Open Access

Proteomic analysis of human follicular fluid associated with successful in vitro fertilization

  • Xiaofang Shen1, 2Email author,
  • Xin Liu1,
  • Peng Zhu1,
  • Yuhua Zhang3,
  • Jiahui Wang1,
  • Yanwei Wang1,
  • Wenting Wang1,
  • Juan Liu1,
  • Ning Li1 and
  • Fujun Liu1Email author
Contributed equally
Reproductive Biology and Endocrinology201715:58

https://doi.org/10.1186/s12958-017-0277-y

Received: 3 May 2017

Accepted: 19 July 2017

Published: 27 July 2017

Abstract

Background

Human follicular fluid (HFF) provides a key environment for follicle development and oocyte maturation, and contributes to oocyte quality and in vitro fertilization (IVF) outcome.

Methods

To better understand folliculogenesis in the ovary, a proteomic strategy based on dual reverse phase high performance liquid chromatography (RP-HPLC) coupled to matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (LC-MALDI TOF/TOF MS) was used to investigate the protein profile of HFF from women undergoing successful IVF.

Results

A total of 219 unique high-confidence (False Discovery Rate (FDR) < 0.01) HFF proteins were identified by searching the reviewed Swiss-Prot human database (20,183 sequences), and MS data were further verified by western blot. PANTHER showed HFF proteins were involved in complement and coagulation cascade, growth factor and hormone, immunity, and transportation, KEGG indicated their pathway, and STRING demonstrated their interaction networks. In comparison, 32% and 50% of proteins have not been reported in previous human follicular fluid and plasma.

Conclusions

Our HFF proteome research provided a new complementary high-confidence dataset of folliculogenesis and oocyte maturation environment. Those proteins associated with innate immunity, complement cascade, blood coagulation, and angiogenesis might serve as the biomarkers of female infertility and IVF outcome, and their pathways facilitated a complete exhibition of reproductive process.

Keywords

Human follicular fluid in vitro fertilization LC-MALDI TOF/TOF MS Folliculogenesis Bioinformatics

Background

In vitro fertilization (IVF) coupled with embryo transfer into uterus has been applied as treatment for infertility several decades. IVF was initially used to assist the reproduction of sub-fertile women caused by tubal factors [1]. With the improvement of IVF techniques, IVF is now a routine treatment for many reproductive diseases. However, the success rate of pregnancy is still a problem in clinical IVF practice, which is only about 50% even if the embryos with normal morphology were used for transfer [2]. In order to select embryos with the best potential good for IVF outcome, morphological assessments of blastocyst and blastocoels have been adopted, but it was still difficult to predict the quality of embryos [3]. Therefore, it was necessary to develop new strategies for embryo quality evaluation. Epidemiologic investigations showed that many intrinsic and extrinsic factors contributed to the quality of embryo. Because oocyte quality directly influences embryo development, HFF (microenvironment of oocyte maturation) became a main factor contributing to the success of IVF treatment [4].

Small antral follicles respond to ovarian stimulation by increasing in size due to rapid accumulation of follicular fluid, as well as granulosa cell divisions, which necessitate follicular basal lamina expansion. The components of HFF had several origins: secretions from granulosa cells, thecal cells, occytes, and blood plasma composition transferred through the thecal capillaries [5]. The major components of HFF were proteins [6], steroid hormones [7], and metabolites [8]. HFF provided a special milieu to facilitate the communications between occyte and follicular cells, the development of follicle and the maturation of occytes. The alteration of HFF proteins reflected disorders of main secretary function of granulosa cells and thecae, and the damage of blood follicular barrier, which was associated with abnormal folliculogenesis [9] and a diminished reproductive potential [10]. In IVF treatment, HFF was easily accessible during the aspiration of oocytes from follicle, and was an ideal source for noninvasive screening of biomakers for oocyte maturation, fertilization success, IVF outcome, pregnancy, and ovarian diseases.

In the postgenomic era, proteomic techniques have been widely used in the field of reproductive medicine. HFF proteome has become a hotspot for research, which not only contributed to discovering proteins related to IVF outcomes, but also improved our comprehensive understanding of physiological process during follicle development and oocyte maturation [11]. Li and co-workers used surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS) combined with weak cation-exchange protein chip (WCX-2) to search for differentially expressed HFF proteins from mature and antral follicles [12]. Two-dimensional gel electrophoresis (2D–GE) followed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was also used to identify 8 differentially expressed HFF proteins related to immune and inflammatory responses from controlled ovarian hyperstimulation (COH) and natural ovulatory cycles [13]. Ambekar and co-workers carried out SDS-PAGE, OFFGEL and SCX-based separation followed by LC-MS/MS analysis to characterize 480 HFF proteins for a better understanding of folliculogenesis physiology [14]. Chen and co-workers explored the HFF biomarkers between successfully fertilized oocytes and unfertilized mature oocytes through nano-scale liquid chromatography coupled to tandem mass spectrometry (nano LC-MS/MS), and found 53 peptides to be potential candidates [15]. Although proteomic researches on HFF deepened our understanding of reproductive process and provided candidates related to oocyte quality, follicle development, IVF outcome and ovarian disorders, it was still essential to fully delineate the HFF networks and pathways involved in the physiology of reproduction and pathophysiology of infertility.

In the present study, we carried out an in-depth proteomic analysis of HFF from women undergoing successful IVF based on dual RP-HPLC coupled to MALDI TOF/TOF MS. The results profiled candidate biomarkers for the prediction of oocyte maturation, fertilization, and pregnancy and provided a new complement for HFF dataset, which will improve the understanding of biological processes and complicated pathways and interaction networks in HFF.

Methods

Patients enrollment and sample preparation

The HFF samples were collected from 10 women who underwent IVF treatment and achieved pregnancy. The selected patients met the following criteria: infertility not caused by tubal factor; aged less than 38 years; serum FSH values <12 mIU/mL; undergoing their first fresh egg retrieval cycle; ovulation stimulated with the long protocol. The patients were also without chromosomal abnormalities, polycystic ovary syndrome (PCOS), endometriosis and or endocrine disease. Cause of infertility was simple male factor. The body mass index (BMI) of patients met the normal criteria proposed by WGOC (18.5 ≤ BMI ≤ 23.9 kg/m2) [1618]. Ovarian stimulation and oocyte retrieval were performed as previously described [19]. Briefly, when more than two follicles exceeded 18 mm in diameter, 10,000 IU of HCG (Merck Serono, Swiss) was injected intramuscular. After 36 h, HFF was collected during trans-vaginal ultrasound guided aspiration of oocytes. The resultant HFF samples were macroscopically clear and without contamination of the flushing medium.

The samples were centrifuged at 10,000×g at 4 °C for 30 min to produce cell debris-free HFF fraction for further analysis. Concentration of HFF was determined by the Bradford method [20]. This work has been approved by the Ethics Committee of Beijing BaoDao Obstetrics and Gynecology Hospital, and written informed consents were obtained from all participants.

First dimension high pH RP chromatography

Equal amounts (50μg) of HFF proteins from each sample were pooled for separation. The samples were sequentially treated with 20 mM dithiothreitol at 37 °C for 120 min, and 50 mM iodoacetamide in dark for 60 min at room temperature. Then the sample was finally digested using trypsin (sequencing grade, Promega, France) (W/W, 1:50 enzyme/protein) overnight at 37 °C. According to the previous method with appropriate modification [21], the first dimension RP separation was performed on PF-2D HPLC System (Rigol) by using a Durashell RP column (5 μm, 150 Å, 250 mm × 4.6 mm i.d., Agela). Mobile phases A (2% acetonitrile, adjusted pH to 10.0 using NH3.H20) and B (98% acetonitrile, adjusted pH to 10.0 using NH3.H20) were used to develop a gradient. The solvent gradient was set as follows: 5% B, 5 min; 5–15% B, 15 min; 15–38% B, 15 min; 38–90% B, 1 min; 90% B, 8.5 min; 90–5% B, 0.5 min; 5% B, 10 min. The tryptic peptides were separated at an eluent flow rate of 0.8 ml/min and monitored at 214 nm. Totally, 28 eluent fractions were collected and dried by a SPD2010 SpeedVac concentrator system (Thermo, USA).

Second dimension low pH RP chromatography coupled with MS/MS measurement

According to the previous method [22], the samples were dried under vacuum and reconstituted in 30 μl of 0.1% (v/v) formic acid, 2% (v/v) acetonitrile in water for subsequent analyses. Each fraction was separated and spotted using the Tempo™ LC-MALDI Spotting System (AB SCIEX, USA). Peptides were separated by a C18 AQ 150 × 0.2 mm column (3 μm, Michrom, USA) using a linear gradient formed by buffer A (2% acetonitrile, 0.1% formic acid) and buffer B (98% acetonitrile, 0.1% formic acid), from 5% to 35% of buffer B over 90 min at a flow rate of 0.5 μL/min. The eluted peptides were mixed with matrix solution (5 mg/mL in 70% acetonitrile, 0.1% trifluoroacetic acid) at a flow rate of 2 μL/min pushed by additional syringe pump. For each fraction, 616 spots were spotted on a 123× 81 mm LC-MALDI plate insert. Then the spots were analyzed using MALDI-TOF/TOF 5800 mass spectrometer (AB SCIEX, USA). A full-scan MS experiment (m/z range from 800 to 4000) was acquired, and then the top 40 ions were detected by MS/MS.

Protein identification

Protein identification was performed with the ProteinPilot™ software (version 4.0.1; AB SCIEX). Each MS/MS spectrum was searched against a database (2017_03 released UniProtKB/Swiss-Prot human database, 20,183 entries) and a decoy database for FDR analysis (programmed in the software). The search parameters were as follows: trypsin enzyme; maximum allowed missed cleavages 1; Carbamidomethyl cysteine; biological modifications programmed in the algorithm. Proteins with high-confidence (FDR < 0.01) were considered as positively identified proteins.

Bioinformatics

The gene ontology enrichment analysis of HFF proteins were performed by using online bioinformatics tools of PANTHER (Protein ANalysis THrough Evolutionary Relationships) classification system (released 11.1, 2016–10-24) (http://pantherdb.org/) [23] and DAVID (The Database for Annotation, Visualization and Integrated Discovery) bioinformatics resources 6.8 (https://david.ncifcrf.gov/) [24]. Each protein was placed in only one category, and those with no annotation and supporting information were categorized as “Unknown”. The pathway map of HFF proteins were achieved through KEGG: Kyoto Encyclopedia of Genes and Genomes (Release 81.0, 2017–01-01) (http://www.kegg.jp) [25]. The protein-protein interaction network for the HFF proteins was annotated using the STRING (search tool for recurring instances of neighbouring genes) database (released 10.0, 2016–04–16) (http://string-db.org/) [26]. The venn diagram was drawn through a online software “Calculate and draw custom Venn diagrams” (http://bioinformatics.psb.ugent.be/webtools/Venn/).

Western blot analysis

According to the method described previously [27, 28], 50 μg HFF protein were separated by a 12% SDS-PAGE gel and then electronically transferred onto a nitrocellulose membrane. The resultant membrane was blocked with 5% (w/v) skimmed milk for 1 h at 37 °C, and then was incubated with the primary antibody (Abcam, Cambridge, USA, diluted 1:2000) at 4 °C overnight. After washing with TBST for three times, the membranes were incubated with horse-radish peroxidase-conjugated secondary antibody (diluted 1:5000, Zhong-Shan Biotechnology, Beijing, China) at room temperature for 1 h. The immunoreactive proteins was visualized by enhanced chemiluminescence detection reagents (Pierce, Rockford, IL, USA) (Additional file 1: Table S1).

Results

Identification of high-confidence HFF proteome by dual RP-HPLC coupled with MALDI TOF/TOF mass spectrometry.

A peptide sequencing strategy was applied by using two-dimensional chromatography-MALDI TOF/TOF mass spectrometry. We employed high pH (pH 10) reverse phase liquid chromatography to decrease the complexity of the tryptic digest of the HFF proteins, and collected 28 fractions. Then each fraction was further separated by low pH (pH 3) reverse phase liquid chromatography, and spotted on the plate using the Tempo™ LC-MALDI Spotting System. After sequencing by a 5800 MALDI TOF/TOF mass spectrometry, the resultant spectra were analyzed by ProteinPilot™ software by searching the reviewed Swiss-Prot human database (20,183 sequences, 2017_03 released). A total of 219 unique high-confidence (FDR < 0.01) proteins were identified by two replicates (Table 1). Experiment 1 and 2 identified 188 with 2747 unique peptides and 179 proteins with 2800 unique peptides, respectively. 148 common proteins were shared between the two experiments. Figure 1 showed representative MS/MS spectra of peptides from the identified HFF proteins. The m/z of precursor (Fig. 2c) was over 2500, and almost all b-ions and y-ions were still obtained based on a 5800 MALDI TOF/TOF mass spectrometry.
Table 1

A list of 219 identified high-confidence HFF proteins from women underwent successful IVF by LC MALDI TOF/TOF mass spectrometry (FDR < 0.01)

No

SwissProt AC

Name protein description

Gene Name

Molecular Weight

experiment 1

experiment 2

Coverage(%)

Matched Peptides number

Coverage(%)

Matched Peptides number

1

P43652

Afamin

AFM

69,069

31.9

10

35.7

10

2

P02763

Alpha-1-acid glycoprotein 1

ORM1

23,512

40.8

17

40.8

15

3

P19652

Alpha-1-acid glycoprotein 2

ORM2

23,603

45.8

15

53.2

15

4

P01011

Alpha-1-antichymotrypsin

SERPINA3

47,651

53

15

44.2

16

5

P01009

Alpha-1-antitrypsin

SERPINA1

46,737

62.7

86

64.4

76

6

P04217

Alpha-1B-glycoprotein

A1BG

54,254

39.8

17

48.5

19

7

P08697

Alpha-2-antiplasmin

SERPINF2

54,566

29.1

9

47.1

11

8

P02765

Alpha-2-HS-glycoprotein

AHSG

39,325

42.8

14

55.9

18

9

P01023

Alpha-2-macroglobulin

A2M

163,291

46.8

47

47.4

46

10

P48728

Aminomethyltransferase, mitochondrial

AMT

43,946

2.2

1

-

-

11

P01019

Angiotensinogen

AGT

53,154

37.7

14

25.8

11

12

C9JTQ0

Ankyrin repeat domain-containing protein 63

ANKRD63

39,620

15

1

-

-

13

P01008

Antithrombin-III

SERPINC1

52,602

61.9

21

54.7

24

14

P02647

Apolipoprotein A-I

APOA1

30,778

73.8

67

82.4

69

15

P02652

Apolipoprotein A-II

APOA2

11,175

70

9

64

9

16

P06727

Apolipoprotein A-IV

APOA4

45,399

67.2

24

63.1

25

17

P02654

Apolipoprotein C-I

APOC1

9332

26.5

3

37.4

3

18

P02655

Apolipoprotein C-II

APOC2

11,284

39.6

2

50.5

3

19

P02656

Apolipoprotein C-III

APOC3

10,852

34.3

2

51.5

6

20

P05090

Apolipoprotein D

APOD

21,276

24.9

3

28.6

3

21

P02649

Apolipoprotein E

APOE

36,154

43.2

6

43.5

4

22

Q13790

Apolipoprotein F

APOF

35,399

-

-

8

1

23

O95445

Apolipoprotein M

APOM

21,253

26.6

2

30.3

2

24

Q9H2U1

ATP-dependent RNA helicase DHX36

DHX36

114,760

-

-

17.9

1

25

O75882

Attractin

ATRN

158,537

15

1

-

-

26

P98160

Basement membrane-specific heparan sulfate proteoglycan core protein

HSPG2

468,830

30.8

43

31

46

27

P02749

Beta-2-glycoprotein 1

APOH

38,298

51

15

41.5

16

28

Q96KN2

Beta-Ala-His dipeptidase

CNDP1

56,706

18.9

1

-

-

29

P43251

Biotinidase

BTD

61,133

9.2

2

14.2

1

30

Q7L273

BTB/POZ domain-containing protein KCTD9

KCTD9

42,567

-

-

30.1

1

31

P04003

C4b-binding protein alpha chain

C4BPA

67,033

11.9

2

27

4

32

Q96IY4

Carboxypeptidase B2

CPB2

48,424

13

2

16.1

2

33

P22792

Carboxypeptidase N subunit 2

CPN2

60,557

-

-

10.8

2

34

Q9ULM6

CCR4-NOT transcription complex subunit 6

CNOT6

63,307

-

-

2.3

1

35

Q8N8E3

Centrosomal protein of 112 kDa

CEP112

112,749

17.4

1

-

-

36

Q5SW79

Centrosomal protein of 170 kDa

CEP170

175,293

-

-

5.9

1

37

P00450

Ceruloplasmin

CP

122,205

59.6

47

58.1

58

38

O14647

Chromodomain-helicase-DNA-binding protein 2

CHD2

211,344

-

-

12

1

39

P10909

Clusterin

CLU

52,495

41.4

14

50.1

12

40

P00740

Coagulation factor IX

F9

51,778

15.2

1

-

-

41

P00742

Coagulation factor X

F10

54,732

24.6

1

14.1

1

42

P00748

Coagulation factor XII

F12

67,792

29.9

4

20.8

4

43

Q5TID7

Coiled-coil domain-containing protein 181

CCDC181

60,103

-

-

7.9

1

44

P02746

Complement C1q subcomponent subunit B

C1QB

26,722

20.2

1

18.6

1

45

Q9NZP8

Complement C1r subcomponent-like protein

C1RL

53,498

8.6

1

6.2

1

46

P06681

Complement C2

C2

83,268

21.5

4

22.7

6

47

P01024

Complement C3

C3

187,148

67.1

121

74.1

119

48

P0C0L4

Complement C4-A

C4A

192,785

46.6

53

54.8

66

49

P0C0L5

Complement C4-B

C4B

192,751

46.3

52

53

66

50

P01031

Complement C5

C5

188,305

20.3

7

27.1

12

51

P13671

Complement component C6

C6

104,786

26

6

25.5

6

52

P10643

Complement component C7

C7

93,518

35.2

8

23.1

5

53

P07357

Complement component C8 alpha chain

C8A

65,163

24.8

5

23.5

4

54

P07358

Complement component C8 beta chain

C8B

67,047

37.1

4

37.2

6

55

P07360

Complement component C8 gamma chain

C8G

22,277

48.5

7

48

5

56

P02748

Complement component C9

C9

63,173

36.5

8

35.8

10

57

P00751

Complement factor B

CFB

85,533

41.4

20

51.4

25

58

P08603

Complement factor H

CFH

139,096

55.4

43

56.9

45

59

Q03591

Complement factor H-related protein 1

CFHR1

37,651

33.9

2

27.3

5

60

P05156

Complement factor I

CFI

65,750

31.1

7

31.7

5

61

P08185

Corticosteroid-binding globulin

SERPINA6

45,141

19.5

3

17.3

2

62

Q9UBG0

C-type mannose receptor 2

MRC2

166,674

3.2

1

-

-

63

P01034

Cystatin-C

CST3

15,799

22.6

1

-

-

64

P30876

DNA-directed RNA polymerase II subunit RPB2

POLR2B

133,897

-

-

10.7

1

65

Q8NHS0

DnaJ homolog subfamily B member 8

DNAJB8

25,686

16.8

1

-

-

66

Q96DT5

Dynein heavy chain 11, axonemal

DNAH11

520,369

-

-

9.8

1

67

Q9C0C9

E2 ubiquitin-conjugating enzyme

UBE2O

141,293

-

-

3.9

1

68

O95071

E3 ubiquitin-protein ligase UBR5

UBR5

309,352

7.6

1

-

-

69

A4FU69

EF-hand calcium-binding domain-containing protein 5

EFCAB5

173,404

8.1

1

-

-

70

Q16610

Extracellular matrix protein 1

ECM1

60,674

20.7

2

11.5

2

71

Q9UGM5

Fetuin-B

FETUB

42,055

12.8

1

18.3

1

72

P02671

Fibrinogen alpha chain

FGA

94,973

44.8

40

47.6

44

73

P02675

Fibrinogen beta chain

FGB

55,928

72.1

53

68.6

42

74

P02679

Fibrinogen gamma chain

FGG

51,512

69.1

36

68

34

75

P02751

Fibronectin

FN1

262,625

30.3

33

31.2

34

76

Q08380

Galectin-3-binding protein

LGALS3BP

65,331

22.9

1

28.7

4

77

P06396

Gelsolin

GSN

85,698

43.9

16

43.6

20

78

P07093

Glia-derived nexin

SERPINE2

44,002

34.7

4

28.6

3

79

P22352

Glutathione peroxidase 3

GPX3

25,552

16.4

2

27

1

80

Q7Z4J2

Glycosyltransferase 6 domain-containing protein 1

GLT6D1

36,274

2.6

1

-

-

81

P0CG08

Golgi pH regulator B

GPR89B

52,917

-

-

7.7

1

82

P00738

Haptoglobin

HP

45,205

61.1

26

58.6

23

83

P00739

Haptoglobin-related protein

HPR

39,030

44.3

10

-

-

84

Q9Y6N9

Harmonin

USH1C

62,211

7.8

1

-

-

85

P69905

Hemoglobin subunit alpha

HBA1/HBA2

15,258

-

-

28.2

1

86

P68871

Hemoglobin subunit beta

HBB

15,998

43.5

2

52.4

1

87

P02790

Hemopexin

HPX

51,676

55.8

44

76.4

50

88

P05546

Heparin cofactor 2

SERPIND1

57,071

21

6

34.9

6

89

Q04756

Hepatocyte growth factor activator

HGFAC

70,682

5.3

1

-

-

90

P04196

Histidine-rich glycoprotein

HRG

59,578

33

15

37.9

18

91

O43365

Homeobox protein Hox-A3

HOXA3

46,369

6.5

1

-

-

92

P78426

Homeobox protein Nkx-6.1

NKX6–1

37,849

16.4

1

-

-

93

Q14520

Hyaluronan-binding protein 2

HABP2

62,672

15.4

2

11.8

3

94

P0DOX2

Immunoglobulin alpha-2 heavy chain

N/A

48,935

39.1

14

40.9

12

95

P0DOX3

Immunoglobulin delta heavy chain

N/A

56,224

19.9

1

23.4

1

96

P0DOX4

Immunoglobulin epsilon heavy chain

N/A

60,323

8.4

2

15.7

2

97

P0DOX5

Immunoglobulin gamma-1 heavy chain

N/A

49,330

70.6

144

71.9

123

98

P01876

Immunoglobulin heavy constant alpha 1

IGHA1

37,655

42.8

23

48.2

16

99

P01859

Immunoglobulin heavy constant gamma 2

IGHG2

35,901

74.9

104

69.9

92

100

P01860

Immunoglobulin heavy constant gamma 3

IGHG3

41,287

72.4

69

78.3

65

101

P01861

Immunoglobulin heavy constant gamma 4

IGHG4

35,941

79.8

101

68.8

85

102

P01871

Immunoglobulin heavy constant mu

IGHM

49,440

33.1

10

34.7

12

103

A0A0C4DH31

Immunoglobulin heavy variable 1–18

IGHV1–18

12,820

53

7

48.7

9

104

P23083

Immunoglobulin heavy variable 1–2

IGHV1–2

13,085

47.9

6

-

-

105

A0A0C4DH33

Immunoglobulin heavy variable 1–24

IGHV1–24

12,824

38.5

2

38.5

3

106

A0A0C4DH29

Immunoglobulin heavy variable 1–3

IGHV1–3

13,008

38.5

3

-

-

107

A0A0A0MS14

Immunoglobulin heavy variable 1–45

IGHV1–45

13,508

9.4

2

-

-

108

P01743

Immunoglobulin heavy variable 1–46

IGHV1–46

12,933

-

-

32.5

5

109

P01742

Immunoglobulin heavy variable 1–69

IGHV1–69

12,659

-

-

34.2

5

110

P01762

Immunoglobulin heavy variable 3–11

IGHV3–11

12,909

38.5

10

53.9

11

111

P01766

Immunoglobulin heavy variable 3–13

IGHV3–13

12,506

60.3

6

-

-

112

A0A0B4J1V0

Immunoglobulin heavy variable 3–15

IGHV3–15

12,926

55.5

8

42.9

7

113

P01764

Immunoglobulin heavy variable 3–23

IGHV3–23

12,582

60.7

15

54.7

10

114

A0A0B4J1X8

Immunoglobulin heavy variable 3–43

IGHV3–43

13,077

-

-

34.8

6

115

A0A0A0MS15

Immunoglobulin heavy variable 3–49

IGHV3–49

13,056

47.1

3

50.4

3

116

A0A075B6Q5

Immunoglobulin heavy variable 3–64

IGHV3–64

12,891

59.3

2

18.6

1

117

A0A0C4DH42

Immunoglobulin heavy variable 3–66

IGHV3–66

12,698

61.2

14

55.2

10

118

P01780

Immunoglobulin heavy variable 3–7

IGHV3–7

12,943

76.9

14

77.8

12

119

A0A0B4J1Y9

Immunoglobulin heavy variable 3–72

IGHV3–72

13,203

55.5

9

-

-

120

A0A0B4J1V6

Immunoglobulin heavy variable 3–73

IGHV3–73

12,858

58

3

58

4

121

P01782

Immunoglobulin heavy variable 3–9

IGHV3–9

12,945

51.7

8

51.7

9

122

P06331

Immunoglobulin heavy variable 4–34

IGHV4–34

13,815

-

-

38.2

4

123

P01824

Immunoglobulin heavy variable 4–39

IGHV4–39

13,917

19.2

4

-

-

124

A0A0C4DH38

Immunoglobulin heavy variable 5–51

IGHV5–51

12,675

66.7

9

50.4

8

125

P01834

Immunoglobulin kappa constant

IGKC

11,765

88.8

50

92.5

37

126

P0DOX7

Immunoglobulin kappa light chain

N/A

23,379

61.2

52

62.6

39

127

P04430

Immunoglobulin kappa variable 1–16

IGKV1–16

12,618

-

-

34.2

2

128

A0A075B6S5

Immunoglobulin kappa variable 1–27

IGKV1–27

12,712

47

8

65

8

129

P01594

Immunoglobulin kappa variable 1–33

IGKV1–33

12,848

49.6

5

42.7

4

130

P01602

Immunoglobulin kappa variable 1–5

IGKV1–5

12,782

30.8

3

30.8

6

131

A0A0C4DH72

Immunoglobulin kappa variable 1–6

IGKV1–6

12,697

47

4

47

5

132

A0A0C4DH69

Immunoglobulin kappa variable 1–9

IGKV1–9

12,715

74.4

5

44.4

5

133

P01611

Immunoglobulin kappa variable 1D-12

IGKV1D-12

12,620

44.4

5

49.6

7

134

A0A0B4J2D9

Immunoglobulin kappa variable 1D-13

IGKV1D-13

12,569

13.7

1

-

-

135

A0A075B6S4

Immunoglobulin kappa variable 1D-17

IGKV1D-17

12,835

28.2

1

43.6

2

136

P04432

Immunoglobulin kappa variable 1D-39

IGKV1D-39

12,737

47

6

47.9

6

137

P06310

Immunoglobulin kappa variable 2–30

IGKV2–30

13,185

50

5

63.3

7

138

P01615

Immunoglobulin kappa variable 2D-28

IGKV2D-28

12,957

33.3

5

40.8

5

139

A0A075B6S2

Immunoglobulin kappa variable 2D-29

IGKV2D-29

13,143

-

-

20.8

5

140

P01614

Immunoglobulin kappa variable 2D-40

IGKV2D-40

13,310

37.2

6

37.2

5

141

P04433

Immunoglobulin kappa variable 3–11

IGKV3–11

12,575

54.8

16

49.6

10

142

P01624

Immunoglobulin kappa variable 3–15

IGKV3–15

12,496

42.6

9

50.4

8

143

P01619

Immunoglobulin kappa variable 3–20

IGKV3–20

12,557

70.7

16

70.7

14

144

A0A087WSY6

Immunoglobulin kappa variable 3D-15

IGKV3D-15

12,534

42.6

10

56.5

8

145

A0A0C4DH25

Immunoglobulin kappa variable 3D-20

IGKV3D-20

12,515

64.7

10

64.7

8

146

P06312

Immunoglobulin kappa variable 4–1

IGKV4–1

13,380

34.7

10

40.5

6

147

A0M8Q6

Immunoglobulin lambda constant 7

IGLC7

11,254

54.7

13

53.8

10

148

A0A0B4J1U3

Immunoglobulin lambda variable 1–36

IGLV1–36

12,478

13.7

1

13.7

1

149

P01703

Immunoglobulin lambda variable 1–40

IGLV1–40

12,302

21.2

2

-

-

150

P01700

Immunoglobulin lambda variable 1–47

IGLV1–47

12,284

54.7

4

39.3

3

151

P01706

Immunoglobulin lambda variable 2–11

IGLV2–11

12,644

22.7

3

-

-

152

A0A075B6K4

Immunoglobulin lambda variable 3–10

IGLV3–10

12,441

40

4

40

3

153

P01714

Immunoglobulin lambda variable 3–19

IGLV3–19

12,042

50

2

42.9

1

154

P80748

Immunoglobulin lambda variable 3–21

IGLV3–21

12,446

35.9

3

-

-

155

P01717

Immunoglobulin lambda variable 3–25

IGLV3–25

12,011

-

-

43.8

3

156

P01721

Immunoglobulin lambda variable 6–57

IGLV6–57

12,566

20.5

2

-

-

157

P0DOX8

Immunoglobulin lambda-1 light chain

N/A

22,830

44.4

23

44.4

20

158

P15814

Immunoglobulin lambda-like polypeptide 1

IGLL1

22,963

23

5

23

5

159

P35858

Insulin-like growth factor-binding protein complex acid labile subunit

IGFALS

66,035

23.1

4

27.4

6

160

P16144

Integrin beta-4

ITGB4

202,167

4.9

1

-

-

161

P19827

Inter-alpha-trypsin inhibitor heavy chain H1

ITIH1

101,389

33.6

20

33.7

25

162

P19823

Inter-alpha-trypsin inhibitor heavy chain H2

ITIH2

106,463

35.9

18

42.6

20

163

Q06033

Inter-alpha-trypsin inhibitor heavy chain H3

ITIH3

99,849

5.2

1

15.5

1

164

Q14624

Inter-alpha-trypsin inhibitor heavy chain H4

ITIH4

103,357

38.4

23

47

26

165

Q15811

Intersectin-1

ITSN1

195,422

-

-

9.9

1

166

P29622

Kallistatin

SERPINA4

48,542

26.5

4

23

5

167

Q92764

Keratin, type I cuticular Ha5

KRT35

50,361

-

-

16.7

1

168

P13645

Keratin, type I cytoskeletal 10

KRT10

58,827

5.8

1

-

-

169

P04264

Keratin, type II cytoskeletal 1

KRT1

66,039

23.6

3

30

2

170

P01042

Kininogen-1

KNG1

71,957

53.7

25

41

23

171

P02750

Leucine-rich alpha-2-glycoprotein

LRG1

38,178

21.6

4

27.1

5

172

P18428

Lipopolysaccharide-binding protein

LBP

53,384

14.8

1

13.3

1

173

P51884

Lumican

LUM

38,429

30.2

3

27.8

3

174

P14174

Macrophage migration inhibitory factor

MIF

12,476

18.3

2

-

-

175

P01033

Metalloproteinase inhibitor 1

TIMP1

23,171

18.8

2

34.8

2

176

Q7Z5P9

Mucin-19

MUC19

805,253

4.3

1

-

-

177

P35579

Myosin-9

MYH9

226,532

-

-

15.8

1

178

Q96PD5

N-acetylmuramoyl-L-alanine amidase

PGLYRP2

62,217

26

7

29.3

6

179

A6NHN0

Otolin-1

OTOL1

49,422

15.3

1

-

-

180

P04180

Phosphatidylcholine-sterol acyltransferase

LCAT

49,578

15.5

2

-

-

181

P36955

Pigment epithelium-derived factor

SERPINF1

46,312

22.3

5

17.9

5

182

P03952

Plasma kallikrein

KLKB1

71,370

23

6

26.5

6

183

P05155

Plasma protease C1 inhibitor

SERPING1

55,154

34.8

9

33.2

16

184

P05154

Plasma serine protease inhibitor

SERPINA5

45,675

13.6

3

-

-

185

P00747

Plasminogen

PLG

90,569

63

30

58.8

32

186

Q96GD3

Polycomb protein SCMH1

SCMH1

73,354

4.7

1

-

-

187

Q8WUM4

Programmed cell death 6-interacting protein

PDCD6IP

96,023

-

-

14.1

1

188

P46013

Proliferation marker protein Ki-67

MKI67

358,694

11.9

1

21.8

1

189

P15309

Prostatic acid phosphatase

ACPP

44,566

25.1

4

17.9

2

190

P02760

Protein AMBP

AMBP

38,999

38.9

11

42.1

12

191

Q9UK55

Protein Z-dependent protease inhibitor

SERPINA10

50,707

15.5

2

18.9

2

192

Q96PF1

Protein-glutamine gamma-glutamyltransferase Z

TGM7

79,941

-

-

7.5

1

193

P00734

Prothrombin

F2

70,037

59.8

33

62.4

31

194

P02753

Retinol-binding protein 4

RBP4

23,010

40.3

11

55.7

13

195

O94885

SAM and SH3 domain-containing protein 1

SASH1

136,653

-

-

10.3

1

196

P04279

Semenogelin-1

SEMG1

52,131

30.5

5

32.3

5

197

Q02383

Semenogelin-2

SEMG2

65,444

21

3

18

5

198

P57059

Serine/threonine-protein kinase SIK1

SIK1

84,902

-

-

7.3

1

199

P02787

Serotransferrin

TF

77,064

71.4

143

79.4

185

200

P02768

Serum albumin

ALB

69,367

89.3

607

91.3

550

201

P35542

Serum amyloid A-4 protein

SAA4

14,747

30

2

49.2

6

202

P02743

Serum amyloid P-component

APCS

25,387

26.5

5

25.1

5

203

P27169

Serum paraoxonase/arylesterase 1

PON1

39,731

24.5

7

19.2

5

204

P04278

Sex hormone-binding globulin

SHBG

43,779

18.7

4

21.9

3

205

P09486

SPARC

SPARC

34,632

-

-

5.3

1

206

Q6N022

Teneurin-4

TENM4

307,957

5.3

1

-

-

207

P05452

Tetranectin

CLEC3B

22,537

22.8

2

30.2

2

208

P05543

Thyroxine-binding globulin

SERPINA7

46,325

14.5

1

23.6

2

209

Q8WZ42

Titin

TTN

3,816,030

10.6

1

-

-

210

P21675

Transcription initiation factor TFIID subunit 1

TAF1

212,677

-

-

7

1

211

Q66K66

Transmembrane protein 198

TMEM198

39,475

2.5

2

2.5

1

212

P02766

Transthyretin

TTR

15,887

69.4

12

69.4

19

213

P13611

Versican core protein

VCAN

372,820

-

-

5.2

2

214

P02774

Vitamin D-binding protein

GC

52,964

63.9

29

60.3

28

215

P04070

Vitamin K-dependent protein C

PROC

52,071

-

-

2.2

1

216

P07225

Vitamin K-dependent protein S

PROS1

75,123

12.6

2

-

-

217

P04004

Vitronectin

VTN

54,306

32.6

11

32.2

15

218

Q6PF04

Zinc finger protein 613

ZNF613

70,143

6.6

1

-

-

219

P25311

Zinc-alpha-2-glycoprotein

AZGP1

34,259

52.7

14

52

17

Fig. 1

Identification of HFF proteins by LC MALDI TOF/TOF MS Spectra. The MS/MS map (a, b) marked with b ions and y ions for vitamin D-binding protein identification. The sequences of precursor at m/z 2053.8506 and 2353.9646 were analyzed by MS/MS to be GQELCADYSENTFTEYK and SYLSMVGSCCTSASPTVCFLK and the protein identified as vitamin D-binding protein. The MS/MS map (c, d) marked with b ions and y ions for retinol-binding protein 4 identification. The sequences of precursor at m/z 2692.0667 and 1197.6047 were analyzed by MS/MS to be GNDDHWIVDTDYDTYAVQYSCR and YWGVASFLQK and the protein identified as retinol-binding protein 4

Fig. 2

Pie diagrams of the proportion of HFF proteins categorized by GO classifications based on their (a) molecular function, (b) subcellular localization, (c) biological process

Bioinformatics analysis of the HFF proteome

The proteins identified by mass spectrometry were broadly placed into several GO categories on the basis of the PANTHER, DAVID and PubMed databases (Fig. 2). Based on molecular function, the majority (31%) of proteins were related to immunity, whereas other involved protein functions were mainly complement and coagulation (17%), protease or inhibitor (14%), and transportation (10%) (Fig. 2a). Based on subcellular localization, the majority (64%) of the identified proteins located in extracellular region. Other main locations were extracellular matix (7%), nuleus (6%), and cytoskeleton (5%) (Fig. 2b). Based on biological process, the majority (28%) of proteins was related to developmental process, and the next prevalence was immunological system process (26%). The other groups were involved into protein metabolic process (12%), reproduction (5%), lipid metabolic process (3%), and transportation (2%) (Fig. 2c).

KEGG pathway analysis was performed to map HFF protein interactions, Pathways associated with complement and coagulation cascades (P_Value = 5.8E-52), vitamin digestion and absorption (P_Value = 0.023), and (P_Value = 0.066) were significantly enriched. Figure 3 showed the complement and coagulation cascades pathway which included 17 (7.8%) and 21 (9.6%) highlighted HFF proteins in coagulation cascade and complement cascade, respectively.
Fig. 3

Presentative Network of protein HSPG2 in the identified HFF proteome. A total of 21 genes are connected with 105 paired relationships annotated by STRING database. The relationships among proteins were derived from evidence that includes textmining, co-expression, protein homology, gene neighborhood, from curated databases, experimentally determined, gene fusions, and gene co-occurrence (as shown in the legend with different color)

A protein-protein interaction network was constructed by retrieving the STRING database. 151 proteins were in connection with other proteins, which lead to 738 paired relationships. As an example, 21 of 151 proteins related to basement membrane-specific heparan sulfate proteoglycan core protein (HSPG) was chosen, and 105 paired relationships were connected (Fig. 4).
Fig. 4

The KEGG pathway of complement and coagulation cascades with the identified HFF proteins highlighted. Generated by the KEGG online (hsa04610), this diagram showed the roles if HFF proteins in complement (Red) and coagulation cascades (Blue)

Comparison of present HFF proteome, the previous reported HFF proteome and human plasma proteome

To disclose the overlap of the HFF proteomes between different labs and to explore the orign of the HFF proteins, the previous reported HFF proteins [14] and the human plasma proteome [29] were selected, whose protein identification criteria were both at a false discovery rate (FDR) of 1%. The results reflected the overlap of our HFF proteins and the previously reported HFF proteins with human plasma proteins (Fig. 5). A total of 49% proteins in our HFF data were common to the previous HFF data. Compared with human plasma proteins, 69% proteins from our HFF data and 64% proteins from previous HFF data were common to human plasma proteins.
Fig. 5

Venn diagram of the overlap of HFF and human plasma protein datasets. Distribution of our present findings or the previously reported HFF proteins (Aditi S. Ambekar et al. 2013) and their overlap with those reported in human plasma (Terry Farrah et al. 2011)

Western blotting analysis

To verify the confidence of the proteome data, the expression patterns of 3 HFF proteins (retinol-binding protein 4, vitamin D-binding protein and lactotransferrin) from 10 women undergoing successful IVF were analyzed by western blotting (Fig. 6). Those three proteins could be detected in all 10 HFF samples. Compared with retinol-binding protein 4 and lactotransferrin, the expression of vitamin D-binding protein was relatively constant level in the HFF of ten women.
Fig. 6

Immunoblot analysis of retinol-binding protein 4, vitamin D-binding protein and lactotransferrin in 10 HFF samples of women underwent successful IVF. Protein lysates prepared from 10 HFF samples were examined by immunoblots using specific antibodies recognizing the retinol-binding protein 4(23 kDa), vitamin D-binding protein (53 kDa) and lactotransferrin (78 kDa)

Discussion

Proteomics has been carried out to discover HFF biomarkers for decades, and liquid chromatography coupled with ion trap MS became widely available with the development of high-throughput sequencing. The identification of HFF proteins from women with and without endometriosis was performed using ESI MS/MS [30]. Nanoflow LC-MS/MS combined with TMT labeling was used to identify HFF biomarkers from women undergoing IVF/ICSI treatment with or without folic acid supplement [31]. Another advance LTQ Orbitrap system coupled with LC was also applied to comparing HFF proteins between fertilized oocytes and non-fertilized oocytes from the same patient [32]. Based on sample pre-fractionation using microscale in-solution isoelectric focusing (IEF), capillary electrophoresis (CE) coupled off-line to matrix assisted laser desorption/ionization time of flight tandem mass spectrometry (MALDI TOF MS/MS) identified 73 unique proteins [33]. Hanrieder and co-workers [34] utilized a proteomic strategy of IEF and reversed-phase nano-liquid chromatography coupled to MALDI TOF/TOF mass spectrometry to identify 69 proteins related to controlled ovarian hyper stimulation (COH) during IVF. However, limited proteins were identified which delayed the research of HFF protein networks.

In the present work, a dual RP-HPLC coupled with MALDI TOF/TOF mass spectrometry was performed to identify HFF protein profiles associated with successful IVF, and 219 unique high-confidence (FDR < 0.01) HFF proteins were identified by searching the reviewed Swiss-Prot human database (20,183 sequences). Meanwhile, the new strategy indicated that the effective dual reverse LC pre-fractionation [21] could identify more HFF proteins.

Ambekar and co-workers carried out SDS-PAGE, OFFGEL and SCX-based separation followed by LC–MS/MS (an LTQ-Orbitrap Velos MS) to identify 480 HFF proteins with high confidence (FDR < 0.01) [14]. A comparison with our results and these data showed that more than 50% proteins in present study were not found in previous dataset (Additional file 2: Figure S1), which indicated that the data from different MS platforms were complementary. Retinol-binding protein 4 and vitamin D-binding protein were verified by western blotting, and the results showed they were all expressed in the 10 HFF samples. Lactotransferrin was uniquely included in Ambekar’s data, and was also successfully detected by western blotting in our study. This result not only testified the good quality of Ambekar’s data, but also facilitated to integrate the data from different MS platform in the future. Interestingly, more than 60% of combined HFF proteins from our data and Ambekar’s data were found in the reported human plasma data [29]. HFF was a complex mixture, and the content of HFF mainly originates from the transfer of blood plasma constituents via theca capillaries, and the secretion of granulosa and thecal cells [5]. From the above contrast, we considered the transfer of plasma proteins was the major source of HFF, and the alternative permeability of theca capillaries would change the HFF compositions which inevitably impaired the oocyte quality, and even caused unsuccessful IVF outcome.

Bioinformatics analysis showed that 5% HFF proteins were involved in lipid metabolism and transport process. It has been reported that ageing could decrease apolipoprotein A1 and apolipoprotein CII, while increase apolipoprotein E, which were associated with the decline in production of mature oocytes and the decline in fertility potential [35]. Preconception folic acid supplementation upregulated apolipoprotein A-I and apolipoprotein C-I of the HDL pathway in human follicular fluid, which increased embryo quality and IVF/ICSI treatment outcome [30]. In our HFF data, apolipoprotein A-I, apolipoprotein A-II, apolipoprotein A-IV, apolipoprotein C-I, apolipoprotein C-II, apolipoprotein C-III, apolipoprotein D, apolipoprotein E, apolipoprotein F, and apolipoprotein M were all found, which indicated that those apolipoproteins were related to cholesterol homeostasis and steroidogenesis and played important roles in the maintenance of oocyte maturation microenvironment.

Pathway analysis showed that complement and coagulation cascades were the most prominent pathways (P_Value = 5.8E-52). Complement cascade promoted coagulation through the inhibition of fibrinolysis, and coagulation cascade in return amplified complement activation. Complement cross_talked with coagulation in a reciprocal way [36]. For example, plasmin, thrombin, elastase and plasma kallikrein could activate C3 [37]. Coagulation activation factor XII could cleave C1 to activate the classical complement pathway [38]. And thrombin could also directly cleave C5 to generate active C5a [39]. Among our HFF proteins, components (F12, KLKB1, PLG, KNG1, F9, F10, SERPINC1, SERPIND1, SERPINA5, F2, PROS1, PROC, SERPINA1, SERPINF2, A2M, CPB2, and FGA) of extrinsic pathway and intrinsic pathway in coagulation cascade and those (FH, FI, FB, C3, C1qrs, SERPING1, C2, C4, C4BP, C5, C6, C7, C8A, C8B, C8G, C9, FGA, FGG, PLG, FGB, F10) of alternative pathway, classical pathway, and lectin pathway in complement cascade were all identified. During follicle development and ovulation, coagulation system in HFF contributed to HFF liquefaction, fibrinolysis and the breakdown of follicle wall [40, 41]. Follicle development had been hypothesized as the controlled inflammatory processes in 1994 [42], and inappropriate complement activation was linked to abortion [43]. Inhibition of complement activation improved angiogenesis failure and rescued pregnancies [44]. The paired comparison of HFF with plasma showed C3, C4, C4a, and C9 as well as complement factor H and clusterin might contribute to the inhibition of complement cascade activity for women undergoing controlled ovarian stimulation for IVF [45]. However there were still debates on the role of complement cascade in IVF. Physiologic complement activation protected the host against infection in normal pregnancy [46]. In comparison with those non-fertilized oocytes, C3 was more abundant in HFF from fertilized oocytes [47]. In the course of IVF treatment, the functions of complement and coagulation cascade were very complicated during ovarian hyperstimulation. More works were still deserved in both mechanism research and clinical practice.

Based on the analysis of STRING, we discovered a profound HFF protein-protein interaction networks. 151 of 219 HFF proteins participated in the network with 738 paired relationships. Basement membrane-specific HSPG was found as a node, which was also a potential biomarker for oocyte maturation in HFF. HSPG was widely distributed on the surface of animal cells, and especially strongly expressed in granulosa cells. HSPG played a critical role in controlling inflammation control through binding and activating antithrombin III during folliculogenesis [48]. Women with PCOS showed HSPG defect in follicular development [49], and on the contrary, HSPG was up-regulated in the fertilized-oocyte HFF [32]. In the network, HSPG interacted with 20 of 219 HFF proteins, and constructed 105 paired relationships. We deduced that the loss of HSPG might affect the function of the whole network or more complicated interaction maps, which might cause subsequent failures of oocyte maturation, fertilization, and IVF treatment.

Conclusions

HFF had a natural advantage for the noninvasive prediction of oocyte quality and IVF treatment outcome. The present study would provide a new complementary dataset for better understanding of oocyte maturation, and also delineate a new networks and pathways involved into the folliculogenesis. Furthermore, those novel findings would facilitate to testify the potential biomarkers associated with oocyte quality and IVF outcome. In the future, international laboratory collaboration should be established to standardize and optimize experimental design, patient selection, HFF handling, analysis methods, data standard, and clinical verification, which will greatly promote basic research of reproductive medicine, and ultimately accelerate the clinical transformation.

Abbreviations

2D–GE: 

Two-dimensional gel electrophoresis

A2M: 

Alpha-2-macroglobulin

BMI: 

Body mass index

C1qrs: 

Complement C1q A chain

C2: 

Complement C2

C3: 

Complement C3

C4: 

Complement C4

C4BP: 

C4b-binding protein alpha chain

C5: 

Complement C5

C6: 

Complement C6

C7: 

Complement C7

C8A: 

Complement component C8 alpha chain

C8B: 

Complement component C8 beta chain

C8G: 

Complement component C8 gamma chain

C9: 

Complement C9

CE: 

Capillary electrophoresis

COH: 

Controlled ovarian hyperstimulation

CPB2: 

Carboxypeptidase B2

DAVID: 

The database for annotation, visualization and integrated discovery

F10: 

Coagulation factor X

F12: 

Coagulation factor XII

F2: 

Prothrombin

F9: 

Coagulation factor IX

FB: 

Complement factor B

FDR: 

False Discovery Rate

FDR: 

False discovery rate

FGA: 

Fibrinogen alpha chain

FGB: 

Fibrinogen beta chain

FGG: 

Fibrinogen gamma chain

FH: 

Complement factor H

FI: 

Complement factor I

HCG: 

Human chorionic gonadotrophin

HFF: 

Human follicular fluid

HSPG: 

Heparan sulfate proteoglycan core protein

IEF: 

Isoelectric focusing

IVF: 

In vitro fertilization

KEGG: 

Kyoto encyclopedia of genes and genomes

KLKB1: 

Plasma kallikrein

KNG1: 

Kininogen-1

MALDI TOF/TOF: 

Matrix-assisted laser desorption/ionization time-of-flight tandem

PANTHER: 

Protein analysis through evolutionary relationships

PCOS: 

Polycystic ovary syndrome

PLG: 

Plasminogen

PROC: 

Vitamin K-dependent protein C

PROS1: 

Vitamin K-dependent protein S

RP-HPLC: 

Reverse phase high performance liquid chromatography

SCX: 

Strong cation exchange

SDS-PAGE: 

One dimensional sodium dodecyl polyacrylamide gel electrophoresis

SELDI-TOF-MS: 

surface-enhanced laser desorption/ionization-time of flight-mass spectrometry

SERPINA1: 

Alpha-1-antitrypsin

SERPINA5: 

Plasma serine protease inhibitor

SERPINC1: 

Antithrombin-III

SERPIND1: 

Heparin cofactor 2

SERPINF2: 

Alpha-2-antiplasmin

SERPING1: 

Plasma protease C1 inhibitor

STRING: 

search tool for recurring instances of neighbouring genes

WCX: 

weak cation-exchange

WGOC: 

Working Group on Obesity in China

Declarations

Acknowledgements

We thank Guo Lihai PhD (Shanghai Asia Pacific Application Support Center, Applied Biosystems, China) for the usage training of LC MALDI TOF/TOF 5800 mass spectrometer (AB SCIEX, USA).

Funding

The current study was supported by the National Natural Science Foundation of China (grant nos. 81300533 81501313 and 81571490), Shandong Provincial Natural Science Foundation, China (grant nos. ZR2014HQ068 and ZR2015HQ031) and Yantai Science and Technology Program (grant no. 2015WS019, 2015WS024 and 2016WS001).

Availability of data and materials

The datasets used and/or analysed during the current study available fromthe corresponding author on reasonable request.

Authors’ contributions

XS, XL, FL conceived of the study, participated in its design and coordination and reviewed the final manuscript for submission. PZ participated in the design of study, carried out the studies and drafted the manuscript. YZ, JW, YW, WW participated in the design of study, carried out the studies and helped to draft the manuscript. XL, FL, PZ performed the proteomic analysis. JL, NL carries out the bioinformatics analysis. XS participated in the study design and performed the HFF collection. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This work has been approved by the Ethics Committee of Beijing BaoDao Obstetrics and Gynecology Hospital, and written informed consents were obtained from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University
(2)
Reproductive center, Beijing BaoDao obstetrics and gynecology hospital
(3)
Reproductive Medicine Centre, Wei fang people’s hospital

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© The Author(s). 2017

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