Modelling dynamics between free-living amoebae and bacteria.

Free-living amoebae (FLA) serve as hosts for a variety of endosymbionts, which are microorganisms that reside and multiply within the FLA. Some of these endosymbionts pose a pathogenic threat to humans, animals, or both. The symbiotic relationship with FLA not only offers these microorganisms protection but also enhances their survival outside their hosts and assists in their dispersal across diverse habitats, thereby escalating disease transmission. This review is intended to offer an exhaustive overview of the existing mathematical models that have been applied to understand the dynamics of FLA, especially concerning their interactions with bacteria. An extensive literature review was conducted across Google Scholar, PubMed, and Scopus databases to identify mathematical models that describe the dynamics of interactions between FLA and bacteria, as published in peer-reviewed scientific journals. The literature search revealed several FLA-bacteria model systems, including Pseudomonas aeruginosa, Pasteurella multocida, and Legionella spp. Although the published mathematical models account for significant system dynamics such as predator-prey relationships and non-linear growth rates, they generally overlook spatial and temporal heterogeneity in environmental conditions, such as temperature, and population diversity. Future mathematical models will need to incorporate these factors to enhance our understanding of FLA-bacteria dynamics and to provide valuable insights for future risk assessment and disease control measures.

Ecological relevance of flagellar motility in soil bacterial communities.

Flagellar motility is a key bacterial trait as it allows bacteria to navigate their immediate surroundings. Not all bacteria are capable of flagellar motility, and the distribution of this trait, its ecological associations, and the life history strategies of flagellated taxa remain poorly characterized. We developed and validated a genome-based approach to infer the potential for flagellar motility across 12 bacterial phyla (26 192 unique genomes). The capacity for flagellar motility was associated with a higher prevalence of genes for carbohydrate metabolism and higher maximum potential growth rates, suggesting that flagellar motility is more prevalent in environments with higher carbon availability. To test this hypothesis, we applied a method to infer the prevalence of flagellar motility in whole bacterial communities from metagenomic data and quantified the prevalence of flagellar motility across four independent field studies that each captured putative gradients in soil carbon availability (148 metagenomes). We observed a positive relationship between the prevalence of bacterial flagellar motility and soil carbon availability in all datasets. Since soil carbon availability is often correlated with other factors that could influence the prevalence of flagellar motility, we validated these observations using metagenomic data from a soil incubation experiment where carbon availability was directly manipulated with glucose amendments. This confirmed that the prevalence of bacterial flagellar motility is consistently associated with soil carbon availability over other potential confounding factors. This work highlights the value of combining predictive genomic and metagenomic approaches to expand our understanding of microbial phenotypic traits and reveal their general environmental associations.

Symbiosis: Did bacteria bias the beetle big bang?

The massive species richness of certain taxonomic groups has long enchanted evolutionary biologists, but even within such groups there are biases in cladogenesis. A study of Metazoa's greatest radiation - the beetles - points to metabolic symbioses with bacteria as a possible driver of enhanced diversification in herbivorous clades.

How do bacterial endosymbionts work with so few genes?

The move from a free-living environment to a long-term residence inside a host eukaryotic cell has profound effects on bacterial function. While endosymbioses are found in many eukaryotes, from protists to plants to animals, the bacteria that form these host-beneficial relationships are even more diverse. Endosymbiont genomes can become radically smaller than their free-living relatives, and their few remaining genes show extreme compositional biases. The details of how these reduced and divergent gene sets work, and how they interact with their host cell, remain mysterious. This Unsolved Mystery reviews how genome reduction alters endosymbiont biology and highlights a "tipping point" where the loss of the ability to build a cell envelope coincides with a marked erosion of translation-related genes.

Communication leads to bacterial heterogeneous adaptation to changing conditions in partial nitrification reactor.

Recently, it is reported that bacterial communication coordinates the whole consortia to jointly resist the adverse environments. Here, we found the bacterial communication inevitably distinguished bacterial adaptation among different species in partial nitrification reactor under decreasing temperatures. We operated a partial nitrification reactor under temperature gradient from 30 °C to 5 °C and found the promotion of bacterial communication on adaptation of ammonia-oxidizing bacteria (AOB) was greater than that of nitrite-oxidizing bacteria (NOB). Signal pathways with single-component sensing protein in AOB can regulate more genes involved in bacterial adaptation than that with two-component sensing protein in NOB. The negative effects of bacterial communication, which were seriously ignored, have been highlighted, and Clp regulator downstream diffusible signal factor (DSF) based signal pathways worked as transcription activators and inhibitors of adaptation genes in AOB and NOB respectively. Bacterial communication can induce differential adaptation through influencing bacterial interactions. AOB inclined to cooperate with DSF synthesis bacteria as temperature declined, however, cooperation between NOB and DSF synthesis bacteria inclined to get weakening. According to the regulatory effects of signal pathways, bacterial survival strategies for self-protection were revealed. This study hints a potential way to govern niche differentiation in the microbiota by bacterial communication, contributing to forming an efficient artificial ecosystem.

Biofilm of petroleum-based and bio-based microplastics in seawater in response to Zn(II): Biofilm formation, community structure, and microbial function.

Microplastic biofilms are novel vectors for the transport and spread of pathogenic and drug-resistant bacteria. With the increasing use of bio-based plastics, there is an urgent need to investigate the microbial colonization characteristics of these materials in seawater, particularly in comparison with conventional petroleum-based plastics. Furthermore, the effect of co-occurring contaminants, such as heavy metals, on the formation of microplastic biofilms and bacterial communities remains unclear. In this study, we compared the biofilm bacterial community structure of petroleum-based polyethylene (PE) and bio-based polylactic acid (PLA) in seawater under the influence of zinc ions (Zn2+). Our findings indicate that the biofilm on PLA microplastics in the late stage was impeded by the formation of a mildly acidic microenvironment resulting from the hydrolysis of the ester group on PLA. The PE surface had higher bacterial abundance and diversity, with a more intricate symbiotic pattern. The bacterial structures on the two types of microplastics were different; PE was more conducive to the colonization of anaerobic bacteria, whereas PLA was more favorable for the colonization of aerobic and acid-tolerant species. Furthermore, Zn increased the proportion of the dominant genera that could utilize microplastics as a carbon source, such as Alcanivorax and Nitratireductor. PLA had a greater propensity to harbor and disseminate pathogenic and drug-resistant bacteria, and Zn promoted the enrichment and spread of harmful bacteria such as, Pseudomonas and Clostridioides. Therefore, further research is essential to fully understand the potential environmental effects of bio-based microplastics and the role of heavy metals in the dynamics of bacterial colonization.

Decoding the influence of low temperature on biofilm development: The hidden roles of c-di-GMP.

Biofilms are widely used and play important roles in biological processes. Low temperature of wastewater inhibits the development of biofilms derived from wastewater activated sludge. However, the specific mechanism of temperature on biofilm development is still unclear. This study explored the mechanism of temperature on biofilm development and found a feasible method to enhance biofilm development at low temperature. The amount of biofilm development decreased by approximately 66 % and 55 % at 4 °C and 15 °C, respectively, as compared to 28 °C. The cyclic dimeric guanosine monophosphate (c-di-GMP) concentration also decreased at low temperature and was positively correlated with extracellular polymeric substance (EPS) content, formation, and adhesion strength. Microbial community results showed that low temperature inhibited the normal survival of most microorganisms, but promoted the growth of some psychrophile bacteria like Sporosarcina, Caldilineaceae, Gemmataceae, Anaerolineaceae and Acidobacteriota. Further analysis of functional genes demonstrated that the abundance of functional genes related to the synthesis of c-di-GMP (K18968, K18967 and K13590) decreased at low temperature. Subsequently, the addition of exogenous spermidine increased the level of intracellular c-di-GMP and alleviated the inhibition effect of low temperature on biofilm development. Therefore, the possible mechanism of low temperature on biofilm development could be the inhibition of the microorganism activity and reduction of the communication level between cells, which is the closely related to the EPS content, formation, and adhesion strength. The enhancement of c-di-GMP level through the exogenous addition of spermidine provides an alternative strategy to enhance biofilm development at low temperatures. The results of this study enhance the understanding of the influence of temperature on biofilm development and provide possible strategies for enhancing biofilm development at low temperatures.

Using metabolic networks to predict cross-feeding and competition interactions between microorganisms.

Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.IMPORTANCEUnderstanding bacterial interactions at the community level is critical for microbiology, and leveraging metabolic networks presents an efficient and effective approach. The introduction of this novel method for predicting interactions through machine learning classifiers has the potential to advance the field by reducing the number of experimental assays required and contributing to the development of more effective bacterial consortia.

Bacteriophage specificity is impacted by interactions between bacteria.

Predators play a central role in shaping community structure, function, and stability. The degree to which bacteriophage predators (viruses that infect bacteria) evolve to be specialists with a single bacterial prey species versus generalists able to consume multiple types of prey has implications for their effect on microbial communities. The presence and abundance of multiple bacterial prey types can alter selection for phage generalists, but less is known about how interactions between prey shape predator specificity in microbial systems. Using a phenomenological mathematical model of phage and bacterial populations, we find that the dominant phage strategy depends on prey ecology. Given a fitness cost for generalism, generalist predators maintain an advantage when prey species compete, while specialists dominate when prey are obligately engaged in cross-feeding interactions. We test these predictions in a synthetic microbial community with interacting strains of Escherichia coli and Salmonella enterica by competing a generalist T5-like phage able to infect both prey against P22vir, an S. enterica-specific phage. Our experimental data conform to our modeling expectations when prey species are competing or obligately mutualistic, although our results suggest that the in vitro cost of generalism is caused by a combination of biological mechanisms not anticipated in our model. Our work demonstrates that interactions between bacteria play a role in shaping ecological selection on predator specificity in obligately lytic bacteriophages and emphasizes the diversity of ways in which fitness trade-offs can manifest. IMPORTANCE: There is significant natural diversity in how many different types of bacteria a bacteriophage can infect, but the mechanisms driving this diversity are unclear. This study uses a combination of mathematical modeling and an in vitro system consisting of Escherichia coli, Salmonella enterica, a T5-like generalist phage, and the specialist phage P22vir to highlight the connection between bacteriophage specificity and interactions between their potential microbial prey. Mathematical modeling suggests that competing bacteria tend to favor generalist bacteriophage, while bacteria that benefit each other tend to favor specialist bacteriophage. Experimental results support this general finding. The experiments also show that the optimal phage strategy is impacted by phage degradation and bacterial physiology. These findings enhance our understanding of how complex microbial communities shape selection on bacteriophage specificity, which may improve our ability to use phage to manage antibiotic-resistant microbial infections.

Bacteriostatic cells instead of bacteriostatic antibiotics?

This year we commemorate the centennial of the birth of the mature concept of bacteriostasis by John W. Churchman at Cornell University Medical School. The term bacteriostasis has primarily been applied to antibiotics (bacteriostatic antibiotics). In this Opinion paper, we are revisiting this concept by suggesting that bacteriostasis essentially reflects a distinct cellular status (or "cell variant") characterized by the inability to be killed as a consequence of an antibiotic-induced stress impacting on bacterial physiology/metabolism (growth). Note that the term "bacteriostasis" should not be associated only with antimicrobials but with many stressful conditions. In that respect, the drug promotion of bacteriostasis might resemble other types of stress-induced cellular differentiation, such as sporulation, in which spores can be considered "bacteriostatic cells" or perhaps as persister bacteria, which can become "normal cells" again when the stressful conditions have abated.IMPORTANCEThis year we commemorate the centennial of the birth of the mature concept of bacteriostasis by John W. Churchman at Cornell University Medical School. The term bacteriostasis has primarily been applied to antibiotics (bacteriostatic antibiotics). In this Opinion paper, we are revisiting this concept by suggesting that some antibiotics are drugs that induce bacteria to become bacteriostatic. Cells that are unable to multiply, thereby preventing the antibiotic from exerting major lethal effects on them, are a variant ("different") type of cells, bacteriostatic cells. Note that the term "bacteriostasis" should not be associated only with antimicrobials but with many stressful conditions. In that respect, the drug promotion of bacteriostasis might resemble other types of stress-induced cellular differentiation, such as sporulation, in which spores can be considered "bacteriostatic cells" or perhaps as persister bacteria, which can become "normal cells" again when the stressful conditions have abated.

Tradeoffs in bacterial physiology determine the efficiency of antibiotic killing.

Antibiotic effectiveness depends on a variety of factors. While many mechanistic details of antibiotic action are known, the connection between death rate and bacterial physiology is poorly understood. A common observation is that death rate in antibiotics rises linearly with growth rate; however, it remains unclear how other factors, such as environmental conditions and whole-cell physiological properties, affect bactericidal activity. To address this, we developed a high-throughput assay to precisely measure antibiotic-mediated death. We found that death rate is linear in growth rate, but the slope depends on environmental conditions. Growth under stress lowers death rate compared to nonstressed environments with similar growth rate. To understand stress's role, we developed a mathematical model of bacterial death based on resource allocation that includes a stress-response sector; we identify this sector using RNA-seq. Our model accurately predicts the minimal inhibitory concentration (MIC) with zero free parameters across a wide range of growth conditions. The model also quantitatively predicts death and MIC when sectors are experimentally modulated using cyclic adenosine monophosphate (cAMP), including protection from death at very low cAMP levels. The present study shows that different conditions with equal growth rate can have different death rates and establishes a quantitative relation between growth, death, and MIC that suggests approaches to improve antibiotic efficacy.

[Visualization method of type â £ pili and its application in the study of pili function].

As an important protein structure on the surface of bacteria, type Ⅳ pili (TFP) is the sensing and moving organ of bacteria. It plays a variety of roles in bacterial physiology, cell adhesion, host cell invasion, DNA uptake, protein secretion, biofilm formation, cell movement and electron transmission. With the rapid development of research methods, technical equipment and pili visualization tools, increasing number of studies have revealed various functions of pili in cellular activities, which greatly facilitated the microbial single cell research. This review focuses on the pili visualization method and its application in the functional research of TFP, providing ideas for the research and application of TFP in biology, medicine and ecology.

Learning from the unknown: exploring the range of bacterial functionality.

Determining the repertoire of a microbe's molecular functions is a central question in microbial biology. Modern techniques achieve this goal by comparing microbial genetic material against reference databases of functionally annotated genes/proteins or known taxonomic markers such as 16S rRNA. Here, we describe a novel approach to exploring bacterial functional repertoires without reference databases. Our Fusion scheme establishes functional relationships between bacteria and assigns organisms to Fusion-taxa that differ from otherwise defined taxonomic clades. Three key findings of our work stand out. First, bacterial functional comparisons outperform marker genes in assigning taxonomic clades. Fusion profiles are also better for this task than other functional annotation schemes. Second, Fusion-taxa are robust to addition of novel organisms and are, arguably, able to capture the environment-driven bacterial diversity. Finally, our alignment-free nucleic acid-based Siamese Neural Network model, created using Fusion functions, enables finding shared functionality of very distant, possibly structurally different, microbial homologs. Our work can thus help annotate functional repertoires of bacterial organisms and further guide our understanding of microbial communities.

Stress-induced metabolic exchanges between complementary bacterial types underly a dynamic mechanism of inter-species stress resistance.

Metabolic cross-feeding plays vital roles in promoting ecological diversity. While some microbes depend on exchanges of essential nutrients for growth, the forces driving the extensive cross-feeding needed to support the coexistence of free-living microbes are poorly understood. Here we characterize bacterial physiology under self-acidification and establish that extensive excretion of key metabolites following growth arrest provides a collaborative, inter-species mechanism of stress resistance. This collaboration occurs not only between species isolated from the same community, but also between unrelated species with complementary (glycolytic vs. gluconeogenic) modes of metabolism. Cultures of such communities progress through distinct phases of growth-dilution cycles, comprising of exponential growth, acidification-triggered growth arrest, collaborative deacidification, and growth recovery, with each phase involving different combinations of physiological states of individual species. Our findings challenge the steady-state view of ecosystems commonly portrayed in ecological models, offering an alternative dynamical view based on growth advantages of complementary species in different phases.

Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns.

The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473-709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%.

Discovery of phage determinants that confer sensitivity to bacterial immune systems.

Over the past few years, numerous anti-phage defense systems have been discovered in bacteria. Although the mechanism of defense for some of these systems is understood, a major unanswered question is how these systems sense phage infection. To systematically address this question, we isolated 177 phage mutants that escape 15 different defense systems. In many cases, these escaper phages were mutated in the gene sensed by the defense system, enabling us to map the phage determinants that confer sensitivity to bacterial immunity. Our data identify specificity determinants of diverse retron systems and reveal phage-encoded triggers for multiple abortive infection systems. We find general themes in phage sensing and demonstrate that mechanistically diverse systems have converged to sense either the core replication machinery of the phage, phage structural components, or host takeover mechanisms. Combining our data with previous findings, we formulate key principles on how bacterial immune systems sense phage invaders.