Specific biomarker mining and rapid detection of Burkholderia cepacia complex by recombinase polymerase amplification

Objective

To mine specific proteins and their protein-coding genes as suitable molecular biomarkers for the Burkholderia cepacia Complex (BCC) bacteria detection based on mega analysis of microbial proteomic and genomic data comparisons and to develop a real-time recombinase polymerase amplification (rt-RPA) assay for rapid isothermal screening for pharmaceutical and personal care products.

Methods

We constructed an automatic screening framework based on Python to compare the microbial proteomes of 78 BCC strains and 263 non-BCC strains to identify BCC-specific protein sequences. In addition, the specific protein-coding gene and its core DNA sequence were validated in silico with a self-built genome database containing 158 thousand bacteria. The appropriate methodology for BCC detection using rt-RPA was evaluated by 58 strains in pure culture and 33 batches of artificially contaminated pharmaceutical and personal care products.

Results

We identified the protein SecY and its protein-coding gene secY through the automatic comparison framework. The virtual evaluation of the conserved region of the secY gene showed more than 99.8% specificity from the genome database, and it can distinguish all known BCC species from other bacteria by phylogenetic analysis. Furthermore, the detection limit of the rt-RPA assay targeting the secY gene was 5.6 × 102 CFU of BCC bacteria in pure culture or 1.2 pg of BCC bacteria genomic DNA within 30 min. It was validated to detect <1 CFU/portion of BCC bacteria from artificially contaminated samples after a pre-enrichment process. The relative trueness and sensitivity of the rt-RPA assay were 100% in practice compared to the reference methods.

Conclusion

The automatic comparison framework for molecular biomarker mining is straightforward, universal, applicable, and efficient. Based on recognizing the BCC-specific protein SecY and its gene, we successfully established the rt-RPA assay for rapid detection in pharmaceutical and personal care products.

Detection of Potential Microbial Contaminants and Their Toxins in Fermented Dairy Products: a Comprehensive Review

Fermented dairy products are dominant constituents of daily diets around the world due to their desired organoleptic properties, long shelf life, and high nutritional value. Probiotics are often incorporated into these products for their health and technological benefits. However, the safety and possible contamination of fermented dairy products during the manufacturing process could have significant deleterious health and economic impacts. Pathogenic microorganisms and toxins from different sources in fermented dairy products contribute to outbreaks and toxicity cases. Although the health and nutritional benefits of fermented dairy products have been extensively investigated, safety hazards due to contamination are relatively less explored. As a preventive measure, it is crucial to accurately identify and determine the associated microbiota or their toxins. It is noteworthy to highlight the importance of detecting not only the pathogenic microbiota but also their toxic metabolites so that putative outbreaks can thereby be prevented or detected even before they cause harmful effects to human health. In this context, this review focuses on describing techniques designed to detect potential contaminants; also, the advantages and disadvantages of these techniques were summarized. Moreover, this review compiles the most recent and efficient analytical methods for detecting microbial hazards and toxins in different fermented dairy products of different origins. Causative agents behind contamination incidences are also discussed briefly to aid in future prevention measures, as well as detection approaches and technologies employed. Such approach enables the elucidation of the best strategies to control contamination in fermented dairy product manufacturing processes.

Invited review: Stress resistance of Cronobacter spp. affecting control of its growth during food production

Members of the Cronobacter genus include food-borne pathogens that can cause infections in infants, with a mortality rate as high as 40 to 80%. The high fatality rate of Cronobacter and its isolation from numerous types of food, especially from powdered infant formula, demonstrate the serious nature of this organism. The source tracking of Cronobacter spp. and the analysis of high-frequency species from different sources are helpful for a more targeted control. Furthermore, the persistence during food processing and storage may be attributed to strong resistance of Cronobacter spp. to environment stresses such as heat, pH, and desiccation. There are many factors that support the survival of Cronobacter spp. in harsh environments, such as some genes, regulatory systems, and biofilms. Advanced detection technology is helpful for the strict monitoring of Cronobacter spp. In addition to the traditional heat treatment, many new control techniques have been developed, and the ability to control Cronobacter spp. has been demonstrated. The control of this bacteria is required not only during manufacture, but also through the selection of packaging methods to reduce postprocessing contamination. At the same time, the effect of inactivation methods on product quality and safety must be considered. This review considers the advances in our understanding of environmental stress response in Cronobacter spp. with special emphasis on its implications in food processing.

Prediction of PCR amplification from primer and template sequences using recurrent neural network

We have developed a novel method to predict the success of PCR amplification for a specific primer set and DNA template based on the relationship between the primer sequence and the template. To perform the prediction using a recurrent neural network, the usual double-stranded formation between the primer and template nucleotide sequences was herein expressed as a five-lettered word. The set of words (pseudo-sentences) was placed to indicate the success or failure of PCR targeted to learn recurrent neural network (RNN). After learning pseudo-sentences, RNN predicted PCR results from pseudo-sentences which were created by primer and template sequences with 70% accuracy. These results suggest that PCR results could be predicted using learned RNN and the trained RNN could be used as a replacement for preliminary PCR experimentation. This is the first report which utilized the application of neural network for primer design and prediction of PCR results.