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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.

Mathematical models designed for describing, predicting, and controlling states of microbial systems promote progress in solving vital problems at the forefront of microbiology. The paper presents a theoretical justification for the modeling and in silico studies of the evolution of bacterial communities cultivated in nutrient medium. We propose a coupled approach to formalize the formation of dendrite patterns of bacteria grown in nutrient medium and the corresponding characteristics of bacterial communication. The conceptualization includes the Allen-Cahn-based model of bacterial colony evolution combined with the model of changes in biomass-dependent nutrient concentration and the reaction-diffusion model of bacterial quorum sensing. The bacterial evolution model is associated with the Landau theory within the core framework of the existence of a growth threshold for bacteria during the growth process, known as the Allee effect. The unique solvability of the initial boundary value problem is proved for the mathematical model of nutrient-dependent bacterial growth. The theoretical results are based on the derivation of new a priori estimates for the solution of the semilinear initial boundary value problem. The general mathematical model was implemented using the finite element method with the COMSOL Multiphysics platform. Various computation experiments attributed to Pseudomonas bacterial strains were performed to examine different scenarios of the spatiotemporal dynamics of key substances of the biosystem. The results obtained indicate that the considered approach can be applied to simulate the growth of dendritic bacterial colonies and to control the optimal population size above which survival is possible despite the Allee effect.

This work evaluated the UASB-septic tank performance using different kinetic models that correlated process efficiency and methane production with hydraulic and organic loading rates through experiments with five different HRT (48 h, 36 h, 24 h, 18 h, and 12 h) using synthetic domestic wastewater. The modified Stover-Kincannon model provided the best fitting to calculate kinetics constants, with an R 2 above 98% for linear regression, and predicted the effluent COD more accurately than the other models. Methane yield was 0.3294 L CH 4 /g COD removed, being closer to the theoretical value, and the Van der Meer and Heertjes model had the highest R 2 for methane production. Organic matter and solids removal were 45% for TS, 70% and 68% for total and soluble COD, and 85% for TSS. Pollutant removal markedly decreased when the reactor operated HRT below 24 h; thus, it is recommended to operate the UASB-septic tank at this HRT.

A major source of anthropogenic polycyclic aromatic hydrocarbon (PAH) inputs into marine environments are diffuse emissions which result in low PAH concentrations in the ocean water, posing a potential threat for the affected ecosystems. However, the remediation of low-dosage PAH contaminations through microbial processes remains largely unknown. Here, we developed a process-based numerical model to simulate batch cultures receiving repeated low-dosage naphthalene pulses compared to the conventionally used one-time high-dosage. Pulsing frequency as well as dosage concentration had a large impact on the degradation efficiency. After 10 days, 99.7%, 97.2%, 86.6%, or 83.5% of the 145 mg L −1 naphthalene was degraded when given as a one-time high-dosage or in 2, 5, or 10 repeated low-concentration dosages equally spaced throughout the experiment, respectively. If the simulation was altered, giving the system that received 10 pulses time to recover to 99.7%, pulsing patterns affected the degradation of naphthalene. When pulsing 10 days at once per day, naphthalene accumulated following each pulse and if the degradation was allowed to continue until the recovered state was reached, the incubation time was prolonged to 17 days with a generation time of 3.81 days. If a full recovery was conditional before the next pulse was added, the scenario elongated to 55 days and generation time increased to 14.15 days. This indicates that dissolution kinetics dominate biodegradation kinetics, and the biomass concentration of PAH-degrading bacteria alone is not a sufficient indicator for quantifying active biodegradation. Applying those findings to the environment, a one-time input of a high dosage is potentially degraded faster than repeated low-dosage PAH pollution and repeated low-dosage input could lead to PAH accumulation in vulnerable pristine environments. Further research on the overlooked field of chronic low-dosage PAH contamination is necessary.

The large-scale use of air-conditioning equipment, while providing a comfortable living environment, has also brought about a series of problems. This study focuses on the growth and reproduction of Staphylococcus aureus on the surface of activated carbon in air-conditioning filtration systems. Experimental data were obtained under temperature conditions of 20 °C and 30 °C and relative humidity conditions of 10%, 50%, and 75% RH. Based on the experimental data, a mathematical model was established to predict the growth and reproduction of Staphylococcus aureus. The Logistic and Gompertz equations were used to fit the growth and reproduction curves under different temperature and humidity conditions, and the two models, commonly used for simulating microbial growth curves, were compared. The model with the best fit was selected to predict the amount of Staphylococcus aureus, providing some guidance for the actual lifespan of the adsorbent in filters.

Predictive microbiology is a relatively novel scientific field belonging to food microbiology; it is aimed at developing mathematical models that account for the effect of intrinsic and extrinsic factors on microorganism responses in food. Predictive models can be classified, according to the type of modelled phenomenon, into growth models, inactivation and survival models and probability models. More recently, transfer models, single‐cell‐based models or genomic‐scale models have been proposed as more mechanistic approaches to reflecting microbial responses in foods. All the relevant advances in predictive microbiology are now the basis of the quantitative microbial risk assessment (QMRA), which is considered a fundamental tool to support decision‐making processes for food risk management. The gamma model is highly applicable in quantitative microbial risk studies, since it can be expanded with new terms and factors. This means that modifications can be made easily, in order to assess new factors, without having to change the whole risk model.

Streptomyces are a genus of ubiquitous soil bacteria from which the majority of clinically utilized antibiotics derive 1 . The production of these antibacterial molecules reflects the relentless competition Streptomyces engage in with other bacteria, including other Streptomyces species 1 , 2 . Here we show that in addition to small-molecule antibiotics, Streptomyces produce and secrete antibacterial protein complexes that feature a large, degenerate repeat-containing polymorphic toxin protein. A cryo-electron microscopy structure of these particles reveals an extended stalk topped by a ringed crown comprising the toxin repeats scaffolding five lectin-tipped spokes, which led us to name them umbrella particles. Streptomyces coelicolor encodes three umbrella particles with distinct toxin and lectin composition. Notably, supernatant containing these toxins specifically and potently inhibits the growth of select Streptomyces species from among a diverse collection of bacteria screened. For one target, Streptomyces griseus , inhibition relies on a single toxin and that intoxication manifests as rapid cessation of vegetative hyphal growth. Our data show that Streptomyces umbrella particles mediate competition among vegetative mycelia of related species, a function distinct from small-molecule antibiotics, which are produced at the onset of reproductive growth and act broadly 3 , 4 . Sequence analyses suggest that this role of umbrella particles extends beyond Streptomyces , as we identified umbrella loci in nearly 1,000 species across Actinobacteria. Streptomyces are discovered to produce antibacterial protein complexes that selectively inhibit the hyphal growth of related species, a function distinct from that of the small-molecule antibiotics they are known for.

Pseudomonas aeruginosa is an opportunistic bacterium causing several health problems and having many virulence factors like biofilm formation on different surfaces. There is a significant need to develop new antimicrobials due to the spreading resistance to the commonly used antibiotics, partly attributed to biofilm formation. Consequently, this study aimed to investigate the anti-biofilm and anti-quorum sensing activities of Dioon spinulosum, Dyer Ex Eichler extract (DSE), against Pseudomonas aeruginosa clinical isolates. DSE exhibited a reduction in the biofilm formation by P. aeruginosa isolates both in vitro and in vivo rat models. It also resulted in a decrease in cell surface hydrophobicity and exopolysaccharide quantity of P. aeruginosa isolates. Both bright field and scanning electron microscopes provided evidence for the inhibiting ability of DSE on biofilm formation. Moreover, it reduced violacein production by Chromobacterium violaceum (ATCC 12,472). It decreased the relative expression of 4 quorum sensing genes ( las I, las R, rhl I, rhl R) and the biofilm gene ( ndv B) using qRT-PCR. Furthermore, DSE presented a cytotoxic activity with IC 50 of 4.36 ± 0.52 µg/ml against human skin fibroblast cell lines. For the first time, this study reports that DSE is a promising resource of anti-biofilm and anti-quorum sensing agents.

The widespread use of antibiotics is selecting for a variety of resistance mechanisms that seriously challenge our ability to treat bacterial infections. Resistant bacteria can be selected at the high concentrations of antibiotics used therapeutically, but what role the much lower antibiotic concentrations present in many environments plays in selection remains largely unclear. Here we show using highly sensitive competition experiments that selection of resistant bacteria occurs at extremely low antibiotic concentrations. Thus, for three clinically important antibiotics, drug concentrations up to several hundred-fold below the minimal inhibitory concentration of susceptible bacteria could enrich for resistant bacteria, even when present at a very low initial fraction. We also show that de novo mutants can be selected at sub-MIC concentrations of antibiotics, and we provide a mathematical model predicting how rapidly such mutants would take over in a susceptible population. These results add another dimension to the evolution of resistance and suggest that the low antibiotic concentrations found in many natural environments are important for enrichment and maintenance of resistance in bacterial populations.