Abstract:Caulobacter crescentus is a model organism for the study of asymmetric division and cell type differentiation, as its cell division cycle generates a pair of daughter cells that differ from one another in their morphology and behavior. One of these cells (called stalked) develops a structure that allows it to attach to solid surfaces and is the only one capable of dividing, while the other (called swarmer) develops a flagellum that allows it to move in liquid media and divides only after differentiating into a… Show more
“…Expression of five core gene types (g1-5) defines the cellular state ( s). Reproduction takes place when a cell completes a trajectory through four states that represent the Caulobacter crescentus cell-cycle (taken from the model of Quiñones-Valles et al, 2014;Sánchez-Osorio et al, 2017). ).…”
Section: Methodsmentioning
confidence: 99%
“…It is The copyright holder for this preprint this version posted July 28, 2021. ; https://doi.org/10.1101/2021.07.28.454145 doi: bioRxiv preprint crescentus cell-cycle have been identified: CtrA, GcrA, DnaA, CcrM and SciP (Tan et al, 2010;Quiñones-Valles et al, 2014). These five genes produce a cyclic expression pattern that drives all downstream cell-cycle functions (Zhou et al, 2015) and which can be reproduced in a simple Boolean model (Quiñones-Valles et al, 2014;Sánchez-Osorio et al, 2017). We use this model as starting point to study the evolution of complex cell-cycle behaviour in cells exposed to more challenging, nutrient-limited conditions.…”
How complexity arises is a fundamental evolutionary question. Complex gene regulation is thought to arise by the interplay between adaptive and non-adaptive forces at multiple organizational levels. Using a computational model, we investigate how complexity arises in cell-cycle regulation. Starting from the well-known Caulobacter crescentus network, we study how cells adapt their cell-cycle behaviour to a gradient of limited nutrient conditions using 10 replicate in silico evolution experiments. We find adaptive expansion of the gene regulatory network: improvement of cell-cycle behaviour allows cells to overcome the inherent cost of complexity. Replicates traverse different evolutionary trajectories leading to distinct eco-evolutionary strategies. In four replicates, cells evolve a generalist strategy to cope with a variety of nutrient levels; in two replicates, different specialist cells evolve for specific nutrient levels; in the remaining four replicates, an intermediate strategy evolves. The generalist and specialist strategies are contingent on the regulatory mechanisms that arise early in evolution, but they are not directly linked to network expansion and overall fitness. This study shows that functionality of cells depends on the combination of gene regulatory network topology and genome structure. For example, the positions of dosage-sensitive genes are exploited to signal to the regulatory network when replication is completed, forming a de novo evolved cell-cycle checkpoint. Complex gene regulation can arise adaptively both from expansion of the regulatory network and from the genomic organization of the elements in this network, demonstrating that to understand complex gene regulation and its evolution, it is necessary to integrate systems that are often studied separately.
“…Expression of five core gene types (g1-5) defines the cellular state ( s). Reproduction takes place when a cell completes a trajectory through four states that represent the Caulobacter crescentus cell-cycle (taken from the model of Quiñones-Valles et al, 2014;Sánchez-Osorio et al, 2017). ).…”
Section: Methodsmentioning
confidence: 99%
“…It is The copyright holder for this preprint this version posted July 28, 2021. ; https://doi.org/10.1101/2021.07.28.454145 doi: bioRxiv preprint crescentus cell-cycle have been identified: CtrA, GcrA, DnaA, CcrM and SciP (Tan et al, 2010;Quiñones-Valles et al, 2014). These five genes produce a cyclic expression pattern that drives all downstream cell-cycle functions (Zhou et al, 2015) and which can be reproduced in a simple Boolean model (Quiñones-Valles et al, 2014;Sánchez-Osorio et al, 2017). We use this model as starting point to study the evolution of complex cell-cycle behaviour in cells exposed to more challenging, nutrient-limited conditions.…”
How complexity arises is a fundamental evolutionary question. Complex gene regulation is thought to arise by the interplay between adaptive and non-adaptive forces at multiple organizational levels. Using a computational model, we investigate how complexity arises in cell-cycle regulation. Starting from the well-known Caulobacter crescentus network, we study how cells adapt their cell-cycle behaviour to a gradient of limited nutrient conditions using 10 replicate in silico evolution experiments. We find adaptive expansion of the gene regulatory network: improvement of cell-cycle behaviour allows cells to overcome the inherent cost of complexity. Replicates traverse different evolutionary trajectories leading to distinct eco-evolutionary strategies. In four replicates, cells evolve a generalist strategy to cope with a variety of nutrient levels; in two replicates, different specialist cells evolve for specific nutrient levels; in the remaining four replicates, an intermediate strategy evolves. The generalist and specialist strategies are contingent on the regulatory mechanisms that arise early in evolution, but they are not directly linked to network expansion and overall fitness. This study shows that functionality of cells depends on the combination of gene regulatory network topology and genome structure. For example, the positions of dosage-sensitive genes are exploited to signal to the regulatory network when replication is completed, forming a de novo evolved cell-cycle checkpoint. Complex gene regulation can arise adaptively both from expansion of the regulatory network and from the genomic organization of the elements in this network, demonstrating that to understand complex gene regulation and its evolution, it is necessary to integrate systems that are often studied separately.
“…We use the techniques we develop to formulate an algorithm for compositional attractor identification which we use to develop tool support. The practical application of the developed techniques and tools is illustrated by applying them to a case study based on a regulatory network for cell differentiation [21], [23] found in the bacteria Caulobacter crescentus [13].…”
Boolean networks are an important qualitative modelling technique that provide techniques for analysing the attractors (important cyclic behaviour) in a model. However, their practical application is limited by the state space explosion problem and this had led to researchers considering compositional techniques. In this paper we take a recently developed compositional framework for Boolean networks based on using logical connectives to merge entities and extend it with compositional techniques for attractor analysis. Our approach is based on using strongly connected components to identify potential cyclic behaviour taking into account the interference arising from a composition. We develop tool support for our approach and illustrate its practical application by a case study.
“…Since asymmetric cell division plays an essential role in survival for C. crescentus, understanding how the asymmetry is regulated provides insight into the life cycle of many bacteria with similar characteristics. Many proteins, genes, and other molecules involved in the asymmetric pattern have been reported [2,3]. CtrA, a master regulator of the C. crescentus life cycle, regulates more than 100 genes involved in flagellum biogenesis, DNA replication, and cell division [4,5].…”
Section: Introductionmentioning
confidence: 99%
“…The stalked cell is equipped with a holdfast to attach to solid surfaces in its environment, whereas the swarmer cell develops a flagellum to move around in search of a suitable nutrient environment. The asymmetric cell cycle affords C. crescentus a certain flexibility to cope with the vagaries of life in an oligotrophic, aquatic environment [ 2 ]. Fig.…”
Background
Second messengers, c-di-GMP and (p)ppGpp, are vital regulatory molecules in bacteria, influencing cellular processes such as biofilm formation, transcription, virulence, quorum sensing, and proliferation. While c-di-GMP and (p)ppGpp are both synthesized from GTP molecules, they play antagonistic roles in regulating the cell cycle. In C. crescentus, c-di-GMP works as a major regulator of pole morphogenesis and cell development. It inhibits cell motility and promotes S-phase entry by inhibiting the activity of the master regulator, CtrA. Intracellular (p)ppGpp accumulates under starvation, which helps bacteria to survive under stressful conditions through regulating nucleotide levels and halting proliferation. (p)ppGpp responds to nitrogen levels through RelA-SpoT homolog enzymes, detecting glutamine concentration using a nitrogen phosphotransferase system (PTS Ntr). This work relates the guanine nucleotide-based second messenger regulatory network with the bacterial PTS Ntr system and investigates how bacteria respond to nutrient availability.
Results
We propose a mathematical model for the dynamics of c-di-GMP and (p)ppGpp in C. crescentus and analyze how the guanine nucleotide-based second messenger system responds to certain environmental changes communicated through the PTS Ntr system. Our mathematical model consists of seven ODEs describing the dynamics of nucleotides and PTS Ntr enzymes. Our simulations are consistent with experimental observations and suggest, among other predictions, that SpoT can effectively decrease c-di-GMP levels in response to nitrogen starvation just as well as it increases (p)ppGpp levels. Thus, the activity of SpoT (or its homologues in other bacterial species) can likely influence the cell cycle by influencing both c-di-GMP and (p)ppGpp.
Conclusions
In this work, we integrate current knowledge and experimental observations from the literature to formulate a novel mathematical model. We analyze the model and demonstrate how the PTS Ntr system influences (p)ppGpp, c-di-GMP, GMP and GTP concentrations. While this model does not consider all aspects of PTS Ntr signaling, such as cross-talk with the carbon PTS system, here we present our first effort to develop a model of nutrient signaling in C. crescentus.
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