Lipidomics in drug discovery

Review

s�P

OSTSCREEN

REVIEWS Drug Discovery Today �Volume 19, Number 2 � February 2014

Lipidomics in drug discovery

Terhi Vihervaara, Matti Suoniemi and Reijo Laaksonen

Zora Biosciences Oy, Biologinkuja 1, 02150 Espoo, Finland

Lipidomics is a rapidly growing technology that can be used in biomedical research to study disease

mechanisms, identify novel disease biomarkers and drug efficacy biomarkers, and reveal off-target

effects. Lipidomics can also be used to elucidate the mechanism of action of different drug compounds or

as readouts in Mendelian randomization approaches. Furthermore, lipidomics can be utilized to identify

deviations in metabolic and/or signaling pathways in different stages of disease. Therefore, as we discuss

here, this emerging technology also has a substantial potential in various drug discovery programs.

Definition of lipidomicsLipid molecules within the human body are enormously complex.

They are subject to active metabolism, where molecular lipids have

essential roles that are only now beginning be to understand. Mass

spectrometry (MS)-driven lipidomics has enabled the resolving of

complex lipidomes at the level of molecular lipid species in a high-

throughput mode, fulfilling the quality requirements of large

clinical cohorts [1–3]. Owing to the selectivity and high sensitivity

of the methods used, lipidomics analysis of minute sample

amounts can be executed. Lipids have essential roles in membrane

dynamics, energy metabolism, and signaling, where lipid structure

is a key determinant of the biological effects. Therefore, lipidomics

is an essential tool for increasing current biological understanding

of molecular lipids in basic research and pharmaceutical drug

discovery and development.

Sphingolipids and glycosphingolipids are among the key

players in many pathophysiological states. The level of complexity

of these lipid molecules is substantial, resulting from different

structural backbone moieties, amide-linked fatty acids, and var-

ious head group structures [4,5]. Although, there is little biological

information about such molecules available, imbalances in sphin-

golipid metabolism have been implicated in many diseases,

including cancer, metabolic syndrome, diabetes, atherosclerosis

and immunity, emphasizing the importance of understanding

lipid biochemistry in disease [6]. Sphingolipids are known to be

involved in apoptosis, proliferation, cellular growth and signaling

Corresponding author: Laaksonen, R. ([email protected])

164 www.drugdiscoverytoday.com 1359-6446/06/$ - see front matt

[7]. However, their molecular structure determines the biological

outcome. For example, ceramides with different chain-length fatty

acids have been shown to have opposing effects on proliferation

[8]. Whereas sphingosine and ceramide lipids promote growth

inhibition and apoptosis, their phosphorylated forms (sphingo-

sine-1-phosphate and ceramide-1-phosphate) support cell survi-

val, differentiation and migration [7].

Lipidomics technologyA lipidomic approach is applicable to all therapeutic areas, including

cardiovascular diseases, diabetes, cancer, neurological diseases and

autoimmune, as well as inflammatory diseases [9]. The currently

used technologies enable studies to be done in all biological materi-

als, including cultured cells, biofluids (blood, serum, plasma, cere-

brospinal fluid, urine, etc.) and all types of tissue from clinical or

animal models. A basic requirement of a successful lipidomic study

is adequate preanalytical sample handling and short storage times,

because some lipids can be unstable. As a common principle,

samples should always be collected using the exact same protocol

across all study sites. Samples should be drawn rapidly and, for

instance, plasma separation should be carried out in cold tempera-

tures. Biological samples should be kept at �808C or in liquid

nitrogen at all times during storage. Samples can be shipped to

the analytical laboratory on dry ice, and it is this step that can be the

most crucial of these expensive and laborious studies; therefore,

careful planning is needed to ensure safe sample transportation.

Fig. 1 summarizes the lipidomic workflow. In analytical labora-

tories, samples undergo preferably automated sample preparation

er � 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.drudis.2013.09.008

mailto:[email protected]

http://dx.doi.org/10.1016/j.drudis.2013.09.008

Drug Discovery Today � Volume 19, Number 2 � February 2014 REVIEWS

Biologicalsamples

Samplepreparation

MS analysis(different platforms)

Dataprocessing

Targeted 3

Targeted 2

Targeted 1

Shotgun

Aliquoting,lipid

extraction

Interpretation

Drug Discovery Today

FIGURE 1

Workflow summarizing the different steps in lipidomics analysis. Abbreviation: MS, mass spectrometry.

Reviews�POSTSCREEN

procedures, including aliquoting and lipid extraction. In an ideal

situation, the analytical process would enable parallel runs with

different lipidomic platforms to speed up the analyses and, impor-

tantly, to minimize the number of unnecessary freeze–thaw cycles

that can substantially affect sample quality and study results.

A complete lipidomic analysis requires several different analy-

tical platforms. Therefore, comprehensive analyses are still time

consuming, sometimes limiting the number of samples that can be

analyzed. Furthermore, different laboratories have their own

methodology set-ups for different instruments, which could lead

to differences in lipidomic data output. Thus, further standardiza-

tion work is needed to make lipidomic data arising from different

laboratories more comparable. However, targeted and quantitative

lipidomic analyses can have an advantage over some untargeted

metabolomic approaches that might also report relative quantifi-

cation of unidentified metabolites.

Usually, lipidomic analyses of given samples are performed by

shotgun and/or targeted approaches depending on the scientific

question. Shotgun technology is an analysis of multiple lipid

classes in one run where lipid extracts are infused directly into

a mass spectrometer. The advantage of the shotgun approach is

that it enables the identification and quantification of hundreds of

molecular lipids in less than 30 min per sample, making it espe-

cially suitable for initial screening. Importantly, the shotgun

approach has been demonstrated to be highly reproducible, meet-

ing good laboratory practice (GLP) requirements [10]. Some lipid

species require preseparation before the MS analysis. In targeted

lipidomics, lipid extracts are primarily separated by liquid chro-

matography (LC) before monitoring by online MS [11,12]. More-

over, multiple reaction-monitoring analyses offer both the high

selectivity and sensitivity necessary for quantification of low-

abundant lipid species [13,14]. Importantly, both shotgun and

targeted analyses can be performed according to regulatory quality

standards when appropriately set up in the required laboratory

environment. Given that these recent technologies meet the GLP

guidelines, performing lipidomic analysis of clinical trial samples

is also feasible.

The MS work in lipidomics is only the half of the complex

technology process that culminates in fully identified and quan-

tified molecular lipid species. Also needed are sophisticated work-

flows for the bioinformatic processing of the wealth of lipid data

that are generated by the analyzers. Indeed, one of the more

challenging areas in the lipidomic process is data handling and

analysis. A key issue for lipidomic providers is the lack of well-

standardized commercial tools and software that can reliably cover

all of the aspects required for proper data management. Several

data-processing options are available for the identification and

quantification of numerous lipid species, including LipidView (AB

Sciex), Lipid Xplorer [15] and LIMSA [16]. However, a comprehen-

sive tool, suitable for high throughput, should also be able to take

care of analytical quality control. Owing to the lack of such

comprehensive commercial solutions, lipidomic laboratories have

typically developed their own systems for data management, as

described, for example, by Jung et al. [17].

Lipidomics in preclinical studiesIn preclinical studies, lipidomic technology might be useful for

evaluating different disease models, to study mechanisms of

action, and for the identification of potential off-target effects

and toxicity signals. Lipidomics can also be utilized for compound

comparison and selection.

The choice of the right animal model is crucial for a successful

drug discovery program. There are multiple options and animal

models currently available to study, for example, diabetes and

atherosclerosis. However, it is often difficult to know how well

these models translate to humans. Lipidomics offers opportunities

to compare different animal models to find the most suitable

model for a specific purpose. This has been demonstrated in a

www.drugdiscoverytoday.com 165


Review

s�P

OSTSCREEN

recent study by Boue et al. [18], where plasma, liver and aorta

samples obtained from apolipoprotein E (ApoE) (�/�) mice

exposed to cigarette smoke or fresh air for 6 months were studied.

These data demonstrated that smoking led to increased concen-

trations of many lipid classes and molecular lipid species. Plasma

concentrations of free cholesterol, ceramides, cerebrosides and

several different phospholipids increased as a result of exposure

to cigarette smoke. Similarly, free and esterified cholesterol, tri-

acylglycerols, phospholipids, sphingomyelins and ceramides were

elevated in liver. Intriguingly, more than twofold higher levels of

cholesteryl ester, lysophosphatidylcholine and glucosyl- and/or

galactosylceramide were seen in the aorta specimens, which were

related in their molecular pattern to the accelerated development

of atherosclerosis in ApoE(�/�) mice on a standard chow diet.

These lipids have been detected in human plaques [19], suggesting

that the plaques of ApoE(�/�) mice resemble those of humans.

Based on these data, the authors concluded that the effect of

smoking on different tissues can be successfully observed using

the ApoE(�/�) mouse model, even in the absence of any addi-

tional factors, such as a high-fat diet. Another recent study com-

pared the use of the lipid profiling of 24 animal models with the

dyslipidemic human plasma in the absence and presence of sim-

vastatin treatment [20]. Not surprisingly, the closest overall

matches were non-human primates, whereas many traditionally

used models, including low density lipoprotein receptor (LDLr)

(�/�) and ApoE(�/�) mice, showed least overall similarity. How-

ever, the use of traditional models is still appropriate during the

preclinical stage of drug discovery.

Importantly, lipidomic applications are not only appropriate for

diseases related to hyperlipidemia or atherosclerosis, but can also

be applied to all diseases and therapeutic areas, including diabetes,

infectious diseases, cancer and central nervous system (CNS)-

related orders. For example, Suhre et al. [21] used lipidomics to

identify an efficacy biomarker for the inhibition of fatty acid-

binding protein 4 (FABP4) using a small-molecule inhibitor

BMS309403, which had been demonstrated to be effective in

the treatment of diabetes and atherosclerosis in mice [22].

Recently, lipid-mediator protectin D1 was identified as the innate

suppressor of influenza virus replication [23]. In cancer research,

several candidate biomarkers have been identified using lipido-

mics [24–27]. Lipids are also of interest in neurological disorders

owing to their high expression in the CNS. A defect in lipid

metabolism has been recognized in many CNS disorders and

injuries, including Alzheimer’s disease, Parkinson’s disease, Nie-

mann–Pick disease, Huntington’s disease [28], multiple sclerosis

[29], schizophrenia [30] and spinal cord injury [31]. For a com-

prehensive review of the role of lipids in CNS-related disorders, see

[32]. In this review, the authors concluded that lipidomic analyses

along with RNA silencing will provide new insights into the role of

lipid intermediates in cell signaling, and could open new avenues

for prevention or treatment options. In line with their conclu-

sions, it seems obvious that lipidomic technology will be a valu-

able tool in evaluating disease models and mechanisms of action

of new drug candidates in various areas.

Lipidomic-based biomarkersHuman plasma is estimated to contain many different lipid spe-

cies, ranging from a few hundred to thousands of lipids. Given that

166 www.drugdiscoverytoday.com

these numerous lipid species are likely to have well-defined roles in

the maintenance of cellular functions in the human body, it has

become obvious that detailed lipid analyses would reveal informa-

tion that will go a long way beyond the knowledge obtained using

current routine clinical lipidology tools, including LDL and high-

density lipoprotein (HDL) cholesterol measurements and apoli-

poprotein assays. Quehenberger et al. [33] described MS-based

lipidomic tools, which were developed by the LIPID MAPS Con-

sortium and used for the systematical identification and quanti-

fication of the human plasma lipidome. As a result of this

undertaking, the authors presented plasma concentrations for

more than 500 different lipid species from six main lipid cate-

gories, including fatty acyls, glycerolipids, glycerophospholipids,

sphingolipids, sterols and prenols in a pooled human plasma; such

a study can be covered routinely in high-throughput fashion [17].

One of the main application areas for lipidomics is as disease-

specific biomarkers. The advantage of the lipidomic approach is

that lipids can be considered as intermediate phenotypes that are

closer to the disease state in question compared with, for example,

genetic information. Thus, it might be possible to demonstrate

strong associations between molecular lipid species and disease

states even with relatively small sample sets, compared with

genetic association studies. Fig. 2 presents a mock data set depict-

ing lipid expression differences between healthy controls and

diseased subjects. The data are presented as a volcano plot, which

enables one to view the whole data set in one glance. Each circle

represents a distinct quantified molecular lipid. The average per-

centage differences between study groups (e.g. cases and controls)

are plotted on the X-axis, whereas the statistical significance of a

particular lipid is indicated on the Y-axis. In this comparison,

certain phospholipid species marked in red are elevated in disease

cases compared with controls and, thus, can be considered as risk

markers for the disease. Lipids indicated with blue circles are

present in higher concentrations in the control group and, there-

fore, can be considered as protective markers. Such data enable one

to search for individual markers of disease, or to build risk-pre-

diction models based on multiple markers.

In a recent publication [27], Zhou et al. identified plasma lipid

biomarkers for prostate cancer by using lipidomics and bioinfor-

matics, providing a good example of a lipidomics-based biomarker

search. The authors analyzed 390 lipid species in plasma samples

obtained from 105 patients with prostate cancer and 36 controls. A

total of 12 individual plasma lipid species were identified, provid-

ing sensitivity above 80%, specificity above 50% and accuracy

above 80% for prostate cancer in that population. A combination

of the top 15 lipid biomarker candidates improved the predictive

power significantly (sensitivity 93.6%, specificity 90.1% and accu-

racy 97.3%), demonstrating the potential of lipidomics in the

disease biomarker field.

Another field with high potential for lipidomics is drug toxicity.

Hepatotoxicity of ximelagatran, an oral thrombin inhibitor, serves

as a good example of the identification of a novel drug toxicity

biomarker [34]. Ximelagatran was withdrawn from the market

owing to increased risk of severe liver damage with an unknown

cause. Lipidomic analyses with human hepatocytes exposed to

ximelagatran revealed a decreased molar ratio of phosphatidyl-

choline:phosphatidylethanolamine (PC:PE) after one hour of

exposure, whereas the total cholesterol:phospholipid molar ratio


-5 0

% Difference

P-v

alue

5 10 15-10-15

1

0,5

0,2

0,1

0,05 0,0500

0,02

0,01

0,005

0,002

0,001

0,0005

0,0002

0,0001

5e-005 SM (d18:1/23:0) (d18:1/22:1-OH)

SM (d18:1/24:0) (d18:1/23:1-OH)

GlcCer(d18:1/24:0)

CE 20:4

Cer(d18:1/26:1)

PC O-16:0/20:4-alkyl

PC 18:1/20:4

PC 17:0/18:2

PE O-18:0/20:4-alkenyl (PE O-18:1/20:4-alkyl)

PC 18:2/18:2

CE 15:0

PC 16:0/16:0 PE 18:0/20:4

LacCer(d18:1/24:0)


FIGURE 2

Lipidomic biomarkers for disease. An imaginative volcano plot representing the average percentage differences (X-axis) in lipid species between cases and

controls plotted against statistical significance (I value; Y-axis). Each circle represents a defined molecular lipid: red or blue circles represent lipids that wereincreased or decreased, respectively, in cases versus controls. Color intensity reflects the magnitude of change. Circles above the horizontal line (P = 0.05)

represent lipids present at significantly different levels between the study groups.


decreased after a 48-hour exposure. Based on these results, the

investigators concluded that the observed lipid changes led to the

loss of membrane integrity and leakage of cellular proteins, which

could explain the observed effects of ximelagatran. These experi-

ments identified distinct molar phospholipid ratios as novel bio-

markers for hepatotoxicity.

Yet another potential lipidomic biomarker application area is the

identification of drug efficacy biomarkers and companion diagnos-

tic solutions. Different proprotein convertase subtilisin/kexin Type

9 (PCSK9) inhibitors are currently being developed by pharmaceu-

tical companies because these new compounds have been identified

to be potent lipid-lowering drugs. A recent lipidomic analysis of

proprotein convertase subtilisin/kexin type 9 (PCSK9) (�/�) ani-

mals and of humans carrying a well-characterized PCSK9 loss-of-

function mutation observed that PCSK9 inhibition lowered plasma

concentrations of certain cholesteryl esters and short-chain sphin-

golipid species much more efficiently than did LDL cholesterol [35].

Thus, the authors suggested that these specific lipid species could be

utilized for the characterization of novel compounds inhibiting

PCSK9 and as sensitive efficacy markers of PCSK9 inhibition

([35]; Fig. 3).

Lipidomics in target discoveryThe utility of detailed lipid measurements in target discovery can be

justified by the numerous roles of lipids in distinct cellular func-

tions. Lipids are known to have significant and vital roles in cellular

trafficking, signaling, structure and energy storage, and, therefore,

are integrated into many important metabolic and signaling path-

ways in human cells. Changes in the expression of different lipid

molecules can be used as probes of the functionality of various



-20 -15

% Difference

-10 -5 0 5-25-30

P-v

alue

1

0,5

0,2

0,1

0,05

0,02

0,0500

0,01

0,005

0,002

0,001

0,0005

0,0002

0,0001

5e-005

2e-005

Improved lipidomicbiomarkers

Conventional biomarkers

CE 20:3

LacCer(d18:1/16:0)

CE 16:1LacCer(d18:1/18:0)

Cer(d18:1/18:0)

Cer(d18:1/16:0)

CE 17:1

CE 18:1

CE 16:0

CE 20:4

CE 14:0

CE 18:3

GlcCer(d18:1/16:0)GlcCer(d18:1/18:0)

Cer(d18:1/20:0)

ApoB

LDL-C

TG

ApoA1

LPC 16:0

SM (d18:1/18:0)

SM (d18:1/16:0) (d18:1/15:1-OH)

SM (d18:1/24:1) (d18:1/23:2-OH) SM (d18:1/16:1) (d18:1/15:2-OH)


FIGURE 3

Lipidomic biomarkers for drug efficacy. A volcano plot representing the average percentage differences (X-axis) in lipid species in response to lipid-loweringtreatment, plotted against statistical significance (P value; Y-axis). Each circle represents a defined molecular lipid: red or blue circles represent lipids that were

increased or decreased, respectively, in cases versus controls. Color intensity reflects the magnitude of change. Circles above the horizontal line (P = 0.05)

represent lipids present at significantly different levels between the study groups. Boxes highlight conventional lipid biomarkers (bottom box) and improved

lipidomic biomarkers (top box). Modified, with permission, from [35].

Review

s�P

OSTSCREEN

metabolic pathways in disease and this information can be used to

identify druggable target enzymes to control the pathway of inter-

est. Fig. 4 presents a typical lipidomic output for eicosanoids derived

from arachidonic acid. This example pathway map could be used to

summarize differences in eicosanoid expression in tissue specimens

obtained from healthy and diseased subjects to reveal potential sites

for interventions. It is often useful to complement the lipidomic

pathway maps with gene expression data to reveal underlying

changes in the corresponding gene expressions, because these

might be useful to better understand multiple changes in complex

pathways, such as the synthetic pathway of sphingolipids [18,36].

Finally, detailed metabolic tracing experiments, also called as

FLUX lipidomics [37], can reveal the kinetics of individual mole-

cular lipids. This approach enables the quantitative measurement

of molecular lipid metabolism, including synthesis and degrada-

tion in real time. In fact, current knowledge of most of the lipid

synthesis pathways covers lipids only to the class (or brutto)


levels, possibly masking several important pathway branches or

enzymatic control points. We believe that lipid-pathway mapping

combined with detailed gene expression analyses and flux experi-

ments will help to reveal crucial insights into lipid metabolism

that might be the key for better target discovery.

The future of clinical lipidomicsLipids offer the promise of novel biomarker solutions in many

areas. In addition, they might also serve as potential pharmaco-

dynamic read-outs for experimental or existing therapies. Thus,

lipids could also fuel companion diagnostic development and,

therefore, support more personalized treatment approaches. Lipi-

domics can also be used for studying various experimental disease

models and this could provide an enormous boost to translational

medicine.

During their discovery stage, comprehensive lipidomic studies

currently demand a repertoire of many different analytical


PGJ2

PGB2

COX pathway LOX pathway

Arachidonic acid pathways

PGC2

PGA2

TXB2 TXA2

PGE2

PGH2

PGF2α

11-epi-PGF2α

11-Dehydro-TXB2

15-Keto-PGF2α

2,3-Dinor-8-Iso PGF1α

2,3-Dinor-8-Iso PGF2α

6-Keto-PGE1

6-Keto-PGF1α Prostacyclin

O

O

O

O

O O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O O O

O

O

PGD2PGG2

8-Iso-prostane

20-OH-LTB4

20-COOH-LTB4

Hepoxilin A3

12-OxoETE12-HETE8-HETE5-HETE

8-HPETE5-HPETE5,6-diHETE

12-Keto-LTB4

LTC4

LTA4

LTD4 LTE4

20-OH-LTE4

5-OxoETE

LTF4

LTB412-HPETE

Trioxilin A3

16-HETE

19-HETE

20-HETE

5,6-EET

5,6-DHET

8,9-EET

9-HPETE

11-HPETE 11-HETE

9-HETE

8,9-DHET

11,12-EET

11,12-DHET

14,15-EET

14,15-DHET

15H-,11,12-EETA

Hepoxilin B3

Trioxilin B3

15-HETE

15-OxoETE

15-HPETE

5,6-Epoxy-tetraene

LXA4

LXB4

15H-,11,12-THETA

11H-,14,15-EETA

11H-,14,15-THETA

CYP pathway ©Zora Biosciences Oy

Δ12-PGJ2

15-Deoxy-Δ12,14-PGJ2

OO

OO

O

OO

O

O O

OO

OO

O O

O

O

O

O

O O

AA


FIGURE 4

Expression of eicosanoid metabolites. Lipidomic output visualized in metabolic pathway map of arachidonic acid metabolites (eicosanoids). Colored

symbols represent the average percentage differences between study groups. Red or blue circles represent lipids that were increased or decreased

between the study groups, respectively. Color intensity reflects the magnitude of change. Arrows and circles represent statistically significant and

nonsignificant differences, respectively. The figure illustrates the specific upregulation of the cyclooxygenase (COX) pathway. Abbreviations: CYP,cytochrome P450; LOX, lipoxygenase.


platforms. Such a quest is currently time consuming and, there-

fore, is only applicable to a limited amount of samples. Never-

theless, technology and process improvements are emerging,

which will be able to output thorough lipidome data sets based

on large sample sets more quickly. Another challenge is the bias of

the final output arising from the chosen methodological set-ups

and instrumentations. Although internal standards are usually

applied to quantify the lipids of interest, owing to the lack of

proper non-endogenous standards, only a subset of lipid species

can be quantified with acceptable precision. It is noteworthy that

the end results depend on the internal standards applied and,

regrettably, most deviations are found in this aspect among users.

Owing to the lack of standardization, it is complicated to compare

or combine lipidomic data from different laboratories. Although

rigorous standardization and validation processes are needed for

transferring lipidomic assays to clinical practice, the benefits are

manifold. MS assays, once set up, are analytically robust and

efficient clinical solutions. The assays can be scaled down from

the discovery throughput mode comprising long per sample scan

times to just 1–2 min analysis time per sample, with no extensive

washing or incubation steps in the overall assays. Lipidomic

analyses are conducted in a 96-well format and, in a robot-assisted

work-flow, can achieve substantial sample throughputs. More

crucial for wider adoption remain the issues centering around

sample transportation, storage, number of freeze–thaw cycles,

preparation and sample handling during the analytical process.

Given the availability of better and more suitable internal

standards and MS analysis approaches, the quality of lipidomic

outputs will be higher with more lipid species determined in

absolute quantities. Bioinformatic solutions will enable the

processing of all data and to put out instantly lipidomic results

in a user-friendly way. Simultaneously, informatics setups can

monitor all processes in the lipidomic workflow, to recognize

automatically any failure in the process and to monitor con-

tinuously sample quality. Putting all these pieces together will

guide lipidomic standardization, which will make lipidomic

solutions attractive for preclinical and clinical studies, and

applicable to the regulatory environment in a cost-effective

manner.

Conflict of interestAll authors are employees of Zora Biosciences.



Review

s�P

OSTSCREEN

1 Ejsing, C.S. et al. (2009) Global analysis of the yeast lipidome by quantitative

shotgun mass spectrometry. Proc. Natl. Acad. Sci. U. S. A. 106, 2136–2141

2 Hiukka, A. et al. (2009) ApoCIII-enriched LDL in type 2 diabetes displays

sphingomyelinase, and increased binding to biglycan. Diabetes 58, 2018–2026

3 Linden, D. et al. (2006) Liver-directed overexpression of mitochondrial glycerol-3-

phosphate acyltransferase results in hepatic steatosis, increased triacylglycerol

secretion and reduced fatty acid oxidation. FASEB J. 20, 434–443

4 Futerman, A.H. and Hannun, Y.A. (2004) The complex life of simple sphingolipids.

EMBO Rep. 5, 777–782

5 Zheng, W. et al. (2006) Ceramides and other bioactive sphingolipid backbones in

health and disease: lipidomic analysis, metabolism and roles in membrane

structure, dynamics, signaling and autophagy. Biochim. Biophys. Acta 1758, 1864–

1884

6 Alewijnse, A.E. and Peters, S.L.M. (2008) Sphingolipid signalling in the

cardiovascular system: good, bad or both? Eur. J. Pharmacol. 585, 292–302

7 Hannun, Y.A. and Obeid, L.M. (2008) Principles of bioactive lipid signalling: lessons

from sphingolipids. Nat. Rev. Mol. Cell Biol. 9, 139–150

8 Hartmann, D. et al. (2012) Long chain ceramides and very long chain ceramides

have opposite effects on human breast and colon cancer cell growth. Int. J. Biochem.

Cell B 44, 620–628

9 Ekroos, K. et al. (2012) Lipidomics Technologies and Applications. Wiley-VCH

10 Heiskanen, L.A. et al. (2013) Long-term performance and stability of molecular

shotgun lipidomic analysis of human plasma samples. Anal. Chem. http://

dx.doi.org/10.1021/ac401857a

11 Ogiso, H. et al. (2008) Development of a reverse-phase liquid chromatography

electrospray ionization mass spectrometry method for lipidomics, improving

detection of phosphatidic acid and phosphatidylserine. Anal. Biochem. 375,

124–131

12 Kuksis, A. et al. (1991) Plasma lipid profiling by liquid chromatography with

chloride-attachment mass spectrometry. Lipids 26, 240–246

13 Hopfgartner, G. et al. (2004) Triple quadrupole linear ion trap mass spectrometer for

the analysis of small molecules and macromolecules. J. Mass Spectrom. 39, 845–855

14 Shaner, R.L. et al. (2009) Quantitative analysis of sphingolipids for lipidomics using

triple quadrupole and quadrupole linear ion trap mass spectrometers. J. Lipid Res.

50, 1692–1707

15 Herzog, R. et al. (2012) LipidXplorer: a software for consensual cross-platform

lipidomics. PLoS ONE 7, e29851

16 Haimi, P. et al. (2006) Software tools for analysis of mass spectrometric lipidome

data. Anal. Chem. 78, 8324–8331

17 Jung, H.R. et al. (2011) High throughput quantitative molecular lipidomics.

Biochim. Biophys. Acta 1811, 925–934

References


18 Boue, S. et al. (2012) Modulation of atherogenic lipidome by cigarette smoke in

apolipoprotein E-deficient mice. Atherosclerosis 225, 328–334

19 Stegemann, C. et al. (2011) Comparative lipidomics profiling of human

atherosclerotic plaques. Circ. Cardiovasc. Genet. 4, 232–242

20 Yin, W. et al. (2012) Plasma lipid profiling across species for the identification of

optimal animal models of human dyslipidemia. J. Lipid Res. 53, 51–65

21 Suhre, K. et al. (2011) Human metabolic individuality in biomedical and

pharmaceutical research. Nature 477, 54–60

22 Furuhashi, M. et al. (2007) Treatment of diabetes and atherosclerosis by inhibiting

fatty-acid-binding protein aP2. Nature 447, 959–965

23 Morita, M. et al. (2013) The lipid mediator protectin D1 inhibits influenza virus

replication and improves severe influenza. Cell 153, 112–125

24 Min, H.K. et al. (2011) Shotgun lipidomics for candidate biomarkers of urinary

phospholipids in prostate cancer. Anal. Bioanal. Chem. 399, 823–830

25 Bougnoux, P. et al. (2008) The lipidome as a composite biomarker of the modifiable

part of the risk of breast cancer. Prostaglandins Leukot. Essent. Fatty Acids 79, 93–96

26 Llorente, A. et al. (2013) Molecular lipidomics of exosomes released by PC-3 prostate

cancer cells. Biochim. Biophys. Acta 1831, 1302–1309

27 Zhou, X. et al. (2012) Identification of plasma lipid biomarkers for prostate cancer by

lipidomics and bioinformatics. PLoS ONE 7, e48889

28 Wenk, M.R. (2005) The emerging field of lipidomics. Nat. Rev. Drug Discov. 4, 594–610

29 Carlson, N.G. and Rose, J.W. (2006) Antioxidants in multiple sclerosis: do they have

a role in therapy? CNS Drugs 20, 433–441

30 McEvoy, J. et al. (2013) Lipidomics reveals early metabolic changes in subjects with

schizophrenia: effects of atypical antipsychotics. PLoS ONE 8, e68717

31 Hall, E.D. and Springer, J.E. (2004) Neuroprotection and acute spinal cord injury: a

reappraisal. NeuroRx 1, 80–100

32 Adibhatla, R.M. and Hatcher, J.F. (2007) Role of lipids in brain injury and diseases.

Future Lipidol. 2, 403–422

33 Quehenberger, O. et al. (2010) Lipidomics reveals a remarkable diversity of lipids in

human plasma. J. Lipid Res. 51, 3299–3305

34 Sergent, O. et al. (2009) Ximelagatran increases membrane fluidity and changes

membrane lipid composition in primary human hepatocytes. Toxicol. In vitro 23,

1305–1310

35 Janis, M.T. et al. (2013) Beyond LDL-C lowering: distinct molecular sphingolipids

are good indicators of proprotein convertase subtilisin/kexin type 9 (PCSK9)

deficiency. Atherosclerosis 228, 380–385

36 Gupta, S. et al. (2011) Integration of lipidomics and transcriptomics data towards a

systems biology model of sphingolipid metabolism. BMC Syst. Biol. 5, 26

37 Kuerschner, L. et al. (2005) Polyene-lipids: a new tool to image lipids. Nat. Meth. 2,

39–45

http://refhub.elsevier.com/S1359-6446(13)00312-7/sbref0005

















































































Documents

Lipidomics in drug discovery