Abstract
Synthetic biology aims to design and construct bacterial genomes harboring the minimum number of genes required for self-replicable life. However, the genome-reduced bacteria often show impaired growth under laboratory conditions that cannot be understood based on the removed genes. The unexpected phenotypes highlight our limited understanding of bacterial genomes. Here, we deploy adaptive laboratory evolution (ALE) to re-optimize growth performance of a genome-reduced strain. The basis for suboptimal growth is the imbalanced metabolism that is rewired during ALE. The metabolic rewiring is globally orchestrated by mutations in rpoD altering promoter binding of RNA polymerase. Lastly, the evolved strain has no translational buffering capacity, enabling effective translation of abundant mRNAs. Multi-omic analysis of the evolved strain reveals transcriptome- and translatome-wide remodeling that orchestrate metabolism and growth. These results reveal that failure of prediction may not be associated with understanding individual genes, but rather from insufficient understanding of the strain’s systems biology.
Highlights
After 807 generations of adaptive laboratory evolution (ALE), we isolated a clone from the evolved population, eMS57, which restored final cell density and growth rate to levels comparable to MG1655 in M9 minimal medium without any nutrient supplementation (Fig. 1d)
We found that the suboptimal metabolic state of MG56 was fixed during ALE through transcriptome reprogramming by RNA polymerase mutation
The mutation was located in the α-C-terminal domain, which is involved in the interaction with CRP and upstream elements of the promoter[39]
Summary
Homologous lambda recombination was used to introduce point mutations in the MS56 genome[46]. The OE-PCR product of the kanamycin resistance gene cassette (amplified from pKD1346) and target gene (amplified from MS56 or eMS56) was introduced into MS56 harboring pKD4646 using electroporation as described above. Target genes were amplified with QuantStudio 3D Digital PCR System (Thermo) as follows. Each PCR reaction was set up total 20 μl reaction volume, comprised of 10 μl of 2× QuantStudio 3D Digital PCR Master Mix (Thermo), 0.5 μl of 40× TaqMan Probe/Primer Mix (8 μM each of reporters and 36 μM each of primers, Supplementary Table 2), and 1 μl of diluted gDNA. The image of the chip was read using a QuantStudio 3D Digital PCR instrument (Thermo) and analyzed by QuantStudio 3D AnalysisSuite Cloud Software (Thermo, https://apps.thermofisher.com/quantstudio3d/). Raw data were extracted and visualized in Microsoft Excel 2010 (Microsoft) and Adobe Illustrator CS6 (Adobe) without modifying data integrity
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