Bacterial persisters are a stochastically formed subpopulation of low-energy cells.

Persisters represent a small subpopulation of non- or slow-growing bacterial cells that are tolerant to killing by antibiotics. Despite their prominent role in the recalcitrance of chronic infections to antibiotic therapy, the mechanism of their formation has remained elusive. We show that sorted cells of Escherichia coli with low levels of energy-generating enzymes are better able to survive antibiotic killing. Using microfluidics time-lapse microscopy and a fluorescent reporter for in vivo ATP measurements, we find that a subpopulation of cells with a low level of ATP survives killing by ampicillin. We propose that these low ATP cells are formed stochastically as a result of fluctuations in the abundance of energy-generating components. These findings point to a general "low energy" mechanism of persister formation

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation

Analyzing Persister Physiology with Fluorescence-Activated Cell Sorting

Bacterial persisters are phenotypic variants that exhibit an impressive ability to tolerate antibiotics. Persisters are hypothesized to cause relapse infections, and therefore, understanding their physiology may lead to novel therapeutics to treat recalcitrant infections. However, persisters have yet to be isolated due to their low abundance, transient nature, and similarity to the more highly abundant viable but non-culturable cells (VBNCs), resulting in limited knowledge of their phenotypic state. This technical hurdle has been addressed through the use of fluorescence activated cell sorting (FACS) and quantification of persister levels in the resulting sorted fractions. These assays provide persister phenotype distributions, which can be compared to the phenotype distributions of the entire population, and can also be used to examine persister heterogeneity. Here we describe two detailed protocols for analysis of persister physiology with FACS. One protocol assays the metabolic state of persisters using a fluorescent metabolic stain, whereas the other assays the growth state of persisters with use of a fluorescent protein

Development of Persister-FACSeq: a method to massively parallelize quantification of persister physiology and its heterogeneity

Bacterial persisters are thought to underlie the relapse of chronic infections. Knowledge of persister physiology would illuminate avenues for therapeutic intervention; however, such knowledge has remained elusive because persisters have yet to be segregated from other cell types to sufficient purity. This technical hurdle has stymied progress toward understanding persistence. Here we developed Persister-FACSeq, which is a method that uses fluorescence-activated cell sorting, antibiotic tolerance assays, and next generation sequencing to interrogate persister physiology and its heterogeneity. As a proof-of-concept, we used Persister-FACSeq on a library of reporters to study gene expression distributions in non-growing Escherichia coli, and found that persistence to ofloxacin is inversely correlated with the capacity of non-growing cells to synthesize protein. Since Persister-FACSeq can be applied to study persistence to any antibiotic in any environment for any bacteria that can harbor a fluorescent reporter, we anticipate that it will yield unprecedented knowledge of this detrimental phenotype