A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions

In Situ and Individual-Based Analysis of the Influence of Polystyrene Microplastics on Escherichia coli Conjugative Gene Transfer at the Single-Cell Level

The impact of microplastic particles of micro- and nanometer sizes on microbial horizontal gene transfer (HGT) remains a controversial topic. Existing studies rely on traditional approaches, which analyze population behavior, leading to conflicting conclusions and a limited understanding. The present study addressed these limitations by employing a novel microfluidic chamber system for in situ visualization and precise quantification of the effects of different concentrations of polystyrene (PS) microbeads on microbial HGT at the single-cell level. The statistical analysis indicated no significant difference in the division times of both the donor and recipient bacteria across different PS microbead concentrations. However, as the concentration of PS microbeads increased from 0 to 2000 mg L-1, the average conjugation frequency of Escherichia coli decreased from 0.028 ± 0.015 to 0.004 ± 0.003. Our observations from the microfluidic experiments revealed that 500 nm PS microbeads created a barrier effect on bacterial conjugative transfer. The presence of microbeads resulted in reduced contact and interaction between the donor and recipient strains, thereby causing a decrease in the conjugation transfer frequency. These findings were validated by an individual-based modeling framework parameterized by the data from the individual-level microfluidic experiments. Overall, this study offers a fresh perspective and strategy for investigating the risks associated with the dissemination of antibiotic resistance genes related to microplastics.

Consider the Anoxic Microsite: Acknowledging and Appreciating Spatiotemporal Redox Heterogeneity in Soils and Sediments

Reduction-oxidation (redox) reactions underlie essentially all biogeochemical cycles. Like most soil properties and processes, redox is spatiotemporally heterogeneous. However, unlike other soil features, redox heterogeneity has yet to be incorporated into mainstream conceptualizations of soil biogeochemistry. Anoxic microsites, the defining feature of redox heterogeneity in bulk oxic soils and sediments, are zones of oxygen depletion in otherwise oxic environments. In this review, we suggest that anoxic microsites represent a critical component of soil function and that appreciating anoxic microsites promises to advance our understanding of soil and sediment biogeochemistry. In sections 1 and 2, we define anoxic microsites and highlight their dynamic properties, specifically anoxic microsite distribution, redox gradient magnitude, and temporality. In section 3, we describe the influence of anoxic microsites on several key elemental cycles, organic carbon, nitrogen, iron, manganese, and sulfur. In section 4, we evaluate methods for identifying and characterizing anoxic microsites, and in section 5, we highlight past and current approaches to modeling anoxic microsites. Finally, in section 6, we suggest steps for incorporating anoxic microsites and redox heterogeneities more broadly into our understanding of soils and sediments.

A subcellular biochemical model for T6SS dynamics reveals winning competitive strategies

The type VI secretion system (T6SS) is a broadly distributed interbacterial weapon that can be used to eliminate competing bacterial populations. Although unarmed target populations are typically used to study T6SS function in vitro, bacteria most likely encounter other T6SS-armed competitors in nature. However, the connection between subcellular details of the T6SS and the outcomes of such mutually lethal battles is not well understood. Here, we incorporate biological data derived from natural competitors of Vibrio fischeri light organ symbionts to build a biochemical model for T6SS at the single-cell level, which we then integrate into an agent-based model (ABM). Using the ABM, we isolate and experiment with strain-specific physiological differences between competitors in ways not possible with biological samples to identify winning strategies for T6SS-armed populations. Through in vitro experiments, we discover that strain-specific differences exist in T6SS activation speed. ABM simulations corroborate that faster activation is dominant in determining survival during competition. Once competitors are fully activated, the energy required for T6SS creates a tipping point where increased weapon building and firing becomes too costly to be advantageous. Through ABM simulations, we identify the threshold where this transition occurs in the T6SS parameter space. We also find that competitive outcomes depend on the geometry of the battlefield: unarmed target cells survive at the edges of a range expansion where unlimited territory can be claimed. Alternatively, competitions within a confined space, much like the light organ crypts where natural V. fischeri compete, result in the rapid elimination of the unarmed population.

Lipopolysaccharide transport regulates bacterial sensitivity to a cell wall-degrading intermicrobial toxin

Gram-negative bacteria can antagonize neighboring microbes using a type VI secretion system (T6SS) to deliver toxins that target different essential cellular features. Despite the conserved nature of these targets, T6SS potency can vary across recipient species. To understand the functional basis of intrinsic T6SS susceptibility, we screened for essential Escherichia coli (Eco) genes that affect its survival when antagonized by a cell wall-degrading T6SS toxin from Pseudomonas aeruginosa, Tae1. We revealed genes associated with both the cell wall and a separate layer of the cell envelope, lipopolysaccharide, that modulate Tae1 toxicity in vivo. Disruption of genes in early lipopolysaccharide biosynthesis provided Eco with novel resistance to Tae1, despite significant cell wall degradation. These data suggest that Tae1 toxicity is determined not only by direct substrate damage, but also by indirect cell envelope homeostasis activities. We also found that Tae1-resistant Eco exhibited reduced cell wall synthesis and overall slowed growth, suggesting that reactive cell envelope maintenance pathways could promote, not prevent, self-lysis. Together, our study reveals the complex functional underpinnings of susceptibility to Tae1 and T6SS which regulate the impact of toxin-substrate interactions in vivo.

Video frame prediction of microbial growth with a recurrent neural network

The recent explosion of interest and advances in machine learning technologies has opened the door to new analytical capabilities in microbiology. Using experimental data such as images or videos, machine learning, in particular deep learning with neural networks, can be harnessed to provide insights and predictions for microbial populations. This paper presents such an application in which a Recurrent Neural Network (RNN) was used to perform prediction of microbial growth for a population of two Pseudomonas aeruginosa mutants. The RNN was trained on videos that were acquired previously using fluorescence microscopy and microfluidics. Of the 20 frames that make up each video, 10 were used as inputs to the network which outputs a prediction for the next 10 frames of the video. The accuracy of the network was evaluated by comparing the predicted frames to the original frames, as well as population curves and the number and size of individual colonies extracted from these frames. Overall, the growth predictions are found to be accurate in metrics such as image comparison, colony size, and total population. Yet, limitations exist due to the scarcity of available and comparable data in the literature, indicating a need for more studies. Both the successes and challenges of our approach are discussed.

The space between us: Modeling spatial heterogeneity in synthetic microbial consortia dynamics

A central endeavor in bioengineering concerns the construction of multistrain microbial consortia with desired properties. Typically, a gene network is partitioned between strains, and strains communicate via quorum sensing, allowing for complex behaviors. Yet a fundamental question of how emergent spatiotemporal patterning in multistrain microbial consortia affects consortial dynamics is not understood well. Here, we propose a computationally tractable and straightforward modeling framework that explicitly allows linking spatiotemporal patterning to consortial dynamics. We validate our model against previously published results and make predictions of how spatial heterogeneity impacts interstrain communication. By enabling the investigation of spatial patterns effects on microbial dynamics, our modeling framework informs experimentalists, helps advance the understanding of complex microbial systems, and supports the development of applications involving them.

Individual-Based Modeling of Spatial Dynamics of Chemotactic Microbial Populations

One important direction of synthetic biology is to establish desired spatial structures from microbial populations. Underlying this structural development process are different driving factors, among which bacterial motility and chemotaxis serve as a major force. Here, we present an individual-based, biophysical computational framework for mechanistic and multiscale simulation of the spatiotemporal dynamics of motile and chemotactic microbial populations. The framework integrates cellular movement with spatial population growth, mechanical and chemical cellular interactions, and intracellular molecular kinetics. It is validated by a statistical comparison of single-cell chemotaxis simulations with reported experiments. The framework successfully captures colony range expansion of growing isogenic populations and also reveals chemotaxis-modulated, spatial patterns of a two-species amensal community. Partial differential equation-based models subsequently validate these simulation findings. This study provides a versatile computational tool to uncover the fundamentals of microbial spatial ecology as well as to facilitate the design of synthetic consortia for desired spatial patterns.

Agent-based modelling of iron cycling bacteria provides a framework for testing alternative environmental conditions and modes of action

Iron-reducing and iron-oxidizing bacteria are of interest in a variety of environmental and industrial applications. Such bacteria often co-occur at oxic-anoxic gradients in aquatic and terrestrial habitats. In this paper, we present the first computational agent-based model of microbial iron cycling, between the anaerobic ferric iron (Fe³⁺)-reducing bacteria Shewanella spp. and the microaerophilic ferrous iron (Fe²⁺)-oxidizing bacteria Sideroxydans spp. By including the key processes of reduction/oxidation, movement, adhesion, Fe²⁺-equilibration and nanoparticle formation, we derive a core model which enables hypothesis testing and prediction for different environmental conditions including temporal cycles of oxic and anoxic conditions. We compared (i) combinations of different Fe³⁺-reducing/Fe²⁺-oxidizing modes of action of the bacteria and (ii) system behaviour for different pH values. We predicted that the beneficial effect of a high number of iron-nanoparticles on the total Fe³⁺ reduction rate of the system is not only due to the faster reduction of these iron-nanoparticles, but also to the nanoparticles' additional capacity to bind Fe²⁺ on their surfaces. Efficient iron-nanoparticle reduction is confined to pH around 6, being twice as high than at pH 7, whereas at pH 5 negligible reduction takes place. Furthermore, in accordance with experimental evidence our model showed that shorter oxic/anoxic periods exhibit a faster increase of total Fe³⁺ reduction rate than longer periods.

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea. We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py. Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R² coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

Defending against the Type Six Secretion System: beyond Immunity Genes

The bacterial type six secretion system (T6SS) delivers toxic effector proteins into neighboring cells, but bacteria must protect themselves against their own T6SS. Immunity genes are the best-characterized defenses, protecting against specific cognate effectors. However, the prevalence of the T6SS and the coexistence of species with heterologous T6SSs suggest evolutionary pressure selecting for additional defenses against it. Here we review defenses against the T6SS beyond self-associated immunity genes, such as diverse stress responses that can recognize T6SS-inflicted damage and coordinate induction of molecular armor, repair pathways, and overall survival. Some of these stress responses are required for full survival even in the presence of immunity genes. Finally, we propose that immunity gene-independent protection is, mechanistically, bacterial innate immunity and that such defenses and the T6SS have co-evolved and continue to shape one another in polymicrobial communities.

Redox Heterogeneity Entangles Soil and Climate Interactions

Interactions between soils and climate impact wider environmental sustainability. Soil heterogeneity intricately regulates these interactions over short spatiotemporal scales and therefore needs to be more finely examined. This paper examines how redox heterogeneity at the level of minerals, microbial cells, organic matter, and the rhizosphere entangles biogeochemical cycles in soil with climate change. Redox heterogeneity is used to develop a conceptual framework that encompasses soil microsites (anaerobic and aerobic) and cryptic biogeochemical cycling, helping to explain poorly understood processes such as methanogenesis in oxygenated soils. This framework is further shown to disentangle global carbon (C) and nitrogen (N) pathways that include CO2, CH4, and N2O. Climate-driven redox perturbations are discussed using wetlands and tropical forests as model systems. Powerful analytical methods are proposed to be combined and used more extensively to study coupled abiotic and biotic reactions that are affected by redox heterogeneity. A core view is that emerging and future research will benefit substantially from developing multifaceted analyses of redox heterogeneity over short spatiotemporal scales in soil. Taking a leap in our understanding of soil and climate interactions and their evolving influence on environmental sustainability then depends on greater collaborative efforts to comprehensively investigate redox heterogeneity spanning the domain of microscopic soil interfaces.

Deep learning predicts microbial interactions from self-organized spatiotemporal patterns

Microbial communities organize into spatial patterns that are largely governed by interspecies interactions. This phenomenon is an important metric for understanding community functional dynamics, yet the use of spatial patterns for predicting microbial interactions is currently lacking. Here we propose supervised deep learning as a new tool for network inference. An agent-based model was used to simulate the spatiotemporal evolution of two interacting organisms under diverse growth and interaction scenarios, the data of which was subsequently used to train deep neural networks. For small-size domains (100 µm × 100 µm) over which interaction coefficients are assumed to be invariant, we obtained fairly accurate predictions, as indicated by an average R2 value of 0.84. In application to relatively larger domains (450 µm × 450 µm) where interaction coefficients are varying in space, deep learning models correctly predicted spatial distributions of interaction coefficients without any additional training. Lastly, we evaluated our model against real biological data obtained using Pseudomonas fluorescens and Escherichia coli co-cultures treated with polymeric chitin or N-acetylglucosamine, the hydrolysis product of chitin. While P. fluorescens can utilize both substrates for growth, E. coli lacked the ability to degrade chitin. Consistent with our expectations, our model predicted context-dependent interactions across two substrates, i.e., degrader-cheater relationship on chitin polymers and competition on monomers. The combined use of the agent-based model and machine learning algorithm successfully demonstrates how to infer microbial interactions from spatially distributed data, presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Three-Way Interactions in an Artificial Community of Bacterial Strains Directly Isolated From the Environment and Their Effect on the System Population Dynamics

This work is motivated by previous studies that have analyzed the population ecology of a collection of culturable thermoresistant bacteria, isolated from the Churince lagoon in Cuatro Cienegas, Mexico. In particular, it is aimed at testing a hypothesis from a modeling study, which states that antagonistic and sensitive bacteria co-exist thanks to resistant bacteria that protect sensitive ones by forming physical barriers. We selected three different bacterial strains from the referred collection: one antagonistic, one sensitive, and one resistant, and studied the population dynamics of mixed colonies. Our results show that, although the proposed protective mechanism does not work in this case, the resistant strain confers some kind of protection to sensitive bacteria. Further modeling and experimental results suggest that the presence of resistant bacteria indirectly improves the probability that patches of sensitive bacteria grow in a mixed colony. More precisely, our results suggest that by making antagonistic bacteria produce and secrete an antagonistic substance (with the concomitant metabolic cost and growth rate reduction), resistant bacteria increase the likelihood that sensitive bacteria locally outcompete antagonistic ones.

A microfluidic device for inferring metabolic landscapes in yeast monolayer colonies

Microbial colonies are fascinating structures in which growth and internal organization reflect complex morphogenetic processes. Here, we generated a microfluidics device with arrays of long monolayer yeast colonies to further global understanding of how intercellular metabolic interactions affect the internal structure of colonies within defined boundary conditions. We observed the emergence of stable glucose gradients using fluorescently labeled hexose transporters and quantified the spatial correlations with intra-colony growth rates and expression of other genes regulated by glucose availability. These landscapes depended on the external glucose concentration as well as secondary gradients, for example amino acid availability. This work demonstrates the regulatory genetic networks governing cellular physiological adaptation are the key to internal structuration of cellular assemblies. This approach could be used in the future to decipher the interplay between long-range metabolic interactions, cellular development and morphogenesis in more complex systems.

Emergent Properties of Microbial Activity in Heterogeneous Soil Microenvironments: Different Research Approaches Are Slowly Converging, Yet Major Challenges Remain

Over the last 60 years, soil microbiologists have accumulated a wealth of experimental data showing that the bulk, macroscopic parameters (e.g., granulometry, pH, soil organic matter, and biomass contents) commonly used to characterize soils provide insufficient information to describe quantitatively the activity of soil microorganisms and some of its outcomes, like the emission of greenhouse gasses. Clearly, new, more appropriate macroscopic parameters are needed, which reflect better the spatial heterogeneity of soils at the microscale (i.e., the pore scale) that is commensurate with the habitat of many microorganisms. For a long time, spectroscopic and microscopic tools were lacking to quantify processes at that scale, but major technological advances over the last 15 years have made suitable equipment available to researchers. In this context, the objective of the present article is to review progress achieved to date in the significant research program that has ensued. This program can be rationalized as a sequence of steps, namely the quantification and modeling of the physical-, (bio)chemical-, and microbiological properties of soils, the integration of these different perspectives into a unified theory, its upscaling to the macroscopic scale, and, eventually, the development of new approaches to measure macroscopic soil characteristics. At this stage, significant progress has been achieved on the physical front, and to a lesser extent on the (bio)chemical one as well, both in terms of experiments and modeling. With regard to the microbial aspects, although a lot of work has been devoted to the modeling of bacterial and fungal activity in soils at the pore scale, the appropriateness of model assumptions cannot be readily assessed because of the scarcity of relevant experimental data. For significant progress to be made, it is crucial to make sure that research on the microbial components of soil systems does not keep lagging behind the work on the physical and (bio)chemical characteristics. Concerning the subsequent steps in the program, very little integration of the various disciplinary perspectives has occurred so far, and, as a result, researchers have not yet been able to tackle the scaling up to the macroscopic level. Many challenges, some of them daunting, remain on the path ahead. Fortunately, a number of these challenges may be resolved by brand new measuring equipment that will become commercially available in the very near future.