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.