Metabolic footprinting for investigation of antifungal properties of <i>Lactobacillus paracasei</i>

Lactic acid bacteria with antifungal properties are applied for biopreservation of food. In order to further our understanding of their antifungal mechanism, there is an ongoing search for bioactive molecules. With a focus on the metabolites formed, bioassay-guided fractionation and comprehensive screening have identified compounds as antifungal. Although these are active, the compounds have been found in concentrations that are too low to account for the observed antifungal effect. It has been hypothesized that the formation of metabolites and consumption of nutrients during bacterial fermentations form the basis for the antifungal effect, i.e., the composition of the exometabolome. To build a more comprehensive view of the chemical changes induced by bacterial fermentation and the effects on mold growth, a strategy for correlating the exometabolomic profiles with mold growth was applied. The antifungal properties were assessed by measuring mold growth of two Penicillium strains on cell-free ferments of three strains of Lactobacillus paracasei pre-fermented in a chemically defined medium. Exometabolomic profiling was performed by reversed-phase liquid chromatography in combination with mass spectrometry in electrospray positive and negative modes. By multivariate data analysis, the three strains of Lb. paracasei were readily distinguished by the relative difference of their exometabolomes. The relative differences correlated with the relative growth of the two Penicillium strains. Metabolic footprinting proved to be a supplement to bioassay-guided fractionation for investigation of antifungal properties of bacterial ferments. Additionally, three previously identified and three novel antifungal metabolites from Lb. paracasei and their potential precursors were detected and assigned using the strategy

Quantitative determination of mold growth and inhibition by multispectral imaging

Quantifying mold growth is of great relevance and interest in microbiology. However, predictive modeling of filamentous fungal growth has been hampered by the lack of an appropriate and accurate method for quantification. An adequate, rapid and objective method will allow studying the effect of many different parameters and conditions on mold growth patterns and can thus provide valuable insight and knowledge. This study outlines a new approach for quantifying mold growth by providing an accurate tool for measuring different segments of mold colonies. The method is based on clustering multispectral images by k-means, an unsupervised and simple clustering algorithm. In order to demonstrate the efficiency of the new approach, three different sample sets were analyzed by the developed method, with the objective of quantifying mold growth and size of the colony segments of Penicillium mold. The results verify the ability of the proposed method to quantify mold growth and colony composition (relative size of the white and green segments) accurately. This provides a robust measure for interpreting inhibition activity against mold in different samples and makes a quantitative comparison possible. Among the virtues of the method are: 1) the ability to quantify very small differences in the size of colonies which cannot be easily discriminated by visual inspection, 2) the ability to quantify mold growth on transparent as well as on opaque media (e.g. milk), and 3) no prior assumptions for the shape and multiplicity of colonies. The accuracy and non-destructive characteristic of the method allow dynamic quantification of mold growth which can be very valuable in predictive microbiology and in studies related to biopreservation of food products

TNFR2/BIRC3-TRAF1 signaling pathway as a novel NK cell immune checkpoint in cancer

Natural Killer (NK) cells control metastatic dissemination of murine tumors and are an important prognostic factor in several human malignancies. However, tumor cells hijack many of the NK cell functional features compromising their tumoricidal activity. Here, we show a deleterious role of the TNFα/TNFR2/BIRC3/TRAF1 signaling cascade in NK cells from the tumor microenvironment (TME). TNFα induces BIRC3/cIAP2 transcripts and reduces NKp46/NCR1 transcription and surface expression on NK cells, promoting metastases dissemination in mice and poor prognosis in GIST patients. NKp30 engagement, by promoting the release of TNFα, also contributes to BIRC3 upregulation, and more so in patients expressing predominantly NKp30C isoforms. These findings reveal that in the absence of IL-12 or a Th1-geared TME, TNFα can be considered as a negative regulatory cytokine for innate effectors