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Salmonella Typhimurium is an invasive enteric pathogen that causes gastroenteritis in humans and life-threatening systemic infections in mice. During infection of the intestine, S. Typhimurium can exploit nitrate as an electron acceptor to enhance its growth. However, the roles of nitrate on S. Typhimurium systemic infection are unknown. In this study, nitrate levels were found to be significantly increased in the liver and spleen of mice systemically infected by S. Typhimurium. Mutations in genes encoding nitrate transmembrane transporter (narK) or nitrate-producing flavohemoprotein (hmpA) decreased the replication of S. Typhimurium in macrophages and reduced systemic infection in vivo, suggesting that nitrate utilization promotes S. Typhimurium systemic virulence. Moreover, nitrate utilization contributes to the acidification of the S. Typhimurium cytoplasm, which can sustain the virulence of S. Typhimurium by increasing the transcription of virulence genes encoding on Salmonella pathogenicity island 2 (SPI-2). Furthermore, the growth advantage of S. Typhimurium conferred by nitrate utilization occurred only under low-oxygen conditions, and the nitrate utilization was activated by both the global regulator Fnr and the nitrate-sensing two-component system NarX-NarL. Collectively, this study revealed a novel mechanism adopted by Salmonella to interact with its host and increase its virulence.

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.