A c-di-GMP binding effector STM0435 modulates flagellar motility and pathogenicity in Salmonella.

Flagella play a crucial role in the invasion process of Salmonella and function as a significant antigen that triggers host pyroptosis. Regulation of flagellar biogenesis is essential for both pathogenicity and immune escape of Salmonella. We identified the conserved and unknown function protein STM0435 as a new flagellar regulator. The ∆stm0435 strain exhibited higher pathogenicity in both cellular and animal infection experiments than the wild-type Salmonella. Proteomic and transcriptomic analyses demonstrated dramatic increases in almost all flagellar genes in the ∆stm0435 strain compared to wild-type Salmonella. In a surface plasmon resonance assay, purified STM0435 protein-bound c-di-GMP had an affinity of ~8.383 µM. The crystal structures of apo-STM0435 and STM0435&c-di-GMP complex were determined. Structural analysis revealed that R33, R137, and D138 of STM0435 were essential for c-di-GMP binding. A Salmonella with STM1987 (GGDEF protein) or STM4264 (EAL protein) overexpression exhibits completely different motility behaviours, indicating that the binding of c-di-GMP to STM0435 promotes its inhibitory effect on Salmonella flagellar biogenesis.

Anchored-fusion enables targeted fusion search in bulk and single-cell RNA sequencing data.

Here, we present Anchored-fusion, a highly sensitive fusion gene detection tool. It anchors a gene of interest, which often involves driver fusion events, and recovers non-unique matches of short-read sequences that are typically filtered out by conventional algorithms. In addition, Anchored-fusion contains a module based on a deep learning hierarchical structure that incorporates self-distillation learning (hierarchical view learning and distillation [HVLD]), which effectively filters out false positive chimeric fragments generated during sequencing while maintaining true fusion genes. Anchored-fusion enables highly sensitive detection of fusion genes, thus allowing for application in cases with low sequencing depths. We benchmark Anchored-fusion under various conditions and found it outperformed other tools in detecting fusion events in simulated data, bulk RNA sequencing (bRNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Our results demonstrate that Anchored-fusion can be a useful tool for fusion detection tasks in clinically relevant RNA-seq data and can be applied to investigate intratumor heterogeneity in scRNA-seq data.