Auto/cross-regulation of Hoxb3 expression in posterior hindbrain and spinal cord

The complex and dynamic pattern of Hoxb3 expression in the developing hindbrain and the associated neural crest of mouse embryos is controlled by three separate cis-regulatory elements: element I (region A), element IIIa, and the r5 enhancer (element IVa). We have examined the cis-regulatory element IIIa by transgenic and mutational analysis to determine the upstream trans-acting factors and mechanisms that are involved in controlling the expression of the mouse Hoxb3 gene in the anterior spinal cord and hindbrain up to the r5/r6 boundary, as well as the associated neural crest which migrate to the third and posterior branchial arches and to the gut. By deletion analysis, we have identified the sequence requirements within a 482-bp element III482. Two Hox binding sites are identified in element III482 and we have shown that in vitro both Hoxb3 and Hoxb4 proteins can interact with these Hox binding sites, suggesting that auto/cross-regulation is required for establishing the expression of Hoxb3 in the neural tube domain. Interestingly, we have identified a novel GCCAGGC sequence motif within element III482, which is also required to direct gene expression to a subset of the expression domains except for rhombomere 6 and the associated neural crest migrating to the third and posterior branchial arches. Element III482 can direct a higher level of reporter gene expression in r6, which led us to investigate whether kreisler is involved in regulating Hoxb3 expression in r6 through this element. However, our transgenic and mutational analysis has demonstrated that, although kreisler binding sites are present, they are not required for the establishment or maintenance of reporter gene expression in r6. Our results have provided evidence that the expression of Hoxb3 in the neural tube up to the r5/r6 boundary is auto/cross-regulated by Hox genes and expression of Hoxb3 in r6 does not require kreisler. © 2002 Elsevier Science (USA).published_or_final_versio

Assessing clusters and motifs from gene expression data

Large-scale gene expression studies and genomic sequencing projects are providing vast amounts of information that can be used to identify or predict cellular regulatory processes. Genes can be clustered on the basis of the similarity of their expression profiles or function and these clusters are likely to contain genes that are regulated by the same transcription factors. Searches for cis-regulatory elements can then be undertaken in the noncoding regions of the clustered genes. However, it is necessary to assess the efficiency of both the gene clustering and the postulated regulatory motifs, as there are many difficulties associated with clustering and determining the functional relevance of matches to sequence motifs. We have developed a method to assess the potential functional significance of clusters and motifs based on the probability of finding a certain number of matches to a motif in all of the gene clusters. To avoid problems with threshold scores for a match, the top matches to a motif are taken in several sample sizes. Genes from a sample are then counted by the cluster in which they appear. The probability of observing these counts by chance is calculated using the hypergeometric distribution. Because of the multiple sample sizes, strong and weak matching motifs can be detected and refined and significant matches to motifs across cluster boundaries are observed as all clusters are considered. By applying this method to many motifs and to a cluster set of yeast genes, we detected a similarity between Swi Five Factor and forkhead proteins and suggest that the currently unidentified Swi Five Factor is one of the yeast forkhead proteins.link_to_OA_fulltex