Salmonella Typhimurium establishes systemic infection by replicating in host macrophages. Here we show that macrophages infected with S. Typhimurium exhibit upregulated glycolysis and decreased serine synthesis, leading to accumulation of glycolytic intermediates. The effects on serine synthesis are mediated by bacterial protein SopE2, a type III secretion system (T3SS) effector encoded in pathogenicity island SPI-1. The changes in host metabolism promote intracellular replication of S. Typhimurium via two mechanisms: decreased glucose levels lead to upregulated bacterial uptake of 2- and 3-phosphoglycerate and phosphoenolpyruvate (carbon sources), while increased pyruvate and lactate levels induce upregulation of another pathogenicity island, SPI-2, known to encode virulence factors. Pharmacological or genetic inhibition of host glycolysis, activation of host serine synthesis, or deletion of either the bacterial transport or signal sensor systems for those host glycolytic intermediates impairs S. Typhimurium replication or virulence.
Shigella is an intracellular pathogen that primarily infects the human colon and causes shigellosis. Shigella virulence relies largely on the type III secretion system (T3SS) and secreted effectors. VirF, the master Shigella virulence regulator, is essential for the expression of T3SS-related genes. In this study, we found that YhjC, a LysR-type transcriptional regulator, is required for Shigella virulence through activating the transcription of virF. Pathogenicity of the yhjC mutant, including colonization in the colons of guinea pigs as well as its ability for host cell adhesion and invasion, was significantly lowered. Expression levels of virF and nearly all VirF-dependent genes were downregulated by yhjC deletion, indicating that YhjC can activate virF transcription. Electrophoretic mobility shift assay analysis demonstrated that YhjC could bind directly to the virF promoter region. Therefore, YhjC is a novel virulence regulator that positively regulates the virF expression and promotes Shigella virulence. Additionally, genome-wide expression analysis identified the presence of other genes in the large virulence plasmid and a genome exhibiting differential expression in response to yhjC deletion, with 169 downregulated and 99 upregulated genes, indicating that YhjC also functioned as a global regulatory factor.
Salmonella enterica serovar Typhi (S. Typhi) is a human-limited intracellular pathogen and the cause of typhoid fever, a severe systemic disease. Pathogen–host interaction at the metabolic level affects the pathogenicity of intracellular pathogens, but it remains unclear how S. Typhi infection influences host metabolism for its own benefit. Herein, using metabolomics and transcriptomics analyses, combined with in vitro and in vivo infection assays, we investigated metabolic responses in human macrophages during S. Typhi infection, and the impact of these responses on S. Typhi intracellular replication and systemic pathogenicity. We observed increased glucose content, higher rates of glucose uptake and glycolysis, and decreased oxidative phosphorylation in S. Typhi-infected human primary macrophages. Replication in human macrophages and the bacterial burden in systemic organs of humanized mice were reduced by either the inhibition of host glucose uptake or a mutation of the bacterial glucose uptake system, indicating that S. Typhi utilizes host-derived glucose to enhance intracellular replication and virulence. Thus, S. Typhi promotes its pathogenicity by inducing metabolic changes in host macrophages and utilizing the glucose that subsequently accumulates as a nutrient for intracellular replication. Our findings provide the first metabolic signature of S. Typhi-infected host cells and identifies a new strategy utilized by S. Typhi for intracellular replication.
In recent years, the rapid development of Deep Learning (DL) has provided a new method for ship detection in Synthetic Aperture Radar (SAR) images. However, there are still four challenges in this task. (1) The ship targets in SAR images are very sparse. A large number of unnecessary anchor boxes may be generated on the feature map when using traditional anchor-based detection models, which could greatly increase the amount of computation and make it difficult to achieve real-time rapid detection. (2) The size of the ship targets in SAR images is relatively small. Most of the detection methods have poor performance on small ships in large scenes. (3) The terrestrial background in SAR images is very complicated. Ship targets are susceptible to interference from complex backgrounds, and there are serious false detections and missed detections. (4) The ship targets in SAR images are characterized by a large aspect ratio, arbitrary direction and dense arrangement. Traditional horizontal box detection can cause non-target areas to interfere with the extraction of ship features, and it is difficult to accurately express the length, width and axial information of ship targets. To solve these problems, we propose an effective lightweight anchor-free detector called R-Centernet+ in the paper. Its features are as follows: the Convolutional Block Attention Module (CBAM) is introduced to the backbone network to improve the focusing ability on small ships; the Foreground Enhance Module (FEM) is used to introduce foreground information to reduce the interference of the complex background; the detection head that can output the ship angle map is designed to realize the rotation detection of ship targets. To verify the validity of the proposed model in this paper, experiments are performed on two public SAR image datasets, i.e., SAR Ship Detection Dataset (SSDD) and AIR-SARShip. The results show that the proposed R-Centernet+ detector can detect both inshore and offshore ships with higher accuracy than traditional models with an average precision of 95.11% on SSDD and 84.89% on AIR-SARShip, and the detection speed is quite fast with 33 frames per second.
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