Book/Dissertation / PhD Thesis

FZJ-2016-03327

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Development and application of single cell biosensors for the improvement of amino acid production in Escherichia coli and Corynebacterium glutamicum

Mahr, R. I. (Corresponding author)FZJ*

2016
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag Jülich
ISBN: 978-3-95806-145-3

Please use a persistent id in citations: http://hdl.handle.net/2128/11602  urn:nbn:de:0001-2016072914

Abstract: In the last decade, the application of genetically-encoded biosensors proved successful to establish novel and elaborated strategies for engineering microbial cell factories by enlarging the repertoire of metabolic engineering tools and by enabling unprecedented insights into bioprocesses at single-cell resolution. Especially, biosensors based on bacterial transcriptional regulators translating intracellular metabolite concentration into a measureable output proved to be of high value for a variety of metabolic engineering approaches. Although nature provides a plethora of transcriptional regulators to sense intrinsic and extrinsic stimuli, only a few regulators and their respective target promoters have been well characterized to date. This hampers the prompt decision for suitable sensor candidates. To this end, an elaborated FACS (fluorescence-activated cell sorting)-based strategy was developed for the rapid identification of effector-responsive promoters as suitable parts for biosensor design. Basically, a library of $\textit{Escherichia coli}$ promoter-auto-fluorescent protein fusions was screened by toggled rounds of positive and negative selection. This approach led to the isolation of the Lphenylalanine-responsive $\textit{mtr}$ promoter. The construction of different biosensors based on the $\textit{mtr}$ promoter revealed a significant influence of the sensor’s architecture on the dynamic range and the sensitivity towards effector molecules. Additionally, the $\textit{mtr}$ biosensor was successfully applied to screen a mutant library of $\textit{E. coli}$ cells for cells with increased L-phenylalanine productivity. Adaptive laboratory evolution (ALE) has widely been applied to adapt microbes to environmental stress or to improve metabolite production. So far, however, the strategy was only applicable to fitness-linked phenotypes. To this end, we established biosensor-driven adaptive laboratory evolution to evolve inconspicuous product formation. Sensor cells with the highest fluorescent output and hence, increased metabolite production, were iteratively isolated by FACS and re-cultivated. This strategy was successfully applied to the pyruvate-dehydrogenase deficient L-valine producer strain $\textit{Corynebacterium glutamicum ΔaceE}$ using the Lrp biosensor, which was developed for the detection of branched-chain amino acids and methionine. Evolved clones featured about 25% increased production and 3-4-fold reduced by-product formation. By genome sequencing and the subsequent evaluation of single mutations in the cured $\textit{ΔaceE}$ background, decreased L-alanine production was attributed to a mutation in the global regulator GlxR. Interestingly, a loss-of-function mutation in the urease accessory protein UreD resulted in about 100% increased L-valine formation in CGXII minimal medium. Further studies demonstrated that urea as part of the cultivation medium imposes a central bottleneck for efficient L-valine production: Urea degradation increases the pH by ammonia release, thereby interfering with growth and L-valine production. Likewise, carbon dioxide formation stimulates anaplerosis leading to a reduced pyruvate pool – the precursor for L-valine production. Altogether, these studies emphasize biosensors as valuable and versatile tools to improve metabolic cell factories with an enormous potential for future applications.

Note: Biotechnologie 1
Note: Dissertation, Universität Düsseldorf, 2016

---

Contributing Institute(s):

1. Biotechnologie (IBG-1)

Research Program(s):

1. 581 - Biotechnology (POF3-581) (POF3-581)

Appears in the scientific report 2016

---

Database coverage:
;

---