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Nat Biotechnol. 2014 Dec;32(12):1241-9. doi: 10.1038/nbt.3063. Epub 2014 Nov 24.

Functional optimization of gene clusters by combinatorial design and assembly.

Author information

1
Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
2
Electrical and Computer Engineering Department, Boston University, Boston, Massachusetts, USA.
3
1] Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. [2] Broad Technology Labs, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
4
Broad Technology Labs, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

Abstract

Large microbial gene clusters encode useful functions, including energy utilization and natural product biosynthesis, but genetic manipulation of such systems is slow, difficult and complicated by complex regulation. We exploit the modularity of a refactored Klebsiella oxytoca nitrogen fixation (nif) gene cluster (16 genes, 103 parts) to build genetic permutations that could not be achieved by starting from the wild-type cluster. Constraint-based combinatorial design and DNA assembly are used to build libraries of radically different cluster architectures by varying part choice, gene order, gene orientation and operon occupancy. We construct 84 variants of the nifUSVWZM operon, 145 variants of the nifHDKY operon, 155 variants of the nifHDKYENJ operon and 122 variants of the complete 16-gene pathway. The performance and behavior of these variants are characterized by nitrogenase assay and strand-specific RNA sequencing (RNA-seq), and the results are incorporated into subsequent design cycles. We have produced a fully synthetic cluster that recovers 57% of wild-type activity. Our approach allows the performance of genetic parts to be quantified simultaneously in hundreds of genetic contexts. This parallelized design-build-test-learn cycle, which can access previously unattainable regions of genetic space, should provide a useful, fast tool for genetic optimization and hypothesis testing.

PMID:
25419741
DOI:
10.1038/nbt.3063
[Indexed for MEDLINE]

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