Browsing by Author "Vargas, Gabriela"

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    Negative Modulation of Macroautophagy by Stabilized HERPUD1 is Counteracted by an Increased ER-Lysosomal Network With Impact in Drug-Induced Stress Cell Survival
    (2022) Vargas, Gabriela; Cortes, Omar; Arias-Munoz, Eloisa; Hernandez, Sergio; Cerda-Troncoso, Cristobal; Hernandez, Laura; Gonzalez, Alexis E.; Tatham, Michael H.; Bustamante, Hianara A.; Retamal, Claudio; Cancino, Jorge; Varas-Godoy, Manuel; Hay, Ronald T.; Rojas-Fernandez, Alejandro; Cavieres, Viviana A.; Burgos, Patricia V.
    Macroautophagy and the ubiquitin proteasome system work as an interconnected network in the maintenance of cellular homeostasis. Indeed, efficient activation of macroautophagy upon nutritional deprivation is sustained by degradation of preexisting proteins by the proteasome. However, the specific substrates that are degraded by the proteasome in order to activate macroautophagy are currently unknown. By quantitative proteomic analysis we identified several proteins downregulated in response to starvation independently of ATG5 expression. Among them, the most significant was HERPUD1, an ER membrane protein with low expression and known to be degraded by the proteasome under normal conditions. Contrary, under ER stress, levels of HERPUD1 increased rapidly due to a blockage in its proteasomal degradation. Thus, we explored whether HERPUD1 stability could work as a negative regulator of autophagy. In this work, we expressed a version of HERPUD1 with its ubiquitin-like domain (UBL) deleted, which is known to be crucial for its proteasome degradation. In comparison to HERPUD1-WT, we found the UBL-deleted version caused a negative role on basal and induced macroautophagy. Unexpectedly, we found stabilized HERPUD1 promotes ER remodeling independent of unfolded protein response activation observing an increase in stacked-tubular structures resembling previously described tubular ER rearrangements. Importantly, a phosphomimetic S59D mutation within the UBL mimics the phenotype observed with the UBL-deleted version including an increase in HERPUD1 stability and ER remodeling together with a negative role on autophagy. Moreover, we found UBL-deleted version and HERPUD1-S59D trigger an increase in cellular size, whereas HERPUD1-S59D also causes an increased in nuclear size. Interestingly, ER remodeling by the deletion of the UBL and the phosphomimetic S59D version led to an increase in the number and function of lysosomes. In addition, the UBL-deleted version and phosphomimetic S59D version established a tight ER-lysosomal network with the presence of extended patches of ER-lysosomal membrane-contact sites condition that reveals an increase of cell survival under stress conditions. Altogether, we propose stabilized HERPUD1 downregulates macroautophagy favoring instead a closed interplay between the ER and lysosomes with consequences in drug-cell stress survival.
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    Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task
    (2023) Vargas, Gabriela; Araya, David; Sepulveda, Pradyumna; Rodriguez-Fernandez, Maria; Friston, Karl J.; Sitaram, Ranganatha; El-Deredy, Wael
    Introduction Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation.Methods We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning.Results Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning.Discussion The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.