Exploring TFE3 Overexpression as a Novel Strategy to Promote Lysosomal Biogenesis and Enhance Cellular Resilience in Vulnerable Neuronal Populations

Overview

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are fatal neurodegenerative disorders characterized by progressive neuronal loss and pathological mislocalization of TDP -43, an RNA -binding protein essential for cellular homeostasis. Im paired lysosomal degradation contributes to disease progression by disrupting protein clearance, leading to toxic accumulation of misfolded proteins and cellular dysfunction. The MiT/TFE transcription factor family, including TFE3, regulates lysosomal biog enesis and autophagy, making it a promising target for therapeutic intervention. However, the precise role of TFE3 in neuronal lysosomal function remains poorly understood. This study investigated whether TFE3 overexpression enhances lysosomal biogenesis, whether forced nuclear localization alters lysosomal function, and whether nuclear mislocalization induces cellular stress. Using stereotaxic AAV-mediated delivery, FLAG-TFE3 was overexpressed in the mouse hippocampus, while a tamoxifen-inducible FLAG-TFE3-ERT construct enabled controlled nuclear translocation. Immunofluorescence and quantitative image analysis were performed to assess lysosomal activity (cathepsin D, CTSD) and DNA damage (γH2AX). TFE3 overexpression significantly increased CTSD expression, confirming enhanced lysosomal biogenesis. However, forced nuclear localization did not elevate overall CTSD levels but altered its subcellular distribution, forming punctate structures indicative of impaired lysosomal trafficking. Additionally, TFE3-ERT expression markedly increased γH2AX levels, signaling elevated DNA damage and cellular stress. These findings suggest that while cytoplasmic TFE3 promotes lysosomal function and biogenesis, its nuclear mislocalization induces toxicity. Future studies should elucidate the mechanisms regulating TFE3 trafficking to harness its lysosomal benefits while minimizing nuclear stress. Understanding these pathways may provide novel therapeutic strategies to enhance TDP -43 clearance and mitigate neurodegeneration in FTD/ALS. Using DeePMD to Predict the Relaxed MoTe₂ Moiré Structure Monisha Bommu Alabama School of Fine Arts, Birmingham, AL The modeling of MoTe₂ bilayers is essential for applications in two-dimensional materials science. Molybdenum ditelluride (MoTe ₂) is a layered transition metal dichalcogenide (TMD) with remarkable electronic and structural properties, making it desirable for next-generation electronic, optoelectronic, and spintronic devices. When stacked as bilayers, MoTe ₂ exhibits intriguing electronic behaviors, which depend critically on the stacking order, interlayer interactions, and strain effects. Density Functional Theory (DFT) is the primary model used to predict the electronic structure of MoTe₂ bilayers, showing detailed predictions of how atomic configurations influence electronic band structures, charge distribution, and interlayer coupling. However, DFT’s high computational cost is a challenge for large -scale simulations. By training AI model s on DFT - generated data for MoTe ₂ bilayers, we can significantly accelerate these simulations while preserving sufficient accuracy, allowing for a deeper exploration of their electronic properties and functional potential. This research investigates bilayer MoTe ₂ systems by examining the atomic displacements and interlayer forces under relaxation. A dataset made up of 144 bilayer configurations was analyzed, each with varying atomic shifts and stacking arrangements. Using DFT simulations, relaxed atomic structures were extracted, providing energy and force data critical for understanding stability and deformation behavior. A machine learning model based on the DeePMD framework uses atomic coordinate to predict interatomic interactions like force and energy with high accuracy, enabling compu tationally efficient exploration of the material's potential energy surface. The DeePMD model was able to predict the energy of a MoTe₂ system with an error of 0.000241 and force with an error of 0.000786.

Competition history

• JSHS 2025 Category not listed

Resources

• National JSHS abstract book ↗

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