Role of metabolic spatiotemporal dynamics in regulating biofilm colony expansion

Stochastic biological system-of-systems modelling for iPSC culture

Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity.

Recent Progress in antibacterial hydrogel coatings for targeting biofilm to prevent orthopedic implant-associated infections

The application of orthopedic implants for bone tissue reconstruction and functional restoration is crucial for patients with severe bone fractures and defects. However, the abiotic nature of orthopedic implants allows bacterial adhesion and colonization, leading to the formation of bacterial biofilms on the implant surface. This can result in implant failure and severe complications such as osteomyelitis and septic arthritis. The emergence of antibiotic-resistant bacteria and the limited efficacy of drugs against biofilms have increased the risk of orthopedic implant-associated infections (OIAI), necessitating the development of alternative therapeutics. In this regard, antibacterial hydrogels based on bacteria repelling, contact killing, drug delivery, or external assistance strategies have been extensively investigated for coating orthopedic implants through surface modification, offering a promising approach to target biofilm formation and prevent OIAI. This review provides an overview of recent advancements in the application of antibacterial hydrogel coatings for preventing OIAI by targeting biofilm formation. The topics covered include: (1) the mechanisms underlying OIAI occurrence and the role of biofilms in exacerbating OIAI development; (2) current strategies to impart anti-biofilm properties to hydrogel coatings and the mechanisms involved in treating OIAI. This article aims to summarize the progress in antibacterial hydrogel coatings for OIAI prevention, providing valuable insights and facilitating the development of prognostic markers for the design of effective antibacterial orthopedic implants.

Spatiotemporal Gene Expression by a Genetic Circuit for Chemical Production in Escherichia coli

Gene expression in spatiotemporal distribution improves the ability of cells to respond to changing environments. For microbial cell factories in artificial environments, reconstruction of the target compound's biosynthetic pathway in a new spatiotemporal dimension/scale promotes the production of chemicals. Here, a genetic circuit based on the Esa quorum sensing and lac operon was designed to achieve the dynamic temporal gene expression. Meanwhile, the pathway was regulated by an l-cysteine-specific sensor and relocalized to the plasma membrane for further flux enhancement to l-cysteine and toxicity reduction on a spatial scale. Finally, the integrated spatiotemporal regulation circuit for l-cysteine biosynthesis enabled a 14.16 g/L l-cysteine yield in Escherichia coli. Furthermore, this spatiotemporal regulation circuit was also applied in our previously constructed engineered strain for pantothenic acid, methionine, homoserine, and 2-aminobutyric acid production, and the titer increased by 29, 33, 28, and 41%, respectively. These results highlighted the applicability of our spatiotemporal regulation circuit to enhance the performance of microbial cell factories.

Involvement of AprD in regulating biofilm structure, matrix secretion, and cell metabolism of meat-borne Pseudomonas fragi during chilled storage

Pseudomonas fragi is by far one of the most threatening species in the spoilage of chilled meat that is stored under aerobic conditions. The membrane protein AprD is a well-established regulator controlling protease secretion in Pseudomonas spp. However, its exact roles in modulating metabolic pathways and spoilage potential of P. fragi at the molecular level remain undefined. Here, an in-frame deletion mutation of aprD was used to explore the impacts on their biofilm structure, matrix secretion, and cell metabolism. The results showed that ΔaprD formed relatively disorganized loose aggregation in biofilm, resulting in a thinner structure and more dead cells. Meanwhile, marked changes in the content of extracellular carbohydrates and proteins were observed. Furthermore, intracellular metabolomic profiling revealed the involvement of aprD in several cellular metabolic pathways, mostly including the carbohydrate pathway, amino acid pathway, and nucleotide pathway, while the characterization of extracellular metabolism clarified the variations in the spoilage-related metabolites (e.g., creatine, IMP, spermine, fatty acids, amino acids, and oligopeptides) could be highly correlated with aprD deletion. In this finding, we indicated that aprD could be responsible for cell reproduction and in situ spoilage potential of P. fragi NMC25 during chilled storage by controlling related metabolism and nutrients utilization. Thus, our results will contribute to an improved understanding of the regulatory mechanism of aprD gene in meat spoilage contaminated with P. fragi, which can be valuable to ensure the quality and safety of meat.

ChemChaste: Simulating spatially inhomogeneous biochemical reaction-diffusion systems for modeling cell-environment feedbacks

BACKGROUND

Spatial organization plays an important role in the function of many biological systems, from cell fate specification in animal development to multistep metabolic conversions in microbial communities. The study of such systems benefits from the use of spatially explicit computational models that combine a discrete description of cells with a continuum description of one or more chemicals diffusing within a surrounding bulk medium. These models allow the in silico testing and refinement of mechanistic hypotheses. However, most existing models of this type do not account for concurrent bulk and intracellular biochemical reactions and their possible coupling.

CONCLUSIONS

Here, we describe ChemChaste, an extension for the open-source C++ computational biology library Chaste. ChemChaste enables the spatial simulation of both multicellular and bulk biochemistry by expanding on Chaste's existing capabilities. In particular, ChemChaste enables (i) simulation of an arbitrary number of spatially diffusing chemicals, (ii) spatially heterogeneous chemical diffusion coefficients, and (iii) inclusion of both bulk and intracellular biochemical reactions and their coupling. ChemChaste also introduces a file-based interface that allows users to define the parameters relating to these functional features without the need to interact directly with Chaste's core C++ code. We describe ChemChaste and demonstrate its functionality using a selection of chemical and biochemical exemplars, with a focus on demonstrating increased ability in modeling bulk chemical reactions and their coupling with intracellular reactions.

AVAILABILITY AND IMPLEMENTATION

ChemChaste version 1.0 is a free, open-source C++ library, available via GitHub at https://github.com/OSS-Lab/ChemChaste under the BSD license, on the Zenodo archive at zendodo doi, as well as on BioTools (biotools:chemchaste) and SciCrunch (RRID:SCR022208) databases.

Structure formation in a conserved mass model of a set of individuals interacting with attractive and repulsive forces

We study a set of interacting individuals that conserve their total mass. In order to describe its dynamics we resort to mesoscopic equations of reaction diffusion including currents driven by attractive and repulsive forces. For the mass conservation we consider a linear response parameter that maintains the mass in the vicinity of a optimal value which is determined by the set. We use the reach and intensity of repulsive forces as control parameters. When sweeping a wide range of parameter space we find a great diversity of localized structures, stationary as well as other ones with cyclical and chaotic dynamics. We compare our results with real situations.

Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution

Capturing the heterogeneous phenotypes of microbial populations at relevant spatiotemporal scales is highly challenging. Here, we present par-seqFISH (parallel sequential fluorescence in situ hybridization), a transcriptome-imaging approach that records gene expression and spatial context within microscale assemblies at a single-cell and molecule resolution. We applied this approach to the opportunistic pathogen Pseudomonas aeruginosa, analyzing about 600,000 individuals across dozens of conditions in planktonic and biofilm cultures. We identified numerous metabolic- and virulence-related transcriptional states that emerged dynamically during planktonic growth, as well as highly spatially resolved metabolic heterogeneity in sessile populations. Our data reveal that distinct physiological states can coexist within the same biofilm just several micrometers away, underscoring the importance of the microenvironment. Our results illustrate the complex dynamics of microbial populations and present a new way of studying them at high resolution.

Understanding the Principles of Pattern Formation Driven by Notch Signaling by Integrating Experiments and Theoretical Models

Notch signaling is an evolutionary conserved cell-cell communication pathway. Besides regulating cell-fate decisions at an individual cell level, Notch signaling coordinates the emergent spatiotemporal patterning in a tissue through ligand-receptor interactions among transmembrane molecules of neighboring cells, as seen in embryonic development, angiogenesis, or wound healing. Due to its ubiquitous nature, Notch signaling is also implicated in several aspects of cancer progression, including tumor angiogenesis, stemness of cancer cells and cellular invasion. Here, we review experimental and computational models that help understand the operating principles of cell patterning driven by Notch signaling. First, we discuss the basic mechanisms of spatial patterning via canonical lateral inhibition and lateral induction mechanisms, including examples from angiogenesis, inner ear development and cancer metastasis. Next, we analyze additional layers of complexity in the Notch pathway, including the effect of varying cell sizes and shapes, ligand-receptor binding within the same cell, variable binding affinity of different ligand/receptor subtypes, and filopodia. Finally, we discuss some recent evidence of mechanosensitivity in the Notch pathway in driving collective epithelial cell migration and cardiovascular morphogenesis.

Extending the minimal model of metabolic oscillations in Bacillus subtilis biofilms

Biofilms are composed of microorganisms attached to a solid surface or floating on top of a liquid surface. They pose challenges in the field of medicine but can also have useful applications in industry. Regulation of biofilm growth is complex and still largely elusive. Oscillations are thought to be advantageous for biofilms to cope with nutrient starvation and chemical attacks. Recently, a minimal mathematical model has been employed to describe the oscillations in Bacillus subtilis biofilms. In this paper, we investigate four different modifications to that minimal model in order to better understand the oscillations in biofilms. Our first modification is towards making a gradient of metabolites from the center of the biofilm to the periphery. We find that it does not improve the model and is therefore, unnecessary. We then use realistic Michaelis-Menten kinetics to replace the highly simple mass-action kinetics for one of the reactions. Further, we use reversible reactions to mimic the diffusion in biofilms. As the final modification, we check the combined effect of using Michaelis-Menten kinetics and reversible reactions on the model behavior. We find that these two modifications alone or in combination improve the description of the biological scenario.

Spatiotemporal pattern formation in E. coli biofilms explained by a simple physical energy balance

While the biofilm growth mode conveys notable thriving advantages to bacterial populations, the mechanisms of biofilm formation are still strongly debated. Here, we investigate the remarkable spontaneous formation of regular spatial patterns during the growth of an Escherichia coli biofilm. These patterns reported here appear with non-motile bacteria, which excludes both chemotactic origins and other motility-based ones. We demonstrate that a minimal physical model based on phase separation describes them well. To confirm the predictive capacity of our model, we tune the cell-cell and cell-surface interactions using cells expressing different surface appendages. We further explain how F pilus-bearing cells enroll their wild type kindred, poorly piliated, into their typical pattern when mixed together. This work supports the hypothesis that purely physicochemical processes, such as the interplay of cell-cell and cell-surface interactions, can drive the emergence of a highly organized spatial structure that is potentially decisive for community fate and for biological functions.

Probabilistic Models for Predicting Mutational Routes to New Adaptive Phenotypes

Understanding the translation of genetic variation to phenotypic variation is a fundamental problem in genetics and evolutionary biology. The introduction of new genetic variation through mutation can lead to new adaptive phenotypes, but the complexity of the genotype-to-phenotype map makes it challenging to predict the phenotypic effects of mutation. Metabolic models, in conjunction with flux balance analysis, have been used to predict evolutionary optimality. These methods however rely on large scale models of metabolism, describe a limited set of phenotypes, and assume that selection for growth rate is the prime evolutionary driver. Here we describe a method for computing the relative likelihood that mutational change will translate into a phenotypic change between two molecular pathways. The interactions of molecular components in the pathways are modeled with ordinary differential equations. Unknown parameters are offset by probability distributions that describe the concentrations of molecular components, the reaction rates for different molecular processes, and the effects of mutations. Finally, the likelihood that mutations in a pathway will yield phenotypic change is estimated with stochastic simulations. One advantage of this method is that only basic knowledge of the interaction network underlying a phenotype is required. However, it can also incorporate available information about concentrations and reaction rates as well as mutational biases and mutational robustness of molecular components. The method estimates the relative probabilities that different pathways produce phenotypic change, which can be combined with fitness models to predict evolutionary outcomes.

Metabolic basis of brain-like electrical signalling in bacterial communities

Information processing in the mammalian brain relies on a careful regulation of the membrane potential dynamics of its constituent neurons, which propagates across the neuronal tissue via electrical signalling. We recently reported the existence of electrical signalling in a much simpler organism, the bacterium Bacillus subtilis. In dense bacterial communities known as biofilms, nutrient-deprived B. subtilis cells in the interior of the colony use electrical communication to transmit stress signals to the periphery, which interfere with the growth of peripheral cells and reduce nutrient consumption, thereby relieving stress from the interior. Here, we explicitly address the interplay between metabolism and electrophysiology in bacterial biofilms, by introducing a spatially extended mathematical model that combines the metabolic and electrical components of the phenomenon in a discretized reaction-diffusion scheme. The model is experimentally validated by environmental and genetic perturbations, and confirms that metabolic stress is transmitted through the bacterial population via a potassium wave. Interestingly, this behaviour is reminiscent of cortical spreading depression in the brain, characterized by a wave of electrical activity mediated by potassium diffusion that has been linked to various neurological disorders, calling for future studies on the evolutionary link between the two phenomena. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.