Human Reproduction

Hum. Reprod. Advance Access originally published online on February 24, 2006
Human Reproduction 2006 21(6):1583-1590; doi:10.1093/humrep/del027

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Characterization and quantification of mRNA transcripts in ejaculated spermatozoa of fertile men by serial analysis of gene expression

Yangxing Zhao¹, Qiaoli Li¹, Chenjiang Yao¹, Zhaoxia Wang¹, Yin Zhou¹, Yajing Wang¹, Limin Liu¹, Yifei Wang¹, Lianyun Wang² and Zhongdong Qiao^1,3

¹ School of Medicine and ² School of Life Science and Technology, Shanghai Jiao Tong University, Shanghai, P.R. China

³ To whom correspondence should be addressed at: School of Medicine, Shanghai Jiao Tong University, Dongchuan Road 800, 200240 Shanghai, P.R. China. E-mail: zdqiao{at}sjtu.edu.cn

	Abstract

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BACKGROUND: Accumulated evidence proves that mature spermatozoa contain a complex yet specific array of mRNA, which could provide information on the past events of spermatogenesis. OBJECTIVE: To quantitatively microdissect these mRNA transcripts by a digital approach. METHODS: Serial analysis of gene expression (SAGE) was used to study the mRNA transcripts from ejaculate of a fertile individual and of a pool of 10 fertile men. Online DAVID software suite was also utilized to cluster the UniGene data. RESULTS: A SAGE library from the individual produced 20 237 raw tags representing 2459 unique tags and that from pooled 10 men generated 21 052 raw tags representing 2712 unique tags. When the unique tags occurring 4 times were analysed, 564 overlapping tags were produced by 638 unique tags from the individual and 682 from the pooled library. Fifty-four of these overlapped tags were considered to be novel genes. Online analysis of the overlapping tags revealed 25 functional gene groups, with the dominant one comprising 96 nuclear protein genes involving transcription and transcription regulation and also a group with 84 ribosomal subunit genes involving protein synthesis. CONCLUSION: A SAGE analysis of ejaculate from fertile men has revealed a large number of transcripts, which occur in steady frequencies and probably have important roles in spermatogenesis and fertilization.

Key words: ejaculated human spermatozoa/male fertility/mRNA profile/reproduction/serial analysis of gene expression

	Introduction

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The mammalian ejaculated spermatozoa are highly differentiated terminal cells with an extremely compacted nucleus of haploid genome. They are considered as dormant cells, because no transcription and translation are possible as a result of nuclear DNA binding with protamine and sperm shedding of cytoplasm during later stage of spermatogenesis (Hecht, 1998). But with the advent of new techniques in fields of molecular andrology, more and more evidences have been accumulating suggesting that human ejaculated spermatozoa convincingly retain a complex yet specific population of RNAs (Pessot et al., 1989; Kumar et al., 1993; Chiang et al., 1994; Rohwedder et al., 1996; Durkee et al., 1998; Richt et al., 1999; Goodwin et al., 2000a,b; Lambard et al., 2004a,b; Miller et al., 2005). However, controversy also arises over the existence of these RNAs in such mature germ cells. It is believed that mature spermatozoa do nothing more than just transfer the paternal genome into oocyte. Are these sperm RNAs remains of the past spermatogenesis, especially spermiogenesis, or necessary package essential to trigger embryo development? Recent studies propose that these RNA transcripts might play important roles in spermatozoa development, chromatin repackaging and genomic imprinting and even the zygote development (Miller et al., 2005).

Therefore, clarifying the make-up of spermatozoal RNA transcripts is important to understand human spermatozoa development and the events surrounding fertilization. By using complementary DNA (cDNA) microarray, an analogue approach for both qualitative and quantitative gene expression, Ostermeier et al. (2002) show that thousands of mRNA species from testis and spermatozoa (pooled and individual) are concordant and that accordingly a spermatozoal mRNA fingerprint can be obtained from normal fertile men and applied to monitor the past event, namely what had happened in gene expression during spermatogenesis.

The spermatozoal mRNA fingerprint is invaluable in assessing sperm’s reproductive potential, in discovering paternal influences to the fetus, in ascertaining whether there are generational consequences of environmental exposures of boys and men and in setting up new strategies for male contraception and even potentially new assisted reproductive techniques (ARTs). Therefore, we were interested to address the question using an alternative approach, the digital approach, of serial analysis of gene expression (SAGE) (Velculescu et al., 1995), to get more insight into the transcript complexity of human ejaculated spermatozoa.

	Materials and methods

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Ejaculate samples
Human ejaculates were obtained from 11 healthy volunteers ranging in age from 27 to 43. They had no special medical record. All of them have fathered a healthy child. The ejaculates were obtained after ethics approval and consent from all participants. And normal semen quality as assessed according to World Health Organization criteria (1999) was applied to all the samples.

Purification of spermatozoa
The ejaculate was purified by swim-up technique to get rid of somatic contaminations (Hargreaves et al., 1998). Briefly, semen was diluted with 2x volume of Earle’s balanced salt solution (EBSS) supplemented with 10% fetal bovine serum (FBS) and then centrifuged at 220 g for 10 min. The supernatant was transferred to another tube. The pellet was resuspended in 2 ml of EBSS with 10% FBS. Both supernatant and pellet were centrifuged at 220 g for 10 min and the supernatants discarded. The pellets were combined and resuspended in 0.5 ml of EBSS with 10% FBS. This suspension was layered gently under 1 ml of EBSS with 10% FBS. The tube was slanted at 45° angle and incubated at 37°C in a 5% CO₂ incubator for 60 min; the swim-up sperm fraction was collected. Microscopy inspection was performed to ensure the reliability and reproducibility of this purification process. Then, the purified spermatozoa were combined for RNA extraction.

RNA preparation
Total RNA of purified spermatozoa was extracted by Trizol RNA isolation reagent (Invitrogen, Carlsbad, CA, USA), by adhering to manufacturer’s protocol (http://www.invitrogen.com/content/sfs/manuals/10296010.pdf). The quantity of extracted RNA was determined by UV absorption.

SAGE procedures
I-SAGE^TM Kit (Invitrogen) was used to construct spermatozoa SAGE library. The manufacturer’s protocol, which is available at http://www.invitrogen.com, was followed strictly. The key steps included the following: poly(A+) mRNAs in the sample were captured by oligo (dT) magnetic beads for synthesizing cDNA; the cDNA was digested with NlaIII (anchoring enzyme) and 3' cDNA isolated by the magnetic beads; the resultant 3' cDNA was split into two fractions and ligated to two SAGE adapters, A and B; SAGE tags were released by the tagging enzyme BsmFI, blunt ended with the Klenow Polymerase Fragment, and the tags from two fractions were ligated to form 100 bp ditags; a 1 : 40 dilution of the ligation product was amplified with 28 cycles of PCR (200 reactions in total); precipitated PCR products were run on 12% polyacrylamide gel (PAGE), and only the 100 bp band containing ditags (a combination of two SAGE tags in a tail-to-tail orientation) was isolated and digested with NlaIII; the products of the digestion were run on a 12% PAGE and the 26 bp bands containing ditags purified and used for self-ligation to form concatamer; concatamers were run on an 8% PAGE and a fraction from 500 to 800 bp was isolated and cloned into pZEro vector digested with SphI; cloned concatamers were used as templates for sequencing by a ABI 310 DNA sequencer (Perkin-Elmer, Boston, MA,USA).

Data analysis
We used SAGE2000 (Version 4.5), a software for analysing SAGE tags from raw sequence data files generated by the I-SAGE^TM Kit (http://www.invitrogen.com/sage), to extract the tags. To denote the genes encoded by the unique tag sequences, the database from SAGEmap (as at 30 September 2005) was used as A reference sequence from http://www.ncbi.nlm.nih.gov/. The SAGE^TM tags (Mappings) from humans based on NlaIII anchoring enzyme were chosen. The significance of the tag variation between individual and pooled samples was based on the method developed by Audic and Claverie (1997), which can be found at http://igs-server.cnrs-mrs.fr./~audic/winflat.cgi.

The online DAVID software suite (http://david.abcc.ncifcrf.gov/) was utilized for the functional gene classification and graphic representation.

	Results

Top Abstract Introduction Materials and methods Results Discussion References

 
Quantification of spermatozoa and their RNA
The aim of the present SAGE study was to identify RNA transcripts existing in the human ejaculated spermatozoa and to characterize their profile patterns. The reliability of this study could be easily influenced by the quality of the start material—the purity of RNA from spermatozoa. It is estimated that each human spermatozoon contains on average 0.015 pg of total RNA (Miller et al., 2005), whereas the typical somatic cell contains 10 pg. With 600-fold more of RNA in a somatic cell than in a sperm, the spermatozoal transcript profile could be easily distorted by somatic RNA contaminant. To enrich spermatozoa and remove somatic contaminants, the semen samples underwent the swim-up process. As indicated by the arrow in Figure 1(A), somatic-cell contaminants were often seen in the crude semen, but after the purifying procedure no somatic contaminant was detected (Figure 1B). The spermatozoa prepared in this way were considered suitable for further experiments.

View larger version (114K): [in this window] [in a new window]   Figure 1. Purity of spermatozoa verified under microscope A: spermatozoa in crude semen, the arrow indicates a somatic cell B: spermatozoa after swim-up purification.

 
Equally important, as purity of sperm RNA, is the quantity of RNA to be available. Our pilot study showed that from a single ejaculate, 4.6–9.2x10⁷ spermatozoa could be harvested, which could generate 0.7–1.4 µg of total RNA. To get enough RNA for SAGE library, the total RNA extracted from a single ejaculate of 10 volunteers was mixed and assigned as ‘pooled’; for the ‘individual’, the extracted RNA from five separate ejaculates of the eleventh volunteer were mixed together. After pooling RNA, 4–7 µg and 7–14 µg of total RNA were recovered and used for constructing SAGE library of individual and pooled, respectively. Thus, the amount of total RNA used in these experiments fell in the range of 5–50 µg demanded by the SAGE protocol. Therefore, the SAGE libraries were based on reliable RNA with respect to its purity and quantity.

Summary of individual and pooled SAGE libraries
Total RNA was prepared from these two populations of sperm and analysed by SAGE. A total of 41 289 tags were generated; approximately half were from individual and half from the pooled library.

Approximately 10 200 unique genes were identified in each of the SAGE libraries (Table I). The characteristics of the frequency distribution of expressed genes were remarkably similar in both libraries: the most abundant tags (frequency 20), whose levels were equal to or >0.1%, were derived from <0.9% of genes analysed but constituted >23% of the total tags. Correspondingly, 74–76% of the genes accounted for those tags present at low abundance, with an average of 0.005%, and contributed only 36–38% of the total tags (Table I).

View this table: [in this window] [in a new window]   Table I. Summary of the serial analysis of gene expression (SAGE) data from individual and pooled human spermatozoa

 

Overlapping analysis between the two libraries
Generally speaking, gene transcriptions are temporally and spatially regulated in response to instant environmental and inner status. It is expected that transcripts responsible for most essential biological process should have a relatively constant production and turnover rate in physiological conditions. So we chose to observe those important transcripts that might be critical to spermatogenesis and spermatozoal functions. One way to realize this objective is to find the overlapped transcripts shared among different individuals.

On the basis of a statistical estimation (see Discussion), the analysis was simply focused on those tags that occurred 4 times, which would guarantee that corresponding transcript in the spermatozoal library had 95% chance to be caught. In this case, 638 and 682 unique tags from individual and pooled library were generated, respectively, and in turn 564 overlapped unique tags had been obtained. These 564 unique tags should be of higher importance in spermatozoal biology, and they became our target for further analysis.

The variation analysis of 564 overlapped unique genes
Though these overlapped unique tags occurred in both libraries, they could still occur at different levels. Comparison procedures were needed to make the question clear. To make the comparing process intuitive, we did this by two steps: the first step was to compare the nine unique genes which have occurred 85 times (Table II); the second step was to plot all those unique genes that had occurred 4–85 times (Figure 2) in a spot diagram. Statistical analysis revealed no significant difference for each unique gene in terms of its frequency in both libraries by a standard of 0.05 confidence intervals. It indicates that mRNA transcripts occur in a steady type in ejaculated spermatozoa.

View this table: [in this window] [in a new window]   Table II. Characterization of the 30 most abundant unique transcripts in human ejaculated spermatozoa

 

View larger version (19K): [in this window] [in a new window]   Figure 2. Variation in transcripts between individual and pooled spermatozoa. Scatter plot of unique tag abundance distributed in the individual and pooled libraries. Only the tags with frequencies of 4–85 were plotted. Each point represents a tag with a given frequency in the pooled (X-axis) and individual (Y-axis) Note that all the tags fell between the two lines which represented p=0.05 values for differential expression according to the test statistics developed by Audic and Claverie (1997). The nine unique tags which had frequencies over 85 are listed in Table II.

 

The top 30 unique genes among 564 overlapped unique genes
A cell lineage is basically defined by the underlying gene transcripts that mediate the specific biological process. Therefore, we investigated the top 30 most abundant unique genes to get the main characteristics of spermatozoa (Table II).

1. These 30 most abundant unique genes originated from 3,306 tags, which accounted for 15.7% of the total tags in the library. Twenty-six of them could be matched to known genes, while four were unknown.
   
2. They happened in very high frequencies of 2,100–27,700 TPM (transcripts per million) within sperm, in contrast to transcripts in somatic cells like beta-actin, a house-keeping gene, of 1070–4632 TPM, or another extreme example of haemoglobin (HBA2) that has 23, 883 TPM in spleen cells.
   
3. The first most abundant tag match was Hs.502244, corresponding to ‘dendritic cell protein’, mapping to 11p13, having an ubiquitous expression pattern, full length of gene being 18633 bp, with mRNA of 1259 bp encoding a type II cell surface membrane protein that can serve as a receptor for entry of herpes simplex virus (Perez et al., 2005). The second most abundant tag match was Hs.372658 corresponding to ‘spermatogenesis- related protein 7’, having an expression in testis only, with mRNA 546 bp encoding a 45 amino acid protein.
   
4. As to their tissue-expression pattern, a few of them were expressed in very specific tissue types: spermatogenesis-related protein 7 in testis only; Hypothetical LOC149018 in testis and placenta exclusively. Some of them were expressed in a limited tissues types: cysteine-rich secretory protein 2 in testis, brain, prostate and lung; PRM2 in testis, placenta, kidney and mammary gland (http://www.ncbi.nlm.nih.gov/UniGene/ESTProfile Viewer.cgi?uglist=Hs.2324) and PRM1 in testis, placenta and brain (http://www.ncbi.nlm.nih.gov/UniGene/ESTProfileViewer.cgi?uglist=Hs.2909) and Semenogelin I in prostate, muscle, kidney and mammary gland. The other unique genes were expressed in a ubiquitous fashion, suggesting they probably played more common and fundamental functions in cell biology.
   
5. As to the unique gene’s molecular functions: (i) Many of these genes were nucleic acid binding related, for example six species of which (PRM1, PRM2, CARHSP1, ZNF580, TFAM, KIAA1618) were DNA binding related and four (RPL17, RBM9, LRRFIP1, EEF1A1) were RNA binding related. Their existence probably provided the molecular participants that mediated the regulation of transcription and translation in the later stage of spermatogenesis and/or in the early stage of the zygote. (ii) Another notable feature was the existence of some catalytic activity protein genes like WBSCR21, IBRDC2, CCNB1IP1, ENO1, FGFR1, COX5B, FADS1. These molecules could shape the metabolic features of the later stage spermatozoa. (iii) and some genes belonged to signal transducer proteins: RPL17, TM4SF6, GRIN2C, IL6ST, FGFR1, VAV2, HLA-E. All these might be related with spermatogenesis and fertilization. (iv) There were fewer structural molecule genes: RPS29, RPL17 and RPS8, the nucleoproteins.
   

Functional grouping analysis of the 564 overlapped unique tags
SAGE of spermatozoa generated a long list of UniGene, DAVID 2.1 using a software suite from Internet integrates functional genomic annotations with intuitive graphical summaries, by which the researchers can look for related gene groups and pathways. Among the 564 overlapped unique tags, 54 had no matches on the SAGEmap and therefore represent potential novel genes. Then the remaining 510 unique tags could result in 914 UniGene ID matches in return. These 914 UniGene IDs served as identifiers and were put into the DAVID analysis system. When the classification stringency was set at medium, with all the parameters of expand option at their default values, which are appropriate for most general cases, 389 clustered and 450 unclustered genes were generated. The clustered genes are summarized in Table III, Supplementary Table I and Figure 3, whereas the unclustered genes were listed in Supplementary Table II.

View this table: [in this window] [in a new window]   Table III. Twenty-five functional groups of clustered genes from human spermatozoa obtained using the DAVID software suitea

 

View larger version (88K): [in this window] [in a new window]   Figure 3. The global view of fuzzy heat map of 25 functional gene groups derived from 389 UniGenes captured in spermatozoa of fertile men. Horizontal gray lines separate genes belonging to each functional group; Vertical gray lines separate annotation terms belonging to each functional group in same order. The heat map patterns in diagonal are the 25 primary genes-term patterns of according functional groups (for more detailed information, see Table III and Supplementary Table I). The heat map patterns above or below the primary patterns are to show the degree of similarity of other functional groups’ term to primary patterns (http://david.abcc.ncifcrf.gov/).

 

By the aid of fuzzy heat map (Figure 3), the dominant cluster genes pattern could be visually identified. The first obvious class was group 21, which claimed one fourth (96/389) of the general genes analysed in spermatozoa as nucleus proteins related to transcription and transcription regulation (DNA-dependent), and the second big gene groups were those 84 ribosomal subunit involving protein biosynthesis; these two sets of genes constitute the major characteristics of mature spermatozoa during spermiogenesis process. Coincident with these two big gene groups were other groups of genes with functions spanning 27 proteolysis and peptidolysis; 24 protein kinase; 22 antigen presentation, cell adhesion and immunity; 18 protein transportation and localization; 16 posttranslational modification. The rest of gene groups pointed to functions like 10 mitochondria electron transmission; 7 ATP synthesis; 9 ATPase activity, 9 protein-nucleus import; 9 RNA-binding and RNA processing; 8 calcium ion binding. The last but not the least were the groups with 4–5 members with functions including protein phosphatase, membrane lipid metabolism, fatty acid metabolism, tetratrico peptide repeat and glycosylphosphatidylinisotol anchor, carbohydrate transport and metabolism, intracellular trafficking, chemotaxis, response to external stimulus, immunoglobulin domain, cytoskeleton organization and biogenesis.

Chromosomal information analysis
With the assistance of DAVID tools, the information on chromosomal distribution of these 510 overlapped unique tags was obtained (Table IV). If we arbitrarily defined the chromosome gene density as ‘number of genes per million bases’, then these sperm-related genes were not evenly distributed among the two dozen chromosomes, with the highest of 1.14 (chromosome 19) and lowest of 0.02 (chromosome Y). When gene number on a single chromosome are referenced, chrome 1 and 19 were stationed with the most high number of genes (101 and 73 respectively) whereas the chromosome Y with the fewest number of genes (1 only). As the exact chromosomal loci for the gene distribution is concerned, Supplementary Table III provides the detailed information.

View this table: [in this window] [in a new window]   Table IV. Chromosomal location of 510 overlapped unique tags

 

	Discussion

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Since its establishment 10 years ago, SAGE has been successfully and widely applied in transcript profile studies of development biology and oncology to characterize the genes that are responsible for the observed biological phenotype changes (Velculescu et al., 1995; Harbers and Carninci, 2005). The purpose of the present study is to introduce SAGE into the exploring of human ejaculated spermatozoa, an area full of argument on the existence and meanings of its RNA species, and to get complementary information to that from microarray. Both the quality and quantity of RNA are always a big concern to studies like this one, and spermatozoal RNA used in this study is further complicated by the contamination of somatic cells in the semen. In order to harvest the most motile cells free of somatic cells, the sperm swim-up method was applied. The removal efficiency of somatic cell has been confirmed by the absence of somatic marker CD45 (Hs.192039, tag = TTAAATCCCA, highly expressed in blood cells), E-cadherin (Hs.461086, tag = TGTGGGTGCT, highly expressed in ubiquitous tissues) and c-kit (Hs.479754, tag = AGTCCTTGAA, highly expressed in germ cells) in our resulted SAGE library.

To fully explore the transcription profile of a specific cell type, the more tags are sequenced in a SAGE library, the better the resulted profile will be. Typically 20,000–80,000 tags are analysed for a specific somatic cell type in recent years. In a summary report of normal and malignant gene expression, we assembled 6,800,316 SAGE tags from 171 SAGE libraries (Boon et al., 2002), the average size is close to 40,000 tags/library.

As a very tiny cell with haploid genome and scarce cytoplasm, tag number limited to 20,000 for spermatozoa library is reasonable. With this in mind, we have sequenced 20237 and 21052 tags in individual and pooled libraries. When the unique tags with frequencies of =2 are taken in consideration, 2459 and 2712 unique tags are found in the libraries. The cumulated raw tags representing these unique tags go up to 64% of the total original tags of the library, indicating that about two-thirds of the raw tag information entered our analysis. The rest are the tags that happened only once, which correspond to either the tags occurring very sparsely or tags having sequencing errors, but they are definitely not the representation of junk RNA. Comparing the present SAGE result with that of microarrary, which has produced 2686–2882 ESTs (Ostermeier et al., 2002), the unique tags in SAGE are in the same range as that of microarray. We tried to compare our SAGE data to Ostermeier’s. Unfortunately, we found that 70% of the UniGene ID stored in that website is out of date.

Another important issue in this study is the sensitivity of the unique transcript detection. It is roughly estimated the total number of RNA molecules that would exist in a single spermatozoon is some 27,000 transcripts, assuming an average RNA size of 1000 bases and yield of RNA of at least 0.015 pg per spermatozoon (Miller et al., 2005). Provided that each transcript has an equal chance to detection, and that 20,000 tags are generated for a SAGE library like in our study, then a specific mRNA with an average of N transcripts in the library will generate on average a total of T = N/1.35 tags. Assuming again a Poisson distribution on the total number of tags actually generated, the probability that no tags are seen is e-T, which is 5% when T = 3. Thus a transcript that appears a mean of four times in the library, i.e., at an average abundance of 0.015%, has a 95% chance of being detected in our library (Chen et al., 1998). Based on this estimation, our data analysis was carried out solely on those tags that occurred = 4 times, which would guarantee that a unique transcript in the spermatozoal library had = 95% chance to be caught. According to statistical estimation above, we have identified 564 overlapped unique tags with high frequencies and stable expression. We believe they are the most important participants in spermatogenesis and sperm functions.

After data processing by DAVID software, the predominant feature that the SAGE data revealed thus far can be cited as the spermatozoal exist of: (1) high quantity of complex mRNA species; (2) lot of transcription-regulation related DNA binding protein genes, protein-synthesis related ribosomal subunits genes; (3) high occurrence of gene with no clear function (for example, GA 17, spermatogenesis-related protein 7). And naturally, these will lead to questions: How do spermatozoal RNAs arise and what do they work for?

In obvious contrast to somatic cells, mammalian spermatozoa experience quite different development and differentiation stages, these including the germ cell renewal, meiotic division and the spermiogenesis. During spermiogenesis, somatic histones are gradually substituted by transition proteins and in turn by protamines, and transcription shutdowns shortly thereafter. But the repackaging proteins (and all other proteins required in spermiogenesis) must be available to the developing spermatocyte despite the shutdown of nuclear transcription that unavoidably precedes their synthesis. To overcome this chronological barrier to gene expression, spermatocytes uncouple transcription from translation and store messages required for spermiogenesis for long periods before their translation (Miller et al., 2005). The mRNAs are stored as ribonucleoprotein particles in a translationally repressed state for several days and are translated in elongating and elongated spermatoids (Steger, 1999). So it is RNAs’ specialized longevity that gives them sufficient stability to survive the final nuclear shutdown and (unlike rRNA) escape removal from the residual cytoplast. In this sense, spermatozoal RNAs are essentially left behind as a consequence of its stability. It is no wonder that mature spermatozoa contain so many genes related to functions like transcription-regulation proteins and ribosomal subunits.

For the question what spermatozoal RNA do, there are several lines of information. Evidence show that mRNAs are delivered to the oocyte on fertilization (Ostermeier et al., 2004), probably have a effect in early embryonic development; spermatozoal RNA may play a role in selective chromatin repackaging, making discrimination between protamine- and histone-packaged DNA, and it may also help mediate imprinting (Miller et al., 2005).

Bache and colleagues show that chromosome 1 harbours high frequency breakpoints among the infertile male and the largest number of breakpoints was reported in 1q21 (Bache et al., 2004). It interesting that chromosome 1 comes top of the list of genes represented by corresponding SAGE tags (Table IV and Supplementary Table III) and 16 out of 101 are located at 1q21. It is highly possible that the genes responsible for infertility in their study could fall into the UniGenes revealed by SAGE.

We have exclusively listed the 30 most frequent unique transcripts, which are closely related to spermatogenesis and sperm function and even fertility and conception. In addition, we dug out many novel genes (54 in 564 overlapped unique tags). Their cloning and identification shall shed light to sperm functional researches. These data could also provide huge numbers of candidate probes for microarray-manufacturing, a promising tool for research and diagnosis in the biological studies of spermatozoa.

Male infertility is a worsening problem in today’s world (Jouannet et al., 2001). One in six couple experience difficulty in conceiving a baby; male-factor infertility causes about half the cases in which ART is recommended. The underlying cause of this problem encompasses a wide range of etiology, making systematic investigation difficult. Traditional positional gene-mapping studies based on family cohorts, for example, have not been particularly helpful because until the recent advent of ART, male infertility was not heritable. and the semen parameters are surprisingly non-uniform. In situation like this, a fine molecular description, such as a transcript fingerprint, of the fertile male ejaculate and comparison with the ejaculates from infertile males probably could identify new candidate genes and or gene pathways responsible for the infertility phenotype. The SAGE result of this study revealed that both the type and the abundances of unique tag population could be incorporated into a transcript profile fingerprint of normal fertile spermatozoa, making the fingerprint more delicate and informative. The future study should take more efforts on the confirming of relationship between the different infertile phenotype and their fingerprints. The study of the spermatozoal influences by environmental factors could also get benefit from spermatozoa transcripts fingerprint. In summary, the present study confirmed the feasibility of SAGE investigation into the spermatozoal transcriptome, revealing large quantity transcripts of constant abundance, which could be utilized to form fingerprint of spermatozoa profile, providing a useful tool for diagnosis or study. Meanwhile, a lot of novel genes were located, and their identification will be important to the study of spermatogenesis, fertilization and infertility.

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Submitted on August 4, 2005; resubmitted on December 19, 2005; accepted on December 29, 2005.

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