Single-molecule investigations of the stringent response machinery in living bacterial cells

English, Brian P.; Hauryliuk, Vasili; Sanamrad, Arash; Tankov, Stoyan; Dekker, Nynke H.; Elf, Johan

doi:10.1073/pnas.1102255108

Cited by 262 publications

(327 citation statements)

References 59 publications

Supporting

Mentioning

298

Contrasting

Unclassified

Order By: Relevance

“…Stringent response has been observed during a lag phase in glucoselactose diauxie of Escherichia coli (11) and carbon starvation in Staphylococcus aureus (12). The stringent response factor RelA produces the phosphorylated purine-derived alarmones (p) ppGpp in response to the presence of uncharged tRNA molecules (13)(14)(15). Stringent response depending on RelA has also been observed in L. lactis (16).…”

Section: Resultsmentioning

confidence: 86%

Bet-hedging during bacterial diauxic shift

Solopova

Gestel

Weissing

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

237

245

View full text Add to dashboard Cite

When bacteria grow in a medium with two sugars, they first use the preferred sugar and only then start metabolizing the second one. After the first exponential growth phase, a short lag phase of nongrowth is observed, a period called the diauxie lag phase. It is commonly seen as a phase in which the bacteria prepare themselves to use the second sugar. Here we reveal that, in contrast to the established concept of metabolic adaptation in the lag phase, two stable cell types with alternative metabolic strategies emerge and coexist in a culture of the bacterium Lactococcus lactis. Only one of them continues to grow. The fraction of each metabolic phenotype depends on the level of catabolite repression and the metabolic state-dependent induction of stringent response, as well as on epigenetic cues. Furthermore, we show that the production of alternative metabolic phenotypes potentially entails a bet-hedging strategy. This study sheds new light on phenotypic heterogeneity during various lag phases occurring in microbiology and biotechnology and adjusts the generally accepted explanation of enzymatic adaptation proposed by Monod and shared by scientists for more than half a century.phenotypic heterogeneity | Gram-positive bacteria | metabolic fitness I n nature, bacteria are confronted with a wide range of environmental conditions that change over time. These conditions often elicit specific metabolic responses that increase the division rate of a cell. For example, when bacterial cells are exposed to multiple sugars, they do not metabolize all sugars simultaneously, but rather use the sugar that allows the highest celldivision rate. Cells switch to the less-preferred sugar when the most-preferred one (in many cases glucose) is depleted. Jacques Monod coined this phenomenon "diauxie" (1). Diauxie is characterized by two growth cycles-the first one on the preferred sugar, followed by a second one on the less-preferred sugar. Both are separated by a short period during which the population apparently does not grow. This period is known as the diauxie lag phase. It is typically assumed that cells need time to make the necessary enzymatic adaptations to switch from one substrate to another (2). However, the behavior of individual cells during the lag phase has not been studied in detail. Here, we examine the diauxic shift at the single-cell level in Lactococcus lactis using time-lapse microscopy, in addition to the traditional approach of studying population growth characteristics. Surprisingly, the lag phase at the switch from glucose to cellobiose consumption by L. lactis largely results from the heterogeneous response of cells to the environmental change, rather than the time it takes for cells to make the necessary metabolic adaptations. This result challenges Monod's view of enzymatic adaptation and asks for a revision on the interpretation of population lag phases.

show abstract

Section: Resultsmentioning

confidence: 86%

Bet-hedging during bacterial diauxic shift

Solopova

Gestel

Weissing

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

237

245

View full text Add to dashboard Cite

show abstract

“…Especially for smaller tracers, which may cover longer distances within the measurement time, or for techniques allowing for full maps of local diffusivities, it turns out that the diffusion coefficient becomes a function of the local tracer position. For both eukaryotic [8] and prokaryotic [9] cells such local diffusivity maps indeed show significant variations. The motion of tracer particles through space may also be impeded by caging effects when the size of the particle is comparable to the local mesh size in structured environments [10,11].…”

Section: Introductionmentioning

confidence: 95%

Ergodicity breaking and particle spreading in noisy heterogeneous diffusion processes

Cherstvy

Metzler

2015

The Journal of Chemical Physics

View full text Add to dashboard Cite

We study noisy heterogeneous diffusion processes with a position dependent diffusivity of the form D(x) ∼ D0|x| α in the presence of annealed and quenched disorder of the environment, corresponding to an effective variation of the exponent α in time and space. In the case of annealed disorder, for which effectively α = α(t) we show how the long time scaling of the ensemble mean squared displacement (MSD) and the amplitude variation of individual realizations of the time averaged MSD are affected by the disorder strength. For the case of quenched disorder, the long time behavior becomes effectively Brownian after a number of jumps between the domains of a stratified medium. In the latter situation the averages are taken over both an ensemble of particles and different realizations of the disorder. As physical observables we analyze in detail the ensemble and time averaged MSDs, the ergodicity breaking parameter, and higher order moments of the time averages.

show abstract

“…English et al [28] employed a photoconvertible GFP variant, mEos2, as a reference to study RelA, a ribosome-dependent ( p)ppGpp synthetase, and ribosomes. They obtained an apparent diffusion coefficient for mEos2 of 8-16 mm 2 s -1 depending on the location within the cell.…”

Section: (C) the Cell Cytoplasmmentioning

confidence: 99%

Single-molecule imaging in live bacteria cells

Ritchie

Lill

Sood

et al. 2013

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

Bacteria, such as Escherichia coli and Caulobacter crescentus , are the most studied and perhaps best-understood organisms in biology. The advances in understanding of living systems gained from these organisms are immense. Application of single-molecule techniques in bacteria have presented unique difficulties owing to their small size and highly curved form. The aim of this review is to show advances made in single-molecule imaging in bacteria over the past 10 years, and to look to the future where the combination of implementing such high-precision techniques in well-characterized and controllable model systems such as E. coli could lead to a greater understanding of fundamental biological questions inaccessible through classic ensemble methods.

show abstract

Single-molecule investigations of the stringent response machinery in living bacterial cells

Cited by 262 publications

References 59 publications

Bet-hedging during bacterial diauxic shift

Bet-hedging during bacterial diauxic shift

Ergodicity breaking and particle spreading in noisy heterogeneous diffusion processes

Single-molecule imaging in live bacteria cells

Contact Info

Product

Resources

About