Cellular heterogeneity is ubiquitous across all domains of life. Genetically identical cells can display heterogeneous metabolic activities, even when grown in identical environments. In bacteria, metabolic heterogeneity can shape the ensemble growth rate and affect antibiotic tolerance. In higher organisms, metabolic activity is functionally related to tumor activity and drug tolerance. While cellular heterogeneities in mRNA and protein abundance have been extensively studied, many questions remain regarding metabolic heterogeneity. For instance, what determines the size and frequency of metabolic fluctuation? How is metabolic heterogeneity regulated? Can we control metabolic heterogeneity and therefore eliminate drug-tolerant cells? The lack of fundamental understanding about this important topic has severely limited the development of effective treatments for multiple diseases in which a small number of transiently tolerant cells often cause disease recurrence. Over the past few years, the Zhang lab has developed several methods to study metabolite heterogeneity, including metabolite-biosensor- assisted single-cell imaging and metabolite quantification by cell sorting. Our work identified large, non-genetic heterogeneity in fatty acid biosynthesis, and we exploited metabolic heterogeneity for biotechnology applications (i.e. overproduction of chemicals). In this MIRA proposal, we aim to obtain a systematic understanding of bacteria metabolic heterogeneity by using our existing and novel methods for single-cell metabolic analysis. Using Escherichia coli as a model, we will construct a tunable metabolic system that produces a unique fluorescent metabolite with controlled flux and metabolite concentration. Microfluidics-assisted time-lapse fluorescent microscopy will be used to simultaneously quantify the concentrations of this metabolite and its biosynthetic enzyme, the cell growth rate, and the age of single cells. Data analysis combined with modeling will be used to determine the origin, dynamics, and propagation of metabolic heterogeneity. Furthermore, using the native fatty acid catabolic pathway as a model, we will study how transcriptional and allosteric regulations affect metabolic heterogeneity. In addition, we will explore the influence of metabolic heterogeneity on drug tolerance and seek to reduce metabolic heterogeneity and multimodality. This project will reveal novel principles that govern metabolic heterogeneity and provide a quantitative framework to explain various single- cell phenomena. Understanding the regulation of metabolic heterogeneity and its influence on drug tolerance will inform strategies to eliminate heterogeneous variants that are insensitive to drugs, thus providing new treatments for recurrent diseases.
This project will provide quantitative understanding about the causation, dynamics, and regulation of metabolic heterogeneity in bacteria as well as new knowledge about single-cell metabolic behaviors. This project will identify methods to control metabolic heterogeneity that is related to bacterial multidrug-tolerance. The results can also inspire new strategies to help eliminate disease recurrence caused by metabolic heterogeneity.
