Most of the transcription factors (TFs) responsible for controlling seed development are not yet known. To identify TF genes expressed at specific stages of seed development, including those unique to seeds, we used Affymetrix GeneChips to profile Arabidopsis genes active in seeds from fertilization through maturation and at other times of the plant life cycle. Seed gene sets were compared with those expressed in prefertilization ovules, germinating seedlings, and leaves, roots, stems, and floral buds of the mature plant. Most genes active in seeds are shared by all stages of seed development, although significant quantitative changes in gene activity occur. Each stage of seed development has a small gene set that is either specific at the level of the GeneChip or up-regulated with respect to genes active at other stages, including those that encode TFs. We identified 289 seed-specific genes, including 48 that encode TFs. Seven of the seed-specific TF genes are known regulators of seed development and include the LEAFY COTYLEDON ( LEC ) genes LEC1, LEC1-LIKE, LEC2 , and FUS3 . The rest represent different classes of TFs with unknown roles in seed development. Promoter-β -glucuronidase ( GUS ) fusion experiments and seed mRNA localization GeneChip datasets showed that the seed-specific TF genes are active in different compartments and tissues of the seed at unique times of development. Collectively, these seed-specific TF genes should facilitate the identification of regulatory networks that are important for programming seed development.

SignificanceWe describe the spatial and temporal profiles of soybean and Arabidopsis seed methylomes during development. CHH methylation increases globally from fertilization through dormancy in all seed parts, decreases following germination, and targets primarily transposons. By contrast, CG- and CHG-context methylation remains constant throughout seed development. Mutant seeds lacking non-CG methylation develop normally, but have a set of up-regulated transposon RNAs suggesting that the CHH methylation increase may be a failsafe mechanism to reinforce transposon silencing. Major classes of seed genes have similar methylation profiles, whether they are active or not. Our results suggest that soybean and Arabidopsis seed methylomes are similar, and that DNA methylation does not play a significant role in regulating many genes important for seed development.

Little is known about the molecular mechanisms by which the embryo proper and suspensor of plant embryos activate specific gene sets shortly after fertilization. We analyzed the upstream region of the scarlet runner bean (Phaseolus coccineus) G564 gene to understand how genes are activated specifically within the suspensor during early embryo development. Previously, we showed that the G564 upstream region has a block of tandem repeats, which contain a conserved 10-bp motif (GAAAAG C /TGAA), and that deletion of these repeats results in a loss of suspensor transcription. Here, we use gain-of-function (GOF) experiments with transgenic globular-stage tobacco embryos to show that only 1 of the 5 tandem repeats is required to drive suspensor-specific transcription. Fine-scale deletion and scanning mutagenesis experiments with 1 tandem repeat uncovered a 54-bp region that contains all of the sequences required to activate transcription in the suspensor, including the 10-bp motif (GAAAAGCGAA) and a similar 10-bp-like motif (GAAAAACGAA). Site-directed mutagenesis and GOF experiments indicated that both the 10-bp and 10-bp-like motifs are necessary, but not sufficient to activate transcription in the suspensor, and that a sequence (TTGGT) between the 10-bp and the 10-bp-like motifs is also necessary for suspensor transcription. Together, these data identify sequences that are required to activate transcription in the suspensor of a plant embryo after fertilization.promoter analysis ͉ scarlet runner bean

The prediction of operons in Mycobacterium tuberculosis (MTB) is a first step toward understanding the regulatory network of this pathogen. Here we apply a statistical model using logistic regression to predict operons in MTB. As predictors, our model incorporates intergenic distance and the correlation of gene expression calculated for adjacent gene pairs from over 474 microarray experiments with MTB RNA. We validate our findings with known examples from the literature and experimentation. From this model, we rank each potential operon pair by the strength of evidence for cotranscription, choose a classification threshold with a true positive rate of over 90% at a false positive rate of 9.1%, and use it to construct an operon map for the MTB genome.