Tuesday, December 6 2016
14:00 - 15:00

Alladi Ramakrishnan Hall

Bacterial growth regulation on the interface of ordered and chaotic regimes

Arnab Bandyopadhyay

University of Kansas

The process of cellular growth is both the distinguishing feature of living matter and
central to the roles of regulatory networks from microbes to metazoa. Growth and
division is also a primary source of phenotypic diversification: for instance, when a
bacterial cell divides, its cellular contents binomally distributed into two daughter cells.

Cellular diversity permits populations to be robust to unpredictable, changing
environments (bet-hedging). Some endogenous effector (e.g. toxin) can regulate growth
and as most of the molecular content in the bacterial cytoplasm undergoes growthmediated
dilution, it is able to create an effective positive feedback via growth regulation,
trapping some cells in a growth arrested state until they can escape (1-2). This
mechanism is associated with antibiotic-tolerant persister cells arising in the population, which cause difficulty in antibiotic treatment. Understanding the processes that result in growth diversification is an important part of solving the impending antibiotic resistance crisis.

An emerging picture of persistence in E. coli shows a hierarchy of toxin-antitoxin
systems and global metabolic regulation (3), with a core mechanism of toxins that are
neutralized by antitoxins. When accounting for gene expression noise, toxin levels will
exceed antitoxin levels in some cells, resulting in the growth feedback mechanism that
ultimately induces growth arrest in suprathreshold cells. The result is skewed phenotypic
distributions, with a core fast-growing group of cells along with rarer, growth arrested
cells, as opposed to regression to mean levels observed in networks without the growth
arrest threshold. Motivated by observations on phenotypic inheritance (4) and the effects
of lineage correlations on daughter cell phenotypes (5-6), we asked how much
phenotypic diversity could be attained for various levels of endogenous growth
regulation. Based on our previous study, we hypothesized that a higher chance of growth
arrest amplifies the effects of cellular lineage on phenotypic correlations.

To explore this hypothesis, we created a minimal multiscale computational
framework of cellular growth and division, with binomially distributed inheritance of a
simplified toxin-antitoxin-like system subject to stochastic molecular kinetics. We used
various specific realizations of the framework to simulate growth of small bacterial
populations from a single common ancestor and growth regulation by the simulated toxin
for various toxin:antitoxin production ratios. Using an established model of threshold based growth arrest in E. coli, we experimentally measured lineages as well. Our results
show that populations that are just supercritical (i.e. with growth rates slightly above
zero), there is an increased level of mutual information between lineage and phenotype.
We also showed how important lineage is to growth regulation and bet-hedging
phenotypes involving growth. Consideration of lineage is now indispensable for studies
on phenotypic heterogeneity, phenotypic memory, and regulation of the growth arrest
transition. We showed that population heterogeneity, inheritance and growth feedback are
fine tuned to phenotypic switching and thus evolved as a bet-hedging strategy.

1. Tan C, Marguet P, & You L (2009) Emergent bistability by a growth-modulating
positive feedback circuit. Nat Chem Biol 5(11):842-848.
2. Ray JCJ, Tabor JJ, & Igoshin OA (2011) Non-transcriptional regulatory processes
shape transcriptional network dynamics. Nat Rev Microbiol 9(11):817-828.
3. Germain E, Roghanian M, Gerdes K, & Maisonneuve E (2015) Stochastic
induction of persister cells by HipA through (p)ppGpp-mediated activation of
mRNA endonucleases. Proc Natl Acad Sci U S A 112(16):5171-5176.
4. Acar M, Becskei A, & van Oudenaarden A (2005) Enhancement of cellular
memory by reducing stochastic transitions. Nature 435(7039):228-232.
5. Hormoz S, Desprat N, & Shraiman BI (2015) Inferring epigenetic dynamics from
kin correlations. Proc Natl Acad Sci U S A 112(18):E2281-E2289.
6. Sandler O, et al. (2015) Lineage correlations of single cell division time as a
probe of cell-cycle dynamics. Nature 519(7544):468-471.

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