http://informahealthcare.com/aan ISSN: 1939-6368 (print), 1939-6376 (electronic) Syst Biol Reprod Med, 2014; 60(6): 329–337 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/19396368.2014.955896
RESEARCH ARTICLE
Functional features and protein network of human sperm-egg interaction Soudabeh Sabetian*, Mohd Shahir Shamsir, and Mohammed Abu Naser
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Department of Biological and Health Sciences, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
Abstract
Keywords
Elucidation of the sperm-egg interaction at the molecular level is one of the unresolved problems in sexual reproduction, and understanding the molecular mechanism is crucial in solving problems in infertility and failed in vitro fertilization (IVF). Many molecular interactions in the form of protein-protein interactions (PPIs) mediate the sperm-egg membrane interaction. Due to the complexity of the problem such as difficulties in analyzing in vivo membrane PPIs, many efforts have failed to comprehensively elucidate the fusion mechanism and the molecular interactions that mediate sperm-egg membrane fusion. The main purpose of this study was to reveal possible protein interactions and associated molecular function during sperm-egg interaction using a protein interaction network approach. Different databases have been used to construct the human sperm-egg interaction network. The constructed network revealed new interactions. These included CD151 and CD9 in human oocyte that interact with CD49 in sperm, and CD49 and ITGA4 in sperm that interact with CD63 and CD81, respectively, in the oocyte. These results showed that the different integrins in sperm may be involved in human spermegg interaction. It was also suggested that sperm ADAM2 plays a role as a protein candidate involved in sperm-egg membrane interaction by interacting with CD9 in the oocyte. Interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity are the major molecular functions in sperm-egg interaction protein network. The disease association analysis indicated that spermegg interaction defects are also reflected in other disease networks such as cardiovascular, hematological, and breast cancer diseases. By analyzing the network, we identified the major molecular functions and disease association genes in sperm-egg interaction protein. Further experimental studies will be required to confirm the significance of these new computationally resolved interactions and the genetic links between sperm-egg interaction abnormalities and the associated disease.
Infertility, protein interaction network, reproduction, sperm-egg interaction History Received 22 April 2014 Revised 15 June 2014 Accepted 24 June 2014 Published online 15 September 2014
Abbreviations: PPIs: protein-protein interactions; UMI: unexplained male infertility; CLU: clusterin; PINs: protein interaction networks; GAD: Genetic Association Database; DAVID: Database for Annotation Visualization and Integrated Discovery; IVF: in vitro fertilization; IUI: intra uterine insemination; MS: mass spectrometry; PPPs: phosphoprotein phosphatase; DIP: Database of Interacting Proteins; BIND: Bimolecular Interaction Network Database; BioGRID: Biological General Repository for Interaction Datasets; Pfam: protein families; InterPro: integrative protein; BioCys: Biological Databases; STRING: Search Tool for the Retrieval of Interacting Genes; GEO: Gene Expression Omnibus; DB: Data Base; ZP: Zona Pellucida; ICSI: Intra Cytoplasmic Sperm Injection
Introduction Infertility is a common clinical problem affecting approximately 15% of couples around the globe [Un-Nahar et al. 2013]. The occurrence differs throughout underdeveloped and developed countries, being greater in the former where resources for treatment and diagnosis are inadequate [Winters and Walsh 2014]. Males are partially or solely connected to
*Address correspondence to Soudabeh Sabetian, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia. Tel: +60177615766. E-mail: [email protected]
20–50% of the cases of infertility [Jarow 2007]. Nevertheless, in spite of developments in diagnostic methods and technologies in the field of andrology, there remains a substantial subsection of these subfertile men who are categorized as holding the key to unexplained male infertility (UMI) [Baker et al. 2012; Hamada et al. 2011]. The type UMI is relevant to men with infertility of unidentified origin, having normal semen parameters and where female infertility elements have been excluded [Sigman et al. 2009]. Therefore normospermic infertile men might have defective sperm that are unable to fertilize. This supposition is established in the reflection of low success
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rates of intra uterine insemination (IUI) and in vitro fertilization (IVF) in specific cases of UMI. In conventional IVF, the chief reason of fertilization failure is owing to abnormalities of sperm-egg membrane interaction [Evans 2012]. Many molecular interactions in the form of protein-protein interactions mediate the sperm-egg membrane interaction [Clark 2010; Krawetz 2005; Rubinstein et al. 2006b]. Janice P. Evans collected the list of candidate sperm proteins for participation in sperm-egg membrane interactions [Evans 2012]. A number of previous studies attempted to find the molecules that were involved in the interaction process. Due to the complexity of the problem such as difficulties in observing membrane protein-protein interactions (PPIs) in vivo, many efforts have failed to comprehensively elucidate the fusion mechanism, leaving the molecular interactions that mediate sperm-egg membrane fusion still poorly understood [Brewis et al. 2005]. Thus the recognition of the candidate proteins involved in the crucial step of fertilization can help to better understand the research into new treatment methods in order to successfully assist reproductive technologies. In this study, all available potential sperm-egg membrane PPIs were retrieved from the selected databases. The retrieved information was then further investigated by constructing and analyzing a PPI network of all the membrane and surface proteins of the sperm and the oocyte proteins, identifying the essential PPI and the corresponding biological roles in the sperm-egg interaction process through various computational tools.
Results and Discussion Construction of the PPI network All collected proteins were loaded into cytoscape 2.8 [Smoot et al. 2011] to construct a sperm and oocyte protein interaction network. The two networks for sperm and egg/ oocyte associated proteins contain 409 protein nodes and 2746 interactions in the oocyte protein network, and 2076 protein nodes and 8565 interactions in the sperm protein network (Figure 1).
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Extraction sperm-egg interaction network The networks were merged and the proteins that contain a signal sequence and/or transmembrane domain were extracted. The extracted protein network consists of 86 nodes and 1,149 interactions (Figure 2). The sperm and oocyte proteins identified by mass spectrometry (MS) (see the first step of Methods) were categorized into two groups: sperm protein group and oocyte protein group (Figure 2). The proteins and their interactions in our network are represented in Supplementary File 1 and Supplementary File 2, respectively. Some of these interactions between sperm and oocyte membranes (see Supplementary File 2) have already been identified and their role in human sperm-egg interaction for fertility has been confirmed. Monoclonal antibodies identified prominent sperm proteins containing ADAM1 and ADAM2 [Primakoff et al. 1987] and IZUMO1 [Okabe et al. 1988]. The ADAM family, builds on the identification of sperm ADAM2 (fertilinb) from studies with a fertilization-blocking antibody and is one of the founding members of the ADAM family [Singson et al. 2008]. Sperm ADAMs are binding partners for numerous members of the integrin family [Edwards et al. 2008]. Many of the integrins are expressed in eggs and can contribute in sperm-egg interaction. ADAM3 has been suggested to be a sperm protein that facilitates sperm-ZP (Zona Pellucida) interaction on the basis that Adam3-null sperm bind poorly to the ZP [Evans 2012]. Even though these data might specify that ADAM3 binds a ZP component(s) directly, it is similarly probable that Adam3-null sperm lack crucial proteins for ZP interaction as Adam3-null sperm have an altered surface proteome, with reduced amounts of several ADAMs. Adam3-null sperm possess an altered surface proteome and, therefore, might lack other proteins [Han et al. 2009; Kim et al. 2006b]. IZUMO1 residing on the sperm surface is an immunoglobulin superfamily member (with an immunoglobulin-like (Ig) domain) that is vital for spermoocyte fusion [Inoue et al. 2005]. The role of IZUMO1 is not fully understood. IZUMO1 might act via IZUMO1-associated proteins (in cis) and/or might function in trans by interacting with a molecule on the egg. Tetraspanin CD9 is the key actor
Figure 1. Sperm and oocyte protein network. A: The sperm associated proteins network; B: The oocyte associated proteins network, using cytoscape 2.8.3. These protein interaction networks consist of A: 2076 nodes and 8565 interaction and B: 409 nodes and 2746 interaction among those protein.
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DOI: 10.3109/19396368.2014.955896
Protein network of human sperm-egg interaction
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Figure 2. The sperm-egg interaction network. The identified sperm and oocyte proteins by MS proteomics technology are represented in red and yellow respectively. The green nodes are the expressed proteins in both organelles.
recognized thus far in mouse eggs and is expected to function in combination with an additional tetraspanin, CD81, as Cd9//Cd81/ female mice are infertile [Rubinstein et al. 2006a]. The CD9 might work by interacting with a sperm protein in trans. Although there are data from antibody inhibition, the contribution of tetraspanin in human fertilization is not fully understood. Human sperm-egg fusion is partly repressed by a tetraspanin CD151 antibody [Ziyyat et al. 2006]. These data suggest that sperm-egg fusion in diverse mammalian species might depend on different members of the tetraspanin family. The protein interaction network was derived using different databases that consist of known and predicted protein interactions. New protein interaction in human sperm-egg interaction New predicted interactions between the groups have been identified based on a ‘combined score’ calculated between any pair of proteins which were predicted using the STRING
database. As a result, with high confidence (40.700) only new sperm-egg interactions that have not been previously reported in human PPIs were identified. A total of 13 proteins participating in 12 high confidence interactions (Table 1) were selected for further analysis. For example, the results indicated that SERPINE1 (plasminogen activator inhibitor) in sperm play a role in sperm-egg interaction by interacting with SERPING1 (plasma protease C1 inhibitor) in the oocyte. The plasminogen activation system is related to a variety of pathological and physiological processes, e.g. regeneration, thrombolysis, wound healing and morphogenesis, in addition to tumor invasion [Deryugina and Quigley 2012]. Plasmin, a protease capable of digesting a number of extracellular matrix proteins and of activating other proteases, is produced from the zymogen plasminogen by proteolytic cleavage [Syrovets et al. 2012]. SERPING1 is present on the surface, in the tail, and in the acrosome of mature spermatozoa [Ferrer et al. 2012; Manske et al. 1994]. The sperm-egg interaction has also been described as sensitive to serine proteinase inhibitors
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Table 1. The new interactions between sperm and oocyte proteins that are extracted from the merged network.
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Sperm proteins
Oocyte protein
Interaction detection method
Combined-score
ITGA3 (CD49C)
CD151
ITGA3 (CD49C) ITGA2 (CD49B)
CD63 CD9
SERPINE1 (plasminogen activator inhibitor) SERPINE1 (plasminogen activator inhibitor) HYOU1 (Hypoxia up-regulated protein 1) ITGA4 (CD49D) CD9
SERPING1 (Plasma protease C1 inhibitor) CLU/Clus (Clusterin)
Co-expression Physical interaction Co-expression Physical interaction Genetic interaction Co-Localization Shared protein domains Co-expression Co-expression
CLU/Clus
Co-expression
0.829
CD81 CD63
0.972 0.949
CD9
CD81
CLU/Clus ADAM2 IZUMO1
SERPING1 CD9 CD9
Co-expression Physical interaction Shared protein domains Co-expression Shared protein domains Co-expression Co-expression Physical interaction Text mining
[Simmons et al. 2013]. Collectively, these outcomes suggest a role of the plasminogen activation in sperm-egg interaction [Shen et al. 2013]. Hypoxia plays a critical part in various pathophysiological situations, comprising cancer biology, whereas hypoxia-inducible factor (HIF) controls transcriptional reactions underneath hypoxia [Shoji et al. 2013]. It has been reported that hypobaric hypoxia is responsible for altered male reproductive function. The mechanism of action concerning fertility has not been clearly established [Vargas et al. 2011]. A number of phosphoprotein phosphatase (PPPs) family members have been revealed to be present in sperm, signifying a noteworthy function in this cell [Fardilha et al. 2011]. One type of protein phosphatase 1 (PP1g2), is localized to the posterior region of the sperm head, the equatorial region, and is implicated in sperm-egg binding [Huang and Vijayaraghavan 2004]. Our protein network indicated that tetraspanins: CD151 and CD9 in human oocyte interacts with CD49 in sperm, and CD49 interacts with CD63 and CD81 in the oocyte. Numerous tetraspanins, comprising CD81 and CD9, have seemingly indirect (and still poorly described) roles in membrane fusion progression [Fanaei et al. 2011]. Cd81-null females have modest damage to reproductive function [Rubinstein et al. 2006a]. CD81 is an associated tetraspanin that is 45% similar to CD9. The Cd81/ mouse also exhibited flaws in sperm-egg interaction with in vitro–fertilized and in vivo–fertilized eggs [Rubinstein et al. 2006a]. Ziyyat and colleagues [2006] found that human sperm-egg fusion is moderately repressed by treating eggs with an antibody to a different tetraspanin, CD151. CD9 is a member of the tetraspanin family (so named as members have four transmembrane domains) [Hemler 2005; Rubinstein 2011]. Cd9/ females, are severely subfertile [Miyado et al. 2000]. It has been demonstrated that the different sperm tetraspanins may be involved in human sperm-egg interaction. This approach showed that the oocyte CD9 plays some role in sperm-egg interaction by interacting with sperm
0.912 0.735 0.748 0.916 0.908
0.996 0.900 0.799 0.766
CD49, IZUMO1, and ADAM2. IZUMO1 is vital for the sperm oocyte binding and CD9 is also essential. It is appealing to suggest that they interact with each other to form a fusogenic compound. If these proteins are actually interacting, it is possible that they are required on both the surfaces of the sperm and the oocyte [Ikawa et al. 2010]. Several sperm ADAMs (where ADAM signifies a ‘metalloprotease’ and a ‘disintegrin’) have been associated with the sperm-egg interaction. Even though no single ADAM is critical, there seems to be a relationship between the capability of sperm to interact with the oocyte membrane and the presence/levels of certain ADAM proteins. In accord with these results, sperm ADAM2 is a protein candidate involved in sperm-egg membrane interaction through the oocyte CD9 and ZP3. In several Adam knockouts, the sperm displays reduced passage into the oviduct via the uterotubal junction, decreased binding to the ZP, and/or reduced fusion and binding to the egg plasma membrane, as well as abnormalities in the sperm surface proteome with the loss of multiple ADAMs [Kim et al. 2006a; Vjugina and Evans 2007]. The Adam2/ knockout has the utmost severe flaws in gamete membrane interactions and has the lowest overall degree of ADAM proteins on the sperm surface [Cho et al. 1998; Han et al. 2009; Nishimura et al. 2001]. Other Adam knockouts have little or no obvious impact on male reproduction [Horiuchi et al. 2003; Kim et al. 2006b]. Clusterin (CLU) have been identified in the sperm proteome, but their sperm function has yet to be elucidated [Dorus et al. 2012]. Our findings show that CLU has an interaction with SERPING1 in oocyte, identified as a co-expression feature. According to these results, CLU may play a significant role in sperm-egg interaction. CD9 also has been identified in sperm [Baker et al. 2007] co-expression detection method indicating that CD9 interacts with some oocyte proteins, e.g. CD63, CD81. Therefore, it is tempting to speculate that human sperm CD9 enables the sperm and egg interaction.
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Figure 3. ClueGO analysis of sperm-egg interaction protein network. The sperm-egg interaction network analyzed with the ClueGO program to identify the major molecular function involved in the protein network. The result showed that the majority of the proteins are involved in growth factor binding, transmembrane receptor protein kinase activity, glycosaminoglycan binding, interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, manganese ion transmembrane transport activity, and receptor signaling protein activity.
Molecular function analysis The sperm-egg interaction network was analyzed with the ClueGO program to identify the major molecular function involved in the protein network. The result suggested that the majority of the proteins are involved in growth factor binding, transmembrane receptor protein kinase activity, glycosaminoglycan binding, interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, manganese ion transmembrane transport activity, and receptor signaling protein activity (Figure 3). Related molecular functions include interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity. The remainder are common signal transduction and receptor activities. Manganese is an anti-oxidant that prevents the damaging effects of oxidative stress (OS) and has been used to improve the in vitro environment to aid successful IVF or ICSI (Intra Cytoplasmic Sperm Injection) [Cheema et al. 2009]. Our results show that the manganese ion transport activity is an important molecular function for sperm-egg interaction that if supressed finally leads to infertility. The other significant molecular function is receptor signaling protein tyrosine kinase activity. Confocal immunofluorescence analysis with antibodies to phosphotyrosine and phosphorylated protein tyrosine kinases allowed detection of minute signaling events localized to the site of sperm-oocyte interaction that were not amenable to biochemical analysis. The results provide evidence for localized accumulation of phosphotyrosine at the site of sperm contact, binding, or fusion, which suggests active protein tyrosine kinase signaling prior to and during sperm incorporation [McGinnis et al. 2013]. Interleukins typically function as intracellular messengers, mediating their effect via specific receptors on target cells. The principal functions of these cytokines include many complementary and many conflicting roles central to the
induction, regulation, and functioning of the immune system in mammals [O’Connell and McInerney 2005]. Their function is governed by binding to a receptor bound to a T cell membrane. The receptor is responsible for transmitting a signal into the cell upon binding the appropriate ligand [Akdis et al. 2011]. In our result IL2RG, IL4R (see Supplementary File 1) are the interleukin receptors in the sperm-egg interaction protein network. Sperm protein Il4i1 (interleukin 4 induced gene) is present in the acrosome and membrane fraction of sperm, however its function remains to be investigated [Stein et al. 2006]. Database for Annotation Visualization and Integrated Discovery (David) analysis The disease genes associated with sperm-egg interaction disorder were identified using the Genetic Association Database (GAD) with the DAVID bioinformatics tools. We restricted disease associated terms using 0.01 as a cutoff for the Benjamini–Hochberg multiple testing correction p value (Table 2). The results indicate that 23 genes of our protein network are involved in cardiovascular, three genes in hematological, and 12 genes are involved in breast cancer disease (the associated genes are provided in Supplementary File 3). It is interesting that others have found a link between parental fertility status and cardiovascular risk but concluded that it is not conclusive if the infertility is associated with an increased risk of cardiovascular disease [Eisenberg et al. 2011; Verit et al. 2014]. Three genes of our network are related to blood transfusion complications. Parental ABO incompatibility has been implicated as a possible contributor to infertility but was not a significant contributor to infertility [Ganitha et al. 2012]. Our results showed that 12 genes from the sperm-egg interaction network were linked to breast cancer. Meggiorini et al. [2011],reported that female
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Table 2. Disease association of candidate genes in sperm-egg interaction network. Category GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE
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GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE
p Value
Term
Count
%
myocardial infarct; atherosclerosis, coronary myocardial infarction; stroke brain hemorrhage cerebrovascular disease; sickle cell anemia thromboembolism, venous myocardial infarct retinal vascular occlusion myocardial infarct; lymphoproliferative disorders; restenosis blood transfusion complications metabolism disorders; myocardial infarction; stroke, ischemic myocardial infarction; stroke, ischemic coronary artery stent thrombosis stroke, ischemic myocardial infarction breast cancer stroke atherosclerosis, coronary
7 4 5 5 6 9 4 3
0.738397 0.421941 0.527426 0.527426 0.632911 0.949367 0.421941 0.316456
3.66E-06 1.18E-04 3.04E-04 3.04E-04 3.58E-04 4.63E-04 5.30E-04 7.01E-04
3 3
0.316456 0.316456
7.01E-04 7.01E-04
3 3 6 6 12 5 8
0.316456 0.316456 0.632911 0.632911 1.265823 0.527426 0.843882
0.001387359 0.001387359 0.001531833 0.001939036 0.003455614 0.006961194 0.008969006
patients with primary infertility might represent a group at high risk for breast cancer. Our findings suggest that defective sperm-egg interaction may be a surrogate for other genes that are involved in different diseases. However further experimental studies are required to clarify the significance of these genetic links between sperm-egg interaction abnormalities and the associated disease (refer to table 2). This protein interaction network of human membrane and surface sperm-egg interaction proteins has been created using several computational approaches. This protein network identified a set of candidate sperm-egg protein interactions. New protein-protein interactions were resolved, e.g. CD49CD151, CD49-CD63, CD49-CD9, ITGA4-CD81, and IZUMO1-CD9. In addition, some proteins of the plasminogen activation system such as SERPINE1 and SERPING1 may play important roles in sperm-egg interaction. These interactions suggested that the different sperm integrins may also be involved in human sperm-egg interaction. The results also revealed that some proteins necessary for fertility like CD151 in humans might play an important role in promoting sperm-– egg interaction. Interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity are the major molecular functions in the sperm-egg interaction protein network. The disease association analysis indicated that successful spermegg interaction may be a surrogate of other diseases such as cardiovascular, hematological, and breast cancer disease. Further experimental studies will be required to confirm the importance of these new predicted interactions and genetic links between human sperm-egg interaction and other associated diseases.
Methods Initial collection of associated proteins with human sperm and egg We only considered spermatozoa and oocyte proteins identified by MS proteomics technology. As a methodological approach, all the articles that were retrieved from PubMed
were considered for inclusion, with the keywords ‘‘human’’, ‘‘sperm’’, ‘‘spermatozoa’’, ‘‘spermatozoon’’, ‘‘human’’, and ‘‘oocyte’’, ‘‘egg’’, ‘‘human’’ combined with the keyword ‘‘proteome", ‘‘proteomics’’, or ‘‘mass spectrometry’’. The only far-reaching human sperm proteome and oocyte proteome analysis available to date are by Baker et al. [2007] on human sperm proteome and Assou et al. [2006] on human egg proteome. We also used one MS-based proteomic database known as the Human Sperm Proteome Database (HSPD at http://reprod.njmu.edu.cn/hspd/). This website was designed to be user-friendly, where the proteome could be easily downloaded. The proteins involved in these studies were collected with our own UniProt ID using UniProt ID mapping (http://www.uniprot.org/?tab¼mapping). Construction of the protein-protein interaction (PPI) network As the first step to construct the PPI network, the proteinprotein interactions of the collected proteins were identified. To achieve this goal we used a variety of databases that catalog PPIs based on different features: human physical PPIs cataloged in Database of Interacting Proteins (DIP) [Salwinski et al. 2004], Bimolecular Interaction Network Database (BIND) [Alfarano et al. 2005], and Biological General Repository for Interaction Datasets (BioGRID) [Chatr-aryamontri et al. 2013]; human gene co-expression derived from DNA microarray studies in Gene Expression Omnibus (GEO) [Barrett et al. 2013]; human genetic interactions from Biological General Repository for Interaction Datasets (BioGRID) [Chatr-aryamontri et al. 2013]; shared protein-protein domain from integrative protein (InterPro) [Mulder et al. 2007], SMART [Letunic et al. 2012], and protein families (Pfam) [Finn et al. 2010]; biological process annotations from Ontology (GO) [Carbon et al. 2009]; copathway interactions from Reactome [Croft et al. 2011], BioCyc [Karp et al. 2005], PathwayCommons [Cerami et al. 2011]; and finally PPIs extracted from biomedical literature by the text-mining engine GeneWays [Rzhetsky et al. 2004]. We also used Search Tool for the Retrieval of Interacting Genes (STRING) database [Franceschini et al. 2013] to
Protein network of human sperm-egg interaction
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DOI: 10.3109/19396368.2014.955896
predict PPI for our candidate proteins. To query our proteins in different databases, different identifiers with various accession numbers were used. All the extracted data are available in the TEXT format and PSI-MI format (Supplementary File 4). All retrieved data from these data sources were combined and loaded into Cytoscape 2.8 [Smoot et al. 2011] to construct the protein interaction network (PIN). These methods were applied to sperm proteins and oocyte proteins separately to map sperm and oocyte PINs. Then, we compared two networks and computed the intersection (overlapping) network using network modification plugin (compare two networks) in Cytoscape. Because our primary aim was to identify protein candidates involved in sperm-egg interactions, we extracted only the proteins that contain a signal sequence and/or transmembrane domain [Stein et al. 2006] by selecting signal peptide and transmembrane features from sequence annotation (features) in extensively curated UniProt database (www.uniprot.org). Possible sperm-egg protein interactions We categorized spermatozoa and oocyte proteins that were identified by MS (see Initial collection of associated proteins with human sperm and egg, the first section of Methods) as two groups (sperm protein nodes and oocyte protein nodes) with the aim to study the interactions and the interconnections between the two groups. PPIs were built using six separate prediction parameters: Neighborhoods, Co-occurrence (phylogenetic profiles), Fusion, Co-expression, Experimental Interactions, and Text-mining. Each of these parameters has its own score (raw) of measurements such as intergenic distances, Euclidean distances, fusion z-score, Pearsoncorrelation coefficient, various experimental score (e.g. qualitative binary score), and log-odds score, respectively. Each raw score was benchmarked using the KEGG database. PPIs that occured on the same metabolic KEGG map were considered to be true positive and those that occurred on a different map were not. Due to the sigmoidal correlation between raw score and fraction of PPIs on the same KEGG map, STRING fits those correlations to the hillequation to derive the confidence score. STRING derived scores correspond to the probability of finding the PPI within the same KEGG pathway or map [Franceschini et al. 2013]. Different scores on the same bench mark provide a platform of comparisons among the scores and equivalent scores can be calculated. This equivalency mapping helps to combine the scores into a single score, which express higher confidence and gives higher coverage (number of predicted PPI) at a specific accuracy. STRING uses a score combiner based on the product of probabilities using the following formula: S¼1
N Y
ð1 Si Þ
i
where Si is the probability score for database I, S the combined score, and N the total number of databases to be combined. Si, i, S, N The combined scores were further rescaled into the confidence range from 0.0 to 0.1 combining all the scores. Those indicate: 50.400 (low confidence), 0.400-0.700 (medium confidence), and 40.700 (high
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confidence). As a result, we selected the high confidence PPIs (40.700). Biological function analysis The major molecular functions of the sperm-egg interaction protein network were identified using the ClueGo plugin of Cytoscape [Bindea et al. 2009]. ClueGo allows us to integrate several ontological sources because in each source, for each gene, there is a large amount of information. ClueGo can extract the non-redundant biological information for a large cluster of genes using, GO, KEGG, BioCarta, REACTOME, and WikiPathways [Bindea et al. 2009]. Disease association GAD [Becker et al. 2004] is an archive of human genetic association studies of complex diseases and disorders. To evaluate candidate with disease association, we mapped our candidate genes to the GAD database using DAVID 6.7 (updated in 2014) (http://david.abcc.ncifcrf.gov/home.jsp), that provides a comprehensive set of functional annotation tools for a set of genes of interest [Huang da et al. 2009]. We selected those terms enriched in our candidate gene list by requiring Benjamini–Hochberg multiple testing corrected p values less than 0.01.
Acknowledgments The authors would like to acknowledge the Universiti Teknologi Malaysia Institutional Research Grant for the funding and Chew Teong Han for technical assistance.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
Author contributions The work was carried out in collaboration between all the authors. Defined the research theme: MSS; Designed methods and experiments, carried out the computational analysis, and analyzed the data: SS, MAN; Interpreted the results and co-wrote the paper: MSS, SS, MAN.
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Supplementary material available online Supplementary files 1–4.
RESEARCH ARTICLE
Functional features and protein network of human sperm-egg interaction Soudabeh Sabetian*, Mohd Shahir Shamsir, and Mohammed Abu Naser
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Department of Biological and Health Sciences, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
Abstract
Keywords
Elucidation of the sperm-egg interaction at the molecular level is one of the unresolved problems in sexual reproduction, and understanding the molecular mechanism is crucial in solving problems in infertility and failed in vitro fertilization (IVF). Many molecular interactions in the form of protein-protein interactions (PPIs) mediate the sperm-egg membrane interaction. Due to the complexity of the problem such as difficulties in analyzing in vivo membrane PPIs, many efforts have failed to comprehensively elucidate the fusion mechanism and the molecular interactions that mediate sperm-egg membrane fusion. The main purpose of this study was to reveal possible protein interactions and associated molecular function during sperm-egg interaction using a protein interaction network approach. Different databases have been used to construct the human sperm-egg interaction network. The constructed network revealed new interactions. These included CD151 and CD9 in human oocyte that interact with CD49 in sperm, and CD49 and ITGA4 in sperm that interact with CD63 and CD81, respectively, in the oocyte. These results showed that the different integrins in sperm may be involved in human spermegg interaction. It was also suggested that sperm ADAM2 plays a role as a protein candidate involved in sperm-egg membrane interaction by interacting with CD9 in the oocyte. Interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity are the major molecular functions in sperm-egg interaction protein network. The disease association analysis indicated that spermegg interaction defects are also reflected in other disease networks such as cardiovascular, hematological, and breast cancer diseases. By analyzing the network, we identified the major molecular functions and disease association genes in sperm-egg interaction protein. Further experimental studies will be required to confirm the significance of these new computationally resolved interactions and the genetic links between sperm-egg interaction abnormalities and the associated disease.
Infertility, protein interaction network, reproduction, sperm-egg interaction History Received 22 April 2014 Revised 15 June 2014 Accepted 24 June 2014 Published online 15 September 2014
Abbreviations: PPIs: protein-protein interactions; UMI: unexplained male infertility; CLU: clusterin; PINs: protein interaction networks; GAD: Genetic Association Database; DAVID: Database for Annotation Visualization and Integrated Discovery; IVF: in vitro fertilization; IUI: intra uterine insemination; MS: mass spectrometry; PPPs: phosphoprotein phosphatase; DIP: Database of Interacting Proteins; BIND: Bimolecular Interaction Network Database; BioGRID: Biological General Repository for Interaction Datasets; Pfam: protein families; InterPro: integrative protein; BioCys: Biological Databases; STRING: Search Tool for the Retrieval of Interacting Genes; GEO: Gene Expression Omnibus; DB: Data Base; ZP: Zona Pellucida; ICSI: Intra Cytoplasmic Sperm Injection
Introduction Infertility is a common clinical problem affecting approximately 15% of couples around the globe [Un-Nahar et al. 2013]. The occurrence differs throughout underdeveloped and developed countries, being greater in the former where resources for treatment and diagnosis are inadequate [Winters and Walsh 2014]. Males are partially or solely connected to
*Address correspondence to Soudabeh Sabetian, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia. Tel: +60177615766. E-mail: [email protected]
20–50% of the cases of infertility [Jarow 2007]. Nevertheless, in spite of developments in diagnostic methods and technologies in the field of andrology, there remains a substantial subsection of these subfertile men who are categorized as holding the key to unexplained male infertility (UMI) [Baker et al. 2012; Hamada et al. 2011]. The type UMI is relevant to men with infertility of unidentified origin, having normal semen parameters and where female infertility elements have been excluded [Sigman et al. 2009]. Therefore normospermic infertile men might have defective sperm that are unable to fertilize. This supposition is established in the reflection of low success
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rates of intra uterine insemination (IUI) and in vitro fertilization (IVF) in specific cases of UMI. In conventional IVF, the chief reason of fertilization failure is owing to abnormalities of sperm-egg membrane interaction [Evans 2012]. Many molecular interactions in the form of protein-protein interactions mediate the sperm-egg membrane interaction [Clark 2010; Krawetz 2005; Rubinstein et al. 2006b]. Janice P. Evans collected the list of candidate sperm proteins for participation in sperm-egg membrane interactions [Evans 2012]. A number of previous studies attempted to find the molecules that were involved in the interaction process. Due to the complexity of the problem such as difficulties in observing membrane protein-protein interactions (PPIs) in vivo, many efforts have failed to comprehensively elucidate the fusion mechanism, leaving the molecular interactions that mediate sperm-egg membrane fusion still poorly understood [Brewis et al. 2005]. Thus the recognition of the candidate proteins involved in the crucial step of fertilization can help to better understand the research into new treatment methods in order to successfully assist reproductive technologies. In this study, all available potential sperm-egg membrane PPIs were retrieved from the selected databases. The retrieved information was then further investigated by constructing and analyzing a PPI network of all the membrane and surface proteins of the sperm and the oocyte proteins, identifying the essential PPI and the corresponding biological roles in the sperm-egg interaction process through various computational tools.
Results and Discussion Construction of the PPI network All collected proteins were loaded into cytoscape 2.8 [Smoot et al. 2011] to construct a sperm and oocyte protein interaction network. The two networks for sperm and egg/ oocyte associated proteins contain 409 protein nodes and 2746 interactions in the oocyte protein network, and 2076 protein nodes and 8565 interactions in the sperm protein network (Figure 1).
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Extraction sperm-egg interaction network The networks were merged and the proteins that contain a signal sequence and/or transmembrane domain were extracted. The extracted protein network consists of 86 nodes and 1,149 interactions (Figure 2). The sperm and oocyte proteins identified by mass spectrometry (MS) (see the first step of Methods) were categorized into two groups: sperm protein group and oocyte protein group (Figure 2). The proteins and their interactions in our network are represented in Supplementary File 1 and Supplementary File 2, respectively. Some of these interactions between sperm and oocyte membranes (see Supplementary File 2) have already been identified and their role in human sperm-egg interaction for fertility has been confirmed. Monoclonal antibodies identified prominent sperm proteins containing ADAM1 and ADAM2 [Primakoff et al. 1987] and IZUMO1 [Okabe et al. 1988]. The ADAM family, builds on the identification of sperm ADAM2 (fertilinb) from studies with a fertilization-blocking antibody and is one of the founding members of the ADAM family [Singson et al. 2008]. Sperm ADAMs are binding partners for numerous members of the integrin family [Edwards et al. 2008]. Many of the integrins are expressed in eggs and can contribute in sperm-egg interaction. ADAM3 has been suggested to be a sperm protein that facilitates sperm-ZP (Zona Pellucida) interaction on the basis that Adam3-null sperm bind poorly to the ZP [Evans 2012]. Even though these data might specify that ADAM3 binds a ZP component(s) directly, it is similarly probable that Adam3-null sperm lack crucial proteins for ZP interaction as Adam3-null sperm have an altered surface proteome, with reduced amounts of several ADAMs. Adam3-null sperm possess an altered surface proteome and, therefore, might lack other proteins [Han et al. 2009; Kim et al. 2006b]. IZUMO1 residing on the sperm surface is an immunoglobulin superfamily member (with an immunoglobulin-like (Ig) domain) that is vital for spermoocyte fusion [Inoue et al. 2005]. The role of IZUMO1 is not fully understood. IZUMO1 might act via IZUMO1-associated proteins (in cis) and/or might function in trans by interacting with a molecule on the egg. Tetraspanin CD9 is the key actor
Figure 1. Sperm and oocyte protein network. A: The sperm associated proteins network; B: The oocyte associated proteins network, using cytoscape 2.8.3. These protein interaction networks consist of A: 2076 nodes and 8565 interaction and B: 409 nodes and 2746 interaction among those protein.
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Figure 2. The sperm-egg interaction network. The identified sperm and oocyte proteins by MS proteomics technology are represented in red and yellow respectively. The green nodes are the expressed proteins in both organelles.
recognized thus far in mouse eggs and is expected to function in combination with an additional tetraspanin, CD81, as Cd9//Cd81/ female mice are infertile [Rubinstein et al. 2006a]. The CD9 might work by interacting with a sperm protein in trans. Although there are data from antibody inhibition, the contribution of tetraspanin in human fertilization is not fully understood. Human sperm-egg fusion is partly repressed by a tetraspanin CD151 antibody [Ziyyat et al. 2006]. These data suggest that sperm-egg fusion in diverse mammalian species might depend on different members of the tetraspanin family. The protein interaction network was derived using different databases that consist of known and predicted protein interactions. New protein interaction in human sperm-egg interaction New predicted interactions between the groups have been identified based on a ‘combined score’ calculated between any pair of proteins which were predicted using the STRING
database. As a result, with high confidence (40.700) only new sperm-egg interactions that have not been previously reported in human PPIs were identified. A total of 13 proteins participating in 12 high confidence interactions (Table 1) were selected for further analysis. For example, the results indicated that SERPINE1 (plasminogen activator inhibitor) in sperm play a role in sperm-egg interaction by interacting with SERPING1 (plasma protease C1 inhibitor) in the oocyte. The plasminogen activation system is related to a variety of pathological and physiological processes, e.g. regeneration, thrombolysis, wound healing and morphogenesis, in addition to tumor invasion [Deryugina and Quigley 2012]. Plasmin, a protease capable of digesting a number of extracellular matrix proteins and of activating other proteases, is produced from the zymogen plasminogen by proteolytic cleavage [Syrovets et al. 2012]. SERPING1 is present on the surface, in the tail, and in the acrosome of mature spermatozoa [Ferrer et al. 2012; Manske et al. 1994]. The sperm-egg interaction has also been described as sensitive to serine proteinase inhibitors
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Table 1. The new interactions between sperm and oocyte proteins that are extracted from the merged network.
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Sperm proteins
Oocyte protein
Interaction detection method
Combined-score
ITGA3 (CD49C)
CD151
ITGA3 (CD49C) ITGA2 (CD49B)
CD63 CD9
SERPINE1 (plasminogen activator inhibitor) SERPINE1 (plasminogen activator inhibitor) HYOU1 (Hypoxia up-regulated protein 1) ITGA4 (CD49D) CD9
SERPING1 (Plasma protease C1 inhibitor) CLU/Clus (Clusterin)
Co-expression Physical interaction Co-expression Physical interaction Genetic interaction Co-Localization Shared protein domains Co-expression Co-expression
CLU/Clus
Co-expression
0.829
CD81 CD63
0.972 0.949
CD9
CD81
CLU/Clus ADAM2 IZUMO1
SERPING1 CD9 CD9
Co-expression Physical interaction Shared protein domains Co-expression Shared protein domains Co-expression Co-expression Physical interaction Text mining
[Simmons et al. 2013]. Collectively, these outcomes suggest a role of the plasminogen activation in sperm-egg interaction [Shen et al. 2013]. Hypoxia plays a critical part in various pathophysiological situations, comprising cancer biology, whereas hypoxia-inducible factor (HIF) controls transcriptional reactions underneath hypoxia [Shoji et al. 2013]. It has been reported that hypobaric hypoxia is responsible for altered male reproductive function. The mechanism of action concerning fertility has not been clearly established [Vargas et al. 2011]. A number of phosphoprotein phosphatase (PPPs) family members have been revealed to be present in sperm, signifying a noteworthy function in this cell [Fardilha et al. 2011]. One type of protein phosphatase 1 (PP1g2), is localized to the posterior region of the sperm head, the equatorial region, and is implicated in sperm-egg binding [Huang and Vijayaraghavan 2004]. Our protein network indicated that tetraspanins: CD151 and CD9 in human oocyte interacts with CD49 in sperm, and CD49 interacts with CD63 and CD81 in the oocyte. Numerous tetraspanins, comprising CD81 and CD9, have seemingly indirect (and still poorly described) roles in membrane fusion progression [Fanaei et al. 2011]. Cd81-null females have modest damage to reproductive function [Rubinstein et al. 2006a]. CD81 is an associated tetraspanin that is 45% similar to CD9. The Cd81/ mouse also exhibited flaws in sperm-egg interaction with in vitro–fertilized and in vivo–fertilized eggs [Rubinstein et al. 2006a]. Ziyyat and colleagues [2006] found that human sperm-egg fusion is moderately repressed by treating eggs with an antibody to a different tetraspanin, CD151. CD9 is a member of the tetraspanin family (so named as members have four transmembrane domains) [Hemler 2005; Rubinstein 2011]. Cd9/ females, are severely subfertile [Miyado et al. 2000]. It has been demonstrated that the different sperm tetraspanins may be involved in human sperm-egg interaction. This approach showed that the oocyte CD9 plays some role in sperm-egg interaction by interacting with sperm
0.912 0.735 0.748 0.916 0.908
0.996 0.900 0.799 0.766
CD49, IZUMO1, and ADAM2. IZUMO1 is vital for the sperm oocyte binding and CD9 is also essential. It is appealing to suggest that they interact with each other to form a fusogenic compound. If these proteins are actually interacting, it is possible that they are required on both the surfaces of the sperm and the oocyte [Ikawa et al. 2010]. Several sperm ADAMs (where ADAM signifies a ‘metalloprotease’ and a ‘disintegrin’) have been associated with the sperm-egg interaction. Even though no single ADAM is critical, there seems to be a relationship between the capability of sperm to interact with the oocyte membrane and the presence/levels of certain ADAM proteins. In accord with these results, sperm ADAM2 is a protein candidate involved in sperm-egg membrane interaction through the oocyte CD9 and ZP3. In several Adam knockouts, the sperm displays reduced passage into the oviduct via the uterotubal junction, decreased binding to the ZP, and/or reduced fusion and binding to the egg plasma membrane, as well as abnormalities in the sperm surface proteome with the loss of multiple ADAMs [Kim et al. 2006a; Vjugina and Evans 2007]. The Adam2/ knockout has the utmost severe flaws in gamete membrane interactions and has the lowest overall degree of ADAM proteins on the sperm surface [Cho et al. 1998; Han et al. 2009; Nishimura et al. 2001]. Other Adam knockouts have little or no obvious impact on male reproduction [Horiuchi et al. 2003; Kim et al. 2006b]. Clusterin (CLU) have been identified in the sperm proteome, but their sperm function has yet to be elucidated [Dorus et al. 2012]. Our findings show that CLU has an interaction with SERPING1 in oocyte, identified as a co-expression feature. According to these results, CLU may play a significant role in sperm-egg interaction. CD9 also has been identified in sperm [Baker et al. 2007] co-expression detection method indicating that CD9 interacts with some oocyte proteins, e.g. CD63, CD81. Therefore, it is tempting to speculate that human sperm CD9 enables the sperm and egg interaction.
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Figure 3. ClueGO analysis of sperm-egg interaction protein network. The sperm-egg interaction network analyzed with the ClueGO program to identify the major molecular function involved in the protein network. The result showed that the majority of the proteins are involved in growth factor binding, transmembrane receptor protein kinase activity, glycosaminoglycan binding, interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, manganese ion transmembrane transport activity, and receptor signaling protein activity.
Molecular function analysis The sperm-egg interaction network was analyzed with the ClueGO program to identify the major molecular function involved in the protein network. The result suggested that the majority of the proteins are involved in growth factor binding, transmembrane receptor protein kinase activity, glycosaminoglycan binding, interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, manganese ion transmembrane transport activity, and receptor signaling protein activity (Figure 3). Related molecular functions include interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity. The remainder are common signal transduction and receptor activities. Manganese is an anti-oxidant that prevents the damaging effects of oxidative stress (OS) and has been used to improve the in vitro environment to aid successful IVF or ICSI (Intra Cytoplasmic Sperm Injection) [Cheema et al. 2009]. Our results show that the manganese ion transport activity is an important molecular function for sperm-egg interaction that if supressed finally leads to infertility. The other significant molecular function is receptor signaling protein tyrosine kinase activity. Confocal immunofluorescence analysis with antibodies to phosphotyrosine and phosphorylated protein tyrosine kinases allowed detection of minute signaling events localized to the site of sperm-oocyte interaction that were not amenable to biochemical analysis. The results provide evidence for localized accumulation of phosphotyrosine at the site of sperm contact, binding, or fusion, which suggests active protein tyrosine kinase signaling prior to and during sperm incorporation [McGinnis et al. 2013]. Interleukins typically function as intracellular messengers, mediating their effect via specific receptors on target cells. The principal functions of these cytokines include many complementary and many conflicting roles central to the
induction, regulation, and functioning of the immune system in mammals [O’Connell and McInerney 2005]. Their function is governed by binding to a receptor bound to a T cell membrane. The receptor is responsible for transmitting a signal into the cell upon binding the appropriate ligand [Akdis et al. 2011]. In our result IL2RG, IL4R (see Supplementary File 1) are the interleukin receptors in the sperm-egg interaction protein network. Sperm protein Il4i1 (interleukin 4 induced gene) is present in the acrosome and membrane fraction of sperm, however its function remains to be investigated [Stein et al. 2006]. Database for Annotation Visualization and Integrated Discovery (David) analysis The disease genes associated with sperm-egg interaction disorder were identified using the Genetic Association Database (GAD) with the DAVID bioinformatics tools. We restricted disease associated terms using 0.01 as a cutoff for the Benjamini–Hochberg multiple testing correction p value (Table 2). The results indicate that 23 genes of our protein network are involved in cardiovascular, three genes in hematological, and 12 genes are involved in breast cancer disease (the associated genes are provided in Supplementary File 3). It is interesting that others have found a link between parental fertility status and cardiovascular risk but concluded that it is not conclusive if the infertility is associated with an increased risk of cardiovascular disease [Eisenberg et al. 2011; Verit et al. 2014]. Three genes of our network are related to blood transfusion complications. Parental ABO incompatibility has been implicated as a possible contributor to infertility but was not a significant contributor to infertility [Ganitha et al. 2012]. Our results showed that 12 genes from the sperm-egg interaction network were linked to breast cancer. Meggiorini et al. [2011],reported that female
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Table 2. Disease association of candidate genes in sperm-egg interaction network. Category GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE
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GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE GENETIC_ASSOCIATION_DB_DISEASE
p Value
Term
Count
%
myocardial infarct; atherosclerosis, coronary myocardial infarction; stroke brain hemorrhage cerebrovascular disease; sickle cell anemia thromboembolism, venous myocardial infarct retinal vascular occlusion myocardial infarct; lymphoproliferative disorders; restenosis blood transfusion complications metabolism disorders; myocardial infarction; stroke, ischemic myocardial infarction; stroke, ischemic coronary artery stent thrombosis stroke, ischemic myocardial infarction breast cancer stroke atherosclerosis, coronary
7 4 5 5 6 9 4 3
0.738397 0.421941 0.527426 0.527426 0.632911 0.949367 0.421941 0.316456
3.66E-06 1.18E-04 3.04E-04 3.04E-04 3.58E-04 4.63E-04 5.30E-04 7.01E-04
3 3
0.316456 0.316456
7.01E-04 7.01E-04
3 3 6 6 12 5 8
0.316456 0.316456 0.632911 0.632911 1.265823 0.527426 0.843882
0.001387359 0.001387359 0.001531833 0.001939036 0.003455614 0.006961194 0.008969006
patients with primary infertility might represent a group at high risk for breast cancer. Our findings suggest that defective sperm-egg interaction may be a surrogate for other genes that are involved in different diseases. However further experimental studies are required to clarify the significance of these genetic links between sperm-egg interaction abnormalities and the associated disease (refer to table 2). This protein interaction network of human membrane and surface sperm-egg interaction proteins has been created using several computational approaches. This protein network identified a set of candidate sperm-egg protein interactions. New protein-protein interactions were resolved, e.g. CD49CD151, CD49-CD63, CD49-CD9, ITGA4-CD81, and IZUMO1-CD9. In addition, some proteins of the plasminogen activation system such as SERPINE1 and SERPING1 may play important roles in sperm-egg interaction. These interactions suggested that the different sperm integrins may also be involved in human sperm-egg interaction. The results also revealed that some proteins necessary for fertility like CD151 in humans might play an important role in promoting sperm-– egg interaction. Interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity are the major molecular functions in the sperm-egg interaction protein network. The disease association analysis indicated that successful spermegg interaction may be a surrogate of other diseases such as cardiovascular, hematological, and breast cancer disease. Further experimental studies will be required to confirm the importance of these new predicted interactions and genetic links between human sperm-egg interaction and other associated diseases.
Methods Initial collection of associated proteins with human sperm and egg We only considered spermatozoa and oocyte proteins identified by MS proteomics technology. As a methodological approach, all the articles that were retrieved from PubMed
were considered for inclusion, with the keywords ‘‘human’’, ‘‘sperm’’, ‘‘spermatozoa’’, ‘‘spermatozoon’’, ‘‘human’’, and ‘‘oocyte’’, ‘‘egg’’, ‘‘human’’ combined with the keyword ‘‘proteome", ‘‘proteomics’’, or ‘‘mass spectrometry’’. The only far-reaching human sperm proteome and oocyte proteome analysis available to date are by Baker et al. [2007] on human sperm proteome and Assou et al. [2006] on human egg proteome. We also used one MS-based proteomic database known as the Human Sperm Proteome Database (HSPD at http://reprod.njmu.edu.cn/hspd/). This website was designed to be user-friendly, where the proteome could be easily downloaded. The proteins involved in these studies were collected with our own UniProt ID using UniProt ID mapping (http://www.uniprot.org/?tab¼mapping). Construction of the protein-protein interaction (PPI) network As the first step to construct the PPI network, the proteinprotein interactions of the collected proteins were identified. To achieve this goal we used a variety of databases that catalog PPIs based on different features: human physical PPIs cataloged in Database of Interacting Proteins (DIP) [Salwinski et al. 2004], Bimolecular Interaction Network Database (BIND) [Alfarano et al. 2005], and Biological General Repository for Interaction Datasets (BioGRID) [Chatr-aryamontri et al. 2013]; human gene co-expression derived from DNA microarray studies in Gene Expression Omnibus (GEO) [Barrett et al. 2013]; human genetic interactions from Biological General Repository for Interaction Datasets (BioGRID) [Chatr-aryamontri et al. 2013]; shared protein-protein domain from integrative protein (InterPro) [Mulder et al. 2007], SMART [Letunic et al. 2012], and protein families (Pfam) [Finn et al. 2010]; biological process annotations from Ontology (GO) [Carbon et al. 2009]; copathway interactions from Reactome [Croft et al. 2011], BioCyc [Karp et al. 2005], PathwayCommons [Cerami et al. 2011]; and finally PPIs extracted from biomedical literature by the text-mining engine GeneWays [Rzhetsky et al. 2004]. We also used Search Tool for the Retrieval of Interacting Genes (STRING) database [Franceschini et al. 2013] to
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DOI: 10.3109/19396368.2014.955896
predict PPI for our candidate proteins. To query our proteins in different databases, different identifiers with various accession numbers were used. All the extracted data are available in the TEXT format and PSI-MI format (Supplementary File 4). All retrieved data from these data sources were combined and loaded into Cytoscape 2.8 [Smoot et al. 2011] to construct the protein interaction network (PIN). These methods were applied to sperm proteins and oocyte proteins separately to map sperm and oocyte PINs. Then, we compared two networks and computed the intersection (overlapping) network using network modification plugin (compare two networks) in Cytoscape. Because our primary aim was to identify protein candidates involved in sperm-egg interactions, we extracted only the proteins that contain a signal sequence and/or transmembrane domain [Stein et al. 2006] by selecting signal peptide and transmembrane features from sequence annotation (features) in extensively curated UniProt database (www.uniprot.org). Possible sperm-egg protein interactions We categorized spermatozoa and oocyte proteins that were identified by MS (see Initial collection of associated proteins with human sperm and egg, the first section of Methods) as two groups (sperm protein nodes and oocyte protein nodes) with the aim to study the interactions and the interconnections between the two groups. PPIs were built using six separate prediction parameters: Neighborhoods, Co-occurrence (phylogenetic profiles), Fusion, Co-expression, Experimental Interactions, and Text-mining. Each of these parameters has its own score (raw) of measurements such as intergenic distances, Euclidean distances, fusion z-score, Pearsoncorrelation coefficient, various experimental score (e.g. qualitative binary score), and log-odds score, respectively. Each raw score was benchmarked using the KEGG database. PPIs that occured on the same metabolic KEGG map were considered to be true positive and those that occurred on a different map were not. Due to the sigmoidal correlation between raw score and fraction of PPIs on the same KEGG map, STRING fits those correlations to the hillequation to derive the confidence score. STRING derived scores correspond to the probability of finding the PPI within the same KEGG pathway or map [Franceschini et al. 2013]. Different scores on the same bench mark provide a platform of comparisons among the scores and equivalent scores can be calculated. This equivalency mapping helps to combine the scores into a single score, which express higher confidence and gives higher coverage (number of predicted PPI) at a specific accuracy. STRING uses a score combiner based on the product of probabilities using the following formula: S¼1
N Y
ð1 Si Þ
i
where Si is the probability score for database I, S the combined score, and N the total number of databases to be combined. Si, i, S, N The combined scores were further rescaled into the confidence range from 0.0 to 0.1 combining all the scores. Those indicate: 50.400 (low confidence), 0.400-0.700 (medium confidence), and 40.700 (high
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confidence). As a result, we selected the high confidence PPIs (40.700). Biological function analysis The major molecular functions of the sperm-egg interaction protein network were identified using the ClueGo plugin of Cytoscape [Bindea et al. 2009]. ClueGo allows us to integrate several ontological sources because in each source, for each gene, there is a large amount of information. ClueGo can extract the non-redundant biological information for a large cluster of genes using, GO, KEGG, BioCarta, REACTOME, and WikiPathways [Bindea et al. 2009]. Disease association GAD [Becker et al. 2004] is an archive of human genetic association studies of complex diseases and disorders. To evaluate candidate with disease association, we mapped our candidate genes to the GAD database using DAVID 6.7 (updated in 2014) (http://david.abcc.ncifcrf.gov/home.jsp), that provides a comprehensive set of functional annotation tools for a set of genes of interest [Huang da et al. 2009]. We selected those terms enriched in our candidate gene list by requiring Benjamini–Hochberg multiple testing corrected p values less than 0.01.
Acknowledgments The authors would like to acknowledge the Universiti Teknologi Malaysia Institutional Research Grant for the funding and Chew Teong Han for technical assistance.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
Author contributions The work was carried out in collaboration between all the authors. Defined the research theme: MSS; Designed methods and experiments, carried out the computational analysis, and analyzed the data: SS, MAN; Interpreted the results and co-wrote the paper: MSS, SS, MAN.
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Supplementary material available online Supplementary files 1–4.
