Biology subjects

Igoshin, O. A.

Publications and source records attributed to Igoshin, O. A..

29 records · Page 2Linked to original sources

bioRxiv2021

Bacteriophage self-counting in the presence of viral replication

When host cells are in low abundance, temperate bacteriophages opt for dormant (lysogenic) infection. Phage lambda implements this strategy by increasing the frequency of lysogeny at higher multiplicity of infection (MOI). However, it remains unclear how the phage reliably counts infecting viral genomes even as their intracellular number increases due to replication. By combining theoretical modeling with single-cell measurements of viral copy number and gene expression, we find that, instead of hindering lambdas decision, replication facilitates it. In a nonreplicating mutant, viral gene expression simply scales with MOI rather than diverging into lytic (virulent) and lysogenic trajectories. A similar pattern is followed during early infection by wildtype phage. However, later in the infection, the modulation of viral replication by the decision genes amplifies the initially modest gene expression differences into divergent trajectories. Replication thus ensures the optimal decision--lysis upon single-phage infection, lysogeny at higher MOI.

systems biology↗

bioRxiv2021

Independent control of mean and noise by convolution of gene expression distributions

Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from a single promoter. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

synthetic biology↗

bioRxiv2020

Chaperone-mediated stress-sensing in Mycobacterium tuberculosis enables fast activation and sustained response

Dynamical properties of gene-regulatory networks are tuned to ensure bacterial survival. In mycobacteria, MprAB-{sigma}E network responds to the presence of stressors, such as surfactants causing surface stress. Positive feedback loops in this network were previously predicted to cause hysteresis, i.e. different responses to identical stressor levels for pre-stressed and unstressed cells. Here we show that hysteresis does not occur in non-pathogenic Mycobacterium smegmatis but occurs in Mycobacterium tuberculosis. However, the observed rapid temporal response in M. tuberculosis is inconsistent with the model predictions. To reconcile these observations, we implement a recently proposed mechanism for stress-sensing: the release of MprB from the inhibitory complex with chaperone DnaK upon the stress exposure. Using modeling and parameter fitting, we demonstrate that this mechanism can accurately describe the experimental observations. Furthermore, we predict perturbations in DnaK expression that can strongly affect dynamical properties. Experiments with these perturbations agree with model predictions, confirming the role of DnaK in fast and sustained response. IMPORTANCEGene-regulatory networks controlling stress response in mycobacterial species have been linked to persistence switches enabling the bacterial dormancy within a host. However, the mechanistic basis of switching and stress sensing is not fully understood. In this paper, combining quantitative experiments and mathematical modeling, we uncover how interactions between two master regulators of stress response -- MprAB two-component system and alternative sigma factor{sigma} E - shape the dynamical properties of surface-stress network. The result show hysteresis (history dependence) in the response of pathogenic bacteria M. tuberculosis to surface stress and lack of the hysteresis in non-pathogenic M. smegmatis. Furthermore, to resolve the apparent contradiction between the existence of hysteresis and fast activation of the response, we utilize a recently proposed role of chaperone DnaK in stress-sensing. The results leads to a novel system-level understanding of bacterial stress-response dynamics.

systems biology↗

bioRxiv2020

Trade-Offs between Speed, Accuracy, and Dissipation in tRNAIle Aminoacylation

Living systems maintain a high fidelity in information processing through kinetic proofreading, a mechanism to preferentially remove incorrect substrates at the cost of energy dissipation and slower speed. Proofreading mechanisms must balance their demand for higher speed, fewer errors, and lower dissipation, but it is unclear how rates of individual reaction steps are evolutionary tuned to balance these needs, especially when multiple proofreading mechanisms are present. Here, using a discrete-state stochastic model, we analyze the optimization strategies in Escherichia coli isoleucyl-tRNA synthetase. Surprisingly, this enzyme adopts an economic proofreading strategy and improves speed and dissipation as long as the error is tolerable. Through global parameter sampling, we reveal a fundamental dissipation-error relation that bounds the enzymes optimal performance and explains the importance of the post-transfer editing mechanism. The proximity of native system parameters to this bound demonstrates the importance of energy dissipation as an evolutionary force affecting fitness. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/027615v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@f4f4e1org.highwire.dtl.DTLVardef@1f8e45forg.highwire.dtl.DTLVardef@1591e16org.highwire.dtl.DTLVardef@1c650b0_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2020

Data-driven models reveal mutant cell behaviors important for myxobacterial aggregation

Single mutations frequently alter several aspects of cell behavior but rarely reveal whether a particular statistically significant change is biologically significant. To determine which behavioral changes are most important for multicellular self-organization, we devised a new methodology using Myxococcus xanthus as a model system. During development, myxobacteria coordinate their movement to aggregate into spore-filled fruiting bodies. We investigate how aggregation is restored in two mutants, csgA and pilC, that cannot aggregate unless mixed with wild type (WT) cells. To this end, we use cell tracking to follow the movement of fluorescently labeled cells in combination with data-driven agent-based modeling. The results indicate that just like WT cells, both mutants bias their movement toward aggregates and reduce motility inside aggregates. However, several aspects of mutant behavior remain uncorrected by WT demonstrating that perfect recreation of WT behavior is unnecessary. In fact, synergies between errant behaviors can make aggregation robust.

biophysics↗

bioRxiv2019

Kinetic control of stationary flux ratios for a wide range of biochemical processes

One of the most intriguing features of biological systems is their ability to regulate the steady-state fluxes of the underlying biochemical reactions, however the regulatory mechanisms and their physicochemical properties are not fully understood. Fundamentally, flux regulation can be explained with a chemical kinetic formalism describing the transitions between discrete states with the reaction rates defined by an underlying free-energy landscape. Which features of the energy landscape affect the flux distribution? Here we prove that the ratios of the steady-state fluxes of quasi-first-order biochemical processes are invariant to energy perturbations of the discrete states and are only affected by the energy barriers. In other words, the non-equilibrium flux distribution is under kinetic and not thermodynamic control. We illustrate the generality of this result for three biological processes. For the network describing protein folding along competing pathways, the probabilities of proceeding via these pathways are shown to be invariant to the stability of the intermediates or to the presence of additional misfolded states. For the network describing protein synthesis, the error rate and the energy expenditure per peptide bond is proven to be independent of the stability of the intermediate states. For molecular motors such as myosin-V, the ratio of forward to backward steps and the number of ATP molecules hydrolyzed per step is demonstrated to be invariant to energy perturbations of the intermediate states. These findings place important constraints on the ability of mutations and drug perturbations to affect the steady-state flux distribution for a wide class of biological processes.

biophysics↗

bioRxiv2018

Synthetic pluripotent bacterial stem cells

A defining property of stem cells is their ability to differentiate via asymmetric cell division, in which a stem cell creates a differentiated daughter cell but retains its own phenotype. Here, we describe a synthetic genetic circuit for controlling asymmetrical cell division in Escherichia coli. Specifically, we engineered an inducible system that can bind and segregate plasmid DNA to a single position in the cell. Upon division, the co-localized plasmids are kept by one and only one of the daughter cells. The other daughter cell receives no plasmid DNA and is hence irreversibly differentiated from its sibling. In this way, we achieved asymmetric cell division though asymmetric plasmid partitioning. We also characterized an orthogonal inducible circuit that enables the simultaneous asymmetric partitioning of two plasmid species - resulting in pluripotent cells that have four distinct differentiated states. These results point the way towards engineering multicellular systems from prokaryotic hosts.

synthetic biology↗

bioRxiv2018

Agent-Based Modeling Reveals Possible Mechanisms for Observed Aggregation Cell Behaviors in Myxococcus xanthus

Myxococcus xanthus is a soil bacterium that serves as a model system for biological self-organization. Cells form distinct, dynamic patterns depending on environmental conditions. An agent-based model (ABM) was used to understand how M. xanthus cells aggregate into multicellular mounds in response to starvation. In this model, each cell is modeled as an agent, represented by a point-particle and characterized by its position and moving direction. At low agent density, the model recapitulates the dynamic patterns observed by experiments and a previous biophysical model. To study aggregation at high cell density, we extended the model based on the recent experimental observation that cells exhibit biased movement towards aggregates. We tested two possible mechanisms for this biased movement and demonstrate that a chemotaxis model with adaptation can reproduce the observed experimental results leading to the formation of stable aggregates. Furthermore, our model reproduces the experimentally observed patterns of cell alignment around aggregates.\n\nAuthor summaryCollective self-organization of cells into multicellular structures is important for lifestyle of many bacterial species. Myxococcus xanthus bacterium is a model system for studying this self-organization. In this work, we investigate how in response to starvation M. xanthus cells aggregate into multicellular mounds. A recent study identified the key cellular behaviors that are necessarily for the aggregation but the mechanisms of these behaviors remained unclear. To uncover these mechanisms, we developed a computational model that simulates interactions among a large number of cells. The results demonstrate that the observed bias in the cell reversal times as they move towards the aggregates can be explained by chemotaxis model. In this model cells secrete a chemical signal and respond to it via a partially-adapting biochemical network. The resulting aggregation dynamics are in good agreement with the experiments. Furthermore, chemotaxis signaling model reproduces the experimentally observed patterns of cell alignment around aggregates. On the other hand, an alternative model, based on contact-dependent signaling between cells, fails to aid in aggregation. Thus our models make important predictions about the cellular interactions that drives multicellular aggregation and can serve as a basis to investigate a wider range of developmental mutant strains.

microbiology↗

bioRxiv2017

On the mechanism of long-range alignment order of fibroblasts

Long-range alignment ordering of fibroblasts have been observed in the vicinity of cancerous tumors and can be recapitulated with in vitro experiments. However, the mechanisms driving their ordering are not understood. Here we show that local collision-driven nematic alignment interactions among fibroblasts are insufficient to explain observed long-range alignment. One possibility is that there exists another orientation field co-evolving with the cells and reinforcing their alignment. We propose that this field reflects the mechanical cross-talk between the fibroblasts and the underlying fibrous material on which they move. We demonstrate that this new long-range interaction can give rise to high nematic order and to the observed patterning of the cancer microenvironment.\n\nSignificance StatementLong-range alignment patterns of fibroblasts have been observed both in vivo and in vitro. However, there has not been much understanding of the underlying mechanism. In this work, we demonstrate that these patterns cannot be simply explained by their steric interaction with one another during collisions. Instead, we propose that fibroblasts may collectively align through non-local interactions arising from their modification of an underlying extracellular matrix. The proposed mechanism explains the observed co-alignment between fibroblasts and collagen fibers around tumors and can be be tested in future experiments that can image the dynamics of this pattern formation in vivo or in vitro

biophysics↗

bioRxiv2017

Modeling mechanical interactions in growing populations of rod-shaped bacteria

Advances in synthetic biology allow us to engineer bacterial collectives with pre-specified characteristics. However, the behavior of these collectives is difficult to understand, as cellular growth and division as well as extra-cellular fluid flow lead to complex, changing arrangements of cells within the population. To rationally engineer and control the behavior of cell collectives we need theoretical and computational tools to understand their emergent spatiotemporal dynamics. Here, we present an agent-based model that allows growing cells to detect and respond to mechanical interactions. Crucially, our model couples the dynamics of cell growth to the cells environment: Mechanical constraints can affect cellular growth rate and a cell may alter its behavior in response to these constraints. This coupling links the mechanical forces that influence cell growth and emergent behaviors in cell assemblies. We illustrate our approach by showing how mechanical interactions can impact the dynamics of bacterial collectives growing in microfluidic traps.

synthetic biology↗

bioRxiv2017

Elucidating interplay of speed and accuracy in biological error correction

One of the most fascinating features of biological systems is the ability to sustain high accuracy of all major cellular processes despite the stochastic nature of underlying chemical processes. It is widely believed that such low errors are the result of the error correcting mechanism known as kinetic proofreading. However, it is usually argued that enhancing the accuracy should result in slowing down the process leading to so-called speed-accuracy trade-off. We developed a discrete-state stochastic framework that allowed us to investigate the mechanisms of the proofreading using the method of first-passage processes. With this framework, we simultaneously analyzed speed and accuracy of the two fundamental biological processes, DNA replication and tRNA selection during the translation. The results indicate that speed-accuracy trade-off is not always observed. However, when the trade-off is present, the biological systems tend to optimize the speed rather than the accuracy of the processes, as long as the error level is tolerable. Additional constraints due to the energetic cost of proofreading also play a role in the error correcting process. Our theoretical findings provide a new microscopic picture of how complex biological processes are able to function so fast with a high accuracy.

biophysics↗