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ON THE INSIDE

The ultimate goal of systems biology is the generation of predictive models of biological processes. An example of systems biology at work is provided in this issue by Thum et al. (pp. 440–452) who report upon their systematic exploration and modeling of the interactions of sugar (Suc) with different light qualities (white, blue, red, and far-red) used at different fluence rates (low or high) in etiolated seedlings and mature green plants. Boolean logic was used to model the effect of these carbon/light interactions on three target genes involved in nitrogen assimilation: Asn synthetase (ASN1 and ASN2) and Gln synthetase (GLN2). Quantitative real-time PCR was used to monitor the transcript abundance of ASN1, ASN2, and GLN2 in etiolated plants treated with light and/or Suc. This analysis enabled them to assess the effects of carbon on light-induced genes (GLN2/ASN2) versus light-repressed genes (ASN1) in this pathway. Overall, light was able to override carbon as a major regulator of ASN1 and GLN2 in etiolated seedlings. This regulation may occur because these plants are not yet photosynthetically active, and because light of all wavelengths is required for the induction of genes encoding proteins involved in chloroplast development, metabolism, and the further development of etiolated plants. By contrast, carbon overrides light as the major regulator of GLN2 and ASN2 in light-grown plants. In particular, the perception or signaling of high fluence blue light appears to be antagonized by carbon in etiolated seedlings. Studies such as this one that include the analysis of genome-scale data should allow for the successful modeling of the networks of genes that are the downstream targets of converging light- and carbon-signaling pathways in plants. Such information may enable scientists to one day predict how changes in light quality and photosynthesis will affect many processes involved in metabolism and plant development.

Pollen Transcriptome of Arabidopsis

Honys and Twell (pp. 640–652) present a genome-wide view of the pollen transcriptome of Arabidopsis based on microarray analysis. Gene-by-gene approaches have previously identified only 20 different genes expressed in Arabidopsis pollen. The data presented in this issue represents a 50-fold increase in knowledge of the number, identity, and relative expression levels of pollen-expressed genes in Arabidopsis. These new microarray measurements demonstrate that the pollen transcriptome is much more unique than early studies indicated. The authors identified 992 pollen-expressed mRNAs, estimated to represent about 30% of the total pollen transcriptome. Nearly 40% of these mRNAs were detected specifically in pollen. The functional specialization of the mature male gametophyte for recognition of target tissues and rapid directional growth was highlighted by the overrepresentation of genes expected to be vital for fulfilling these tasks. These included genes involved in cell wall metabolism, the cytoskeleton, and cell signaling. Their data also highlight the diminished role for transcription and the important role of mRNA storage in pollen function. Finally, the observed 61% overlap between gametophyte and sporophyte suggests, from an evolutionary perspective, the possibility of improving the fitness of the sporophytic generation through gametophytic competition and selection.

Both complementing and confirming the aforementioned study, Lee and Lee (pp. 517–529) characterized the global gene expression patterns of Arabidopsis pollen at different temperatures using Serial Analysis of Gene Expression (SAGE). The expression level of the great majority of transcripts was unaffected by cold treatment at 0°C for 72 h, whereas pollen tube growth and seed production were substantially reduced. Interestingly, many genes thought to be responsible for cold acclimation that are highly induced in Arabidopsis leaves were only expressed at their normal level or weakly induced in the pollen. The expression patterns of the cold-responsive transcripts identified by SAGE were confirmed by microarray analysis. These results suggest the hypothesis that the cold sensitivity of Arabidopsis pollen may be due to the poor accumulation of proteins that play a role in tolerance to cold stress in sporophytic tissues.

Genomic Analysis of Nitrate Response in Arabidopsis

Nitrate is an important source of inorganic N for plants. It also initiates rapid changes in metabolism that include the induction of the synthesis of nitrate assimilatory enzymes and shifting from starch biosynthesis to the production of organic acids to assimilate ammonium. In this issue, Wang et al. (pp. 556–567) report upon the genomic response of Arabidopsis to low levels of nitrate. Over 1,000 genes are found to respond to low levels of nitrate after only 20 min (almost 10% of the genes with detectable mRNA levels). The response to nitrate was much stronger in roots (1,176 genes showing increased or decreased mRNA levels) than in shoots (183 responding genes). In addition to known nitrate-responsive genes (e.g. those encoding nitrate transporters, nitrate reductase, nitrite reductase, ferredoxin reductase, and enzymes in the pentose phosphate pathway), genes encoding novel metabolic and potential regulatory proteins were found. These include genes that encode enzymes involved in glycolysis (Glc-6-phosphate isomerase and phosphoglycerate mutase), in trehalose-6-P biosynthesis (trehalose-6-P synthase and trehalose-6-P phosphatase), in iron transport/metabolism (nicotianamine synthase), and in sulfate uptake/reduction. The expression of one form of aquaporin was severely repressed in the roots. An interesting feature of nitrate regulation observed in these experiments is that only select members of small gene families respond to nitrate in a significant way.

Rapid Screening for Perturbations of Metabolism and Growth

In many areas of plant biology and agrochemical research, there is an increasing requirement for more rapid-screening techniques to identify plants with impaired metabolism and growth, and to handle effectively the ever increasing number of candidate molecules being screened for agricultural purposes. For many years, conventional screening methods involved screening 5,000 to 20,000 new chemical entities per year in greenhouse screens, which for herbicides involved pot-grown plants in greenhouses. In this issue, Barbagallo et al. (pp. 485–493) describe a rapid, noninvasive technique involving imaging of chlorophyll fluorescence parameters for detecting perturbations of leaf metabolism and growth in seedlings. Arabidopsis seedlings were grown in 96-well microtitre plates for 4 d and then treated with eight herbicides with differing modes of action to induce perturbations in a range of different metabolic processes. Imaging of chlorophyll fluorescence emissions from 96 seedlings growing on a microtitre plate enabled images of a number of fluorescence parameters to be rapidly and simultaneously produced for the plants in each well. Considerable herbicide-induced perturbations in metabolism, even in metabolic reactions not directly associated with photosynthetic metabolism, were detected from the changes in the images of fluorescence parameters before any visual effects on seedling growth were observed.

Smart Plants Signal Phosphate Starvation

Phosphorus (P) is an essential mineral nutrient for plants that is required in large amounts to maintain growth. The leaching of P fertilizers into nearby water, however, can lead to the eutrophication of lakes and environmental degradation. Moreover, it has been estimated that cheap sources of P will be exhausted within the next 60 to 90 years. One approach to partially solving this problem is the development of "smart" plants that inform the grower when P deficiency is looming. In this issue, Hammond et al. (pp. 578–586) have identified Arabidopsis genes whose expression increases specifically in response to P starvation when the P content of plant tissues begins to decline but before the lack of P affects growth. They have generated transgenic Arabidopsis bearing a construct containing a marker gene (GUS) under the control of the promoter sequence for one of these P-sensitive genes (SQD1), and they demonstrate that these smart plants can be used to monitor P deficiency in plants. Transferring this technology to crop plants will help to manage the application of phosphate fertilizers for sustainable agriculture.

Circadian Transcriptome

Rhythmic gene expression underlies many circadian rhythms. Current efforts to define the subset of the transcriptome that is regulated by the circadian clock have largely relied on transcript profiling, in which large numbers of genes were assayed in parallel to identify transcripts that showed circadian oscillations in abundance. Such microarray experiments have revealed that the circadian clock regulates mRNA abundance of about 10% of the transcriptome in plants, invertebrates, and mammals. In contrast, the circadian clock regulates the transcription of the virtually all cyanobacterial genes. In this issue, Michael and Mc-Clung (pp. 629–639) use in vivo luciferase enhancer trapping to determine the extent to which the circadian clock controls transcription in Arabidopsis. They report that 36% of their enhancer trap lines display circadian-regulated transcription, which suggests that the transcriptional control of the Arabidopsis circadian clock may be more widespread then previously demonstrated through microarray experiments. The discrepancies between estimates of clock regulation based on transcription versus mRNA abundance, together with the observation that clock-regulated transcripts are enriched in the set of relatively unstable transcripts, suggests that mRNA stability may be an important component of circadian regulation of gene expression. The authors also identified individual lines identified by enhancer trapping that exhibit peak transcription rates at circadian phases spanning the complete circadian cycle.

We also direct the reader's attention to an update article by Erickson and Millar (pp. 732–738) that reviews the state of our knowledge concerning how the circadian clock synchronizes the internal biology of plants with the environment.

Peter V. Minorsky

Department of Natural Sciences Mercy College Dobbs Ferry, NY 10522

FOOTNOTES

www.plantphysiol.org/cgi/doi/10.1104/pp.900078.

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