Transcriptional response ofEscherichia colito ammonia and glucose fluctuations

Abstract

SummaryIn large‐scale production processes, metabolic control is typically achieved by limited supply of essential nutrients such as glucose or ammonia. With increasing bioreactor dimensions, microbial producers such as Escherichia coli are exposed to changing substrate availabilities due to limited mixing. In turn, cells sense and respond to these dynamic conditions leading to frequent activation of their regulatory programmes. Previously, we characterized short‐ and long‐term strategies of cells to adapt to glucose fluctuations. Here, we focused on fluctuating ammonia supply while studying a continuously running two‐compartment bioreactor system comprising a stirred tank reactor (STR) and a plug‐flow reactor (PFR). The alarmone ppGpp rapidly accumulated in PFR, initiating considerable transcriptional responses after 70 s. About 400 genes were repeatedly switched on/off when E. coli returned to the STR. E. coli revealed highly diverging long‐term transcriptional responses in ammonia compared to glucose fluctuations. In contrast, the induction of stringent regulation was a common feature of both short‐term responses. Cellular ATP demands for coping with fluctuating ammonia supply were found to increase maintenance by 15%. The identification of genes contributing to the increased ATP demand together with the elucidation of regulatory mechanisms may help to create robust cells and processes for large‐scale application.

Modelling concentration gradients in fed‐batch cultivations of E. coli – towards the flexible design of scale‐down experiments

“…The duration, frequency and magnitude of concentration gradients in an actual large‐scale bioreactor are all dynamic parameters . In effect, the exposure time in the scale‐down bioreactor should be a flexible parameter that can be changed easily, to suit a specific large‐scale bioreactor . In the current contribution, this flexibility of scale‐down design is offered by the modelling framework of the pulse‐based system.…”

Comparative performance of different scale‐down simulators of substrate gradients in Penicillium chrysogenum cultures: the need of a biological systems response analysis

“…Metabolic flux analysis, using a previously established stoichiometric model for growth and penicillin production in P. chrysogenum (van Gulik et al, 2000), shows that there is a 34% and 16% larger ATP gap in the IFRs of 6 min and 3 min, respectively, but in 30 s IFR, the ATP gap is nearly the same as in the chemostat cultures. Similarly, in E. coli, it has been found that the cellular energy demands for coping with fluctuating carbon or nitrogen source supply become higher under simulated large-scale conditions, due to continuous on-off switching of related genes and the increased cellular ATP costs of transcription and translation (Loffler et al, 2016Simen et al, 2017).…”

Repetitive Short-Term Stimuli Imposed in Poor Mixing Zones Induce Long-Term Adaptation of E. coli Cultures in Large-Scale Bioreactors: Experimental Evidence and Mathematical Model

“…Among them, the frequent activation of the tryptophan operon could be observed (Simen et al, 2017). The mathematical model comprising the (Equations 2–16) was used to describe not only short- and long-term transcriptional dynamics but also to estimate the impact on protein formation by linking the transcription with the translation machinery.…”

“…Transcript measurements are published in Simen et al (2017) and are available under GEO Accession GSE90743.…”