Quorum sensing (QS) is a type of microbial communication used by bacteria to coordinate their behavior based on population density, regulating complex processes like biofilm formation and virulence, among other behaviors. Quorum quenching (QQ), on the other hand, disrupts this communication, usually by degradation of the QS signaling molecule. QQ offers a potential strategy for controlling bacterial behaviors linked to pathogenicity and biofouling. Despite significant advances in understanding and modeling the spatial-temporal behavior of QS, predictive modeling of QQ remains nascent, with a notable gap in the quantitative assessment of QQ's impact on QS. Here we show quantitative evaluation and characterization of the effect of QQ on QS in agar-based experiments, combined with an experimentally validated computational model. This research utilizes green fluorescence in E. coli MG 1655 as an indicator of QS activation, focusing on the degradation of Acyl-Homoserine Lactone (AHL), a key QS molecule in Gram-negative bacteria linked to pathogenicity, by the AiiA enzyme in engineered AiiA-producing Salmonella Typhimurium 14028. Our findings suggest that QQ more effectively influences QS in spatial configurations of the populations with larger interaction surfaces and shorter diffusion distances. Contrary to our initially held hypothesis, the primary effect of QQ is not a delay in QS onset but rather an attenuation of QS activity, with the area-under-the-curve of fluorescence serving as a quantitative metric. This study also introduces, to the best of our knowledge, one of the first instances of experimentally validated predictive modeling for QQ, applied to agar-based experimental setups. We posit that the quantitative experimental characterization and modeling framework presented in this research will enhance the understanding of bacterial community interactions. Enhanced comprehension of QQ and QS behaviors holds significant promise for advancing practical applications, particularly in mitigating or diminishing undesirable QS-associated activities. This is especially relevant in areas like biofouling, waste treatment, and the reduction of infections and progression of diseases in plants and animals, areas increasingly important as concerns about drug resistance in microbes and food security escalates. / Master of Science / One of the ways bacteria communicate with each other is called quorum sensing (QS), where they use chemical signals to organize and time group behavior, including forming communities encapsulated in protective layers, called biofilms, and engaging in virulent attacks against hosts. Quorum quenching (QQ) in bacteria, however, disrupts this communication system, usually by breaking down the chemical signals that bacteria use to send messages to each other.
Even though QS has been studied extensively, determining how to predict and control QQ is still a nascent area of research. Here, we studied and characterized how QQ affects QS by doing experiments with bacteria populations in agar (a jelly-like substance) and applied a computational model to explain and ultimately predict the experimental observations. Engineered QS population (E. coli MG 1655) produced Acyl-Homoserine Lactone (AHL) signaling molecules, and engineered QQ bacteria (S. Tm 14028) used the Autoinducer Inactivation A (AiiA) enzyme to break down the AHL.
According to our results, QQ doesn't delay the QS bacteria's group behaviors (in our case, green fluorescent signal production); it weakens the signal instead.
Understanding QQ and QS better, especially through measurements and modeling, could lead to expanded methods of deterring harmful bacterial behavior, managing waste better, and stopping diseases in plants, animals, and humans, especially with the concerning rise of drug-resistant microbes and food security. One exciting possibility is using QQ to protect plants from bacterial infections. This could be a way to shield our crops without always relying on antibiotics.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/117877 |
| Date | 05 February 2024 |
| Creators | Thielman, Maria-Fe Sayon |
| Contributors | Mechanical Engineering, Behkam, Bahareh, Leonessa, Alexander, Senger, Ryan S. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/x-zip-compressed |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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