Glutathione limits Ero1-dependent oxidation in the endoplasmic reticulum

Many proteins of the secretory pathway contain disulfide bonds that are essential for structure and function. In the endoplasmic reticulum (ER), Ero1alpha and Ero1beta oxidize protein disulfide isomerase (PDI), which in turn transfers oxidative equivalents to newly synthesized cargo proteins. However, oxidation must be limited, as some reduced PDI is necessary for disulfide isomerization and ER-associated degradation. Here we show that in semipermeable cells, PDI is more oxidized, disulfide bonds are formed faster, and high molecular mass covalent protein aggregates accumulate in the absence of cytosol. Addition of reduced glutathione (GSH) reduces PDI and restores normal disulfide formation rates. A higher GSH concentration is needed to balance oxidative folding in semipermeable cells overexpressing Ero1alpha, indicating that cytosolic GSH and lumenal Ero1alpha play antagonistic roles in controlling the ER redox. Moreover, the overexpression of Ero1alpha significantly increases the GSH content in HeLa cells. Our data demonstrate tight connections between ER and cytosol to guarantee redox exchange across compartments: a reducing cytosol is important to ensure disulfide isomerization in secretory proteins

Towards Empathetic Social Robots: Investigating the Interplay between Facial Expressions and Brain Activity

The pursuit of creating empathetic social robots that can understand and respond to human emotions is a critical challenge in Robotics and Artificial Intelligence. Social robots, designed to interact with humans in various settings, from healthcare to customer service, require a sophisticated understanding of human emotional states to resonate and effectively assist truly. Our research contributes to this ambitious goal by exploring the relationship between natural facial expressions and brain activity in these human-robot interactions, as captured by electroencephalogram (EEG) signals. This paper presents our initial steps towards this attempt. We want to find which areas in the participant user’s brain are most activated and how these activations correlate with facial expressions. Understanding these correlations is essential for developing social robots that recognize and empathize with various human emotions. Our approach combines neuroscience and computer science, offering a novel perspective in the quest to enhance the emotional intelligence of social robots. We share some preliminary results on a new multimodal dataset that we are developing, providing valuable insights into the potential of our work to improve the personalization and emotional depth of social robot interactions