Effects of glucose and lactate on Streptococcus mutans abundance in a novel multispecies oral biofilm model.

The oral microbiome plays an important role in protecting oral health. Here, we established a controlled mixed-species in vitro biofilm model and used it to assess the impact of glucose and lactate on the ability of Streptococcus mutans, an acidogenic and aciduric species, to compete with commensal oral bacteria. A chemically defined medium was developed that supported the growth of S. mutans and four common early colonizers of dental plaque: Streptococcus gordonii, Actinomyces oris, Neisseria subflava, and Veillonella parvula. Biofilms containing the early colonizers were developed in a continuous flow bioreactor, exposed to S. mutans, and incubated for up to 7 days. The abundance of bacteria was estimated by quantitative polymerase chain reaction (qPCR). At high glucose and high lactate, the pH in bulk fluid rapidly decreased to approximately 5.2, and S. mutans outgrew other species in biofilms. In low glucose and high lactate, the pH remained above 5.5, and V. parvula was the most abundant species in biofilms. By contrast, in low glucose and low lactate, the pH remained above 6.0 throughout the experiment, and the microbial community in biofilms was relatively balanced. Fluorescence in situ hybridization confirmed that all species were present in the biofilm and the majority of cells were viable using live/dead staining. These data demonstrate that carbon source concentration is critical for microbial homeostasis in model oral biofilms. Furthermore, we established an experimental system that can support the development of computational models to predict transitions to microbial dysbiosis based on metabolic interactions.IMPORTANCEWe developed a controlled (by removing host factor) dynamic system metabolically representative of early colonization of Streptococcus mutans not measurable in vivo. Hypotheses on factors influencing S. mutans colonization, such as community composition and inoculation sequence and the effect of metabolite concentrations, can be tested and used to predict the effect of interventions such as dietary modifications or the use of toothpaste or mouthwash on S. mutans colonization. The defined in vitro model (species and medium) can be simulated in an in silico model to explore more of the parameter space.

Multilayered Networks of SalmoNet2 Enable Strain Comparisons of the Salmonella Genus on a Molecular Level.

Serovars of the genus Salmonella primarily evolved as gastrointestinal pathogens in a wide range of hosts. Some serotypes later evolved further, adopting a more invasive lifestyle in a narrower host range associated with systemic infections. A system-level knowledge of these pathogens could identify the complex adaptations associated with the evolution of serovars with distinct pathogenicity, host range, and risk to human health. This promises to aid the design of interventions and serve as a knowledge base in the Salmonella research community. Here, we present SalmoNet2, a major update to SalmoNet1, the first multilayered interaction resource for Salmonella strains, containing protein-protein, transcriptional regulatory, and enzyme-enzyme interactions. The new version extends the number of Salmonella networks from 11 to 20. We now include a strain from the second species in the Salmonella genus, a strain from the Salmonella enterica subspecies arizonae and additional strains of importance from the subspecies enterica, including S. Typhimurium strain D23580, an epidemic multidrug-resistant strain associated with invasive nontyphoidal salmonellosis (iNTS). The database now uses strain specific metabolic models instead of a generalized model to highlight differences between strains. The update has increased the coverage of high-quality protein-protein interactions, and enhanced interoperability with other computational resources by adopting standardized formats. The resource website has been updated with tutorials to help researchers analyze their Salmonella data using molecular interaction networks from SalmoNet2. SalmoNet2 is accessible at http://salmonet.org/. IMPORTANCE Multilayered network databases collate interaction information from multiple sources, and are powerful both as a knowledge base and subject of analysis. Here, we present SalmoNet2, an integrated network resource containing protein-protein, transcriptional regulatory, and metabolic interactions for 20 Salmonella strains. Key improvements to the update include expanding the number of strains, strain-specific metabolic networks, an increase in high-quality protein-protein interactions, community standard computational formats to help interoperability, and online tutorials to help users analyze their data using SalmoNet2.

Transitory Shifts in Skin Microbiota Composition and Reductions in Bacterial Load and Psoriasin following Ethanol Perturbation.

Personal care and hygiene regimens may substantially alter the composition of the skin microbiota through direct and indirect mechanisms. An understanding of the timescales of commensal skin microbiota reestablishment following perturbation is required to inform consumer safety risk assessment, and support product development. In the current investigation, the microbiota of the volar and dorsal forearm of 10 volunteers was sampled immediately before and after wiping with 70% ethanol and at up to 24 h afterwards. Quantitative PCR and amplicon sequencing were used to measure microbial load and composition, and concentrations of the antimicrobial peptide psoriasin were measured using an enzyme-linked immunosorbent assay (ELISA). Ethanol wiping significantly reduced the total bacterial abundance at 2 h post-wipe. Recovery was observed after 6 h for total bacterial populations and for Staphylococcus epidermidis depending on the site tested. Microbiome diversity recovered by 6 h after wiping. Psoriasin concentrations were highly variable between volunteers, ranging from 42 to 1,569 ng/mL, and dorsal concentrations were significantly higher than volar concentrations (P < 0.05). For most of the volunteers, the application of ethanol decreased psoriasin concentrations, particularly for the dorsal samples, but the overall effect was not significant. This work extends observations of skin microbiome stability and demonstrates resilience in a key antimicrobial peptide. IMPORTANCE An understanding of the timescales of commensal skin microbiota reestablishment following perturbation is required to inform consumer safety risk assessment and support product development. Following ethanol exposure, total bacterial populations and microbiome diversity recovered after 6 h. For most of the volunteers, the application of ethanol decreased psoriasin concentrations, but the overall effect was not significant. This work extends observations of skin microbiome stability and demonstrates resilience in a key antimicrobial peptide.

Next generation microbiological risk assessment-Potential of omics data for hazard characterisation.

According to the World Health Organization estimates in 2015, 600 million people fall ill every year from contaminated food and 420,000 die. Microbial risk assessment (MRA) was developed as a tool to reduce and prevent risks presented by pathogens and/or their toxins. MRA is organized in four steps to analyse information and assist in both designing appropriate control options and implementation of regulatory decisions and programs. Among the four steps, hazard characterisation is performed to establish the probability and severity of a disease outcome, which is determined as function of the dose of toxin and/or pathogen ingested. This dose-response relationship is subject to both variability and uncertainty. The purpose of this review/opinion article is to discuss how Next Generation Omics can impact hazard characterisation and, more precisely, how it can improve our understanding of variability and limit the uncertainty in the dose-response relation. The expansion of omics tools (e.g. genomics, transcriptomics, proteomics and metabolomics) allows for a better understanding of pathogenicity mechanisms and virulence levels of bacterial strains. Detection and identification of virulence genes, comparative genomics, analyses of mRNA and protein levels and the development of biomarkers can help in building a mechanistic dose-response model to predict disease severity. In this respect, systems biology can help to identify critical system characteristics that confer virulence and explain variability between strains. Despite challenges in the integration of omics into risk assessment, some omics methods have already been used by regulatory agencies for hazard identification. Standardized methods, reproducibility and datasets obtained from realistic conditions remain a challenge, and are needed to improve accuracy of hazard characterisation. When these improvements are realized, they will allow the health authorities and government policy makers to prioritize hazards more accurately and thus refine surveillance programs with the collaboration of all stakeholders of the food chain.

SalmoNet, an integrated network of ten Salmonella enterica strains reveals common and distinct pathways to host adaptation.

Salmonella enterica is a prominent bacterial pathogen with implications on human and animal health. Salmonella serovars could be classified as gastro-intestinal or extra-intestinal. Genome-wide comparisons revealed that extra-intestinal strains are closer relatives of gastro-intestinal strains than to each other indicating a parallel evolution of this trait. Given the complexity of the differences, a systems-level comparison could reveal key mechanisms enabling extra-intestinal serovars to cause systemic infections. Accordingly, in this work, we introduce a unique resource, SalmoNet, which combines manual curation, high-throughput data and computational predictions to provide an integrated network for Salmonella at the metabolic, transcriptional regulatory and protein-protein interaction levels. SalmoNet provides the networks separately for five gastro-intestinal and five extra-intestinal strains. As a multi-layered, multi-strain database containing experimental data, SalmoNet is the first dedicated network resource for Salmonella. It comprehensively contains interactions between proteins encoded in Salmonella pathogenicity islands, as well as regulatory mechanisms of metabolic processes with the option to zoom-in and analyze the interactions at specific loci in more detail. Application of SalmoNet is not limited to strain comparisons as it also provides a Salmonella resource for biochemical network modeling, host-pathogen interaction studies, drug discovery, experimental validation of novel interactions, uncovering new pathological mechanisms from emergent properties and epidemiological studies. SalmoNet is available at http://salmonet.org.

Piecewise linear approximations to model the dynamics of adaptation to osmotic stress by food-borne pathogens.

Addition of salt to food is one of the most ancient and most common methods of food preservation. However, little is known of how bacterial cells adapt to such conditions. We propose to use piecewise linear approximations to model the regulatory adaptation of Escherichiacoli to osmotic stress. We apply the method to eight selected genes representing the functions known to be at play during osmotic adaptation. The network is centred on the general stress response factor, sigma S, and also includes a module representing the catabolic repressor CRP-cAMP. Glutamate, potassium and supercoiling are combined to represent the intracellular regulatory signal during osmotic stress induced by salt. The output is a module where growth is represented by the concentration of stable RNAs and the transcription of the osmotic gene osmY. The time course of gene expression of transport of osmoprotectant represented by the symporter proP and of the osmY is successfully reproduced by the network. The behaviour of the rpoS mutant predicted by the model is in agreement with experimental data. We discuss the application of the model to food-borne pathogens such as Salmonella; although the genes considered have orthologs, it seems that supercoiling is not regulated in the same way. The model is limited to a few selected genes, but the regulatory interactions are numerous and span different time scales. In addition, they seem to be condition specific: the links that are important during the transition from exponential to stationary phase are not all needed during osmotic stress. This model is one of the first steps towards modelling adaptation to stress in food safety and has scope to be extended to other genes and pathways, other stresses relevant to the food industry, and food-borne pathogens. The method offers a good compromise between systems of ordinary differential equations, which would be unmanageable because of the size of the system and for which insufficient data are available, and the more abstract Boolean methods.

Data for the qualitative modeling of the osmotic stress response to NaCl in Escherichia coli.

Qualitative modeling approaches allow to provide a coarse-grained description of the functioning of cellular networks when experimental data are scarce and heterogeneous. We translate the primary literature data on the response of Escherichia coli to hyperosmotic stress caused by NaCl addition into a piecewise linear (PL) model. We provide a data file of the qualitative model, which can be used for simulation of changes of protein concentrations and of DNA coiling during the physiological response of the bacterium to the stress. The qualitative model predictions are directly comparable to the available experimental data. This data is related to the research article entitled "Piecewise linear approximations to model the dynamics of adaptation to osmotic stress by food-borne pathogens" (Metris et al., 2016) [1].

A proposed essential gene discovery pipeline: a Campylobacter jejuni case study.

Genes required for an organism's growth and survival are termed essential and represent potential intervention targets. Following in the footsteps of the genomics era, the "next-gen" genomic era provides vast amounts of genetic information. Sequencing of a representative bacterial pathogen genome has been superseded by sequencing of whole strain collections, whether from environmental or clinical sources (Harris et al., Science 327:469-474, 2010; Lewis et al., J Hosp Infect 75:37-41, 2010; Beres et al., Proc Natl Acad Sci U S A 107:4371-4376, 2010; Qi et al., PLoS Pathog 5:e1000580, 2009; He et al., Proc Natl Acad Sci U S A 107:7527-7532, 2010; Barrick et al., Nature 461:1243-1247, 2009; Sheppard et al., Mol Ecol 22:1051-1064, 2013). However, the challenge of using this information to gain biological insight remains. Nonetheless, this information, in combination with experimental data from the literature, can serve as the framework for gaining a better understanding of an organism's biology. Generic metabolic pathways have long been known, and a number of websites (e.g., KEGG and BioCyc) attempt to map information from genome annotation to metabolic pathways (Kanehisa et al., Nucleic Acids Res 40:D109-D114, 2010; Karp et al., Nucleic Acids Res 33:6083-6089, 2005). Extending this analysis to incorporate metabolic flux models further allows in silico prediction of potential essential genes. Such efforts are of value, either to highlight novel generic antimicrobials or to seek novel treatments for non-paradigm organisms. Such in silico approaches are attractive as they can highlight pathways and genes that would otherwise only be identified by costly and time-consuming laboratory methods.

Modelling osmotic stress by Flux Balance Analysis at the genomic scale.

Predictive microbiology for food safety is still primarily based on empirical models describing the effect of the environmental conditions of the food on the kinetics of the growth of foodborne pathogens. One way to make these models more mechanistic is to use systems biology methods such as Flux Balance Analysis (FBA). FBA consists of evaluating the possible fluxes through the metabolic reactions taking place in a cell. Using this method, the specific growth rate of Escherichia coli can be predicted by assuming, as an objective function, that the cells maximise their biomass production during balanced growth. Whilst this works under favourable environmental conditions, our simulations show that this objective function is not sufficient to explain the decrease of the growth rate due to osmotic stress. One feature of the FBA models is that the parameters and objective function in general refer to chemostat experiments where the carbon source is the main limiting factor. This may be relevant to some foods where the carbon to nitrogen balance is limiting but, in general, it is the physico-chemical conditions which are the most stringent. We therefore need to examine the effect of such constraints on the fluxes and/or modify the objective function, or to elaborate the metabolic model by taking into account other functional levels of the cell in order to develop mechanistic predictive models for osmotic stress conditions.

In vivo and in silico determination of essential genes of Campylobacter jejuni.

BACKGROUND: In the United Kingdom, the thermophilic Campylobacter species C. jejuni and C. coli are the most frequent causes of food-borne gastroenteritis in humans. While campylobacteriosis is usually a relatively mild infection, it has a significant public health and economic impact, and possible complications include reactive arthritis and the autoimmune diseases Guillain-Barré syndrome. The rapid developments in "omics" technologies have resulted in the availability of diverse datasets allowing predictions of metabolism and physiology of pathogenic micro-organisms. When combined, these datasets may allow for the identification of potential weaknesses that can be used for development of new antimicrobials to reduce or eliminate C. jejuni and C. coli from the food chain. RESULTS: A metabolic model of C. jejuni was constructed using the annotation of the NCTC 11168 genome sequence, a published model of the related bacterium Helicobacter pylori, and extensive literature mining. Using this model, we have used in silico Flux Balance Analysis (FBA) to determine key metabolic routes that are essential for generating energy and biomass, thus creating a list of genes potentially essential for growth under laboratory conditions. To complement this in silico approach, candidate essential genes have been determined using a whole genome transposon mutagenesis method. FBA and transposon mutagenesis (both this study and a published study) predict a similar number of essential genes (around 200). The analysis of the intersection between the three approaches highlights the shikimate pathway where genes are predicted to be essential by one or more method, and tend to be network hubs, based on a previously published Campylobacter protein-protein interaction network, and could therefore be targets for novel antimicrobial therapy. CONCLUSIONS: We have constructed the first curated metabolic model for the food-borne pathogen Campylobacter jejuni and have presented the resulting metabolic insights. We have shown that the combination of in silico and in vivo approaches could point to non-redundant, indispensable genes associated with the well characterised shikimate pathway, and also genes of unknown function specific to C. jejuni, which are all potential novel Campylobacter intervention targets.

Physiological state of single cells of Listeria innocua in organic acids.

The growth of Listeria innocua in three organic acids was monitored by optical density measurements in a Bioscreen C automatic plate reader (Labsystems, Finland). The method described by Metris et al. [Metris, A., George, S.M., Baranyi, J., 2006. Use of optical density detection times to assess the effect of acetic acid on single-cell kinetics. Applied and Environmental Microbiology 72, 6674-6679.], was used to estimate both the growth rate and the lag time of single cells. It was found that the logarithm of both the growth rates and the population lag times increased linearly with sorbic acid concentration in the same way as previously described with acetic acid but the relationship was not linear with lactic acid. Of the three acids tested, sorbic was the most inhibitory for an equivalent undissociated acid concentration. The effect of lactic acid was dependent on both the growth phase of the inoculum and the inoculum concentration.

The effect of reuterin on the lag time of single cells of Listeria innocua grown on a solid agar surface at different pH and NaCl concentrations.

The lag time of single cells of Listeria innocua grown on the surface of Brain Heart Infusion Agar was studied by microscopy and image analysis. An experimental set-up that enabled relocation of the cells on the agar surface was developed and used to collect data from 50 to 100 individual cells at a time. Reuterin was added at different concentrations (0-10 AU/ml) and it was observed that it increased both the lag time of the cells and its variance. Furthermore, for a large proportion of cells, reuterin completely prevented the cell division within the time of observation. Reuterin in combination with low pH inhibited the cell division even more efficiently. A similar effect was observed for the combination of reuterin and sodium chloride. Our experimental set-up provides a good model system for generating data on the lag time of single cells on solid surfaces, which can improve the predictions of microbial growth on solid food matrices.

Distribution of turbidity detection times produced by single cell-generated bacterial populations.

The distributions of the times to turbidity for wells inoculated with single cells of Listeria innocua were determined in different environmental conditions (pH 4.5 to 7 and with 0.5% to 8% of NaCl at 30 degrees C). It was established by statistical analysis that the main source of the variability of the detection times, T, is the variability of individual lag times. A linear relation dev(T) approximately T was observed between the detection times and their standard deviation. At slow growth, other sources of variability became increasingly significant.