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The multifaceted nature of CRISPR screens has propelled advancements in the field of functional genomics. Pooled CRISPR screens involve creating programmed genetic perturbations across multiple genomic sites in a pool of host cells subjected to a challenge, empowering researchers to identify genetic causes of desirable phenotypes. These genome-wide screens have been widely used in mammalian cells to discover biological mechanisms of diseases and drive the development of targeted drugs and therapeutics. Their use in non-model organisms, especially in microbes to improve bioprocessing-relevant phenotypes, has been limited. Further compounding this issue is the lack of bioinformatic algorithms for analyzing microbial screening data with high accuracy. Here, we describe the general approach and underlying principles for conducting pooled CRISPR knockout screens in non-conventional yeasts and performing downstream analysis of the screening data, while also reviewing state-of-the-art algorithms for identification of CRISPR screening outcomes. Application of pooled CRISPR screens to non-model yeasts holds considerable potential to uncover novel metabolic engineering targets and improve industrial bioproduction.

This mini-review describes experimental and computational approaches for functional genomic screening using CRISPR technologies in non-conventional microbes.

 

Plants sense abscisic acid (ABA) using chemical-induced dimerization (CID) modules, including the receptor PYR1 and HAB1, a phosphatase inhibited by ligand-activated PYR1. This system is unique because of the relative ease with which ligand recognition can be reprogrammed. To expand the PYR1 system, we designed an orthogonal ‘*’ module, which harbors a dimer interface salt bridge; X-ray crystallographic, biochemical and in vivo analyses confirm its orthogonality. We used this module to create PYR1*^MANDI/HAB1* and PYR1*^AZIN/HAB1*, which possess nanomolar sensitivities to their activating ligands mandipropamid and azinphos-ethyl. Experiments inArabidopsis thalianaandSaccharomyces cerevisiaedemonstrate the sensitive detection of banned organophosphate contaminants using living biosensors and the construction of multi-input/output genetic circuits. Our new modules enable ligand-programmable multi-channel CID systems for plant and eukaryotic synthetic biology that can empower new plant-based and microbe-based sensing modalities.

 

High throughput CRISPR screens are revolutionizing the way scientists unravel the genetic underpinnings of engineered and evolved phenotypes. One of the critical challenges in accurately assessing screening outcomes is accounting for the variability in sgRNA cutting efficiency. Poorly active guides targeting genes essential to screening conditions obscure the growth defects that are expected from disrupting them. Here, we develop acCRISPR, an end-to-end pipeline that identifies essential genes in pooled CRISPR screens using sgRNA read counts obtained from next-generation sequencing. acCRISPR uses experimentally determined cutting efficiencies for each guide in the library to provide an activity correction to the screening outcomes via calculation of an optimization metric, thus determining the fitness effect of disrupted genes. CRISPR-Cas9 and -Cas12a screens were carried out in the non-conventional oleaginous yeastYarrowia lipolyticaand acCRISPR was used to determine a high-confidence set of essential genes for growth under glucose, a common carbon source used for the industrial production of oleochemicals. acCRISPR was also used in screens quantifying relative cellular fitness under high salt conditions to identify genes that were related to salt tolerance. Collectively, this work presents an experimental-computational framework for CRISPR-based functional genomics studies that may be expanded to other non-conventional organisms of interest.

 

Chemical-induced dimerization (CID) modules enable users to implement ligand-controlled cellular and biochemical functions for a number of problems in basic and applied biology. A special class of CID modules occur naturally by plants involving a hormone receptor which binds hormone, triggering a conformational change in the receptor which enables recognition by a second binding protein. Two recent reports show that such hormone receptors can be engineered to sense dozens of structurally diverse compounds. As a closed form model for molecular ratchets would be of immense utility in forward engineering of biological systems, here we have developed a closed form model for these distinct CID modules. These modules, which we call molecular ratchets, are distinct from more common CID modules called molecular glues in that they engage in saturable binding kinetics and are well characterized by a Hill equation. A defining characteristic of molecular ratchets is that the sensitivity of the response can be tuned by increasing the molar ratio between the hormone receptor and binding protein. Thus, the same molecular ratchet can have a picomolar or micromolar EC50 depending on the concentration of the different receptor and binding proteins. Closed form models are derived for a base elementary reaction rate model, for ligand-independent complexation of receptor and binding protein, and for homodimerization of the hormone receptor. Useful governing equations for a variety of in vitro and in vivo applications are derived, including ELISA-like microplate assays, transcriptional activation in prokaryotes and eukaryotes, and ligand-induced split protein complementation. 

Genome-wide functional genetic screens have been successful in discovering genotype-phenotype relationships and in engineering new phenotypes. While broadly applied in mammalian cell lines and inE. coli, use in non-conventional microorganisms has been limited, in part, due to the inability to accurately design high activity CRISPR guides in such species. Here, we develop an experimental-computational approach to sgRNA design that is specific to an organism of choice, in this case the oleaginous yeastYarrowia lipolytica. A negative selection screen in the absence of non-homologous end-joining, the dominant DNA repair mechanism, was used to generate single guide RNA (sgRNA) activity profiles for both SpCas9 and LbCas12a. This genome-wide data served as input to a deep learning algorithm, DeepGuide, that is able to accurately predict guide activity. DeepGuide uses unsupervised learning to obtain a compressed representation of the genome, followed by supervised learning to map sgRNA sequence, genomic context, and epigenetic features with guide activity. Experimental validation, both genome-wide and with a subset of selected genes, confirms DeepGuide’s ability to accurately predict high activity sgRNAs. DeepGuide provides an organism specific predictor of CRISPR guide activity that with retraining could be applied to other fungal species, prokaryotes, and other non-conventional organisms.

 

Abstract Background 2-phenylethanol (2-PE) is a rose-scented flavor and fragrance compound that is used in food, beverages, and personal care products. Compatibility with gasoline also makes it a potential biofuel or fuel additive. A biochemical process converting glucose or other fermentable sugars to 2-PE can potentially provide a more sustainable and economical production route than current methods that use chemical synthesis and/or isolation from plant material. Results We work toward this goal by engineering the Shikimate and Ehrlich pathways in the stress-tolerant yeast Kluyveromyces marxianus . First, we develop a multigene integration tool that uses CRISPR-Cas9 induced breaks on the genome as a selection for the one-step integration of an insert that encodes one, two, or three gene expression cassettes. Integration of a 5-kbp insert containing three overexpression cassettes successfully occurs with an efficiency of 51 ± 9% at the ABZ1 locus and was used to create a library of K. marxianus CBS 6556 strains with refactored Shikimate pathway genes. The 3 3 -factorial library includes all combinations of KmARO4 , KmARO7 , and KmPHA2 , each driven by three different promoters that span a wide expression range. Analysis of the refactored pathway library reveals that high expression of the tyrosine-deregulated KmARO4 K221L and native KmPHA2 , with the medium expression of feedback insensitive KmARO7 G141S , results in the highest increase in 2-PE biosynthesis, producing 684 ± 73 mg/L. Ehrlich pathway engineering by overexpression of KmARO10 and disruption of KmEAT1 further increases 2-PE production to 766 ± 6 mg/L. The best strain achieves 1943 ± 63 mg/L 2-PE after 120 h fed-batch operation in shake flask cultures. Conclusions The CRISPR-mediated multigene integration system expands the genome-editing toolset for K. marxianus, a promising multi-stress tolerant host for the biosynthesis of 2-PE and other aromatic compounds derived from the Shikimate pathway. 

A general method to generate biosensors for user-defined molecules could provide detection tools for a wide range of biological applications. Here, we describe an approach for the rapid engineering of biosensors using PYR1 (Pyrabactin Resistance 1), a plant abscisic acid (ABA) receptor with a malleable ligand-binding pocket and a requirement for ligand-induced heterodimerization, which facilitates the construction of sense–response functions. We applied this platform to evolve 21 sensors with nanomolar to micromolar sensitivities for a range of small molecules, including structurally diverse natural and synthetic cannabinoids and several organophosphates. X-ray crystallography analysis revealed the mechanistic basis for new ligand recognition by an evolved cannabinoid receptor. We demonstrate that PYR1-derived receptors are readily ported to various ligand-responsive outputs, including enzyme-linked immunosorbent assay (ELISA)-like assays, luminescence by protein-fragment complementation and transcriptional circuits, all with picomolar to nanomolar sensitivity. PYR1 provides a scaffold for rapidly evolving new biosensors for diverse sense–response applications.