Center for Quantitative Medicine, University of Connecticutgrid.208078.5grid.63054.34 School of Medicine, Farmington, Connecticut, USA.
Department of Oral Health and Diagnostic Sciences, University of Connecticutgrid.208078.5grid.63054.34 School of Dental Medicine, Farmington, Connecticut, USA.
mSphere. 2021 Dec 22;6(6):e0087521. doi: 10.1128/mSphere.00875-21. Epub 2021 Dec 15.
As common commensals residing on mucosal tissues, 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 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 secretes a surfactant, and another where 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 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. 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. 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 in biofilm growth. The inhibitory nature of on S. oralis in biofilms may be exploited as a means of preventing or alleviating mucosal fungal infections.
作为居住在黏膜组织上的常见共生菌，一些链球菌属物种被认为能促进健康，而有些链球菌属物种则会增强这些环境中其他生物体的致病性。在这项研究中，我们结合使用活体生物膜成像和计算建模来探索口腔念珠菌病辅助病原体口腔链球菌和具有已知益生菌特性的副干酪乳杆菌之间的生物膜相互作用。创建了一个计算基于代理的模型，其中两种物种仅通过竞争空间、氧气和葡萄糖相互作用。活体生物膜中细菌生长的定量分析表明，S. oralis 的生物量和细胞数量远低于模型预测的数量。然后创建了另外两个模型来研究这两种物种之间更复杂的相互作用，一个模型中 分泌表面活性剂，另一个模型中 分泌 S. oralis 生长抑制剂。我们观察到 S. oralis 的生长可能受到这两种机制的影响。进一步的生物膜实验支持了这样一种假设，即 可能分泌 S. oralis 生长抑制剂，尽管它们不排除表面活性剂也可能参与其中。这项研究表明，基于代理的建模和实验可以协同使用，以解决多种物种生物膜相互作用的问题，这对于黏膜健康和疾病具有重要作用。我们之前发现口腔共生链球菌属作为辅助病原体的作用。S. oralis 通过促进组织破坏性生物膜形成的机制，增加了口腔念珠菌感染在小鼠口腔念珠菌病和上皮细胞模型中的毒力。 种已知对许多微生物的生物膜形成具有抑制作用，包括链球菌属物种。基于代理的建模作为探索复杂环境（如生物膜）中生物体之间多方面关系的一种手段具有很大的优势。在这里，我们使用实验和建模之间的迭代协作过程来揭示 S. oralis 和 在生物膜生长中的大部分未探索关系的某些方面。 对生物膜中 S. oralis 的抑制作用可能被用作预防或缓解黏膜真菌感染的一种手段。
