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Biozentrum and Swiss Institute of Bioinformatics, Klingelbergstrasse 50-70, 4056 Basel, Switzerland
Correspondence should be addressed to Michael Primig; Email: michael.primig{at}unibas.ch
| Abstract |
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| Introduction |
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For practical and analytical purposes, this review covers highly comparable and comprehensive work describing the transcriptional profile of approximately 12 000 rat and 20 000 mouse loci respectively, in testicular or enriched germ cells (Schultz et al. 2003, Schlecht et al. 2004, Shima et al. 2004). The studies were carried out using Affymetrix rat U34 or mouse U74v2 GeneChips (Fig. 1B
) (Lockhart & Winzeler 2000). To focus on the important group of conserved genes, the dataset analyzed is limited to mouse and rat loci that have similar peptide sequences. A convenient graphical display of the data from these experiments is available online via an innovative cross-species community annotation database, GermOnline (http://www.germonline.org/), which combines curated knowledge contributed by life scientists about genes relevant for germ cell differentiation with high-throughput microarray data (Primig et al. 2003, Wiederkehr et al. 2004a,b).
| Expression profiling of mammalian spermatogenesis |
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Major waves of transcriptional up-regulation/peak expression were found to be associated with (i) mitotic growth of spermatogonial stem cells, (ii) the onset of meiosis and (iii) entry into post-meiotic haploid germ cell maturation in experiments using rodent total testis samples or enriched germ cells (Schultz et al. 2003, Schlecht et al. 2004, Shima et al. 2004). Remarkably, up to 30% of all loci covered by mouse microarrays were claimed to be differentially transcribed during spermatogenesis. This indicates that a large portion of the genes in the genome of a higher eukaryote may play a role during male gametogenesis and fertility (Schultz et al. 2003, Shima et al. 2004). The available data suggests that only very few transcripts are specific for dividing spermatogonial stem cells, while several hundred loci may be expressed exclusively (or preferentially) in meiotic and/or post-meiotic germ cells (Schultz et al. 2003). Note that the mouse and rat spermatogonia used in these studies were purified from 8- and 9-day-old animals respectively, so they cover only the first wave of spermatogenesis and not subsequent waves that occur at later stages (Eddy 2002). It should be interesting to determine if their transcriptional signatures differ substantially.
Schultz et al.(2003) identified a group of 1652 meiotically up-regulated mouse loci that included 351 genes apparently expressed only in the germ line. Importantly, this group contains 17 genes that were demonstrated to be required for gametogenesis and/or fertility in transgenic mouse deletion experiments (Schultz et al. 2003 and references therein). Similarly, 1268 transcripts differentially regulated in Sertoli cells and germ cells during rat spermatogenesis include a sub-group of 121 mRNAs potentially specific for meiotic and/or post-meiotic germ cells because it is not detected in three somatic controls and vegetatively growing germ cells. This group contains a large number of loci essential for the process which suggests that tissue specific (or at least substantially enriched) expression may be a good indicator for an important function in gametogenesis and hence fertility (Fig. 6 in Schlecht et al. 2004; see also Figs 3
and 4 in Shima et al. 2004 for loci enriched in specific testicular cell types). Shima et al.(2004) report that mouse somatic Sertoli nurse cells display a complex pattern of differential gene expression whereby approximately 45% of the enriched transcripts are detected during meiosis at 10 to 14 days post partum. It is noteworthy that only very little Sertoli-specific gene expression was detected at early or late (post-meiotic) stages of development. This suggests that transcripts peaking during meiosis may encode proteins important for the interaction of Sertoli cells with developing spermatocytes and spermatids which is essential for the production of mature sperm and fertility (Gow et al. 1999, Akama et al. 2002).
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| Consolidated expression profiling of spermatogenesis in mouse and rat |
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These results are going to improve our understanding of male fertility from several perspectives. First, many of the germ cell specific/enriched transcripts (or expressed sequence tags (ESTs)) are poorly characterized and may encode novel factors important for reproduction. Secondly, a global picture of the regulatory network governing germ cell gene expression is emerging that includes several hundred target genes that appear to be expressed only during meiotic development. Comparative analysis of their promoter regions across species is likely to provide clues about novel regulatory elements and ultimately their cognate transcription factors. Thirdly, conserved rodent germ cell transcripts may serve as markers to help pinpoint genetic defects that disrupt human spermatogenesis once expression profiles of infertile patients become available. Fourthly, since such a huge number of potentially important genes are expressed in the germ line, we can search for targets of drugs that reversibly inhibit spermatogenesis. A possible route may be to use comparative modeling to predict the three-dimensional structure of proteins present in meiotic or post-meiotic cells (Schwede et al. 2003, Kopp & Schwede 2004). By simulating the interaction of proteins with small molecules (docking predicts the energetically most favorable complex between a protein and a compound, Campbell et al. 2003) it should be possible to identify lead compounds that block spermiogenesis or cause the production of sperm with impaired motility or defective spermegg interaction. Since these drug targets were pre-selected for being present only in meiotic or post-meiotic germ cells, any highly specific compound should have few, if any, side-effects on somatic tissue (Schultz et al. 2003).
| GeneOntology (GO) annotation and expression patterns |
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In a different approach we sought to identify GO terms statistically associated with expression patterns across the complete data set from all experiments. The tool used is currently being developed as an R package and will be made available in the near future via the BioConductor project (G Wrobel and M Primig, manuscript in preparation). To illustrate the efficiency of such an approach a cluster of 11 co-regulated genes that bear annotation belonging to the category reproduction (GO:0000003) is shown in Fig. 3
(see legend for more details on the analysis approach). The loci associated with this GO term were sorted by hierarchical clustering of their transcriptional profiles across all four data sets. A number of the resulting genes are known to be involved in reproduction. Interesting examples are the cell adhesion factor, Adam2, that participates in spermegg membrane binding (Zhu et al. 2000, Kim et al. 2003), the testis-specific factor, Spam, involved in fertility (Zheng & Martin-Deleon 1997) and the transition proteins, Tnp1 and Tnp2, required for chromatin condensation during spermiogenesis (Meistrich et al. 2003). These genes and many others shown to be involved in spermatogenesis and/or fertility were also detected as transcriptionally up-regulated during spermatogenesis in related studies using PCR microarrays and mouse total testis samples (see Table III in Ellis et al. 2004 and Fig. 1
in Almstrup et al. 2004). Note that poorly characterized transcripts or ESTs are included in the group of 11 loci, underlining the gene discovery aspect of microarray profiling experiments. For example, AA944027
[GenBank]
is likely to be the rat homolog of mouse Fscn3 (actin-bundling protein) expressed specifically in the elongated spermatid head (Tubb et al. 2002). AA900574
[GenBank]
and AA850130
[GenBank]
are highly similar to mouse Tbpl1 (TATA-boxbinding protein-like protein 1) and Clgn, that were demonstrated to be required for spermiogenesis (Martianov et al. 2001) and fertility (Ikawa et al. 1997) respectively.
| Next generation microarray technology and functional genomics |
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The combination of expression data and functional genomics has proven to be an efficient approach to identify important genes in yeast and worm (Rabitsch et al. 2001, Colaiacovo et al. 2002, Deutschbauer et al. 2002). Along similar lines, it is likely that an ongoing functional genomics project aiming at the large-scale production of mutant mice that display a fertility phenotype will produce valuable data on the important question as to whether expression and function positively correlate in mammals as well (Ward et al. 2003). Moreover, the upcoming generation of microarrays that cover the entire genome of an organism independently of gene annotation (Yamada et al. 2003) will be yet another powerful means to dramatically increase our knowledge about transcripts (hence proteins) present in mammalian male germ cells.
| Acknowledgements |
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| Footnotes |
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| References |
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