1-s2.0-S0003267013009884-main

8/9/2019 1-s2.0-S0003267013009884-main

1/13

Analytica Chimica Acta 802 (2013) 113

Contents lists available atScienceDirect

Analytica Chimica Acta

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a c a

Review

Metabolomics, peptidomics and proteomics applications of capillaryelectrophoresis-mass spectrometry in Foodomics: A review

Clara Ibnez, Carolina Sim, Virginia Garca-Canas, Alejandro Cifuentes ,Mara Castro-PuyanaLaboratory of Foodomics, CIAL, CSIC, Nicolas Cabrera 9, 28009 Madrid, Spain

h i g h l i g h t s

Foodomics allows studying food andnutrition through the application ofadvanced omics approaches.

CE-MS plays a crucial role as ana-lytical platform to carry out omicsstudies.

CE-MS applications for foodmetabolomics, proteomics andpeptidomics are presented.

g r a p h i c a l a b s t r a c t

a r t i c l e i n f o

Article history:Received 8 March 2013Received in revised form 20 June 2013Accepted 17 July 2013Available online 25 July 2013

Keywords:

Capillary electrophoresis-massspectrometryFoodomicsMetabolomicsPeptidomicsProteomics

a b s t r a c t

In the current post-genomic era, Foodomics has been defined as a discipline that studies food and nutri-tion through the application of advanced omics approaches. Foodomics involves the use of genomics,transcriptomics, epigenetics, proteomics, peptidomics, and/or metabolomics to investigate food quality,safety, traceability and bioactivity. In this context, capillary electrophoresis-mass spectrometry (CE-MS)hasbeen applied mainly in food proteomics, peptidomics andmetabolomics. Theaim of this reviewworkis to present an overview of the most recent developments and applications of CE-MS as analytical plat-form for Foodomics, covering the relevant works published from 2008 to 2012. The review provides alsoinformation about the integration of several omics approaches in the new Foodomics field.

2013 Elsevier B.V. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22. Role of CE-MS for omics approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43. Data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44. CE-MS in Foodomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

4.1. CE-MS for food metabolomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54.2. CE-MS for food proteomics and peptidomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104.3. Foodomics integration of various omics approaches. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Corresponding author at: Laboratory of Foodomics, CIAL, CSIC, Nicolas Cabrera 9, 28049 Madrid, Spain. Tel.: +34 91 0017955.E-mail address:[email protected](A. Cifuentes).

0003-2670/$ see front matter 2013 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.aca.2013.07.042
http://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.aca.2013.07.042http://www.sciencedirect.com/science/journal/00032670http://www.elsevier.com/locate/acamailto:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.aca.2013.07.042http://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.aca.2013.07.042mailto:[email protected]://crossmark.crossref.org/dialog/?doi=10.1016/j.aca.2013.07.042&domain=pdfhttp://www.elsevier.com/locate/acahttp://www.sciencedirect.com/science/journal/00032670http://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.aca.2013.07.042

8/9/2019 1-s2.0-S0003267013009884-main

2/13

2 C. Ibnez et al. / Analytica Chimica Acta 802 (2013) 113

5. Future developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Clara Ibnezis a PhD student at the Foodomics labo-ratory in theNationalResearchCouncilof Spain(CSIC)

in Madrid. She works in the development of newMetabolomic methodologies as well as new meth-ods for data processing and statistical analysis. Herexperience includes the integration of Metabolomicswith other omics to follow different Foodomicsapproaches.

CarolinaSim is a Tenured Researcher at theNationalResearch Council of Spain (CSIC) in Madrid. Her run-

ning research lines include (i) method developmentand application of capillary electrophoresis/liquidchromatographymass spectrometry, (ii) chiral anal-ysis forfood analysis (quality,safety,traceability),(iii)Proteomics and Metabolomics in food quality, safety,and food bioactivity studies.

Virginia Garca-Canasis a Tenured Researcher at theNational Research Council of Spain (CSIC) in Madrid.

Shedevelopsher scientific activitiesin theLaboratoryof Foodomics. Her activities include the developmentof advanced analytical methods for Genomics andTranscriptomicsin foodquality,safetyand foodbioac-tivity studies.

AlejandroCifuentes isa Full ResearchProfessor attheNational Research Council of Spain (CSIC) in Madrid

and Head of the Laboratory of Foodomics. His activityincludes advanced analytical methods developmentfor Foodomics, food quality and safety, food bioac-tivity as well as isolation and characterization ofbiologically active natural products. He is Editor ofTrAC and Electrophoresisand member of theEditorialBoard of 12 international journals.

Mara Castro-Puyana is a contracted researcherunder the Juan de la Cierva program at the NationalResearch Council of Spain (CSIC) in Madrid. Her mainresearch activities include the development of greenextraction for functional ingredients production andadvanced analytical methodologies for food ingredi-ents characterization.

1. Introduction

In order to meet the exigent demands from official laboratories,consumers and regulatory agencies on food safety, quality, trace-ability, and bioactivity in the globalized 21st century, it is noweven more necessary the development of faster, more powerful,cleaner, and cheaper analytical methodologies, capable to provideinformation about chemical composition of foods, adulteration,contamination, product tampering, processing, traceability, etc.,while ensuring compliance with food and trade laws [1,2]. There isalso currently a general trend in food science toward the consider-ation of food as an affordable way to prevent diseases. In this sense,oneof the main challenges is to improve ourlimited understandingon the interaction of food compounds with genes and their subse-quent effect on proteins and metabolites; this knowledge should

allow a rational design of strategies to manipulate cell functions

through diet, which is expected to have an extraordinary impacton our health in the non-distant future[3].

In this context, Foodomics has been defined by our researchgroup as a new discipline that studies the food and nutritiondomains through the application of advanced omics technologiesto improve consumerswell-being, health, and confidence[46]. Tocarry out a Foodomics study, it is essential to take resort of mod-ern analytical approaches (seeFig. 1)capable to provide molecularinformation on the different expression levels, i.e., gene, transcript,protein or metabolite[712].Fig. 1presents an ideal Foodomicsscheme and the expected outcomes (adapted from[13]).By usingthis global strategy it should be possible to identify all the smallchanges induced by bioactive food ingredient/s on a given system(cell, tissue, organ, or organism) at different expression levels[2].Among the advanced analytical methodologies that can be usedto carry out the necessary omics studies, capillary electrophoresis

(CE) hyphenated to mass spectrometry (MS) has already shown toplay a crucial role (seeTable 1).As can be seen inTable 1,CE-MSis mainly applied for proteomics, peptidomics and metabolomicsstudies, a brief discussion on their fundamentals is given below.

Proteomics is the large-scale analysis of a proteome, thatincludes allthe expressed proteins in a particular biological systemat a given time, whereas peptidomics is the analysis of all peptidecontent within an organism, tissue or cell (peptidome) includingnot only the peptide present in the system but also transientprod-ucts of protein degradation[32].In general, the major difficultyin the analysis of protein and peptides comes from the differentphysic-chemical properties of proteins, the high number of pep-tidic sequences that can become available, and the huge dynamicconcentration range of both families of compounds in real sam-

ples. Proteomics and peptidomics offer multiple applications in

8/9/2019 1-s2.0-S0003267013009884-main

3/13

C. Ibnez et al. / Analytica Chimica Acta 802 (2013) 113 3

Fig. 1. Ideal Foodomics strategy, including methodologies and expected outcomes, to investigate the health benefits from dietary constituents.Redrawn from Ibnez et al.[13]. Copyright (2012) with permission from Elsevier.

Table 1Review papers on CE-MS for omics approaches published in the period covered by this work (20082012).

Subject/title Publication year Reference

Capillary electrophoresis-electrospray-mass spectrometry in peptide analysis and peptidomics. 2008 [14]Glycosylation analysis of glycoproteins and proteoglycans using capillary electrophoresis-mass spectrometry strategies. 2008 [15]CE at the omics level: Towards systems biologyAn update. 2008 [16]CE-MS in metabolomics. 2009 [17]

Profiling of primary metabolite by means of capillary electrophoresis-mass spectrometry and its application for plantscience. 2009 [18]

Capillary electrophoresis-mass spectrometry as a powerful tool in biomarker discovery and clinical diagnosis: An updateof recent developments.

2009 [19]

Capillary electrophoresis applied to proteomic analysis. 2009 [20]The role of capillary electrophoresis-mass spectrometry to proteome analysis and biomarker discovery. 2009 [21]Growing trend of CE at the omics level: The frontier of systems biology. 2010 [22]CE-MS for metabolomics: Developments and applications in the period 20082010. 2011 [23]Capillary electrophoresis as a metabolomics tool for non-targeted fingerprinting of biological samples. 2011 [24]Analysis of glycans derived from glycoconjugates by capillary electrophoresis-mass spectrometry. 2011 [25]Capillary electrophoresis-mass spectrometry for the analysis of intact proteins 20072010. 2011 [26]On-line CE/ESI/MS interfacing: Recent developments and applications in proteomics. 2012 [27]Modern analytical techniques in metabolomics analysis. 2012 [28]CE-MS for proteomics: Advances in interface development and application. 2012 [29]Growing trend of CE at the omics level: The frontier of systems biologyAn update. 2012 [30]Recent advances in the application of capillary electromigration methods for food analysis and Foodomics 2012 [7]CE-MS for metabolomics: Developments and applications in the period 20102012. 2013 [31]

8/9/2019 1-s2.0-S0003267013009884-main

4/13


food science including food processing, food quality, food safety,characterization of healthy food ingredients, etc.[3336].

Metabolomics focuses on the analysis of a metabolome whichhas been defined as the full set of endogenous or exogenous lowmolecular weight entities of approximately

8/9/2019 1-s2.0-S0003267013009884-main

5/13


of genomic data. Algorithms for de novo search have been recentlyreviewed [56] and individually explained elsewhere[57,58]. Ontheother hand, database search is the most common approach due tothe higher simplicity and reliability of the results obtained. In thiscase, mass spectra are matched with available databases (Mascot[59],OMSSA (http://pubchem.ncbi.nlm.nih.gov/omssa/), MassWiz[60], X!Tandem (http://www.thegpm.org/TANDEM/index.html),SEQUEST[61],SIMS[62],etc.) containing all amino acid sequencesexpected to be present in the sample analyzed[61]. Obviously,most of the bottom-up bioinformatic tools can be directly usedin peptidomics. However, the development of specific tools andvalidation procedures for peptidomics are required to be entitiesseparated from proteomics tools due to subtle but important dif-ferences in peptidomics analysis [63]. For instance, informationobtained from naturally occurring peptides (non-tryptic peptides)isinadequatelyunderstooduptodaterequiringspecificpeptidomicbioinformatics tools[63].An overview of the available softwarecan be found in several websites (www.proteomesoftware.com;www.proteomecommons.org/tools.jsp;www.ms-utils.org).

Like in peptidomics, specific bioinformatics tools are alsorequired to process CE-MS for metabolomics data. Target anal-ysis is the most developed analytical approach in metabolomicsfor which data processing is basically performed by creating cal-ibration curves, calculating LOD and LOQ for the metabolite/s ofinterest and then, to detect and quantify these metabolites inthe sample. This approach requires the previous knowledge ofthe compounds of interest (molecular weight, physico-chemicalproperties, etc.), its major limitation is that the metabolites ofinterest have to be available as standards to address them fromthe beginning of the study. This approach is therefore useless toidentify novel metabolic markers[64].This fact has been deter-minant for the growing of non-target metabolomics in clinic andnutrition studies. Processing and analyzing the large and com-plex raw data obtained from non-targeted metabolomics is achallenge for researchers and requires specialized bioinformat-ics tools[65].MS data processing comprises some general stagesin non-targeted metabolomics studies. Data has to be converted

generally to netCDF or mzXML formats commonly by the samesoftware used for MS acquisition or, if needed, by other bioinfor-matic tool. Some frequently used vendor-specific converters areTrapper (Agilent), CompassXport (Bruker) and Masslynx (Waters),among others. In thissense,proteowizardis a highlyrecommendedtool as can be used for all MS vendors (with some exceptions) andcan be freely downloaded (http://proteowizard.sourceforge.net/).Data once is converted to the suitable format is then submittedto the following data processing stage, the different steps for MSdata processing have been tackled in a recent review work[66].Briefly, main steps in MS data processing are feature detection,baseline removal, migration time alignment and peak annotation.The wide variety and complexity of peak shapes in CE-MS (com-pared to LCMS or GCMS) and the large variation in migration

time between runs that frequently are obtained conform the mainissues to be solved in the processing of CE-MS data[67].Hetero-geneity ofpeak shapesmay affectthe feature detectionstep,for thisreason, when a fully automated detection is carried out, detailedchecking of the results is mandatory. It is also recommended theuse of algorithms that permit user-interactive adjustment of theparameters of the algorithm. In both cases (fully-automated oruser-interactive), optimization of each parameter adjusted in thisfeature detection algorithm represents one of the most importantand difficult tasks in data processing because it can bias all thesubsequent data processing, and consequently the metabolomicsresults and the following interpretation. The most critical draw-back of CE-MS compared to LCMS or GCMS is the larger variationin migration time between runs mainlydue to changes in the capil-

lary wall or electrolyte solution induced by the sample matrix. This

fact makes the choice of a highly interactive and versatile bioinfor-matic tool and its parameters one of the main critical decisions ofthe whole metabolomic analysis.

Although there are a variety of bioinformatic tools to performan automatic or semi-automatic data processing of CE-MS data, themajority of algorithms and softwares were originally developedfor other analytical platforms[6872].From all these softwares,MZmine is characterized to present high flexibility, user-friendlyinterfaceandsupporthighresolutionMSdata [73]. Alltheseadvan-tages make this bioinformatic tool highly suitable for CE-MS dataprocessing, even for handling a substantial number of data fromruns analyzed in different days as has been demonstrated before[74].The most significant disadvantage is that some of the algo-rithmsare very time consuming leading in some cases to theforcedshutdown of the program. Other free available softwares stronglyrecommended,especiallyifthetimeshiftisminimumandthenum-ber of samples is lower, are XCMS package for R program [71],Metaboanalyst[75],and MetAlign[76].

From all the above, it can clearly be concluded the need todevelop new and specific software for the analysis of CE-MS data.Some researchers have focused on the development of softwarespecially designed for CE-MS data called JDAMP [67]. This toolincorporates all dataprocessing steps needed including normaliza-tion with internal standards commonly used in CE-MS, followed bya 2D(time and m/zdimension) map of data enabling visual inspec-tion[49],quality check, differentially expressed metabolites andidentification of redundant ions.Fig. 2shows the screenshots ofthe graphical user JDAMP interface.

Independently of the processingsoftwareapplied,in general thepeak list obtained has to be normalized by internal standards co-injectionortotalareacalculation,andthenstatisticalanalysishastobe performed(if these steps were notincluded in the previous dataprocessing software). It is then possible to obtainvaluableinforma-tion about the quality of the data processing using unsupervisedstatistical techniques (PCA, clustering, etc.). Moreover, statisticalanalysis requires all the peaks to be migration time-aligned toprevent false statistically differences between samples. Thus, sta-

tistical analysis is not only useful to differentiate samples understudy butalso to evaluate thequality andadequacyof allthe CE-MSdata analysis process.

4. CE-MS in Foodomics

CE-MS provides impressive possibilities as analytical platformfor Foodomics studies at different levels. In order to provide a morein-depth look on the high potential of CE-MS, this section providesa detailed description of different applications of this technique infood metabolomics, proteomics and peptidomics. Besides, integra-tion of several omics and recent CE-MS developments will be alsodiscussed.

4.1. CE-MS for food metabolomics

Nutrition and food ingredients are now known to influencethe molecular mechanisms that lead to individual health sta-tus. However, the elucidation of the correlation between dietor food ingredients and their preventive or health promotingeffects are not clear yet. Synergic effects between diet compo-nents,inter-individualvariability andenvironmentalfactorshinderany interpretation. Besides, single nutrients may have multiplebiochemical targets and subsequent physiological actions, whichmay not be easily addressed with classical target biomarker anal-ysis. As a result, the current use of single biomarkers as indicatorsof health/disease is being replaced by comprehensive profiling of

individual metabolites linked to understand health and human
http://pubchem.ncbi.nlm.nih.gov/omssa/http://www.thegpm.org/TANDEM/index.htmlhttp://www.proteomesoftware.com/http://www.proteomecommons.org/tools.jsphttp://www.ms-utils.org/http://proteowizard.sourceforge.net/http://proteowizard.sourceforge.net/http://www.ms-utils.org/http://www.proteomecommons.org/tools.jsphttp://www.proteomesoftware.com/http://www.thegpm.org/TANDEM/index.htmlhttp://pubchem.ncbi.nlm.nih.gov/omssa/

8/9/2019 1-s2.0-S0003267013009884-main

6/13


Fig. 2. Screenshots of JDAMP results windows. Panel A displays the location of detected significant differences (red labels) and of known compounds (blue labels). Details ofthe differences identified are shown in Panel B. An electropherogram overlay is shown for the selected features in Panel C.

Reprinted from Sugimoto et al. [67]. Copyright (2010) with permission from Springer.

metabolism [77]. In this new scenario, the emerging field ofmetabolomics is receiving increasing attention by the scientificcommunity working in food science as can be deduced from thenumerous review works published on this issue in the last years[39,7880].However, one of the main challenges of metabolomicsin food science is still the study and understanding of the modifi-cations produced in the metabolome and metabolic pathways byspecific dietary interventions.

An additional consideration is that themain causesof morbidity

and mortality worldwide are those pathologies known as non-communicable diseases (cardiovascular diseases, diabetes, cancersand chronic respiratory diseases)[81] which are not infectious nei-thertransmissibleamongpeople.Thesediseasesarelessinfluencedby genetics than by lifestyle factors, physical activity and diet. Thisfact shows an increasing need for developing powerful researchstrategies as the ones proposed by Foodomics, to promote individ-uals health and prevent these diseases through diet understandingthe molecular mechanisms behind its positive effect.

Previous to any dietary intervention is necessary to charac-terize all the metabolites present in a dietary extract or foodingredient. Some ingredients present in food have received specialattention due to their potential health-promoting activity. Pheno-lic compounds have been claimed to show promising antioxidant,antimicrobial and anticancer activities among others, and have

been evaluated in several nutritional studies [82]. Polyphenols andother compounds were detected from a lemon verbena leavesextract by CE-MS[83]among which 16 compounds were tenta-tively identified. Furthermore, four of the identified compoundsweredescribedforthefirsttimeinlemonverbena.Characterizationofthisherbhasrelevanceduetoitstraditionaluseforthetreatmentof asthma, fever, gastrointestinal disorders and skin diseases[84].

Natural phenolic compounds as isoflavones are one of themajorclasses of phytoestrogens. Isoflavones are widely distributed

throughout the plant kingdom, but accumulate predominantly inplants of the Leguminosae family. As potential functional foods,isoflavoneshave been claimed to present several health-promotingproperties, such as antimutagenic effects, reducing the symptomsof postmenopausal women, reducing the risk of osteoporosis andpreventing cardiovascular diseases. Isoflavones bioavailability hasbeen demonstrated to be crucial for their benefitial impact[85].Soy products are rich in isoflavones and they have a growingscientific interest due to their increasing consumption. In thisdirection, Bustamante-Rangel et al. not only separated but alsoquantified seven isoflavones in soy drink samples [86]. Gluco-sides and aglycones were analyzed in that work by CE-MS. Thedeveloped method was able to separate the analytes as anions inpositive separation and ionization mode. Samples were injectedhydrodynamicallyusinganaqueoussolutionofammoniumacetate

8/9/2019 1-s2.0-S0003267013009884-main

7/13


adjusted to pH 11.0 as BGE. At this pH all compounds of interestwere ionized as anions. In addition, an enhanced resolution viaa programmed nebulizing gas pressure along the analysis wasachieved, namely gas pressure was set at 1psi from 0 to 7minand then 5psi from 7 to 15min. This method demonstrated animproved separation and allowed analytes to reach the ESI asanions[86].Other works have focused on the evaluation of pos-sible metabolic alterations in soybean due to genetic engineering[87]. The transgenic soybean variety tolerant to glyphosate hasbeen the most cultivated transgenic plant worldwide[88].In thelast decades, several aspects of transgenic crops have been criti-cized and scientific and public debate is open about their influenceon the environment and their safety as food and feed. In thisregard, the research and development of new and more power-ful analytical methods is becoming imperative [9]. To shed light onthis controversial topic, Garca-Villalba et al.[87]used CE-TOF-MSto compare the metabolic profiles of conventional and geneti-cally modified soybeans. A total of 45 molecules were identifiedincluding isoflavones, amino acids and carboxylic acids. Differentexpression of three free amino acids (i.e. proline, histidine andasparagine) as well as of one amino acid derivative (i.e. 4-hydroxy-l-threonine) was observed when the transgenic line was comparedto the corresponding isogenic soybean variety. In other study, aCE-TOF MS methodology was optimized to identify and quantifythe main metabolites found in three varieties of transgenic maizewith their corresponding isogenic wild lines [89].In this study,27 metabolites were identified in each line finding as main dif-ferences their quantity in the different lines. A similar approachwas carried out combining CE-TOF MS and FT-ICR MS analyticalplatforms[90].Carnitine, arginine, tyrosine, beta-alanynarginine,subaphyllin, graveoline, caffeoyl-beta-glucose, lunarine, apigenini-din, and 5,6-dihydroxyindole were identified by both techniques.Additionally, some altered metabolic pathways due to the geneticmodification were also identified in this work[90].

There are processes (i.e. transport, storage, food manipulation,fermentation, etc.) that can affect food integrity, quality, securityand/or composition. In some cases, the characterization of foods

has to be completed by studies concerning possible alterationsduring the above mentioned processes. This issue was recentlyaddressed on the alteration of a soybean during a fermentationprocess[91].A traditional Korean food based on fermented soy-beanpaste(Cheonggukjang) wasanalyzedsincethe consumptionofthisfoodhasbeenlinkedtoantioxidant,anti-inflammatory,antihy-pertensive and antidiabetic properties[92,93].This paste containsvitamins, minerals, isoflavonoids and saponins among others. Inthis work, changes dueto soyfermentation as a function of fermen-tationtime(at0,12,24,36,48,60and72h)andthecorrelationwithmetabolic pathways changes, were explored by CE-TOF-MS andGCMS. CE-TOF-MS analysis was performed in both negative andpositive ion mode detecting 24 and 14 metabolites, respectively.Together with the molecules detected by GCMS and after apply-

ing a multivariate statistical analysis, changes in amino acid andnucleotidemetabolism, glycolysisand tricarboxylic acidcycle wereobserved.ThiscanbeobservedinFig.3whichdepictstheschematicoverview of changes in the metabolite pathway of Cheongguk-

jang fermentation of three microorganisms. Sugimoto et al.[94]studied the temporal changes in the metabolic profiles and sen-sory characteristics ofedamame, a popular vegetable bean, duringtransportation from the site of harvest to the site of purchase orconsumption. Metabolicanalyses wereperformedat different stor-age times and temperatures. A significant change in amino acidlevels were correlated with sensory attributes alteration while anincrease in phospholipids and GABA were correlated with the riseoftemperaturestorage.Morerecently,thesameresearchgrouphasstudied the temporal changesin the metabolite profiles of pasteur-

ized and unpasteurized Japanesesake(rice wine) during storage

[95]. Samples (pasteurized andnot) stored at 8 Cor20 Cfor0,1,2or 4 months were analyzed by two platforms (CE-MS and LCMS).CE-MS identified a decrease in the total amino acid concentrationwith storage time being more severe in the case of pasteurizedsamples.

Apart from all the stages related to food processing, storage,transportand so on, availability of all the single nutrients is limitedby human body processes. Digestive tract starts at the mouth anddental caries, one of the most prevalent diseases, can take place.This bacterial infection causes demineralization and destruction ofthe hard tissues usually by production of acid by bacterial fermen-tation of the food accumulated on the tooth surface[96].Dentalplaque study is essential to elucidate the metabolic regulation ofbacterial acid production, not only to understand caries etiologyfrom a bacterial perspective, butalso to develop more effective andsafercaries-preventive protocols.Takahashi et al.[97] analyzed themetabolic profiles related to the central carbon metabolism, theEmbden Meyerhof Parnas pathway, the pentose-phosphate path-way,andthetricarboxylicacidscycleinsupragingivaldentalplaqueand representative oral bacteria by CE-MS. This work studied themetabolic regulation in dental plaque through the comparison ofmetabolite profiles between supragingival dental plaque and rep-resentative plaque bacteriaobservingmarkedlydifferent metabolicprofiles after glucose rinse in both bacteria cases.

Gut flora (or intestine bacteria) is involved in the absorption ofdiet components such as fiber (undigested polysaccharides) that ismetabolized to short chain fatty acids and absorbed by the humanbody.ItalsoplaysaroleinthesynthesisofvitaminsBandKandthemetabolismofbileacids,othersterolsandxenobiotics [98]. Bacteriametabolism knowledge can help to understand the bioavailabilityof certain nutrients in the gut.Escherichia coli is one example ofgut bacteria needed for the correct performance of the digestiveprocess and by its collaboration in the vitamin B and K production.Escherichiacoli isabletoproducelactosethroughglucosefermenta-tion and helps in nutrient absorption. Yamamotoya et al.[99] stud-ied the dynamic glucose metabolism changes inE. coli. Althoughmetabolomics analyses have mainly been performed using steady

state culture, this does not allow observing the transition of bac-terial growth phases. For that reason, a profiling of concentrationof metabolites by using batch culture was performed to analyzethe dynamic changes in cellular metabolism. Namely, glucose-6-phosphate, phosphoenolpyruvate and 3-phosphoglycerate con-centration were quantified by CE-MS. Surprisingly glycogen, awell-known molecule with energy storage function, was observedto be the main sugar source in the lag phase (maturation phase,before cell division phase) and begins to be accumulated duringthe exponential phase (division phase) of this bacteria.

Once a food ingredient hasbeen characterized andis related to ahypothetical beneficial activity, the comprehensive determinationand evaluation of its effect at molecular level is necessary. In thisregard, plasma, urine and saliva are the most common biofluids

analyzed in metabolomics. Fecal extracts are the preferred sampleto study gut microflora metabolism in order to detect changes inmicrobiota,howeveritsusefulnessindietaryinterventionstudiesislimited since metabolic profiling of fecal extracts does not indicatemetabolites absorbed by the human body[100].

In this sense,cell cultures offer thepossibilityto investigate foodingredients (as well as new drugs or xenobiotics) on human bio-logical material in a more economic manner. Besides, cell culturesavoid major error sources in metabolomics due to heterogene-ity on individuals responses, habits (smoking, physical activity,etc.),environmental interferences, etc. In addition,other additionalsources of variation in metabolomic studies can take place duringthe analysis with special mention to the sample purification step.Although the influence of this step is frequently underestimated in

metabolomics, it has a great impact in the metabolomics results.

8/9/2019 1-s2.0-S0003267013009884-main

8/13


Fig.3. Schematicoverviewof changes in themetabolite pathway ofcheonggukjang(CGJ) fermentationof three microorganisms.(A) Aminoacid andnucleosides ofpyrimidinemetabolism; (B) nucleosides of purine metabolism.

Reprinted from Kim et al.[91].Copyright (2012) with permission from the American Chemical Society.

This fact has been demonstrated by a non-targeted metabolomicstudy by CE-MS using as case-study cell culture samples [101].Namely, metabolite purification by four different methodologies(methanol deproteinization, ultrafiltration and two solid phaseextraction methods using a C18 and a more polar polymer-basedcartridge) were investigated. More than 80 metabolites weredetected in a single CE-MS run in less than 20min per sample.Besides, important differences were found among the four sampletreatments demonstrating the great influence of this step that insomecasescanhaveacrucialinfluenceontheconclusionsachievedfrom a metabolomic study (e.g., the extraction of a higher num-ber of metabolites increases the metabolome coverage whereasthe extraction of a lower number of metabolite could involve aloss of information). That work highlights the problems derivedfrom the lack of standardized protocols[101].Thus, the design of

the study is one of the most complicated and critical phases inany metabolomics research where uncontrolled and undesirablesources of variability may hinder interpretation.

The chemopreventive effect of polyphenols from rosemary incolon cancer cell cultures has been recently studied using threemetabolomic analytical platforms (CE-, RP/UPLC- and HILIC/UPLC-TOFMS) [102]. After thedetermination of a decreasing colon cancercell proliferation induced by the rich-polyphenol extract fromrosemary, authors correlated this fact with significant metabo-lite differences. Among other modifications, an alteration in thepolyamine metabolism and an increase in ratio between theglutathione reduced and oxidized were observed. Integrationwith transcriptomics and proteomics studies was then performedfollowing a global Foodomics strategy [13] (see Section 4.3).Other strategy was performed to elucidate the effect of Cistus

8/9/2019 1-s2.0-S0003267013009884-main

9/13


Fig. 4. Examples of electropherograms of urine. (A) Total ion electropherogram; (B) Extracted compound electropherogram.Reprinted from Moraes et al.[107]. Copyright (2011) with permission from Wiley.

monspeliensisin human intestinal cells[103].This herb, predom-inantly consumed in the Mediterranean area as tea and spice,has been claimed to have antioxidant, antibacterial, and anti-inflammatory effects[104].Shimoda et al. [103]focused on theenergy metabolism of this extract on intestinal epithelium cellsobserving that a low concentration extract increased ATP pro-duction without changing the viability of the cells, and a moreconcentrated extract decreased ATP production (when comparingwith the low concentrated extract) and increased cell viability. Inthis work[103], it was pointed out the needof a futurevalidationof

these results in vivo. Ideally, food ingredients with confirmed ben-efits in cell cultures or tissues should be further analyzed applyingin vivo approaches since, even with the numerous advantages thatisolated cell cultures and tissue samples offer, there are many fac-tors interacting to bring about physiological responses impossibleto predict or measure in cells [105]. In a recentinvestigation, Omoriet al. [106] used tissue sample (cardiac fibroblast) together with ananimal model of hypertensive rat (Dahl salt-sensitive rat fed witha high-salt diet) to identify a potential therapeutic intervention fora particular type of heart failure. Through a target CE-MS analy-sis on l-carnitine and related metabolites, the authors suggestedl-carnitine supplementation as mitigating agent of cardiac fibrosisand promising therapy for this type of heart failure[106].

Moraes et al. [107]have deepened in the research on possi-

ble antioxidant therapies using rats treated with streptozotocin,

proposed as the most appropriate animalmodel of systemic oxida-tive stress related to diabetes. Metabolic fingerprint of rat urinewere obtained by CE-TOF-MS. An example of the total ion andextracted compound electropherograms for urine can be observedin Fig.4. Some metabolites associatedto lysine glycation and cleav-age from proteins were increased in diabetic animals. After rattreatment with a potential antioxidant extract from a brown alga(Cystoseira), a marked decreased fructoselysine levelwas observed.This fact couldbe associated toan improvementof thediabetic statesince it has been demonstrated that higher levels of fructosely-

sine in hair and nails of diabetic patients have been related to theseverity of the diabetes[108,109].Oxidative stress and antioxidant properties are frequently

investigated by metabolomics to find new effective ingredientshelpful in preventing undesirable effects of aging and degenerativediseases. Lee et al. [110] designed a metabolomic strategy to assessthe efficacy of a nutritional intervention with N-acetyl-l-cysteine(NAC), to attenuate oxidative stress induced by strenuous exer-cise. Non-targeted metabolomics of filtered red blood cell lysatesrevealed significant attenuation of cellular oxidation after a high-dose oral NAC intake. Significant changes in oxidized and reducedglutathione, 3-methylhistidine, l-carnitine, O-acetyl-l-carnitine,and creatine were related to the dietary intervention. In this study,the same healthy individual was used as control and as NAC intake

case, what is called a paired sample. Paired samples is the most

8/9/2019 1-s2.0-S0003267013009884-main

10/13


correct study design in nutritional interventions in order to avoidthe effect from inter-individual variations in metabolomics. Pairedsamples have been also used in another study where human urinesamples from 13 healthy individuals were analyzed[111].Eachindividual collectedurine 2 h after intakeof three beverages(water,coffee and tea) for 3 days. Urine samples were analyzed by CE-MS in both, the negative and positive ion mode and as a result, 12potentialmetaboliteswerehighlighted as the mostimportant com-pounds in differentiating among the three beverage intakes. Thesemolecules are thought to be potentially identified with tandem MSin a future work[111].

4.2. CE-MS for food proteomics and peptidomics

Analysis of proteins andpeptidesis of special interest since theyaremajorconstituentsoffoods.Oneofthemostchallengingaspectsin protein andpeptide analysis in foodstuffs is their complexity anddynamic concentration range. Still nowadays, the most powerfulmethod for the separation of complex protein mixtures is 2D elec-trophoresis. However, separation efficiency of CE can be extremelyhigh for large molecules, such as proteins, and thus, it has emergedas a powerful technique for the analysis of proteins and peptides.Since MS dominates todays proteomic research, CE-MS couplingis of special interest in this field[26].Although the sheathliquidinterface is by far the most widely used for CE-ESI MS in proteinanalysis, recently, the potential of new CE-MS interfacing tech-niques has been showed within the field of proteomics[29].Theadsorption of proteins on the negatively charged fused-silica innersurfaceofthecapillaryisatypicalprobleminCEofproteins.Proteinadsorption may cause changes of the EOF which leads to nega-tive effects on peak broadening, peak tailing and poor migrationtime reproducibility. To prevent these adverse effects a numberof coatings have been described[112]. Using a capillary coat-ing with ethylpyrrolidine methacrylate-N,N-dimethylacrylamidecopolymerprotein adsorption was avoided and a stable spraycouldbe achieved for the analysis of the zein fraction of different maizecultivars[49].Substantial equivalence of three transgenic maize

lines (Aristis, Tietar, and PR33P66) and their corresponding naturallines,was studied using a novel CE-TOF-MSapproach[113]. Amongmilk components, proteins exert a wide range of nutritional, func-tionalandbiologicalactivities.Milkanddairyproductsadulterationis a common practice that arises from a mixing of high qualityfoodproducts with cheaper ingredients.CE-IT-MS was successfullyapplied to the detection of adulteration of expensive ovine/caprinemilk with less expensive bovine milk[114].In that work, an acidicand high-ionic strength running electrolyte (1M formic acid) wasused to minimize the adsorption of proteins on the inner surface ofthefused-silicacapillaryandtogaintheeffectiveproductionofpos-itive ions in electrospray interface[114].Through-lactalbuminand-lactoglobulin analysis, concentrations below 5% of bovinemilk in caprine milk could be detected. Capillary electrofocusing

(CIEF) is a very interesting separation mode for proteins since itallows separation based on their different isoelectric point. How-ever, the carrier ampholytes used to separate proteins in CIEF arenot volatile and thus, background signals and ionization suppres-sion of the proteins are observed, hindering general applicationof CIEF-MS to this field. Lecoeur et al. [115]proposed a new on-line CIEF-MS method for the qualitative and quantitative analysisofmilkwheyproteinsofclosepIvalues.Wheyproteinsfrombovinemilk (i.e.,-lactoglobulin A,-lactoglobulin B,-lactalbumin andbovine serum albumin) with close pIvalues, were successfully sep-arated and quantified with CIEF-MS achieving limits of detectionfrom 10to 70nM. Moreover,in order to removeresidualadsorptionof proteins onto the inner capillary wall a systematic rinsing pro-cedure (with TFA, ammonia and ethanol) was found to be essential

for good method repeatability.

Global profiling approaches have also been applied to the studyand exploration of complex peptidome in food science. CE is con-sidered complementary to LC and a powerful technique for theanalysis of peptides as it exhibits a selectivity that is based oncharge-to-size ratio differences. Since peptides are in general lessdifficult to analyze by CE than proteins, characterization of com-plex mixtures of peptides by CE-MS is one of the most promisingapproaches in proteomic analysis[14].Recently CE-MS has beenused to identify the presence of bioactive peptides in several com-mercial hypoallergenic infant milk formulas prepared from bovinemilk protein hydrolysates [34]. CE-IT-MS allowed the tentativeidentificationofninebioactivepeptides.Fiveofthemwerereportedto present angiotensin-converting enzyme inhibitory, and werelater confirmed by CE-TOF MS (namely, LKP, IPY, ALPM, PGPIHNand VAGTWY)[34].

Digestion of complex protein extracts with endoproteinases ofknown specificity prior to CE separation and MS detection, hasbeen shown to be an effective approach for proteomic character-ization. Following this approach substantial equivalence betweena transgenic soybean and its isogenic non-transgenic counterpart,was studied by CE-TOF MS in order to detect possible unintendedeffects from the genetic modification [35]. The results showed thatno significant changes were found in the specific proteins fractionstudied between the conventional and the GM soybean.

Peptidomics via CE-MS analysis has been scarcely applied tostudy the effect of nutrients on health. Mullen et al. [116]car-ried out a pilot study showing the obtained results on the effectsof polyphenol rich dietary products on subjects with coronaryartery disease (CAD). For that purpose, urinary peptides from mid-dle aged and overweight subjects treated with polyphenol richdrink (n = 20) and withplacebo (n = 19) were studied using CE-TOF-MS. Polyphenol rich drink contained green tea flavan-3-ols, grapeseed and pomace procyanidins, apple dihydrochalcones, procyani-dins, chlorogenic acids, lemon flavanones, and grape anthocyanins[117].The complex urinary proteomic profile obtained by CE-TOF-MS from volunteers ingesting placebo (A) and the polyphenol richdrink (B) is shown inFig. 5.Levels of 27 polypeptides were greater

than 4-fold different between the two groups. Remarkably, 7 ofthese peptides were previously found to be part of a CAD specificurinary biomarker and their direction of expression was closer tothe healthy state in the polyphenol rich drink group and closer toCAD state in the placebo [116]. The data obtained in this study sug-gest that the polyphenol rich drink may have beneficial effects onurinary biomarkers of cardiovascular disease.

4.3. Foodomics integration of various omics approaches.

As omics techniques have become more accessible and afford-able, more reports based on the use of different high-throughputomics platforms are now carried out. The idea behind the appli-cation of different high-throughput techniques for analyzing

biological systems relies on that recording as much information aspossible will increase the opportunities to identify thosemoleculeswith a relevant role in a biologic system under specific conditions,which in the last term should increase our knowledge on the sys-tem under study. In addition, the possibility of analyzing differenttypes of biomolecules implies that inter-relationships can also beexplored. However, cross-platform studies entail a higher level ofcomplexity that requires the development and application of newintegrative tools. A general trend, in the studies in which severalomics technologies are applied, is to perform statistical analysison each omics dataset separately and then, to approach integra-tion over the results by different means in order to verify somecorrelations between molecular levels.

The application of CE-MS in cross-platform studies has been

scarce. A representative example involves the application of

8/9/2019 1-s2.0-S0003267013009884-main

11/13


Fig. 5. Urinary proteomicprofile data from controls (ingesting placebo) (A)and volunteers ingestingthe polyphenol rich drink (B). On theXaxis theCE migrationtime (min)is plotted against the molecular mass (kDa) on the Yaxis on a logarithmic scale. The Zaxis represents the mean signal intensity.

Reprinted from Mullen et al.[116].Copyright (2011) with permission from the American Chemical Society.

CE-MS for profiling the metabolome of rice under ozone exposi-tion[118].In that study, gene expression microarray and 2-DGEcombined with nanoLCMS/MS were used for transcriptome andproteome profiling analysis of the same rice samples, respectively.Prior to CE-MS analysis, samples were extracted with chloro-form/ethanol/water containing methionine sulfone and PIPES asinternal standards and then, subjected to ultracentrifugation. CE-MS analyses were carried out using an ESI interface coupled toan ion trap mass spectrometer. In this case, CE-MS analysis wasessential to confirm the interplay between some amino acids andother metabolites with genes involved in metabolic pathways acti-vated upon ozone exposure. More recently, Valds et al.[119] haveapplied a global Foodomics strategy, involving transcriptomics

and metabolomics profiling, to study the antiproliferative effect ofdietary polyphenols from rosemary on human leukemia K562 celllines[119].Comparative transcriptomic analysis was performedon leukemia cells incubated with polyphenol-enriched rosemaryextracts, and their respective untreated controls using whole-transcriptome microarray. Similarly, comparative metabolomicsapproach including CE-TOF MS and UPLC-Q/TOF MS analysis wasalso performed on treated and control leukemia cells. Also, func-tional enrichment and transcription factor analysis was performedon transcriptomics data suggesting that rosemary pholyphenolsmay inhibit Myc transcription factor function, giving a possibleexplanation to the observed antiproliferative effect of rosemaryextracts in leukemia cells[119].Metabolomic data suggested thatthe treatment altered reduced/oxidized glutathione ratioas well as

the levels of some metabolites involved in the polyamine pathwayin leukemia cells. In this case, integration of omics data from thetwo platforms was attempted by overlaying datasets on canonical(defined) metabolic pathways using Ingenuity Pathway Analysis(IPA) software[119].This strategy facilitated the identification ofseveral differentially expressed genes in the metabolic pathwaysmodulated by polyphenols, which provided more evidences on theeffect of these compounds.

In another report, a similar strategy has been applied to inves-tigate the health benefits of rosemary polyphenols against coloncancer [13]. In this case, the approach was expanded to com-parative proteomics besides whole-genome transcriptomics andmetabolomics. A battery of advanced analytical techniques wereapplied to obtain the three data sets, including high-densitygene expression microarray, 2-DGE combined with MALDI-TOF

MS for proteomics, and CE-TOF MS and UPLC-Q/TOF MS forwide metabolome coverage[13].Omics data were independentlyprocessed using dedicated software and bioinformatics tools. Theanalysis of individual datasets provided some insights of the effectof rosemary polyphenols on thecolon cancer cell lines. Results sug-gested a transcriptional activation of typical nuclear factor NRF2target genes as well as a moderated down-regulation of a num-ber of genes related with M phase, chromosome segregation andcytokinesis. Then, an integrative strategy based on the networkingand mapping capabilities of IPA software was explored in orderto tackle the cross-platform analysis and biological interpreta-tion of data. Using this strategy, direct associations were achievedby overlaying transcriptomic data onto a metabolite-centric net-

work associated with amino acid metabolism, molecular transportand small molecule biochemistry[13].On the other hand, usingthe same approach with proteomics results, no direct associationscouldbeidentifiedbyIPAsoftwarebetweenmetabolomicsandpro-teomic data. It has been widely recognized that biological systemscannot be modeled by analyzing their constituents separately, butrather require suitable integrative strategies[120].In this context,appropriate holistic Systems Biology approaches providing newprocedures able to integrate, summarize and visualize omics dataare still necessary[121].Systems biology aims to study biologicalprocesses using network modeling with an integrated approach, asopposed to the traditional reductionist approach. Data integration,in turn,requiresthe development of computationalframeworks fordescribing molecular systems and connecting omics databases as

well as effective integrative statistical approaches that can revealnew relationships, which cannot be found otherwise[122,123].

5. Future developments

Over the last five years, the increased number of research paperreportingCE-MSmethodologiesformetabolomics,proteomics,andpeptidomics indicate a growing interest in this coupling as ana-lytical platform for omics research. However, the low sensitivity,reproducibility and robustness hamper the wider use of CE-MSin this field compared to other analytical techniques such as LCor GC. It is expected that various solutions, mainly related to thedesign of new capillary coatings and interfaces combined withcutting-edgemethodologicaladvances,willhelptoovercomethese

important limitations or at least to reduce their negative impact.

8/9/2019 1-s2.0-S0003267013009884-main

12/13

8/9/2019 1-s2.0-S0003267013009884-main

13/13


[70] M. Katajamaa, M. Oresic, Bioinformatics 6 (2005) 179.[71] C.A.Smith,E.J.Want,G.OMaille,R.Abagyan,G.Siuzdak,Anal.Chem.78(2006)

779787.[72] R. Tautenhahn, C. Bottcher, S. Neumann, BMC Bioinformatics 9 (2008) 504.[73] T. Pluskal, S. Castillo, A. Villar-Briones, M. Oresic, BMC Bioinformatics 11

(2010) 395.[74] C. Ibnez, C. Sim, P.J. Martn-lvarez, M. Kivipelto, B. Winblad, A. Cedazo-

Mnguez, A. Cifuentes, Anal. Chem. 84 (2012) 85328540.[75] J. Xia, D.S. Wishart, Nat. Protoc. 6 (2011) 743760.[76] A. Lommen, Anal. Chem. 81 (2009) 30793086.[77] J.B. German, S.M. Watkins, L.B. Fay, J. Am.Diet.Assoc.105 (2005)14251432.

[78] R. Llorach, M. Garcia-Aloy, S. Tulipani, R. Vzquez-Fresno, C. Andres-Lacueva,J. Agric. Food Chem. 60 (2012) 87978808.[79] E.M.S. McNiven, J.B. German, C.M. Slupsky, J. Nutr. Biochem. 22 (2011)

9951002.[80] S. Collino, F.P.J. Martin, S. Kochhar, S. Rezzi, Chimia 65 (2011) 396399.[81] A. Alwan, Global Status Report on Noncommunicable Diseases 2010, World

Health Organization, Italy, 2011.[82] J. Dai, R.J. Mumper, Molecules 15 (2010) 73137352.[83] R. Quirantes-Pin, D. Arrez-Romn, A. Segura-Carretero, A. Fernndez-

Gutirrez, J. Sep. Sci. 33 (2010) 28182827.[84] C.A. Newall, L.A. Anderson, J.D. Phillipson, Herbal Medicines. A Guide for

Health Care Professionals, The Pharmaceutical Press, London, 1996.[85] D.C. Vitale, C. Piazza, B. Melilli, F. Drago, S. Salomone, Eur. J. Drug Metab.

Pharmacokinet. (2012),http://dx.doi.org/10.1007/s13318-012-0112-y.[86] M. Bustamante-Rangel, M.M. Delgado-Zamarreno, R. Carabias-Martnez, J.

Domnguez-lvarez, Anal. Chim. Acta 709 (2012) 113119.[87] R. Garca-Villalba, C. Len, G. Dinelli, A. Segura-Carretero, A. Fernndez-

Gutirrez, V. Garca-Canas, A. Cifuentes, J. Chromatogr. A 1195 (2008)164173.

[88] S. Bonny, Agron. Sustain. Dev. 28 (2008) 2132.[89] T. Levandi, C. Leon, M. Kaljurand, V. Garcia-Ca nas, A. Cifuentes, Anal. Chem.

80 (2008) 63296335.[90] C. Leon, I. Rodriguez-Meizoso, M. Lucio,V. Garcia-Canas,E.Ibanez,P. Schmitt-

Kopplin, A. Cifuentes, J. Chromatogr. A 1216 (2009) 73147323.[91] J. Kim, J.N. Choi, K.M. John, M. Kusano, A. Oikawa, K. Saito, C.H. Lee, J. Agric.

Food Chem. 60 (2012) 97469753.[92] N.Y. Kim, E.J. Song, D.Y. Kwon, H.P. Kim, M.Y. Heo, Food Chem. Toxicol. 46

(2008) 11841189.[93] D.Y. Kwon, J.W.I. Daily, H.J. Kim, S. Park, Nutr. Res. 30 (2010) 113.[94] M. Sugimoto, H. Goto, K. Otomo, M. Ito, H. Onuma, A. Suzuki, M. Sugawara, S.

Abe, M. Tomita, T. Soga, J. Agric. Food Chem. 58 (2010) 84188425.[95] M. Sugimoto, M. Kaneko, H. Onuma, Y. Sakaguchi, M. Mori, S. Abe, T. Soga, M.

Tomita, J. Agric. Food Chem. 60 (2012) 25862593.[96] J.M. Hardie, Br. Dent. J. 172 (1992) 271278.[97] N. Takahashi, J. Washio, G. Mayanagi, J. Dent. Res. 89 (2010) 13831388.[98] J.H. Cummings, G.T. Macfarlane, Clin. Nutr. 16 (1997) 311.[99] T. Yamamotoya, H. Dose, Z. Tian, A. Faur, Y. Toya, M. Honma, K. Igarashi, K.

Nakahigashi,T. Soga,H. Mori,H. Matsuno, Biochim.Biophys.Acta 1824(2012)14421448.[100] M.J. Gibney, M.Walsh, L.Brennan,H.M.Roche,B. German,B. vanOmmen,Am.

J. Clin. Nutr. 82 (2005) 497503.[101] C. Sim, C. Ibnez, A. Gmez-Martnez, J.A. Ferragut, A. Cifuentes, Elec-

trophoresis 32 (2011) 17651777.

[102] C. Ibnez, C. Sim, V. Garca-Canas, A. Gmez-Martnez, J.A. Ferragut, A.Cifuentes, Electrophoresis 33 (2012) 23282336.

[103] Y.Shimoda,J. Han, K.Kawada,A. Smaoui, H.Isoda, J.Biomed.Biotechnol.2012(2012) 428514.

[104] H.Bouamama,T. Noel, J.Villard, A. Benharref, M. Jana,J. Ethnopharmacol.104(2006) 104107.

[105] H.C. Murphy, Trans. Nebr. Acad. Sci. 18 (1991) 105108.[106] Y. Omori,T. Ohtani, Y. Sakata, T. Mano, Y. Takeda, S. Tamaki, Y. Tsukamoto,D.

Kamimura,Y. Aizawa,T. Miwa, I. Komuro, T.Soga,K. Yamamoto,J. Hypertens.30 (2012) 18341844.

[107] E.P. Moraes, F.J. Ruprez, M. Plaza, M. Herrero, C. Barbas, Electrophoresis 32

(2011) 20552562.[108] M. Oimomi, H. Hatanaka,K. Ishikawa,S. Kubota, Y. Yoshimura, S. Baba, J. Mol.Med. 62 (1984) 477478.

[109] M. Oimomi, S. Nishimoto, Y. Kitamura, S. Matsumoto, H. Hatanaka, K.Ishikawa, S. Baba, Klin. Wochenschr. 63 (1985) 728730.

[110] R. Lee, D. West, S.M. Phillips, P. Britz-McKibbin, Anal. Chem. 82 (2010)29592968.

[111] E. Allard, D. Bckstrm, R. Danielsson, P.J. Sjberg, J. Bergquist, Anal. Chem.80 (2008) 89468955.

[112] C. Huhn, R. Ramautar, M. Wuhrer, G.W. Somsen, Anal. Bioanal. Chem. 396(2010) 297314.

[113] G.L. Erny, M.L. Marina, A. Cifuentes, Electrophoresis 28 (2007) 41924201.[114] L. Muller, P. Bartk, P. Bednr, I. Frysov, J. Sevck, K. Lemr, Electrophoresis 29

(2008) 20882093.[115] M. Lecoeur, P. Gareil, A. Varenne, J. Chromatogr. A 1217 (2010) 72937301.[116] W. Mullen, J. Gonzalez, J. Siwy, J. Franke, N. Sattar, A. Mullan, S.

Roberts, C. Delles, H. Mischak, A. Albalat, J. Agric. Food Chem. 59 (2011)1285012857.

[117] W. Mullen, G. Borges, M.E.J.Lean,S.A. Roberts, A. Crozier, J. Agric.Food Chem.58 (2010) 25862595.

[118] K. Cho, J. Shibato, G.K. Agrawal, Y.H. Jung, A. Kubo, N.S. Jwa, S. Tamogami,K. Satoh, S. Kikuchi, T. Higashi, S. Kimura, H. Saji, Y. Tanaka, H. Iwahashi, Y.Masuo, R. Rakwal, J. Proteome Res. 7 (2008) 29802988.

[119] A. Valds, C. Sim, C. Ibnez, L. Rocamora-Reverte, J.A. Ferragut, V. Garca-Canas, A. Cifuentes, Electrophoresis 33 (2012) 23142327.

[120] L. Martn, A. Anguita,V. Maojo,J. Crespo, W.C.S.Cho (Eds.), AnOmicsperspec-tive on Cancer Research, Springer, New York, 2010, pp. 249266.

[121] N.Gehlenbor,S.I.ODonoghue,N.S.Baliga,A.Goesmann,M.A.Hibbs,H.Kitano,Ol. Kohlbacher, H. Neuweger, R. Schneider, D. Tenenbaum, A.C. Gavin, Nat.Methods 7 (2010) S56S68.

[122] S.P. Akula,R.N.Miriyala, H.Thota, A.A. Rao, S.Gedela,Bioinformation3 (2009)284286.

[123] A. Conesa, J.M. Prats-Montalbn, S. Tarazona, M.J. Nueda, A. Ferrer, Chemom.Intell. Lab. Syst. 104 (2010) 101111.

[124] R. Wojcik, Y. Li, M.J. MacCoss, N.J. Dovichi, Talanta 88 (2012) 324329.[125] J.-M. Busnel, B. Schoenmaker, R. Ramautar, A. Carrasco-Pancorbo, C. Rat-

nayake, J.S. Feitelson, J.D. Chapman, A.M. Deelder, O.A. Mayboroda, Anal.

Chem. 82 (2010) 94769483.[126] A.A.M. Heemskerk, J.-M. Busnel, B. Schoenmaker, R.J.E. Derks, O. Klych-nikov, P.J. Hensbergen, A.M. Deelder, O.A. Mayboroda, Anal. Chem. 84 (2012)45524559.

[127] T. Sikanen, S. Aura, S. Franssila, T. Kotiaho, R. Kostiainen, Anal. Chim.Acta 711(2012) 6976.
http://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1007/s13318-012-0112-yhttp://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1007/s13318-012-0112-y

1-s2.0-S0003267013009884-main

Documents

Transcript of 1-s2.0-S0003267013009884-main