Search bioRxiv⌕ Search

bioRxiv · 10.1101/494161

Forecasting when cells die during antibiotic exposure using stochastic gene expression

Abstract

Antibiotic killing does not occur at a single, precise time for all cells within a population. Variability in time to death can be caused by stochastic expression of genes, resulting in differences in endogenous stress-resistance levels between individual cells in a population. This variability can be part of a bet-hedging strategy where cells leverage noise to ensure a subset of the population can tolerate the drug, while decreasing the overall cost of expressing resistance genes. We asked whether single-cell differences in gene expression prior to antibiotic addition were related to cell survival times after antibiotic exposure for a range of genes of diverse function. We quantified the time to death of single cells under antibiotic exposure in combination with expression of reporters. For some reporters, the time to cell death had a strong relationship with the initial expression level of the genes. Reporters that could forecast cell fate included stress response genes, but also genes involved in a variety of other cellular processes like metabolism. Our results highlight the single-cell level non-uniformity of antibiotic killing and also provide examples of key genes where cell-to-cell variation in expression prior to antibiotic exposure is strongly linked to extended durations of antibiotic survival.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rossi, N., El Meouche, I., Dunlop, M. J.. 2018-12-13. Forecasting when cells die during antibiotic exposure using stochastic gene expression. https://doi.org/10.1101/494161

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

A three-factor model can explain symmetry breaking and the n-1 rule during X-chromosome inactivation

X-chromosome inactivation (XCI) is a mammalian gene regulatory process that via chromosome-scale transcriptional silencing, leaves active a single X chromosome per diploid genome complement. The mechanisms that enable cells to select the appropriate number of X chromosomes to inactivate while leaving others active remain unclear. We demonstrate that all known chromosomal counting scenarios during XCI can be accounted for by a three-factor mathematical model that includes (1) Xist, the long noncoding RNA (lncRNA) responsible for gene silencing during XCI in eutherians, (2) an X-linked transcriptional activator of Xist, and (3) the autosomally-encoded SPEN, an Xist RNA-binding protein that is essential for gene silencing during XCI. In our model, symmetry between active X chromosomes is broken by stochastic differences in the number of Xist molecules that associate with the X chromosomes at the onset of XCI. Subsequently, integrated positive and negative feedback loops amplify then propagate superstoichiometric assemblies of SPEN complexed with Xist on the inactive chromosome(s), and select for levels of X-linked activator that enable only one X chromosome per diploid genome complement to remain active. Thus, asymmetric propagation of the SPEN-Xist ribonucleoprotein complex may play an essential role in symmetry breaking and chromosomal counting during XCI, independently of or in concert with cis-acting networks of DNA regulatory elements. Our results also have conceptual implications for gene regulatory circuits on autosomes.

systems biology↗

stem.MAP: Identification of cell-type-specific protein complexes in mammalian embryonic stem cells.

Protein complexes carry out critical functions during development. Mapping cell-type-specific protein complexes in early embryogenesis is essential for uncovering the molecular mechanisms of embryonic development. We unfortunately currently lack a catalog of protein complexes specific for embryogenesis including embryonic stem cells. Here we develop a proteomics-based machine learning workflow to predict protein complexes present in mouse embryonic stem cells (mESC). Our method integrates data from a cell-type agnostic protein complex resource with co-fractionation mass spectrometry (CF-MS) experiments on mESC. Our resource, stem.MAP, identifies 3,986 complexes encompassing ~133,000 unique interactions, and 8,616 proteins. stem.MAP captures variant complexes critical for stem cell function including the PRC2, BAF and NuRD complexes. stem.MAP also discriminates mutually exclusive interactions specific to embryonic stem cells (e.g. Eif4ebp2 - Eif4e). Finally we observe 1,388 high confidence stem.MAP interactions found only in mESC CF-MS data, many with support from AlphaFold modeling and a gastruloid coregulation network. We make stem.MAP protein complexes available through our web resource: stem.proteincomplexes.org. We anticipate stem.MAP will be beneficial to the study of early development and we expect our proteomics-based machine learning workflow to be applicable to the creation of any context-specific protein interaction network.

systems biology↗

Gene regulatory architecture in antibiotic resistance is optimized for specific dynamic regimens

Cellular responses to antibiotics face a fundamental trade-off: resistance must be deployed quickly enough to prevent the accumulation of drug inside the cell, but expression of resistance genes is costly in the absence of the drug. Therefore, antibiotic responses are typically regulated, controlling not only whether resistance is expressed, but also when, how strongly, and for how long. These regulatory strategies are implemented through the strength and arrangement of promoters and operators. However, it is still not clear how the dynamics of drug exposure select for specific regulatory strategies. Here, we develop a dynamical model of a typical antibiotic response, where a transcriptional repressor senses the drug and regulates expression of a resistance protein, coupled to the effects of the drug on cell growth and gene expression. We define a cost function that weighs the different burdens imposed on the cell over the course of the response, and perform in silico optimization of the regulatory parameters under different selective pressures for three architectures of increasing complexity: constitutive expression of resistance, repression by a constitutively expressed repressor, and repression by a self-regulated repressor. We find that strong drug exposures favor constitutive expression of resistance, high costs of basal expression favor repression by a constitutive repressor, and self-regulation of the repressor is favored only when the response must provide protection while also restraining overexpression of resistance. Stochastic simulations further show that self-regulation of the repressor protects cells against fluctuations in repressor levels, which cause unnecessary induction of resistance before exposure and failed induction after exposure. This framework links the dynamics of the environment to the evolution of gene regulation, providing a basis to predict which regulatory strategies are expected to arise under specific selective pressures.

systems biology↗