V1R promoters are well conserved and exhibit common putative regulatory motifs

摘要

Background The mouse vomeronasal organ (VNO) processes chemosensory information, including pheromone signals that influence reproductive behaviors. The sensory neurons of the VNO express two types of chemosensory receptors, V1R and V2R. There are ~165 V1R genes in the mouse genome that have been classified into ~12 divergent subfamilies. Each sensory neuron of the apical compartment of the VNO transcribes only one of the repertoire of V1R genes. A model for mutually exclusive V1R transcription in these cells has been proposed in which each V1R gene might compete stochastically for a single transcriptional complex. This model predicts that the large repertoire of divergent V1R genes in the mouse genome contains common regulatory elements. In this study, we have characterized V1R promoter regions by comparative genomics and by mapping transcription start sites. Results We find that transcription is initiated from ~1 kb promoter regions that are well conserved within V1R subfamilies. While cross-subfamily homology is not evident by traditional methods, we developed a heuristic motif-searching tool, LogoAlign, and applied this tool to identify motifs shared within the promoters of all V1R genes. Our motif-searching tool exhibits rapid convergence to a relatively small number of non-redundant solutions (97% convergence). We also find that the best motifs contain significantly more information than those identified in controls, and that these motifs are more likely to be found in the immediate vicinity of transcription start sites than elsewhere in gene blocks. The best motifs occur near transcription start sites of ~90% of all V1R genes and across all of the divergent subfamilies. Therefore, these motifs are candidate binding sites for transcription factors involved in V1R co-regulation. Conclusion Our analyses show that V1R subfamilies have broad and well conserved promoter regions from which transcription is initiated. Results from a new motif-finding algorithm, LogoAlign, designed for this context and more generally for searching large, hierarchical datasets, suggest the existence of common information-rich regulatory motifs that are shared across otherwise divergent V1R subfamilies.