Effect of different Peptidoglycan on Clostridium perfringens spore germination and quantitative prediction

Abstract: Clostridium perfringens (C. perfringens) is a Gram-positive, anaerobic, spore forming pathogenic bacterium causing gastrointestinal (GI) diseases in humans and animals. The most important type that causes C. perfringens-associated food poisoning (FP) in humans is C. perfringens type A, and this illness is the third most commonly reported food-borne disease in the United States. C .perfringens spores are resistant to many environmental stresses and remain dormant in the environment for a long period of time. Once conditions are favorable, they can break their dormancy and initiate germination in response to a variety of compounds. Bacterial shape and cellular resistance to cytoplasmic turgor pressure are determined by peptidoglycan (PG), a polymer of repeated subunits of an N-acetylglucosamine (GlcNAc) and N-acetylmuramic acid (MurNAc) peptide monomer that surrounds the cytoplasmic membrane. PG can be targeted to a single germination receptor to efficiently induce C. perfringens spore germination. In this study, C. perfringens vegetative and its spore cortex peptidoglycan were used for spore germination rate (S), turbidity (OD600%) and the release rate of Ca2+-DPA%. Among the existing spectroscopic methods, near-infrared spectroscopy (NIR) has been proven to be one of the most powerful tools for the qualitative and quantitative analysis of constituents in food, agricultural, wood and pharmaceutical products. The S, and (OD600%) and Ca2+-DPA% were compared the effect of different peptidoglycans on spore germination, and the time-consuming and laborious shortage of spore germination rate detection, a study based on NIR combined with chemometric methods to quantitatively predict spore germination rates under different PG concentration conditions. Three preprocessed method, including MSC, SNV and centralization, were used to preprocess the original spectral. The optimal preprocessing method is SNV, and then using principal component analysis (PCA) and GA-joint interval Partial least squares (GA-siPLS) for spectral data dimensionality reduction and feature variable screening, and finally using GA-siPLS was used to rapidly predict spore S, OD600%, and Ca2+-DPA% in different concentrations of PG. The results showed that C. perfringens PG could effectively induce spore germination, and the best effect was induced by 10-1 mg.mL-1. The results of were showed that the S was 95.28%, the OD600% was 29.41%, and the Ca2+-DPA release rate was 58%, while the spore PG effect was not obvious. Using GA-siPLS to screen for spore germination characteristic variables, the optimal feature intervals for S, OD600%, and Ca2+-DPA% were3, 9, 11, 14, 1, 7, 12, 15, and 7, 8, 12, 17, respectively. For the S, the correlation coefficients R of the calibration set and prediction set are 0.8924 and 0.8726, respectively, and the root mean square error are 0.711 and 0.769 respectively. For the OD600%, the R are 0.8963 and 0.8611, respectively. The root mean square error are 0.189% and 0.218% respectively. For Ca2+-DPA%, the R of the most training set and prediction set are 0.9037 and 0.8841, respectively, and the root mean square error is 39.53% and 42.34%. The results show that the NIR combined with chemometric methods can quickly predict the spore germination rate of C. perfringens. This study can rapidly predict the spore germination rate, which can provide an effective means to ensure the safety of meat products.