This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.
Seminal plasma is an acellular fluid conglomerate, constituted by the combined contribution of the epididymis and accessory sexual glands. Human SP contains many proteins that are important in the successful fertilization of the oocyte by the spermatoÂÂÂÂzoa. As a consequence, SP represents a good sample for proteomic analysis in the evaluation of male fertility/infertility.
Proteomics is a research area that has developed rapidly in the last decade. It studies the large-scale characterization of the full protein components of a tissue, biological fluid or whole organism. In the last decade, clinical proteomics has developed new technology and bioinformatics useful in identifying molecular markers of pathology. The next decade might be the era of proteomics.
This paper reviews the employed proteomic methods, such as one dimensional polyacrylamide gel electrophoresis (1D-PAGE), two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), and mass spectrometry in order to detect human seminal markers involved in fertility and infertility. Proteomic studies have identified numerous seminal-specific proteins. Recent reports provided further understanding of protein function involved in the differentiation between male fertility and infertility.
Upon further validation, these proteins may be useful in the clinical differentiation between fertility and infertility status. Proteomic studies will help the development of new techniques to identify novel biomarkers for a better clinical diagnosis and treatment of male infertility.
Clinicians usually rely on semen analysis in evaluating male fertility, as it represents the surrogate measure of male fecundity in clinical practice (Zinaman MJ et al. 2000; Menkveld R et al. 2001). However, since World Health Organization (WHO) when reference values were adopted, it has become evident that a basic semen analysis is insufficient to determine the fertility status of the male partner (Milardi D et al. 2012 a). Additional sperm function tests, such as swelling and/or eosine test, do not provide additional information in the assessment of the fertility status (Wang C et al. 1988).
New molecular insights into sperm properties and the way in which the sperm is capable of fertilizing the egg are recently emerging. Increased knowledge of sperm or seminal proteome might allow us to identify new molecular markers of male fertility (Milardi D et al. 2012B).
This paper reviews the use of proteomic technologies to identify seminal plasma (SP) composition with an ultimate focus on the identification of novel proteic markers for male fertility and infertility.
Seminal plasma: origin, proteic composition and functions.
SP is an acellular fluid conglomerate produced by several different glands. It is made by the combined contribution of the epididymis and accessory sexual glands. These components are not completely mixed during ejaculation. The first part of the ejaculate (5%) derives by Cowper and Littre glands; the second portion is made up of secretions from the prostate (15-30%). The last portion is contributed by the ampulla and epididymis and finally by seminal vesicles (Owen DH and Katz DF, 2005). Apart from these organ-specific constituents, human SP is rich in other constituents, whose origin and function are not completely clear.
The different composition of the secretions by different glands is used in clinical practice in the diagnostic evaluation of male fertility. The secretion of the prostate represents the uppermost source of acid phosphatase, inositol, citric acid, calcium, magnesium and zinc found in the ejaculate. The secretion by seminal vesicles contains fructose, ascorbic acid and prostaglandins. Epididymal secretion is rich in L-carnitine and neutral alpha-glucosidase. A small amount of the seminal fructose originates from the ampulla of the ductus deferens (Owen DH and Katz DF, 2005).
The proteic and aminoacid content of SP is much higher than that of blood plasma (Frohlich JU et al. 1980). In fact, human SP contains many proteins that are important in the capacitation of the spermatozoa, in the modulation of the immune responses in the uterus, in the formation of the tubal sperm reservoir (Evans JP et al. 1998; Jansen S et al. 2001), and finally in both the sperm-zona pellucida (ZP) interaction and in the sperm and egg fusion (Primakoff P et al. 2002; Yi YJ et al. 2007). The complex content of SP allows for the successful fertilization of the oocyte by the spermatozoa.
Therefore, SP represents a good sample for proteomic analysis.
The era of proteomics
Omics refers to the study of biological systems on a large scale. A newcomer to the -omics era, proteomics, is a wide instrument intensive research area that has made large and rapid progress over the past twenty years (Thelen JJ and Miernyk JA, 2012). The term 'proteomics' has been coined in 1995 to define the large-scale characterization of the full protein components of a cell type, tissue or biological fluids (Wasinger VC et al., 1995).
In the post-genome era, the proteomics has focused on the identification of protein biomarkers in complex biological systems, due to the demonstrated ability by mass spectrometry (MS) of characterizing a broad number of proteins and their post-translational changes (Boja ES and Rodriguez H, 2011).
Proteomics, in fact, studies the overall profile of expression of the protein rather than the behaviour of individual proteins. The study of genes may not provide complete information on the properties of proteins, since proteins can undergo more than 200 different types of post-translational modifications (Krishna RG and Wold F, 1993). Only through the study of proteins themselves it is possible to identify their characteristics and functions since such a wide range of post translational modifications cannot be predicted purely from DNA sequences.
Although we have obtained an estimated number of proteins encoded by the genome from the data gathered in the genome project, it is difficult to provide the actual number of proteins encoded on the basis of genomic data, because the exon-intron cannot be accurately predicted from genomic DNA (Dunham I, 1998). Alternative splicing of a transcript can produce more than one type of protein (A Newman, 1998) and, the same protein can be obtained with different properties and functions in different places as a result of the partitioning and translocation (Colledge M Scott and JD, 1999) . These problems can only be solved by proteomics, which can identify directly the proteins and provide the useful information through the correct integration of genomic and proteomic data.
Indeed, proteomics has been broadly employed in different fields of science, from deciphering molecular pathogenesis of diseases to the characterization of new pharmacological targets, and the identification of diagnostic and prognostic biomarkers.
Proteomic technology is able to identify and quantify proteins associated with the beginning of a specific pathology by means of their altered levels of expression (Bateman NW et al., 2010; An HJ and Lebrilla CB, 2010). Using this methodology, proteomic analysis can differentiate the healthy state from the pathological state, thus identifying disease-related biomarkers.
Over the last decade, the innovation in technology/platform development and bioinformatics permitted to identify molecular signatures of diseases according to proteomic profiles which might become standard practice in the clinical laboratory (Boja ES and Rodriguez H, 2011). Proteomic technique undoubtedly represents a real promise for early disease diagnosis, disease prognosis, and prediction of response to therapy on an individualized basis. There is little doubt that the next decade will be the era of proteomics.
Seminal proteome identification by one dimensional and two dimensional electrophoresis
Attempts at identifying constituents of SP have a long history. The first electrophoretic studies of human SP dates back to the forties (Ross V et al. 1942) and the major proteins, including phosphatases, glycosidases, aminopeptidases, and mucin, have been known since the sixties (Mann T, 1964). In 1978 Sensabaugh identified some 40 distinct components in SP by one dimensional (1D) electrophoresis (Sensabaugh GF et al. 1978). Overall, electrophoresis limits the motion of molecules based on their mass and charge through an applied electromotive force to a cross-linked polymer gel matrix.
1D electrophoresis is a common gel-based technique, which allows to separate deoxyribonucleic acid (DNA), ribonucleic acid (RNA) and proteins from complex mixtures. This approach permits the approximate quantification as well as the identification of proteins, but has limited resolving power in separating complex mixtures of proteins, especially low-abundance proteins. For this reason, a two dimensional (2D) approach has been currently used to overcome these problems.
2D electrophoresis separates proteins based on isoelectric point (pI) in the first dimension. Proteins separated in this way are then separated using protein mass (molecular weight) as separation property. In the second dimension, a sodium dodecyl sulphate polyacrylamide gel electrophoresis is applied (2D-PAGE). 2D-PAGE is a method that analyzes qualitative and quantitative evaluation of proteins change at high resolution on a large scale. It can be used as an initial screening method to acquire hypotheses and gives guidance on future research. However, 2D-PAGE can fail to determine adequately low-molecular weight proteins (<10000Da) (du Plessis SS et al., 2011)
In the eighties, the 2D-PAGE technique allowed a deeper understanding of SP proteome. Rui et al. studied in 1984 the fraction of split ejaculates from normal men, revealing the differences between the prostate-enriched fractions and seminal vesicles secretions. Aside from prostate acid phosphatase, four additional proteins were apparently associated with the prostatic fraction, one of which shared the biochemical characteristics of the specific ventral lobe protein of the rat prostate, prostatein. The presumptive vesicular fractions contained a large number of low molecular mass proteins (10-20000), with widely varying pI values. The concentration of albumin, which acts binding cholesterol and transferrin appeared to be at the highest level in the sperm cell enriched fractions, indicating that a major contribution of the ejaculate derives from the testis and epididymis (Rui H et al., 1984)
Seminal proteome identification by mass spectrometry
Mass spectrometry (MS) is a technique whereby the mass to charge ratio (m/z) of a gas phase ion is determined essentially making it a particle counting method that yields knowledge of the particle's mass and charge (m/z ratio) (Yates JR et al., 2009). The mass spectrometer is functionally defined by the ionization source, the mass analyzer, and the detector.
The most frequent MS techniques that have been applied in the study of seminal proteins are matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS), surface enhancement laser desorption/ionization time of flight (SELDI-TOF) and liquid chromatography tandem mass spectrometry (LC-MS/MS).
The importance of MALDI-TOF-MS has been recognized by the award of 2002 Nobel Prize in Chemistry to K Tanaka for its invention. MALDI-TOF-MS, invented in the late 1980s, is an analytical sensitive technique which allows the analysis of peptides and proteins in a range of mass from 1 to 500 KDa. A very small quantities of sample (103-106 pmol), with an accuracy of 0.1-0.01%, can be used for the analysis (Low TY et al., 2002). In MALDI-TOF-MS the proteic sample, co-crystallized with a matrix on a metal support, is ionized by a laser pulse. The ionized samples are accelerated through a vacuum tube by an electrical field and reach a detector. The ions are separated on the base of their m/z ratio reaching the detector at different times. The flight time is inversely proportional to the mass: the greater the protein mass, the more slowly it reaches the detector. The charge (z) of the ionized proteins is often +1, making the m/z value equal to the mass value. The spectral output produced by MALDI protein profiling consists of a number of protein peaks, which are described by an m/z value on the horizontal axis and by a peak-intensity value on the vertical axis, and is referred to as a "protein profile" (Schuchardt S and Sickmann A, 2007). This technology presented several variables that caused considerable concern, in particular the reproducibility of the technique across different laboratories (Diamandis EP, 2004).
SELDI-TOF mass spectrometry has been used widely in biomarker discovery because it is sensitive and requires only a small amount of protein for analysis. This technique showed a significant improvement in interlaboratory reproducibility (Kiehntopf M et al., 2007).
Yang H et al used SELDI-TOF proteomics to screen specific biomarkers of oligospermic candidates from SP by proteomics technology (Yang H et al., 2007) and Baj J et al used this method to analyse protein alterations in the SP of non-obstructive azoospermic patients (Baj J et al., 2008).
Seminal proteome identification by multidimensional approaches
A new approach in the study of complex protein mixtures, such as SP, has been the use of Multidimensional Protein Identification Technology (MudPIT), a highly sensitive and specific analytical technique that unites the protein separation by bidimensional liquid chromatography with mass determination and consequent sequence in tandem mass (MS/MS) set up. The use of high performance liquid chromatography-mass spectrometry (HPLC) separation of proteomics sample prior to MS has enabled the researcher to dig deeper into the proteome over the past few years. The first application of HPLC for protein separation is based on the number of unique properties such as hydrophobicity, charge and the presence of a specific amino acids (Mitulovic G and Mechtler K, 2006). The subsequent coupling to a mass spectrometer permits a rapid separation and comprehensive identification of components within a complex protein mixture.
This approach overcomes some of the limitations of 2DE because the system allows for complete recovery of proteins, including small basic and hydrophobic types (Veveris-Lowe TL et al., 2007).
2D gel electrophoresis separation associated with identification by either MALDI-TOF-MS or capillary liquid chromatography tandem mass spectrometry (LC-MS/MS) was used in 2004 by Fung et al to visualize the whole seminal proteome (Fung KY et al., 2004). Including all isoforms, over 100 unique species were identified. Analysis by 2DE also revealed many proteins <30 kDa in seminal plasma. These proteins were identified as truncated forms of semenogelin I and II, cystatin S, cystatin C, and variants of prolactin inducible protein.
Recent years have witnessed a significant shift from the use of ion traps, single- and triple-quadrupole mass spectrometers towards employing mass spectrometers that provide accurate mass of analytes, such as linear ion trap (LTQ) with a high capacity and sequencing speed which has been matched to a Fourier transform ion cyclotron resonance analyzer (LTQ-FT-ICR) and LTQ-Orbitrap (Makarov A and Scigelova M, 2010).
Orbitrap was invented in 1999 by Makarov and was regarded as a tool for proteomics research by Hu in 2005 (Hu Q et al., 2005). Orbitrap is a mass analyzer that couples high resolution (up to 150,000) with high mass accuracy (2-5 ppm), a mass-to-charge range of 6,000 and a dynamic range greater than 103 (Hu Q et al., 2005; Makarov A et al., 2006).
The high mass accuracy of the Orbitrap considerably contributes to the amount of acquired data and analytic approaches when compared with low-resolution instruments (Yates YR et al., 2009). The LQT additionally permits routine use of two subsequent stages of MS fragmentation (MS/MS/MS or MS3). The advantage of this technique is that it makes possible to confidently characterize proteins on the basis of a single peptide (Pilch B and Mann M, 2006).
Data analysis and Gene Ontology system in seminal plasma proteomics
In proteomic analysis, different database search algorithms can result in varied protein, including qualitative and quantitative differences (Alves G et al., 2008). One of the most challenging problems nowadays concerns the proper interpretation of proteomics is to give a large amount of data collected so far. Bioinformatics, by means of statistical analyzes and algorithms, is capable of supporting proteomics in the interpretation of the data analysis and in the identification the proteins of clinical interest (Baldwin MA, 2004). Therefore, the discrepancy between data from different authors may be due to the use of different database search software. The most common system applied by bioinformatics is Gene Ontology (GO), which can uncover meaningful patterns found in proteins (Lan T, 2003). The Gene Ontology (GO) Consortium has, over the last 10 years, revolutionized the use of structured vocabularies in biology, and provides GO annotations of gene products that describe biological function from the molecular to organism level (Consortium GO, 2009). The Gene Ontology hierarchy is widely used to provide some insight concerning the function, biological process or cellular location of collections of genes or proteins. The GO data is organized as a directed acyclic graph starting from one parent node, which means that particular ontology categories can have multiple parents as well as multiple children. Given the complexity of the graph, multiple tools have been designed to navigate it and summarize GO information (Pascovici D et al., 2012). It is very important to note that the absence of a GO annotation does not mean that a function is absent from a particular gene product. Even the limited knowledge that we have about biological function is not yet completely represented by GO annotations, due to limitations of time and resources (Thomas PD et al., 2012).
As far as SP proteomics is concerned, GO annotations have been used to perform a descriptive analysis of protein distribution and function.
The first major group of seminal proteins includes a number of enzymes engaged in catalytic activities, comprising 33-65% of proteins. An additional 5-14% of proteins is classified as their regulators, implying that 39-79% of the seminal proteome is involved in enzymatic activity. In fact, most of these proteins, such as prostatic specific antigen (PSA), Sg I, and Sg II, are involved in the regulation, processing or degradation of seminal fluid proteins and coagulation of semen. This large number of enzymes is consistent with the statement that a great number of seminal proteins are involved in semen coagulum liquefaction and sperm capacitation (Pilch B and Mann M, 2006; Batruch I et al., 2011).
Some papers reported binding activity as the most frequent annotation reported in seminal proteins (Milardi D et al., 2012B; Batruch I et al., 2011). It may be possible that in many cases protein binding function represents an auxiliary role to the main one of that protein, which is closely linked to enzymatic or transport activity. Some of these proteins are bound to the sperm surface during ejaculation and thus protein-coating layers are formed. In this GO functional category we can also classify some proteins that bind heparin which are in turn involved in stabilizing the plasma membrane over the acrosome prior to capacitation. Seminal fluid heparin binding proteins (HBPs) are presumed to adhere themselves to the sperm surface. Hence, seminal fluid HBPs plays a vital role in spermatozoon survival and in the overall fertilization process. Any alteration of the proteins involved can cause infertility.
Kumar in 2008 identified and characterized HBPs from human seminal fluid. The authors adopted a proteomic strategy based on affinity chromatography followed by 2-DE, in conjunction with subsequent enzymatic digestion, followed by MALDI-TOF/MS for proteomic analysis of human HBPs. Forty different types of proteins were identified. Functional analysis revealed that 38% of the proteins belonged to the enzyme category, 20% were regarded in RNA processing and transcription, 18% in structure and transport function, 16% in cell recognition and signal transduction and 8% had no known function. This experimental approach has also permitted the identification of a significant number of new HBPs (Kumar V et al., 2009).
Another important class of proteins corresponds to structural and transport proteins. The most abundant structural proteins are gel-forming proteins, which are secreted by the seminal vesicles: semenogelin I, semenogelin II and fibronectin, which are cleaved by kallikrein-like proteases.
The most abundant transport protein is lactoferrin. Lactoferrin was first identified in human SP in 1966 (Masson PL et al., 1966). Previous studies reported that it has antibacterial, antioxidative, and an immune-modulating role in SP. It is also involved in maintaining normal sperm structure and motility and in modulating the composition and quality of the semen during sperm maturation and migration through the male genital tract (Gambera L et al., 2007; Piomboni P et al., 2008). It has been demonstrated that the increased lactoferrin concentration in some cases of leukocytospermia, oligospermia, and asthenospermia is beneficial in the reduction of leukocyte concentration, in increasing sperm motility, in rescuing sperm morphology and functions and in improving the semen quality (Buckett WM et al., 1997). Recently, Wang et al (Wang P et al., 2011) demonstrated that lactoferrin receptor is expressed in the testis and is anchored to the sperm membrane by glycophosphatidylinositol during spermatogenesis, playing an important role in spermatogenesis by binding lactoferrin.
Membrane proteins are present with a frequency of 52% in seminal proteome (Batruch I et al., 2011). The high percentage of membrane proteins, even though SP is a secreted fluid may depend on the fact that some of these proteins are adhered to the sperm surface during ejaculation, thus forming protein-coated layers. This is confirmed by the high number of membrane proteins that are annotated both as membrane proteins and as extracellular or surface proteins.
The largest group of proteins, according to GO Biological Process annotations, is reported to be composed of proteins involved in the cellular process, followed by proteins annotated as involved in regulation (Batruch I et al., 2011; Milardi D et al., 2012B). The high incidence of proteins in these annotations is justified by the presence of enzymes involved in the regulation, processing, or degradation of seminal fluid proteins, coagulation of semen and in the basic cellular processes.
Proteomic identification of fertility and infertility markers
Comparative proteomic analysis directed to identifying markers related to male infertility in SP has been only partially performed.
Ayyagari et al in 1987 studied postliquefacion proteolytic breakdown of SP proteins, pointing out a greater proteolysis of SP proteins in oligospermic men compared to normospermic men (Ayyagari RR et al., 1987).
The first detailed characterization of proteins in the human SP of infertile patients was made in 2001 by Starita-Geribaldi (Starita-Geribaldi M et al., 2001), who compared 2-D electrophoretic profiles of SP from fertile men with abnormal SP from vasectomized or azoospermic men. About 750 spots were detected in the two-dimensional map of SP coming from fertile men. These Authors demonstrated that groups of spots and individual spots present in the 2-D map of SP from a fertile man were undetectable in patients who had undergone vasectomy or in a man with testicular bilateral anorchidy. The Authors concluded that 2-D PAGE may be useful in identifying seminal markers of secretory azoospermia.
In 2003 another 2D-electrophoresis study, carried out by applying narrow immobilized pH gradients covering one pH unit as first dimension, allowed the identification of proteins spots changing in abundance in azoospermia and a more accurate differential expression analysis of markers associated with impaired spermatogenesis (Starita-Geribaldi M et al., 2003).
In 2003, Utleg et al reported proteomic analysis of prostasomes, membrane-enveloped secretory vesicles present in SP and a rich source of intracellular protein, highlighting the important role of prostasomes in sperm survival. A total of 139 protein were identified by 1D-gel electrophoresis followed by MS analysis, but many of them were identified only with a single peptide and with low identification scores (Utleg AG et al., 2003).
In 2006, Pilch and Mann identified 923 seminal proteins using a bottom-up approach in a seminal sample from one healthy individual of unknown fertility status (Pilch B and Mann M, 2006). They used a nano-HPLC coupled to LTQ-FT mass spectrometer in order to perform the first large-scale and high-confidence proteomic analysis of human SP. The main proteins characterized in the seminal fluid proteome were: (1) those secreted by the accessory glands, the so-called gelforming proteins such as PSA, PAP (prostatic acid phoshatase), lactoferrin, albumin and extracellular matrix proteins (semenogelins, fibronectin, laminin) (2) proteins contained in prostasome; (3) epithelial-derived proteins, which presumably result from cell shedding from the accessory organs, as well as ductal tubes. Only 10% of the reported proteins had previously been identified as coming from the male reproductive tract. Essentially, all of the proteins found by Fung et al in seminal fluid and many of those detected by Utleg et al in prostasomes were also identified.
Yamakawa et al identified up to 501 polypeptide spots in SP from fertile men by a proteomic approach based on 2D difference gel electrophoresis (2D-DIGE) and used this normalized standard map of normal SP proteins to identify the differences in the proteic expression between fertile and azoospermic patients (Yamakawa K et al., 2007).
Wang et al. identified 625 proteins in SP from fertile men by LC-MS/MS analysis. They identified 45 up-regulated proteins and of 56 down-regulated proteins in a group of asthenozoospermic patients compared with the control subjects. Among these proteins, the researchers identified DJ-1, a protein involved in the control of oxidative stress, as a down-regulated protein in SP of asthenozoospermic group. Moreover the quantities of two epididymal secretory proteins, protein E1 and epididymal secretory protein E4, were increased in asthenozoospermic SP. This study identified a rich source of biomarker candidates for male infertility and suggested that functional abnormalities of the epididymis, prostate and vesicle can impact on sperm quality during the post-testicular process (Wang J et al., 2009).
Drake et al reported the identification of 916 unique proteins in 9 samples of expressed prostatic secretion by the MudPIT approach and by LTQ-Orbitrap XL mass spectrometer (Drake RR et al., 2010).
Batruch et al. identified more than 2,000 proteins, using the offline MudPIT approach and by LTQ-Orbitrap XL mass spectrometer, in a pool of SP samples by five controls. The same proteomic study was performed in pooled seminal plasma from post-vasectomy (PV) men and comparative analysis of controls and PV data sets. With semiquantitative analysis using spectral counting, they classified 32 proteins unique to Control, 49 at lower abundance in PV, 3 unique to PV, and 25 at higher abundance in PV (Batruch I et al., 2011).
The last two papers in particular provided a very exhaustive protein list. It will be interesting to continue investigation in this list regarding the possible absence of some specific proteins in SP in case of male infertility.
More recently, the same authors catalogued 2048 proteins in seminal plasma from subjects presenting non-obstructive azoospermia (NOA). Using spectral-counting, they compared the NOA proteome to previously published proteomes of fertile control men and of PV men. They identified 34 proteins elevated in Control relative to NOA, 18 decreased in Control relative to NOA, 59 increased in NOA relative to PV, and 16 decreased in NOA relative to PV. The majority of these proteins are linked to fertility and have expression in the testis and the epididymis. Some of these proteins may represent non-invasive biomarkers in discriminating NOA cases from OA, including LDHC, ELSPBP1, CES7, A2M, OVCH2, PTGDS, GPR64 and ALDH1A1 (Batruch I et al., 2012).
Moreover, Kagedan et al compared the previously published list of seminal plasma proteins in the pooled seminal plasma of 5 healthy fertile controls by Kumar et al, with the set of 1708 proteins in the pooled seminal plasma of 5 patients affected by prostatitis (Kagedan D et al., 2012). 1464 proteins were commonly expressed in fertile controls and in patients affected by prostatitis, 413 proteins were found only in the control group, and 254 were found only in the prostatitis group. set of criteria to this dataset, they created a high-confidence list of 59 candidate prostatitis biomarkers, 33 of which were significantly elevated in prostatitis patients matched to control, and 26 of which were decreased. The authors reported that the levels of SERPINA1 (alpha-1-antitrypsin), SERPINA5, SERPING1, SERPINF1, Lipocalin-1, and TIMP3 as protease inhibitors increased in the seminal plasma in prostatitis.
In a previous paper, we studied human SP proteome of fertile men, by LTQ-Orbitrap XL mass spectrometer, in order to identify a panel of common seminal proteins in fertile men. We identified 83 common proteins in SP in a group of fertile subjects (Milardi D et al., 2012B). It was the first identification using high-resolution MS of the common pattern of seminal proteins in male fertility, including some proteins involved in male fertility, such as semenogelin I, semenogelin II, olfactory receptor 5R1, lactoferrin, hCAP18, spindlin, and clusterin.
All the quoted studies confirm that SP protein profiling is a very hot topic. The combination of proteomic analysis and functional studies is a successful tool which will enable us to discover new cellular pathways involved in the physiological processes and to provide better insights into the nature of male infertility. The ability to predict fertility using biomarkers is a promising goal. Upon further validation, these proteins may be useful in clinical differentiation between fertility and infertility status. Proteomic studies will help the development of new techniques in order to identify novel biomarkers for a better clinical diagnosis and treatment of male infertility.