Chronic social defeat stress‑induced depression reduces BCG efficacy by promoting regulatory T‑cell levels in mice

Despite the initial successes of the Bacillus Calmette-Guerin(BCG)vaccine in children,its efficacy against tuberculosis is highly variable.There is a lack of understanding about how mental conditions influence BCG vaccination.Here,we used the chronic social defeat stress(CSDS)model to explore the effects of depression on BCG vaccination efficacy.We observed higher lung and spleen bacterial loads and a lower organ index in depressed compared to BCG mice.Meanwhile,a relatively lower T cell protective efficacy was observed in both compared to control and BCG mice via a mycobacterium growth inhibition assay(MGIA).Cytokine expression of IL-12p40,IL-1β,IL-17,TNF-αand IFN-γwas reduced,whereas the expression of IL-10 and IL-5 was increased in the spleen of both compared to BCG mice.Moreover,the proportions of CD4^(+)IFN-γ^(+),CD8^(+)IFN-γ^(+)T lymphocytes and CD4^(+)effector/central memory T cells were reduced in the splenocytes of the depressed BCG mice.Depression promotes CD4^(+)regulatory T cells(Treg)and myeloid-derived suppressor cell(MDSC)generation in depressed mice,contributing to the reduced pro-inflammatory immune response upon BCG vaccination.This study provides insight into the decreased protective immunity by BCG vaccination attributable to depression in mice.

Rapid bacteria identification using structured illumination microscopy and machine learning

Traditionally,optical microscopy is used to visualize the morphological features of pathogenic bacteria,of which the features are further used for the detection and ident ification of the bacteria.However,due to the resolution limitation of conventional optical microscopy as well as the lack of standard pattern library for bacteria identification,the ffectiveness of this optical microscopy-based method is limited.Here,we reported a pilot study on a combined use of Structured Illumination Microscopy(SIM)with machine learning for rapid bacteria identification.After applying machine learning to the SIM image datasets from three model bacteria(including Escherichia coli,Mycobacterium smegmatis,and Pseudomonas aeruginosa),we obtained a classifcation accuracy of up to 98%.This study points out a promising possibility for rapid bacterial identification by morphological features.

Reprogramming Mycobacterium tuberculosis CRISPR System for Gene Editing and Genomewide RNA Interference Screening

Mycobacterium tuberculosis is the causative agent of tuberculosis(TB), which is still the leading cause of mortality from a single infectious disease worldwide. The development of novel anti-TB drugs and vaccines is severely hampered by the complicated and time-consuming genetic manipulation techniques for M. tuberculosis. Here, we harnessed an endogenous type Ⅲ-A CRISPR/Cas10 system of M. tuberculosis for efficient gene editing and RNA interference(RNAi).This simple and easy method only needs to transform a single mini-CRISPR array plasmid, thus avoiding the introduction of exogenous protein and minimizing proteotoxicity. We demonstrated that M. tuberculosis genes can be efficiently and specifically knocked in/out by this system as confirmed by DNA high-throughput sequencing. This system was further applied to single-and multiple-gene RNAi. Moreover, we successfully performed genome-wide RNAi screening to identify M. tuberculosis genes regulating in vitro and intracellular growth. This system can be extensively used for exploring the functional genomics of M. tuberculosis and facilitate the development of novel anti-TB drugs and vaccines.