This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. In nature most organisms synchronize gene expression with daily light/dark and ambient temperature cycles to ensure that specific biological activities occur at the correct times of day. Synchronicity results in peak and trough, or diurnal, gene expression over the 24-hour period of the day. In order to understand the specific roles of temperature and light in synchronizing diurnal gene expression, and identify underlying transcriptional networks, we monitored the entire Arabidopsis thaliana transcriptome (~22,000 genes) using Affymetrix Microarrays over two days and seven conditions of light/dark, and temperature. Utilizing new model based pattern-matching methods we estimate that light, temperature and/or both diurnally regulate more than half of the Arabidopsis transcriptome. We have identified genes with peak expression at every time, or phase of the day with one-hour resolution. Furthermore, the phase of peak expression depends on the specific regime of temperature and light/dark cycle. Now we wish to dissect the underlying transcriptional regulation networks through extensive regulatory DNA (i.e. promoter) analysis. The regulation of gene expression in eukaryotes is complex and is largely mediated by multiple transcription factors that bind within regulatory regions upstream of the coding sequence. Transcription factors recognize specific DNA motifs, bind, and in turn interact with each other and the basal transcriptional machinery to regulate the expression of adjacent genes. Thus, the modular and combinatorial nature of promoter architecture enables the integration of multiple signaling inputs through the binding of different classes of transcription factors operating in different pathways. In the simplest model it is generally assumed that because co-expressed genes exhibit similar expression characteristics they may be co-regulated. A number of algorithms have been developed to identify known and putative unknown regulatory motifs in the promoter sequences of co-regulated genes. Whether the motifs are known or unknown, the fundamental assumption underlying the computational approaches aimed at identifying these elements is that co-regulated genes should contain similar regulatory motifs in their promoters, and these motifs should be significantly overrepresented in a set of co-regulated promoters. We plan to primarily use an enumerative method which estimates the probability of a given DNA sequence occurring some number of times in a set of promoters by counting by comparing the observed count against an expected count based on random sampling to identify novel and known DNA motifs that are overrepresented in the sets of diurnally-regulated genes we have identified.
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