Host metabolic shift during systemic <i>Salmonella</i> infection revealed by comparative proteomics

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a food-borne bacterium that causes acute gastroenteritis in humans and typhoid fever in mice. Salmonella pathogenicity island II (SPI-2) is an important virulence gene cluster responsible for Salmonella survival and replication within host cells, leading to systemic infection. Previous studies have suggested that SPI-2 function to modulate host vesicle trafficking and immune response to promote systemic infection. However, the molecular mechanism and the host responses triggered by SPI-2 remain largely unknown. To assess the roles of SPI-2, we used a differential proteomic approach to analyse host proteins levels during systemic infections in mice. Our results showed that infection by WT S. Typhimurium triggered the reprogramming of host cell metabolism and inflammatory response. Salmonella systemic infection induces an up-regulation of glycolytic process and a repression of the tricarboxylic acid (TCA) cycle. WT-infected tissues prefer to produce adenosine 5′-triphosphate (ATP) through aerobic glycolysis rather than relying on oxidative phosphorylation to generate energy. Moreover, our data also revealed that infected macrophages may undergo both M1 and M2 polarization. In addition, our results further suggest that SPI-2 is involved in altering actin cytoskeleton to facilitate the Salmonella-containing vacuole (SCV) biogenesis and perhaps even the release of bacteria later in the infection process. Results from our study provide valuable insights into the roles of SPI-2 during systemic Salmonella infection and will guide future studies to dissect the molecular mechanisms of how SPI-2 functions in vivo.</p

Dielectric Barrier Discharge Plasma Synergistic Catalytic Pyrolysis of Waste Polyethylene into Aromatics-Enriched Oil

The dielectric barrier discharge (DBD) plasma synergistic catalytic pyrolysis scheme was developed for upgrading polyethylene (PE) pyrolysis volatiles to obtain aromatics-enriched oil. The effects of the PE/HZSM-5 ratio, discharge power, and discharge time interval on the product yield, oil composition, and carbon deposition were studied by the experiment. When the PE/HZSM-5 ratio shifted from 5:1 to 1:5, the oil yield decreased first and then ramped to 51.32%. The selectivity for aromatics in oil increased from 69.93 to 98.43%. In the discharge power range of 0–15 W, increasing the power increased the aromatic content with the maximum of 98.14%, consequently decreasing the alkane and alkene contents of the product oils. The further addition of the power resulted in a small decrease in the selectivity for the aromatics. The coke showed a decrease in yield with increasing discharge power when the power did not exceed 20 W. The maximum selectivities for aromatics (97.69%) and the minimum coke yield (2.82%) were obtained at the discharge time intervals of 2.5 and 5 ms, respectively. In general, both of the free radicals excited by the plasma and the specific surface area, acidity, and strength of HZSM-5 changed by the plasma favored the aromatization reaction

Cumulative incidence of recurrent ischemic stroke over 5 years of follow-up.

Cumulative incidence of recurrent ischemic stroke over 5 years of follow-up.</p

Simplified schematic of the recurrent stroke Markov micro-simulation model.

Simplified schematic of the recurrent stroke Markov micro-simulation model.</p

Incremental Cost-effectiveness Ratios.

Incremental Cost-effectiveness Ratios.</p

Comparison of the baseline cumulative hazards of the Cox model and flexible parametric survival model.

Comparison of the baseline cumulative hazards of the Cox model and flexible parametric survival model.</p

Characteristics of the study cohort and original cohort.

Characteristics of the study cohort and original cohort.</p

Hazard ratios (95% confidence intervals) of the flexible parametric survival model (FPSM) and Cox proportional hazards model (Cox).

Hazard ratios (95% confidence intervals) of the flexible parametric survival model (FPSM) and Cox proportional hazards model (Cox).</p

Key parameters used in the Markov model.

Key parameters used in the Markov model.</p