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Small-molecule sensing in plants is dominated by chemical-induced dimerization modules. In the abscisic acid (ABA) system, allosteric receptors recruit phosphatase effectors and achieve nM in vivo responses from µM receptor–ligand interactions. This sensitivity amplification could enable ABA receptors to serve as generic scaffolds for designing small-molecule sensors. To test this, we screened collections of mutant ABA-receptors against 2,726 drugs and other ligands and identified 553 sensors for 6.6% of these ligands. The mutational patterns indicate strong selection for ligand-specific binding pockets. We used these data to develop a sensor design pipeline and isolated sensors for multiple plant natural products, 2,4,6-trinitrotoluene (TNT), and “forever” per- and polyfluoroalkyl substances (PFAS). Thus, the ABA sensor system enables design and isolation of small-molecule sensors with broad chemical scope and antibody-like simplicity. 

Abstract Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas genome-wide screens are powerful tools for unraveling genotype–phenotype relationships, enabling precise manipulation of genes to study and engineer industrially useful traits. Traditional genetic methods, such as random mutagenesis or RNA interference, often lack the specificity and scalability required for large-scale functional genomic screens. CRISPR systems overcome these limitations by offering precision gene targeting and manipulation, allowing for high-throughput investigations into gene function and interactions. Recent work has shown that CRISPR genome editing is widely adaptable to several yeast species, many of which have natural traits suited for industrial biotechnology. In this review, we discuss recent advances in yeast functional genomics, emphasizing advancements made with CRISPR tools. We discuss how the development and optimization of CRISPR genome-wide screens have enabled a host-first approach to metabolic engineering, which takes advantage of the natural traits of nonconventional yeast—fast growth rates, high stress tolerance, and novel metabolism—to create new production hosts. Lastly, we discuss future directions, including automation and biosensor-driven screens, to enhance high-throughput CRISPR-enabled yeast engineering. 

Abstract Designing CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) single guide RNA (sgRNA) libraries targeting entire kingdoms of life will significantly advance genetic research in diverse and underexplored taxa. Current sgRNA design tools are often species-specific and fail to scale to large, phylogenetically diverse datasets, limiting their applicability to comparative genomics, evolutionary studies, and biotechnology. Here, we introduce ALLEGRO, a combinatorial optimization algorithm designed to compose minimal, yet highly effective sgRNA libraries targeting thousands of species at the same time. Leveraging integer linear programming, ALLEGRO identified compact sgRNA sets simultaneously targeting multiple genes of interest for over 2000 species across the fungal kingdom. We experimentally validated sgRNAs designed by ALLEGRO in Kluyveromyces marxianus, Komagataella phaffii, Yarrowia lipolytica, and Saccharomyces cerevisiae, confirming successful genome edits. Additionally, we employed a generalized Cas9–ribonucleoprotein delivery system to apply ALLEGRO’s sgRNA libraries to untested fungal genomes, such as Rhodotorula araucariae. Our experimental findings, together with cross-validation, demonstrate that ALLEGRO facilitates efficient CRISPR genome editing, enabling the development of universal sgRNA libraries applicable to entire taxonomic groups. 

Abstract Designing CRISPR single guide RNA (sgRNA) libraries targeting entire kingdoms of life will significantly advance genetic research in diverse and underexplored taxa. Current sgRNA design tools are often species-specific and fail to scale to large, phylogenetically diverse datasets, limiting their applicability to comparative genomics, evolutionary studies, and biotechnology. Here, we present ALLEGRO, a combinatorial optimization algorithm able to design minimal, yet highly effective sgRNA libraries targeting thousands of species. Leveraging integer linear programming, ALLEGRO identified compact sgRNA sets simultaneously targeting several genes of interest for over 2,000 species across the fungal kingdom. We experimentally validated the sgRNAs designed by ALLEGRO inKluyveromyces marxianus, Komagataella phaffii, andYarrowia lipolytica. In addition, we adopted a generalized Cas9-Ribonucleoprotein delivery system coupled with protoplast transformation to extend ALLEGRO’s sgRNA libraries to other untested fungal genomes, such asRhodotorula araucariae. Our experimental results, along with cross-validation, show that ALLEGRO enables efficient CRISPR genome editing, supporting the development of universal sgRNA libraries applicable to entire taxonomic groups. 

Precise control of gene expression is critical for optimizing cellular metabolism and improving the production of valuable biochemicals. However, hard-wired approaches to pathway engineering, such as optimizing promoters, can take time and effort. Moreover, limited tools exist for controlling gene regulation in non-conventional hosts. Here, we develop a two-channel chemically-regulated gene expression system for the multi-stress tolerant yeast Kluyveromyces marxianus and use it to tune ethyl acetate production, a native metabolite produced at high titers in this yeast. To achieve this, we repurposed the plant hormone sensing modules (PYR1ABA/HAB1 and PYR1*MANDI/HAB1*) for high dynamic-range gene activation and repression controlled by either abscisic acid (ABA) or mandipropamid (mandi). To redirect metabolic flux towards ethyl acetate biosynthesis, we simultaneously repress pyruvate dehydrogenase (PDA1) and activate pyruvate decarboxylase (PDC1) to enhance ethyl acetate titers. Thus, we have developed new tools for chemically tuning gene expression in K. marxianus and S. cerevisiae that should be deployable across many non-conventional eukaryotic hosts. 

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