Random Gene Knockout to Maximize Product Yield

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The Experiment

The Problem

Researchers Hal Alper, Kohei Miyaoku and Greg Stephanopoulos have recently demonstrated the effectiveness of using directed evolution to optimize a synthetic construct. Previously, the researchers engineered a strain of E. coli to synthesize lycopene, a carotenoid that naturally occurs in tomatoes. Lycopene is an antioxidant that has recently been incorporated into vitamin tablets, creating a commercial pressure to increase the efficiency and yield of lycopene synthesis.

Computer Modeling to Increase Product Yield

To further optimize lycopene production by the E. coli strain, the authors used computer modling to identify a series of gene knockouts sites that were predicted to increase lycopene production. However, the strains engineered to contain these knockouts were still unable to produced lycopene at the predicted “stoichiometric maximum.” This finding led the researchers to hypothesize that lycopene production was “limited by unknown kinetic or regulatory factors unaccounted for in the stoichiometric models.”

Directed Evolution: Random Gene Knockout

The researchers used directed evolution to find other gene knockouts that might affect these unexplored regions of the cell. A library of lycopene-producing E. coli with random gene knockout was achieved by introducing genome-wide integrating transposons to the cells in vivo. This random knockout library was then plated and tested for lycopene production. In this assay, lycopene production was proportional to the red color of bacterial colonies. The three best knockouts selected by this screening were there molecularly characterized. All three knockout sites were differed that had been predicted to be beneficial using their models. Two of these knockouts interrupted genes which had been previously undescribed. Thus, by using a random strategy, they discovered new genes whose expression affected the production of the desired product.

Computer Modeling Versus Random Gene Knockout

The team went on to determine the relative effectiveness of the three gene knockouts at increasing lycopene production when compared to the knockouts they had predicting with computer modeling. To answer this question, the researchers created 64 unique strains of the lycopene-producing E. coli representing all possible combinations of the three knockouts selected by directed evolution, the seven model-predicted knockouts, and the two parental strains from which the “evolved” and model-predicted strains were derived. Lycopene production of the 64 strains was measured by extracting the lycopene from the each colony after a defined interval of time and quantifying lycopene level by absorbance spectroscopy at 475. Of the two global maxima of this experiment, one had a knockout selected through directed evolution testing (Fig. 1a). Furthermore, this particular knockout strain also showed an earlier peak in lycopene production when compared to the completely systematically-predicted knockout strain in batch fed culture (Fig. 1b).


(Alper, 2005 - Permission Pending)

Figure 1: The two measurements lycopene production in knockout strains of lycopene producing bacteria. (a) A landscape displaying the 64 strains resulting for all possible combinations of gene knockouts selected through systematic modeling and directed evolution (combinatorial knockouts). Lycopene production for each strain was measured at the end of a 48-h shake-flask fermentation and amount of lycopene produced was quantified through extraction form the cell pellet with acetone and supernatant absorbance at 475 nm. Of interest is global maximum strain ΔgdhA ΔaceE ΔPyjiD, which contains a knockout of the ΔPyjiD gene selected through directed evolution testing. (b) Lycopene production of the best knockout strains in batch-fed culture. From left to right, the K12 strain from which combinatorial mutants were derived, the preengineered parental strain from which the systematically-selected knockout strains were derived, global maximum strain ΔgdhA ΔaceE ΔfdhF, global maximum strain ΔgdhA ΔaceE ΔPyjiD, and the global minimum strain and two local maximum strains from landscape 1a. Of interest is knockout strain ΔgdhA ΔaceE ΔPyjiD (fourth from the left). This strain, which has a gene knockout selected through directed evolution testing, shows the same maximum in lycopene productivity as the entirely systematically-predicted strain ΔgdhA ΔaceE ΔfdhF (third from left), but also shows an earlier peak in this productivity and more sustained lycopene production.

Advantages of the Method

  1. The researchers were able to improve an existing phenotype using a method of directed evolution which tested changes across the entire genome rather than in simply one gene.
  2. Unbiased selection found three genes to improve the studied phenotype which had not been predicted to optimize the lycopene production pathway in systematic modeling. Furthermore, two of these genes were previously undescribed.
  3. The gene knockouts selected through directed evolution in this experiment worked in concert with knockouts selected through systematic modeling to improve lycopene production in one of the best strains. This fact indicates the method is adaptable to use with other synthetically-engineered cells.

Disadvantages of the Method

  1. In this experiment, random transposon intergration could only test how gene knockouts increased lycopene production. The method of directed evolution described in this paper cannot test changes in gene expression levels or the genetic code itself to optimize phenotypes.
  2. While some randomly-selected knockouts were able to work with systematic knockouts to increase lycopene yield, others did not increase (and sometimes decreased) lycopene yields compared to the wild-type (Fig. 1a). Combining random knockouts and systematically predicted knockouts does not seem to have a 100% success rate.


Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets. Nature Biotechnology 23(5): 612-616.