Genome-Wide Expression Analysis of Plant Cell Cycle Modulate Genes

SAGE (serial analysis of gene expression):

In a review article entitled “Genome-wide expression analysis of plant cell cycle modulated genes” published in the April 2001 issue of Current Opinion in Plant Biology (4(2):136-42), Drs. Peter Breyne and Marc Zabeau at the Flemish Institute for Biotechnology – University of Gent, Belgium, present the first comprehensive survey of cell cycle modulated genes in plants. In this article, they have described how technologies for genome-wide expression analysis may be applied to determine the functioning of groups, sets, or subsets of genes involved in vital biological processes such as cell division or various developmental and environmental signal responses. They discuss cDNA-AFLP in detail, describing this technique as not only suitable for gene discovery but also for quantitative expression profiling – the latter providing an additional advantage of this approach not shown before.
The authors devote the first half of their paper to a short review of current technologies for genome-wide expression analysis. Quoting the authors, “these methods depend on three different principles each having their strengths and weaknesses; hybridization of probes to microarrays, counting of sequence tags or signatures from cDNA fragments and gel-based analysis of cDNA tags.” Oligo and cDNA microarrays are discussed followed by the sequence-based methods of SAGE (serial analysis of gene expression) and the newer MPSS (massively parrallel signature sequencing).
One of the limitations of the current microarray technology is that many rarely expressed genes are overlooked. According to one estimate, the 105,000 known Arabidopsis ESTs* represent only 60% of all the genes in this species. The second limitation is that transcripts of genes belonging to multi-gene families are difficult to distinguish from one another, as cross-hybridization may occur. Gene duplication seems to be common within the Arabidopsis genome, so this may well be a notable drawback. In addition, a relatively large amount of RNA is still necessary for microarray analysis at present.
Finally, attention is devoted to the originally qualitative PCR-based technique known as cDNA-AFLP , where “a complex starting mixture of cDNAs is fractionated into smaller subsets, whereafter cDNA tags are PCR amplified and separated on high-resolution gels.” Although microarrays can also be used in cases where only limited genomic resources are available, technologies that do not rely on sequence information such as cDNA-AFLP are more appropriate for gene discovery. This is exemplified by the discovery of fruit ripening genes in strawberries using such techniques. The authors then describe their modifications that permit quantitative genome-wide transcription analysis with cDNA-AFLP.
The authors point out that the main prerequisite of the microarray technique for identification and monitoring of genes involved in cell cycle regulation is the availability of a completely sequenced genome or a large, representative set of cDNA clones. In addition, one must be able to synchronize cell division. Because budding yeast cells fulfill the above two criteria, its gene expression was analyzed by the microarray technique. As a result, about 800 out of a total of 8000 genes have been implicated in regulating different stages of a mitotic cell division cycle in dividing yeast. Subsets of genes are activated, one after another in sequential order, corresponding to one of the four stages of a mitotic division: S, G2, mitosis, and G1. Almost twice as many yeast genes regulating different stages of meiosis were found to be differentially expressed during a meiotic cell cycle.

The results of these studies demonstrate that each division cycle stage corresponds to a unique and particular set of genes governing function of the dividing cell at that time point. Activation of each set of genes allows progression to the next stage of the cell cycle.
In bacterial cell division cycles, 553 open reading frames (ORFs) out of 3000, or over one fifth of the total number, were found to be expressed in a sequential order matching the division stage. In synchronously dividing animal cells, the number of sequentially activated genes corresponding to the stage of division was only 700 out of a total of 40,000.
While the cDNA microarray technique was successfully applied to identify Arabidopsis genes involved in wounding, defense and circadian rhythm-modulated gene responses, this approach is not feasible for identifying and monitoring genes involved in cell divisions, because Arabidopsis cells cannot be properly induced to divide synchronously.
Therefore, to study cell cycle gene expression in green plant tissue, the authors selected the Bright Yellow 2 (BY2) cell line of tobacco, which can be induced to divide synchronously, thus facilitating the identification of cell cycle modulated genes. The authors used the cDNA-AFLP transcript profiling approach and 12 time points covering most of the cell cycle to screen for cell-cycle regulatory genes. Their data have shown that the majority of the cell cycle modulated genes can be grouped into three large functional classes corresponding to S1-, G2-, and M-specific genes.
In conclusion, the authors express hope that there will be further refinements in the microarray-based technique and that this technique will continue to be widely used in plants to study different biological processes. The availability of the complete genomic sequence of Arabidopsis will make it possible to build a microarray containing all ORFs from this model plant species. Simultaneous improvements in DNA fragment-based technologies such as cDNA-AFLP will facilitate in-depth analysis of gene expression, particularly for organisms for which complete genome sequence data is not available, and will offer a solution for some of the problems resulting from the high levels of redundancy in plant genomes.

*ESTs: Short cDNA fragments unique for specific expressed genes are called Expressed Sequence Tags (ESTs). These are generated from mRNA transcripts, as they appear in a particular tissue at the time of extraction.


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