@article {53990, title = {Using stochastic cell division and death to probe minimal units of cellular replication.}, journal = {New J Phys}, volume = {20}, year = {2018}, month = {2018 Mar}, pages = {035004}, abstract = {

The invariant cell initiation mass measured in bacterial growth experiments has been interpreted as a minimal unit of cellular replication. Here we argue that the existence of such minimal units induces a coupling between the rates of stochastic cell division and death. To probe this coupling we tracked live and dead cells in populations treated with a ribosome-targeting antibiotic. We find that the growth exponent from macroscopic cell growth or decay measurements can be represented as the difference of microscopic first-order cell division and death rates. The boundary between cell growth and decay, at which the number of live cells remains constant over time, occurs at the minimal inhibitory concentration (MIC) of the antibiotic. This state appears macroscopically static but is microscopically dynamic: division and death rates exactly cancel at MIC but each is remarkably high, reaching 60\% of the antibiotic-free division rate. A stochastic model of cells as collections of minimal replicating units we term {\textquoteright}widgets{\textquoteright} reproduces both steady-state and transient features of our experiments. Sub-cellular fluctuations of widget numbers stochastically drive each new daughter cell to one of two alternate fates, division or death. First-order division or death rates emerge as eigenvalues of a stationary Markov process, and can be expressed in terms of the widget{\textquoteright}s molecular properties. High division and death rates at MIC arise due to low mean and high relative fluctuations of widget number. Isolating cells at the threshold of irreversible death might allow molecular characterization of this minimal replication unit.

}, issn = {1367-2630}, doi = {10.1088/1367-2630/aab197}, author = {Chib, Savita and Das, Suman and Venkatesan, Soumya and Seshasayee, Aswin Sai Narain and Thattai, Mukund} } @article {32028, title = {Using stochastic cell division and death to probe minimal units of cellular replication}, journal = {New Journal of Physics}, volume = {20}, year = {2018}, month = {03/2018}, abstract = {

The invariant cell initiation mass measured in bacterial growth experiments has been interpreted as a minimal unit of cellular replication. Here we argue that the existence of such minimal units induces a coupling between the rates of stochastic cell division and death. To probe this coupling we tracked live and dead cells in Escherichia coli populations treated with a ribosome-targeting antibiotic. We find that the growth exponent from macroscopic cell growth or decay measurements can be represented as the difference of microscopic first-order cell division and death rates. The boundary between cell growth and decay, at which the number of live cells remains constant over time, occurs at the minimal inhibitory concentration (MIC) of the antibiotic. This state appears macroscopically static but is microscopically dynamic: division and death rates exactly cancel at MIC but each is remarkably high, reaching 60\% of the antibiotic-free division rate. A stochastic model of cells as collections of minimal replicating units we term {\textquoteright}widgets{\textquoteright} reproduces both steady-state and transient features of our experiments. Sub-cellular fluctuations of widget numbers stochastically drive each new daughter cell to one of two alternate fates, division or death. First-order division or death rates emerge as eigenvalues of a stationary Markov process, and can be expressed in terms of the widget{\textquoteright}s molecular properties. High division and death rates at MIC arise due to low mean and high relative fluctuations of widget number. Isolating cells at the threshold of irreversible death might allow molecular characterization of this minimal replication unit.

}, doi = {10.1088/1367-2630/aab197}, author = {Chib, Savita and Das, Suman and Venkatesan, S and Seshasayee, Aswin S N and Thattai, Mukund} } @article {18677, title = {Genomewide Mutational Diversity in Escherichia coli Population Evolving in Prolonged Stationary Phase.}, journal = {mSphere}, volume = {2}, year = {2017}, month = {2017 May-Jun}, abstract = {

Prolonged stationary phase is an approximation of natural environments presenting a range of stresses. Survival in prolonged stationary phase requires alternative metabolic pathways for survival. This study describes the repertoire of mutations accumulating in starving Escherichia\ coli populations in lysogeny broth. A wide range of mutations accumulates over the course of 1\ month in stationary phase. Single nucleotide polymorphisms (SNPs) constitute 64\% of all mutations. A majority of these mutations are nonsynonymous and are located at conserved loci. There is an increase in genetic diversity in the evolving populations over time. Computer simulations of evolution in stationary phase suggest that the maximum frequency of mutations observed in our experimental populations cannot be explained by neutral drift. Moreover, there is frequent genetic parallelism across populations, suggesting that these mutations are under positive selection. Finally, functional analysis of mutations suggests that regulatory mutations are frequent targets of selection. IMPORTANCE Prolonged stationary phase in bacteria, contrary to its name, is highly dynamic, with extreme nutrient limitation as a predominant stress. Stationary-phase cultures adapt by rapidly selecting a mutation(s) that confers a growth advantage in stationary phase (GASP). The phenotypic diversity of starving E.\ coli populations has been studied in detail; however, only a few mutations that accumulate in prolonged stationary phase have been described. This study documented the spectrum of mutations appearing in Escherichia coli during 28\ days of prolonged starvation. The genetic diversity of the population increases over time in stationary phase to an extent that cannot be explained by random, neutral drift. This suggests that prolonged stationary phase offers a great model system to study adaptive evolution by natural selection.

}, doi = {10.1128/mSphere.00059-17}, author = {Chib, Savita and Ali, Farhan and Seshasayee, Aswin Sai Narain} }