The effect of macromolecular crowding on single-round transcription by<i>Escherichia coli</i>RNA polymerase

Chung, SangYoon; Lerner, Eitan; Jin, Yan; Kim, Soo-Hong; Alhadid, Yazan; Grimaud, Logan; Zhang, Irina X; Knobler, Charles M.; Gelbart, William M.; Weiss, Shimon

doi:10.1093/nar/gky1277

Cited by 35 publications

(31 citation statements)

References 54 publications

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“…Considering the low probe binding rate in the experiment, the start points of the transcription signal in Fig. 4c, d likely do not indicate the start of transcription, rather 54 the binding of the fastFISH probe after the transcription has already started. On the other hand, the Alexa 488 bleaching time (the 99% confidence level: 17.7 ± 12.4 s, Supplementary Fig.…”

Section: D-smartmentioning

confidence: 95%

“…The probe DNA concentration was kept low (200 pM) to reduce fluorescence background, resulting in a low transcription detection rate in these experiments (6 of 628 trajectories) since hybridization rate is only 1.2 × 10 −3 s −1 without the presence of glycerol. Moreover, the presence of glycerol will further decrease the hybridization kinetics 54,55 . Considering the low probe binding rate in the experiment, the start points of the transcription signal in Fig.…”

Section: D-smartmentioning

confidence: 99%

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Real-time 3D single molecule tracking

2020

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To date, single molecule studies have been reliant on tethering or confinement to achieve long duration and high temporal resolution measurements. Here, we present a 3D singlemolecule active real-time tracking method (3D-SMART) which is capable of locking on to single fluorophores in solution for minutes at a time with photon limited temporal resolution. As a demonstration, 3D-SMART is applied to actively track single Atto 647 N fluorophores in 90% glycerol solution with an average duration of~16 s at count rates of~10 kHz. Active feedback tracking is further applied to single proteins and nucleic acids, directly measuring the diffusion of various lengths (99 to 1385 bp) of single DNA molecules at rates up to 10 µm 2 /s. In addition, 3D-SMART is able to quantify the occupancy of single Spinach2 RNA aptamers and capture active transcription on single freely diffusing DNA. 3D-SMART represents a critical step towards the untethering of single molecule spectroscopy.

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Section: D-smartmentioning

confidence: 95%

Section: D-smartmentioning

confidence: 99%

Real-time 3D single molecule tracking

2020

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“…This volume exclusion affects various enzymatic reactions, either by increasing or decreasing their rates, depending on the change in size of the reactants [114][115][116][117]. While it has been shown that macromolecular crowding demonstrates mostly advantageous effects on cell metabolism [114,[117][118][119], it can also function as a constraint for cells which exhibit high metabolic rates [120]. Beg et al [121] developed a flux balance model of E. coli (FBAwMC), including a constraint for the enzyme concentrations, considering the macromolecular crowding.…”

Section: Short-term Uptake Dynamicsmentioning

confidence: 99%

Escherichia coli metabolism under short-term repetitive substrate dynamics: adaptation and trade-offs

2020

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Background: Microbial metabolism is highly dependent on the environmental conditions. Especially, the substrate concentration, as well as oxygen availability, determine the metabolic rates. In large-scale bioreactors, microorganisms encounter dynamic conditions in substrate and oxygen availability (mixing limitations), which influence their metabolism and subsequently their physiology. Earlier, single substrate pulse experiments were not able to explain the observed physiological changes generated under large-scale industrial fermentation conditions. Results: In this study we applied a repetitive feast-famine regime in an aerobic Escherichia coli culture in a timescale of seconds. The regime was applied for several generations, allowing cells to adapt to the (repetitive) dynamic environment. The observed response was highly reproducible over the cycles, indicating that cells were indeed fully adapted to the regime. We observed an increase of the specific substrate and oxygen consumption (average) rates during the feast-famine regime, compared to a steady-state (chemostat) reference environment. The increased rates at same (average) growth rate led to a reduced biomass yield (30% lower). Interestingly, this drop was not followed by increased by-product formation, pointing to the existence of energy-spilling reactions. During the feast-famine cycle, the cells rapidly increased their uptake rate. Within 10 s after the beginning of the feeding, the substrate uptake rate was higher (4.68 μmol/g CDW /s) than reported during batch growth (3.3 μmol/g CDW /s). The high uptake led to an accumulation of several intracellular metabolites, during the feast phase, accounting for up to 34% of the carbon supplied. Although the metabolite concentrations changed rapidly, the cellular energy charge remained unaffected, suggesting well-controlled balance between ATP producing and ATP consuming reactions. Conclusions: The adaptation of the physiology and metabolism of E. coli under substrate dynamics, representative for large-scale fermenters, revealed the existence of several cellular mechanisms coping with stress. Changes in the substrate uptake system, storage potential and energy-spilling processes resulted to be of great importance. These metabolic strategies consist a meaningful step to further tackle reduced microbial performance, observed under large-scale cultivations.

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“…This volume exclusion affects various enzymatic reactions, either by increasing or decreasing their rates, depending on the change in size of the reactants [124–127]. While it has been shown that macromolecular crowding demonstrates mostly advantageous effects on cell metabolism [124, 127–129], it can also function as a constraint for cells which exhibit high metabolic rates [130]. Beg QK , et al [131] developed a flux balance model of E.coli (FBAwMC), including a constraint for the enzyme concentrations, considering the macromolecular crowding.…”

Section: Discussionmentioning

confidence: 99%

Escherichia colimetabolism under short-term repetitive substrate dynamics: Adaptation and trade-offs

Vasilakou

Loosdrecht

Wahl

2020

Preprint

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7Background 8 Microbial metabolism is highly dependent on the environmental conditions. Especially, the 9 substrate concentration, as well as oxygen availability, determine the metabolic rates. In large-10 scale bioreactors, microorganisms encounter dynamic conditions in substrate and oxygen 11 availability (mixing limitations), which influence their metabolism and subsequently their 12 physiology. Earlier, single substrate pulse experiments were not able to explain the observed 13 physiological changes generated under large-scale industrial fermentation conditions. 14 Results 15In this study we applied a repetitive feast-famine regime in an aerobic Escherichia coli culture 16 in a time-scale of seconds. The regime was applied for several generations, allowing cells to 17 adapt to the (repetitive) dynamic environment. The observed response was highly reproducible 18 over the cycles, indicating that cells were indeed fully adapted to the regime. We observed an 19 increase of the specific substrate and oxygen consumption (average) rates during the feast-20 famine regime, compared to a steady-state (chemostat) reference environment. The increased 21 rates at same (average) growth rate led to a reduced biomass yield (30% lower). Interestingly, 22 this drop was not followed by increased by-product formation, pointing to the existence of 23 energy-spilling reactions and/or less effective ATP synthesis. During the feast-famine cycle, 24 the cells rapidly increased their uptake rate. Within 10 seconds after the beginning of the 25 feeding, the substrate uptake rate was higher (4.68 μmol/gCDW/s) than reported during batch 26 growth (3.3 μmol/gCDW/s). The high uptake led to an accumulation of several intracellular 27 metabolites, during the feast phase, accounting for up to 34 % of the carbon supplied. Although 28 the metabolite concentrations changed rapidly, the cellular energy charge remained unaffected, 29 3 suggesting well-controlled balance between ATP producing and ATP consuming reactions. The 30 role of inorganic polyphosphate as an energy buffer is discussed. 31 Conclusions 32The adaptation of the physiology and metabolism of Escherichia coli under substrate dynamics, 33 representative for large-scale fermenters, revealed the existence of several cellular mechanisms 34 coping with stress. Changes in the substrate uptake system, storage potential and energy-spilling 35 processes resulted to be of great importance. These metabolic strategies consist a meaningful 36 step to further tackle reduced microbial performance, observed under large-scale cultivations. 37 38 Keywords 39 Escherichia coli; feast-famine; substrate dynamics; dynamic metabolic responses; energy 40 homeostasis 41 4 Introduction 42Microorganisms are widely used for the production of chemicals, ranging from small organic 43 acids to large proteins, including biopharmaceuticals, biochemicals and bulk biofuels [1][2][3]. In 44 order to meet the cost targets and demands, large-scale production cultivations are and will be 45 required [4]. ...

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The effect of macromolecular crowding on single-round transcription byEscherichia coliRNA polymerase

Cited by 35 publications

References 54 publications

Real-time 3D single molecule tracking

Real-time 3D single molecule tracking

Escherichia coli metabolism under short-term repetitive substrate dynamics: adaptation and trade-offs

Escherichia colimetabolism under short-term repetitive substrate dynamics: Adaptation and trade-offs

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