Research Article

Received: 16 July 2009, Revised: 11 November 2009, Accepted: 12 November 2009, Published online in Wiley InterScience: 24 March 2010

(www.interscience.wiley.com) DOI:10.1002/nbm.1488

NMR metabolic profile of humanfollicular fluidEva Pinero-Sagredoa,b, Sofıa Nunesc, Ma Jose de los Santosc,Bernardo Celdaa and Vicent Estevea,b*

The environment of the oocyte during its in vivo m

NMR Biom

aturation consists of follicular fluid (FF) and is surrounded bygranulosa cells. The FF is derived from the sanguineous plasma and secretions, synthesised in the follicle wall, thatcontain a large variety of growth factors, cytokines, amino acids, and other metabolites. These metabolites arepresumably involved in the physiology of the oocyte. The identification, quantification and study of FF metabolitescan provide additional information about the oocyte state which can be helpful in distinguishing those oocytes thathave a greater capacity to be fertilised and to develop properly. The aim of this work is to identify the metabolicprofile of FF samples exhaustively using High Resolution Nuclear Magnetic Resonance (NMR). A total of 30 FF samplesfrom oocyte donors (<35 years) were analysed. Different monodimensional (1D) and bidimensional (2D) (homo andheteronuclear) NMR experiments were acquired. A total of 131 chemical shifts were assigned and 42 metabolites,including as example glucose, lactate, acetate, acetoacetate, pyruvate and b-hydroxybutyrate, were identified. Highcorrelations were found between these important intermediaries of the energetic metabolic pathways of the folliclewhich can indicate the importance of these pathways in oocyte development. Some of these identified metabolitesmight be useful as biomarkers of the follicular maturation state, allowing oocytes with a higher fertilisation potentialto be selected, thereby increasing pregnancy rates in women following in vitro fertilisation (IVF) treatments.Copyright � 2010 John Wiley & Sons, Ltd.

Keywords: NMR spectroscopy; chemical shift; follicular fluid; metabolomics; fertility

* Correspondence to: V. Esteve, Department of Physical-Chemistry. University ofValencia, C/ Dr. Moliner 50, Valencia, Spain.E-mail: Vicent.Esteve@uv.es

a E. Pinero-Sagredo, B. Celda, V. Esteve

Physical-Chemistry Department, University of Valencia, Valencia, Spain

b E. Pinero-Sagredo, V. Esteve

CIBER Bioengineering, Biomaterials and Nanomedicine, ISC III, Valencia, Spain

c S. Nunes, M. Jose de los Santos

Fertility Department. Valencia Infertility Institute (IVI), Valencia, Spain

Contract/grant sponsor: Ministerio de Educacion y Ciencia del Gobierno de

Espana; contract/grant number: SAF2007-6547, SAF2007-29393-E, SAF2007-

29394-E, SAF2007-29455-E.

Contract/grant sponsor: European project eTUMOUR; contract/grant number:

FP6-2002-LIFESCIHEALTH503094.

Abbreviations used: CPMG, Carr–Purcell–Meiboom–Gill; DOSY, diffusion-

ordered spectroscopy; DQF-COSY, double quantum filtered correlation spec-

troscopy; DSS, 2,2-dimethylsilapentane-5-sulfonic acid; HSQC, heteronuclear

single quantum coherence spectroscopy; IVF, in vitro fertilisation; TOCSY, 1H-1H

total correlation spectroscopy; TPPI, time proportional phase increment. 4

INTRODUCTION

Follicular Fluid (FF) and granulosa cells provide a specialenvironment to the oocyte during its maturation in vivo. Follicularfluid has been described as a derivate of the sanguineous plasmaand secretions that are synthesised in the follicle wall (1). Thisfluid is probably involved in several physiological processes, andprovides supplies of nourishment to the oocyte and thegranulosa cells. It may also offer some protection during bothoocyte development and ovulation.Follicular fluid contains important metabolites that will be

accumulated in the oocyte, which can be critical for oocytegrowth and development. Thus, this fluid reflects the biochemicalactivity of the follicle and it can provide useful information aboutthe growth and differentiation of the follicle (1). This relationshipbetween FF and oocyte development is often recognized. Forexample, in bovids, the FF composition varies throughout theoestrous cycle (2), and in humans, the follicular fluid showschanges depending on the maturity of the ovarian follicle (3).Investigation of the FF has increased over the recent years, but

its metabolic composition is unfortunately still relativelyunknown. Several metabolites have been identified usingcommercial kits or chromatographic techniques; e.g. the aminoacid composition of human FF (4) and that of some animals (2,5).The ionic composition (5–8), the presence of glucose, lactate,urea and other metabolites (2,4–9) and lipids (5,7,8) have alsobeen determined. Some protein analyses have been alsoreported (8,10–12).Qualitative and quantitative alterations in the composition

of the FF composition could affect the quality of the oocyte

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and hence affect the potential fecundity. These alterationsin the FF could be due to metabolic changes in the serum(7). Moreover, granulosa cells could play an active role inaltering the FF by selectively filtering and perhaps transformingsome of the metabolites. There is increasing interest indetermining whether the metabolic composition of the FFcan provide information about the quality of the oocytesand embryo viability (13–17), or even about the developmentof possible diseases in the adult (18). The studies that

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have been performed to find fertility biomarkers havemainly focussed on the hormonal composition of thepreovulatory follicle and the influence of hormonal alterationson female fertility. Steroids (progesterone, 17-oestradiol-b,androstenedione) (13), gonadotropins, prolactin (14) orgrowth factors (16) have been particularly studied. Inaddition, the role of myo-inositol in follicular maturity (19)and of N-acylethanolamines, which have been found inhuman reproductive fluids, has been of particular interest(20). These substances may modulate follicular maturationand ovulation, normal and pathological ovarian function,placental and foetal physiology, lactation, infant physiologyand behaviour, and may help to regulate multiple physiologicaland pathological processes in the reproductive system (20).The main objective of this work is the exhaustive, detailed

and reliable analysis of metabolite composition in FF byusing an extensive and complementary set of HighResolution NMR experiments. Additionally, we characteriseda set of samples from 30 ovum donors in a preliminary wayas a reference for future work. Thus, we have analysedsome basic statistical parameters about the populationdistribution and possible correlations between some metab-olites and basic parameters potentially related to the qualityof oocytes.NMR, the main technique used in this study, can provide the

metabolic profiles of tissues and biofluids, yielding qualitativeand potentially quantitative data, thereby allowing the detectionof metabolic disorders and organ damage, investigations into thebiochemical basis of drug and xenobiotic metabolism, anddiagnoses for paediatric pathologies (21). In addition, thistechnique can be successfully used to distinguish a large varietyof organic components in different corporal fluids (21,22), and ithas been widely used in metabolomic studies during the last 20years. Some of these studies emphasise the research ofbiomarkers that have metabolic roles that make them usefulas indicators of the state and progression of various diseases andof the response to therapeutic treatments, among others. Alimited number of FF components have been determined usingNMR in different animals (23–25). However, we were not able tofind any study of a complete metabolic analysis of human FFusing NMR and the relationship between human FF and oocytesuitability.

EXPERIMENTALSamples

FF samples from 30 ovum donors were provided from ValenciaInfertility Institute. These donors, ranging from 18–35 years of agehad familiar case histories that were negative for genetically-transmitted diseases, a normal karyotype, negative results forsexually-transmitted diseases, a normal reproductive system,good mental and physical status, and, finally, a previous casehistory of fertility and/or the correct response to ovarystimulation treatment. All of this work was done in agreementwith applicable ethical norms that have been approved by the IVIethical committee.These samples were collected by means of transvaginal

puncture guided by vaginal ultrasound scans. The FF wasseparated from the oocytes, before they were centrifuged at10,000 rpm for 5min. The FF samples were stored at �808C.

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NMR Spectroscopy

FF samples were defrosted at room temperature, 600mL werecentrifuged at 10,000 rpm for 5min and the supernatant wasmixedwith 60mL of D2O to provide a field-frequency lock. The pHof the samples ranged from 7.7–8.2.The NMR spectra were recorded principally on a 500MHz

Bruker Avance DRX spectrometer that was equipped with a TXIcryo-probe (5mm 1H{13C/15N}) at 298 K (other temperaturestested were 292, 304 and 310 K; 298 K was selected as theworking temperature looking for optimal peak width and stabilityof the spectra over the measurement time). The 1H spectral widthwas 6009Hz, the relaxation delay between the transients was 5 sand the FID was zero-filled by a factor of two prior to Fouriertransformation. The chemical shifts of lactate, relative to DSS,were used as a reference (1H (1.318 and 4.108 ppm) and13C (22.80 ppm)).One-dimensional experiments were obtained for all of the FF

samples. The water signal was attenuated using presaturation(1H presat). For the 1D experiments, 64 scans and 16 K points(TD) were acquired. The spectra were zero filled (SI¼ 32 K)and apodised using an exponential function prior to Fouriertransform. Line broadening of 0.3 Hz was applied.In addition, several two-dimensional experiments were

performed to aid in the assignment of the signals and in theidentification of the metabolites in FF. Phase sensitive (States-TPPI) DQF-COSY and several TOCSY experiments with differentmixing time (30ms, 60ms and 80ms) were used to identify thedifferent spin systems through chemical bond magnetisationtransfer, and 1H-13C HSQC provided the carbon chemical shifts ofsome characteristic chemical groups.For the DQF-COSY experiment, transients were acquired into

2048 data points with 16 scans per increment, using a total of 512increments on the F1 axis. The TOCSY experiments wereperformed using the MLEV17 pulse sequence for the spin-lockand 1024 points were collected in 512 increments with 16 scansper increment. For the 1H-13C HSQC, 1024 time domain pointswere obtained in 160 increments with 128 scans per incrementand zero filled to 512 points. The data were zero filled andapodised using a squared sine-bell function in both dimensions.Two-dimensional J-resolved (JRES) spectra were recorded with

1024 data points in the time domain dimension and 128increments with 128 transients and a 60Hz spectral width in theJ-coupling domain. The data were apodised using a sine-bellfunction and, after being transformed, a 45 degrees tilt andsymmetrisation step provided the 2D orthogonal chemical shiftand J-coupled dimensions required.CPMG spin-echo spectra were recorded with 31 and 136ms

total echo times and 16 K data points. The T2 relaxation time wasdetermined using a series of 16 CPMG spectra with echo timesranging from 1 to 500ms for a set of separated signals.Finally, different monodimensional diffusion spectra (DOSY)

were recorded using a stimulated echo with different diffusiontimes (10, 200, 500 and 1000ms).The strong signal of the lactate methyl group was used to

provide reference peaks, at chemical shift values of 1.318 and22.80 ppm for the 1H and 13C a signals, respectively.

Data analysis and statistics

The Kolmogorov–Smirnov and Lilliefors normality tests wereapplied for estimating the goodness of the fitting of the

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NMR METABOLIC PROFILE OF HUMAN FOLLICULAR FLUID

data using a standard normal distribution. The test results wereconsidered stastistically significant at the 5% level.Two analytic approximations were used for the quantitative

evaluation. First, the spectrum was divided into 118 bucketsof variable widths that were integrated independently.Second, the intensity of a selection of 215 peaks wasmeasured. The absolute values and the normalised values(calculated by dividing by the total sum) were used tocalculate the statistical descriptive parameters and the linealcorrelation between the metabolites and the characteristicsample factors.The concentration of lactate and glucose were estimated

from CPMG spectra by adjusting a linear combination of therespective reference spectra obtained from known solutionsof both metabolites (all CPMG spectra in the same conditionsand the same order of magnitude in the concentrations).The data were grouped by age [group A1, 16 samples

(�25 years old) and A2, 14 samples (�27 years old)], bynormalised fertilisation rates [groups B1, 11 samples(>60%) and B2, 9 samples (<50%)], and by pregnancyresults [groups C1, 13 samples (positive pregnancy) andC2, 11 samples (no pregnancy or embryo transfer)]. Thenormalised fertilisation rates were calculated relative tothe total number of oocytes that were obtained afterstimulation (independently of the maturing state). Thecorrelations were calculated inside each group using astudent-t probability test.

Figure 1. 1D NMR spectra (d 0 to 4.5 ppm) of follicular fluid measured at 500

assignments. b) 1H CPMG NMR spectrum TE 136ms. c) 1H DOSY NMR spect

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RESULTS

All of the recorded spectra showed a suitable resolution andsignal/noise ratio.The 1H presat spectrum (Fig. 1a) is themost complete spectrum

because there is was no particular filter used for the signals of FF.All of the signals from the FF are visible, and a large number ofoverlapped peaks can be observed (particularly in the regionfrom 3–4 ppm, which can preclude the identification of thosemetabolites present at low concentration). Moreover, the spectrashow baseline distortions produced by the contribution of thebroad peaks from compounds that have short T2 values, e.g.macromolecules.Several experiments were recorded to separate the signals

from molecules that have different sizes to simplify the 1Dspectrum. The 1D projection of the J-resolved experimentsprovides simplified spectra without multiple splitting of the mainsignals, but despite this, the peaks are still too broad and theoverlap dominates in many regions. On the other hand, some ofthe broad signals from the macromolecules disappeared due tothe effects of relaxation. This effect is more evident in the CPMGexperiments, which provides spectra that lack nearly all of thesignals from macromolecules, when an echo time of 136ms isused (Fig. 1b). In this type of spectra, only the signals of thesmaller metabolites or molecules with very mobile groups (andtherefore a long T2) can be observed and the baseline isimproved.

MHz and 298 K. a) 1H presaturation NMR spectrum with most significant

rum with most significant assignments.

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In 1D diffusion experiments, signals from molecules thatdiffuse more slowly (i.e. macromolecules or macromolecularcomplexes) can be selected. A large number of macromoleculescan be detected in the FF samples using these experiments(Fig. 1c).The exhaustive analysis of these 1D spectra does not

provide enough information to completely assign the spectra,due to their complexity and the variety of metabolites thathave similar chemical shifts. Therefore, 2D spectra wererequired to separate the overlapping chemical shifts andto determine the connectivity of each spin system, eitherbetween protons of contiguous carbons (COSY and TOCSY)or between carbons and their directly attached protons[HSQC (Fig. 2)]. The signals were assigned by comparison withthe chemical shifts obtained from other biofluids andbiological samples (26–30), and from standard solutions ofsome metabolites.A total of 42 metabolites were identified by the assignment of

131 chemical shifts in the region between 0.8 and 8.5 ppm. Acomplete list of these chemical shifts can be found in Table 1.When available, the 13C chemical shifts corresponding to thedirectly bonded carbons have also been shown.

Figure 2. 1H-13C HSQC NMR spectrum of follicular fluid at 500MHz and 29

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Glucose is a very important metabolite that is used as anenergy source, and prominent signals can be seen in the 1Dspectra. However, some of the signals are overlapped in a verycrowded region. In order to identify every chemical shift,corresponding to both the a and b isomers of glucose, the protonand carbon backbone connectivities were checked using TOCSYand HSQC experiments and compared to standard solutions ofglucose.Several signals were identified with greater difficulty. Some of

the peaks corresponding to glutamine and glutamate weredistinguished using 1D spectra of standard solutions and theinformation obtained from TOCSY experiments. The signals ofglutamate were only clearly observed in the TOCSY experiment,and the Cb signal was barely detectable using HSQC. Thedetermination of 3-methylhistidine was difficult due to its lowconcentration. The 1H correlations of 3-methylhistidine andserine were observed using the TOCSY spectra. Myo-inositol wasonly assigned through the identification of its H5 resonance inJ-resolved spectra.High-intensity peaks arising from the methyl and methylene

groups of lipids dominate the aliphatic part of the 1D spectrumand were differentiated from the underlying low weight

8 K showing the assignments of the significant metabolites.

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Table 1. Resonance assignments with 1H and 13C chemical shifts of metabolites identified in NMR spectra of follicular fluid at500MHz and 298 K

d1H d 13C Multiplicity Molecule Group Experiments

0,81 25,18 m Cholesterol C26 and C27 1D, CPMG, HSQC0,82 16,62 — Lipid CH3(CH2)n 1D, CPMG, TOCSY, COSY, HSQC0,87 20,91 — Cholesterol C21 1D, HSQC0,93 20,98 — Cholesterol C19 1D, HSQC0,93 23,55 d Leucine d2H HSQC0,95 24,85 d Leucine d1H TOCSY, COSY, HSQC0,98 19,32 d Valine g1H 1D, CPMG, JRES, TOCSY, COSY, HSQC1,03 20,69 d Valine g2H 1D, CPMG, JRES, TOCSY, COSY, HSQC1,11 d Isobutyrate CH3 TOCSY1,18 21,48 d b-Hydroxybutyrate gH 1D, CPMG, JRES, TOCSY, HSQC1,21 34,30 — Lipid CH3CH2CH2 1D, CPMG, HSQC1,21 32,15 — Lipid (CH2)n 1D, CPMG, HSQC1,24 25,22 — Lipid CH3CH2(CH2)n TOCSY, COSY, HSQC1,30 32,03 — Lipid CH2CH2CH2CO TOCSY, COSY, HSQC1,31 21,82 d Threonine gH 1D, CPMG, TOCSY, COSY, HSQC1,32 22,80 d Lactate bH 1D, CPMG, JRES, TOCSY, COSY, HSQC1,45 m Lysine gH TOCSY1,46 — Lipid CH2CH2C––C TOCSY1,47 18,93 d Alanine bH 1D, CPMG, JRES, TOCSY, COSY, HSQC1,57 26,91 — Lipid CH2CH2CO HSQC1,66 m Arginine gH TOCSY1,70 m Lysine dH TOCSY1,70 42,63 m Leucine bHgH TOCSY, COSY, HSQC1,88 32,70 m Lysine bH TOCSY, COSY, HSQC1,89 30,38 m Arginine bH TOCSY, HSQC1,91 25,97 s Acetate bH 1D, CPMG, JRES, TOCSY, HSQC1,97 29,65 — Lipid CH––CHCH2 1D, COSY, HSQC1,99 29,65 — Lipid CH––CHCH2 1D, TOCSY, COSY, HSQC2,00 26,48 m Proline gH TOCSY, COSY, HSQC2,03 — Lipid CH2CH––CH2,03 24,84 s Glycoprotein Nac 1D, CPMG, JRES, TOCSY, HSQC2,04 m Proline b1H TOCSY2,08 29,79 m Glutamic Acid bH TOCSY, HSQC2,11 29,08 m Glutamine bH TOCSY, COSY, HSQC2,11 16,94 s Methionine SCH3 HSQC2,21 32,97 s Acetoacetate gH 1D, CPMG, JRES, TOCSY, HSQC2,23 36,06 — Lipid CH2CO 1D, HSQC2,25 31,90 m Valine bH TOCSY, COSY, HSQC2,30 m b-Hydroxybutyrate a1H TOCSY2,33 m Glutamic Acid gH TOCSY2,34 m Proline b2H TOCSY2,35 28,98 s Pyruvate bH 1D, CPMG, JRES, TOCSY, HSQC2,38 m b-Hydroxybutyrate a2H TOCSY2,43 33,51 m Glutamine gH 1D, CPMG, JRES, TOCSY, COSY, HSQC2,51 d Citrate a2Hg2H 1D, CPMG, JRES, TOCSY, COSY2,53 s Methylamine CH3 TOCSY2,65 d Citrate a1Hg1H 1D, CPMG, JRES, TOCSY, COSY2,66 dd Aspartic Acid b1H TOCSY2,69 28,04 — Lipid CH––CHCH2CH––CH HSQC2,71 Sarcosine (tentatively) NCH3 JRES2,74 28,04 — Lipid CH––CHCH2CH––CH HSQC2,80 dd Aspartic Acid b2H TOCSY, COSY2,81 s Trimethylamine CH3 TOCSY2,83 dd Asparagine b1H TOCSY

(Continues)

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Table 1. (Continued)

d1H d 13C Multiplicity Molecule Group Experiments

2,92 dd Asparagine b2H TOCSY

3,01 41,95 t Lysine eH TOCSY, HSQC3,02 39,48 s Creatine NCH 1D, CPMG, JRES, HSQC3,03 32,85 s Creatinine NCH 1D, CPMG, JRES, HSQC3,04 dd Tyrosine b1H TOCSY, COSY3,09 dd Phenylalanine b1H TOCSY, COSY3,17 56,42 s Choline NCH3 HSQC3,18 dd Tyrosine b2H TOCSY, COSY3,20 dd Histidine bH TOCSY3,20 56,50 s Glycerophosphocholine NH 1D, CPMG, HSQC3,20 56,50 s Phosphocholine NH 1D, CPMG, HSQC3,23 43,27 t Arginine dH TOCSY, HSQC3,23 76,90 dd b-Glucose H2 1D, CPMG, JRES, TOCSY, COSY, HSQC3,23 t Taurine H2N TOCSY, COSY3,27 Myo-inositol (tentatively) H5 JRES3,34 s TMAO CH3 TOCSY3,35 48,84 m Proline d1H TOCSY, COSY, HSQC3,39 72,33 t b-Glucose H4 1D, CPMG, JRES, TOCSY, COSY, HSQC3,41 72,34 t a-Glucose H4 1D, CPMG, JRES, TOCSY, HSQC3,41 38,10 t Taurine H2S TOCSY, HSQC3,41 48,84 m Proline d2H TOCSY, COSY, HSQC3,46 78,42 ddd b-Glucose H5 1D, CPMG, JRES, TOCSY, HSQC3,48 78,51 t b-Glucose H3 1D, CPMG, JRES, TOCSY, COSY, HSQC3,51 70,05 m Choline bH TOCSY, HSQC3,53 74,23 dd a-Glucose H2 1D, CPMG, JRES, TOCSY, HSQC3,53 74,01 ddd a-Glucose H5 1D, CPMG, JRES, HSQC3,54 44,24 s Glycine aH 1D, CPMG, JRES, HSQC3,55 65,28 dd Glycerol 1,3Ha TOCSY, HSQC3,55 63,19 d Threonine aH 1D, CPMG, JRES, TOCSY, COSY, HSQC3,59 Sarcosine (tentatively) aH JRES3,60 63,21 d Valine aH 1D, CPMG, JRES, TOCSY, COSY, HSQC3,64 65,28 dd Glycerol 1,3Hb TOCSY, HSQC3,65 69,07 m Phosphocholine bH TOCSY, HSQC3,67 68,49 m Glycerophosphocholine bH HSQC3,70 75,39 t a-Glucose H3 HSQC3,71 63,48 dd b-Glucose H6 TOCSY, HSQC3,71 dd Leucine aH TOCSY3,74 63,29 dd o m a-Glucose H6 HSQC3,74 t Lysine aH TOCSY, COSY3,74 t Arginine aH TOCSY3,74 m Glutamic Acid aH TOCSY3,74 57,10 t Glutamine aH TOCSY, COSY, HSQC3,76 74,52 m Glycerol H2 TOCSY, HSQC3,77 53,16 q Alanine aH TOCSY, COSY, HSQC3,79 54,55 dd Aspartic Acid aH TOCSY, COSY, HSQC3,82 63,29 dd o m a-Glucose H6 HSQC3,82 dd Serine aH TOCSY3,83 74,10 a-Glucose H5 HSQC3,89 63,48 dd b-Glucose H6 TOCSY, HSQC3,92 56,49 s Creatine aH HSQC3,92 dd Tyrosine aH TOCSY, COSY3,94 dd Serine b2H TOCSY3,96 dd Histidine aH TOCSY3,96 dd Phenylalanine aH TOCSY, COSY3,97 dd Serine b1H TOCSY3,97 dd Asparagine aH TOCSY

(Continues)

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Table 1. (Continued)

d1H d 13C Multiplicity Molecule Group Experiments

4,04 58,85 s Creatinine aH 1D, CPMG, HSQC4,06 58,31 m Choline aH TOCSY, HSQC4,10 71,13 q Lactate aH 1D, CPMG, JRES, TOCSY, COSY, HSQC4,12 63,96 m Proline aH TOCSY, HSQC4,15 67,91 m b-Hydroxybutyrate bH TOCSY, HSQC4,23 68,87 m Threonine bH TOCSY, COSY, HSQC4,28 61,89 m Phosphocholine aH TOCSY, HSQC4,32 61,95 m Glycerophosphocholine aH HSQC4,64 98,77 d b-Glucose H1 1D, CPMG, JRES, TOCSY, COSY, HSQC5,23 94,89 d a-Glucose H1 1D, CPMG, JRES, TOCSY, HSQC5,26 — Lipid CH––CHCH2CH––CH 1D, COSY5,29 — Lipid CH––CHCH2CH––CH 1D, COSY6,70 s 3-Methylhistidine H4 TOCSY6,88 m Tyrosine 3,5H 1D, CPMG, JRES, TOCSY, COSY7,03 s Histidine H4 1D, CPMG, JRES, TOCSY7,17 m Tyrosine 2,6H 1D, CPMG, JRES, TOCSY, COSY7,31 m Phenylalanine H4 TOCSY7,40 m Phenylalanine 2,6H TOCSY7,60 s 3-Methylhistidine H2 TOCSY7,75 s Histidine H2 1D, CPMG, JRES, TOCSY8,45 s Formate H 1D, CPMG, JRES, TOCSY

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4

metabolites using diffusion experiments (Fig. 1c). The methylgroups appeared at 0.821 ppm, whereas the methylene groupsappeared at 1.210 and 1.238 ppm. Protons corresponding to theunsaturation of fatty acid moieties such as oleate, linoleate,palmitoleate were identified at 5.255 and 5.285 ppm. Protonsbetween double unsaturations in fatty acids moieties, such aslinoleate, were found at 2.685 and 2.735 ppm. A series ofmethylenes close to unsaturation sites were found at 1.458, 1.565,1.969, 1.985, and 2.025 ppm (Table 1). The connectivity of all ofthese was obtained from TOCSY experiments, and the13C chemical shifts were determined using HSQC. These signalsprobably correspond to the fatty acid chains of phospholipidsand other single chain lipids. Cholesterol signals were onlyidentified using the methyl groups in the HSQC experiments(Table 1). These lipids seem to be associated with HDL particles ashas been determined using some chemical analysis of FF (5),where this is in agreement with the chemicals shifts of themethyl and methylene signals that were found at 0.821 and1.21 ppm (31).Spectra obtained at different temperatures (292, 304 and

310 k) showed that the resolution of the macromoleculesincreased with temperature, particularly for the lipids. Thisproved useful in confirming some assignments.The number of samples that we examined is too limited to

obtain a statistical characterisation of the general population.Nevertheless, it may prove useful to check whether somemetabolic tendencies can be detected in the samples of FFstudied.When the spectra of the 30 samples were compared, no

appreciable variation in the position of the peaks was apparent.However, a heterogeneous level of variation in the intensities wasfound in different regions of the spectra. To obtain a more preciseestimation of the intrinsic variation of these metabolic profiles,two analytic approximations were used. First, the spectrum wasdivided into buckets that were integrated independently.Second, the intensity of a selection peaks was measured. In

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both cases, the absolute values were not normally distributed.However, after normalisation (dividing by the total sum of theselected buckets or peaks), the normalised values followed anormal distribution. The absolute value comparison shows thatsome samples were more diluted than the others, probablydue to differences in the size of the follicles collected in eachcase (7).The spectral regions were divided in different sets of buckets

for analysis, and a total of 118 buckets of variable widths wereevaluated. The more variable regions found were (all in ppm):(3.55–3.26), (3.17–3.11), (2.91–2.71), (1.35–1.32) and (1.28–1.18).These regions had a variation coefficient of greater than 10%. Theless variable regions were (3.11–2.91), (2.07–1.37) and (1.13–0.88),which had variation coefficients lower than 5%. The peaks thatwere analysed followed a variation that was congruent with thatof the buckets. However, all these analyses were carried out in thepresence of the broad signals from the macromolecules, andtherefore a number of influences can be expected, probablydominated by the lipid content. However, when the CPMGspectra using a TE of 136ms were used, the signals of themacromolecules were no longer visible and the variations due tothe low weight metabolites could be estimated independently.The results of this later analysis showed a greater variation in thesignals of individual low weight metabolites after normalisation,relative to the total integral of the 118 buckets that werescreened. Some metabolites could be evaluated because theirsignals were well separated or their multiplicities were readilyidentifiable. For example, glucose, lactate and alanine showed acoefficient of variation (cv) of 20%; valine and creatine, 25%, andformate, pyruvate and b-hydroxybutyrate, 30%. The lowest valuewas found for glycerol, 10%. All of these were compatible with anormal distribution, except that of acetoacetate, which had also avery high variation (cv of 60%).A systematic analysis of the 1D spectra, using both presat and

the CPMG TE 136ms, was achieved by integrating all of theobserved peaks and calculating the lineal correlation with some

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factors, e.g. the age and the fertilisation rate of the oocytes. Nostatistically significant correlation was found.The correlations between some important intermediaries in

the energetic pathways of the follicle were analysed throughintegration of well-separated peaks in the CPMG spectra at TE136ms. The data for glucose, lactate, acetate, acetoacetate,b-hydroxybutyrate and pyruvate were divided into two agegroups that were well-separated and balanced: �25 years(group A1) and�27 years (group A2). The correlation coefficientsand probabilities of the correlations inside each group aregathered in Table 2. A negative correlation can be observedbetween glucose with b-hydroxybutyrate, lactate, pyruvateand acetoacetate, whereas the correlation between glucoseand acetate is positive.Finally, it is worth mentioning that some of these correlations

between metabolites were also found when the samples weregrouped by the normalised fertilisation rates (group B1: > 60%;group B2: < 50%) or by the results of IVF (group C1: positivepregnancy; group C2: no pregnancy or no embryo transfer).

DISCUSSION

Extensive analysis of the different NMR spectra has allowed theidentification of 17 out of the 20 natural amino-acids, 10 ofthem correspond to the group classified as the essentialamino-acids which cannot be synthesised by the organismand must be incorporated in the diet. These were valine, leucine,isoleucine, phenylalanine, tyrosine, methionine, threonine, lysine,arginine and histidine. Likewise, some metabolic derivatives fromthe latter, such as 3-methylhistidine, and other non-proteinamino acids such as creatine and taurine were also identified.Contradictory results have been reported between the amountsof amino acids (AA) in the FF and those found in plasma (32). Onestudy (4) reported that all of the essential AAs and most of thenon-essential AAs had significantly lower concentrations in FF

Table 2. Metabolite-metabolite correlations in follicular fluid of ovparenthesis). Group A: grouped by age; Group A1: donors � 25 yenormalized fertilisation rates; Group B1: >60%; Group B2: <50%. GGroup C2: no pregnancy or not embryo transfer.

Correlation metabolite-metabolite Group A1 Group A2

Glucose-b-Hydroxybutyrate �0.68 (0.009) �0.69 (0.008)Glucose-Lactate �0.79 (0.004) �0.84 (0.002)Glucose-Pyruvate �0.75 (0.004) �0.83 (0.002)Glucose-Acetoacetate �0.59 (0.027) �

Glucose-Acetate 0.59 (0.035) 0.58 (0.031)Pyruvate-b-Hydroxybutyrate 0.82 (0.000) 0.75 (0.002)Pyruvate-Lactate 0.80 (0.000) 0.95 (0.000)Pyruvate-Acetoacetate 0.68 (0.007) �

Pyruvate-Acetate �0.65 (0.012) �

Acetoacetate-b-Hydroxybutyrate 0.77 (0.001) �

Acetoacetate-Lactate 0.75 (0.003) �

Acetoacetate-Acetate �0.73 (0.003) �

Acetate-Lactate �0.77 (0.002) �

Lactate-b-Hydroxybutyrate 0.69 (0.006) 0.66 (0.014)

�Not significant correlation was found.

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than in plasma, with the particular exception of glutamate, whichis higher (around threefold) in FF. Interestingly, L-glutamate isused in the very early stages of embryo development, and it hasbeen suggested that it can act as a protective agent againstoxidative stress (33,34). The low concentration of AAs, relative toplasma, may reflect the utilisation of these AAs by the oocyteand/or by the follicular cells, or by selective transport throughthese cells. This second possibility would confirm that thefollicular fluid is a selective transudate from the plasma as hasbeen described in earlier research (1). The concentration ofglutamine has been reported to be negatively correlated with thedifference in the concentration of glutamate between plasmaand follicular fluids, and this has been construed as support forthe transformation of glutamine to glutamate and ammonia inthe cells of the follicle (4). This metabolic reaction could beimportant in the synthesis of nucleic acid bases that are requiredfor the completion of meiosis in the maturing oocyte (35).On the other hand, some important intermediaries in the

energetic pathways of the follicle are closely correlated (Fig. 3).Glucose is metabolised to acetyl-CoA (the active form of acetate)producing an intermediate, pyruvate, that can generate lactate.Acetoacetate and b-hydroxybutyrate can be synthesised fromacetyl-CoA by fatty acid metabolism.There are contradictory studies in relation to the proportion

between glucose and lactate: some authors report that theproportion is 2:1 (36), while others determine the concentrationsto be 3.39mM for glucose and 3.17mM for lactate, i.e., close to 1:1(9). A negative correlation between glucose and lactate wasreported in both studies (9,36). Although, in this study, theconcentration of all the metabolites identified cannot beprecisely calculated, we have calculated that the concentrationsof lactate and glucose were approximately 5mM and 2mM,respectively, which is close to the 2:1 ratio proposed by the firstauthors (36). In our analysis, the coefficient of the correlationbetween glucose and lactate was –0.80 (p< 0.0001). This resultsupports the presence of an important anaerobic metabolism in

um donors obtained from CPMG spectra (probability values inars old; Group A2: donors �27 years old. Group B: grouped byroup C: grouped by IVF results; Group C1: positive pregnancy;

Group B1 Group B2 Group C1 Group C2

�0.66 (0.027) �0.75 (0.021) �0.70 (0.010) �0.69 (0.021)�0.87 (0.002) �0.79 (0.017) �0.79 (0.004) �0.90 (0.003)�0.84 (0.003) �0.74 (0.025) �0.74 (0.002) �0.83 (0.002)�0.61 (0.045) � �0.61 (0.029) �

0.79 (0.004) �0.69 (0.039) 0.76 (0.003) 0.66 (0.035)0.76 (0.007) 0.92 (0.000) 0.81 (0.001) 0.71 (0.014)0.91 (0.000) 0.84 (0.007) 0.90 (0.000) 0.93 (0.000)0.73 (0.010) � 0.75 (0.003) �

�0.67 (0.025) � �0.57 (0.042) �

0.82 (0.002) � 0.81 (0.001) 0.71 (0.014)0.70 (0.016) � 0.70 (0.009) �

�0.71 (0.013) � �0.64 (0.026) �0.64 (0.037)0.60 (0.049) 0.85 (0.033) 0.67 (0.013) 0.72 (0.012)

� � � �

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Figure 3. Connections between the most important intermediaries in energetic metabolic pathways of the follicle.

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hyperstimulated follicles (9). In fact, it has been reported that asoocyte grows, the activity of glucose-6-phosphate dehydrogen-ase and lactate dehydrogenase-1 increase significantly, plateau-ing in medium size follicles (6).The high correlation found between the concentration of

glucose, lactate and pyruvate (Table 2) seems to indicate that FFcould play a role providing an energy source as lactate andpyruvate. Earlier studies report pyruvate being the main energysource of support for oocyte maturation, at least in vitro (37). Infact, the concentration of lactate is very high in FF and fallopiantube secretions. Thus, lactate could provide an energy supply(perhaps via transformation to pyruvate by the granulosa cells) tothe maturing oocyte in the follicle and in the first moments afterovulation. This metabolic balance and the specific glycolyticactivity seem to be important not only from an energetic point ofview, but also for the maturation signalling conditions, probablythrough the reduction potential (glutathione, NADH, and NADPH)(18).Another important metabolite that we identified was

myo-inositol, the precursor of two intermediate messengersthat play an important role in morphogenesis and cytogen-esis, lipid synthesis, cell membrane structure and cell growth(38,39). The concentration of myo-inositol has been deter-mined in FF by enzymatic assays (19), and it was found thatthe myo-inositol concentration is larger in the FF of goodquality oocytes than of low quality ones. However, in thepresent work, only a weak signal from myo-inositol wasdetected in the J-resolved spectra, suggesting that it ispresent at low concentration. The variation in the quantity ofthis metabolite, which is involved in the energetic metabolismof the follicle, will probably influence the quality of thedeveloped oocyte, and can therefore be proposed as apotential biomarker to decide the suitability of the folliclesemployed for in vitro fertilisation.In general, the chemical shifts obtained were similar to those

found in plasma (28,30); this could support the idea that the FF isan exudate of the plasma (1). Some of the signals that werereported in plasma were not found in FF, perhaps because ofthe low concentration of the corresponding metabolites, orbecause they were masked by other metabolites that werepresent at a higher concentration.

NMR Biomed. 2010; 23: 485–495 Copyright � 2010 John Wiley

Rich lipid content is present in FF, and our study of this bymass spectrometry is ongoing. Similarly, a great abundanceof proteins and glycoproteins has been reported (10–12). It isworth noting that in our analysis, a large amount of albumincarrying fatty acids has been detected by mass spectrometry(data not shown). A higher lipid metabolism has been detected inmaturing pig oocytes (40), probably providing an importantenergy source for the developing oocyte. The aerobic degra-dation of the excess of fatty acids is limited by the availability ofoxaloacetate, and this will eventually produce a proportionalamount of ketone bodies (as occurs under starvation conditionsor in diabetes). We have observed a high correlation betweenthe concentration of acetoacetate and b-hydroxybutyrate(Table 2) with other metabolites of the glycolytic pathway,which can be the result of a strong connection between aerobicdegradation and the metabolism of fatty acids. These ketonebodies could be an additional source of acetyl groups that iseasily transported through the cellular membranes into theoocyte.CPMG experiments were used to evaluate the T2 relaxation

times for some relatively isolated signals. An exponentialor biexponential function was adjusted to the decay ofthe signals. The T2values of the low molecular weightmetabolites lay in the range of 15-50ms, and those formacromolecules were about 1–2ms. However, the adjust-ments were not accurate enough to provide exact T2values. In general, the metabolites seem to follow a complex,nonexponential relaxation behaviour. When a biexponentialfunction was used, a better fitting was obtained, and a T2component of around 1.5 ms was found for high molecularweight metabolites. This may be due to the distortioncaused by residual overlap (particularly the case formacromolecules) and to complex relaxation behaviour causedby interactions between the metabolites and macromol-ecules. The lipid signal at 0.83 ppm also showed a better fit to abiexponential decay with T2 values of 0.9 and 6.4 ms. Thiscould be related to the heterogeneous size of the HDLparticles or to different associations between the lipidcomponents. Some protein contribution cannot be dismissed.More detailed experiments have to be done to elucidate suchcomplex relationships.

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CONCLUSIONS

This study of the metabolic profile of the FF by NMR mayrepresent an important step in the search for particularbiochemical markers for oocyte quality. This has potentialimplication for the improvement of the pregnancy rate ofin vitro fertilisation treatments, which is the final objective of ourwork.An easy and direct evaluation of the lipid and protein

abundance, or the proportions between the metabolitesimplicated in essential metabolic pathways can be obtained(i.e. the glycolytic anaerobic pathway). These parameters canindicate the quality of the oocyte and can be used for balancingthe ex vivo culture conditions in which the gametes andembryos are normally cultured in human reproductionlaboratories.Finally, our data are in agreement with results that imply the

existence of an important anaerobic glycolytic metabolism inthe follicles, and support the hypothesis that there is a strongrelation between the glycolytic pathway and fatty acidsynthesis. Interestingly, the connection between the glycolyticpathway and fatty acid metabolism seems to be enhanced inthe groups where the fertilisation was more successful, as wellas in the group of younger donors. Thus, while no individualmetabolite concentration provides a marker that determinesthe potential of the oocytes in this set of samples, the analysisseems to indicate that more subtle aspects of the control ofmetabolism can be related to a successful fertilisation andpregnancy. We consider that it is worth studying thishypothesis more extensively.

Acknowledgements

Thanks are due to the SCSIE of the University of Valencia forproviding access to the NMR facility. This study was partlyfinanced by grants from the Ministerio de Educacion y Cienciadel Gobierno de Espana (SAF2007-6547), SAF2007-29393-E,SAF2007-29394-E, SAF2007-29455-E and European project eTU-MOUR (contract no. FP6-2002-LIFESCIHEALTH 503094).

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