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Agent-based modeling of the central amygdala and pain using cell-type specific physiological parameters.

ABSTRACT: The amygdala is a brain area involved in emotional regulation and pain. Over the course of the last 20 years, multiple researchers have studied sensory and motor connections within the amygdala in trying to understand the ultimate role of this structure in pain perception and descending control of pain. A number of investigators have been using cell-type specific manipulations to probe the underlying circuitry of the amygdala. As data have accumulated in this research space, we recognized a critical need for a single framework to integrate these data and evaluate emergent system-level responses. In this manuscript, we present an agent-based computational model of two distinct inhibitory neuron populations in the amygdala, those that express protein kinase C delta (PKCδ) and those that express somatostatin (SOM). We utilized a network of neural links to simulate connectivity and the transmission of inhibitory signals between neurons. Type-specific parameters describing the response of these neurons to noxious stimuli were estimated from published physiological and immunological data as well as our own wet-lab experiments. The model outputs an abstract measure of pain, which is calculated in terms of the cumulative pro-nociceptive and anti-nociceptive activity across neurons in both hemispheres of the amygdala. Results demonstrate the ability of the model to produce changes in pain that are consistent with published studies and highlight the importance of several model parameters. In particular, we found that the relative proportion of PKCδ and SOM neurons within each hemisphere is a key parameter in predicting pain and we explored model predictions for three possible values of this parameter. We compared model predictions of pain to data from our earlier behavioral studies and found areas of similarity as well as distinctions between the data sets. These differences, in particular, suggest a number of wet-lab experiments that could be done in the future.

SUBMITTER: Miller Neilan R

PROVIDER: S-EPMC8213159 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background:Physiochemical factors such as temperature, pH and cofactors are well known parameters that confer conformational changes in a protein structure. With S100? protein being a metal binding brain-specific receptor for both extracellular and intracellular functions, a change in conformation due to the above-mentioned factors, can compromise their cellular functions and therefore result in several pathological conditions such as Alzheimer's disease, Ischemic stroke, as well as Myocardial Infarction. Objective:The studies conducted sought to elucidate the effect of these physiological factors on the conformational dynamics of S100? protein using computational modeling approaches. Method:Temperature-dependent and protein-cofactor complexes molecular dynamics simulations were conducted by varying the temperature from 100 to 400K using GROMACS 5.0.3. Additionally, the conformational dynamics of the protein was studied by varying the pH at 5.0, 7.4 and 9.0 using Ambertools17. This was done by preparing the protein molecule, solvating and minimizing its energy level as well as heating it to the required temperature, equilibrating and simulating under desired conditions (NVT and NPT ensembles). Results:The results show that the protein misfolds as a function of increasing temperature with alpha helical content at 100K and 400K being 57.8% and 43.3%, respectively. However, the binding sites of the protein was not appreciably affected by temperature variations. The protein displayed high conformational instability in acidic medium (pH ~5.0). The binding sites of Ca2+, Mg2+ and Zn2+ were identified and each exhibited different groupings of the secondary structural elements (binding motifs). The secondary structure analysis revealed different conformational changes with the characteristic appearance of two beta hairpins in the presence of Zn2+and Mg2+. Conclusion:High temperatures, different cofactors and acidic pH confer conformational changes to the S100? structure and these results may inform the design of novel drugs against the protein.

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Agent-based modeling of the central amygdala and pain using cell-type specific physiological parameters.

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