Comparative Distribution of Relaxin-3 Inputs and Calcium-Binding Protein-Positive Neurons in Rat Amygdala

The neural circuits involved in mediating complex behaviors are being rapidly elucidated using various newly developed and powerful anatomical and molecular techniques, providing insights into the neural basis for anxiety disorders, depression, addiction, and dysfunctional social behaviors. Many of these behaviors and associated physiological processes involve the activation of the amygdala in conjunction with cortical and hippocampal circuits. Ascending subcortical projections provide modulatory inputs to the extended amygdala and its related nodes (or ‘hubs’) within these key circuits. One such input arises from the nucleus incertus (NI) in the tegmentum, which sends amino acid- and peptide-containing projections throughout the forebrain. Notably, a distinct population of GABAergic NI neurons expresses the highly-conserved neuropeptide, relaxin-3, and relaxin-3 signaling has been implicated in the modulation of reward/motivation and anxiety- and depressive-like behaviors in rodents via actions within the extended amygdala. Thus, a detailed description of the relaxin-3 innervation of the extended amygdala would provide an anatomical framework for an improved understanding of NI and relaxin-3 modulation of these and other specific amygdala-related functions. Therefore, in this study, we examined the distribution of NI projections and relaxin-3-positive elements (axons/fibers/terminals) within the amygdala, relative to the distribution of neurons expressing the calcium-binding proteins, parvalbumin, calretinin and/or calbindin. Anterograde tracer injections into the NI revealed a topographic distribution of NI efferents within the amygdala that was near identical to the distribution of relaxin-3-immunoreactive fibers. Highest densities of anterogradely-labeled elements and relaxin-3-immunoreactive fibers were observed in the medial nucleus of the amygdala, medial divisions of the bed nucleus of the stria terminalis (BST) and in the endopiriform nucleus. In contrast, sparse anterogradely-labeled and relaxin-3-immunoreactive fibers were observed in other amygdala nuclei, including the lateral, central and basal nuclei and the nucleus accumbens lacked any innervation. Using synaptophysin as a synaptic marker, we identified relaxin-3 positive synaptic terminals in the medial amygdala, BST and endopiriform nucleus of amygdala. Our findings demonstrate the existence of topographic NI and relaxin-3-containing projections to specific nuclei of the extended amygdala, consistent with a likely role for this putative integrative arousal system in the regulation of amygdala-dependent social and emotional behaviors

Validation of ‘Somnivore’, a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, SomnivoreTM, for automated wake–sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons (n = 5). Training and testing sets for validation were previously scored manually by 1–2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 ± 0.01; N1 0.57 ± 0.01; N2 0.81 ± 0.01; N3 0.86 ± 0.01; REM 0.87 ± 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 ± 0.01; NREM 0.94 ± 0.01; REM 0.91 ± 0.01) and pigeon (wake 0.96 ± 0.006; NREM 0.97 ± 0.01; REM 0.86 ± 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies

Development of a single-chain peptide agonist of the relaxin-3 receptor using hydrocarbon stapling

Structure activity studies of the insulin super family member, relaxin-3, have shown that its G protein-coupled receptor (RXFP3) binding site is contained within its central B-chain a-helix and this helical structure is essential for receptor activation. We sought to develop a single B-chain mimetic that retained agonist activity. This was achieved by use of solid phase peptide synthesis together with on-resin ruthenium-catalyzed ring closure metathesis of a pair of judiciously placed i,i+4 alpha-methyl, alpha-alkenyl amino acids. The resulting hydrocarbon stapled peptide was shown by solution NMR spectroscopy to mimic the native helical conformation of relaxin-3 and to possess potent RXFP3 receptor binding and activation. Alternative stapling procedures were unsuccessful, highlighting the critical need to carefully consider both the peptide sequence and stapling methodology for optimal outcomes. Our result is the first successful minimization of an insulin-like peptide to a single-chain alpha-helical peptide agonist which will facilitate study of the function of relaxin-3