Ba, J. | bioRxiv Science

bioRxiv2024

Capsaicin acts as a novel NRF2 agonist to suppress ethanol induced gastric mucosa oxidative damage by directly disrupting the KEAP1-NRF2 interaction

Excessive alcohol consumption poses significant health risks and is closely associated with oxidative damage. The KEAP1-NRF2-ARE signaling pathway serves as the primary antioxidant system. However, current small molecule inhibitors are all covalently bound to KEAP1, meaning that once bound, they are not easily dissociated, while continuous inhibition of KEAP1 exhibits severe side effects. In this study, BLI, CETSA, Pull-down, Co-IP and HDX-MS assay analysis were conducted to detect the KEAP1 binding behavior of natural product, capsaicin (CAP), both in vitro and in cells. The ethanol-induced acute gastric mucosal damage rat model was also established to evaluate the therapeutic effect of CAP. Our findings demonstrated that CAP mitigated mitochondrial damage, facilitated the nuclear translocation of NRF2, leading to the up-regulation of downstream antioxidant response elements, HMOX1, TXN, GSS and NQO1 in GES-1 cells. Furthermore, CAP directly bind to KEAP1 and inhibit the interaction between KEAP1 and NRF2. In the KEAP1-knockout 293T cells, CAP failed to activate NRF2 expression. We identified that CAP non-covalently bound to Kelch domain and allosterically regulated three specific regions of KEAP1 : L342-L355, D394-G423 and N482-N495. To improve drug solubility and delivery efficiency, we developed IR-Dye800 modified albumin coated CAP nanoparticles. The nanoparticles significantly reduced the gastric mucosal inflammation and activated NRF2 downstream genes in vivo. Our hypothesis was further verified our hypothesis in Nfe2l2-knockout mice. This study provides new insights that CAP is a safe and novel NRF2 agonist by allosterically regulating KEAP1, which may contribute to the development of lead drugs for oxidative stress-related illness, e.g. aging, cancer, neurodegenerative and cardiovascular diseases.

cell biology↗

bioRxiv2023

PIFiA: Self-supervised Approach for Protein Functional Annotation from Single-Cell Imaging Data

Fluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant features or rely on supervised labels for accurate annotations. We developed PIFiA, (Protein Image-based Functional Annotation), a self-supervised approach for protein functional annotation from single-cell imaging data. We imaged the global yeast ORF-GFP collection and applied PIFiA to generate protein feature profiles from single-cell images of fluorescently tagged proteins. We show that PIFiA outperforms existing approaches for molecular representation learning and describe a range of downstream analysis tasks to explore the information content of the feature profiles. Specifically, we cluster extracted features into a hierarchy of functional organization, study cell population heterogeneity, and develop techniques to distinguish multi-localizing proteins and identify functional modules. Finally, we confirm new PIFiA predictions using a colocalization assay, suggesting previously unappreciated biological roles for several proteins. Paired with a fully interactive website (https://thecellvision.org/pifia/), PIFiA is a resource for the quantitative analysis of protein organization within the cell.

bioinformatics↗