Insights into the role of legionella effectors on host metabolic perturbations.

Legionella infection, the causative agent of Legionnaires' disease, represents a significant threat to human health. The pathogenesis of this infection is intricately linked to the complex interactions between the bacterium and its host, resulting in profound metabolic perturbations. Central to these metabolic shifts is the bacterium's modulation of lipid metabolism, with changes in lipid synthesis and breakdown modifying membrane composition and function. These alterations can influence cellular signaling and immune responses, further contributing to disease progression. It also disrupts glucose utilization and lipid metabolism, altering cellular energy production and immune responses. Additionally, Legionella infection perturbs amino acid and protein metabolism, affecting protein synthesis and degradation, leading to changes in cellular functions and immune responses. This mini-review underscores the complexity of metabolic perturbations in Legionella infection and their significance in host-pathogen interactions. Understanding these metabolic shifts provides valuable insights into the pathogenesis of Legionnaires' disease and could lead to the development of novel therapeutic strategies.

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements.

Nondestructive prediction of ingredient contents of farm products is useful to ship and sell the products with guaranteed qualities. Here, near-infrared spectroscopy is used to predict nondestructively total sugar, total organic acid, and total anthocyanin content in each blueberry. The technique is expected to enable the selection of only delicious blueberries from all harvested ones. The near-infrared absorption spectra of blueberries are measured with the diffuse reflectance mode at the positions not on the calyx. The ingredient contents of a blueberry determined by high-performance liquid chromatography are used to construct models to predict the ingredient contents from observed spectra. Partial least squares regression is used for the construction of the models. It is necessary to properly select the pretreatments for the observed spectra and the wavelength regions of the spectra used for analyses. Validations are necessary for the constructed models to confirm that the ingredient contents are predicted with practical accuracies. Here we present a protocol to construct and validate the models for nondestructive prediction of ingredient contents in blueberries by near-infrared spectroscopy.