Physiological effects of pH gradients on Escherichia coli during plasmid DNA production.

ABSTRACT

A two-compartment scale-down system was used to mimic pH heterogeneities that can occur in large-scale bioreactors. The system consisted of two interconnected stirred tank reactors (STRs) where one of them represented the conditions of the bulk of the fluid and the second one the zone of alkali addition for pH control. The working volumes ratio of the STRs was set to 201 in order to simulate the relative sizes of the bulk and alkali addition zones, respectively, in large-scale bioreactors. Residence times (tR ) in the alkali addition STR of 60, 120, 180, and 240 s were simulated during batch cultures of an engineered Escherichia coli strain that produced plasmid DNA (pDNA). pH gradients of up to 0.9 units, between the two compartments, were attained. The kinetic, stoichiometric, and pDNA topological changes due to the pH gradients were studied and compared to cultures at constant pH of 7.2 and 8.0. As the tR increased, the pDNA and biomass yields, as well as pDNA final titer decreased, whereas the accumulation of organic acids increased. Furthermore, the transcriptional response of 10 selected genes to alkaline stress (pH 8.0) and pH gradients was monitored at different stages of the cultures. The selected genes coded for ion transporters, amino acids catabolism enzymes, and transcriptional regulators. The transcriptional response of genes coding for amino acids catabolism, in terms of relative transcription level and stage of maximal expression, was different when the alkaline stress was constant or transient. This suggests the activation of different mechanisms by E. coli to cope with pH fluctuations compared to constant alkaline pH. Moreover, the transcriptional response of genes related to negative control of DNA synthesis did not correlate with the lower pDNA yields. This is the first study that reports the effects of pH gradients on pDNA production by E. coli cultures. The information presented can be useful for the design of better bioreactor scale-up strategies.