Microbial metabolic production is an attractive alternative to traditional chemical synthesis for a wide array of commercially relevant molecules. Coaxing microbes to produce a target chemical efficiently often requires substantial modification of host cell metabolism, which necessitates searching a vast genetic space of enzyme genes and expression levels. Millions of pathway designs can now be built, but identifying the most productive cells remains low throughput. The ability to detect and report on the presence of any arbitrary target molecule within individual cells would transform the field of metabolic engineering.
To this end, we developed strains of E. coli that survive an antibiotic challenge only in the presence of a specific small molecule, by regulating resistance gene expression via transcription factors responsive to sugars, alkanes, macrolides, flavonoids, vitamins or other molecules. Using two of these whole cell biosensors, responsive to glucaric acid or naringenin, we evolved respective biosynthetic pathways for each compound toward higher production. We used oligonucleotide-mediated genomic editing to simultaneously target up to 20 enzyme genes for expression modulation or knockout, creating billions of unique strains. Demonstrating the first example of iterative, whole-pathway engineering via a metabolite biosensor, we discovered E. coli strains that had increased production of naringenin by 36 times, or glucaric acid by 22 times.
However, for many target molecules, especially those that are synthetic, no natural biosensor may exist. We developed a platform to engineer natural allosteric transcription factors with specificity to new inducer molecules. We computationally design for binding, synthesize and clone in multiplex thousands of specified sequences, and use a bidirectional screen to identify new responsive variants that retain allostery. We demonstrate by generating E. coli LacI variants responsive to gentiobiose, fucose, lactitol and sucralose. We uncovered significant plasticity in the ligand recognition of LacI, which may be a hallmark of allosteric transcription factors. Our method relies only on protein structure and operator DNA sequence, making it applicable to many other proteins.
These methods together advance the ability to engineer microbial biosynthesis of any target molecule using evolution. Additionally, designer transcription factors can enable broad applications from dynamic metabolic control to cell biology. / Medical Sciences
| Identifer | oai:union.ndltd.org:harvard.edu/oai:dash.harvard.edu:1/26718755 |
| Date | 01 March 2017 |
| Creators | Taylor, Noah David |
| Publisher | Harvard University |
| Source Sets | Harvard University |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation, text |
| Format | application/pdf |
| Rights | open |
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