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Erschienen in: Reproductive Biology and Endocrinology 1/2013

Open Access 01.12.2013 | Research

Proteomic analysis of human spermatozoa proteins with oxidative stress

verfasst von: Rakesh Sharma, Ashok Agarwal, Gayatri Mohanty, Alaa J Hamada, Banu Gopalan, Belinda Willard, Satya Yadav, Stefan du Plessis

Erschienen in: Reproductive Biology and Endocrinology | Ausgabe 1/2013

Abstract

Background

Oxidative stress plays a key role in the etiology of male infertility. Significant alterations in the sperm proteome are associated with poor semen quality. The aim of the present study was to examine if elevated levels of reactive oxygen species cause an alteration in the proteomic profile of spermatozoa.

Methods

This prospective study consisted of 52 subjects: 32 infertile men and 20 normal donors. Seminal ejaculates were classified as ROS+ or ROS- and evaluated for their proteomic profile. Samples were pooled and subjected to LC-MS/MS analysis through in-solution digestion of proteins for peptide characterization. The expression profile of proteins present in human spermatozoa was examined using proteomic and bioinformatic analysis to elucidate the regulatory pathways of oxidative stress.

Results

Of the 74 proteins identified, 10 proteins with a 2-fold difference were overexpressed and 5 were underexpressed in the ROS+ group; energy metabolism and regulation, carbohydrate metabolic processes such as gluconeogenesis and glycolysis, protein modifications and oxidative stress regulation were some of the metabolic processes affected in ROS+ group.

Conclusions

We have identified proteins involved in a variety of functions associated with response and management of oxidative stress. In the present study we focused on proteins that showed a high degree of differential expression and thus, have a greater impact on the fertilizing potential of the spermatozoa. While proteomic analyses identified the potential biomarkers, further studies through Western Blot are necessary to validate the biomarker status of the proteins in pathological conditions.
Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1477-7827-11-48) contains supplementary material, which is available to authorized users.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

RK: participated in the study conception/design, review of the data and writing of the manuscript and final approval; SD: acquisition and preparation of samples for analysis; BW, acquisition of samples for analysis; interpretation of the results; SY: acquisition of the samples, interpretation of the data, discussion o results; BG: contributed to bioinformatic analysis, data interpretation and participated in the paper redaction; AH: drafting of the article; GM participated in the review of the data and writing of the manuscript; AA contributed to the study design, and review of the data. All authors read and approved the final manuscript.

Background

Male-factor infertility contributes to 50% of infertile couples worldwide, and while the causes are multifactorial, current research has focused on oxidative stress. Specifically, oxidative stress may lead to alterations in protein expression levels in spermatozoa, causing molecular and genetic defects. Research has already shown that oxidative stress is associated with a variety of male infertility diagnosis such as varicocele, idiopathic infertility, spinal cord injury, prostatitis and leukocytospermia [18]. Varicocele is implicated in 35% of men with primary infertility and in 80% of infertile men with secondary infertility, and oxidative stress is one of the many causes of varicocele related infertility [911]. Oxidative stress in men with varicocele is also associated both with motility and grade of varicocele.
Understanding the protein profile of spermatozoa is essential for identifying the protein alterations that occur as a result of increased production of ROS and for better diagnosis of male infertility. The development of 2-dimensional-polyacrylamide gel electrophoresis (2-DE) coupled with Western blot have aided in the identification of proteins, in relation to sperm composition and function [12, 13]. Furthermore advancements in mass spectrometry analyses of spermatozoa have broadened our knowledge and aided in the identification of pivotal sperm proteins [14, 15]. Numerous studies have reported the presence of proteins in studies involving human spermatozoa through the implementation of proteomic techniques such as 2-DE, liquid chromatography tandem mass spectrometry (LC-MS/MS), matrix assisted laser desorption and ionization- time of flight tandem mass spectrometry (MALDI-TOF-MS/MS), and 2-dimensional difference in gel electrophoresis (DIGE) [1621].
In this comparative study, we employed LC- MS/ MS and functional bioinformatic analyses to identify the relative abundance of proteins in spermatozoa obtained from seminal ejaculates of infertile men who were referred to the andrology clinical laboratory for advanced semen analysis (including parameters of oxidative stress such as levels of ROS in spermatozoa, concentration of antioxidant in the seminal plasma and the extent of DNA damage). We also included healthy men with high and low levels of ROS in their seminal ejaculates compared to those with low levels of ROS. Oxidative stress plays a major role in male infertility, but the crucial proteins that are differentially expressed in ROS are not known. Understanding the role of these proteins is therefore important in our understanding of the post-genomic events. It will help identify potential proteins that could serve as biomarkers to better manage and treat oxidative stress, and consequently, male infertility.

Methods

The study was approved by the Institutional Review Board of Cleveland Clinic and was conducted in accordance with national and international guidelines. A total of 52 subjects were enrolled in the study. Twenty healthy male volunteers of unproven fertility (normal semen analysis results but had not established a pregnancy) were selected on the basis of normal semen analysis. Thirty two infertile men attending our infertility clinic were referred to the andrology lab for advanced semen parameters of oxidative stress. The breakdown of the infertile subjects was as follows: primary infertility: n = 25 (25/ 32; 78.1%); Secondary infertility: n = 7 (7/25; 28%). Of the 25 men with primary infertility, 64% (16/ 25) had <2 years and 36% (9/ 25) >2 years. Clinical varicocele (grade 1-2) was present in 10 (62.5%) of the 16 men who presented with <2 years of primary infertility and in 5 (55.6%) of the 9 men with >2 years of primary infertility. In the 7 patients with secondary infertility, 6 (85.7%) had a clinical varicocele (grade 1-2).
Semen samples were collected by masturbation after 2-3 days of sexual abstinence and analyzed according to WHO 2010 criteria [22]. A complete semen analysis was performed on both donors and patients and included both macroscopic (volume, pH, color, viscosity, liquefaction time, split or complete ejaculate) and microscopic parameters (concentration, motility, morphology, round cells and peroxidase or Endtz test). All subjects were also examined for levels of reactive oxygen species (ROS) in the seminal ejaculate. The remainder of the seminal ejaculate was frozen without any cryoprotectant and stored at -55°C for proteomic analysis.

Semen analysis

After liquefaction, complete semen analysis was performed to evaluate the sperm parameters (sperm count, percentages motility and morphology), according to WHO guidelines [22]. Semen analysis was done using a MicroCell counting chamber (Vitrolife, San Diego, CA). Smears of the raw semen were stained with a Diff-Quik kit (Baxter Healthcare Corporation, Inc., McGaw Park, IL) for assessment of sperm morphology according to WHO criteria.

Measurement of reactive oxygen species

In the freshly ejaculated and completely liquefied semen, ROS levels were measured by a chemiluminescence assay using luminol (5-amino-2, 3- dihydro-1, 4-phthalazinedione). Test samples consisted of luminol (10 μL, 5 mM) and 400 μL of semen. Negative controls were prepared by replacing the sperm suspension with phosphate buffered saline. Chemiluminescence was measured for 15 min using a Berthold luminometer (Autolumat Plus 953). Results were expressed as relative light units (RLU)/sec/ × 106 sperm. Reference value for ROS – group was: <20 RLU/s/×106 sperm. ROS levels >20 RLU/s/×106 sperm were considered positive [23].

Separation of spermatozoa and In-solution Digestion of Proteins

We were interested in examining the effect of ROS levels on the distribution / alteration of proteins in an effort to understand the underlying mechanism of infertility. Based on our published reference levels, all samples with ROS levels below 20 RLU/sec/ × 106 sperm were considered to be ROS – and those with ROS levels >20 RLU/sec/ × 106 sperm were considered to be ROS + [23]. For proteomic analysis, frozen seminal ejaculates from both the donors and patients were divided into 2 groups: ROS+ and ROS-, irrespective of the subject’s clinical diagnoses. Samples were centrifuge at 10,000 g for 10 minutes to pellet the spermatozoa and the clear seminal plasma was discarded. Samples were pooled into ROS+ and ROS – groups and solubilized in lysis buffer containing 2% octyl-β-glucopyranoside, 100 mM dithiothreitol (DTT), 9.8 M urea and protease inhibitors [12]. Spermatozoa samples were stored overnight in the refrigerator to allow for complete lysis of the spermatozoa. The samples were first precipitated in cold acetone, solubilized in 6 M urea, reduced with DTT, and alkylated with iodoacetamide. The samples were subsequently diluted to give a urea concentration <2 M and then digested with trypsin. The tryptic digestion was subjected to a C18 clean up and then brought up in 50 μL of 1% acetic acid [12].

Liquid chromatography- Mass Spectrometer analysis (LC-MS-MS)

The LC-MS/MS system was a Finnigan LTQ linear ion trap mass spectrometer system (Thermo Finnigan). Ten μL volumes of the extract were injected on a self-packed high performance liquid chromatography column (Phenomenex Jupiter C18 reversed-phase capillary chromatography column). Peptides were eluted from the column by an acetonitrile/0.1% formic acid gradient at a flow rate of 0.25 μL/min. They were introduced into the source of the mass spectrometer on-line. The microelectrospray ion source was operated at 2.5 kV. The digest was analyzed using the data dependent multitask capability of the instrument acquiring full scan mass spectra to determine peptide molecular weights and product ion spectra to determine amino acid sequence in successive instrument scans [12].

Data analysis

The data were analyzed using all collision- induced dissociation spectra collected in the experiment to search the National Center for Biotechnology Information (NCBI) human reference sequence database with the search program MASCOT (Matrix Science, Boston, MA). Mascot is a mass spectral search algorithm that uses mass spectrometry data to identify proteins from primary sequence databases. These searches were used to identify the proteins present in the in-solution digestions. After identification, a database consisting of all proteins identified in these searches was created and used for a second set of searches performed with SEQUEST (Thermo Finnigan). SEQUEST is a tandem mass spectrometry data analysis program used for protein identification. Sequest identifies collections of tandem mass spectra to peptide sequences that have been generated from databases of protein sequences. The results from these SEQUEST searches were used to determine the spectral counts. These spectral count values were normalized by the total number of spectral counts for all proteins in the sample and the number of amino acids present in the protein. As the precision of the proteomic analysis has an average error of 10-20%, a 2-fold change in protein expression was considered significant.
Furthermore functional bioinformatics analysis were done using publicly available (Gene Ontology (GO) annotations from GO Term Finder [24] and GO Term Mapper [25], UNIPROT [26], STRAP [27] and BioGPS [28] and proprietary software packages (Ingenuity Pathway Analysis (IPA) from Ingenuity® Systems [29] and Metacore™ from GeneGo Inc. [30] to identify the differentially affected processes, pathways, interactions and cellular distribution of the proteins in the two study groups.

Results

Semen parameters and oxidative stress parameters

Most of the infertile men were diagnosed with primary infertility and varicocele. The median and 25th and 75th percentile for sperm concentration (×106/ mL) in controls was 64.9 (45.4-74.9) versus patients 23.0 (13.0-41.4). The median and 25th and 75th percentile motility in controls was 58.3(50.0-65.7) compared to infertile men 50.5 (36.8-58.0). Among the patients, 46.8% (15/32) had > 1 × 106 round cells / mL compared to donors 35% (7/20) of the donors, of these 15 patients, 15.6% were positive for the Endtz test – a marker for granulocytes, which are the major contributors of ROS production. None of the donors was positive for the Endtz test. An overlap in morphology was seen in both the donors (3.5% ± 1.6%) and infertile men (3.6% ± 3.2%).
Among the patients 22 of 32 (68.8%) were positive for ROS compared to 9 of 20 (45%) in the controls. Levels of ROS were significantly lower (RLU/s/×106 sperm) median and 25th and 75th percentile were seen in donors [4.4(0, 300.2)] compared to infertile men [104(0, 1341)]. Among the primary infertility patients with <2 years of infertility, 80% were positive for ROS and 55.6% were positive in the patients with >2 years of primary infertility. A significantly larger percentage of men (83%) were positive for ROS in those with secondary infertility. Teratozoospermia was present in 48% of men with primary infertility.
When all subjects were categorized into ROS+ and ROS- groups, ROS levels (RLU/s/106sperm: median, 25th, 75th percentile) were significantly different and higher in the ROS+ group [2236(33, 4439)] group compared to the ROS – group [4 (0, 9); P < 0.01)].

MS identification of the differentially expressed proteins in ROS+ and ROS- samples

In this study, we analyzed the differential expression of proteins in spermatozoa obtained from seminal ejaculates with high levels of ROS+ and those with normal levels (ROS-). Tables 1 and 2 show the primary proteins with their NCBI database index number, calculated molecular weight, isoelectric point (pI), peptide coverage and Mascot scores for ROS+ and ROS- groups. Based on the SEQUEST score, a total of 74 proteins were identified and differential expression was calculated based on the normalized spectral count ratios between ROS+ and ROS-. In the ROS+ group, proteins that had >50% peptide coverage were: A-Kinase anchor protein 4 isoform 2; Lactotransferrin precursor; Tubulin, beta, 2; Tubulin, beta 4; Tubulin, alpha 3c; Ropporin, rhophilin associated protein 1B; Prolactin-induced protein; Histone cluster 1, H2aa; Beta actin and Triosephosphate isomerase 1 isoform 1. In the ROS- group, proteins that had >50% peptide coverage were: A-Kinase anchor protein 4 isoform 2; Lactotransferrin precursor; Spermatogenic Tubulin, beta, 2 precursor; Tubulin, beta 4; Tubulin, alpha 3c; Ropporin, rhophilin associated protein 1B; Prolactin-induced protein; Histone cluster 1, H2aa and Triosephosphate isomerase 1 isoform 1;
Table 1
Protein name, NCBI database index number, calculated MW, pI, peptide coverage and Mascot score in ROS+ group
No.
Protein name
NCBI database index number
Calculated MW (kDa)
pI
LTQ
Peptides
Mascot
(Coverage)
score
1
A-Kinase anchor protein 4 isoform 2
21493039
94
6.7
34 (54%)
10988
2
lactotransferrin precursor
54607120
80
8.5
36 (64%)
7287
3
tubulin, beta, 2
5174735
50
4.7
24 (68%)
6702
4
tubulin, beta 4
21361322
50
4.8
19 (57%)
5071
5
glyceraldehyde-3-phosphate dehydrogenase, spermatogenic
7657116
44
8.4
12 (48%)
5711
6
tubulin, alpha 3c
17921993
50
4.9
15 (51%)
2942
7
ropporin, rhophilin associated protein 1B
59891409
24
5.1
7 (56%)
2587
8
semenogelin II precursor
4506885
65
9.1
13 (22%)
2277
9
outer dense fiber of sperm tails 2 isoform 2
24430183
73
7.2
16 (31%)
2051
10
semenogelin I isoform a preproprotein
4506883
52
9.3
12 (31%)
1983
11
sperm protein associated with the nucleus, X chromosome, family member C
13435137
11
5.0
3 (45%)
1892
12
tubulin, alpha, ubiquitous
57013276
50
4.9
13 (46%)
1616
13
glutathione S-transferase mu 3
23065552
26
5.3
7 (37%)
1595
14
prolactin-induced protein
4505821
16
8.2
8 (62%)
1559
15
A Kinase anchor protein 3
21493041
95
5.8
20 (35%)
1416
16
histone cluster 1, H2ba
24586679
14
10.3
1 (11%)
1371
17
mitochondrial malate dehydrogenase precursor
21735621
35
8.9
8 (33%)
1342
18
histone cluster 1, H2aa
25092737
14
10.8
6 (58%)
1334
19
clusterin isoform 1
42716297
58
6.2
6 (16%)
1204
20
ropporin
21359920
24
5.5
6 (43%)
1184
21
sorbitol dehydrogenase
156627571
38
8.2
7 (35%)
1095
22
mitochondrial ATP synthase beta subunit precursor
32189394
56
5.2
16 (53%)
976
23
beta actin
4501885
42
5.2
9 (54%)
909
24
triosephosphate isomerase 1 isoform 1
4507645
26
6.4
9 (55%)
761
25
glyceraldehyde-3-phosphate dehydrogenase
7669492
36
8.5
6 (32%)
706
26
L-lactate dehydrogenase C– 1 peptide
4504973
36
7.0
1 (5%)
674
27
fatty acid synthase
41872631
275
6.0
3 (8%)
669
28
sperm autoantigenic protein 17
8394343
17
4.7
2 (22%)
592
29
pyruvate kinase, muscle isoform M2
33286418
58
7.9
11 (38%)
560
30
ATP synthase, H + transporting, mitochondrial F1 complex, alpha subunit precursor
4757810
59
9.1
8 (23%)
542
31
brain creatine kinase
21536286
42
5.3
7 (32%)
511
32
heat shock protein 90 kDa beta, member 1
4507677
92
4.7
6 (10%)
502
33
heat shock 70 kDa protein 2
13676857
70
5.5
10 (21%)
448
34
heat shock 90 kDa protein 1, alpha isoform 1
153792590
98
5.0
8 (13%)
447
35
saccharopine dehydrogenase (putative)
55770836
47
9.2
5 (29%)
395
36
actin, alpha 1, skeletal muscle
4501881
42
5.2
6 (36%)
374
37
glutamine synthetase
19923206
42
6.4
3 (9%)
374
38
fructose-bisphosphate aldolase A
4557305
39
8.3
5 (29%)
366
39
eukaryotic translation elongation factor 1 alpha 1
4503471
50
9.1
8 (36%)
347
40
fibronectin 1 isoform 3 preproprotein
16933542
262
5.4
6 (4%)
321
41
prostate, ovary, testis expressed protein on chromosome 2
153791352
123
5.8
4 (8%)
317
42
acetyl-Coenzyme A acetyltransferase 1 precursor
4557237
45
8.9
9 (33%)
316
43
voltage-dependent anion channel 2
42476281
32
7.4
4 (22%)
293
44
outer dense fiber of sperm tails 1
194248060
30
8.4
4 (26%)
283
45
phosphoglycerate kinase 2
31543397
45
8.7
5 (30%)
253
46
histone cluster 1, H2ae
10645195
14
11.0
4 (49%)
251
47
protein disulfide – isomerase A3 precursor
21361657
57
5.9
4 (13%)
243
48
acid phosphatase, prostate short isoform precursor
6382064
44
5.8
5 (16%)
240
49
voltage-dependent anion channel 3 isoform b
25188179
30
8.8
5 (27%)
226
50
phospholipase A2, group IIA precursor
4505849
16
9.4
2 (23%)
202
51
transglutaminase 4 (prostate)
156627577
77
6.3
8 (17%)
187
52
glutathione peroxidase 4 isoform A precursor
75709200
22
8.6
4 (32%)
181
53
heat shock 70 kDa protein 5
16507237
72
5.0
4 (8%)
161
54
heat shock protein beta-1
4504517
22
5.9
3 (31%)
160
55
angiotensin I converting enzyme 1 isoform 1 precursor
4503273
150
5.9
3 (4%)
160
56
peroxiredoxin 6
4758638
25
6.0
2 (16%)
145
57
chaperonin containing TCP1, subunit 5 (epsilon)
24307939
60
5.4
4 (12%)
142
58
valosin-containing protein
6005942
89
5.1
3 (7%)
142
59
sperm acrosomal membrane protein 14 – 1 peptide
19424138
13
5.4
1 (11%)
132
60
chaperonin containing TCP1, subunit 8 (theta)
48762932
60
5.4
4 (14%)
131
61
L-lactate dehydrogenase A isoform 1
5031857
36
8.4
2 (13%)
124
62
phosphoglycerate dehydrogenase
23308577
57
6.2
3 (13%)
112
63
clathrin heavy chain 1
4758012
193
5.4
4 (5%)
108
64
chaperonin containing TCP1, subunit 4 (delta)
38455427
58
7.9
5 (21%)
99
65
peptidylprolyl isomerase A
10863927
18
7.6
2 (23%)
96
66
fumarate hydratase precursor
19743875
54
8.8
8 (31%)
92
67
RAB2A, member RAS oncogene family
4506365
23
6.0
3 (18%)
92
68
olfactomedin 4 precursor
32313593
57
5.5
2 (5%)
90
Table 2
Protein name, NCBI database index number, calculated MW, pI, peptide coverage and Mascot score in ROS- group
No.
Protein name
NCBI database index number
Calculated MW (kDa)
pI
LTQ
Peptides (Coverage)
Mascot score
1
A-Kinase anchor protein 4 isoform 2
21493039
94
6.7
34 (54%)
11284
2
lactotransferrin precursor
54607120
80
8.5
32 (56%)
6049
3
gyceraldehyde-3-phosphate dehydrogenase,
7657116
44
8.4
12 (48%)
5959
4
spermatogenic tubulin, beta, 2 precursor
5174735
50
4.7
25 (68%)
5842
5
semenogelin I isoform a preproprotein
4506883
52
9.3
11(24%)
4965
6
tubulin, beta 4
21361322
50
4.8
19 (58%)
4879
7
semenogelin II precursor
4506885
65
9.1
15 (25%)
4205
8
tubulin, alpha 3c
17921993
50
4.9
15 (56%)
2645
9
ropporin, rhophilin associated protein 1B
59891409
24
5.1
6 (53%)
2427
10
prolactin-induced protein
4505821
16
8.2
8 (62%)
2164
11
A-kinase anchor protein 3
21493041
95
5.8
16 (28%)
1800
12
outer dense fiber of sperm tails 2 Isoform 2
24430183
73
7.2
16 (31%)
1722
13
sperm protein associated with the Nucleus, X chromosome, family Member C
13435137
11
5.0
3 (45%)
1437
14
tubulin alpha 6
14389309
50
4.9
11 (45%)
1374
15
ropporin
21359920
24
5.5
6 (43%)
1280
16
clusterin isoform 1
42716297
58
6.2
5 (15%)
1257
17
sorbitol dehydrogenase
156627571
38
8.2
4 (23%)
1249
18
beta actin
4501885
42
5.2
6 (34%)
1232
19
pyruvate kinase, muscle isoform M1
3328642
58
7.6
13 (47%)
1133
20
histone cluster 1, H2aa
25092737
14
10.8
4 (51%)
1121
21
mitochondrial ATP synthase beta subunit precursor
32189394
56
5.2
13 (38%)
1031
22
fatty acid synthase
41872631
275
6.0
10 (9%)
935
23
glutathione S-transferase mu 3
23065552
26
5.3
7 (32%)
929
24
histone cluster 1, H2ba -1 peptide
24586679
14
10.3
1 (11%)
823
25
fibronectin 1 isoform 3 preproprotein
16933542
262
5.4
16 (11%)
790
26
L-lactate dehydrogenase C
4504973
36
7.0
2 (7%)
693
27
heat shock 90 kDa protein 1, alpha isoform 2
154146191
85
4.9
10 (18%)
682
28
glyceraldehyde-3-phosphate dehydrogenase
7669492
36
8.5
3 (15%)
621
29
sperm autoantigenic protein 17
8394343
17
4.7
2 (22%)
552
30
triosephosphate isomerase 1 isoform 1
4507645
26
6.4
6 (61%)
530
31
mitochondrial malate dehydrogenase precursor
21735621
35
8.9
5 (23%)
512
32
phosphoglycerate kinase 2
31543397
45
8.7
4 (17%)
506
33
heat shock 70 kDa protein 2
13676857
70
5.5
8 (22%)
423
34
histone cluster 1, H2ae
10645195
14
11.0
3 (43%)
392
35
acetyl-Coenzyme A acetyltransferase 1 precursor
4557237
45
8.9
8 (34%)
388
36
eukaryotic translation elongation factor 1 alpha 1
4503471
50
9.1
7 (30%)
383
37
ATP synthase, H + transporting, mitochondrial F1 complex, alpha subunit precursor
4757810
59
9.1
8 (22%)
378
38
outer dense fiber of sperm tails 1
194248060
30
8.4
3 (22%)
334
39
voltage-dependent anion channel 2
42476281
32
7.4
3 (19%)
332
40
acid phosphatase, prostate short isoform precursor
6382064
44
5.8
5 (16%)
326
41
brain creatine kinase
21536286
42
5.3
7 (29%)
268
42
saccharopine dehydrogenase (putative)
55770836
47
9.2
4 (29%)
265
43
protein disulfide-isomerase A3 precursor
21361657
57
5.9
2 (9%)
261
44
eukaryotic translation elongation factor 2
4503483
96
6.4
3 (8%)
261
45
heat shock protein 90 kDa beta, member 1
4507677
92
4.7
5 (8%)
250
46
heat shock protein beta-1
4504517
22
5.9
4 (36%)
191
47
glutamine synthetase
19923206
42
6.4
3 (14%)
179
48
valosin-containing protein
6005942
89
5.1
7 (18%)
177
49
phospholipase A2, group IIA precursor
4505849
16
9.4
2 (23%)
170
50
tyrosine 3/tryptophan 5 -monooxygenase activation protein, zeta polypeptide
4507953
27
4.7
2 (19%)
167
51
L-lactate dehydrogenase A isoform 1
5031857
36
8.4
2 (7%)
167
52
fructose-bisphosphate aldolase A
4557305
39
8.3
3 (16%)
167
53
chaperonin containing TCP1, subunit 5 (epsilon)
24307939
60
5.4
3 (9%)
157
54
voltage-dependent anion channel 3 isoform b
25188179
30
8.8
4 (26%)
147
55
peroxiredoxin 6
4758638
25
6.0
4 (33%)
140
56
glutathione peroxidase 4 isoform A precursor
75709200
22
8.6
5 (43%)
139
57
transglutaminase 4 (prostate)
156627577
77
6.3
5 (9%)
136
58
enolase 1
4503571
47
7.0
3 (12%)
134
59
T-complex protein 1 isoform b
57863259
44
7.5
5 (30%)
129
60
CSE1 chromosome segregation 1-like protein
29029559
111
5.5
2 (3%)
128
61
fumarate hydratase precursor
19743875
54
8.8
3 (12%)
115
62
lectin, mannose-binding 2 precursor
5803023
40
6.4
2 (13%)
115
63
clathrin heavy chain 1
4758012
193
5.4
6 (7%)
111
64
chaperonin containing TCP1, subunit 4 (delta)
38455427
58
7.9
4 (19%)
107
65
RAB2A, member RAS oncogene family
4506365
23
6.0
3 (18%)
106
66
chromosome 20 open reading frame 3
24308201
46
5.8
3 (19%)
105
Out of the total 74 proteins identified, 47 were overexpressed and 27 underexpressed in the ROS+ group; 15 proteins were significantly (2-fold) differentially expressed in the ROS+ samples compared to the ROS- samples.
Of the differentially expressed proteins, 15 proteins were overexpressed in the ROS+ samples while 5 were underexpressed in comparison to ROS- samples (Table 3). The overexpressed proteins included the histone cluster1; H2ba (HIST1H2BA); mitochondrial malate dehydrogenase precursor (MDH2); heat shock protein 90 kDa beta, member 1 (HSP90B1); heat shock 70 kDa protein 5 (HSPA5); glutamine synthetase (GLUL); transglutaminase 4 (prostate) (TGM4); glutathione peroxidase 4 isoform A precursor (GPX4); sperm acrosomal membrane protein 14 (SPACA4); olfactomedin 4 precursor (OLFM4) and chromosome 20 open reading frame 3 (C20orf3). Proteins that were found to be under expressed were: semenogelin II precursor (SEMG2); peroxiredoxin 6 (PRDX6); clathrin heavy chain 1 (CLTC); eukaryotic translation elongation factor 2 (EEF2) and enolase 1 (ENO1).
Table 3
Differentially expressed proteins in ROS- and ROS+ groups along with the spectral count and the spectral count ratio for the two groups
Protein
NCBI #
SC ROS-
NSC-ROS-
SC ROS+
NSC-ROS+
NSC ratio in ROS+/ROS-
acetyl-Coenzyme A acetyltransferase 1 precursor (ACAT1)
4557237
20
0.006
26.000
0.008
1.4
acid phosphatase, prostate short isoform precursor (ACPP)
6382064
17
0.005
27.000
0.009
1.7
actin, alpha 1, skeletal muscle (ACTA1)
4501881
0
0.000
8.000
0.003
ROS + only
A-kinase anchor protein 3 (AKAP3)
21493041
76
0.022
70.000
0.022
1.0
A-kinase anchor protein 4 isoform 2 (AKAP4)
21493039
497
0.145
467.000
0.147
1.0
angiotensin I converting enzyme 1 isoform 1 precursor (ACE)
4503273
5
0.001
5.000
0.002
1.1
ATP synthase, H + transporting, mitochondrial F1 complex, alpha subunit precursor (ATP5A1)
4757810
28
0.008
30.000
0.009
1.2
beta actin (ACTB)
4501885
53
0.015
36.000
0.011
0.7
brain creatine kinase (CKB)
21536286
19
0.006
21.000
0.007
1.2
chaperonin containing TCP1, subunit 4 (delta) (CCT4)
38455427
4
0.001
6.000
0.002
1.6
chaperonin containing TCP1, subunit 5 (epsilon) (CCT8)
24307939
4
0.001
7.000
0.002
1.9
chaperonin containing TCP1, subunit 8 (theta)
48762932
5
0.001
4.000
0.001
0.9
chromosome 20 open reading frame 3 (C20orf3)
24308201
2
0.001
4.000
0.001
2.2
clathrin heavy chain 1 (CLTC)
4758012
14
0.004
6.000
0.002
0.5
clusterin preproprotein (CLU)
355594753
56
0.016
55.000
0.017
1.1
CSE1 chromosome segregation 1-like protein (CSE1L)
29029559
3
0.001
0.000
0.000
ROS- only
enolase 1 (ENO1)
4503571
7
0.002
2.000
0.001
0.3
eukaryotic translation elongation factor 1 alpha 1 (EEF1A1)
4503471
28
0.008
33.000
0.010
1.3
eukaryotic translation elongation factor 2 (EEF2)
4503483
12
0.003
5.000
0.002
0.5
fatty acid synthase (FASN)
41872631
40
0.012
35.000
0.011
1.0
fibronectin 1 isoform 3 preproprotein (FN1)
16933542
41
0.012
21.000
0.007
0.6
fructose-bisphosphate aldolase A (ALDOA)
4557305
15
0.004
17.000
0.005
1.2
fumarate hydratase precursor (FH)
19743875
5
0.001
7.000
0.002
1.5
glutamine synthetase (GLUL)
19923206
4
0.001
12.000
0.004
3.2
glutathione peroxidase 4 isoform A precursor (GPX4)
75709200
8
0.002
21.000
0.007
2.8
glutathione S-transferase mu 3 (GSTM3)
23065552
50
0.015
65.000
0.020
1.4
glyceraldehyde-3-phosphate dehydrogenase (GAPDH)
7669492
14
0.004
22.000
0.007
1.7
glyceraldehyde-3-phosphate dehydrogenase, spermatogenic (GAPDHS)
7657116
210
0.061
185.000
0.058
1.0
heat shock 70 kDa protein 2 (HSPA2)
13676857
26
0.008
22.000
0.007
0.9
heat shock 70 kDa protein 5 (HSPA5)
16507237
5
0.001
11.000
0.003
2.4
heat shock 90 kDa protein 1, alpha isoform 1 (HSP90AA1)
153792590
39
0.011
26.000
0.008
0.7
heat shock protein 90 kDa beta, member 1 (HSP90B1)
4507677
11
0.003
22.000
0.007
2.2
heat shock protein beta-1 (HSPB1)
4504517
5
0.001
4.000
0.001
0.9
histone cluster 1, H2aa (HIST1H2AA)
25092737
47
0.014
56.000
0.018
1.3
histone cluster 1, H2ae (HISTH1AE)
10645195
14
0.004
13.000
0.004
1.0
histone cluster 1, H2ba (HIST1H2BA)
24586679
22
0.006
49.000
0.015
2.4
lactotransferrin precursor (LTF)
54607120
246
0.072
292.000
0.092
1.3
lectin, mannose-binding 2 precursor
5803023
3
0.001
5.000
0.002
1.8
L-lactate dehydrogenase A isoform 1 (LDHA)
5031857
10
0.003
9.000
0.003
1.0
L-lactate dehydrogenase C (LDHC)
4504973
17
0.005
12.000
0.004
0.8
mitochondrial ATP synthase beta subunit precursor (ATP5B)
32189394
50
0.015
70.000
0.022
1.5
mitochondrial malate dehydrogenase precursor (MDH2)
21735621
22
0.006
44.000
0.014
2.2
olfactomedin 4 precursor (OLFM4)
32313593
1
0.000
4.000
0.001
4.3
outer dense fiber of sperm tails 1 (ODF1)
194248060
15
0.004
14.000
0.004
1.0
outer dense fiber of sperm tails 2 isoform 2 (ODF2)
24430183
90
0.026
91.000
0.029
1.1
peptidylprolyl isomerase A (PPIA)
10863927
4
0.001
2.000
0.001
0.5
peroxiredoxin 6 (PRDX6)
4758638
4
0.001
1.000
0.0003
0.3
phosphoglycerate dehydrogenase (PHGDH)
23308577
3
0.001
5.000
0.002
1.8
phosphoglycerate kinase 2 (PGK2)
31543397
17
0.005
9.000
0.003
0.6
phospholipase A2, group IIA precursor (PLA2G2A)
4505849
6
0.002
10.000
0.003
1.8
prolactin-induced protein (PIP)
4505821
118
0.034
101.000
0.032
0.9
prostate specific antigen isoform 1 preproprotein (KLK3)
4502173
3
0.001
2.000
0.001
0.7
protein disulfide-isomerase A3 precursor (PDIA3)
21361657
7
0.002
7.000
0.002
1.1
pyruvate kinase, muscle isoform M2 (PKM)
33286418
49
0.014
31.000
0.010
0.7
RAB2A, member RAS oncogene family (RAB2A)
4506365
3
0.001
3.000
0.001
1.1
ropporin (ROPN1)
21359920
20
0.006
31.000
0.010
1.7
ropporin, rhophilin associated protein 1B (ROPN1B)
59891409
73
0.021
73.000
0.023
1.1
saccharopine dehydrogenase (putative) (SCCPDH)
55770836
13
0.004
16.000
0.005
1.3
semenogelin I isoform a preproprotein (SEMG1)
4506883
171
0.050
101.000
0.032
0.6
semenogelin II precursor (SEMG2)
4506885
513
0.149
226.000
0.071
0.5
sorbitol dehydrogenase (SORD)
156627571
36
0.010
27.000
0.009
0.8
sperm acrosomal membrane protein 14
19424138
1
0.000
2.000
0.001
2.2
sperm autoantigenic protein 17 (SPA17)
8394343
15
0.004
20.000
0.006
1.4
sperm protein associated with the nucleus, X chromosome, family member C (SPANX)
13435137
54
0.016
80.000
0.025
1.6
transglutaminase 4 (prostate) (TGM4)
156627577
4
0.001
9.000
0.003
2.4
triosephosphate isomerase 1 isoform 1 (TPI1)
4507645
28
0.008
31.000
0.010
1.2
tubulin alpha 6 (TUBA1C)
14389309
9
0.003
11.000
0.003
1.3
tubulin, alpha 3c (TUBA3C)
17921993
117
0.034
134.000
0.042
1.2
tubulin, beta 4 (TUBB4A)
21361322
36
0.010
30.000
0.009
0.9
tubulin, beta, 2 (TUBB4B)
5174735
232
0.068
235.000
0.074
1.1
tyrosine 3/tryptophan 5 -monooxygenase activation protein, zeta polypeptide (YWHAZ)
4507953
4
0.001
3.000
0.001
0.8
valosin-containing protein (VCP)
6005942
16
0.005
9.000
0.003
0.6
voltage-dependent anion channel 2 (VDAC2)
42476281
11
0.003
11.000
0.003
1.1
voltage-dependent anion channel 3 isoform b
25188179
7
0.002
12.000
0.004
1.9
A cut-off of 2-fold difference in NSC ratio was considered as significantly differentially expressed in ROS +. Overexpressed and underexpressed proteins are shown in bold.

Gene ontology annotations of the proteins identified in ROS+ and ROS- samples

The cellular distributions of the 74 proteins present in the ROS+ and ROS- samples showed that there was a greater distribution of proteins in the intracellular region compared to the extracellular region (Figure 1A). A significant number of proteins were also distributed in the cytoplasmic region. The proteins involved in the biological processes showed a greater distribution in cellular processes (21%) followed by metabolic processes (17%) (Figure 1B).

Gene ontology annotation comparisons of the differentially expressed proteins in ROS+ and ROS- samples

Based on the Gene Ontology classifications, the majority of the differentially expressed proteins that were over or underexpressed in ROS+ group compared to the ROS- group were localized to the cytoplasmic and intracellular regions (Figure 2). Specifically, the cellular compartments of intracellular, organelle, macromolecular complex region and mitochondria were predominantly occupied by the overexpressed proteins. The cytosolic, cytoplasm, extracellular, plasma membrane, protein complex and the vesicular region showed an abundance of underexpressed proteins when compared to the overexpressed ones. Strikingly, the endosome, lipid particle, membrane-bound organelles and the microtubules showed only the presence of overexpressed proteins in the ROS+ group while the proteinaceous extracellular matrix was restricted only to the underexpressed proteins.
GO analysis also revealed that the majority of over and under expressed proteins in the ROS+ group was found to be involved in cellular processes (Figure 3). The proteins involved in developmental processes, cellular process, interactions with cells and organisms, localization, metabolic processes and transport were common to both under and overexpressed proteins. Overexpressed proteins were abundantly expressed for these functions. Many of the processes such as cellular amino acid metabolic processes, cellular component biogenesis, chromosome organization, cytoskeleton organization, embryo development, gluconeogenesis and homeostatic processes were found to be restricted only to the overexpressed proteins. Similarly, the processes such as carbohydrate catabolic processes, cellular component movement, glycolysis, and response to unfolded protein were found to be restricted to the underexpressed proteins.

Pathways and network analysis of differentially expressed in ROS+ proteins using Ingenuity Pathway Analysis (IPA) and Metacore™ software packages

A list of significantly enriched or topmost pathways and/or process networks associated with the over and underexpressed proteins in the ROS+ group is shown in Table 3. Cellular processes were comprised of cell cycle, cell adhesion, cellular morphology, cellular movement and cell death and cell survival. Protein modifications included protein folding and degradation; general metabolic pathways were: carbohydrate metabolism (such as glycolysis and gluconeogenesis), amino acid metabolism, energy metabolism and oxidative stress regulation. With respect to the pathways related to carbohydrate metabolism, enzymes operating in gluconeogenesis and glycolysis including phosphoglycerate kinase 2 (PGK2) and glyceraldehyde phosphate dehydrogenase-S (GAPD-S) were found to be underexpressed in the ROS+ group for both gluconeogenesis and glycolysis while glyceraldehyde-3-phosphate dehydrogenase (GAPDH), fructose-biphosphate aldolase A(ALDOA) and mitochondrial malate dehydrogenase precursor (MDH2) were overexpressed. The overexpressed proteins, especially in the glycolytic pathway, were triosephosphate isomerase 1 (isoform 1(TPI1), glyceraldehyde-3 phosphate dehydrogenase (GAPDH) and fructose-biphosphate aldolase A (ALDOA). Two enzymes involved in the fermentation of pyruvate to lactate – L-lactate dehydrogenase C (LDHC) and L-lactate dehydrogenase A isoform 1(LDHA) were underexpressed. In addition, two enzymes functional in sucrose degradation pathways - triosephosphate isomerase 1 (TPI1) and fructose biphosphate aldolase A (ALDO A), were overexpressed.
Transcriptional regulatory network analysis of the differentially expressed proteins, using Metacore™, showed that the androgen receptor was one of the topmost regulators with 21 differentially expressed proteins in the ROS+ group interacting with the receptor (Figure 4). As a result of the established connection between germ cell development and the levels of androgens, it was important to elucidate the potential interaction of these over- and underexpressed proteins with the androgen receptor. One of the pathways that may be relevant to spermatogenesis is cAMP responsive element modulator (CREM) signaling in the testis (Figure 5). In this pathway, 3 proteins were found to be differentially expressed. Angiotensin I converting isoform 1 precursor (ACE). The outer dense fiber of sperm tails 1 (ODF1) was found to be underexpressed whereas the L- lactate dehydrogenase C (LDHC) was overexpressed in ROS+ sample compared with ROS- sample.

Discussion

Oxidative stress plays a key role in the etiology of male infertility, possibly by altering protein expression levels in spermatozoa, causing molecular and genetic defects. Therefore, understanding the function of each protein involved and the post-translational modifications that occur during sperm-maturation is important and may lead to a useful biomarker for male infertility. In earlier studies, we demonstrated that ROS are independent of the semen parameters [6]. Therefore, in the current study, we compared the sperm proteome from a group of men with high levels of ROS (ROS+) with that from men with low or physiological levels of ROS (ROS-). The ROS grouping was done irrespective of the patient’s clinical diagnoses and semen parameters: the only criteria was the presence or absence of ROS.
We have identified 74 proteins, 15 of which had a >2-fold difference in their expression levels in the ROS+ group when compared to the ROS- group. Based on the spectral counts of the 74 proteins, we identified 47 were overexpressed and 27 that were underexpressed in the ROS+ group compared to the ROS- group. Of the 74 proteins, 17 were determined to be either in their precursor or pre-protein form. The presence of these incompletely processed proteins is indicative of post-translational processing problems as reported by some investigators [31, 32]. Accumulation of precursor forms of protein may result in deregulation of the downstream functions of the mature protein [31]. Oxidative stress may result in the accumulation of precursors or preproteins. The cellular distribution of all the proteins as ascertained by GO analysis (Figure 1A) suggested that the majority of the proteins (68 proteins) were intracellular or organellar (62 proteins) in origin, whereas a few were localized in the extracellular (13 proteins) region. The presence of extracellular proteins may be attributed to the presence of round cells in the ejaculate from both the donors and patients. We did not remove these from subsequent processing for proteomic analysis.
Similar comparative studies have been performed previously by other groups in an effort to understand the sperm proteome [31, 33, 34]. Most notable amongst these are two studies where the expression profile of proteins was compared in asthenozoospermic and normozoospermic donors [31, 33]. In these studies, 10 proteins and 17 spots were identified using 2-DE suggesting their role in motility-related male infertility. Xu et al conducted a comparative study between infertile patients and normozoospermic group and identified 24 differentially expressed proteins that could potentially serve as diagnostic markers in identifying the underlying pathology of male infertility [35]. Our findings in this study are novel, as we have compared and identified the proteins that are differentially expressed in spermatozoa obtained from seminal ejaculates with high levels of ROS (ROS+) to those normal levels of ROS (ROS-).
On examining the association of the differentially expressed proteins with the biological processes (Figure 1B), we concluded that the majority of the proteins were involved in cellular, metabolic, and regulatory processes. A smaller distribution of proteins (3-5%) was also found in biological processes such as de novo post translational (8 proteins) and protein folding (14 proteins), and these were identified as ‘enriched processes’ for our list of proteins, by the GO Term Finder.
Even though the spermatozoa become transcriptionally inactive after spermatogenesis, we found the presence of the translational proteins and this may be indicative of the presence of some leftovers from inefficient spermatogenesis. They may also have a role in the normal physiology of the spermatozoa or affect fertilization and embryo development. Further validation through the pathway and process network analysis using IPA and Metacore™, suggested that the topmost function of the differentially expressed proteins was carbohydrate metabolism and included pathways of gluconeogenesis, glycolysis, fermentation of pyruvate to lactate and sucrose degradation. There is a great controversy pertaining to the major pathway of energy metabolism during sperm motility [3639]. In this context, our findings showed that while oxidative phosphorylation is important for sperm function, the major pathway for energy production for sperm motility was via the glycolytic pathway. To support this view, various respiratory enzymes involved in the glycolytic pathway including phosphoglycerate kinase 2 (PGK2), glyceraldehyde-3phosphate dehydrogenase (GAPDH), fructose- biphosphate aldolase A (ALDOA), glyceraldehyde-3 phosphate dehydrogenase-S (GAPDHS), triosephosphate isomerase 1 isoform (TPI1) were identified in our study. Furthermore, GAPDH-S protein was overexpressed in the ROS+ sample. Oxidation of GAPDH-S as a result of H2O2 generation has been reported to decrease sperm motility and inhibit GAPDH-S activity [40].
Various isoforms of histone proteins were also identified and these included histone cluster 1, H2aa (HIST1H2AA), histone cluster1 H2ae (HISTH1AE) and histone cluster1 and H2ba (HIST1H2BA). Histones are a group of proteins that are replaced by the protamines during sperm maturation in the epididymal region [41]. Their presence in the ejaculated spermatozoa is indicative of improper packaging of sperm chromatin and subsequent DNA damage. These findings have been attributed to oxidative stress [5, 41].
We found 17 precursor proteins to be dysregulated; of these, 10 were over-expressed and 5 were underexpressed with a 2-fold difference between the ROS+ and ROS- groups. The differentially expressed proteins were mainly intracellular, cytoplasmic or organellar in distribution. This is contrary to the findings of Xu et al, who reported a higher distribution of differentially expressed proteins in the extracellular region in the infertile groups with normal semen parameters [35]. This difference may be attributed to the import/export of epididymal and seminal vesicle proteins. Furthermore, the distribution of overexpressed and underexpressed proteins in the ROS+ group revealed their involvement in various processes. We noted that many proteins showing significant changes in expression between the ROS+ and ROS- groups had roles in sexual reproduction, metabolic processes and stress response (Figures 2 and 3).
Our data suggests that semenogelin II precursor (SEMG2), which is involved in sexual reproduction, was underexpressed in the ROS+ group. Overexpression of semenogelin I and II precursor has been reported in previous comparative studies [31, 35]. Xu et al reported an increase in both isoforms of semenogelin in the spermatozoa of infertile patients compared to the normozoospermic men [35]. Semenogelin -1 and semenogelin -2 have been reported in men with varicocele only as well as in men with varicocele with a history of moderate as well as heavy smoking. Semenogelin is known to cause a decrease in sperm motility, but it was within the normal range in the ROS+ samples. Semenogelin (SEMG) is the major protein of seminal fluid proteins (20-40%) and is secreted from seminal vesicles, epididymis, and prostate [42, 43]. It is an androgen dependent protein that exists in two forms I (52 kDa) and II (71-76 kDa). SEMG helps in the formation of the semen coagulum that is degraded later by prostate-specific antigen. SEMG counteracts oxidative stress by reducing the generation of free radicals through several mechanisms: slow sperm motility of the entrapped sperm, reduced energy consumption, free radical generation and inhibition of superoxide radical generation through direct interference with sperm NADH oxidase [44].
A great number of proteins are involved in the energy production required for the spermatozoon tail movement. We identified several proteins that are involved in energy producing processes such as the carbohydrate metabolic pathways of glycolysis and gluconeogenesis. Enolase I (ENO1) was found to be significantly underexpressed while respiratory enzyme mitochondrial malate dehydrogenase precursor (MDH2) was found to be overexpressed. MDH2 is an ROS producing enzyme localized in the sperm mitochondria [45]. It catalyzes the last step of the Krebs cycle when malate is converted into oxaloacetate using NAD+/NADH as intermediates. Several of the chaperone proteins such as heat shock protein 90 kDa beta, member 1 (HSP90B1) and heat shock 70 kDa protein 5 (HSPA5) were also seen to be overexpressed in response to stress. The association of these proteins with various processes related to cellular stressors such as heat, glucose deprivation, free radical attack, and infection indicates that ROS induces a stress response in the spermatozoa [4648].
The GO annotations further revealed the involvement of overexpressed proteins in cellular amino acid metabolic processes. Examples of proteins that were found to be overexpressed included transglutaminases (TGM4) and glutamine synthetases. Transglutaminases are a family of calcium-dependent enzymes that catalyze the post-translational modification of the selected glutamine residues on proteins by cross-linking with the peptide-bound lysine residues or incorporating polyamines [49, 50]. Transglutaminase that is secreted from the prostate is known as TGM4 prostate. A recent study showed increased levels of transglutaminase in response to oxidative stress in the prostate [51]. Excessive production of ROS has been reported to induce cell senescence, oxidative protein modifications, and cell membrane damage, increase intracellular calcium and activate certain cytokines such as TGFb [51]. Oxidative stress impacts the aggregation, increased molecular weight, unfolding, denaturation and proteolysis of cellular proteins. These effects may result in the initiation of cellular hyperamonemia, which probably triggers the expression of glutamate synthetase to redirect the production of the released ammonium into glutamate [52].
Glutathione peroxidase 4 isoform A precursor (GPX4) and peroxiredoxin 6 (PRDX6) function to reduce oxidative stress. The former was overexpressed and the latter was underexpressed in the ROS+ group. GPX4 is a well-known antioxidant enzyme and its increased expression in sperm in the ROS+ samples is reflective of its influence on genes synthesizing this enzyme. PRDX6 also belongs to a family of antioxidant enzyme known as peroxiredoxins. They detoxify H2O2 similar to catalase or glutathione peroxidases (GPXs) [53, 54]. Peroxiredoxin 6 is localized in the sperm head (postacrosomal region and equatorial segment) and sperm tail [55]. Reduced levels of peroxiredoxins (PRDX1 and PRDX6) have been reported in infertile men with oxidative stress. Reduced levels of PRDX6 have also been reported in the seminal plasma from men with varicocele. Differential amounts of peroxiredoxin 6 have been reported (41% in men with varicocele and 65% in men with idiopathic infertility) [56]. Human spermatozoa are reported to have high sensitivity to ROS and may be a result of reduced recycling of peroxiredoxins after exposure to oxidative stress [55].
Optimal levels of expression and function of androgens are central to the process of spermatogenesis. To understand the dependency of the differentially expressed proteins in the ROS+ groups, a network was derived to identify proteins regulated by androgen receptors. Spermatogenesis is dependent on androgen action and androgens act by stimulating the receptors present on the Sertoli cells [57, 58]. Transcriptional network analysis (Figure 4) showed that 21 proteins were regulated by the androgen receptor. In our study, several proteins appeared to be transcriptionally activated by the androgen receptor but the effect of the androgen receptor on several other proteins is still unknown (Figure 5). Stanton et al reported that any loss to the androgen stimulus during meiosis induces changes in the proteins that are associated with various molecular processes such as apoptosis, cell signaling, oxidative stress and RNA processing [58]. These investigators further validated this observation by immunostaining for oxidized DNA adducts – they showed that spermatocytes that undergo oxidative stress induced DNA damage during androgen suppression. The completion of meiosis requires androgen action via the Sertoli cell [59]. Low levels of testicular androgens support meiosis while a high level is required for spermiogenesis.
Testicular testosterone suppression has been shown to adversely influence antioxidant activity [60]. The present study utilizing Network analysis revealed heat shock protein beta-1 (HSPB1) and clathrin heavy chain 1 (CLTC), which are known to bind and activate androgen receptor were actually underexpressed in the ROS+ group. We also observed that GAPDH levels were overexpressed, and that it was bound with the androgen receptor to impart a stimulatory effect in the ROS+ sample. The mechanism by which the androgen stimulus is transduced by the Sertoli cell to the germ cells is still unclear. In addition, it is well known that in majority of the infertility cases attributed to a male factor, spermatogenic arrest is a common feature. The process of spermatogenesis is dependent on the cAMP responsive element modulator (CREM) signaling pathway in the testis which operates in coordination with the CREM modulators (repressors and activators) [61]. CREM is essential for male fertility and the absence of CREM-dependent transcription in post-meiotic germ cells is known to result in an arrest of spermatid differentiation and apoptosis [62, 63]. CREM repressors are expressed during pre-meiotic germ cells while CREM activators are expressed in the post-meiotic germ cells. In order to understand the impact of oxidative stress on impaired spermatogenesis, we examined the CREM signaling in testis. Our findings revealed that three of the identified proteins that were differentially expressed in the ROS+ group were modulated by CREM activators. This included ODF1 and ACE1 proteins that were underexpressed as well LDHC, which was overexpressed. These findings suggest that oxidative stress may affect the switching of genes at the spermatogenic level. Comparative studies on CREM signaling in testis have been conducted on normo- and oligozoospermic men and men with round spermatids vs. round spermatid maturation arrest [64]. These findings further support the fact that a lack of a switch in the expression of CREM gene isoforms may have an adverse impact on spermatogenesis in humans.
To summarize, in this study, we have identified various proteins that are implicated in a multitude of functions associated with response and management of oxidative stress. The increased expression of histone cluster 1H2ba (HIST1H2BA), mitochondrial malate dehydrogenase precursor (MDH2), transglutaminase 4 (TGM4), glutathione peroxidase 4 isoform A precursor (GPX4), glutamine synthetase (GLUL), heat shock proteins (HSP90B1 and HSPA5) in the ROS+ sample suggests that these proteins can serve as potential biomarkers of oxidative stress. Thus alterations reported in our study in men with and without oxidative stress may help explain pathways leading to the altered semen phenotype especially in men exhibiting oxidative stress both in infertile men as well as in infertile men with varicocele. However, the interaction between the proteins and the androgen receptors is still unclear and warrants further investigation. It is important to accurately assess the protein levels and verify that the differentially expressed proteins are indeed involved in the process of oxidative stress and are possibly modified as a result of oxidative stress. In future studies, as a follow-up to the present study, quantitative estimation of the differentially (overexpressed and underexpressed) expressed proteins, utilizing western blotting and antibodies is warranted. The findings of our study provide the groundwork for further testing including the proposition that these newly identified sperm proteins play crucial roles in oxidative stress and the pathophysiology of male infertility.

Acknowledgements

This study was supported by Cleveland Clinic, Research Program Committee. Authors wish to thank the Cleveland Clinic Andrology laboratory personnel for their help with scheduling of subjects used in this study. This research project was supported in part by funds from the Cleveland Clinic Research Program Committee and the Center for Reproductive Medicine. Visit of GM supported in part by the INSPIRE fellowship from the Department of Science and Technology, New Delhi, India.
Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://​creativecommons.​org/​licenses/​by/​2.​0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

RK: participated in the study conception/design, review of the data and writing of the manuscript and final approval; SD: acquisition and preparation of samples for analysis; BW, acquisition of samples for analysis; interpretation of the results; SY: acquisition of the samples, interpretation of the data, discussion o results; BG: contributed to bioinformatic analysis, data interpretation and participated in the paper redaction; AH: drafting of the article; GM participated in the review of the data and writing of the manuscript; AA contributed to the study design, and review of the data. All authors read and approved the final manuscript.
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Metadaten
Titel
Proteomic analysis of human spermatozoa proteins with oxidative stress
verfasst von
Rakesh Sharma
Ashok Agarwal
Gayatri Mohanty
Alaa J Hamada
Banu Gopalan
Belinda Willard
Satya Yadav
Stefan du Plessis
Publikationsdatum
01.12.2013
Verlag
BioMed Central
Erschienen in
Reproductive Biology and Endocrinology / Ausgabe 1/2013
Elektronische ISSN: 1477-7827
DOI
https://doi.org/10.1186/1477-7827-11-48

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