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Erschienen in: Reproductive Biology and Endocrinology 1/2017

Open Access 01.12.2017 | Research

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

verfasst von: Xiaofang Shen, Xin Liu, Peng Zhu, Yuhua Zhang, Jiahui Wang, Yanwei Wang, Wenting Wang, Juan Liu, Ning Li, Fujun Liu

Erschienen in: Reproductive Biology and Endocrinology | Ausgabe 1/2017

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.
Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12958-017-0277-y) contains supplementary material, which is available to authorized users.
Abkürzungen
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

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

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.
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).

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.

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.

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.

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.
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.
Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Metadaten
Titel
Proteomic analysis of human follicular fluid associated with successful in vitro fertilization
verfasst von
Xiaofang Shen
Xin Liu
Peng Zhu
Yuhua Zhang
Jiahui Wang
Yanwei Wang
Wenting Wang
Juan Liu
Ning Li
Fujun Liu
Publikationsdatum
01.12.2017
Verlag
BioMed Central
Erschienen in
Reproductive Biology and Endocrinology / Ausgabe 1/2017
Elektronische ISSN: 1477-7827
DOI
https://doi.org/10.1186/s12958-017-0277-y

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