No abstract

The advent and growth of synthetic biology has demonstrated its potential as a promising avenue of research to address many societal needs. However, plant synthetic biology efforts have been hampered by a dearth of DNA part libraries, versatile transformation vectors and efficient assembly strategies. Here, we describe a versatile system (named jStack) utilizing yeast homologous recombination to efficiently assemble DNA into plant transformation vectors. We demonstrate how this method can facilitate pathway engineering of molecules of pharmaceutical interest, production of potential biofuels and shuffling of disease-resistance traits between crop species. Our approach provides a powerful alternative to conventional strategies for stacking genes and traits to address many impending environmental and agricultural challenges.

Transformation of engineered Escherichia coli into a robust microbial factory is contingent on precise control of metabolism. Yet, the throughput of omics technologies used to characterize cell components has lagged far behind our ability to engineer novel strains. To expand the utility of quantitative proteomics for metabolic engineering, we validated and optimized targeted proteomics methods for over 400 proteins from more than 20 major pathways in E. coli metabolism. Complementing these methods, we constructed a series of synthetic genes to produce concatenated peptides (QconCAT) for absolute quantification of the proteins and made them available through the Addgene plasmid repository (www.addgene.org). To facilitate high sample throughput, we developed a fast, analytical-flow chromatography method using a 5.5-min gradient (10 min total run time). Overall this toolkit provides an invaluable resource for metabolic engineering by increasing sample throughput, minimizing development time and providing peptide standards for absolute quantification of E. coli proteins.