Predicting microbial growth dynamics in response to nutrient availability

High-throughput ecological interaction mapping of dairy microorganisms

To engineer efficient microbial management strategies in the food industry, a comprehensive understanding of microbial interactions is crucial. Microorganisms live in communities where they influence each other in several ways. Although much attention has been paid to the production of antagonistic metabolites in lactic acid bacteria (LAB), research that accounts for the complexity of their ecological interactions and their dynamics remains limited. This study explores binary interactions within a mock community of 94 strains, including 23 LAB from culture collections and 71 isolated from dairy products. Using a colony-picking robot and image analysis, bidirectional interactions were measured at high throughput on solid media, where one test strain was challenged against other mock community members as the target strains. Assays of 15 test strains (14 LAB and one yeast) yielded 1,142 bidirectionally mapped interactions, classified by ecological type over seven days. The results showed variation in the strength, directionality, and type of interactions depending on the origin of the target strains. Cooperation rates increased for strains isolated from raw milk to pasteurized milk and cheese, while exploitation was more common in raw milk strains. Cooperating strains also exhibited more similar ecological behaviors than competing strains, suggesting the presence of microbial cliques. Interestingly, Lactococcus cremoris ATCC 19257 developed pink-red pigmentation when co-cultured with Pseudomonas aeruginosa. Overall, these findings present an unprecedented exploratory data set that significantly improves our understanding of microbial interactions at the system level, with potential applications in strain selection for food processes such as fermentation, bioprotection, and probiotics.

A comparative assesment of biostimulants in microbiome-based ecorestoration of polycyclic aromatic hydrocarbon polluted soil

Polycyclic aromatic hydrocarbons (PAHs) pose severe environmental and public health risks due to their harmful and persistent nature. Therefore, developing sustainable and effective methods for PAH remediation is crucial. This study explores the biostimulation potential of various nutrient supplements in enhancing the metabolic activities of indigenous oleophilic bacteria to PAH degradation and removal. The physicochemical and microbiological characterization of the soil sample obtained from the aged crude oil spill site prior to bioremediation revealed the presence of PAH and other hydrocarbons, reduced nutrient availability as well as an appreciable population of PAH degrading bacteria such as strains of Pseudomonas, Enterobacter, Kosakonia and Staphylococcus. The polluted soil treatment was conducted in six microcosms representing each nutrient supplement: casmes-CM, cocodust-CCD and osmocote-OSM slow-release fertilizers, NPK 20:10:10, casmes + cow dung - CM + CD and a control (unamended soil). Each pot contained 4 kg of soil spiked with 4% Escravos crude oil to a final concentration of 989 mg/kg of PAH, respectively. All treatments enhanced the activity of the indigenous bacteria to promote PAH removal (> 50%) after 35 days although CM + CD had the highest biostimulation effect (B. E.) of 56% with 71.77% PAH attenuation followed by NPK treatment with B. E. of 54.9% and 70.4% PAH removal, respectively. The order of degradation of PAHs from lowest to highest is: control > casmes > osmocote > cocodust > NPK > CM + CD. First-order kinetic model revealed soil microcosm amended with CM + CD had a higher k value (0.0342 day-1) and lower t½ (18.48 day) and this was relatively followed by NPK treated soil. Biostimulation is an effective bioremediation approach to PAH degradation, however, a combined nutrient regimen in the presence of PAH-degrading microbes is more potent and eco-friendly in driving this process.

Competition for resources can reshape the evolutionary properties of spatial structure

Many evolving ecosystems have spatial structures that can be conceptualized as networks, with nodes representing individuals or homogeneous subpopulations and links the patterns of spread between them. Prior models of evolution on networks do not take ecological niche differences and eco-evolutionary interplay into account. Here, we combine a resource competition model with evolutionary graph theory to study how heterogeneous topological structure shapes evolutionary dynamics under global frequency-dependent ecological interactions. We find that the addition of ecological competition for resources can produce a reversal of roles between amplifier and suppressor networks for deleterious mutants entering the population. We show that this effect is a nonlinear function of ecological niche overlap and discuss intuition for the observed dynamics using simulations and analytical approximations. We use these theoretical results together with spatial representations from imaging data to show that, for ductal carcinoma, where tumor growth is highly spatially constrained, with cells confined to a tree-like network of ducts, the topological structure can lead to higher rates of deleterious mutant hitchhiking with metabolic driver mutations, compared to tumors characterized by different spatial topologies.

Bacterial cell size modulation along the growth curve across nutrient conditions

Under stable growth conditions, bacteria maintain cell size homeostasis through coordinated elongation and division. However, fluctuations in nutrient availability result in dynamic regulation of the target cell size. Using microscopy imaging and mathematical modelling, we examine how bacterial cell volume changes over the growth curve in response to nutrient conditions. We find that two rod-shaped bacteria, Escherichia coli and Salmonella enterica, exhibit similar cell volume distributions in stationary phase cultures irrespective of growth media. Cell resuspension in rich media results in a transient peak with a five-fold increase in cell volume ≈ 2h after resuspension. This maximum cell volume, which depends on nutrient composition, subsequently decreases to the stationary phase cell size. Continuous nutrient supply sustains the maximum volume. In poor nutrient conditions, cell volume shows minimal changes over the growth curve, but a markedly decreased cell width compared to other conditions. The observed cell volume dynamics translate into non-monotonic dynamics in the ratio between biomass (optical density) and cell number (colony-forming units), highlighting their non-linear relationship. Our findings support a heuristic model comparing modulation of cell division relative to growth across nutrient conditions and providing novel insight into the mechanisms of cell size control under dynamic environmental conditions.

Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics

The ability to effectively represent microbiome dynamics is a crucial challenge in their quantitative analysis and engineering. By using autoencoder neural networks, we show that microbial growth dynamics can be compressed into low-dimensional representations and reconstructed with high fidelity. These low-dimensional embeddings are just as effective, if not better, than raw data for tasks such as identifying bacterial strains, predicting traits like antibiotic resistance, and predicting community dynamics. Additionally, we demonstrate that essential dynamical information of these systems can be captured using far fewer variables than traditional mechanistic models. Our work suggests that machine learning can enable the creation of concise representations of high-dimensional microbiome dynamics to facilitate data analysis and gain new biological insights.

Metabolic efficiency reshapes the seminal relationship between pathogen growth rate and virulence

A cornerstone of classical virulence evolution theories is the assumption that pathogen growth rate is positively correlated with virulence, the amount of damage pathogens inflict on their hosts. Such theories are key for incorporating evolutionary principles into sustainable disease management strategies. Yet, empirical evidence raises doubts over this central assumption underpinning classical theories, thus undermining their generality and predictive power. In this paper, we identify a key component missing from current theories which redefines the growth-virulence relationship in a way that is consistent with data. By modifying the activity of a single metabolic gene, we engineered strains of Magnaporthe oryzae with different nutrient acquisition and growth rates. We conducted in planta infection studies and uncovered an unexpected non-monotonic relationship between growth rate and virulence that is jointly shaped by how growth rate and metabolic efficiency interact. This novel mechanistic framework paves the way for a much-needed new suite of virulence evolution theories.

Phosphate Solubilizing Microorganism Bacillus sp. MVY-004 and Its Significance for Biomineral Fertilizers' Development in Agrobiotechnology

In this study, a phosphate solubilizing microorganism was isolated from the soil of an agricultural field in Lithuania. Based on 16S rRNA gene sequence analysis, the strain was identified as Bacillus sp. and submitted to the NCBI database, Sector of Applied Bio-catalysis, University Institute of Biotechnology, Vilnius, Lithuania and allocated the accession number KY882273. The Bacillus sp. was assigned with the number MVY-004. The culture nutrient medium and growth conditions were optimized: molasses was used as a carbon source; yeast extract powder was used as an organic source; NH₄H₂PO₄ was used as a nitrogen source; the culture growth temperature was 30 ± 0.5 °C; the initial value of pH was 7.0 ± 0.5; the partial pressure of oxygen (pO₂) was 60 ± 2.0; the mixer revolutions per minute (RPM) were 25-850, and the incubation and the fermentation time was 48-50 h. Analysis using Liquid Chromatography Time-of-Flight Mass Spectrometry (LC-TOF/MS) results showed that Bacillus sp. MVY-004 produced organic acids such as citric, succinic, 2-ketogluconic, gluconic, malic, lactic, and oxalic acids. Furthermore, the experiment showed that Bacillus sp. MVY-004 can also produce the following phytohormones: indole-3-acetic (IAA), jasmonic (JA), and gibberellic (GA3) acids. In the climate chamber, the experiment was performed using mineral fertilizer (NPS-12:40:10 80 Kg ha^-1) and mineral fertilizers in combination with Bacillus sp. MVY-004 cells (NPS-12:40:10 80 Kg ha^-1 + Bacillus sp. MVY-004) in loamy soil. Analysis was performed in three climate conditions: normal (T = 20 °C; relative humidity 60%); hot and dry (T = 30 °C; relative humidity 30%); hot and humid (T = 30 °C; relative humidity 80%).