scholarly journals Robust, linear correlations between growth rates and β-lactam–mediated lysis rates

2018 ◽  
Vol 115 (16) ◽  
pp. 4069-4074 ◽  
Author(s):  
Anna J. Lee ◽  
Shangying Wang ◽  
Hannah R. Meredith ◽  
Bihan Zhuang ◽  
Zhuojun Dai ◽  
...  
Keyword(s):  
Growth Rate ◽  
Single Cells ◽  
Bacterial Species ◽  
Population Level ◽  
Instantaneous Growth Rate ◽  
Bacterial Cells ◽  
Bacterial Growth Rate ◽  
Lysis Rate ◽  
Dynamic Signature ◽  
Linear Correlations

It is widely acknowledged that faster-growing bacteria are killed faster by β-lactam antibiotics. This notion serves as the foundation for the concept of bacterial persistence: dormant bacterial cells that do not grow are phenotypically tolerant against β-lactam treatment. Such correlation has often been invoked in the mathematical modeling of bacterial responses to antibiotics. Due to the lack of thorough quantification, however, it is unclear whether and to what extent the bacterial growth rate can predict the lysis rate upon β-lactam treatment under diverse conditions. Enabled by experimental automation, here we measured >1,000 growth/killing curves for eight combinations of antibiotics and bacterial species and strains, including clinical isolates of bacterial pathogens. We found that the lysis rate of a bacterial population linearly depends on the instantaneous growth rate of the population, regardless of how the latter is modulated. We further demonstrate that this predictive power at the population level can be explained by accounting for bacterial responses to the antibiotic treatment by single cells. This linear dependence of the lysis rate on the growth rate represents a dynamic signature associated with each bacterium–antibiotic pair and serves as the quantitative foundation for designing combination antibiotic therapy and predicting the population-structure change in a population with mixed phenotypes.

scholarly journals Precise regulation of the relative rates of surface area and volume synthesis in bacterial cells growing in dynamic environments

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Handuo Shi ◽  
Yan Hu ◽  
Pascal D. Odermatt ◽  
Carlos G. Gonzalez ◽  
Lichao Zhang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Time Delay ◽  
Surface Area ◽  
Environmental Changes ◽  
Volume Ratio ◽  
Dynamic Environments ◽  
Bacterial Cells ◽  
Bacterial Growth Rate ◽  
State Size ◽  
Insight Into

AbstractThe steady-state size of bacterial cells correlates with nutrient-determined growth rate. Here, we explore how rod-shaped bacterial cells regulate their morphology during rapid environmental changes. We quantify cellular dimensions throughout passage cycles of stationary-phase cells diluted into fresh medium and grown back to saturation. We find that cells exhibit characteristic dynamics in surface area to volume ratio (SA/V), which are conserved across genetic and chemical perturbations as well as across species and growth temperatures. A mathematical model with a single fitting parameter (the time delay between surface and volume synthesis) is quantitatively consistent with our SA/V experimental observations. The model supports that this time delay is due to differential expression of volume and surface-related genes, and that the first division after dilution occurs at a tightly controlled SA/V. Our minimal model thus provides insight into the connections between bacterial growth rate and cell shape in dynamic environments.

scholarly journals Noise-driven growth rate gain in clonal cellular populations

2016 ◽  
Vol 113 (12) ◽  
pp. 3251-3256 ◽  
Author(s):  
Mikihiro Hashimoto ◽  
Takashi Nozoe ◽  
Hidenori Nakaoka ◽  
Reiko Okura ◽  
Sayo Akiyoshi ◽  
...  
Keyword(s):  
Growth Rate ◽  
Population Growth ◽  
Single Cell ◽  
Single Cells ◽  
Population Level ◽  
Growth Conditions ◽  
Cell Dynamics ◽  
Generation Times ◽  
Complex Population ◽  
The Mean

Cellular populations in both nature and the laboratory are composed of phenotypically heterogeneous individuals that compete with each other resulting in complex population dynamics. Predicting population growth characteristics based on knowledge of heterogeneous single-cell dynamics remains challenging. By observing groups of cells for hundreds of generations at single-cell resolution, we reveal that growth noise causes clonal populations of Escherichia coli to double faster than the mean doubling time of their constituent single cells across a broad set of balanced-growth conditions. We show that the population-level growth rate gain as well as age structures of populations and of cell lineages in competition are predictable. Furthermore, we theoretically reveal that the growth rate gain can be linked with the relative entropy of lineage generation time distributions. Unexpectedly, we find an empirical linear relation between the means and the variances of generation times across conditions, which provides a general constraint on maximal growth rates. Together, these results demonstrate a fundamental benefit of noise for population growth, and identify a growth law that sets a “speed limit” for proliferation.

scholarly journals Bioremediation of Diazinon, Pirimicarb and Atrazine Pesticides Using Bacterial Species

2021 ◽  
pp. 14-20
Keyword(s):  
Growth Rate ◽  
Bacterial Growth ◽  
Bacterial Species ◽  
Serial Dilution ◽  
Bacterial Cells ◽  
E Coli ◽  
Highly Efficient

Bacterial species such as E.coli, S. aureus and Sa. bongori were isolated from soil by using serial dilution. Bioremediation results showed the S. aureus was highly efficient on Diazinon removal by 62%, 63.2% and 68.6%, Pirimicarb removal was 44%, 52.4% and 53.8%, and Atrazine removal was 61%, 65.6% and 70.6%. and the efficiency of E.coli removal on Diazinon was 59%, 60.8% and 63.8%; on Pirimicarb was 44%, 52.4% and 53.8%; and for Atrazine 57%, 60.8% and 64.4%. Sa. bongori efficiency on Diazinon was 49%, 51.2% and 55.8%; on Pirimicarb removal was 61%, 63.2% and 68.4%; Also, in Atrazine removal 48%, 50.4% and 57.2%. When comparing the growth rate of bacterial cells. The bacterial cells before treatment with S. aureus was 22.01×10^4, Results after treatment showed Diazinon of 35.58×10^4. The Pirimicarb 32.41×10^4 and Atrazine was 38.45 ×10^4. Either E. coli Its bacterial growth was before treatment 17.09×10^4 To show the results of growth on diazinon 30.43×10^4, Pirimicarb 27.71×10^4 and Atrazine 24.34 ×10^4. While the growth was in Sa. bongori Before treatment 10.09×10^4 While recorded a growth rate on Diazinon 18.82×10^4, Pirimicarb 19.98×10^4 and Atrazine 17.08 ×10^4.These bacterial species efficiencies on bioremediation of these three pesticides proved to be promising It can be used safely in the process of removing pesticides, yet more research on safety, mechanisms and kinetics needs to be further investigated.

scholarly journals Response of single bacterial cells to stress gives rise to complex history dependence at the population level

2016 ◽  
Vol 113 (15) ◽  
pp. 4224-4229 ◽  
Author(s):  
Roland Mathis ◽  
Martin Ackermann
Keyword(s):  
Cell Cycle ◽  
Sodium Chloride ◽  
Caulobacter Crescentus ◽  
Single Cells ◽  
Population Level ◽  
Time Interval ◽  
Bacterial Cells ◽  
History Dependence ◽  
Cell Cycle Synchronization ◽  
Subsequent Exposure

Most bacteria live in ever-changing environments where periods of stress are common. One fundamental question is whether individual bacterial cells have an increased tolerance to stress if they recently have been exposed to lower levels of the same stressor. To address this question, we worked with the bacteriumCaulobacter crescentusand asked whether exposure to a moderate concentration of sodium chloride would affect survival during later exposure to a higher concentration. We found that the effects measured at the population level depended in a surprising and complex way on the time interval between the two exposure events: The effect of the first exposure on survival of the second exposure was positive for some time intervals but negative for others. We hypothesized that the complex pattern of history dependence at the population level was a consequence of the responses of individual cells to sodium chloride that we observed: (i) exposure to moderate concentrations of sodium chloride caused delays in cell division and led to cell-cycle synchronization, and (ii) whether a bacterium would survive subsequent exposure to higher concentrations was dependent on the cell-cycle state. Using computational modeling, we demonstrated that indeed the combination of these two effects could explain the complex patterns of history dependence observed at the population level. Our insight into how the behavior of single cells scales up to processes at the population level provides a perspective on how organisms operate in dynamic environments with fluctuating stress exposure.

scholarly journals Noise propagation in an integrated model of bacterial gene expression and growth

2018 ◽  
Author(s):  
Istvan T. Kleijn ◽  
Laurens H. J. Krah ◽  
Rutger Hermsen
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Integrated Approach ◽  
Control Analysis ◽  
Instantaneous Growth Rate ◽  
Bacterial Cells ◽  
Noise Propagation ◽  
Bacterial Gene ◽  
Trade Offs ◽  
Cross Correlations

AbstractIn bacterial cells, gene expression, metabolism, and growth are highly interdependent and tightly coordinated. As a result, stochastic fluctuations in expression levels and instantaneous growth rate show intricate cross-correlations. These correlations are shaped by feedback loops, trade-offs and constraints acting at the cellular level; therefore a quantitative understanding requires an integrated approach. To that end, we here present a mathematical model describing a cell that contains multiple proteins that are each expressed stochastically and jointly limit the growth rate. Conversely, metabolism and growth affect protein synthesis and dilution. Thus, expression noise originating in one gene propagates to metabolism, growth, and the expression of all other genes. Nevertheless, under a small-noise approximation many statistical quantities can be calculated analytically. We identify several routes of noise propagation, illustrate their origins and scaling, and establish important connections between noise propagation and the field of metabolic control analysis. We then present a many-protein model containing > 1000 proteins parameterized by previously measured abundance data and demonstrate that the predicted cross-correlations between gene expression and growth rate are in broad agreement with published measurements.

scholarly journals Comparative activity of Ceftriaxone, Ciprofloxacin and Gentamicin as a function of bacterial growth rate probed by Escherichia coli chromosome replication in the mouse peritonitis model

2018 ◽  
Author(s):  
Maria Schei Haugan ◽  
Anders Løbner-Olesen ◽  
Niels Frimodt-Møller
Keyword(s):  
Growth Rate ◽  
Bacterial Growth ◽  
Growth Dynamics ◽  
Chromosome Replication ◽  
Bacterial Cells ◽  
Bacterial Growth Rate ◽  
Pre Treatment

AbstractCommonly used antibiotics exert their effect predominantly on rapidly growing bacterial cells, yet growth dynamics taking place during infection in a complex host environment remain largely unknown. Hence, means to measure in situ bacterial growth rate is essential to predict the outcome of antibacterial treatment. We have recently validated chromosome replication as readout for in situ bacterial growth rate during Escherichia coli infection in the mouse peritonitis model. By the use of two complementary methods (qPCR and fluorescence microscopy) for differential genome origin and terminus copy number quantification, we demonstrated the ability to track bacterial growth rate, both on a population average and on a single-cell level; from one single biological specimen. Here, we asked whether the in situ growth rate could predict antibiotic treatment effect during infection in the same model. Parallel in vitro growth experiments were conducted as proof-of-concept. Our data demonstrate that the activity of commonly used antibiotics Ceftriaxone and Gentamicin correlated with pre-treatment bacterial growth rate; both drugs performing better during rapid growth than during slow growth. Conversely, Ciprofloxacin was less sensitive to bacterial growth rate, both in a homogenous in vitro bacterial population and in a more heterogeneous in vivo bacterial population. The method serves as a platform to test any antibiotic’s dependency upon active in situ bacterial growth. Improved insight into this relationship in vivo could ultimately prove helpful in evaluating future antibacterial strategies.ImportanceMost antibiotics in clinical use exert their effect predominantly on rapidly growing bacterial cells, yet there is a lack of insight into bacterial growth dynamics taking place during infection in vivo. We have applied inexpensive and easily accessible methods for extraction of in situ bacterial growth rate from bacterial chromosome replication during experimental murine infection. This approach not only allows for a better understanding of bacterial growth dynamics taking place during the course of infection, but also serves as a platform to test the activity of different antibiotics as a function of pre-treatment in situ growth rate. The method has the advantage that bacterial growth rate can be probed from a single biological sample, with the potential for extension into clinical use in pre-treatment infected biological specimens. A better understanding of commonly used antibiotics’ level of dependency upon bacterial growth, combined with measurements of in situ bacterial growth rate in infected clinical specimens, could prove helpful in evaluating future antibacterial treatment regimens.

scholarly journals A Population-level Strain Genotyping Method to Study Pathogen Strain Dynamics in Human Infections

2021 ◽  
Author(s):  
Sarah J Morgan ◽  
Samantha L Durfey ◽  
Sumedha Ravishankar ◽  
Peter Jorth ◽  
Wendy Ni ◽  
...  
Keyword(s):  
Relative Abundance ◽  
Bacterial Infections ◽  
Ex Vivo ◽  
Bacterial Species ◽  
Pcr Amplification ◽  
Population Level ◽  
Bacterial Cells ◽  
Chronic Infections ◽  
Single Strain ◽  
Sequencing Errors

A hallmark of chronic bacterial infections is the long-term persistence of one or more pathogen species at the compromised site. Repeated detection of the same bacterial species can suggest that a single strain or lineage is continually present. However, infection with multiple strains of a given species, strain acquisition and loss, and changes in strain relative abundance can occur. Detecting strain-level changes and their effects on disease is challenging as most methods require labor intensive isolate-by-isolate analyses, thus, only a few cells from large infecting populations can be examined. Here we present a population-level method for enumerating and measuring the relative abundance of strains called PopMLST. The method exploits PCR amplification of strain-identifying polymorphic loci, next-generation sequencing to measure allelic variants, and informatic methods to determine whether variants arise from sequencing errors or low abundance strains. These features enable PopMLST to simultaneously interrogate hundreds of bacterial cells that are either cultured en masse from patient samples, or are present in DNA directly extracted from clinical specimens without ex vivo culture. This method could be used to detect epidemic or super-infecting strains, facilitate understanding of strain dynamics during chronic infections, and enable studies that link strain changes to clinical outcomes.

Oscillating conditions for influencing the composition of mixed biological cultures

1998 ◽  
Vol 37 (4-5) ◽  
pp. 259-262 ◽  
Author(s):  
Bjarne R. Horntvedt ◽  
Morten Rambekk ◽  
Rune Bakke
Keyword(s):  
Growth Rate ◽  
Specific Growth ◽  
Heterotrophic Bacteria ◽  
Bacterial Species ◽  
Rate Reduction ◽  
Similar Strategy ◽  
Sinusoidal Oscillations ◽  
Specific Growth Rates ◽  
Rate Limiting ◽  
Concentration Levels

This paper presents a strategy in which mixed biological cultures are exposed to oscillating concentration levels, to improve the potential for coexistence of desired bacterial species. A mechanistic mathematical model is constructed to investigate and illustrate this strategy. This paper is focused on competition between nitrifying, denitrifying and aerobic heterotrophic bacteria in a CSTR with sludge recycle. For nitrifying and aerobic heterotrophic cultures, the effect of sinusoidal oscillations in DO levels with an amplitude of 1.0 mg/l is a 16% specific growth rate reduction compared to that at a constant DO level. The denitrifiers growth rate is increased by an average of 59%, compared to the constant DO level situation. A similar strategy has been tested in a pilot plant. It is concluded that the influence on specific growth rates is a function of the amplitude of the oscillations. The effects are greatest when concentrations fluctuate around the half saturation concentration of the rate limiting component(s).

Modulation of Gut Microbiota through Dietary Phytochemicals as a Novel Anti-infective Strategy

2020 ◽  
Vol 17 (4) ◽  
pp. 498-506 ◽  
Author(s):  
Pavan K. Mujawdiya ◽  
Suman Kapur
Keyword(s):  
Microbial Community ◽  
Quorum Sensing ◽  
Population Density ◽  
Gut Microbiota ◽  
Biofilm Formation ◽  
Chemical Interaction ◽  
Bacterial Species ◽  
Critical Role ◽  
Bacterial Cells ◽  
Gut Microbes

: Quorum Sensing (QS) is a phenomenon in which bacterial cells communicate with each other with the help of several low molecular weight compounds. QS is largely dependent on population density, and it triggers when the concentration of quorum sensing molecules accumulate in the environment and crosses a particular threshold. Once a certain population density is achieved and the concentration of molecules crosses a threshold, the bacterial cells show a collective behavior in response to various chemical stimuli referred to as “auto-inducers”. The QS signaling is crucial for several phenotypic characteristics responsible for bacterial survival such as motility, virulence, and biofilm formation. Biofilm formation is also responsible for making bacterial cells resistant to antibiotics. : The human gut is home to trillions of bacterial cells collectively called “gut microbiota” or “gut microbes”. Gut microbes are a consortium of more than 15,000 bacterial species and play a very crucial role in several body functions such as metabolism, development and maturation of the immune system, and the synthesis of several essential vitamins. Due to its critical role in shaping human survival and its modulating impact on body metabolisms, the gut microbial community has been referred to as “the forgotten organ” by O`Hara et al. (2006) [1]. Several studies have demonstrated that chemical interaction between the members of bacterial cells in the gut is responsible for shaping the overall microbial community. : Recent advances in phytochemical research have generated a lot of interest in finding new, effective, and safer alternatives to modern chemical-based medicines. In the context of antimicrobial research various plant extracts have been identified with Quorum Sensing Inhibitory (QSI) activities among bacterial cells. This review focuses on the mechanism of quorum sensing and quorum sensing inhibitors isolated from natural sources.

scholarly journals Simplified Approach to Predict Food Safety through the Maximum Specific Bacterial Growth Rate as Function of Extrinsic and Intrinsic Parameters

2021 ◽  
Vol 5 (2) ◽  
pp. 22
Author(s):  
Pedro D. Gaspar ◽  
Joel Alves ◽  
Pedro Pinto
Keyword(s):  
Growth Rate ◽  
Food Safety ◽  
Information And Communication Technologies ◽  
Bacterial Growth ◽  
Low Cost ◽  
Extrinsic Factors ◽  
Bacterial Growth Rate ◽  
Modeling Tools ◽  
Simplified Approach ◽  
Food Safety And Quality

Currently, we assist the emergence of sensors and low-cost information and communication technologies applied to food products, in order to improve food safety and quality along the food chain. Thus, it is relevant to implement predictive mathematical modeling tools in order to predict changes in the food quality and allow decision-making for expiration dates. To perform that, the Baranyi and Roberts model and the online tool Combined Database for Predictive Microbiology (Combase) were used to determine the factors that define the growth of different bacteria. These factors applied to the equation that determines the maximum specific growth rate establish a relation between the bacterial growth and the intrinsic and extrinsic factors that define the bacteria environment. These models may be programmed in low-cost wireless biochemical sensor devices applied to packaging and food supply chains to promote food safety and quality through real time traceability.

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