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Abstract The lag phase is a temporary, nonreplicative period observed when a microbial population is introduced to a new, nutrient-rich environment. Although the theoretical concept of growth phases is clear, the practical application of methods for estimating lag lengths is often challenging. In fact, there are two distinct assumptions: (i) that cells do not divide at all during the lag phase or (ii) that they divide but at a suboptimal rate. Therefore, the choice of method should consider not only technical limitations but also consistency with the biological context. Here, we investigate the performance of the most common lag estimation methods, using empirical and simulated datasets. We apply different biological scenarios and simulate curves with varying parameters (i.e. growth rate, noise level, and frequency of measurements) to test their impact on the estimated lag phase duration. Our validation shows that infrequent measurements, low growth rate, longer lag phases, or higher level of noise in the measurements result in higher bias and higher variance of lag estimation. Additionally, in case of noisy data, the methods relying on model fitting perform best. The effect of method assumptions (i.e. no cell divisions or divisions at a suboptimal rate) on the estimated lag phase lengths was investigated using empirical and simulated growth curves.

The duration of lag phase can be used as an organismal fitness marker; however, it is often underreported as its estimation may be challenging and method and parameters dependent. Moreover, there are no publicly available tools to calculate lag duration by different methods. We developed a shiny‐based web application (https://microbialgrowth.shinyapps.io/lag_calulator/) where the lag duration can be calculated based on the user‐specified growth curve data, and for various explicitly specified methods, parameters and data preprocessing techniques. Additionally, we release an R package ‘miLAG’ that can be further customised and developed. We also describe in short the assumptions, advantages and disadvantages of the most popular lag calculation methods and propose a decision tree to choose a method most suited to one's data. Finally, we show some working examples of how to calculate lag duration using our shiny server.

The shelf life of meat products is a critical factor in ensuring both consumer safety and product quality. Traditional methods for determining shelf life are labor-intensive and time-consuming, making it challenging for manufacturers to adapt to market demands. The accelerated shelf life testing (ASLT) methodology offers a viable solution by exposing products to controlled elevated conditions that simulate long-term storage, allowing for faster shelf life predictions. This study evaluates the ASLT methodology as a predictive tool for determining the shelf life of high-pressure (HPP)-treated meat products. The present study includes experiments to verify the shelf life of frankfurter-type sausages under accelerated conditions. By simulating long-term storage at elevated temperatures (4–18 °C), a kinetic model was developed to monitor spoilage bacteria growth, with validation under dynamic temperature conditions. The results indicate that the main spoilage population of frankfurter-type sausages was lactic acid bacteria (LAB), which was strongly correlated with the total mesophilic microflora of the products. The findings show that elevated storage temperatures (8 and 18 °C) provide accurate shelf life predictions, offering an efficient approach to ensure product quality and meet consumer demands.

The problem of segmenting linearly ordered data is frequently encountered in time-series analysis, computational biology, and natural language processing. Segmentations obtained independently from replicate data sets or from the same data with different methods or parameter settings pose the problem of computing an aggregate or consensus segmentation. This Segmentation Aggregation problem amounts to finding a segmentation that minimizes the sum of distances to the input segmentations. It is again a segmentation problem and can be solved by dynamic programming. The aim of this contribution is (1) to gain a better mathematical understanding of the Segmentation Aggregation problem and its solutions and (2) to demonstrate that consensus segmentations have useful applications. Extending previously known results we show that for a large class of distance functions only breakpoints present in at least one input segmentation appear in the consensus segmentation. Furthermore, we derive a bound on the size of consensus segments. As show-case applications, we investigate a yeast transcriptome and show that consensus segments provide a robust means of identifying transcriptomic units. This approach is particularly suited for dense transcriptomes with polycistronic transcripts, operons, or a lack of separation between transcripts. As a second application, we demonstrate that consensus segmentations can be used to robustly identify growth regimes from sets of replicate growth curves.

Plant growth-promoting rhizobacteria (PGPR) engage in complex chemical exchange and signalling processes to enhance their survival, rhizosphere colonisation, and plant-beneficial roles. These microbial interactions are mediated by various chemical cues, including quorum sensing (QS) molecules, cyclic peptides, lipopeptides, nutrients, volatile organic compounds (VOC), and phytohormones. Cross-feeding, where one microorganism consumes metabolites produced by another, exemplifies direct chemical communication that shapes community dynamics and metabolic cooperation. However, the effects of cross-feeding among different PGPR strains remain insufficiently characterised. In this study, an LC–MS-based metabolomics approach, combined with multivariate statistical analysis, was employed to investigate metabolic perturbations induced by cross-feeding among PGPR strains. Growth curve analysis revealed that cross-fed PGPR exhibited growth patterns comparable to controls, with a slight reduction in biomass. Metabolic profiling indicated time-dependent shifts in the metabolic state of the cross-fed organisms, suggesting adaptive metabolic reprogramming in response to the donor-conditioned media. Multivariate analysis identified distinct metabolite alterations between cross-fed and control groups across different time points, highlighting the influence of nutrient availability on microbial growth dynamics. Notably, cross-fed groups showed decreased levels of primary metabolites such as amino acids and sugars alongside increased production of secondary metabolites, including surfactins, salicylic acid, and carboxylic acids. These secondary metabolites are implicated in plant growth promotion and defence, indicating their potential as natural biostimulants. The findings advance the understanding of PGPR interactions and chemical communication in the rhizosphere, supporting the development of sustainable agricultural practices by leveraging beneficial microbial interactions. Future research should explore these interactions within more complex microbial communities.

Mannosylerythritol lipids (MELs) are novel biosurfactants performing excellent physical-chemical properties as well as bioactivities. This study is aimed to explore the antibacterial and antibiofilm activity of mannosylerythritol lipids against foodborne gram-positive Staphylococcus aureus. The results of growth curve and survival rate revealed the significant inhibitory effect of MELs against S. aureus. The visualized pictures by scanning electron microscope and transmission electron microscope exposed apparent morphological and ultrastructure changes of MEL-treated cells. Furthermore, flow cytometry confirmed that MELs have promoted cell apoptosis and damaged the cell membrane. Notably, MEL-A also exhibited outstanding antibiofilm activity against S. aureus biofilm on different material surfaces including polystyrene, glass, and stainless steel, verified by confocal laser scanning microscope. These findings suggest that the antimicrobial activity of MELs is related to inhibit planktonic cells and biofilm of S. aureus, indicating that it has potential to be an alternative to antibacterial agents and preservatives applied into food processing.Key Points• MELs have strong antibacterial activity against Staphylococcus aureus.• MELs mainly damage the cell membrane of Staphylococcus aureus.• Mannosylerythritol lipids inhibit the bacterial adhesion to remove biofilm.

Background Plate readers can measure the growth curves of many microbial strains in a high-throughput fashion. The hundreds of absorbance readings collected simultaneously for hundreds of samples create technical hurdles for data analysis. Results Growthcurver summarizes the growth characteristics of microbial growth curve experiments conducted in a plate reader. The data are fitted to a standard form of the logistic equation, and the parameters have clear interpretations on population-level characteristics, like doubling time, carrying capacity, and growth rate. Conclusions Growthcurver is an easy-to-use R package available for installation from the Comprehensive R Archive Network (CRAN). The source code is available under the GNU General Public License and can be obtained from Github (Sprouffske K, Growthcurver sourcecode, 2016).

Microbes are capable of physiologically adapting to diverse environmental conditions by differentially varying the rates at which they uptake different nutrients. In particular, microbes can switch hierarchically between different energy sources, consuming first those that ensure the highest growth rate. Experimentally, this can result in biphasic growth curves called \"diauxic shifts\" that typically arise when microbes are grown in media containing several nutrients. Despite these observations are well known in microbiology and molecular biology, the mathematical models generally used to describe the population dynamics of microbial communities do not account for dynamic metabolic adaptation, thus implicitly assuming that microbes cannot switch dynamically from one resource to another. Here, we introduce dynamic metabolic adaptation in the framework of consumer-resource models, which are commonly used to describe competitive microbial communities, allowing each species to temporally change its preferred energy source to maximize its own relative fitness. We show that dynamic metabolic adaptation enables the community to self-organize, allowing several species to coexist even in the presence of few resources, and to respond optimally to a time-dependent environment, thus showing that dynamic metabolic adaptation could be an important mechanism for maintaining high levels of diversity even in environments with few energy sources. We show that introducing dynamic metabolic strategies in consumer-resource models is necessary for reproducing experimental growth curves of the baker's yeast Saccharomyces cerevisiae growing in the presence of two carbon sources. Even though diauxic shifts emerge naturally from the model when two resources are qualitatively very different, the model predicts that the existence of such shifts is not a prerequisite for species coexistence in competitive communities.

Determining the fitness of specific microbial genotypes has extensive application in microbial genetics, evolution, and biotechnology. While estimates from growth curves are simple and allow high throughput, they are inaccurate and do not account for interactions between costs and benefits accruing over different parts of a growth cycle. For this reason, pairwise competition experiments are the current “gold standard” for accurate estimation of fitness. However, competition experiments require distinct markers, making them difficult to perform between isolates derived from a common ancestor or between isolates of nonmodel organisms. In addition, competition experiments require that competing strains be grown in the same environment, so they cannot be used to infer the fitness consequence of different environmental perturbations on the same genotype. Finally, competition experiments typically consider only the end-points of a period of competition so that they do not readily provide information on the growth differences that underlie competitive ability. Here, we describe a computational approach for predicting density-dependent microbial growth in a mixed culture utilizing data from monoculture and mixed-culture growth curves. We validate this approach using 2 different experiments with Escherichia coli and demonstrate its application for estimating relative fitness. Our approach provides an effective way to predict growth and infer relative fitness in mixed cultures.

Microbial fitness screens are a key technique in functional genomics. We present an all-in-one solution, pyphe, for automating and improving data analysis pipelines associated with large-scale fitness screens, including image acquisition and quantification, data normalisation, and statistical analysis. Pyphe is versatile and processes fitness data from colony sizes, viability scores from phloxine B staining or colony growth curves, all obtained with inexpensive transilluminating flatbed scanners. We apply pyphe to show that the fitness information contained in late endpoint measurements of colony sizes is similar to maximum growth slopes from time series. We phenotype gene-deletion strains of fission yeast in 59,350 individual fitness assays in 70 conditions, revealing that colony size and viability provide complementary, independent information. Viability scores obtained from quantifying the redness of phloxine-stained colonies accurately reflect the fraction of live cells within colonies. Pyphe is user-friendly, open-source and fully documented, illustrated by applications to diverse fitness analysis scenarios.