Partial or Complete Loss of Norepinephrine Differentially Alters Contextual Fear and Catecholamine Release Dynamics in Hippocampal CA1.

Background: Contextual fear learning is heavily dependent on the hippocampus. Despite evidence that catecholamines contribute to contextual encoding and memory retrieval, the precise temporal dynamics of their release in the hippocampus during behavior is unknown. In addition, new animal models are required to probe the effects of altered catecholamine synthesis on release dynamics and contextual learning. Methods: We generated 2 new mouse models of altered locus coeruleus-norepinephrine (NE) synthesis and utilized them together with GRABNE and GRABDA sensors and in vivo fiber photometry to investigate NE and dopamine (DA) release dynamics in the dorsal hippocampal CA1 during contextual fear conditioning. Results: Aversive foot shock increased both NE and DA release in the dorsal CA1, while freezing behavior associated with recall of fear memory was accompanied by decreased release. Moreover, we found that freezing at the recent time point was sensitive to both partial and complete loss of locus coeruleus-NE synthesis throughout prenatal and postnatal development, similar to previous observations of mice with global loss of NE synthesis beginning postnatally. In contrast, freezing at the remote time point was compromised only by complete loss of locus coeruleus-NE synthesis beginning prenatally. Conclusions: Overall, these findings provide novel insights into the role of NE in contextual fear and the precise temporal dynamics of both NE and DA during freezing behavior and highlight complex relationships between genotype, sex, and NE signaling.

Towards a comprehensive regulatory map of Mammalian Genomes.

Genome mapping studies have generated a nearly complete collection of genes for the human genome, but we still lack an equivalently vetted inventory of human regulatory sequences. Cis-regulatory modules (CRMs) play important roles in controlling when, where, and how much a gene is expressed. We developed a training data-free CRM-prediction algorithm, the Mammalian Regulatory MOdule Detector (MrMOD) for accurate CRM prediction in mammalian genomes. MrMOD provides genome position-fixed CRM models similar to the fixed gene models for the mouse and human genomes using only genomic sequences as the inputs with one adjustable parameter - the significance p-value. Importantly, MrMOD predicts a comprehensive set of high-resolution CRMs in the mouse and human genomes including all types of regulatory modules not limited to any tissue, cell type, developmental stage, or condition. We computationally validated MrMOD predictions used a compendium of 21 orthogonal experimental data sets including thousands of experimentally defined CRMs and millions of putative regulatory elements derived from hundreds of different tissues, cell types, and stimulus conditions obtained from multiple databases. In ovo transgenic reporter assay demonstrates the power of our prediction in guiding experimental design. We analyzed CRMs located in the chromosome 17 using unsupervised machine learning and identified groups of CRMs with multiple lines of evidence supporting their functionality, linking CRMs with upstream binding transcription factors and downstream target genes. Our work provides a comprehensive base pair resolution annotation of the functional regulatory elements and non-functional regions in the mammalian genomes.