Cha, S.-K. | bioRxiv Science

bioRxiv2025

Perilysosomal Ca2+ overload impairs autophagic degradation in β-cell lipotoxicity

Saturated fatty acids impose lipotoxic stress on pancreatic {beta}-cells, leading to {beta}-cell failure and diabetes. In this study, we investigate the critical role of organellar Ca2+ disturbance on defective autophagy and {beta}-cell lipotoxicity. Palmitate, a saturated fatty acid, induced perilysosomal Ca2+ elevation, sustained mTORC1 activation on the lysosomal membrane, suppression of the lysosomal transient receptor potential mucolipin 1 (TRPML1) channel, and accumulation of undigested autophagosomes in {beta}-cells. These Ca2+ aberrations with autophagy defects by palmitate were prevented by a mTORC1 inhibitor or a mitochondrial superoxide scavenger. To alleviate perilysosomal Ca2+ overload, strategies such as lowering extracellular Ca2+, employing voltage-gated Ca2+ channel blocker or ATP-sensitive K+ channel opener effectively abrogated mTORC1 activation and preserved autophagy. Furthermore, redirecting perilysosomal Ca2+ into the endoplasmic reticulum (ER) with an ER Ca2+ ATPase activator, restores TRPML1 activity, promotes autophagic flux, and improves survival of {beta}-cells exposed to palmitate-induced lipotoxicity. Our findings suggest oxidative stress-Ca2+ overload-mTORC1 pathway involves in TRPML1 suppression and defective autophagy during {beta}-cell lipotoxicity. Restoring perilysosomal Ca2+ homeostasis emerges as a promising therapeutic strategy for metabolic diseases.

physiology↗

bioRxiv2023

GraphMHC: neoantigen prediction model applying the graph neural network to molecular structure

Neoantigens are biomarkers that can predict the prognosis associated with immune checkpoint inhibition by estimating the binding potential of candidate peptides to somatic mutation and major histocompatibility complex (MHC) proteins. Although deep neural networks have been primarily used for these prediction models, it is difficult to consider the models reported thus far as accurately representing the interactions between biomolecules. In this study, we propose the GraphMHC model, which utilizes a graph neural network model through molecular structure to simulate the binding between MHC proteins and peptide sequences. Amino acid sequences sourced from the immune epitope database (IEDB) undergo conversion into molecular structures. Subsequently, atomic intrinsic informations and inter-atomic connections are extracted and structured as a graph representation. Bindings are classified by feeding them into the GraphMHC network, comprising stacked graph attention and convolution layers. The prediction results from the test set using the GraphMHC model showed a high performance with an area under the receiver operating characteristic curve of 92.2% (91.9-92.5%), surpassing the baseline model. Moreover, by applying the GraphMHC model to melanoma patient data from the Cancer Genome Atlas project, we found a borderline difference in overall survival and a significant difference in stromal score between the high and low neoantigen load groups. This distinction was not present in the baseline model. This study presents the first feature-intrinsic method based on biochemical molecular structure for modeling the binding between MHC protein sequences and neoantigen candidate peptide sequences. The model can provide highly accurate suitability information for cancer patients who want to apply immune checkpoint inhibitors. Author summary

bioinformatics↗