Designing an optimal microbial cell factory often requires overexpression, knock-down, and knock-out of multiple gene targets. Unfortunately, such rewiring of cellular metabolism is often carried out sequentially and with low throughput. Here, we report a combinatorial metabolic engineering strategy based on an orthogonal tri-functional CRISPR system that combines transcriptional activation, transcriptional interference, and gene deletion (CRISPR-AID) in the yeast Saccharomyces cerevisiae. This strategy enables perturbation of the metabolic and regulatory networks in a modular, parallel, and high-throughput manner. We demonstrate the application of CRISPR-AID not only to increase the production of β-carotene by 3-fold in a single step, but also to achieve 2.5-fold improvement in the display of an endoglucanase on the yeast surface by optimizing multiple metabolic engineering targets in a combinatorial manner.

We developed a CRISPR-Cas9- and homology-directed-repair-assisted genome-scale engineering method named CHAnGE that can rapidly output tens of thousands of specific genetic variants in yeast. More than 98% of target sequences were efficiently edited with an average frequency of 82%. We validate the single-nucleotide resolution genome-editing capability of this technology by creating a genome-wide gene disruption collection and apply our method to improve tolerance to growth inhibitors.

Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods. Here, we report the application of an integrated robotic system coupled with machine learning algorithms to fully automate the DBTL process for biosystems design. As proof of concept, we have demonstrated its capacity by optimizing the lycopene biosynthetic pathway. This fully-automated robotic platform, BioAutomata, evaluates less than 1% of possible variants while outperforming random screening by 77%. A paired predictive model and Bayesian algorithm select experiments which are performed by Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB). BioAutomata excels with black-box optimization problems, where experiments are expensive and noisy and the success of the experiment is not dependent on extensive prior knowledge of biological mechanisms.

Genome-scale engineering is an indispensable tool to understand genome functions due to our limited knowledge of cellular networks. Unfortunately, most existing methods for genome-wide genotype–phenotype mapping are limited to a single mode of genomic alteration, i.e. overexpression, repression, or deletion. Here we report a multi-functional genome-wide CRISPR (MAGIC) system to precisely control the expression level of defined genes to desired levels throughout the whole genome. By combining the tri-functional CRISPR system and array-synthesized oligo pools, MAGIC is used to create, to the best of our knowledge, one of the most comprehensive and diversified genomic libraries in yeast ever reported. The power of MAGIC is demonstrated by the identification of previously uncharacterized genetic determinants of complex phenotypes, particularly those having synergistic interactions when perturbed to different expression levels. MAGIC represents a powerful synthetic biology tool to investigate fundamental biological questions as well as engineer complex phenotypes for biotechnological applications.

Nuclear organization has an important role in determining genome function; however, it is not clear how spatiotemporal organization of the genome relates to functionality. To elucidate this relationship, a method for tracking any locus of interest is desirable. Recently clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) or transcription activator-like effectors were adapted for imaging endogenous loci; however, they are mostly limited to visualization of repetitive regions. Here, we report an efficient and scalable method named SHACKTeR (Short Homology and CRISPR/Cas9-mediated Knock-in of a TetO Repeat) for live cell imaging of specific chromosomal regions without the need for a pre-existing repetitive sequence. SHACKTeR requires only two modifications to the genome: CRISPR/Cas9-mediated knock-in of an optimized TetO repeat and its visualization by TetR-EGFP expression. Our simplified knock-in protocol, utilizing short homology arms integrated by polymerase chain reaction, was successful at labeling 10 different loci in HCT116 cells. We also showed the feasibility of knock-in into lamina-associated, heterochromatin regions, demonstrating that these regions prefer non-homologous end joining for knock-in. Using SHACKTeR, we were able to observe DNA replication at a specific locus by long-term live cell imaging. We anticipate the general applicability and scalability of our method will enhance causative analyses between gene function and compartmentalization in a high-throughput manner.

Golden Gate assembly is one of the most widely used DNA assembly methods due to its robustness and modularity. However, despite its popularity, the need for BsaI-free parts, the introduction of scars between junctions, as well as the lack of a comprehensive study on the linkers hinders its more widespread use. Here, we first developed a novel sequencing scheme to test the efficiency and specificity of 96 linkers of 4-bp length and experimentally verified these linkers and their effects on Golden Gate assembly efficiency and specificity. We then used this sequencing data to generate 200 distinct linker sets that can be used by the community to perform efficient Golden Gate assemblies of different sizes and complexity. We also present a single-pot scarless Golden Gate assembly and BsaI removal scheme and its accompanying assembly design software to perform point mutations and Golden Gate assembly. This assembly scheme enables scarless assembly without compromising efficiency by choosing optimized linkers near assembly junctions.

Thanks to its ease of use, modularity, and scalability, the clustered regularly interspaced short palindromic repeats (CRISPR) system has been increasingly used in the design and engineering of Saccharomyces cerevisiae, one of the most popular hosts for industrial biotechnology. This review summarizes the recent development of this disruptive technology for metabolic engineering applications, including CRISPR-mediated gene knock-out and knock-in as well as transcriptional activation and interference. More importantly, multi-functional CRISPR systems that combine both gain- and loss-of-function modulations for combinatorial metabolic engineering are highlighted.

Xylose is a major component of lignocellulosic biomass, one of the most abundant feedstocks for biofuel production. Therefore, efficient and rapid conversion of xylose to ethanol is crucial in the viability of lignocellulosic biofuel plants. In this study, RNAi Assisted Genome Evolution (RAGE) was used to improve the xylose utilization rate in SR8, one of the most efficient publicly available xylose utilizing Saccharomyces cerevisiae strains. To identify gene targets for further improvement, we created a genome-scale library consisting of both genetic over-expression and down-regulation mutations in SR8. Followed by screening in media containing xylose as the sole carbon source, yeast mutants with 29% faster xylose utilization, and 45% higher ethanol productivity were obtained relative to the parent strain. Two known and two new effector genes were identified in these mutant strains. Notably, down-regulation of CDC11, an essential gene, resulted in faster xylose utilization, and this gene target cannot be identified in genetic knock-out screens.