The ability ofMycobacterium tuberculosis(Mtb) to persist in the host complicates and prolongs tuberculosis (TB) patient chemotherapy. Here we demonstrate that a neglected two-component system (TCS) of Mtb, TcrXY, is an autoregulated acid-sensing TCS that controls a functionally diverse 70-gene regulon required for bacterial persistence. Characterisation of two representatives of this regulon,Rv3706candRv3705A, implicate these genes as key determinants for the survival of Mtb in vivo by serving as important effectors to mitigate redox stress at acidic pH. We show that genetic silencing of the response regulatortcrXusing CRISPR interference attenuates the persistence of Mtb during chronic mouse infection and improves treatment with the two front-line anti-TB drugs, rifampicin and isoniazid. We propose that targeting TcrXY signal transduction blocks the ability of Mtb to sense and respond to acid stress, resulting in a disordered program of persistence to render the organism vulnerable to existing TB chemotherapy.

Fermented beverages have been consumed for millennia and today support a global industry producing diverse products. Saccharomyces yeasts currently dominate the fermented beverage industry, but consumer demands for alternative products with a variety of sensory profiles and actual or perceived health benefits are driving the diversification and use of non-Saccharomyces yeasts. The diversity of flavours, aromas, and other sensory characteristics that can be obtained by using non-Saccharomyces yeasts in fermentation is, in large part, due to the diverse secondary metabolites they produce compared to conventional Saccharomyces yeast. Here, we review the use of metabolomic analyses of non-Saccharomyces yeasts to explore their impact on the sensory characteristics of fermented beverages. We highlight several key species currently used in the industry, including Brettanomyces, Torulaspora, Lachancea, and Saccharomycodes, and emphasize the future potential for the use of non-Saccharomyces yeasts in the production of diverse fermented beverages.

Foam-related parameters are associated with beer quality and dependent, among others, on the protein content. This study aimed to develop a machine learning (ML) model to predict the pattern and presence of 54 proteins. Triplicates of 24 beer samples were analyzed through proteomics. Furthermore, samples were analyzed using the RoboBEER to evaluate 15 physical parameters (color, foam, and bubbles), and a portable near-infrared (NIR) device. Proteins were grouped according to their molecular weight (MW), and a matrix was developed to assess only the significant correlations (p < 0.05) with the physical parameters. Two ML models were developed using the NIR (Model 1), and RoboBEER (Model 2) data as inputs to predict the relative quantification of 54 proteins. Proteins in the 0–20 kDa group were negatively correlated with the maximum volume of foam (MaxVol; r = −0.57) and total lifetime of foam (TLTF; r = −0.58), while those within 20–40 kDa had a positive correlation with MaxVol (r = 0.47) and TLTF (r = 0.47). Model 1 was not as accurate (testing r = 0.68; overall r = 0.89) as Model 2 (testing r = 0.90; overall r = 0.93), which may serve as a reliable and affordable method to incorporate the relative quantification of important proteins to explain beer quality.