MraW is a 16S rRNA methyltransferase and plays a role in the fine-tuning of the ribosomal decoding center. It was recently found to contribute to the virulence of Staphylococcus aureus. In this study, we examined the function of MraW in Escherichia coli O157:H7 and found that the deletion of mraW led to decreased motility, flagellar production and DNA methylation. Whole-genome bisulfite sequencing showed a genome wide decrease of methylation of 336 genes and 219 promoters in the mraW mutant including flagellar genes. The methylation level of flagellar genes was confirmed by bisulfite PCR sequencing. Quantitative reverse transcription PCR results indicated that the transcription of these genes was also affected. MraW was furtherly observed to directly bind to the four flagellar gene sequences by electrophoretic mobility shift assay (EMSA). A common flexible motif in differentially methylated regions (DMRs) of promoters and coding regions of the four flagellar genes was identified. Reduced methylation was correlated with altered expression of 21 of the 24 genes tested. DNA methylation activity of MraW was confirmed by DNA methyltransferase activity assay in vitro and repressed by DNA methylation inhibitor 5-aza-2′-deoxycytidine (5-aza). In addition, the mraW mutant colonized poorer than wild type in mice. We also found that the expression of mraZ in the mraW mutant was increased confirming the antagonistic effect of mraW on mraZ. In conclusion, mraW was found to be a DNA methylase and have a wide-ranging effect on E. coli O157:H7 including motility and virulence in vivo via genome wide methylation and mraZ antagonism.

Rapid and accurate identification of Clostridium botulinum is of great importance because it has been considered as an emerging food-borne pathogen and potential zoonotic agent. Raman spectroscopy can differentiate bacteria based on Raman scattering spectral patterns of whole cells in a fast, reagentless, and easy-to-use manner. This study demonstrates that confocal Raman microspectroscopy (CRM) combined with chemometrics can serve as a fast, reliable, and nondestructive method for detection and identification of C. botulinum at both species and serotypes level without any laborious pre-treatments. Three significant bacillus pathogens including C. botulinum, C. perfringens, and C. difficile were investigated with CRM. Additionally, two main C. botulinum strains causing botulism, C. botulinum type A, and C. botulinum type B were examined with CRM. Principal component analysis (PCA) was performed to differentiate the three species. PCA and linear discrimination analysis (LDA) were used for serotyping C. botulism strains. Four common and important preprocessing methods including Savitzky-Golay algorithm smoothing (SG), standard normal variate (SNV), multivariate scatter correction (MSC), and Savitzky-Golay algorithm 1st Derivative (SG 1st Der) were applied to improve the accuracy of identification and explore the impact of various single preprocessing methods on the model. The results proved that CRM coupled with chemometrics can be utilized for fast, reliable, and nondestructive identification of clostridia and serotypes of C. botulinum strains. This study proves for the first time that the CRM combined with chemometrics methods can be used as a potential means to detect and identify C. botulinum.

Rapid and accurate identification of foodborne pathogenic bacteria is of great importance because they are often responsible for the majority of serious foodborne illnesses. The confocal Raman microspectroscopy (CRM) is a fast and easy-to-use method known for its effectiveness in detecting and identifying microorganisms. This study demonstrates that CRM combined with chemometrics can serve as a rapid, reliable, and efficient method for the detection and identification of foodborne pathogenic bacteria without any laborious pre-treatments. Six important foodborne pathogenic bacteria including S. flexneri, L. monocytogenes, V. cholerae, S. aureus, S. typhimurium, and C. botulinum were investigated with CRM. These pathogenic bacteria can be differentiated based on several characteristic peaks and peak intensity ratio. Principal component analysis (PCA) was used for investigating the difference of various samples and reducing the dimensionality of the dataset. Performances of some classical classifiers were compared for bacterial detection and identification including decision tree (DT), artificial neural network (ANN), and Fisher’s discriminant analysis (FDA). Correct recognition ratio (CRR), area under the receiver operating characteristic curve (ROC), cumulative gains, and lift charts were used to evaluate the performance of models. The impact of different pretreatment methods on the models was explored, and pretreatment methods include Savitzky–Golay algorithm smoothing (SG), standard normal variate (SNV), multivariate scatter correction (MSC), and Savitzky–Golay algorithm 1st Derivative (SG 1st Der). In the DT, ANN, and FDA model, FDA is more robust for overfitting problem and offers the highest accuracy. Most pretreatment methods raised the performance of the models except SNV. The results revealed that CRM coupled with chemometrics offers a powerful tool for the discrimination of foodborne pathogenic bacteria.

Clostridium botulinum is the causative pathogen of botulism. Laboratory detection of C. botulinum is essential for clinical therapy treatment of botulism due to the difficulty in diagnosis, especially in infant botulism. The extreme toxicity of botulinum neurotoxin (BoNT) requires a sensitive detection method. Due to the detection limit of real-time quantitative PCR (q-PCR), a more sensitive detection method, micro-drop digital PCR (ddPCR) was applied in C. botulinum main serotypes A and B. The following performance criteria were evaluated by ddPCR: analytical sensitivity; repeatability; and diagnostic specificity. The limit of detection (LOD) was 0.84 and 0.88 copies/μl for BoNT A and B genes, respectively, by ddPCR with high specificity, compared to 5.04×102 and 6.91×102 copies/μl by q-PCR. It was increased 10 times compared with q-PCR in spiked stool samples. This improvement in sensitivity was especially important in clinical samples as more positive samples were detected by digital PCR compared with q-PCR. Meanwhile, enrichment time for low bacteria content samples was shortened by four hours both in serotypes A and B C. botulinum by ddPCR compared with q-PCR, which are important for laboratory diagnosis and epidemiology work.