Selvaraj, J. | Search bioRxiv

bioRxiv2025

Maltose facilitates Salmonella growth under nitrogen stress while impairing adhesion to epithelial cells

The composition of nutrients in the intestine defines a niche for colonising gut pathogens. The lack of nutrients suitable for pathogens due to competition from resident intestinal microbiota or dietary preferences leads to colonisation failure by pathogens. In this study, we investigated the impact of the disaccharide sugar maltose on Salmonella Typhimurium (STM) pathogenicity during the early phases of infection in C57BL/6 mice and human colon carcinoma cells (Caco-2). We found that supplementation of maltose at lower concentrations inhibited STM colonisation in the ileum of mice. To understand this, we investigated the role of maltose metabolism in human colon epithelial (Caco-2) cells. Deleting the maltose metabolism gene (malQ) increased adhesion to Caco-2 cells. The increased adhesion was due to increased expression of type 1 fimbriae. Inhibiting the type 1 fimbriae-mediated binding to host epithelial cells by incubating with mannose resulted in similar adhesion of STM WT and STM {Delta}malQ. We further identified that malQ regulates the adhesion of Salmonella through the sigma factor RpoE. Overall, malQ in Salmonella inhibits infection in epithelial cells by reducing its adhesion to host epithelial cells.

microbiology↗

bioRxiv2024

CryoTEN: Efficiently Enhancing Cryo-EM DensityMaps Using Transformers

MotivationCryogenic Electron Microscopy (cryo-EM) is a core experimental technique used to determine the structure of macromolecules such as proteins. However, the effectiveness of cryo-EM is often hindered by the noise and missing density values in cryo-EM density maps caused by experimental conditions such as low contrast and conformational heterogeneity. Although various global and local map sharpening techniques are widely employed to improve cryo-EM density maps, it is still challenging to efficiently improve their quality for building better protein structures from them. ResultsIn this study, we introduce CryoTEN - a three-dimensional U-Net style transformer to improve cryo-EM maps effectively. CryoTEN is trained using a diverse set of 1,295 cryo-EM maps as inputs and their corresponding simulated maps generated from known protein structures as targets. An independent test set containing 150 maps is used to evaluate CryoTEN, and the results demonstrate that it can robustly enhance the quality of cryo-EM density maps. In addition, the automatic de novo protein structure modeling shows that the protein structures built from the density maps processed by CryoTEN have substantially better quality than those built from the original maps. Compared to the existing state- of-the-art deep learning methods for enhancing cryo-EM density maps, CryoTEN ranks second in improving the quality of density maps, while running > 10 times faster and requiring much less GPU memory than them. Availability and implementationThe source code and data is freely available at https://github.com/jianlin-cheng/cryoten

bioinformatics↗