The seminal plasma proteome of the giant panda
Abstract
For the ex-situconservation of giant pandas, both collecting and preserving semen are important methods. The seminal plasma is rich in nutrients and bioactive substances, such as proteins, carbohydrates, lipids, amino acids, and hormones, which play an important role in the reproduction and reproductive health of the species. This is the first study to analyze the seminal plasma proteins of giant pandas through proteomics and identified 1125 proteins. These proteins are related to protein turnover, translation, and metabolism. The seminal plasma proteins of giant pandas were then compared to those of humans, pigs and sheep, with many unique proteins found in giant panda samples. Among these proteins, the WD40 repeat-containing proteins have been identified and implicated in sperm function and fertility. Understanding the composition and function of proteins in the giant panda seminal plasma proteome can provide valuable insights into their reproductive biology and help develop strategies to improve their reproductive success in captivity, which is essential for giant panda conservation.
KEYWORDS
Giant panda, seminal plasma, proteome, WD40 repeats proteins
The giant panda (Ailuropoda melanoleuca ) is a unique and vulnerable species endemic to China. It is not only a flagship species for global biodiversity conservation, but also serves as a political and diplomatic ambassador for China, and a cultural icon. Conserving the giant panda is of particular ecological, social, political and economic significance.
In recent years, both in-situ and ex-situ conservation measures have been implemented to different degrees of success, however, the species is still vulnerable to extinction. For the captive population, the low proportion of naturally mating male giant pandas is a major limiting factor for their reproductive efficiency, utilization, and genetic diversity. With artificial insemination techniques, this problem can be overcome and genetic management can also be facilitated, making the preservation and efficient use of individual giant panda semen significant for maintaining the entire captive population.
Seminal plasma is a fluid that is produced by the male reproductive system and is a mixture of secretions from the testis, epididymis and male accessory sex glands. Sperm motility is an index tightly associated with male fertility(Jia et al., 2021). Seminal plasma proteins have important roles in sperm functionality, and different mechanisms including micro-vesicle transport of proteins are involved in the regulation of sperm biology. Due to the role of seminal plasma, specific proteins present in seminal plasma may be used as discriminant variables with the potential to predict sperm motility and fertility(Gaitskell-Phillips et al., 2022). The seminal plasma proteome refers to the complete set of proteins in the fluid that makes up semen, including enzymes, transport proteins, and immune system components. The seminal plasma proteome contains thousands of proteins and includes many tissue-specific proteins that might accurately indicate a pathological process in the tissue of origin(Drabovich et al., 2014). Several seminal plasma proteins are associated with male fertility but most of these have not been studied in detail until now(Candenas & Chianese, 2020; Cannarella et al., 2020).
Research on the seminal plasma proteome has been ongoing for many years and has yielded important insights into male reproductive biology and the mechanisms underlying male infertility. Camargo, M. et al used proteomic techniques to analyze the seminal plasma proteome of men with and without a varicocele, a common cause of male infertility (Camargo et al., 2013). The researchers identified several differentially expressed proteins in the two groups, suggesting that these proteins may be involved in the pathogenesis of varicocele-associated male infertility. Another study used mass spectrometry to analyze the seminal plasma proteome of fertile and infertile men(da Silva et al., 2016). The researchers identified several proteins that were present at significantly different levels in the two groups, suggesting that these proteins may be useful biomarkers for male infertility. Recently, Martins, A.D. et al elucidated the potential role of differentially expressed proteins in the seminal plasma as a diagnostic biomarker for primary and secondary infertility, their results showed overexpression of ANXA2, CDC42 and under-expression of SEMG2 proteins in primary infertility; and overexpression of ANXA2 and APP proteins in secondary infertility(Martins et al., 2020). For instance, the correlation between semen protein composition, sperm activity and fertility in animals such as cattle(Westfalewicz et al., 2017), goats(Jia et al., 2021), pigs(Mills et al., 2020) and chicken(Li et al., 2020) has been explored.
Zhu et al quantified 35 metabolome molecules with distinct age-related trends in the giant panda seminal plasma(Zhu et al., 2022). However, the metabolic profile of giant panda seminal plasma has not yet been reported. The aim of the present study was to identify the whole proteome profiles of giant panda seminal plasma using a gel-free, label-free shotgun proteomics approach. Semen from four sexually mature giant pandas (aged between 9 and 16 years, the average was 11.5±3.32 ) was collected by electroejaculation during the breeding season according to the previous methodology(Cai et al., 2018). Semen was collected into a plastic container and immediately placed in a centrifuge. An aliquot of 0.5 mL of fresh semen was centrifuged at 900 × g for 30 min at 4℃ to separate seminal plasma from spermatozoa. Seminal plasma was then transported at 4℃ and frozen at -80℃ until further use. All samples were collected during artificial insemination and cryogenically stored following a standard, routine procedure at the Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife, Chengdu Research Base of Giant Panda Breeding.
All samples were initially sonicated three times in ice and lysed in lysis buffer containing 100 mM NH4HCO3(pH 8), 6 M Urea and 0.2% SDS, followed by 5 min of ultrasonication on ice. The lysate was centrifuged at 12000 g for 15 min at 4°C and the supernatant was transferred to a clean tube. Extracts from each sample were reduced with 2mM DTT for 1 h at 56℃ and subsequently alkylated with sufficient Iodoacetamide for 1 h at room temperature in the dark. Then 4 times the volume of precooled acetone was mixed with samples by well vortexing and incubated at -20°C for at least 2h. Samples were then centrifuged, and the precipitation was collected. After washing twice with cold acetone, the pellet was dissolved by a dissolution buffer containing 0.1 M triethylammonium bicarbonate (TEAB, pH 8.5) and 6 M urea. Protein concentration was determined again by Bradford protein assay.
The supernatant from each sample, containing precisely 0.12 mg of protein was digested with Trypsin Gold (Promega) at 1:50 enzyme-to-substrate ratio. After 16 h of digestion at 37°C, peptides were desalted with a C18 cartridge to remove the high urea, and desalted peptides were dried by vacuum centrifugation.
Shotgun proteomics analyses were performed using an EASY-nLCTM 1200 UHPLC system (Thermo Fisher) coupled with an Orbitrap Q Exactive HF-X mass spectrometer (Thermo Fisher) operating in the data-dependent acquisition (DDA) mode. A sample volume containing 2 μg of total peptides was injected onto a home-made C18 Nano-Trap column (2 cm×100 μm, 3 μm). Peptides were separated on a home-made analytical column (15 cm×150 μm, 1.9 μm), using a 60 min linear gradient from 5 to 100% eluent B (0.1% FA in 80% ACN) in eluent A (0.1% FA in H2O) at a flow rate of 600 nL/min. The detailed solvent gradient is listed as follows: 5-10% B, 2 min; 10-30% B, 49 min; 30-50% B, 2 min; 50-90% B, 2 min; 90-100% B, 5 min. Q-Exactive HF-X mass spectrometer was operated in positive polarity mode with a spray voltage of 2.3 kV and capillary temperature of 320°C. Full MS scans ranging from 350 to 1500 m/z were acquired at a resolution of 60000 (at 200 m/z) with an automatic gain control (AGC) target value of 3×106 and a maximum ion injection time of 20 ms. The 40 most abundant precursor ions from a full MS scan were selected for fragmentation using higher energy collisional dissociation (HCD) fragment analysis at a resolution of 15000 (at 200 m/z) with an AGC target value of 1×105, a maximum ion injection time of 45 ms, a normalized collision energy of 28%, an intensity threshold of 2.2e4, and the dynamic exclusion parameter of 20 s.
The resulting spectra from each fraction were searched separately against ‘P101SC18111984-01-ailuropoda_melanoleuca.fasta’ by the search engines: Proteome Discoverer 2.2 (PD 2.2, thermo). The searched parameters were as follows, a mass tolerance of 10 ppm for precursor ion scans and a mass tolerance of 0.02 Da for the product ion scans were used, carbamidomethyl was specified in PD 2.2 as fixed modifications, oxidation of methionine (M) and acetylation of the N-terminus were specified in PD 2.2 as variable modifications and a maximum of 2 miscleavage sites were allowed.
For protein identification, a protein with at least one unique peptide was identified at FDR less than 1.0% on peptide and protein levels, respectively. Proteins containing similar peptides that could not be distinguished based on MS/MS analysis were grouped separately as protein groups. Precursor quantification based on intensity was used for label-free quantification. Gene Ontology (GO) and InterPro (IPR) analysis were conducted using the InterProScan-5 program against the non-redundant protein database (including Pfam, PRINTS, ProDom, SMART, ProSiteProfiles, PANTHER)(Jones et al., 2014), and the databases COG (Clusters of Orthologous Groups) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used to analyze the protein family and pathway. Based on the related species, the probable interacting partners were predicted using the STRING-db server (http://string.embl.de/). STRING is a database of both known and predicted protein-protein interactions(Franceschini et al., 2013). The enrichment pipeline (Huang da et al., 2009) was used to perform GO, IPR and KEGG enrichment analysis, respectively.