Asymmetric division during sporulation by Bacillus subtilis generates a mother cell that undergoes a 5-h program of differentiation. The program is governed by a hierarchical cascade consisting of the transcription factors: σE, σK, GerE, GerR, and SpoIIID. The program consists of the activation and repression of 383 genes. The σE factor turns on 262 genes, including those for GerR and SpoIIID. These DNA-binding proteins downregulate almost half of the genes in the σE regulon. In addition, SpoIIID turns on ten genes, including genes involved in the appearance of σK . Next, σK activates 75 additional genes, including that for GerE. This DNA-binding protein, in turn, represses half of the genes that had been activated by σK while switching on a final set of 36 genes. Evidence is presented that repression and activation contribute to proper morphogenesis. The program of gene expression is driven forward by its hierarchical organization and by the repressive effects of the DNA-binding proteins. The logic of the program is that of a linked series of feed-forward loops, which generate successive pulses of gene transcription. Similar regulatory circuits could be a common feature of other systems of cellular differentiation.

We propose MOTIF REGRESSOR for discovering sequence motifs upstream of genes that undergo expression changes in a given condition. The method combines the advantages of matrix-based motif finding and oligomer motif-expression regression analysis, resulting in high sensitivity and specificity. MOTIF REGRESSOR is particularly effective in discovering expression-mediating motifs of medium to long width with multiple degenerate positions. When applied to Saccharomyces cerevisiae, MOTIF REGRESSOR identified the ROX1 and YAP1 motifs from Rox1p and Yap1p overexpression experiments, respectively; predicted that Gcn4p may have increased activity in YAP1 deletion mutants; reported a group of motifs (including GCN4, PHO4, MET4, STRE, USR1, RAP1, M3A, and M3B) that may mediate the transcriptional response to amino acid starvation; and found all of the known cell-cycle regulation motifs from 18 expression microarrays over two cell cycles.sequence motif discovery ͉ microarray data ͉ correlation ͉ transcription regulation D irect experimental determination of transcription factor DNA-binding motifs (TFBM) is not practical or efficient in many biological systems. Therefore, computational algorithms such as the word-enumeration (1-4), the position-specific matrix update (5-7), and the dictionary (8) methods have been developed to identify putative motifs and guide experimentation. One of the most successful computational tactics for TFBM discovery is to cluster genes based on their expression profiles, and then search for motifs in the sequences upstream of tightly clustered genes (9). When noise is introduced into the cluster through spurious correlations, however, such an approach may result in false positives. A filtering method (10) based on the specificity of the motif occurrences has been shown to effectively eliminate false positives, but the sensitivity of the algorithm is still low in some cases. An iterative procedure for simultaneous clustering and motif finding has been suggested (11), but no effective algorithm has been implemented to demonstrate its advantage in biological data. Two novel methods for TFBM discovery via the association of gene expression values with oligomer motif abundances have been proposed (12, 13). They first conduct word enumeration and then use regression to check whether the genes whose upstream sequences contain a set of words have significant changes in their expression. These methods are effective for discovering conserved short motifs and sometimes interactions among them, but are not effective with longer motifs and may lose sensitivity in cases where TFBMs have multiple degenerate positions. ʈ We present an alternative approach operating under the explicit assumption that, in response to a given biological condition, the effect of a TFBM is strongest among genes with the most dramatic increase or decrease in mRNA expression. We first use a fast and sensitive motif-finding method, MDSCAN (14), to generate a large set of motif candidates that are enriched in the DNA sequence upstream o...

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