Estimating the optimal efflux inhibitor concentration of carvacrol as a function of the bacterial physiological state.

Our aim was to find the optimal efflux inhibitor concentration of a natural component, carvacrol, as a function of the physiological state of Escherichia coli. Using fluorescence-based measurements with two strains of E. coli, the effect of carvacrol was assessed at 17 sub-inhibitory concentrations, at which the bacterial efflux mechanism was compromised. The efficacy of carvacrol, as an efflux inhibitor, was compared to synthetic inhibitors and we found carvacrol the most efficient one. We considered the accumulation of Ethidium Bromide (EtBr) as a proxy for drugs spreading in the cell, thus measuring the efflux activity indirectly. The change in membrane integrity caused by the exposure to carvacrol was monitored using the LIVE/DEAD BacLight Bacterial Viability kit. To find the optimal inhibitory concentration of carvacrol, we used predictive microbiology methods. This optimum varied with the bacterial physiological state, as non-growing cultures were less susceptible to the effect of carvacrol than growing cultures were. Moreover, we point out, for the first time, that the efflux-mediated resistance of untreated cultures was also stronger in the non-growing than in the growing phase at population level.

MilkyBase, a database of human milk composition as a function of maternal-, infant- and measurement conditions.

This study describes the development of a database, called MilkyBase, of the biochemical composition of human milk. The data were selected, digitized and curated partly by machine-learning, partly manually from publications. The database can be used to find patterns in the milk composition as a function of maternal-, infant- and measurement conditions and as a platform for users to put their own data in the format shown here. The database is an Excel workbook of linked sheets, making it easy to input data by non-computationally minded nutritionists. The hierarchical organisation of the fields makes sure that statistical inference methods can be programmed to analyse the data. Uncertainty quantification and recording dynamic (time-dependent) compositions offer predictive potentials.

Roles of Alternative Sigma Factors in Invasion and Growth Characteristics of Listeria monocytogenes 10403S Into Human Epithelial Colorectal Adenocarcinoma Caco-2 Cell.

Listeria monocytogenes is a Gram-positive facultative intracellular bacterium with a broad host range. With its housekeeping sigma factor and four alternative ones (namely SigB, SigC, SigH, and SigL), L. monocytogenes can express genes in response to changing environments. However, the roles of these sigma factors in intracellular survival are still unclear. The objectives of this study were to characterize the role of each alternative σ factor on L. monocytogenes invasion and growth inside human epithelial colorectal adenocarcinoma Caco-2 cells. We used L. monocytogenes 10403S wild type and its 15 alternative sigma factor deletion mutants at a multiplicity of infection of 100 and 1 in invasion and intracellular growth assays in the Caco-2 cells, respectively. At 1.5, 2, 4, 6, 8, 10, and 12 h post-infection, Caco-2 cells were lysed, and intracellular L. monocytogenes were enumerated on brain-heart infusion agar. Colony-forming and growth rates were compared among strains. The results from phenotypic characterization confirmed that (i) SigB is the key factor for L. monocytogenes invasion and (ii) having only SigA (ΔsigBCHL strain) is sufficient to invade and multiply in the host cell at similar levels as the wild type. Our previous study suggested the negative role of SigL in bile stress response. In this study, we have shown that additional deletion of the rpoN (or sigL) gene to ΔsigB, ΔsigC, or ΔsigH could restore the impaired invasion efficiencies of the single mutant, suggesting the absence of SigL could enhance host invasion. Therefore, we further investigated the role of SigL during extracellular and intracellular life cycles. Using RNA sequencing, we identified 118 and 16 SigL-dependent genes during the extracellular and intracellular life cycles, respectively. The sigL gene itself was induced by fivefolds prior to the invasion, and 5.3 folds during Caco-2 infection, further suggesting the role of SigL in intracellular growth.

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.

Distribution of bacterial single cell parameters and their estimation from turbidity detection times.

The stochastic growth of homogeneous bacterial populations in the wells of a microtiter plate was studied as a function of the random initial cell number and their random individual lag times. These significantly affected the population growth in the well, while the maximum specific growth rate of the population was constant (or its variance was negligible) for each well. We showed the advantages of the mathematical assumption that a transformation of the single cell lag time, called the single cell physiological state (or, more accurately, that of the sub-population generated by the single cell) follow the Beta distribution. Simulations demonstrated what patterns would such assumption generate for the distribution of the detection times observed in the wells. An estimation procedure was developed, based on the beta-assumption, that resulted in an explicit expression for the expected value of the single cell physiological state as a function of measured "time to detection" values using turbidity experiments. The method was illustrated using laboratory data with Escherichia coli, Salmonella enterica subsp. enterica strains. The results gave a basis to quantify the difference between the studied organisms in terms of their single-cell kinetics.

Corrigendum: Modeling Bacillus cereus Growth and Cereulide Formation in Cereal-, Dairy-, Meat-, Vegetable-Based Food and Culture Medium.

[This corrects the article DOI: 10.3389/fmicb.2021.639546.].

The effect of pH on the growth rate of Bacillus cereus sensu lato: Quantifying strain variability and modelling the combined effects of temperature and pH.

In this study, the effect of pH, alone or in combination with temperature, on the maximum growth rate (µmax) of B. cereus sensu lato was investigated. In phase 1, the effect of pH at 30 °C was studied for 16 mesophilic strains and 2 psychrotrophic strains of Bacillus cereus sensu lato. The µmax vs. pH relationship was found to show a similar pattern for all the strains. Several pH models from literature were evaluated and the best performing 'growth rate vs. pH' model selected. A stochastic model was then developed to predict the maximum specific growth rate of mesophilic B. cereus at 30 °C as a function of pH, the intra-species variability being incorporated via considering the model parameters (e.g. pHmin) randomly distributed. The predicted maximum specific growth rates were acceptably close to independent published data. In phase 2, the combined effects of temperature and pH were studied. Growth rates were also generated at 15, 20 and 40 °C for a selection of strains and the pH model was fitted at each temperature. Interestingly, the results showed that the estimates for the pHmin parameter for mesophilic strains were lower at 20-30 °C than near the optimum temperature (40 °C), suggesting that experiments for the determination of this parameter should be conducted at lower-than-optimum temperatures. New equations were proposed for the relationship between temperature and the minimum pH-values, which were also consistent with the experimental growth boundaries. The parameters defining this equation quantify the minimum temperature for growth observed experimentally, the temperature of maximum enzyme stability and the maximum temperature for growth. Deviations from the Gamma hypothesis (multiplicative effects of environmental factors on the maximum specific growth rate) were observed near the growth limits, especially at 40 °C. To improve model performance, two approaches, one based on a minimum pH-term (doi: https://doi.org/10.3389/fmicb.2019.01510) and one based on an interaction term (doi: http://dx.doi.org/10.1016/S0168-1605(01)00640-7) were evaluated.

Determining optimum carvacrol treatment as a cardinal value of a secondary model.

Predictive microbiology methods were used to study the effect of carvacrol on the bacterial resistance to antimicrobials. Our objective was to estimate the optimum dose of carvacrol at concentrations below its MIC value (Minimum Inhibitory Concentration). As a fluorescent marker, ethidium bromide (EtBr) was applied to Escherichia coli to acquire raw data. The accumulation of EtBr was measured by its fluorescence signal (Fs), in the unit of RFU (Relative Fluorescence Unit). The temporal change of the fluorescence values, at a constant concentration of carvacrol, was described by a saturation curve (primary model). The difference, within the observation interval, between the fitted initial and maximum fluorescent values was chosen as the primary parameter to be fitted in the secondary model: a convex, asymmetric, bi-linear function of the carvacrol concentration changing between 0 and 0.5 MIC. Its breakpoint is the optimum value of the carvacrol, a cardinal parameter of the secondary model, where the chosen primary parameter assumes its highest value. This optimum was estimated with high uncertainty for individual experiments, but F-test showed that, with appropriate experimental and numerical procedure, its existence and value can be claimed with confidence. Our results demonstrate that the estimation of the optimum of the secondary model can be robust even if the full secondary model is uncertain.

A stochastic approach for modelling the effects of temperature on the growth rate of Bacillus cereus sensu lato.

A stochastic model that predicts the maximum specific growth rate (µmax) of Bacillus cereus sensu lato as a function of temperature was developed. The model integrates the intra-species variability by incorporating distributions of cardinal parameters (Tmin, Topt, Tmax) in the model. Growth rate data were generated for 22 strains, covering 5 major phylogenetic groups of B. cereus, and their cardinal temperatures identified. Published growth rate data were also incorporated in the model fitting, resulting in a set of 33 strains. Based on their cardinal temperatures, we identified clusters of Bacillus cereus strains that show similar response to temperature and these clusters were considered separately in the stochastic model. Interestingly, the µopt values for psychrotrophic strains were found to be significantly lower than those obtained for mesophilic strains. The model developed within this work takes into account some correlations existing between parameters (µopt, Tmin, Topt, Tmax). In particular, the relationship highlighted between the b-slope of the Ratkowsky model and Tmin (doi: https://doi.org/10.3389/fmicb.2017.01890) was adapted to the case of the popular Cardinal Temperature Model. This resulted in a reduced model in which µopt is replaced by a function of Tmin, Topt and 2 strain-independent parameters. A correlation between the Tmin parameter and the experimental minimal growth temperature was also highlighted and integrated in the model for improved predictions near the temperature growth limits. Compared to the classical approach, the model developed in this study leads to improved predictions for temperatures around Tmin and more realistic tails for the predicted distributions of µmax. It can be useful for describing the variability of the Bacillus cereus Group in Quantitative Microbial Risk Assessment (QMRA). An example of application of the stochastic model to Reconstituted Infant Formulae (RIF) was proposed.

Modeling Bacillus cereus Growth and Cereulide Formation in Cereal-, Dairy-, Meat-, Vegetable-Based Food and Culture Medium.

This study describes the simultaneous Bacillus cereus growth and cereulide formation, in culture medium and cereal-, dairy-, meat-, and vegetable-based food matrices. First, bacterial growth experiments were carried out under a wide range of temperatures (from 9 to 45°C), using the emetic reference strain F4810/72, in the above-mentioned matrices. Then, the generated data were put in a modeling framework where the response variable was a vector of two components: the concentration of B. cereus and that of its toxin, cereulide. Both were considered time-, temperature- and matrix-dependent. The modeling was carried out in a series of steps: the parameters fitted in one step became the response variable of the following step. Using the square root link function, the maximum specific growth rate of the organism and the time to the appearance of quantifiable cereulide were modeled against temperature by cardinal parameters models (CPM), for each matrix. Finally, a validation study was carried out on an independent data set obtained in the same matrices and using various Bacillus cereus strains. Results showed that both growth and toxin-formation depended on the food matrix and on the environment but not in the same way. Thus, the matrix (culture medium), where the highest growth rate of B. cereus was observed, was not the medium where the shortest time to quantifiable cereulide occurred. While the cereal-based matrix generated the smallest growth rates (0.41-times smaller than culture medium did), quantifiable cereulide appeared in it at earlier times compared to the other tested matrices. In fact, three groups of matrices could be distinguished based on their ability to support cereulide formation (1) the cereal-based matrix (highest), (2) the culture medium and the dairy-based matrix (intermediate), and (3) the meat- and vegetable-based matrices (lowest). This ranking between the matrices is quite different from that based on their suitability to the growth of the organism. Our models can be used in HACCP studies, to improve shelf-life predictions and, generally, microbiological food safety assessments of products for which B. cereus is the main concern.

Optimization of turbidity experiments to estimate the probability of growth for individual bacterial cells.

Using turbidity measurements to quantify bacterial growth is a common and well-established practice in microbiology. Automated devices offering high throughput analyses have largely contributed to the increase of its use. A main difficulty of this method is that it detects growth only at late exponential phase, making turbidity measurements limited for studies focussing on low cell numbers. This work proposes an improved estimator for the probability of growth of individual cells using turbidity-based measurements, when the initial number of cells is low and random. We modify the currently used estimator for the expected cell number per well, a Poisson-parameter, and show that an optimal scenario is when ca 20% of the wells do not become turbid, resulting in improved accuracy and precision.

Microbiological Testing for the Proper Assessment of the Hygiene Status of Beef Carcasses.

Microbiological testing is an important quality management tool in the food industry. In this study, the hygiene status of beef carcasses sampled in eight Brazilian slaughterhouses was assessed by enumeration of different hygiene indicator microorganisms, and a model to establish potential associations among these counts was proposed. The carcasses (n = 464) were surface sampled at four slaughtering steps (step 1: Hide after bleeding; step 2: Carcass after hide removal; step 3: Carcass after evisceration; step 4: Carcass after end washing) and subjected to a counting of mesophilic aerobes (MA), Enterobacteriaceae (EB), total coliforms (TC), and Escherichia coli (EC) using Petrifilm™ plates. Among the sampled beef carcasses (step 4), 32 (6.9%) and 71 (15.3%) presented counts above the microbiological criteria established by (EC) No. 1441/2007 for MA and EB, respectively. Thus, indicating that improvements in slaughter hygiene and a review of process controls are demanded in some of the studied slaughterhouses. The log count differences of EC, TC, and EB from MA were considered as response variables as a function of the slaughtering steps. Differential log counts changed consistently with the steps. The measurements, including the patterns in their inherently random variability, were fairly predictable from steps 1 and 4. The results indicated that differential log counts for TC and EC are not relevant, as their concentrations and random pattern can be inferred from counts of MA and EB. The proposed model can be used as a valuable tool for the design and adoption of feasible quality control programs in beef industries. The adoption of such a tool should have a positive contribution on consumers' health and enhance product quality.

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.

From Culture-Medium-Based Models to Applications to Food: Predicting the Growth of B. cereus in Reconstituted Infant Formulae.

Predictive models of the growth of foodborne organisms are commonly based on data generated in laboratory medium. It is a crucial question how to apply the predictions to realistic food scenarios. A simple approach is to assume that the bias factor, i.e., the ratio between the maximum specific growth rate in culture medium and the food in question is constant in the region of interest of the studied environmental variables. In this study, we investigate the validity of this assumption using two well-known link functions, the square-root and the natural logarithm, both having advantageous properties when modeling the variation of the maximum specific growth rate with temperature. The main difference between the two approaches appears in terms of the respective residuals as the temperature decreases to its minimum. The model organism was Bacillus cereus. Three strains (B594, B596, and F4810/72) were grown in Reconstituted Infant Formulae, while one of them (F4810/72) was grown also in culture medium to calculate the bias factor. Their growth parameters were estimated using viable count measurements at temperatures ranging from 12 to 25°C. We utilized the fact that, if the bias factor is independent of the temperature, then the minimum growth temperature parameter of the square-root model of Ratkowsky et al. (1982) is the same for culture medium and food. We concluded, supported also by mathematical analysis, that the Ratkowsky model works well but its rearrangement for the natural logarithm of the specific growth rate is more appropriate for practical regression. On the other hand, when analyzing mixed culture data, available in the ComBase database, we observed a trend different from the one generated by pure cultures. This suggests that the identity of the strains dominating the growth of mixed cultures depends on the temperature. Such analysis can increase the accuracy of predictive models, based on culture medium, to food scenarios, bringing significant saving for the food industry.

Rethinking Tertiary Models: Relationships between Growth Parameters of Bacillus cereus Strains.

The maximum specific growth rates of 12 strains, pair-wise belonging to six groups of Bacillus cereus sensu lato, were fitted against temperature by a reparametrized version of the model of Ratkowsky et al. (1983). This way, the interpretation of the new parameter set was similar to that of the cardinal-values-model of Rosso and Robinson (2001), both models including the minimum, optimum and maximum temperatures for growth as well as a fourth parameter scaling along the dependent variable. The modularity of the reparametrized version of the Ratkowsky model was utilized to show a so-far undetected relationship between this scaling parameter and the cardinal temperatures, which linked even distant (e.g., mesophilic and psychotropic) strains of B. cereus. We propose that the name "tertiary modeling" should be used for investigations like ours, as logically derived from the concepts of "primary" and "secondary" modeling. Such tertiary models may reveal biological relationships between kinetic parameters within a group of strains. It can also be used to create an overarching predictive model for mixed cultures, when different strains grow together but independently of each other.

Predicting the kinetics of Listeria monocytogenes and Yersinia enterocolitica under dynamic growth/death-inducing conditions, in Italian style fresh sausage.

Traditional Italian pork products can be consumed after variable drying periods, where the temporal decrease of water activity spans from optimal to inactivating values. This makes it necessary to A) consider the bias factor when applying culture-medium-based predictive models to sausage; B) apply the dynamic version (described by differential equations) of those models; C) combine growth and death models in a continuous way, including the highly uncertain growth/no growth range separating the two regions. This paper tests the applicability of published predictive models on the responses of Listeria monocytogenes and Yersinia enterocolitica to dynamic conditions in traditional Italian pork sausage, where the environment changes from growth-supporting to inhibitory conditions, so the growth and death models need to be combined. The effect of indigenous lactic acid bacteria was also taken into account in the predictions. Challenge tests were carried out using such sausages, inoculated separately with L. monocytogenes and Y. enterocolitica, stored for 480h at 8, 12, 18 and 20°C. The pH was fairly constant, while the water activity changed dynamically. The effects of the environment on the specific growth and death rate of the studied organisms were predicted using previously published predictive models and parameters. Microbial kinetics in many products with a long shelf-life and dynamic internal environment, could result in both growth and inactivation, making it difficult to estimate the bacterial concentration at the time of consumption by means of commonly available predictive software tools. Our prediction of the effect of the storage environment, where the water activity gradually decreases during a drying period, is designed to overcome these difficulties. The methodology can be used generally to predict and visualise bacterial kinetics under temporal variation of environments, which is vital when assessing the safety of many similar products.

The use of predictive models to optimize risk of decisions.

The purpose of this paper is to set up a mathematical framework that risk assessors and regulators could use to quantify the "riskiness" of a particular recommendation (choice/decision). The mathematical theory introduced here can be used for decision support systems. We point out that efficient use of predictive models in decision making for food microbiology needs to consider three major points: (1) the uncertainty and variability of the used information based on which the decision is to be made; (2) the validity of the predictive models aiding the assessor; and (3) the cost generated by the difference between the a-priory choice and the a-posteriori outcome.

Incorporating prior knowledge improves detection of differences in bacterial growth rate.

BACKGROUND: Robust statistical detection of differences in the bacterial growth rate can be challenging, particularly when dealing with small differences or noisy data. The Bayesian approach provides a consistent framework for inferring model parameters and comparing hypotheses. The method captures the full uncertainty of parameter values, whilst making effective use of prior knowledge about a given system to improve estimation. RESULTS: We demonstrated the application of Bayesian analysis to bacterial growth curve comparison. Following extensive testing of the method, the analysis was applied to the large dataset of bacterial responses which are freely available at the web-resource, ComBase. Detection was found to be improved by using prior knowledge from clusters of previously analysed experimental results at similar environmental conditions. A comparison was also made to a more traditional statistical testing method, the F-test, and Bayesian analysis was found to perform more conclusively and to be capable of attributing significance to more subtle differences in growth rate. CONCLUSIONS: We have demonstrated that by making use of existing experimental knowledge, it is possible to significantly improve detection of differences in bacterial growth rate.

Interactions of Salmonella enterica subspecies enterica serovar Typhimurium with gut bacteria.

The aim of this study was to evaluate the impact of the gut microbiota on the growth and survival of S. Typhimurium. This was tested in two-species co-cultures and in mixed cultures with a simplified gut model microbiota. Subsequently, interactions between S. Typhimurium and human faecal bacteria were quantified in both batch and continuous culture systems simulating the human colon. The exponential growth of S. Typhimurium was halted when the population of Escherichia coli reached the maximum population density in a two-compartment co-culture system where the two species were separated by a 0.45 µm pore membrane. Furthermore, the growth of some gut bacteria such as Lactobacillus gasseri and Bifidobacterium bifidum was inhibited by the presence of S. Typhimurium in the other compartment. The survival of S. Typhimurium was severely affected in mixed batch cultures with human faecal samples; a reduction of 10(3)-10(4) cfu/ml in the concentration of S. Typhimurium was observed in these cultures. However, no effect on S. Typhimurium survival was observed in mixed batch cultures with a simplified gut model microbiota under the same conditions. The effect of human faecal samples on S. Typhimurium in a three-stage continuous culture was different to that obtained in batch cultures; its growth rather than survival was affected under these conditions. S. Typhimurium growth was inhibited, and the bacterium was therefore eliminated by the continuous flow of the medium. Depending upon culturing conditions, the gut microbiota caused either growth inhibition, inactivation or did not affect S. Typhimurium.

Interstrain interactions between bacteria isolated from vacuum-packaged refrigerated beef.

The formation of bacterial spoilage communities in food is influenced by both extrinsic and intrinsic environmental factors. Although many reports describe how these factors affect bacterial growth, much less is known about interactions among bacteria, which may influence community structure. This study investigated interactions among representative species of bacteria isolated from vacuum-packaged (VP) beef. Thirty-nine effectors and 20 target isolates were selected, representing 10 bacterial genera: Carnobacterium, Pseudomonas, Hafnia, Serratia, Yersinia, Rahnella, Brochothrix, Bacillus, Leuconostoc, and Staphylococcus. The influence of live effectors on growth of target isolates was measured by spot-lawn agar assay and also in liquid culture medium broth using live targets and effector cell-free supernatants. Inhibition on agar was quantified by diameter of inhibition zone and in broth by measuring detection time, growth rate, and maximum population density. A number of interactions were observed, with 28.6% of isolates inhibiting and 4.2% promoting growth. The majority of Pseudomonas isolates antagonized growth of approximately one-half of target isolates. Two Bacillus spp. each inhibited 16 targets. Among lactic acid bacteria (LAB), Carnobacterium maltaromaticum inhibited a wider range of isolates compared to other LAB. The majority of effector isolates enhancing target isolate growth were Gram-negative, including Pseudomonas spp. and Enterobacteriaceae. These findings markedly improve the understanding of potential interactions among spoilage bacteria, possibly leading to more mechanistic descriptions of bacterial community formation in VP beef and other foods.