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Engineered microbes can be used for producing value-added chemicals from renewable feedstocks, relieving the dependency on nonrenewable resources such as petroleum. These microbes often are composed of synthetic metabolic pathways; however, one major problem in establishing a synthetic pathway is the challenge of precisely controlling competing metabolic routes, some of which could be crucial for fitness and survival. While traditional gene deletion and/or coarse overexpression approaches do not provide precise regulation, cis-repressors (CRs) are RNA-based regulatory elements that can control the production levels of a particular protein in a tunable manner. Here, we describe a protocol for a generally applicable fluorescence-activated cell sorting technique used to isolate eight subpopulations of CRs from a semidegenerate library in Escherichia coli, followed by deep sequencing that permitted the identification of 15 individual CRs with a broad range of protein production profiles. Using these new CRs, we demonstrated a change in production levels of a fluorescent reporter by over two orders of magnitude and further showed that these CRs are easily ported from E. coli to Pseudomonas putida. We next used four CRs to tune the production of the enzyme PpsA, involved in pyruvate to phosphoenolpyruvate (PEP) conversion, to alter the pool of PEP that feeds into the shikimate pathway. In an engineered P. putida strain, where carbon flux in the shikimate pathway is diverted to the synthesis of the commodity chemical cis,cis-muconate, we found that tuning PpsA translation levels increased the overall titer of muconate. Therefore, CRs provide an approach to precisely tune protein levels in metabolic pathways and will be an important tool for other metabolic engineering efforts.

DNA libraries are important resources to derive targets to be used for a wide range of applications, from structural and functional studies to intracellular protein interference studies to developing new diagnostics and therapeutics. Whatever the goal, the key parameter for a DNA library is its complexity (also known as diversity), i.e. the number of distinct elements in the collection. Quantitative evaluation of a DNA library complexity and quality has been for a long time inadequately addressed, due to the high similarity and length of the sequences of the library. Complexity was usually inferred by the transformation efficiency and tested by sequencing of a few random library elements. Inferring complexity from such a small sampling is, however, very rudimental and gives limited information about the real diversity, because complexity does not scale linearly with sample size. Next-generation sequencing (NGS) has opened new ways to tackle the DNA library complexity quality assessment. However, much remains to be done to fully exploit the potential of NGS for the quantitative analysis of DNA repertoires and to overcome current limitations. Even with the recent advances in NGS, it remains difficult to directly measure the representation of variant libraries, as the number of reads is insufficient to cover the size of a large library. As an example, a 1 kbp combinatorial DNA library with a billion variants has equivalent base pair content to that of 300 human genomes. Thus, brute force measurements of individual library members is impractical even with field-leading sequencing capabilities; i.e. >300 million reads at ~150 base lengths. To obtain a more reliable DNA library complexity estimate, here we show a NGS approach to sequence DNA libraries on Illumina platform, coupled to with a bioinformatic analysis and software that allows to reliably estimate the complexity, taking in consideration the sequencing error

Understanding PCR amplification derived sequence bias is integral to understanding and interpreting NGS library diversity analsis. For this reason, it is vital to experimentally determine the potential bias that can arise when amplifiying the products of library manipulation via NGS oligo pool generation.

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