ABSTRACT
To become fertile, mammalian spermatozoa require completing a complex biochemical maturation that begins in the testis and ends within the female oviduct. Here, we paid attention to the events occurring at the membrane level during the epididymal transit. Indeed, in the epididymis, the molecular composition and the physical-chemical proprieties of sperm membranes markedly change, with functional cross talking among the spermatozoa, the epithelium, and the luminal content (particularly the epididymosomes). To study this process, we undertook a biological networks study, representing the involved molecules as nodes and their interactions as links. The analysis of network topology revealed that it has a scale free and small world architecture and it is robust against random failure. That assures a fast and efficient transmission of information and it leads to identifying the molecules exerting a higher level of control on the system, among which cholesterol plays a pivotal role. The reactome enrichment analysis allowed the reconstruction of the biochemical pathways involved in sperm epididymal maturation and STRING analysis permitted the identification of molecular events possibly involved in that process. In conclusion, this approach allows inferring interesting information, thus contributing to the knowledge on this process and suggesting staring points for further research.
Introduction
Spermatozoa from mammalian species need to undergo a complex process of maturation to become fully fertile. This process occurs both before and after the ejaculation. In the latter case, it happens within the female genital tract and it is called ‘capacitation’. In the former case, it occurs within the testis, where the stem cells, the spermatogonia, differentiate into spermatozoa (spermatogenesis) and reach the lumen of seminiferous tubule (spermiation), and within the epididymis. This last structure is the site where important events related with the functional maturation of male gametes occur. In particular, the protein composition of spermatozoa deeply changes, by losing or modifying several surface proteins and changing the expression of proteins involved in the immune response [Skerget et al. Citation2015]. All of these modifications are well modulated in this confined space, because of a functional and anatomical segmentation of epididymis, which is divided in head (caput), middle (corpus), and tail (cauda) segments. Indeed, each epididymal segment has a specific function and, during the transit of male gametes, confers to the maturing spermatozoa a specific blueprint.
To date, the data about the biochemical changes occurring in spermatozoa within the epididymis are quickly growing, because of the adoption of high-throughput technologies and of bioinformatics tools [Skerget et al. 2013], allowing the identification of thousands of proteins involved, and increasing our knowledge on this very interesting and important issue. At the same time, it is becoming clear that spermatozoal epididymal maturation involves also the epigenetic profile of male germ cells [Dada et al. Citation2012; Rousseaux et al. Citation2008; Sendler et al. Citation2013] as well as the control of gene expression, as it is the case of the presence of microRNA in epididymal spermatozoa [Hu et al. Citation2013; Wu et al. Citation2013].
In this context, it will be of great importance to increase our knowledge about membrane remodelling process, which confers to the sperm membrane the ability to undergo capacitation, once the spermatozoa are ejaculated into the female genital tract, and reach the oviduct. Here, the lipid composition of spermatozoa membrane changes, due to the modification of cholesterol/phospholipids ratio [Botto et al. Citation2010a; Gadella, and Luna Citation2014; Gadella and Harrison Citation2002]. This causes the increase in membrane disorder and isotropy, with the consequence of an increased ability of plasma membrane (PM) and outer acrosome membrane (OAM) to fuse, when it meets the oocyte zona pellucida [Gadella, and Harrison Citation2002; Barboni et al. Citation2011].
The events occurring during epididymal transit targeted to spermatozoal membrane physiology, are preparatory to achieving capacitation, and in pathological conditions, they could compromise the ability of male gametes to correctly respond to the activator/inhibitory messengers present in the female tract. In light of this consideration, the deep knowledge of the molecular events that drive membrane maturation of epididymal spermatozoa could contribute to the understanding of male reproductive pathophysiology.
Results and discussion
Network analysis
In this work, we realized and analyzed different networks: EPIDIDYMIS_NETWORK, which represents all the molecules and the structure involved in epididymal maturation of sperm membrane; Epididymis Molecule Interaction Network Model (EMINeM), which represents only the molecules involved in membrane remodelling and their interactions; and Main Component of EMINeM (MC_EMINeM), which is the main connected component found in EMINeM. The topological parameters analyzed in the three networks are summarized in considering them as undirected. Their Cytoscape 3.1.2 [Shannon et al. Citation2003] analyses, summarized in , highlight important inferences of relevant interest to understanding epididymal physiology. The key players controlling spermatozoa epididymal maturation were suggested by examining the EMINeM topology: the hubs, i.e., those with a degree at least one standard deviation above the network mean [Sporns et al. Citation2007], are listed in . These nodes have been subjected to a multivariate cluster analysis, considering the node degree (i.e., the number of links per node), the betweenness centrality (which is a measure of the centrality of a node within the network), and clustering coefficient (which is a measure of the node tendency to develop clusters). The analysis was carried out using a paired group algorithm (UPGMA) with a Euclidean similarity index with the open source software Past3. The resulting dendrogram allows identifying some cluster of nodes characterized by a different behavior, in particular cholesterol has a specific behavior, different from that of all the others nodes (). Reactome FI Enrichment Analysis was then undertaken, revealing the pathways that could likely be expressed in epididymis maturation of spermatozoa (p<0.05 and FDR <5%) as outlined in . All the molecules participating in these processes are aggregated generating a new network (REACTOME_EMINeM) that represents the putative interactome involved in epididymal maturation of spermatozoa.
Table 1. Main topological parameters assessed in this study.
Table 2. Table showing the results of topological analyses of obtained networks.
Table 3. Hubs of EMINeM, with respective node degree, betweenness centrality, and clustering coefficient.
Table 4. Results of pathway enrichment analysis of EMINeM.
Figure 1. Result of multivariate cluster analysis performed on EMINeM hubs. The dendrogram represents the result of a multivariate hierarchical cluster analysis carried out based on the node degree, the clustering coefficient, and the betweenness centrality of EMINeM hubs, realized by using Past3 software (refer to the text for details). As a result, it is evident that the hubs tend to form different clusters and that the cholesterol has a specific behavior, different from that of all the other nodes.
EMINeM was classified based on the already known network models: random networks, scale free Barabási–Albert (BA) networks, and scale free hierarchical networks. The network is considered to be a scale free network following the BA model. Indeed the distribution of node degree (i.e., the number of links per node) follows an exponential law, with the negative exponent (γ = -1.073) and the node degree and the clustering coefficient are not correlated (R2 = 0.383), as shown in . This is an interesting observation because this specific topology has well defined biological features. As first, it confers to the networks a high robustness against random failure and, in parallel, a high susceptibility to targeted attacks. Indeed, most of the nodes within the network are characterized by a low node degree, consequently, random damage will have the higher probability of effect, with negligible effects on whole network topology. The probability that it could affect a hub is very low, estimated at about 10% (19 hubs on 161 nodes in EMINeM). From an evolutionary perspective, this kind of topology represents an advantage. It maximizes the chances to preserve biological function excluding random damage controlling it by using only a small number of nodes (molecules). In comparison, if the same networks had a more ‘democratic’ structure spermatozoal epididymal maturation would be under the control of tens or hundreds of different molecules, with an enormous increase in energy cost [Bernabò et al. Citation2015]. In addition, the ultra-small world topology of the system allows it to respond in the shorter possible time and in the most specific possible way to internal and external fluctuation, thus maintaining homeostasis. Indeed, each molecule can interact with the others through a small number of passages, thus the loss of information is minimized and the signaling efficiency is maximized. Moreover, any local perturbation could influence the whole network in a very short time, thus increasing the ability to adapt the biological function to intracellular and extracellular stimuli. Interestingly, this specific topology seems to be characteristic of signal transduction [Bernabò et al. Citation2015].
Further, the scale free topology enables one to identify the unique contribution of different compartments involved in sperm maturation. The analysis of the EPIDIDYMIS_NETWORK allows one to observe the involvement of different epididymis components (epithelial cells from the different segments of the organ, male gametes, and epididymal fluid) in biochemical events. Indeed, as reported in , the whole epididymis has 142 interactions, the spermatozoa 99, and the luminal compartment 48. About 50% (69 out 142) of the interactions reported in epididymis are found in caput, whereas corpus and cauda account for about 5% of interactions (7 out 142 and 9 out 142, respectively), thus confirming the different role of the epididymal regions in spermatozoa maturation. One can hypothesize that the caput epididymis could be the structure where a massive process of sperm remodeling takes place, in keeping with the notion that the caput epididymis is the most metabolically active region of this organ, and that 70–80% of the total overall protein secretion in the epididymal lumen takes place here [Cornwall Citation2009]. Moreover, in this context it is interesting to note that the sperm membrane is highly involved in this process in all its regions, and that epididymal fluid [Orgebin-Crist Citation1967; Bedford Citation2015] and epididymosomes [Sharma et al. Citation2015; Belleannée et al. Citation2013a; Belleannée et al. Citation2013b] are actively participating in this process.
Table 5. Structures involved in epididymal maturation of male gamete membranes and number of interactions found.
The analysis of the hubs of the system (EMINeM and MC_ EMINeM) () gives other interesting views. In particular, the most connected node is cholesterol. This molecule plays a key role in pre- and post-ejaculatory life of spermatozoa [Sheriff and Ali 2010; Schwarz et al. 2013; Saez et al. 2011; Rajasekharan et al. 2013]. Indeed, it infers the physical-chemical behavior of male gamete membranes. Its hydroxyl group interacts with the polar head groups near phospholipids and sphingolipids, while its steroid and hydrocarbon chain are embedded in the membrane. Where membranes are rich in unsaturated fatty acids, it increases the lipid’s density and thus membrane fluidity is reduced [Botto et al. Citation2010; Leahy and Gadella Citation2015]. On the contrary, where cholesterol intercalates in the presence of a high concentration of saturated fatty acids, it causes an increase in membrane fusogenicity and permeability. In addition, cholesterol is involved in maintaining the microdomain architecture. These are small ‘lipid ordered’ (Lo phase) portions of membrane, rich in cholesterol, and other lipids (sphingomyelin, gangliosides, saturated long-chain acyl chains phospholipids) and proteins (glycosylphosphatidylinositol-anchored proteins, caveolin, and flotillin). This is surrounded by a more fluid ‘disordered liquid’ membrane (Ld phase). Epididymal maturation and capacitation are characterized by a complex process of lipid trafficking aimed, in one hand, to maintain the integrity of membrane architecture and, on the other hand, to allow the plasma membrane (PM) and outer acrosome membrane (OAM) to fuse at the time of acrosome reaction. In this context, it is not surprising that our model indicates that cholesterol is the molecule acting with the higher degree of control, and that several other hubs are proteins involved in lipid trafficking. Clustering analysis based on cluster coefficient, betweenness centrality, and node degree, confirms that cholesterol has a behavior different from the other nodes, strengthening these results. Interestingly, it has been found that hypercholesterolemia in rat [Shalaby et al. Citation2004], rabbit [Saez Lancellotti et al., Citation2013], and human [Zhang et al. Citation2013] negatively affects male fertility, giving a direct functional confirmation of these inferences.
The reconstruction of the biochemical pathways involved in epididymis physiology based on Reactome FI Pathway Enrichment Analysis complexes revealed various reaction pathways. As summarized in these include the ABCA transporters in lipid homeostasis, HDL-mediated lipid transport, ABC-family proteins mediated transport, detoxification of reactive oxygen species, and lipoprotein metabolism. To the best of our knowledge, this is the first datum describing the epididymal biochemical interactome. It provides an opportunity to consider the trafficking of lipids as a central role in epididymis biochemistry, emphasizing the key role of cholesterol in epididymal physiology. During spermatozoal transit in this organ, their membrane composition changes reflected as a change in their physical and chemical status. The complex phenomenon of lipid remodeling of sperm membrane in the epididymis is a very fascinating and, at least in part, still not completely understood event, involving male gametes, epithelial cells, and epididymosomes. These last entities are spherical vesicles of 50 to 800 nm, secreted by principal cells of the epididymis, that play a key role in determining the evolution of sperm membrane within the epididymis. They are involved in component exchange with the sperm membrane [Sullivan et al. Citation2007; Frenette et al. Citation2010]. As hypothesized by Rejraji “…it does not seem too farfetched to imagine that epididymosomes (and aposomes in general) could exchange lipids and protein materials with sperm cells, contributing to the formation of structures such as rafts in sperm cells membrane” [Rejraji et al. Citation2006, p. 1111]. Suggestively, in mouse, it has been suggested that the epididymosomes membrane is more fluid in the head of the epididymis and its fluidity decreases in the cauda, while the fluidity on the sperm membrane increases as the male gametes progress along the organ [Rejraji et al. Citation2006]. This is due to the change in spermatozoa membrane composition that occurs during epididymal transit. The phosphatidylethanolamine (PE): phosphatidylcholine (PC) ratio does not change, while the concentration of sphingomyelin (SM) increases from 20.9% in caput epididymis to over 29% in cauda epididymis [Rejraji et al. Citation2006]. The cholesterol: phospholipids remains constant, but the polyunsaturated fatty acids relative amount increases significantly during epididymal transit, particularly for 22:5 n-6 and 22:6 n-3. In absolute and relative terms, the cholesterol concentration changes: cholesterol 10−15 mol/spermatozoon: caput 6.9±1.4; cauda 2.4±0.4; cholesterol/phospholipid ratio: caput 0.24±0.04; cauda: 0.289±0.07 [Rejraji et al. Citation2006].
Our computational results are in agreement with the experimental data and contribute to the understanding of the role exerted by cholesterol and by the proteins involved in lipid trafficking. Consider ATP-binding cassette transporters (ABC transporters) and HDL. ABC transporters are a protein superfamily characterized by the presence of ATP-binding cassette (ABC) domains that are involved in inward and outward transport of a wide variety of substrates such as metabolites, drugs, and lipids. In particular, our analysis demonstrates that the ABC transporters play a key role in epididymal biochemistry of sperm membrane rearrangement [Ouvrier et al. Citation2009; Jones and Cry Citation2011; Morales et al. Citation2012; Caballero et al. Citation2012; Gregory and Cyr Citation2014; Palme et al. Citation2014]. They are a superfamily of more than 250 transmembrane proteins characterized by a region that spans about 180 amino acids and contains three highly conserved motifs: the Walker A/P-loop (12 amino acids), a signature motif/C-loop (5 amino acids), and the Walker B motif (5 amino acids) [George and Jones Citation2012]. ABCs are involved in transport of a wide variety of substrates, such as drugs, metabolic products, lipids, and sterols. In addition, recent evidences showed that they also participate in non-transport-related processes such as translation of RNA and DNA repair [Tarling et al. Citation2013]. Lipoproteins are structures constituted by the assembly of proteins and lipids, and are involved in the processes that regulate the moving through the membranes of fats, by emulsifying the lipid molecules. They are classified in five different classes based on electrophoresis and ultracentrifugation. These include chylomicrons, very-low-density lipoproteins (VLDL), intermediate-density lipoproteins (IDL), low-density lipoproteins (LDL), and high density lipoproteins (HDL). The chylomicrons carry triglycerides from the intestines to the liver, to skeletal muscle, and to adipose tissue, VLDL carry triglycerides from the liver to adipose tissue, and IDL are intermediate between VLDL and LDL that carry phospholipids, cholesterol, triglycerides, etc. around the body. In contrast, HDL collect phospholipids, cholesterol, triglycerides, etc. from the cells, and transport it back to the liver. Morales and co-workers proposed an interesting model of lipid transport in epididymis. They hypothesized that apoA-I and apoJ, once synthesized and secreted by the epididymal epithelium, bind to ABCA1, ABCA7, and ABCG1 on the epididymal plasma membrane in the caput epididymis. Here, they translocate cholesterol and phospholipids from plasma membrane of epithelial cells to the apolipoproteins bound to the transporters. This leads to the formation of HDL particles, which bind to the ABCA1 and ABCA7 on the sperm head and ABCG1 on the sperm tail. They, also, make possible the efflux of cholesterol and phospholipids from the plasma membrane. Then, HDL is removed by the epididymal epithelium, via megalin/LRP-2 and cubilin mediated endocytosis [Morales et al. Citation2008]. It is very interesting to note that after ejaculation, an analogous process takes place within the female genital tract. Here, during the process of capacitation, HDL particles shuttle cholesterol and phospholipids from the male gamete’s membrane to the uterine and oviduct epithelium, via the action of the ABC transporters, megalin/LRP-2, and cubilin [Argraves and Morales Citation2004].
One of the other important pathways expressed in epididymis is ROS detoxification. This could be of great functional importance for two reasons. On one hand, the sperm membrane is a very rich source of polyunsaturated fatty acids that is highly susceptible to the oxidative damage. ROS are chemically reactive molecules containing oxygen such as peroxides, superoxide, hydroxyl radical, and singlet oxygen. They are involved in cell damage as well in several signalling pathways. In particular, their harmful effects are the damage of DNA, the oxidation of amino acids, the deactivation of enzymes, and the oxidation of polyunsaturated fatty acids in lipids (lipid peroxidation). Obviously, all these events could play a very important role in determining infertility, when they are directed against the spermatozoa within the epididymis. Consequently, it has evolved a complex system of molecules aimed to protect male gametes from the harmful effect of ROS accumulation in oxidative stress. However, ROS are also actively involved in several pathways related to the acquisition of spermatozoa fertilizing ability [FordCitation2004; Aitken Citation2011; Aitken et al. Citation2015; Bernabò et al. Citation2015].
Using the protein referred to EMINeM to query the STRING database, we realized the STRING_EMINeM network, and then we used a Markov Cluster Algorithm (MCL), a cluster algorithm for graphs based on simulation of stochastic flow in graphs (http://micans.org/mcl/). It allows assessing if the network contains clusters of nodes, and to identify them. The results of MCL clustering and of subsequent network relaxation are displayed in . As it is evident, based on the possible interactions among the proteins, it was possible to identify three bigger clusters (cluster A, cluster B, and cluster C) and a few smaller ones. Cluster A and B contain several classes of proteins involved in sperm maturation, in protection against oxidative damage, and in sperm-oocyte binding and recognition process. These include the ADAMS, lipocalins, ubiquitin and Hsp’s, glutathione peroxidases, and defensins.
Figure 2. Results of MCL clustering on STRING_EMINeM network. The figure shows the result of a clustering analysis carried out on STRING_EMINeM network using the online resource Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, available at: http://string-db.org/). It is a database including known and predicted protein interactions, based on data from genomic context, high-throughput experiments, conserved co-expression, and previous knowledge. The analysis was carried out with the aim to identify clusters of molecules within the network, by using a Markov Cluster Algorithm (MCL), setting the inflation parameter = 4. As a result, three main clusters (Cluster A, Cluster B, and Cluster C) have been identified.
ADAMs (a disintegrin and metalloproteinases), also known as the adamalysin family or MDC family (metalloproteinase-like, disintegrin-like, cysteine rich) are a family of peptidase proteins involved in several biological processes, such as cell differentiation, adhesion, and binding. Interestingly, more than half of the ADAM family members, that are present in both mice and humans, are expressed exclusively or predominantly in testis or epididymis. They can be divided in three groups: group I (ADAM1, ADAM4, ADAM6, ADAM20, ADAM21, ADAM24, ADAM25, ADAM26, ADAM29, ADAM30, and ADAM34), group II (ADAM2, ADAM3, ADAM5, ADAM18, and ADAM32), and group III (ADAM7 and ADAM28). The two ADAMs mainly present in epididymis (ADAM7 and ADAM28) seem to play a different role, nevertheless they show high amino acid sequence similarity to one another (53%). ADAM7 is synthesized and secreted within all the epididymis and is transferred to sperm membrane [Shannon et al. Citation2003; Oh et al., Citation2005] via the epididymosomes. In maturing sperm membranes it is associated with calnexin (CANX), heat shock protein 5 (HSPA5), and integral membrane protein 2B (ITM2B). Differently, ADAM28 is expressed only in the caput where it concurs in regulating the function of other proteins implicated in epididymis and sperm function [Oh et al. Citation2005].
The lipocalins 5, 8, and 9 belong to a family of proteins that share limited regions of sequence homology and a common tertiary structure. They are involved in transport of small hydrophobic molecules such as steroids, bilins, retinoids, and several classes of lipids [Dacheux and Dacheux Citation2013; Lee et al. Citation2003]. Ubiquitin and Hsp are proteins involved in the control of the folding, interaction, and degradation of other proteins. Ubiquitin controls the metabolism of proteins by binding them (ubiquitination or ubiquitylation), thus affecting their degradation via the proteasome, modifying their intra-cellular location, modulating their activity and their interactions [Sutovsky et al. Citation2001; Baska et al. Citation2008]. HSPs are intra-cellular chaperones for other proteins. Consequently, they control the protein conformation shape and prevent unwanted protein aggregation [Dun et al. Citation2012]. In addition, they participate in the transport of other proteins across cellular membranes and are involved in binding antigens and presenting them to the immune system [Binder Citation2014].
Lastly, glutathione peroxidases are a family of proteins with peroxidase activity, which protect the organism from oxidative damage, by reducing the lipid hydroperoxides to their corresponding alcohols and reducing the free hydrogen peroxide to water [Arthur Citation2000]. Glutathione S-transferase Mu 1 belongs to the family of glutathione S-transferases, which play an important role in the detoxification of electrophilic compounds, such as carcinogens, therapeutic drugs, environmental toxins, and products of oxidative stress, by conjugation with glutathione.
In our opinion, the independent cluster formed by defensins, Cluster C, is of very high interest. It contains several proteins belonging to the superfamily of defensins. They are small cysteine-rich cationic peptides consisting of 18-45 amino acids including six to eight conserved cysteine residues. They are involved in immune response, being active against bacteria, fungi, and many enveloped and non-enveloped viruses. Defensins act by binding to the pathogen cell membrane, and by forming pore-like membrane defects, that cause the efflux of essential molecules [Bonucci et al. Citation2013]. In the epididymis, they could be involved in protecting the epididymis itself and the testes by ascending microbial infections. Recently, it has been demonstrated that segmental boundaries in the epididymis restrict the ascent of bacteria in the cauda [Stammler et al. Citation2015]. Our data seem to complete this anatomical relief with the identification of molecular determinants of antimicrobial activity of the epididymis. Suggestively, we can also hypothesize that defensins could be involved in maturation of sperm membranes. Indeed, they are able to specifically bind lipids, thus concurring in changing the physical and chemical characteristics of membranes. This intriguing hypothesis is strengthened by the finding that the defensins are located in the head of epididymis, it is where the more important events involved in sperm membrane remodelling take place and not in the cauda, where the immune defences are active. These data are in agreement with those from several other groups that propose that defensins are actively involved in spermatozoa epidydimal maturation [Chan and Zhang, Citation2005; Dorin and Barratt Citation2014] and that are finely regulated during embryo [Ribeiro et al. Citation2016] as well as during adult life [Ribeiro et al. Citation2016; Hu et al. Citation2014].
In conclusion, we presented data from a computational biology study that could be useful for increasing knowledge of membrane remodelling of spermatozoa during their epididymal maturation. In particular, it could offer an interesting perspective in developing a new research project, towards the experimental validation or implementation of these in silico results.
This could have positive consequences for designing new diagnostic and therapeutic strategies for male infertility.
Materials and methods
Data collection, network creation, and analysis
Since a database describing the molecular interaction occurring during the epidydimal maturation of spermatozoa membranes did not exist, a new one was realized by considering the scientific literature published in peer-reviewed international papers indicized in PubMed archive (http://www.ncbi.nlm.nih.gov/pubmed) in the last 15 years [Bernabò et al. Citation2013; Citation2015]. As reference, we used the mouse. In particular, two researchers expert on spermatozoa biology carried out an independent literature analysis on papers that referred to the epidydimal maturation of mice spermatozoa using the same key words. Then, the databases were compared, and a third researcher verified the correctness of the record inserted and resolved eventual conflicts. The freely available and diffusible molecules such as H2O, CO2, Pi, and H+, O2 were omitted, when not necessary, and in some cases the record did not represent a single molecule but a complex event, such as ‘protein tyrosine phosphorylation’ because all the single molecular determinants of the phenomenon are still unknown.
This database (interaction database), was realized in Microsoft Excel 2013 and contained the following fields:
Molecule or anatomical structure involved in biochemical reaction (source): here are reported the molecules participating in the interaction or the anatomical structure (i.e., caput, corpus, cauda epididymis, sperm head membrane, etc.) where the interaction has been reported to occur.
Interaction: here is described what kind of interaction the molecules or the structures carry out.
Molecule or anatomical structure involved in biochemical reaction (target): here are reported the molecules participating in the interaction or the anatomical structure (i.e., caput, corpus, cauda epididymis, sperm head membrane, etc.) where the interaction has been reported to occur.
In a further database (attributes database) for each molecule were recorded:
Alias: eventual aliases.
Role: the physiological and/or pathological role of the molecule in epididymis.
Reference: the paper reporting the above mentioned data.
Notes: any further information that could be useful in the study.
Once the databases were created, we obtained three networks:
EPIDIDYMIS_NETWORK: that represents all the molecules and the structure involved in epididymal maturation of sperm membrane.
Epididymis Molecule Interaction Network Model, EMINeM: that represents only the molecules involved in membrane remodelling and their interactions.
Main Component of EMINeM, MC_EMINeM: the main connected component found in EMINeM.
Network enrichment and pathway reconstruction
The network enrichment and the reconstruction of the pathways of our interest has been carried out using Reatome FI (http://wiki.reactome.org/index.php/ReactomeFIViz). It is a Cytoscape plugin, designed to find pathways and network patterns related to relevant biological events. As database, it refers to Reactome, which is a free, open-source, curated, and peer-reviewed pathway database, whose aim is to offer bioinformatics tools for the visualization, interpretation, and analysis of biochemical pathway involved in relevant biological events [Joshi-Tope et al. Citation2005]. By using as query the molecules contained in EMINeM, we performed an analysis (Pathway Enrichment Analysis) that allowed us to identify the pathway in which they are expressed, weighing the data for a p<0.05 and for a false discovery rate (FDR) <5%.
Once the pathways were identified, we downloaded and merged them. The obtained new network was tuned (the duplicated edges and the self-loops were removed), thus we obtained the REACTOME_EMINeM network, that represent the putative complex of biochemical reaction occurring during epididymal maturation of spermatozoa membranes.
Identification of predict interactions with STRING, and node MCL clustering
To identify and predict new molecular interactions of molecules contained in EMINeM, we used Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), http://string-db.org/newstring_cgi/show_input_page.pl?UserId=eNOo92_OQ_LS&sessionId=Cfz4mDP5ayne) [Szklarczyk et al. Citation2015]. It is a database including known and predicted protein interactions. They could be either direct (physical) or indirect (functional) associations, and are derived from different sources: genomic context, high-throughput experiments, conserved co-expression, and previous knowledge. From the data obtained using STRING, a new network was obtained (STRING-EMINeM) by filtering the data for the species (Mus musculus) and adopting a medium confidence score (0.400). Further, to identify clusters of molecules within the network, we used a Markov Cluster Algorithm (MCL), setting the inflation parameter = 4.
Cluster analysis
The hubs of LR were subjected to a multivariate cluster analysis based on their node degree, their clustering coefficient, and their betweenness centrality. This last parameter was computed as follows:
where s and t are nodes in the network different from n, σst denotes the number of shortest paths from s to t, and σst (n) is the number of shortest paths from s to t that n lies on. It ranges between 0 and 1, and it is a measure of the nodes centrality in a network, assuming that the information transfer within the network follows the shortest paths. The analysis was performed using a Paired group algorithm (UPGMA) with a Euclidean similarity index (Past3).
Declaration of interest
The authors report no declarations of interest.
Additional information
Notes on contributors
Nicola Bernabò
Conceived the work, performed the data analysis, and wrote the article: NB; Participated in data collection and analysis: RDA, AO; Revised the manuscript critically for important intellectual content: MM, BB.
Raffaele Di Agostino
Conceived the work, performed the data analysis, and wrote the article: NB; Participated in data collection and analysis: RDA, AO; Revised the manuscript critically for important intellectual content: MM, BB.
Alessandra Ordinelli
Conceived the work, performed the data analysis, and wrote the article: NB; Participated in data collection and analysis: RDA, AO; Revised the manuscript critically for important intellectual content: MM, BB.
Mauro Mattioli
Conceived the work, performed the data analysis, and wrote the article: NB; Participated in data collection and analysis: RDA, AO; Revised the manuscript critically for important intellectual content: MM, BB.
Barbara Barboni
Conceived the work, performed the data analysis, and wrote the article: NB; Participated in data collection and analysis: RDA, AO; Revised the manuscript critically for important intellectual content: MM, BB.
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