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Plasmid‐encoded protein: The principal factor in the “metabolic burden” associated with recombinant bacteria
Author(s) -
Bentley William E.,
Mirjalili Noushin,
Andersen Dana C.,
Davis Robert H.,
Kompala Dhinakar S.
Publication year - 1990
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.260350704
Subject(s) - recombinant dna , plasmid , escherichia coli , bacteria , growth rate , biology , cole1 , chloramphenicol , yield (engineering) , bacterial growth , expression vector , biochemistry , gene , genetics , mathematics , geometry , materials science , metallurgy
Experimental elucidation of the metabolic load placed on bacteria by the expression of foreign protein is presented. The host/vector system is Escherichia coli RR1/pBR329 (amp r , cam r , and let r ). Plasmid content results, which indicate that the plasmid copy number monotonically increases with decreasing growth rate, are consistent with the literature on ColE1‐like plasmids. More significantly, we have experimentally quantified the reduction in growth rate brought about by the expression of chloramphenicol‐acetyl‐transferase (CAT) and β‐lactamase. Results indicate a nearly linear decrease in growth rate with increasing foreign protein content. Also, the change in growth rate due to foreign protein expression depends on the growth rate of the cells. The observed linear relationship is media independent and, to our knowledge, previously undocumented. Furthermore, the induction of CAT, mediated by the presence of chloramphenicol, is shown to occur only at low growth rates, which further increases the metabolic load. Results are vdelineated with the aid of a structured kinetic model representing the metabolism of recombinant E. coli. In this article, several previous hypotheses and model predictions are justified and validated. This work provides an important step in the development of comprehensive, methabolically‐structured, kinetic models capable of prediciting optimal conditions for maximizing product yield.