surgery 2005, 138,464–473.

[61] Engelmann, B., Plasmalogens: targets for oxidants andmajor lipophilic antioxidants. Bioch. Soc. Trans. 2004, 32,147–150.

[62] Pietilainen, K. H., Rog, T., Seppanen-Laakso, T., Virtue, S.et al., Association of lipidome remodeling in the adipocytemembrane with acquired obesity in humans. PLoS Biol.2011, 9, e1000623.

[63] Karelis, A. D., Metabolically healthy but obese individuals.Lancet 2008, 372, 1281–1283.

[64] Barber, M. N., Risis, S., Yang, C., Meikle, P. J. et al., Plasmalysophosphatidylcholine levels are reduced in obesity andtype 2 diabetes. PLoS One 2012, 7, e41456.

[65] Han, M. S., Lim, Y. M., Quan, W., Kim, J. R. et al., Lysophos-phatidylcholine as an effector of fatty acid-induced insulinresistance. J. Lipid Res. 2011, 52, 1234–1246.

[66] Yea, K., Kim, J., Yoon, J. H., Kwon, T. et al., Lysophos-phatidylcholine activates adipocyte glucose uptake andlowers blood glucose levels in murine models of diabetes.J. Biol. Chem. 2009, 284, 33833–33840.

[67] Caesar, R., Manieri, M., Kelder, T., Boekschoten, M. et al., Acombined transcriptomics and lipidomics analysis of sub-cutaneous, epididymal and mesenteric adipose tissue re-veals marked functional differences. PLoS One 2010, 5,e1525.

[68] Wopereis, S., Radonjic, M., Rubingh, C., van Erk, M. et al.,Identification of prognostic and diagnostic biomarkers ofglucose intolerance in ApoE3Leiden mice. Phys. Genom.2012, 44, 293–304.


Mol. Nutr. Food Res. 2013, 57, 1306–1318 1317

[69] Huss, J. M., Kelly, D. P., Mitochondrial energy metabolismin heart failure, a question of balance. J. Clin. Invest. 2005,115, 547–555.

[70] Kinsey, G. R., McHowat, J., Beckett, C. S., Schnellmann,R.G., Identification of calcium-independent phospholipaseA2� in mitochondria and its role in mitochondrial oxidativestress. Am. J. Phys. Renal Physiol. 2007, 292, F853–F860.

[71] Gadd, M. E., Broekemeier, K. M., Crouser, E. D.,Kumar, J. et al., Mitochondrial iPLA2 activity modulatesthe release of cytochrome C from mitochondria andinfluences the permeability transition. J. Biol. Chem. 2006,281, 6931–6939.

[72] Mancuso, D. J., Sims, H. F., Yang, K., Kiebish, M. A. et al.,Genetic ablation of calcium-independent phospholipaseA2gamma prevents obesity and insulin resistance duringhigh fat feeding by mitochondrial uncoupling and increasedadipocyte fatty acid oxidation. J. Biol. Chem. 2010, 285,36495–36510.

[73] Han, X., Yang, K., Yang, J., Cheng, H. et al., Shotgunlipidomics of cardiolipin molecular species in lipid extractsof biological samples. J. Lipid Res. 2006, 47, 864–879.

[74] Xu, H., Barnes, G. T., Yang, Q., Tan, G. et al., Chronic in-flammation in fat plays a crucial role in the development ofobesity-related insulin resistance. J. Clin. Invest. 2003, 112,1821–1830.

[75] Lu, M., Patsouris, D., Li, P., Flores-Riveros, J. et al., A new an-tidiabetic compound attenuates inflammation and insulinresistance in Zucker diabetic fatty rats. Am. J. Physiol. En-docrinol. Metab. 2010, 298, E1036–E1048.

[76] Carlsten, H., Immune responses and bone loss, the estro-gen connection. Immunol. Rev. 2005, 208, 194–206.

[77] Serhan, C. N., Lipoxins and aspirin-triggered 15-epi-lipoxins are the first lipid mediators of endogenous anti-inflammation and resolution. Prostaglandins, Leukot. Es-sent. Fatty Acids 2005, 73, 141–162.

[78] Poulsen, R. C., Gotlinger, K. H., Serhan, C. N., Kruger,M. C., Identification of inflammatory and proresolvinglipid mediators in bone marrow and their lipidomic pro-files with ovariectomy and omega-3 intake. AJM 2008,83, 437–445.

[79] Hibbeln, J. R., Depression, suicide and deficiencies ofomega-3 essential fatty acids in modern diets. World Rev.Nutr. Dietetics 2009, 99, 17–30.

[80] Morris, M. C., The role of nutrition in Alzheimer’s disease,epidemiological evidence. Eur. J. Neurol. 2009, 16, 1–7.

[81] Lamaziere, A., Richard, D., Barbe, U., Kefi, K. et al., Differ-ential distribution of DHA-phospholipids in rat brain afterfeeding: a lipidomic approach. Prostaglandins, Leukot. Es-sent. Fatty Acids 2011, 84, 7–11.

[82] Bazan, N. G., Musto, A. E., Knott, E. J., Endogenous signal-ing by omega-3 docosahexaenoic acid-derived mediatorssustains homeostatic synaptic and circuitry integrity. Mol.Neurobiol. 2011, 44, 216–222.

[83] Brand, A., Crawford, M. A., Yavin, E., Retailoring docosa-hexaenoic acid-containing phospholipid species during im-paired neurogenesis following omega-3 -linolenic acid de-privation. J. Neurochem. 2010, 114, 1393–1404.

[84] Srinath, R. K., Katan, M. B., Diet, nutrition and the pre-vention of hypertension and cardiovascular diseases. Pub.Health Nutr. 2004, 7, 167–186.

[85] Dutro, M. P., Gerthoffer, T. D., Peterson, E. D., Tang, S. S. K.et al., Treatment of hypertension and dyslipidemia or theircombination among US managed-care patients. J. Clin. Hy-pertens. 2007, 9, 684–691.

[86] Hu, C., Kong, H., Qu, F., Li, Y. et al., Application of plasmalipidomics in studying the response of patients with es-sential hypertension to antihypertensive drug therapy. Mol.BioSystems. 2011, 7, 3271–3279.

[87] Griffiths, W. J., Wang, Y., Mass spectrometry: from pro-teomics to metabolomics and lipidomics. Chem. Soc. Rev.200, 938, 1882–1896.

[88] Clugston, R. D., Jiang, H., Lee, M. X., Piantedosi, R. et al.,Altered hepatic lipid metabolism in C57BL/6 mice fed al-cohol, a targeted lipidomic and gene expression study. JLipid Res. 2011, 52, 2021–2031.

[89] Bird, S. S., Marur, V. R., Sniatynski, M. J., Green-berg, H. K. et al., Lipidomics profiling by high-resolutionLCMS and high-energy collisional dissociation fragmen-tation, focus on characterization of mitochondrial cardi-olipins and monolysocardiolipins. Anal. Chem. 2011, 83,940–949.

[90] Hilvo, M., Oresic, M., Regulation of lipid metabolism inbreast cancer provides diagnostic and therapeutic oppor-tunities. Clin. Lipidol. 2012, 7, 177–188.

[91] Hilvo, M., Denkert, C., Lehtinen, L., Muller, B. et al., Noveltheranostic opportunities offered by characterization of al-tered membrane lipid metabolism in breast cancer progres-sion. Cancer Res. 2011, 71, 3236–3245.

[92] Marshall, J. R., Nutrition and colon cancer prevention. Curr.Opin. Clin. Nutr. Metab. Care 2009, 12, 539–543.

[93] Yeh, C., Wang, J., Cheng, T., Juan, C. et al., Fatty acidmetabolism pathway plays an important role in car-cinogenesis of human colorectal cancers by microarray-bioinformatics analysis. Cancer Lett. 2006, 233, 297–308.

[94] Punnen, S., Hardin, J., Cheng, I., Klein, E. A. et al., Impact ofmeat consumption, preparation, and mutagens on aggres-sive prostate cancer. PLoS One 2011, 6, e27711.

[95] Luci, S., Konig, B., Giemsa, B., Huber, S. et al., Feeding ofa deep-fried fat causes PPARalpha activation in the liver ofpigs as a non-proliferating species. Br. J. Nutr. 2007, 97,872–882.

[96] Chapkin, R. S., Davidson, L. A., Ly, L., Weeks, B. R. et al.,Immunomodulatory effects of n-3 fatty acids, putative linkto inflammation and colon cancer. J. Nutr. 2007, 137,200S–204S.

[97] Scharlau, D., Borowicki, A., Habermann, N., Hofmann, T.et al., Mechanisms of primary cancer prevention by bu-tyrate and other products formed during gut flora-mediatedfermentation of dietary fibre. Mutat. Res./Rev. Mutat. Res.2009, 682, 39–53.

[98] Aiello, D., De Luca, D., Gionfriddo, E., Naccarato, A. et al.,Review: multistage mass spectrometry in quality, safetyand origin of foods. Eur. J. Mass Spectrom. 2011, 17,1–31.



[99] Kvasnicka, F., Proteomics, general strategies and applica-tion to nutritionally relevant proteins. J. Chromatogr. B2003, 787, 77–89.

[100] Kirsch, S., Fourdrilis, S., Dobson, R., Scippo, M. L. et al.,Quantitative methods for food allergens, a review. Anal.Bioanal. Chem. 2009, 395, 57–67.

[101] Cifuentes, A., Food analysis and Foodomics. J. ChromatogrA. 2009, 1216, 7109–7110.

[102] Herrero, M., Simo, C., Garcıa-Canas, V., Ibanez, E. et al.,Foodomics: MS-based strategies in modern food sci-ence and nutrition. Mass Spectrom. Rev. 2012, 31,49–69.

[103] McAnoy, A. M., Wu, C. C., Murphy, R. C., Direct qualitativeanalysis of triacylglycerols by electrospray mass spectrom-etry using a linear ion trap. J. Am. Soc. Mass Spectrom.2005, 16, 1498–1509.

[104] Lin, J., Arcinas, A., Analysis of regiospecific triacylglycerolsby electrospray ionization mass spectrometry of lithiatedadducts. J. Agric. Food Chem. 2008, 56, 4909–4915.

[105] Shen, Q., Wang, Y., Gong, L., Guo, R. et al., Shotgunlipidomics strategy for fast analysis of phospholipids infisheries waste and its potential in species differentiation.J. Agric. Food Chem. 2012, 60, 9384–9393.

[106] Abbas, K. A., Mohamed, A., Jamilah, B., Ebrahimian, M.,A review on correlations between fish freshness and pHduring cold storage. Am. J. Biochem. Biotechnol. 2008, 4,416–421.

[107] Caddy, J. F., Wickens, P. A., Sugunan, V. V., Mahon, R., Qual-ity and quality changes in fresh fish. Food Agric. Organ. FAORome 1995, 348, 68–92.

[108] Wang, Y., Zhang, H., Tracking phospholipid profiling ofmuscle from Ctennopharyngodon idellus during storageby shotgun lipidomics. J. Agric Food Chem. 2011, 59,11635–11642.

[109] Calvano, C. D., Carulli, S., Palmisano, F., 1H-pteridine-2,4-dione lumazine, a new MALDI matrix for complex phos-pholipid mixtures analysis. Anal. Bioanal. Chem. 2010, 398,499–507.


eview lipidomics in nutrition and food research...tgs). these neutral lipids have a glycerol...

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