scholarly journals Stochasticity in Colonial Growth Dynamics of Individual Bacterial Cells

2013 ◽  
Vol 79 (7) ◽  
pp. 2294-2301 ◽  
Author(s):  
Konstantinos P. Koutsoumanis ◽  
Alexandra Lianou
Keyword(s):  
Kinetic Parameters ◽  
Single Cells ◽  
Growth Curves ◽  
Growth Dynamics ◽  
Time Lapse ◽  
Initial Population ◽  
Bacterial Cells ◽  
Stochastic Growth ◽  
Bacterial Populations ◽  
Content Type

ABSTRACTConventional bacterial growth studies rely on large bacterial populations without considering the individual cells. Individual cells, however, can exhibit marked behavioral heterogeneity. Here, we present experimental observations on the colonial growth of 220 individual cells ofSalmonella entericaserotype Typhimurium using time-lapse microscopy videos. We found a highly heterogeneous behavior. Some cells did not grow, showing filamentation or lysis before division. Cells that were able to grow and form microcolonies showed highly diverse growth dynamics. The quality of the videos allowed for counting the cells over time and estimating the kinetic parameters lag time (λ) and maximum specific growth rate (μmax) for each microcolony originating from a single cell. To interpret the observations, the variability of the kinetic parameters was characterized using appropriate probability distributions and introduced to a stochastic model that allows for taking into account heterogeneity using Monte Carlo simulation. The model provides stochastic growth curves demonstrating that growth of single cells or small microbial populations is a pool of events each one of which has its own probability to occur. Simulations of the model illustrated how the apparent variability in population growth gradually decreases with increasing initial population size (N0). For bacterial populations withN0of >100 cells, the variability is almost eliminated and the system seems to behave deterministically, even though the underlying law is stochastic. We also used the model to demonstrate the effect of the presence and extent of a nongrowing population fraction on the stochastic growth of bacterial populations.

scholarly journals Escherichia coli can survive stress by noisy growth modulation

2018 ◽  
Author(s):  
Om Patange ◽  
Christian Schwall ◽  
Matt Jones ◽  
Douglas Griffith ◽  
Andrew Phillips ◽  
...  
Keyword(s):  
Gene Expression ◽  
Response Regulator ◽  
Single Cells ◽  
Phenotypic Variability ◽  
Growth Dynamics ◽  
Time Lapse ◽  
Survival Strategies ◽  
Growth Modulation ◽  
Bacterial Populations ◽  
E Coli

Gene expression can be noisy1,2, as can the growth of single cells3,4. Such cell-to-cell variation has been implicated in survival strategies for bacterial populations5–7. However, it remains unclear how single cells couple gene expression with growth to implement these survival strategies. Here we show how noisy expression of a key stress response regulator, rpoS8, allows E. coli to modulate its growth dynamics to survive future adverse environments. First, we demonstrate that rpoS has a long-tailed distribution of expression in an unstressed population of cells. We next reveal how a dynamic positive feedback loop between rpoS and growth rate produces multi-generation rpoS pulses, which are responsible for the rpoS heterogeneity. We do so experimentally with single-cell, time-lapse microscopy9 and microfluidics10 and theoretically with a stochastic model11,22. Finally, we demonstrate the function of the coupling of heterogeneous rpoS activity and growth. It enables E. coli to survive oxidative attack by causing prolonged periods of slow growth. This dynamic phenotype is captured by the rpoS-growth feedback model. Our synthesis of noisy gene expression, growth, and survival paves the way for further exploration of functional phenotypic variability.

scholarly journals Modeling Bacterial Population Growth from Stochastic Single-Cell Dynamics

2014 ◽  
Vol 80 (17) ◽  
pp. 5241-5253 ◽  
Author(s):  
Antonio A. Alonso ◽  
Ignacio Molina ◽  
Constantinos Theodoropoulos
Keyword(s):  
Differential Equation ◽  
Population Growth ◽  
Single Cell ◽  
Bacterial Cells ◽  
Estimation Of Parameters ◽  
Deterministic Models ◽  
Cell Dynamics ◽  
Bacterial Populations ◽  
Content Type ◽  
Number Of Individuals

ABSTRACTA few bacterial cells may be sufficient to produce a food-borne illness outbreak, provided that they are capable of adapting and proliferating on a food matrix. This is why any quantitative health risk assessment policy must incorporate methods to accurately predict the growth of bacterial populations from a small number of pathogens. In this aim, mathematical models have become a powerful tool. Unfortunately, at low cell concentrations, standard deterministic models fail to predict the fate of the population, essentially because the heterogeneity between individuals becomes relevant. In this work, a stochastic differential equation (SDE) model is proposed to describe variability within single-cell growth and division and to simulate population growth from a given initial number of individuals. We provide evidence of the model ability to explain the observed distributions of times to division, including the lag time produced by the adaptation to the environment, by comparing model predictions with experiments from the literature forEscherichia coli,Listeria innocua, andSalmonella enterica. The model is shown to accurately predict experimental growth population dynamics for both small and large microbial populations. The use of stochastic models for the estimation of parameters to successfully fit experimental data is a particularly challenging problem. For instance, if Monte Carlo methods are employed to model the required distributions of times to division, the parameter estimation problem can become numerically intractable. We overcame this limitation by converting the stochastic description to a partial differential equation (backward Kolmogorov) instead, which relates to the distribution of division times. Contrary to previous stochastic formulations based on random parameters, the present model is capable of explaining the variability observed in populations that result from the growth of a small number of initial cells as well as the lack of it compared to populations initiated by a larger number of individuals, where the random effects become negligible.

scholarly journals BCM3D 2.0: Accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations

2021 ◽  
Author(s):  
Ji Zhang ◽  
Yibo Wang ◽  
Eric Donarski ◽  
Andreas Gahlmann
Keyword(s):  
Image Analysis ◽  
Bacterial Cell ◽  
Single Cells ◽  
Three Dimensional ◽  
Time Lapse ◽  
Cell Segmentation ◽  
Bacterial Cells ◽  
Deep Convolutional Neural Networks ◽  
Fluorescence Images ◽  
Image Representations

Accurate detection and segmentation of single cells in three-dimensional (3D) fluorescence time-lapse images is essential for measuring individual cell behaviors in large bacterial communities called biofilms. Recent progress in machine-learning-based image analysis is providing this capability with every increasing accuracy. Leveraging the capabilities of deep convolutional neural networks (CNNs), we recently developed bacterial cell morphometry in 3D (BCM3D), an integrated image analysis pipeline that combines deep learning with conventional image analysis to detect and segment single biofilm-dwelling cells in 3D fluorescence images. While the first release of BCM3D (BCM3D 1.0) achieved state-of-the-art 3D bacterial cell segmentation accuracies, low signal-to-background ratios (SBRs) and images of very dense biofilms remained challenging. Here, we present BCM3D 2.0 to address this challenge. BCM3D 2.0 is completely complementary to the approach utilized in BCM3D 1.0. Instead of training CNNs to perform voxel classification, we trained CNNs to translate 3D fluorescence images into intermediate 3D image representations that are, when combined appropriately later, more amenable to conventional mathematical image processing than a single experimental image. Using this approach, improved segmentation results are obtained even for very low SBRs and/or high cell density biofilm images. The improved cell segmentation accuracies in turn enable improved accuracies of tracking individual cells through 3D space and time, which opens the door to investigating time-dependent phenomena in bacterial biofilms at the cellular level.

scholarly journals High-Content Imaging to Phenotype Antimicrobial Effects on Individual Bacteria at Scale

mSystems ◽  
2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Sushmita Sridhar ◽  
Sally Forrest ◽  
Ben Warne ◽  
Mailis Maes ◽  
Stephen Baker ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
High Resolution ◽  
Antimicrobial Resistance ◽  
Morphological Characteristics ◽  
Bacterial Cells ◽  
Bacterial Populations ◽  
Content Type ◽  
High Content Imaging ◽  
Serovar Typhimurium ◽  
Multiple Cells

ABSTRACT High-content imaging (HCI) is a technique for screening multiple cells in high resolution to detect subtle morphological and phenotypic variation. The method has been commonly deployed on model eukaryotic cellular systems, often for screening new drugs and targets. HCI is not commonly utilized for studying bacterial populations but may be a powerful tool in understanding and combatting antimicrobial resistance. Consequently, we developed a high-throughput method for phenotyping bacteria under antimicrobial exposure at the scale of individual bacterial cells. Imaging conditions were optimized on an Opera Phenix confocal microscope (Perkin Elmer), and novel analysis pipelines were established for both Gram-negative bacilli and Gram-positive cocci. The potential of this approach was illustrated using isolates of Klebsiella pneumoniae, Salmonella enterica serovar Typhimurium, and Staphylococcus aureus. HCI enabled the detection and assessment of subtle morphological characteristics, undetectable through conventional phenotypical methods, that could reproducibly distinguish between bacteria exposed to different classes of antimicrobials with distinct modes of action (MOAs). In addition, distinctive responses were observed between susceptible and resistant isolates. By phenotyping single bacterial cells, we observed intrapopulation differences, which may be critical in identifying persistence or emerging resistance during antimicrobial treatment. The work presented here outlines a comprehensive method for investigating morphological changes at scale in bacterial populations under specific perturbation. IMPORTANCE High-content imaging (HCI) is a microscopy technique that permits the screening of multiple cells simultaneously in high resolution to detect subtle morphological and phenotypic variation. The power of this methodology is that it can generate large data sets comprised of multiple parameters taken from individual cells subjected to a range of different conditions. We aimed to develop novel methods for using HCI to study bacterial cells exposed to a range of different antibiotic classes. Using an Opera Phenix confocal microscope (Perkin Elmer) and novel analysis pipelines, we created a method to study the morphological characteristics of Klebsiella pneumoniae, Salmonella enterica serovar Typhimurium, and Staphylococcus aureus when exposed to antibacterial drugs with differing modes of action. By imaging individual bacterial cells at high resolution and scale, we observed intrapopulation differences associated with different antibiotics. The outlined methods are highly relevant for how we begin to better understand and combat antimicrobial resistance.

scholarly journals Stochasticity of cellular growth: sources, propagation and consequences

2018 ◽  
Author(s):  
Philipp Thomas ◽  
Guillaume Terradot ◽  
Vincent Danos ◽  
Andrea Y. Weiße
Keyword(s):  
Evolutionary Biology ◽  
Cell Model ◽  
Single Cells ◽  
Growth Dynamics ◽  
Cellular Growth ◽  
Bacterial Cells ◽  
Cell Processes ◽  
Cellular Machinery ◽  
Cross Correlations ◽  
Phenotypic Responses

Cellular growth impacts a range of phenotypic responses. Identifying the sources of fluctuations in growth and how they propagate across the cellular machinery can unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. In addition to several population-averaged data, the model quantitatively recovers how growth fluctuations in single cells change across nutrient conditions. We develop a framework to analyse stochastic chemical reactions coupled with cell divisions and use it to identify sources of growth heterogeneity. By visualising cross-correlations we then determine how such initial fluctuations propagate to growth rate and affect other cell processes. We further study antibiotic responses and find that complex drug-nutrient interactions can both enhance and suppress heterogeneity. Our results provide a predictive framework to integrate single-cell and bulk data and draw testable predictions with implications for antibiotic tolerance, evolutionary biology and synthetic biology.

scholarly journals RadD Contributes to R-Loop Avoidance in Sub-MIC Tobramycin

mBio ◽  
2019 ◽  
Vol 10 (4) ◽  
Author(s):  
Veronica Negro ◽  
Evelyne Krin ◽  
Sebastian Aguilar Pierlé ◽  
Thibault Chaze ◽  
Quentin Giai Gianetto ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Coupling Factor ◽  
Resistant Bacteria ◽  
Bacterial Cells ◽  
Dna Breaks ◽  
Bacterial Populations ◽  
Content Type ◽  
Double Strand Dna Breaks ◽  
Translation Coupling ◽  
Recombination Pathway

ABSTRACTWe have previously identifiedVibrio choleraemutants in which the stress response to subinhibitory concentrations of aminoglycoside is altered. One gene identified, VC1636, encodes a putative DNA/RNA helicase, recently named RadD inEscherichia coli. Here we combined extensive genetic characterization and high-throughput approaches in order to identify partners and molecular mechanisms involving RadD. We show that double-strand DNA breaks (DSBs) are formed upon subinhibitory tobramycin treatment in the absence ofradDandrecBCDand that formation of these DSBs can be overcome by RNase H1 overexpression. Loss of RNase H1, or of the transcription-translation coupling factor EF-P, is lethal in theradDdeletion mutant. We propose that R-loops are formed upon sublethal aminoglycoside treatment, leading to the formation of DSBs that can be repaired by the RecBCD homologous recombination pathway, and that RadD counteracts such R-loop accumulation. We discuss how R-loops that can occur upon translation-transcription uncoupling could be the link between tobramycin treatment and DNA break formation.IMPORTANCEBacteria frequently encounter low concentrations of antibiotics. Active antibiotics are commonly detected in soil and water at concentrations much below lethal concentration. Although sub-MICs of antibiotics do not kill bacteria, they can have a major impact on bacterial populations by contributing to the development of antibiotic resistance through mutations in originally sensitive bacteria or acquisition of DNA from resistant bacteria. It was shown that concentrations as low as 100-fold below the MIC can actually lead to the selection of antibiotic-resistant cells. We seek to understand how bacterial cells react to such antibiotic concentrations usingE. coli, the Gram-negative bacterial paradigm, andV. cholerae, the causative agent of cholera. Our findings shed light on the processes triggered at the DNA level by antibiotics targeting translation, how damage occurs, and what the bacterial strategies are to respond to such DNA damage.

scholarly journals Contributions ofCandida albicansDimorphism, Adhesive Interactions, and Extracellular Matrix to the Formation of Dual-Species Biofilms withStreptococcus gordonii

mBio ◽  
2019 ◽  
Vol 10 (3) ◽  
Author(s):  
Daniel Montelongo-Jauregui ◽  
Stephen P. Saville ◽  
Jose L. Lopez-Ribot
Keyword(s):  
Extracellular Matrix ◽  
Oral Cavity ◽  
Bacterial Species ◽  
Fungal Species ◽  
Bacterial Cells ◽  
Mixed Species ◽  
Bacterial Populations ◽  
Content Type ◽  
Mutant Strains ◽  
Adhesive Interactions

ABSTRACTFungal and bacterial populations coexist in the oral cavity, frequently forming mixed-species biofilms that complicate treatment against polymicrobial infections. However, despite relevance to oral health, the bidirectional interactions between these microbial populations are poorly understood. In this study, we aimed to elucidate the mechanisms underlying the interactions between the fungal speciesCandida albicansand the bacterial speciesStreptococcus gordoniias they coexist in mixed-species biofilms. Specifically, the interactions of differentC. albicansmutant strains deficient in filamentation (efg1Δ/Δ andbrg1Δ/Δ), adhesive interactions (als3Δ/Δ andbcr1Δ/Δ), and production of matrix exopolymeric substances (EPS) (kre5Δ/Δ, mnn9Δ/Δ,rlm1Δ/Δ, andzap1Δ/Δ) were evaluated withS. gordoniiunder different conditions mimicking the environment in the oral cavity. Interestingly, our results revealed that growth of the biofilm-deficientC. albicansals3Δ/Δandbcr1Δ/Δmutant strains in synthetic saliva or withS. gordoniirestored their biofilm-forming ability. Moreover, challenging previous observations indicating an important role of morphogenetic conversions in the interactions between these two species, our results indicated a highly synergistic interaction betweenS. gordoniiand theC. albicansfilamentation-deficientefg1Δ/Δandbrg1Δ/Δdeletion mutants, which was particularly noticeable when the mixed biofilms were grown in synthetic saliva. Importantly, dual-species biofilms were found to exhibit increase in antimicrobial resistance, indicating that components of the fungal exopolymeric material confer protection to streptococcal cells against antibacterial treatment. Collectively, these findings unravel a high degree of complexity in the interactions betweenC. albicansandS. gordoniiin mixed-species biofilms, which may impact homeostasis in the oral cavity.IMPORTANCEMicrobial communities have a great impact in health and disease.C. albicansinteracts with multiple microorganisms in the oral cavity, frequently forming polymicrobial biofilms. We report on the synergistic interactions betweenC. albicansand the Gram-positive bacteriumS. gordonii, for which we have examined the different contributions of adhesive interactions, filamentation, and the extracellular matrix to the formation of dual-species biofilms. Our results demonstrate that growth in the presence of the bacterium can restore the biofilm-forming ability of differentC. albicansmutant strains with defects in adhesion and filamentation. The mixed-species biofilms also show high levels of resistance to antibacterial and antifungal antibiotics, and our results indicate that the fungal biofilm matrix protects bacterial cells within these mixed-species biofilms. Our observations add to a growing body of evidence indicating a high level of complexity in the reciprocal interactions and consortial behavior of fungal/bacterial biofilms.

Antagonistic effect ofLactobacillusstrains againstEscherichia coliandListeria monocytogenesin milk

2010 ◽  
Vol 78 (2) ◽  
pp. 136-143 ◽  
Author(s):  
Catalina Aguilar ◽  
Consuelo Vanegas ◽  
Bernadette Klotz
Keyword(s):  
Kinetic Parameters ◽  
Pathogenic Bacteria ◽  
Bacterial Culture ◽  
Growth Curves ◽  
Antagonistic Activity ◽  
Antagonistic Effect ◽  
Lag Phase ◽  
Bacterial Populations ◽  
Food Borne ◽  
Mechanism Of Growth

The current work studied four types of binary antagonist/pathogen bacterial culture system, in order to determine the effect of interaction between two strains ofLactobacillus plantarumand two food-borne pathogens,Listeria monocytogenesandEscherichia coli, in whole UHT milk at 37°C. To determine the type of interaction between the two bacterial populations in co-cultures and to evaluate the antagonistic activity of the lactic acid bacteria (LAB) on the pathogenic bacteria, the growth curves, the kinetic parameters, and the pH profiles of mono- and co-cultures were compared. TheLb. plantarumstrains showed different bacteriocin-like inhibitory substance (BLIS) production, auto- and co-inducible. The antibacterial effect of neutralized supernatants of mono and co-cultures harvested at different times of incubation was assessed in order to establish the presence of bacteriocin-like inhibitory-substances (BLIS) and their possible relation to the growth inhibition of the pathogen. The LAB reduced the growth ofEsch. coliand ofList. monocytogenesby 4 and ∼5 log cycles, respectively and influenced other growth kinetic parameters, such as μmaxand lag phase, in the different binary combinations. The growth of the LAB was not relevantly altered by simultaneous growth with the pathogenic strains showing an interaction of amensalism. The pattern of inhibition exerted by the LAB on the pathogens was different;Lb. plantarumLB279 inhibited the growth ofList. monocytogenesmore effectively than that ofEsch. coli. The behaviour ofEsch. coliin co-culture withLb. plantarumWS4174 suggested the presence of metabolic crowding in the mechanism of growth suppression. This exploratory study showed the complexity and specific particularities of the inhibition phenomena between bacterial communities.

scholarly journals Following the Fate of Bacterial Cells Experiencing Sudden Chromosome Loss

mBio ◽  
2015 ◽  
Vol 6 (3) ◽  
Author(s):  
Maya Elbaz ◽  
Sigal Ben-Yehuda
Keyword(s):  
De Novo ◽  
Vaccination Strategy ◽  
Time Lapse ◽  
Chromosome Loss ◽  
Bacterial Cells ◽  
Chromosomal Dna ◽  
Entire Genome ◽  
Content Type ◽  
Intact Membrane ◽  
Efficient Induction

ABSTRACTChromosomal DNA is a constant source of information, essential for any given cell to respond and adapt to changing conditions. Here, we investigated the fate of exponentially growing bacterial cells experiencing a sudden and rapid loss of their entire chromosome. UtilizingBacillus subtiliscells harboring an inducible copy of the endogenous toxinyqcG, which encodes an endonuclease, we induced the formation of a population of cells that lost their genetic information simultaneously. Surprisingly, these DNA-less cells, termed DLCs, did not lyse immediately and exhibited normal cellular morphology for a period of at least 5 h after DNA loss. This cellular integrity was manifested by their capacity to maintain an intact membrane and membrane potential and cell wall architecture similar to those of wild-type cells. Unlike growing cells that exhibit a dynamic profile of macromolecules, DLCs displayed steady protein and RNA reservoirs. Remarkably, following DLCs by time lapse microscopy revealed that they succeeded in synthesizing proteins, elongating, and dividing, apparently formingde novoZ rings at the midcell position. Taken together, the persistence of key cellular events in DLCs indicates that the information to carry out lengthy processes is harbored within the remaining molecular components.IMPORTANCEPerturbing bacterial growth by the use of antibiotics targeting replication, transcription, or translation has been a subject of study for many years; however, the consequences of a more dramatic event, in which the entire bacterial chromosome is lost, have not been described. Here, we followed the fate of bacterial cells encountering an abrupt loss of their entire genome. Surprisingly, the cells preserved an intact envelope and functioning macromolecules. Furthermore, cells lacking their genome could still elongate and divide hours after the loss of DNA. Our data suggest that the information stored in the transient reservoir of macromolecules is sufficient to carry out complex and lengthy processes even in the absence of the chromosome. Based on our study, the formation of DNA-less bacteria could serve as a novel vaccination strategy, enabling an efficient induction of the immune system without the risk of bacterial propagation within the host.

scholarly journals Genetic Reporter System for Positioning of Proteins at the Bacterial Pole

mBio ◽  
2012 ◽  
Vol 3 (2) ◽  
Author(s):  
Kathryn R. Fixen ◽  
Anuradha Janakiraman ◽  
Sean Garrity ◽  
Daniel J. Slade ◽  
Andrew N. Gray ◽  
...  
Keyword(s):  
Cell Division ◽  
Protein Function ◽  
Large Scale ◽  
Spatial Organization ◽  
Protein Localization ◽  
Bacterial Cells ◽  
Reporter System ◽  
Bacterial Populations ◽  
Content Type ◽  
Cell Division Protein

ABSTRACTSpatial organization within bacteria is fundamental to many cellular processes, although the basic mechanisms underlying localization of proteins to specific sites within bacteria are poorly understood. The study of protein positioning has been limited by a paucity of methods that allow rapid large-scale screening for mutants in which protein positioning is altered. We developed a genetic reporter system for protein localization to the pole within the bacterial cytoplasm that allows saturation screening for mutants inEscherichia coliin which protein localization is altered. Utilizing this system, we identify proteins required for proper positioning of theShigellaautotransporter IcsA. Autotransporters, widely distributed bacterial virulence proteins, are secreted at the bacterial pole. We show that the conserved cell division protein FtsQ is required for localization of IcsA and other autotransporters to the pole. We demonstrate further that this system can be applied to the study of proteins other than autotransporters that display polar positioning within bacterial cells.IMPORTANCEMany proteins localize to specific sites within bacterial cells, and localization to these sites is frequently critical to proper protein function. The mechanisms that underlie protein localization are incompletely understood, in part because of the paucity of methods that allow saturation screening for mutants in which protein localization is altered. We developed a genetic reporter assay that enables screening of bacterial populations for changes in localization of proteins to the bacterial pole, and we demonstrate the utility of the system in identifying factors required for proper localization of the polarShigellaautotransporter protein IcsA. Using this method, we identify the conserved cell division protein FtsQ as being required for positioning of IcsA to the bacterial pole. We demonstrate further that the requirement for FtsQ for polar positioning applies to other autotransporters and that the method can be applied to polar proteins other than autotransporters.

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