Role of Ca2+,Mg2+-ATPases in Diabetes-Induced Alterations in Calcium Homeostasis in Input Neurons of the Nociceptive System

Affection of Comt1 genetype on anxiety and nociceptive sensitivity: an Ego-network analysis approach

Purpose

The aim of this study was to predict key genes and their relationships for anxiety and nociceptive sensitivity related to Comt1 genetype.

Methods

The raw data of E-GEOD-20160 related to anxiety and nociceptive sensitivity were obtained. Pearson correlation coefficient of interaction in protein–protein interaction was calculated. Topological analysis was processed for protein–protein interaction network, and genes in this network were ranked based on their degrees. Ego genes were identified, and models were searched and refined. A total of 1000 randomized tests were processed for ego networks. The classification accuracy of each ego network was obtained in this process.

Results

The interactions with genes in gene expression profiles were extracted, and protein–protein interaction was constructed. The protein–protein interaction included 4639 genes and 43,837 relationships. Differential co-expression network was constructed, and 74 ego genes were obtained. Thereinto, top five ego genes were ADCY2, GRM8, S1PR3, ADCY6, and ANXA1. After module searching and refinement, a total of 11 candidate modules were obtained, including module 14, module 51, and module 9. In addition, these 11 modules were confirmed to be with significance. Module 14 contained 10 genes, such as HRH3, DRD2, and CXCR3. Similarly, module 51 included six genes, such as HELZ2, NCOA3, and MED30.

Conclusions

Ego network analysis was a useful and comprehensive method for biomarkers screening. Several modules such as module 3 and module 36 were important subnetworks. Potential genes in these modules including ADCYs, GNAI1, DRD2, PNOC, CCR2, DRD2, and LPAR1 might be important genes in the research of anxiety and nociceptive sensitivity.

Modeling and analysis of the molecular basis of pain in sensory neurons

Intracellular calcium dynamics are critical to cellular functions like pain transmission. Extracellular ATP plays an important role in modulating intracellular calcium levels by interacting with the P2 family of surface receptors. In this study, we developed a mechanistic mathematical model of ATP-induced P2 mediated calcium signaling in archetype sensory neurons. The model architecture, which described 90 species connected by 162 interactions, was formulated by aggregating disparate molecular modules from literature. Unlike previous models, only mass action kinetics were used to describe the rate of molecular interactions. Thus, the majority of the 252 unknown model parameters were either association, dissociation or catalytic rate constants. Model parameters were estimated from nine independent data sets taken from multiple laboratories. The training data consisted of both dynamic and steady-state measurements. However, because of the complexity of the calcium network, we were unable to estimate unique model parameters. Instead, we estimated a family or ensemble of probable parameter sets using a multi-objective thermal ensemble method. Each member of the ensemble met an error criterion and was located along or near the optimal trade-off surface between the individual training data sets. The model quantitatively reproduced experimental measurements from dorsal root ganglion neurons as a function of extracellular ATP forcing. Hypothesized architecture linking phosphoinositide regulation with P2X receptor activity explained the inhibition of P2X-mediated current flow by activated metabotropic P2Y receptors. Sensitivity analysis using individual and the whole system outputs suggested which molecular subsystems were most important following P2 activation. Taken together, modeling and analysis of ATP-induced P2 mediated calcium signaling generated qualitative insight into the critical interactions controlling ATP induced calcium dynamics. Understanding these critical interactions may prove useful for the design of the next generation of molecular pain management strategies.