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Comment
. 2018 Dec 11;115(50):12547-12549.
doi: 10.1073/pnas.1818285115. Epub 2018 Nov 21.

Navigating the fitness landscape using multiallele genome editing

Neil T Miller  1 , Robert L Burnap  2
Affiliations
Comment

Navigating the fitness landscape using multiallele genome editing

Neil T Miller et al. Proc Natl Acad Sci U S A. .
No abstract available

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Adaptive landscape for the cyanobacterium Synechococcus 2973 (Syn2973, green spheres) and the nearly genetically identical, widely used experimental model Synechococcus 7942 (Syn7942, blue spheres). Syn2973 exhibits high fitness in Environment A, characterized by high light, but is not as well adapted to Environment B, characterized by low light. Syn7942 has the opposite fitness profile. Environmental factors other than light are likely involved in the fitness profiles of both. With only 55 single-nucleotide differences between the two genomes, a systematic genome editing study using CRISPR/Cpf1 was conducted to identify the loci by exchanging alleles in both directions. The number of critical differences was surprisingly small: Only five SNPs in three genes, atpA, ppnK, and rpaA, account for the large phenotypic differences. Interestingly, the introduction of the fast genes to Syn7942 + alleles 1,2,3 (orange spheres) showed considerable synergy shown in terms of the relative rate constants for exponential growth, μ (obtained from the doubling times in ref. 1), and this combination apparently renders the converted strain highly fit in Environment A.
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References

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