Impact of genomics on microbial food safety

The Genetic Determinants of Listeria monocytogenes Resistance to Bacteriocins Produced by Lactic Acid Bacteria

BACKGROUND

Listeria monocytogenes is a Gram-positive bacterium responsible for listeriosis, a serious foodborne disease that can lead to serious health complications. Pregnant women, newborns, the elderly, and patients with weakened immune systems are particularly susceptible to infection. Due to the ability of L. monocytogenes to survive in extreme environmental conditions, such as low temperatures, high salinity, and acidity, this bacterium poses a serious threat to food production plants and is particularly difficult to eliminate from these plants. One of the promising solutions to reduce the presence of this bacterium in food products is bacteriocins as natural control agents. These are substances with antibacterial activity produced by other bacteria, mainly lactic acid bacteria (LAB), which can effectively inhibit the development of pathogens such as L. monocytogenes. The use of bacteriocins in the food industry is beneficial due to their natural origin, specificity of action, and consumer safety. However, the problem of resistance to these substances exists.

RESULTS

This review focuses on the mechanisms of bacteriocin resistance, such as modifications of bacteriocin docking receptors, changes in the structure of the cell wall and membrane, and the occurrence of cross-resistance to different bacteriocins. Genetic factors determining these mechanisms and strategies to cope with the problem of resistance are also presented.

CONCLUSIONS

Research on this issue is crucial for developing effective preventive methods that will enable the safe and long-term use of bacteriocins in food production.

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data

Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready-to-eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.

Omics in traditional vegetable fermented foods and beverages

For a long time, food microbiota has been studied using traditional microbiological techniques. With the arrival of molecular or culture-independent techniques, a strong understanding of microbiota dynamics has been achieved. However, analyzing the functional role of microbial communities is not an easy task. The application of omics sciences to the study of fermented foods would provide the metabolic and functional understanding of the microbial communities and their impact on the fermented product, including the molecules that define its aroma and flavor, as well as its nutritional properties. Until now, most omics studies have focused on commercial fermented products, such as cheese, wine, bread and beer, but traditional fermented foods have been neglected. Therefore, the information that allows to relate the present microbiota in the food and its properties remains limited. In this review, reports on the applications of omics in the study of traditional fermented foods and beverages are reviewed to propose new ways to analyze the fermentation phenomena.

Culture-independent approaches to chlamydial genomics

The expanding field of bacterial genomics has revolutionized our understanding of microbial diversity, biology and phylogeny. For most species, DNA extracted from culture material is used as the template for genome sequencing; however, the majority of microbes are actually uncultivable, and others, such as obligate intracellular bacteria, require laborious tissue culture to yield sufficient genomic material for sequencing. Chlamydiae are one such group of obligate intracellular microbes whose characterization has been hampered by this requirement. To circumvent these challenges, researchers have developed culture-independent sample preparation methods that can be applied to the sample directly or to genomic material extracted from the sample. These methods, which encompass both targeted [immunomagnetic separation-multiple displacement amplification (IMS-MDA) and sequence capture] and non-targeted approaches (host methylated DNA depletion-microbial DNA enrichment and cell-sorting-MDA), have been applied to a range of clinical and environmental samples to generate whole genomes of novel chlamydial species and strains. This review aims to provide an overview of the application, advantages and limitations of these targeted and non-targeted approaches in the chlamydial context. The methods discussed also have broad application to other obligate intracellular bacteria or clinical and environmental samples.

First step in using molecular data for microbial food safety risk assessment; hazard identification of Escherichia coli O157:H7 by coupling genomic data with in vitro adherence to human epithelial cells

The potential for using whole genome sequencing (WGS) data in microbiological risk assessment (MRA) has been discussed on several occasions since the beginning of this century. Still, the proposed heuristic approaches have never been applied in a practical framework. This is due to the non-trivial problem of mapping microbial information consisting of thousands of loci onto a probabilistic scale for risks. The paradigm change for MRA involves translation of multidimensional microbial genotypic information to much reduced (integrated) phenotypic information and onwards to a single measure of human risk (i.e. probability of illness).

In this paper a first approach in methodology development is described for the application of WGS data in MRA; this is supported by a practical example. That is, combining genetic data (single nucleotide polymorphisms; SNPs) for Shiga toxin-producing Escherichia coli (STEC) O157 with phenotypic data (in vitro adherence to epithelial cells as a proxy for virulence) leads to hazard identification in a Genome Wide Association Study (GWAS).

This application revealed practical implications when using SNP data for MRA. These can be summarized by considering the following main issues: optimum sample size for valid inference on population level, correction for population structure, quantification and calibration of results, reproducibility of the analysis, links with epidemiological data, anchoring and integration of results into a systems biology approach for the translation of molecular studies to human health risk.

Future developments in genetic data analysis for MRA should aim at resolving the mapping problem of processing genetic sequences to come to a quantitative description of risk. The development of a clustering scheme focusing on biologically relevant information of the microbe involved would be a useful approach in molecular data reduction for risk assessment.

A filter paper-based microdevice for low-cost, rapid, and automated DNA extraction and amplification from diverse sample types

A plastic microfluidic device that integrates a filter disc as a DNA capture phase was successfully developed for low-cost, rapid and automated DNA extraction and PCR amplification from various raw samples. The microdevice was constructed by sandwiching a piece of Fusion 5 filter, as well as a PDMS (polydimethylsiloxane) membrane, between two PMMA (poly(methyl methacrylate)) layers. An automated DNA extraction from 1 μL of human whole blood can be finished on the chip in 7 minutes by sequentially aspirating NaOH, HCl, and water through the filter. The filter disc containing extracted DNA was then taken out directly for PCR. On-chip DNA purification from 0.25-1 μL of human whole blood yielded 8.1-21.8 ng of DNA, higher than those obtained using QIAamp® DNA Micro kits. To realize DNA extraction from raw samples, an additional sample loading chamber containing a filter net with an 80 μm mesh size was designed in front of the extraction chamber to accommodate sample materials. Real-world samples, including whole blood, dried blood stains on Whatman® 903 paper, dried blood stains on FTA™ cards, buccal swabs, saliva, and cigarette butts, can all be processed in the system in 8 minutes. In addition, multiplex amplification of 15 STR (short tandem repeat) loci and Sanger-based DNA sequencing of the 520 bp GJB2 gene were accomplished from the filters that contained extracted DNA from blood. To further prove the feasibility of integrating this extraction method with downstream analyses, "in situ" PCR amplifications were successfully performed in the DNA extraction chamber following DNA purification from blood and blood stains without DNA elution. Using a modified protocol to bond the PDMS and PMMA, our plastic PDMS devices withstood the PCR process without any leakage. This study represents a significant step towards the practical application of on-chip DNA extraction methods, as well as the development of fully integrated genetic analytical systems.

Development of rapid detection and genetic characterization of salmonella in poultry breeder feeds

Salmonella is a leading cause of foodborne illness in the United States, with poultry and poultry products being a primary source of infection to humans. Poultry may carry some Salmonella serovars without any signs or symptoms of disease and without causing any adverse effects to the health of the bird. Salmonella may be introduced to a flock by multiple environmental sources, but poultry feed is suspected to be a leading source. Detecting Salmonella in feed can be challenging because low levels of the bacteria may not be recovered using traditional culturing techniques. Numerous detection methodologies have been examined over the years for quantifying Salmonella in feeds and many have proven to be effective for Salmonella isolation and detection in a variety of feeds. However, given the potential need for increased detection sensitivity, molecular detection technologies may the best candidate for developing rapid sensitive methods for identifying small numbers of Salmonella in the background of large volumes of feed. Several studies have been done using polymerase chain reaction (PCR) assays and commercial kits to detect Salmonella spp. in a wide variety of feed sources. In addition, DNA array technology has recently been utilized to track the dissemination of a specific Salmonella serotype in feed mills. This review will discuss the processing of feeds and potential points in the process that may introduce Salmonella contamination to the feed. Detection methods currently used and the need for advances in these methods also will be discussed. Finally, implementation of rapid detection for optimizing control methods to prevent and remove any Salmonella contamination of feeds will be considered.

Gene expression profiling of Listeria monocytogenes strain F2365 during growth in ultrahigh-temperature-processed skim milk

To study how Listeria monocytogenes survives and grows in ultrahigh-temperature-processed (UHT) skim milk, microarray technology was used to monitor the gene expression profiles of strain F2365 in UHT skim milk. Total RNA was isolated from strain F2365 in UHT skim milk after 24 h of growth at 4 degrees C, labeled with fluorescent dyes, and hybridized to "custom-made" commercial oligonucleotide (35-mers) microarray chips containing the whole genome of L. monocytogenes strain F2365. Compared to L. monocytogenes grown in brain heart infusion (BHI) broth for 24 h at 4 degrees C, 26 genes were upregulated (more-than-twofold increase) in UHT skim milk, whereas 14 genes were downregulated (less-than-twofold decrease). The upregulated genes included genes encoding transport and binding proteins, transcriptional regulators, proteins in amino acid biosynthesis and energy metabolism, protein synthesis, cell division, and hypothetical proteins. The downregulated genes included genes that encode transport and binding proteins, protein synthesis, cellular processes, cell envelope, energy metabolism, a transcriptional regulator, and an unknown protein. The gene expression changes determined by microarray assays were confirmed by real-time reverse transcriptase PCR analyses. Furthermore, cells grown in UHT skim milk displayed the same sensitivity to hydrogen peroxide as cells grown in BHI, demonstrating that the elevated levels of expression of genes encoding manganese transporter complexes in UHT skim milk did not result in changes in the oxidative stress sensitivity. To our knowledge, this report represents a novel study of global transcriptional gene expression profiling of L. monocytogenes in a liquid food.

Nutritional genomics: food industry applications from farm to fork

Nutritional genomics is a new and promising science area which can broadly be defined as the application of high throughput genomics (transcriptomics, proteomics, metabolomics/metabonomics) and functional genomic technologies to the study of nutritional sciences and food technology. First utilised in the food industry by plant biotechnologists to manipulate plant biosynthetic pathways, the use of genomic technologies has now spread within the agriculture sector, unleashing a host of new applications (e.g. approaches for producing novel, non-transgenic plant varietals; identification of genetic markers to guide plant and animal breeding programmes; exploration of diet-gene interactions for enhancing product quality and plant/animal health). Beyond agriculture, genomic technologies are also contributing to the improvement of food processing, food safety and quality assurance as well as the development of functional food products and the evolution of new health management concepts such as 'personalised nutrition', an emerging paradigm in which the diet of an individual is customised, based on their own genomic information, to optimise health and prevent disease. In this review the relevance of nutritional genomics to the food industry will be considered and examples given on how this science area is starting to be leveraged for economic benefits and to improve human nutrition and health.

Oligonucleotide fingerprint identification for microarray-based pathogen diagnostic assays

MOTIVATION

Advances in DNA microarray technology and computational methods have unlocked new opportunities to identify 'DNA fingerprints', i.e. oligonucleotide sequences that uniquely identify a specific genome. We present an integrated approach for the computational identification of DNA fingerprints for design of microarray-based pathogen diagnostic assays. We provide a quantifiable definition of a DNA fingerprint stated both from a computational as well as an experimental point of view, and the analytical proof that all in silico fingerprints satisfying the stated definition are found using our approach.

RESULTS

The presented computational approach is implemented in an integrated high-performance computing (HPC) software tool for oligonucleotide fingerprint identification termed TOFI. We employed TOFI to identify in silico DNA fingerprints for several bacteria and plasmid sequences, which were then experimentally evaluated as potential probes for microarray-based diagnostic assays. Results and analysis of approximately 150 in silico DNA fingerprints for Yersinia pestis and 250 fingerprints for Francisella tularensis are presented.

AVAILABILITY

The implemented algorithm is available upon request.

Using the stress response to monitor process control: pathways to more effective bioremediation

Environmental contamination with a variety of pollutants has prompted the development of effective bioremediation strategies. But how can these processes be best monitored and controlled? One avenue under investigation is the development of stress response systems as tools for effective and general process control. Although the microbial stress response has been the subject of intensive laboratory investigation, the environmental reflection of the laboratory response to specific stresses has been little explored. However, it is only within an environmental context, in which microorganisms are constantly exposed to multiple changing environmental stresses, that there will be full understanding of microbial adaptive resiliency. Knowledge of the stress response in the environment will facilitate the control of bioremediation and other processes mediated by complex microbial communities.

The role of sigmaB in the stress response of Gram-positive bacteria -- targets for food preservation and safety

The alternative sigma factor sigmaB modulates the stress response of several Gram-positive bacteria, including Bacillus subtilis and the food-borne human pathogens Bacillus cereus, Listeria monocytogenes and Staphylococcus aureus. In all these bacteria, sigmaB is responsible for the transcription of genes that can confer stress resistance to the vegetative cell. Recent findings indicate that sigmaB also plays an important role in antibiotic resistance, pathogenesis and cellular differentiation processes such as biofilm formation and sporulation. Although there are important differences in the regulation of sigmaB and in the set of genes regulated by sigmaB in B. subtilis, B. cereus, L. monocytogenes and S. aureus, there are also some conserved themes. A mechanistic understanding of the sigmaB activation processes and assessment of its regulon could provide tools for pathogen control and inactivation both in the food industry and clinical settings.