A new mathematical model of bacterial interactions in two-species oral biofilms

Advancing dental biofilm models: the integral role of pH in predicting S. mutans colonization

Mathematical models can provide insights into complex interactions and dynamics within microbial communities to complement and extend experimental laboratory approaches. For dental biofilms, they can give a basis for evaluating biofilm growth or the transition from health to disease. We have developed mathematical models to simulate the transition toward a cariogenic microbial biofilm, modeled as the overgrowth of Streptococcus mutans within a five-species dental community. This work builds on experimental data from a continuous flow reactor with hydroxyapatite coupons for biofilm growth, in a chemically defined medium with varying concentrations of glucose and lactic acid. The biofilms formed on the coupons were simulated using individual-based models (IbMs), with bacterial growth modeled using experimentally measured kinetic parameters. The IbM assumes that the maximum theoretical growth yield for biomass is dependent on the local concentration of reactants and products, while the growth rates were described using traditional Monod equations. We have simulated all the conditions studied experimentally, considering different initial relative abundance of the five species, and also different initial clustering in the biofilm. The simulation results only reproduced the experimental dominance of S. mutans at high glucose concentration after we considered the species-specific effect of pH on growth rates. This highlights the significance of the aciduric property of S. mutans in the development of caries. Our study demonstrates the potential of combining in vitro and in silico studies to gain a new understanding of the factors that influence dental biofilm dynamics.IMPORTANCEWe have developed in silico models able to reproduce the relative abundance measured in vitro in the synthetic dental biofilm communities growing in a chemically defined medium. The advantage of this combination of in vitro and in silico models is that we can study the influence of one parameter at a time and aim for direct validation. Our work demonstrates the utility of individual-based models for simulating diverse conditions affecting dental biofilm scenarios, such as the frequency of glucose intake, sucrose pulsing, or integration of pathogenic or probiotic species. Although in silico models are reductionist approaches, they have the advantage of not being limited in the scenarios they can test by the ethical consideration of an in vivo system, thus significantly contributing to dental biofilm research.

Corrected and Republished from: "Understanding Lactobacillus paracasei and Streptococcus oralis Biofilm Interactions through Agent-Based Modeling"

As common commensals residing on mucosal tissues, Lactobacillus species are known to promote health, while some Streptococcus species act to enhance the pathogenicity of other organisms in those environments. In this study we used a combination of in vitro imaging of live biofilms and computational modeling to explore biofilm interactions between Streptococcus oralis, an accessory pathogen in oral candidiasis, and Lactobacillus paracasei, an organism with known probiotic properties. A computational agent-based model was created where the two species interact only by competing for space, oxygen, and glucose. Quantification of bacterial growth in live biofilms indicated that S. oralis biomass and cell numbers were much lower than predicted by the model. Two subsequent models were then created to examine more complex interactions between these species, one where L. paracasei secretes a surfactant and another where L. paracasei secretes an inhibitor of S. oralis growth. We observed that the growth of S. oralis could be affected by both mechanisms. Further biofilm experiments support the hypothesis that L. paracasei may secrete an inhibitor of S. oralis growth, although they do not exclude that a surfactant could also be involved. This contribution shows how agent-based modeling and experiments can be used in synergy to address multiple-species biofilm interactions, with important roles in mucosal health and disease. IMPORTANCE We previously discovered a role of the oral commensal Streptococcus oralis as an accessory pathogen. S. oralis increases the virulence of Candida albicans infections in murine oral candidiasis and epithelial cell models through mechanisms which promote the formation of tissue-damaging biofilms. Lactobacillus species have known inhibitory effects on biofilm formation of many microbes, including Streptococcus species. Agent-based modeling has great advantages as a means of exploring multifaceted relationships between organisms in complex environments such as biofilms. Here, we used an iterative collaborative process between experimentation and modeling to reveal aspects of the mostly unexplored relationship between S. oralis and L. paracasei in biofilm growth. The inhibitory nature of L. paracasei on S. oralis in biofilms may be exploited as a means of preventing or alleviating mucosal fungal infections.

Nisin and Nisin Probiotic Disrupt Oral Pathogenic Biofilms and Restore Their Microbiome Composition towards Healthy Control Levels in a Peri-Implantitis Setting

Peri-implantitis is characterized by chronic inflammation of the peri-implant supporting tissues that progressively and irreversibly leads to bone loss and, consequently, implant loss. Similar to periodontal disease, oral dysbiosis is thought to be a driver of peri-implantitis. However, managing peri-implantitis with traditional treatment methods, such as nonsurgical debridement or surgery, is not always successful. Thus, novel strategies have been proposed to address these shortcomings. One strategy is the use of probiotics as antimicrobial agents since they are considered safe for humans and the environment. Specifically, the probiotic Lactococcus lactis produces nisin, which has been used worldwide for food preservation. The objective of this study was to determine whether nisin and the wild-type (WT) nisin-producing L. lactis probiotic can disrupt oral pathogenic biofilms and promote a healthier oral microbiome within these oral biofilms on titanium discs. Using confocal imaging and 16S rRNA sequencing, this study revealed that nisin and WT L. lactis probiotic disrupt oral pathogenic biofilms in a peri-implantitis setting in vitro. More specifically, nisin decreased the viability of the pathogen-spiked biofilms dose-dependently from 62.53 ± 3.69% to 54.26 ± 3.35% and 44.88 ± 2.98%, respectively. Similarly, 105 CFU/mL of WT L. lactis significantly decreased biofilm viability to 52.45 ± 3.41%. Further, both treatments shift the composition, relative abundance, and diversity levels of these biofilms towards healthy control levels. A total of 1 µg/mL of nisin and 103 CFU/mL of WT L. lactis were able to revert the pathogen-mediated changes in the Proteobacteria (from 80.5 ± 2.9% to 75.6 ± 2.0%, 78.0 ± 2.8%, and 75.1 ± 5.3%, respectively) and Firmicutes (from 11.6 ± 1.6% to 15.4 ± 1.3%, 13.8 ± 1.8%, and 13.7 ± 2.6%, respectively) phyla back towards control levels. Thus, nisin and its nisin-producing L. lactis probiotic may be useful in treating peri-implantitis by promoting healthier oral biofilms, which may be useful for improving patient oral health.

"Electroactive biofilms: how microbial electron transfer enables bioelectrochemical applications"

Microbial biofilms are ubiquitous. In marine and freshwater ecosystems, microbe–mineral interactions sustain biogeochemical cycles, while biofilms found on plants and animals can range from pathogens to commensals. Moreover, biofouling and biocorrosion represent significant challenges to industry. Bioprocessing is an opportunity to take advantage of biofilms and harness their utility as a chassis for biocommodity production. Electrochemical bioreactors have numerous potential applications, including wastewater treatment and commodity production. The literature examining these applications has demonstrated that the cell–surface interface is vital to facilitating these processes. Therefore, it is necessary to understand the state of knowledge regarding biofilms’ role in bioprocessing. This mini-review discusses bacterial biofilm formation, cell–surface redox interactions, and the role of microbial electron transfer in bioprocesses. It also highlights some current goals and challenges with respect to microbe-mediated bioprocessing and future perspectives.

A Multi-Skilled Mathematical Model of Bacterial Attachment in Initiation of Biofilms

The initial step of biofilm formation is bacteria attachment to biotic or abiotic surfaces and other bacteria through intra or interspecies interactions. Adhesion can be influenced by physicochemical conditions of the environment, such as iron. There is no available mathematical model of bacterial attachment giving realistic initiation rather than random adhesion. We describe a simple stochastic attachment model, from the simplest case in two dimensions with one bacterial species attaching on a homogeneous flat surface to more complex situations, with either several bacterial species, inhomogeneous or non-flat surfaces, or in three dimensions. The model depends on attachment probabilities (on the surface, laterally, or vertically on bacteria). Effects of each of these parameters were analyzed. This mathematical model is then applied to experimental oral microcolonies of Porphyromonas gingivalis, Streptococcus gordonii, and Treponema denticola, either as mono-, two, or three species, under different iron concentrations. The model allows to characterize the adhesion of three bacterial species and explore the effect of iron on attachment. This model appears as a powerful tool for initial attachment analysis of bacterial species. It will enable further modeling of biofilm formation in later steps with biofilm initialization more relevant to real-life subgingival biofilms.

Understanding Lactobacillus paracasei and Streptococcus oralis Biofilm Interactions through Agent-Based Modeling

As common commensals residing on mucosal tissues, Lactobacillus species are known to promote health, while some Streptococcus species act to enhance the pathogenicity of other organisms in those environments. In this study, we used a combination of in vitro imaging of live biofilms and computational modeling to explore biofilm interactions between Streptococcus oralis, an accessory pathogen in oral candidiasis, and Lactobacillus paracasei, an organism with known probiotic properties. A computational agent-based model was created where the two species interact only by competing for space, oxygen and glucose. Quantification of bacterial growth in live biofilms indicated that S. oralis biomass and cell numbers were much lower than predicted by the model. Two subsequent models were then created to examine more complex interactions between these species, one where L. paracasei secretes a surfactant, and another where L. paracasei secretes an inhibitor of S. oralis growth. We observed that the growth of S. oralis could be affected by both mechanisms. Further biofilm experiments support the hypothesis that L. paracasei may secrete an inhibitor of S. oralis growth, although they do not exclude that a surfactant could also be involved. This contribution shows how agent-based modeling and experiments can be used in synergy to address multiple species biofilm interactions, with important roles in mucosal health and disease. IMPORTANCE We previously discovered a role of the oral commensal Streptococcus oralis as an accessory pathogen. S. oralis increases the virulence of Candida albicans infections in murine oral candidiasis and epithelial cell models through mechanisms which promote the formation of tissue-damaging biofilms. Lactobacillus species have known inhibitory effects on biofilm formation of many microbes, including Streptococcus species. Agent-based modeling has great advantages as a means of exploring multifaceted relationships between organisms in complex environments such as biofilms. Here, we used an iterative collaborative process between experimentation and modeling to reveal aspects of the mostly unexplored relationship between S. oralis and L. paracasei in biofilm growth. The inhibitory nature of L. paracasei on S. oralis in biofilms may be exploited as a means of preventing or alleviating mucosal fungal infections.

Method for screening antimicrobial gels against multi-species oral biofilms

We described a microtiter plate-based method that was effectively tailored for testing gel formulations against oral multispecies biofilms established on peg-lids. This method lifts the limitations imposed mainly by the anaerobic nature of the targeted bacterial species and the viscous properties of the targeted treatments.

Co-culturing microbial consortia: approaches for applications in biomanufacturing and bioprocessing

The application of microbial co-cultures is now recognized in the fields of biotechnology, ecology, and medicine. Understanding the biological interactions that govern the association of microorganisms would shape the way in which artificial/synthetic co-cultures or consortia are developed. The ability to accurately predict and control cell-to-cell interactions fully would be a significant enabler in synthetic biology. Co-culturing method development holds the key to strategically engineer environments in which the co-cultured microorganism can be monitored. Various approaches have been employed which aim to emulate the natural environment and gain access to the untapped natural resources emerging from cross-talk between partners. Amongst these methods are the use of a communal liquid medium for growth, use of a solid-liquid interface, membrane separation, spatial separation, and use of microfluidics systems. Maximizing the information content of interactions monitored is one of the major challenges that needs to be addressed by these designs. This review critically evaluates the significance and drawbacks of the co-culturing approaches used to this day in biotechnological applications, relevant to biomanufacturing. It is recommended that experimental results for a co-cultured species should be validated with different co-culture approaches due to variations in interactions that could exist as a result of the culturing method selected.

Modeling of Symbiotic Bacterial Biofilm Growth with an Example of the Streptococcus-Veillonella sp. System

We present a multi-dimensional continuum mathematical model for modeling the growth of a symbiotic biofilm system. We take a dual-species namely, the Streptococcus-Veillonella sp. biofilm system as an example for numerical investigations. The presented model describes both the cooperation and competition between these species of bacteria. The coupled partial differential equations are solved by using an integrative finite element numerical strategy. Numerical examples are carried out for studying the evolution and distribution of the bio-components. The results demonstrate that the presented model is capable of describing the symbiotic behavior of the biofilm system. However, homogenized numerical solutions are observed locally. To study the homogenization behavior of the model, numerical investigations regarding on how random initial biomass distribution influences the homogenization process are carried out. We found that a smaller correlation length of the initial biomass distribution leads to faster homogenization of the solution globally, however, shows more fluctuated biomass profiles along the biofilm thickness direction. More realistic scenarios with bacteria in patches are also investigated numerically in this study.

Agent Based Models of Polymicrobial Biofilms and the Microbiome-A Review

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.

Optimization of Agent-Based Models Through Coarse-Graining: A Case Study in Microbial Ecology

Optimization and control are important objectives across biology and biomedicine, and mathematical models are a key enabling technology. This paper reports a computational study of model-based multi-objective optimization in the setting of microbial ecology, using agent-based models. This modeling framework is well-suited to the field, but is not amenable to standard control-theoretic approaches. Furthermore, due to computational complexity, simulation-based optimization approaches are often challenging to implement. This paper presents the results of an approach that combines control-dependent coarse-graining with Pareto optimization, applied to two models of multi-species bacterial biofilms. It shows that this approach can be successful for models whose computational complexity prevents effective simulation-based optimization.

Future directions for studying resilience of the oral ecosystem

The oral ecosystem is shaped by complex interactions between systemic health disease and the resident oral microbiota. Research in the last two decades has produced datasets describing the genetics and physiology of the host and the oral microbiome in health and disease. There are inter-individual differences in the ability to tolerate oral disease-promoting challenges. Identification of the key factors that drive a healthy and resilient oral ecosystem is urgently needed. So far, progress is being made towards replicating the host-microbiota interplay in vitro. Clinical studies may shed light on the mechanisms of oral health resilience. However, most clinical studies are cross-sectional and are insufficient for understanding resilience or for identifying biomarkers that correlate with the point of transition from oral health to dysbiosis. Mathematical and computational models, including artificial intelligence approaches, offer an opportunity to inform the design of clinical studies by identifying key biomarkers and interaction networks in complex datasets and predicting important parameters. This paper discusses some of the challenges and opportunities for understanding the biological basis of resilience of the oral ecosystem. It discusses the current status and challenges, and proposes a way forward to better understand resilience towards oral diseases.

Modulation of pathogenic oral biofilms towards health with nisin probiotic

Background

Oral dysbiosis is an imbalance in the oral microbiome and is associated with a variety of oral and systemic diseases, including periodontal disease, caries, and head and neck/oral cancer. Although antibiotics can be used to control this dysbiosis, they can lead to adverse side effects and superinfections. Thus, novel strategies have been proposed to address these shortcomings. One strategy is the use of probiotics as antimicrobial agents, since they are considered safe for humans and the environment. Specifically, the Gram-positive Lactococcus lactis, a species present in the oral and gut microbiota, is able to produce nisin, which has been used worldwide for food preservation.

Objective

The objective of this study was to test whether a nisin probiotic can promote a healthier oral microbiome in pathogen-spiked oral biofilms.

Results

We found that L. lactis can prevent oral biofilm formation and disrupt 24-h and 48-h pre-formed biofilms. Finally, we demonstrate that both treatments, a nisin-producing L. lactis probiotic and nisin can decrease the levels of pathogens in the biofilms and return the diversity levels back to control or ‘healthy’ levels.

Conclusion

A nisin-producing probiotic, can be used to treat ‘disease-altered’ biofilms and promote healthier oral biofilms, which may be useful for improving patient oral health.

Challenges of biofilm control and utilization: lessons from mathematical modelling

This article reviews modern applications of mathematical descriptions of biofilm formation. The focus is on theoretically obtained results which have implications for areas including the medical sector, food industry and wastewater treatment. Examples are given as to how models have contributed to the overall knowledge on biofilms and how they are used to predict biofilm behaviour. We conclude that the use of mathematical models of biofilms has demonstrated over the years the ability to significantly contribute to the vast field of biofilm research. Among other things, they have been used to test various hypotheses on the nature of interspecies interactions, viability of biofilm treatment methods or forces behind observed biofilm pattern formations. Mathematical models can also play a key role in future biofilm research. Many models nowadays are analysed through computer simulations and continue to improve along with computational capabilities. We predict that models will keep on providing answers to important challenges involving biofilm formation. However, further strengthening of the ties between various disciplines is necessary to fully use the tools of collective knowledge in tackling the biofilm phenomenon.

In silico modelling to differentiate the contribution of sugar frequency versus total amount in driving biofilm dysbiosis in dental caries

Dental caries is the most prevalent infection globally and a substantial economic burden in developed countries. Dietary sugars are the main risk factor, and drive increased proportions of acid-producing and acid-tolerating (aciduric) bacterial species within dental biofilms. Recent longitudinal studies have suggested that caries is most strongly correlated with total sugar intake, contrasting with the prevailing view that intake frequency is the primary determinant. To explore this possibility, we employed a computational model for supragingival plaque to systematically sample combinations of sugar frequency and total amount, allowing their independent contributions on the ratio of aciduric (i.e. cariogenic) to non-aciduric bacteria to be unambiguously determined. Sugar frequency was found to be irrelevant for either very high or very low daily total amounts as the simulated biofilm was predicted to be always or never cariogenic, respectively. Frequency was a determining factor for intermediate total amounts of sugar, including the estimated average human consumption. An increased risk of caries (i.e. high prevalence of aciduric/non-aciduric species) was predicted for high intake frequencies. Thus, both total amount and frequency of sugar intake may combine to influence plaque cariogenicity. These findings could be employed to support public guidance for dietary change, leading to improved oral healthcare.