<p> <p>&nbsp / </p> </p> <p align="left">This study utilized large-scale gene expression data to define the regulatory networks of genes expressing in the hippocampus to which multiple disease pathologies may be associated. Specific aims were: ident i fy key regulatory transcription factors (TFs) responsible for observed gene expression patterns, reconstruct transcription regulatory networks, and prioritize likely TFs responsible for anatomically restricted gene expression. Most of the analysis was restricted to the CA3 sub-region of Ammon&rsquo / s horn within the hippocampus. We identified 155 core genes expressing throughout the CA3 sub-region and predicted corresponding TF binding site (TFBS) distributions. Our analysis shows plausible transcription regulatory networks for twelve clusters of co-expressed genes. We demonstrate the validity of the predictions by re-clustering genes based on TFBS distributions and found that genes tend to be correctly assigned to groups of previously identified co-expressing genes with sensitivity of 67.74% and positive predictive value of 100%. Taken together, this study represents one of the first to merge anatomical architecture, expression profiles and transcription regulatory potential on such a large scale in hippocampal sub-anatomy.</p>

<p> <p>&nbsp / </p> </p> <p align="left">This study utilized large-scale gene expression data to define the regulatory networks of genes expressing in the hippocampus to which multiple disease pathologies may be associated. Specific aims were: ident i fy key regulatory transcription factors (TFs) responsible for observed gene expression patterns, reconstruct transcription regulatory networks, and prioritize likely TFs responsible for anatomically restricted gene expression. Most of the analysis was restricted to the CA3 sub-region of Ammon&rsquo / s horn within the hippocampus. We identified 155 core genes expressing throughout the CA3 sub-region and predicted corresponding TF binding site (TFBS) distributions. Our analysis shows plausible transcription regulatory networks for twelve clusters of co-expressed genes. We demonstrate the validity of the predictions by re-clustering genes based on TFBS distributions and found that genes tend to be correctly assigned to groups of previously identified co-expressing genes with sensitivity of 67.74% and positive predictive value of 100%. Taken together, this study represents one of the first to merge anatomical architecture, expression profiles and transcription regulatory potential on such a large scale in hippocampal sub-anatomy.</p>