A forebrain neural substrate for behavioral thermoregulation.

Thermoregulatory behavior is a basic motivated behavior for body temperature homeostasis. Despite its fundamental importance, a forebrain region or defined neural population required for this process has yet to be established. Here, we show that Vgat-expressing neurons in the lateral hypothalamus (LHVgat neurons) are required for diverse thermoregulatory behaviors. The population activity of LHVgat neurons is increased during thermoregulatory behavior and bidirectionally encodes thermal punishment and reward (P&R). Although this population also regulates feeding and caloric reward, inhibition of parabrachial inputs selectively impaired thermoregulatory behaviors and encoding of thermal stimulus by LHVgat neurons. Furthermore, two-photon calcium imaging revealed a subpopulation of LHVgat neurons bidirectionally encoding thermal P&R, which is engaged during thermoregulatory behavior, but is largely distinct from caloric reward-encoding LHVgat neurons. Our data establish LHVgat neurons as a required neural substrate for behavioral thermoregulation and point to the key role of the thermal P&R-encoding LHVgat subpopulation in thermoregulatory behavior.

Coarse grained normal mode analysis vs. refined Gaussian Network Model for protein residue-level structural fluctuations.

We investigate several approaches to coarse grained normal mode analysis on protein residual-level structural fluctuations by choosing different ways of representing the residues and the forces among them. Single-atom representations using the backbone atoms C(α), C, N, and C(ß) are considered. Combinations of some of these atoms are also tested. The force constants between the representative atoms are extracted from the Hessian matrix of the energy function and served as the force constants between the corresponding residues. The residue mean-square-fluctuations and their correlations with the experimental B-factors are calculated for a large set of proteins. The results are compared with all-atom normal mode analysis and the residue-level Gaussian Network Model. The coarse-grained methods perform more efficiently than all-atom normal mode analysis, while their B-factor correlations are also higher. Their B-factor correlations are comparable with those estimated by the Gaussian Network Model and in many cases better. The extracted force constants are surveyed for different pairs of residues with different numbers of separation residues in sequence. The statistical averages are used to build a refined Gaussian Network Model, which is able to predict residue-level structural fluctuations significantly better than the conventional Gaussian Network Model in many test cases.