Hossain, M. Z. | Search bioRxiv

bioRxiv2023

Bilayer tension-induced clustering of the UPR sensor IRE1

The endoplasmic reticulum acts as a protein quality control center where a range of chaperones and foldases facilitates protein folding. IRE1 is a sensory trans-membrane protein that transduces signals of proteotoxic stress by forming clusters and activating a cellular program called the unfolded protein response (UPR). Recently, membrane thickness variation due to membrane compositional changes have been shown to drive IRE1 cluster formation, activating the UPR even in the absence of proteotoxic stress. Here, we demonstrate a direct relationship between bilayer tension and UPR activation based on IRE1 dimer stability. The stability of the IRE1 dimer in a (50%DOPC-50%POPC) membrane at different applied bilayer tensions was analyzed via molecular dynamics simulations. The potential of mean force for IRE1 dimerization predicts a higher concentration of IRE1 dimers for both tensed and compressed ER membranes. This study shows that IRE1 may be a mechanosensitive membrane protein and establishes a direct biophysical relationship between bilayer tension and UPR activation. HighlightsO_LIMechanical perturbation of the ER membrane favor oligomerization. C_LIO_LIBoth tension and compression promote IRE1 dimer formation. C_LIO_LIIRE1 is mechanosensitive, potentially the UPR to changes in ER membrane tension and compression. C_LI

biophysics↗

bioRxiv2023

Spatial Transcriptomics Analysis of Gene Expression Prediction using Exemplar Guided Graph Neural Network

Spatial transcriptomics (ST) is essential for understanding diseases and developing novel treatments. It measures the gene expression of each fine-grained area (i.e., different windows) in the tissue slide with low throughput. This paper proposes an exemplar guided graph network dubbed EGGN to accurately and efficiently predict gene expression from each window of a tissue slide image. We apply exemplar learning to dynamically boost gene expression prediction from nearest/similar exemplars of a given tissue slide image window. Our framework has three main components connected in a sequence: i) an extractor to structure a feature space for exemplar retrievals; ii) a graph construction strategy to connect windows and exemplars as a graph; iii) a graph convolutional network backbone to process window and exemplar features, and a graph exemplar bridging block to adaptively revise the window features using its exemplars. Finally, we complete the gene expression prediction task with a simple attention-based prediction block. Experiments on standard benchmark datasets indicate the superiority of our approach when compared with past state-of-the-art methods. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/534914v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@6507f5org.highwire.dtl.DTLVardef@ed7ea5org.highwire.dtl.DTLVardef@159fd5org.highwire.dtl.DTLVardef@1b09270_HPS_FORMAT_FIGEXP M_FIG C_FIG In this paper, we aim to predict gene expression of each window in a tissue slide image. Given a tissue slide image, we encode the windows to feature space, retrieve its exemplars from the reference dataset, construct a graph, and then dynamically predict gene expression of each window with our exemplar guided graph network. HighlightsO_LIWe propose an exemplar guided graph network to accurately predict gene expression from a slide image window. C_LIO_LIWe design a graph construction strategy to connect windows and exemplars for performing exemplar learning of gene expression prediction. C_LIO_LIWe propose a graph exemplar bridging block to revise the window feature by using its nearest exemplars. C_LIO_LIExperiments on two standard benchmark datasets demonstrate our superiority when compared with state-of-the-art approaches. C_LI

bioinformatics↗