Transcriptional analysis and adaptive evolution of Escherichia coli strains growing on acetate

Abstract

Eighteen strains of Escherichia coli were compared for maximum specific growth rate (μ MAX) on 85 mM acetate as the sole carbon source. The C strain ATCC8739 had the greatest growth rate (0.41 h(-1)) while SCS-1 had the slowest growth rate (0.15 h(-1)). Transcriptional analysis of three of the strains (ATCC8739, BL21, SMS-3-5) was conducted to elucidate why ATCC8739 had the greatest maximum growth rate. Seventy-one genes were upregulated 2-fold or greater in ATCC8739, while 128 genes were downregulated 2-fold or greater in ATCC8739 compared to BL21 and SMS-3-5. To generate a strain that could grow more quickly on acetate, ATCC8739 was cultured in a chemostat using a progressively increasing dilution rate. When the dilution rate reached 0.50 h(-1), three isolated colonies each grew faster than ATCC8739 on 85 mM acetate, with MEC136 growing the fastest with a growth rate of 0.51 h(-1), about 25 % greater than ATCC8739. Transcriptional analysis of MEC136 showed that eight genes were downregulated 2-fold or greater and one gene was upregulated 2-fold or greater compared to ATCC8739. Genomic sequencing revealed that MEC136 contained a single mutation, causing a serine to proline change in amino acid 266 of RpoA, the α subunit of the RNA polymerase core enzyme. The 260-270 amino acid region of RpoA has been shown to be a key region of the protein that affects the interaction of the α subunit of the RNA polymerase core enzyme with several global transcriptional activators, such as CRP and FNR.

The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

“…The most ubiquitous organism studied to improve uptake is the yeast Saccharomyces cerevisiae (Cadière et al, 2011;Garcia Sanchez et al, 2010;Guimarães et al, 2008;Kim et al, 2012;Smith et al, 2014;Sonderegger and Sauer, 2003;Zha et al, 2014;Zhang et al, 2018;Zhou et al, 2012), with the CEN.PK strain and its derivatives the most common (de Kok et al, 2012;Ho et al, 2017;Jansen et al, 2004;Klimacek et al, 2014;Kuyper et al, 2005Kuyper et al, , 2004Marques et al, 2017;Merico et al, 2011;Novy et al, 2014;Ochoa-Estopier et al, 2011;Papapetridis et al, 2018;Scalcinati et al, 2012;Strucko et al, 2018;van Rossum et al, 2016), mainly due to its widespread use in industry, robust fermentative capability, and inherent ethanol tolerance. There has been considerable interest in generating microbial strains that can utilize the fermentable sugars in lignocellulosic biomass (Clark et al, 2012;Sanderson, 2011); S. cerevisiae has again been the driving force in discovery to this end (Klimacek et al, 2014;Marques et al, 2017;Zha et al, 2014), with some work done in E. coli strains (Lee and Palsson, 2010;Rajaraman et al, 2016;Sandberg et al, 2017Sandberg et al, , 2016Utrilla et al, 2012) and other various bacteria and yeast (Cordova et al, 2016;Latif et al, 201...…”

“…ALE has been used to tackle challenges in the utilization of plant biomass as a nutrient source, such as the presence of toxic or inhibitory byproducts like furfural and acetate (Bellissimi et al, 2009;Heer and Sauer, 2008). A study by Rajaraman et al addressed this with a chemostat ALE on several E. coli strains with acetate as the sole carbon source (Rajaraman et al, 2016). Evolved strains had a specific growth rate increase of ~25% and significantly altered expression for a number of genes, enabled by a single amino acid substitution in the RNA polymerase complex subunit RpoA;…”

An antiphage Escherichia coli mutant for higher production of L‐threonine obtained by atmospheric and room temperature plasma mutagenesis

“…Enhancement of NADPH availability with higher metabolic flux of the PP pathway effectively increased amino acid synthesis, with higher production of L‐threonine by E. coli TRFC‐AP through improvement of NADPH supply 11 . Less flux entering the EMP pathway reduced the flux of lactate and acetate synthesis, which resulted in reduced lactate and acetate accumulation, decreasing inhibition of threonine synthesis and increasing the effective availability of glucose for threonine formation 38 . Higher production of L‐threonine and higher glucose conversion percentage were obtained with E. coli TRFC‐AP.…”

Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

“…Furthermore, if a strain's fitness has been severely disrupted by a designed change, ALE can identify mutations that rebalance the cell's homeostasis 4,[7][8][9][10][11] . ALE can additionally serve to harden strains against industrial conditions 3,4,12,13 and improve their utilization of secondary or non-native substrates 4,[14][15][16][17][18][19] .…”