Acute ACAT1/SOAT1 Blockade Increases MAM Cholesterol and Strengthens ER-Mitochondria Connectivity.

Cholesterol is a key component of all mammalian cell membranes. Disruptions in cholesterol metabolism have been observed in the context of various diseases, including neurodegenerative disorders such as Alzheimer's disease (AD). The genetic and pharmacological blockade of acyl-CoA:cholesterol acyltransferase 1/sterol O-acyltransferase 1 (ACAT1/SOAT1), a cholesterol storage enzyme found on the endoplasmic reticulum (ER) and enriched at the mitochondria-associated ER membrane (MAM), has been shown to reduce amyloid pathology and rescue cognitive deficits in mouse models of AD. Additionally, blocking ACAT1/SOAT1 activity stimulates autophagy and lysosomal biogenesis; however, the exact molecular connection between the ACAT1/SOAT1 blockade and these observed benefits remain unknown. Here, using biochemical fractionation techniques, we observe cholesterol accumulation at the MAM which leads to ACAT1/SOAT1 enrichment in this domain. MAM proteomics data suggests that ACAT1/SOAT1 inhibition strengthens the ER-mitochondria connection. Confocal and electron microscopy confirms that ACAT1/SOAT1 inhibition increases the number of ER-mitochondria contact sites and strengthens this connection by shortening the distance between these two organelles. This work demonstrates how directly manipulating local cholesterol levels at the MAM can alter inter-organellar contact sites and suggests that cholesterol buildup at the MAM is the impetus behind the therapeutic benefits of ACAT1/SOAT1 inhibition.

Metabolic interventions improve HBV envelope-specific T-cell responses in patients with chronic hepatitis B.

BACKGROUND: Restoration of HBV-specific T cell immunity is a promising approach for the functional cure of chronic Hepatitis B (CHB), necessitating the development of valid assays to boost and monitor HBV-specific T cell responses in patients with CHB. METHODS: We analyzed hepatitis B virus (HBV) core- and envelope (env)-specific T cell responses using in vitro expanded peripheral blood mononuclear cells (PBMCs) from patients with CHB exhibiting different immunological phases, including immune tolerance (IT), immune activation (IA), inactive carrier (IC), and HBeAg-negative hepatitis (ENEG). Additionally, we evaluated the effects of metabolic interventions, including mitochondria-targeted antioxidants (MTA), polyphenolic compounds, and ACAT inhibitors (iACAT), on HBV-specific T-cell functionality. RESULTS: We found that HBV core- and env-specific T cell responses were finely coordinated and more profound in IC and ENEG than in the IT and IA stages. HBV env-specific T cells were more dysfunctional but prone to respond to metabolic interventions using MTA, iACAT, and polyphenolic compounds than HBV core-specific T-cells. The responsiveness of HBV env-specific T cells to metabolic interventions can be predicted by the eosinophil (EO) count and the coefficient of variation of red blood cell distribution width (RDW-CV). CONCLUSION: These findings may provide valuable information for metabolically invigorating HBV-specific T-cells to treat CHB.

Bio-activity of aminosulfonyl ureas in the light of nucleic acid bases and DNA base pair interaction.

The quantum chemical descriptors based on density functional theory (DFT) are applied to predict the biological activity (log IC50) of one class of acyl-CoA: cholesterol O-acyltransferase (ACAT) inhibitors, viz. aminosulfonyl ureas. ACAT are very effective agents for reduction of triglyceride and cholesterol levels in human body. Successful two parameter quantitative structure-activity relationship (QSAR) models are developed with a combination of relevant global and local DFT based descriptors for prediction of biological activity of aminosulfonyl ureas. The global descriptors, electron affinity of the ACAT inhibitors (EA) and/or charge transfer (ΔN) between inhibitors and model biosystems (NA bases and DNA base pairs) along with the local group atomic charge on sulfonyl moiety (∑QSul) of the inhibitors reveals more than 90% efficacy of the selected descriptors for predicting the experimental log (IC50) values.