High-Throughput Discovery of Germ-Cell-Specific Genes

 

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ABSTRACT

As a specialized cell population that plays the unique role of transmitting genetic information to subsequent generations, germ cells have been intensively studied to unravel their unique physiology from the specification of primordial germ cells to the fateful reunion of gametes during fertilization. For their differential expression, germ-cell-specific genes are the keys to understanding these unique features. In the last decade, the emerging methodologies designed for large-scale and high-throughput analysis have created an ever-increasing amount of data. Among these methodologies, expressed sequence tag libraries, serial analysis of gene expression, and microarrays provide valuable expression data that can be further analyzed. Using the mouse as a model system, we describe a strategy starting from the quick identification of germ-cell-specific genes using public domain expression data to the functional characterization of the identified genes using targeted gene disruption. This strategy should accelerate the process to fill in the missing pieces of the germ cell physiology puzzle and the construction of genetic networks to help us to understand the etiology of infertility. Furthermore, these identified germ-cell-specific genes may lead to the development of new contraceptives targeted specifically to germ cells.

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APPENDIX A

Compile germ-cell-specific gene candidate list using McCarrey Eddy round spermatid EST library as example.

1. Go to CGAP cDNA DGED (http://cgap.nci.nih.gov/Tissues/GXS).

2. Select [Organism-Mus musculus], [Library group-All EST libraries], [Mininum number-200], [Pool A (Tissue type-include testis; Tissue histology-normal)], [Pool B (Tissue type-exclude testis; Tissue histology-normal)], and submit query.

3. In pool A, unselect all libraries except [McCarrey Eddy round spermatid]; in pool B, use the browser command, Find, to locate and unselect [NIH_MGC_169], [NIA Mouse 15k cDNA clone set], and all libraries containing pooled tissues. Increase p value in statistical parameter to 0.7 to allow identification of singleton during in silico subtraction. Submit query to start subtraction.

4. When the result returns, select the [Full Text] link to access the whole list

5. Copy the result and convert it to a table using a text editor. By setting a cutoff at [1 sequence in pool A versus 2 sequences in pool B], a list of GCSGs from round spermatids is ready for further inspection.

6. Go to GNF SymAtlas (http://symatlas.gnf.org/SymAtlas/) and paste either UniGene cluster IDs or representative transcript accessions for batch query.

7. Available GNF multitissue microarray expression profiles can be browsed to validate the germ-cell-specific or gonad-specific expression patterns.

8. More expression profiles can be searched in NCBI GEO database (Table [3]). Using Hils1 as example, search term can be limited with Boolean qualifiers as [(Hils1 AND GSE640) OR (Hils1 AND GDS565)]. Genes that satisfy most criteria serve as good GCSG candidates for further analysis.

   Notes: (A) Human brain medulla library (Lib.9725) is contaminated with testis cDNAs and should be avoided for in silico subtraction of human data. (B) The human ovary tissue samples used by GNF were from two postmenopausal females (http://symatlas.gnf.org/GeneAtlasv2_sample_info.html); thus they are not applicable to the analysis of female germ-cell-specific genes since few germ cells remain. (C) The human tissue sample preparations, Testis Leydig Cell and Testis Interstitial, are contaminated with germ cells, judging by the high expression level of germ-cell-specific genes, such as protamines.

Martin M MatzukM.D. Ph.D. 

Stuart A. Wallace Chair and Professor, Department of Pathology, Baylor College of Medicine

One Baylor Plaza, Houston, TX 77030

Email: mmatzuk@bcm.tmc.edu