Promotion of healthier lifestyles has led to an increase in consumption of fresh produce. Such foodstuffs may expose consumers to increased risk of foodborne disease, as often they are not subjected to processing steps to ensure effective removal or inactivation of pathogenic microorganisms before consumption. Consequently, reports of ready‐to‐eat fruit and vegetable related disease outbreak occurrences have increased substantially in recent years, and information regarding these events is often not readily available. Identifying the nature and source of microbial contamination of these foodstuffs is critical for developing appropriate mitigation measures to be implemented by food producers. This review aimed to identify the foodstuffs most susceptible to microbial contamination and the microorganisms responsible for disease outbreaks from information available in peer‐reviewed scientific publications. A total of 571 outbreaks were identified from 1980 to 2016, accounting for 72,855 infections and 173 deaths. Contaminated leafy green vegetables were responsible for 51.7% of reported outbreaks. Contaminated soft fruits caused 27.8% of infections. Pathogenic strains of Escherichia coli and Salmonella , norovirus, and hepatitis A accounted for the majority of cases. Large outbreaks resulted in particular biases such as the observation that contaminated sprouted plants caused 31.8% of deaths. Where known, contamination mainly occurred via contaminated seeds, water, and contaminated food handlers. There is a critical need for standardized datasets regarding all aspects of disease outbreaks, including how foodstuffs are contaminated with pathogenic microorganisms. Providing food business operators with this knowledge will allow them to implement better strategies to improve safety and quality of fresh produce.

The Clermont PCR method for phylotyping Escherichia coli remains a useful classification scheme even though genome sequencing is now routine, and higher-resolution sequence typing schemes are now available. Relating present-day whole-genome E. coli classifications to legacy phylotyping is essential for harmonizing the historical literature and understanding of this important organism. Therefore, we present EzClermont – a novel in silico Clermont PCR phylotyping tool to enable ready application of this phylotyping scheme to whole-genome assemblies. We evaluate this tool against phylogenomic classifications, and an alternative software implementation of Clermont typing. EzClermont is available as a web app at www.ezclermont.org, and as a command-line tool at https://nickp60.github.io/EzClermont/.

Sigma B (σ ^B ) is an alternative sigma factor that controls the transcriptional response to stress in Listeria monocytogenes and is also known to play a role in the virulence of this human pathogen. In the present study we investigated the impact of a sigB deletion on the proteome of L. monocytogenes grown in a chemically defined medium both in the presence and in the absence of osmotic stress (0.5 M NaCl). Two new phenotypes associated with the sigB deletion were identified using this medium. (i) Unexpectedly, the strain with the Δ sigB deletion was found to grow faster than the parent strain in the growth medium, but only when 0.5 M NaCl was present. This phenomenon was independent of the carbon source provided in the medium. (ii) The Δ sigB mutant was found to have unusual Gram staining properties compared to the parent, suggesting that σ ^B contributes to the maintenance of an intact cell wall. A proteomic analysis was performed by two-dimensional gel electrophoresis, using cells growing in the exponential and stationary phases. Overall, 11 proteins were found to be differentially expressed in the wild type and the Δ sigB mutant; 10 of these proteins were expressed at lower levels in the mutant, and 1 was overexpressed in the mutant. All 11 proteins were identified by tandem mass spectrometry, and putative functions were assigned based on homology to proteins from other bacteria. Five proteins had putative functions related to carbon utilization (Lmo0539, Lmo0783, Lmo0913, Lmo1830, and Lmo2696), while three proteins were similar to proteins whose functions are unknown but that are known to be stress inducible (Lmo0796, Lmo2391, and Lmo2748). To gain further insight into the role of σ ^B in L. monocytogenes , we deleted the genes encoding four of the proteins, lmo0796, lmo0913, lmo2391, and lmo2748. Phenotypic characterization of the mutants revealed that Lmo2748 plays a role in osmotolerance, while Lmo0796, Lmo0913, and Lmo2391 were all implicated in acid stress tolerance to various degrees. Invasion assays performed with Caco-2 cells indicated that none of the four genes was required for mammalian cell invasion. Microscopic analysis suggested that loss of Lmo2748 might contribute to the cell wall defect observed in the Δ sigB mutant. Overall, this study highlighted two new phenotypes associated with the loss of σ ^B . It also demonstrated clear roles for σ ^B in both osmotic and low-pH stress tolerance and identified specific components of the σ ^B regulon that contribute to the responses observed.

System approaches to elucidate ecosystem functioning constitute an emerging area of research within microbial ecology. Such approaches aim at investi

Some of the most transformative discoveries promising to enable the resolution of this century's grand societal challenges will most likely arise from environmental science and particularly environmental microbiology and biotechnology. Understanding how microbes interact in situ, and how microbial communities respond to environmental changes remains an enormous challenge for science. Systems biology offers a powerful experimental strategy to tackle the exciting task of deciphering microbial interactions. In this framework, entire microbial communities are considered as metaorganisms and each level of biological information (DNA, RNA, proteins and metabolites) is investigated along with in situ environmental characteristics. In this way, systems biology can help unravel the interactions between the different parts of an ecosystem ultimately responsible for its emergent properties. Indeed each level of biological information provides a different level of characterisation of the microbial communities. Metagenomics, metatranscriptomics, metaproteomics, metabolomics and SIP-omics can be employed to investigate collectively microbial community structure, potential, function, activity and interactions. Omics approaches are enabled by high-throughput 21st century technologies and this review will discuss how their implementation has revolutionised our understanding of microbial communities.