Kashi, A. | Search bioRxiv

bioRxiv2026

Chronic ER Stress Disrupts Mitochondrial-Associated ER Membrane Integrity in Corneal Endothelial Cells.

PurposeFuchs endothelial corneal dystrophy (FECD) is an age-related degenerative disease of the corneal endothelium cells (CEnCs), affecting 4% of the US population over 40. While Endoplasmic reticulum (ER) and mitochondrial stress have been independently associated with FECD pathogenesis, few studies have examined ER-mitochondrial interactions/ER-mitochondrial contact sites/mitochondria-associated ER membrane (MAM), or MAM proteins, and their contribution to ER and mitochondrial stress in FECD. This study aims to characterize alterations in MAMs and identify key MAM proteins associated with ER and mitochondrial stress in FECD. MethodHuman corneal endothelial cell line (HCEnC-21T) and Fuchs corneal endothelial cell line (F35T) were cultured and subjected to ER stressor tunicamycin (1, 10 g/ml) for 6 and 24 hours. MAM proteins were isolated by subcellular fractionation, and key ER and mitochondrial-damage-sensor proteins, such as PERK and Parkin, respectively, were identified by immunoblotting. ER-mitochondrial contact sites were quantified using the MAM plasmid and transmission electron microscopy (TEM) in normal and Fuchs cell lines, as well as in human tissues under chronic ER stress. ResultsER-mitochondrial contact distance significantly increased in Fuchs tissues compared with normal tissues, and a similar increase was observed in 21T cell line after tunicamycin treatment. There was a significant increase in the intensity of the MAM plasmid upon tunicamycin treatment at 6 hours in the 21T cell line compared to the non-treated control. However, MAM plasmid intensity significantly decreased at 24 hours compared to 6 hours post-tunicamycin treatment in 21T cell line. Analysis of MAM function by quantifying phosphatidylserine synthase 1 (PSS1 [gene PTDSS1]) expression in 21T cells showed a reduction in PTDSS1 expression after 24 hours of tunicamycin treatment. ER stress protein PERK and mitochondria damage sensor protein (Parkin) significantly increased in the MAM fraction after tunicamycin at 24 hours in 21T cell line. ConclusionsFuchs cell lines and tissues demonstrate decreased ER-mitochondrial interactions/MAMs, which are also seen in 21T cell line after chronic ER stress. Under chronic ER stress, ER and mitochondrial stress mediator proteins are translocated to MAM. This study highlights the importance of MAMs as a potential mediator of ER-mitochondria crosstalk in degenerating corneal endothelial cells for FECD.

cell biology↗

bioRxiv2024

Diverse Database and Machine Learning Model to narrow the generalization gap in RNA structure prediction

Understanding macromolecular structures of proteins and nucleic acids is critical for discerning their functions and biological roles. Advanced techniques--crystallography, NMR, and CryoEM--have facilitated the determination of over 180,000 protein structures, all cataloged in the Protein Data Bank (PDB). This comprehensive repository has been pivotal in developing deep learning algorithms for predicting protein structures directly from sequences. In contrast, RNA structure prediction has lagged, and suffers from a scarcity of structural data. Here, we present the secondary structure models of 1098 pri-miRNAs and 1456 human mRNA regions determined through chemical probing. We develop a novel deep learning architecture, inspired from the Evoformer model of Alphafold and traditional architectures for secondary structure prediction. This new model, eFold, was trained on our newly generated database and over 300,000 secondary structures across multiple sources. We benchmark eFold on two new test sets of long and diverse RNA structures and show that our dataset and new architecture contribute to increasing the prediction performance, compared to similar state-of-the-art methods. All together, our results reveal that merely expanding the database size is insufficient for generalization across families, whereas incorporating a greater diversity and complexity of RNAs structures allows for enhanced model performance.

biochemistry↗