Activation Pathways of Neurotensin Receptor 1 Elucidated Using Statistical Machine Learning

Construction of a Lung Adenocarcinoma Prognostic Model Utilizing Serine and Glycine Metabolism-Related Genes

The objective of this study was to construct a prognostic model by utilizing serine/glycine metabolism-related genes (SGMGs), thus establishing a risk score for lung adenocarcinoma (LUAD). Based on the TCGA-LUAD and SGMG data set, two subtypes with different SGMG expression levels were identified by clustering analysis. Thirteen differential expression genes were used to construct RiskScore by Cox regression. GSE72094 data set was used for validation. The survival characteristics, immune features, and potential benefits of chemotherapy drugs were analyzed for two risk groups. RiskScore was constructed based on the genes ABCC12, RIC3, CYP4B1, SFTPB, CACNA2D2, IGF2BP1, NTSR1, DKK1, CREG2, PITX3, RGS20, FETUB, and IGFBP1. Patients in the low-risk (LR) group exhibited a superior overall survival. In addition, aDCs, iDSs, mast cells, neutrophils, HLA, and type II IFN were more abundant in the LR group with higher IPS scores and lower TIDE scores. In contrast, NK cells, APC coinhibition, and MHC-I were more common in the high-risk (HR) group, which may be more sensitive to chemotherapy drugs such as cisplatin, oxaliplatin, and nilotinib. RiskScore was a promising biomarker that can be used to distinguish LUAD prognosis, immune features, and sensitivity to chemotherapy drugs.

Mechanistic Elucidation of Activation/Deactivation Signal Transduction within Neurotensin Receptor 1 Triggered by 'Driver Chemical Groups' of Modulators: A Comparative Molecular Dynamics Simulation

Small-molecule modulators of neurotensin receptor 1 (NTSR1), a class A G-protein-coupled receptor (GPCR), has emerged as promising therapeutic agent for psychiatric disorders and cancer. Interestingly, a chemical group substitution in NTSR1 modulators can launch different types of downstream regulation, highlighting the significance of deciphering the internal fine-tuning mechanism. Here, we conducted a synergistic application of a Gaussian accelerated molecular dynamics simulation, a conventional molecular dynamics simulation, and Markov state models (MSM) to investigate the underlying mechanism of 'driver chemical groups' of modulators triggering inverse signaling. The results indicated that the flexibility of the leucine moiety in NTSR1 agonists contributes to the inward displacement of TM7 through a loosely coupled allosteric pathway, while the rigidity of the adamantane moiety in NTSR1 antagonists leads to unfavorable downward transduction of agonistic signaling. Furthermore, we found that R322^6.54, Y319^6.51, F353^7.42, R148^3.32, S356^7.45, and S357^7.46 may play a key role in inducing the activation of NTSR1. Together, our findings not only highlight the ingenious signal transduction within class A GPCRs but also lay a foundation for the development of targeted drugs harboring different regulatory functions of NTSR1.