Quantifying and Engineering Bacterial Population Dynamics in Time and Space

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You, Lingchong

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Recent technological advances enable us to examine bacterial population dynamics with high temporal resolution with capacity for collecting high throughput data. Precise quantification of bacterial population dynamics can help us to further extend our understanding of how bacteria respond to environmental conditions. Such analysis provides critical information for improving antibiotic treatment protocols and for predictable engineering cellular behavior with synthetic gene circuits.

A fundamental question in bacterial population dynamics is how fast bacteria are killed in response to antibiotics. Due to their mode of action, β-lactams are more effective against fast-growing bacteria than against slow-growing bacteria. Indeed, it has been recognized that the rate of lysis by β-lactam antibiotics depends on the growth rate of the bacteria, based on previous works. However, past studies examined the growth rate modulation of lysis only during balanced growth and for very limited combinations of bacteria and drugs. Although there is evidence that growth plays key role in determining bacterial response to antibiotics, more comprehensive understanding on how wide range of growth rates affect antibiotic dose response had been overlooked. Instead, bacterial growth has been largely described to be in either growing or non-growing states.

To examine the general applicability of this growth rate dependence of antibiotic response, I examined how growth rate influences the lysis rate induced by beta-lactams. I found that there is a robust correlation between growth and lysis rates beyond what had been demonstrated in the previous work. Even during unbalanced growth, and regardless of how growth rate was modulated, the robust correlation between growth and lysis rates in bacterial populations were observed. Also, my data suggested a striking versatility of this correlation in different bacterial specie-drug pairs. Thus, my quantification greatly expands previous work by further examining the dependence of lysis rate on growth rate, and extends our understanding of the phenomenon associated with β-lactam antibiotic treatment, and of possible consequences arising from variable lysis rate. My strategy on modulating growth rates and measuring corresponding lysis rates demonstrates a simple and robust method for examining this phenomenon. These results have direct implications in two aspects.

First, my quantification method allows greater degree of freedom in modulating growth states of bacteria. Indeed, I was able to examine a wide range of growth rates in bacteria that allowed analyses of robust correlation in growth and lysis rates. The simple correlation reported from my work suggests the underlying reasoning for slow or fast lysis of bacterial population that can lead to designing optimal protocols depending on the growth rates of bacterial population. Due to frequent observation of slow-growing cells under conditions such as biofilm of pathogenic bacteria that complicates clinical symptoms and treatments in patients, they have been an important aspect of study for antibiotic tolerance. A quantitative understanding of the robust correlation between growth and lysis rates is critical for designing effective treatment protocols using β-lactams.

Second, the robust correlation serves as a foundation for predicting dynamics of synthetic gene circuits engineered for practical applications. In my work, I developed a prototype microbial swarmbot, which employs spatial arrangement to control growth dynamics of engineered bacteria. I demonstrated an engineered safeguard strategy to prevent unintended bacterial proliferation with this platform technology. In this work, I adopted several synthetic gene circuits to program collective survival in Escherichia coli: the engineered bacteria could only survive when present at sufficiently high population densities. When encapsulated by permeable membranes, these bacteria can sense the local environment and respond accordingly. The cells inside microbial swarmbots will survive due to their high densities. Those escaping from a capsule, however, will be killed due to a decrease in their densities. In this work, using antibiotics to control growth dynamics of the engineered populations was critical, and optimization of the growth dynamics depended on their environmental conditions that modulated their growth rates.

Together, my investigation on quantifying and analyzing bacterial growth dynamics demonstrated that understanding of bacterial population dynamics is crucial in addressing antibiotic tolerance in bacteria as well as in using them for engineered functions. By further examining the dependence of lysis rate on growth rate, we extended our understanding of the phenomenon associated with β-lactam antibiotic treatment, and of possible consequences arising from variable lysis rate. This information is important in designing a modular and readily generalizable platform technology as well. Therefore, my work demonstrates quantitative approach towards understanding of bacterial populations, and lays the foundation for engineering integrated and programmable control of hybrid biological-material systems for diverse applications.





Lee, Anna Jisu (2016). Quantifying and Engineering Bacterial Population Dynamics in Time and Space. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/13422.


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