Rapid changes in extracellular osmolarity are one of many insults microbial cells face on a daily basis. To protect against such shocks, Escherichia coli and other microbes express several types of transmembrane channels which open and close in response to changes in membrane tension. In E. coli, the most abundant of these channels is the mechanosensitive channel of large conductance (MscL). While this channel has been heavily characterized through structural methods, electrophysiology, and theoretical modeling, our understanding of its physiological role in preventing cell death by alleviating high membrane tension remains tenuous. In this work, we examine the contribution of MscL alone to cell survival after osmotic shock at single cell resolution using quantitative fluorescence microscopy. We conduct these experiments in an E. coli strain which is lacking all mechanosensitive channel genes save for MscL whose expression is tuned across three orders of magnitude through modifications of the Shine-Dalgarno sequence. While theoretical models suggest that only a few MscL channels would be needed to alleviate even large changes in osmotic pressure, we find that between 500 and 700 channels per cell are needed to convey upwards of 80% survival. This number agrees with the average MscL copy number measured in wild-type E. coli cells through proteomic studies and quantitative Western blotting. Furthermore, we observe zero survival events in cells with less than 100 channels per cell. This work opens new questions concerning the contribution of other mechanosensitive channels to survival as well as regulation of their activity.
All single-cell measurements of the osmotic-shock experiments can be downloaded as a
Raw images and MCMC chains are hosted on the CaltechDATA data repository and can be downloaded through the links below.
All Python code for processing the raw data sets can be reached through the
master branch of this repository,
github.com/rpgroup-pboc/mscl_survival.git. All MCMC was performed using the Stan probabailistic programming language. The
.stan models used for the hierarchical logistic regression can be downloaded below:
complete_analysis.stan| This model samples both hierarchical models simultaneously.
We have also included a Jupyter Notebook which describes the image processing procedure undertaken in this work.
Griffin Chure | Biochemistry & Molecular Biophysics PhD Candidate, California Institute of Technology, Pasadena CA
Heun Jin Lee | Staff Scientist, California Institute of Technology, Pasadena CA
Rob Phillips | Phillip and Nancy Morris Professor of Biophysics and Biology, California Institute of Technology, Pasadena CA