Development and applications of a CRISPR activation system for facile genetic overexpression in Candida albicans

Abstract

For the fungal pathogen Candida albicans, genetic overexpression readily occurs via a diversity of genomic alterations, such as aneuploidy and gain-of-function mutations, with important consequences for host adaptation, virulence, and evolution of antifungal drug resistance. Given the important role of overexpression on C. albicans biology, it is critical to develop and harness tools that enable the analysis of genes expressed at high levels in the fungal cell. Here, we describe the development, optimization, and application of a novel, single-plasmid-based CRISPR activation (CRISPRa) platform for targeted genetic overexpression in C. albicans, which employs a guide RNA to target an activator complex to the promoter region of a gene of interest, thus driving transcriptional expression of that gene. Using this system, we demonstrate the ability of CRISPRa to drive high levels of gene expression in C. albicans, and we assess optimal guide RNA targeting for robust and constitutive overexpression. We further demonstrate the specificity of the system via RNA sequencing. We highlight the application of CRISPR activation to overexpress genes involved in pathogenesis and drug susceptibility, and contribute toward the identification of novel phenotypes. Consequently, this tool will facilitate a broad range of applications for the study of C. albicans genetic overexpression.

Introduction

Fungal pathogens are an increasingly important cause of human illness, with significant impacts on morbidity and mortality (Fisher et al. 2020). Candida albicans is an opportunistic fungal pathogen that inhabits the microbiome of most healthy adults as a commensal organism (Gow et al. 2011; Pérez 2019). However, it is also responsible for invasive infections, particularly among immunocompromised individuals, which are often fatal (Bongomin et al. 2017). Instances of C. albicans infections are becoming more prevalent globally, in part due to an increasing population of susceptible elderly and immunocompromised individuals (Bongomin et al. 2017). Illustratively, systemic Candida infections have been identified in up to 15% of patients hospitalized for COVID-19, where patients with invasive candidiasis spent longer in the hospital intensive care unit on average and had more severe symptoms than those COVID-19 patients without a Candida infection (Segrelles-Calvo et al. 2021). The economic cost of Candida infections is estimated to be ∼1.4 billion dollars yearly in the United States of America alone, hospitalizing >26,000 people (Benedict et al. 2018). With limited development of novel antifungal drugs (Perfect 2017) and increasing prevalence of antifungal drug-resistant strains and species (Sanguinetti et al. 2015; Arendrup and Patterson 2017; Geddes-McAlister and Shapiro 2018), Candida pathogens remain a serious threat to human health.

Given the importance of C. albicans and other fungal pathogens, it is imperative that tools are developed and applied to manipulate these organisms and dissect genetic functions. For C. albicans, along with countless other microbial species, CRISPR-based genetic manipulation tools have revolutionized the ability of scientists to modify and study gene function (Shapiro, Chavez, Collins 2018; Uthayakumar et al. 2021). Such CRISPR platforms typically rely on a CRISPR-associated endonuclease protein, often Cas9, targeted to a genomic region of interest via a single-guide RNA (sgRNA), resulting in targeted DNA double-strand breaks, and subsequent repair via nonhomologous end-joining or homology-directed repair via a donor DNA repair template (Adli 2018; Bock et al. 2022). Since the initial development of CRISPR tools for genetic editing in C. albicans (Vyas et al. 2015), numerous systems have been developed to modify this fungal genome with efficiency and specificity (Min et al. 2016, 2018; Huang and Mitchell 2017; Ng and Dean 2017; Nguyen et al. 2017; Shapiro, Chavez, Porter, et al. 2018; Vyas et al. 2018; Román, Prieto, et al. 2019; Morio et al. 2020; Uthayakumar et al. 2021), and to build and screen genetic mutant libraries (Shapiro, Chavez, Porter, et al. 2018; Rosiana et al. 2021). Together, these gene editing systems have played an important role in providing novel biological insight into this fungal pathogen.

In addition to these canonical CRISPR-based genetic modification systems that introduce mutations, insertions, or deletions into the genome, alternative CRISPR technologies have been developed that modulate the expression of C. albicans genes (Román, Coman, et al. 2019; Wensing et al. 2019). These CRISPR systems rely on the inactivation of the Cas9 endonuclease, to generate a nuclease-dead (dCas9) protein, which can be fused to constructs from a transcriptional repressor or activator that generate CRISPR interference (CRISPRi) or CRISPR activation (CRISPRa) systems, respectively (Dominguez et al. 2016; Wang et al. 2016). These dCas9 fusions, when targeted to the promoter region of a gene via the sgRNA, can repress or activate gene transcription from a gene of interest, and have been widely applied in diverse organisms (Peters et al. 2015; Kampmann 2018; Xu and Qi 2019; Schultenkämper et al. 2020; Todor et al. 2021; Bock et al. 2022). In C. albicans, these modified CRISPR systems have enabled the modulation and study of important fungal genes, including essential genes (Román, Coman, et al. 2019; Wensing et al. 2019; Wensing and Shapiro 2022).

While a majority of CRISPR systems and other genetic manipulation platforms focus on gene mutation, deletion, or downregulation, it is also critical to develop techniques that enable the upregulation or overexpression of genes. Indeed, genetic overexpression in C. albicans occurs readily via gene copy number amplification and aneuploidy, and these copy number amplification events are frequently observed in genetically diverse clinical isolates (Coste et al. 2006; Forche et al. 2018; Todd et al. 2019). The upregulation of gene expression plays a critical role in diverse aspects of fungal biology (Rai et al. 2021), including the evolution of antifungal drug resistance (Selmecki et al. 2008; Sanglard et al. 2009; Schubert et al. 2011; Flowers et al. 2012; Lohberger et al. 2014; Todd and Selmecki 2020), host colonization (Prieto et al. 2017; Znaidi et al. 2018), and virulence (Fu et al. 2008; Lohberger et al. 2014). Additionally, in other microbial systems, including bacteria, protozoa, and the model yeast Saccharomyces cerevisiae, genetic overexpression libraries have proved to be critical tools for identifying targets of drugs with uncharacterized mechanisms of action, as mutants overexpressing the drug target are typically more resistant to the drug in question (Li et al. 2004; Luesch et al. 2005; Smith et al. 2010; Begolo et al. 2014). Overexpression screens can also help dissect molecular pathways (Sopko et al. 2006; Sahni et al. 2010; Polvi et al. 2019). In C. albicans, several techniques exist for genetic overexpression (Rai et al. 2021), including promoter replacement strategies (Delgado et al. 2003; Park and Morschhäuser 2005; Eckert and Mühlschlegel 2009; Sahni et al. 2010), the generation of ORFeome collection strains (Chauvel et al. 2012; Legrand et al. 2018), and CRISPR-based methods (Román, Coman, et al. 2019). While these tools have made important contributions to understanding overexpression in C. albicans, there remains a need for simple and rapid methods to efficiently target fungal genes of interest for overexpression, and to do so in diverse strain backgrounds.

Here, we introduce a CRISPR-based tool for genetic overexpression in C. albicans, to bolster the existing functional genetic toolbox available to manipulate genes in this critical fungal pathogen. Our CRISPRa tool exploits a C. albicans-optimized tripartite activator complex fused to dCas9, which was previously developed for highly efficient transcriptional regulation in S. cerevisiae (Chavez et al. 2015). This CRISPRa system is distinct from other CRISPR-based gene activation systems for C. albicans, as it is an efficient single-plasmid system designed for rapid Golden Gate cloning and facile fungal strain generation, which can be readily applied to diverse strain backgrounds including clinical isolates. We test CRISPRa guide targeting principles based on predicted transcriptional start sites, confirm on-target guide efficiency, and demonstrate the ability of this CRISPRa system to drive high levels of expression of C. albicans genes. Using this optimized CRISPRa technique, we validate its ability to drive the overexpression of genes with established roles in antifungal drug susceptibility and biofilm formation. Together, this work introduces a novel CRISPRa system for genetic overexpression in C. albicans, with a wide range of future applications.

Materials and methods

Plasmid design and cloning

The plasmid backbone used in this study was the C. albicans-optimized CRISPR-dCas9 plasmid (pRS143, Addgene #122377) used in our previous study (Wensing et al. 2019), containing the NEUT5L integration site, sgRNA cloning site (SNR52 promoter, SapI cloning locus, and sgRNA tail), and dCAS9. The dCas9-VP64-p65-Rta (VPR) fusion construct was generated via Gibson assembly. The VPR tripartite complex was codon-optimized for C. albicans expression and synthesized as gBlocks gene fragments from Integrated DNA Technologies (IDT). This gene fragment was cloned with Gibson assembly into the dCAS9 plasmid backbone. We have made the CRISPRa (dCas9-VPR) plasmid available via Addgene (reference #182707). All plasmids are listed in Supplementary Table 1.

sgRNA design and cloning

CRISPRa strain construction was broadly performed as described in detail (Wensing and Shapiro 2022), with some minor modifications. The sgRNA CRISPR RNAs (crRNA; 20 nucleotide sequence complementary to the target DNA genomic DNA) were designed based on efficiency and predicted specificity via the sgRNA design tool Eukaryotic Pathogen CRISPR gRNA Design Tool (EuPaGDT; http://grna.ctegd.uga.edu; Peng and Tarleton 2015). Generally, designing sgRNAs around −100 to −350 bp upstream of both the start codon (ATG) and transcriptional start site of the target gene led to most successful overexpression. In all cases, employing 4-6 sgRNAs in total (2-3 sgRNAs per site) led to significant overexpression in at least one of the corresponding strains. These sgRNA crRNA N20 sequences were cloned into the dCas9-VPR CRISPRa plasmid at the sgRNA cloning locus (SapI cloning site, flanked by SNR52 promoter, and sgRNA tail) using Golden Gate cloning (Engler et al. 2008), as previously described (Halder et al. 2019; Wensing and Shapiro 2022). crRNA N20 sequences were obtained as two oligonucleotides from IDT in forward and reverse complement orientation, each containing a SapI cloning site, and were reconstituted to 100 µM in a nuclease-free duplex buffer from IDT. Equal volumes of the two complementary oligonucleotides were combined after being heated separately at 94°C, and then duplexed by heating to 94°C for 2 min and cooling to room temperature. The duplexed fragment was then cloned into the CRISPRa plasmid with the following Golden Gate cloning reaction: 10 µl miniprepped CRISPRa plasmid, 1 µl duplexed oligonucleotide, 2 µl 10X CutSmart buffer, 2 µl ATP, 1 µl SapI, 1 µl T4 DNA ligase, and 3 µl nuclease-free water. This mixture was incubated in a thermocycler under the following cycling conditions: (37°C, 2 min; 16°C, 5 min) for 99 cycles; 65°C, 15 min; 80°C, 15 min. After cycling, 1 µl of additional SapI was added to each reaction mixture, and the reaction was incubated at 37°C for 1 h. All sgRNAs are listed in Supplementary Table 1.

Media and growth conditions

Escherichia coli DH5α cells were grown at 37°C in Lysogeny Broth (LB) and LB plates supplemented with 100 mg/ml ampicillin (AMP) and 250 mg/ml nourseothricin (NAT) for plasmid selection. Candida albicans cells were grown at 30°C or 37°C in yeast peptone dextrose (YPD) broth and YPD plates supplemented with 250 mg/ml NAT for plasmid selection.

Bacterial transformation

High-efficiency 5-alpha competent E. coli (NEB) cells were thawed on ice. 1µl of Golden Gate cloning reagents was added to 50 µl of competent cells and incubated on ice for 30 min, heat shocked for 30 s at 42°C, and incubated on ice for an additional 5 min. About 950 µl of SOC outgrowth media was added to each cell culture and incubated at 30°C for 1.5 h at 250 RPM. Transformed cells were then plated on LB media containing AMP and NAT and grown at 30°C for 1 day.

Candida albicans transformation

Plasmids were transformed into C. albicans via a chemical transformation strategy, as previously described (Halder et al. 2019; Wensing and Shapiro 2022). Briefly, miniprepped CRISPRa plasmids were linearized via a 1.5X restriction digest mix using the PacI enzyme. A transformation master mix was prepared as follows: 800 µl of 50% polyethylene glycol, 100 µl of 10X Tris-EDTA buffer solution, 100 µl of 1 M lithium acetate, 40 µl of 10 mg/ml salmon sperm DNA, and 20 µl of 1 M dithiothreitol. The transformation mix was added to C. albicans cells and linearized CRISPRa plasmid and left to incubate at 30°C for 1 h, then heat shocked at 42°C for 45 min. Cells were washed with fresh YPD, then grown in YPD for 4 h to allow for expression of the NAT resistance construct. Transformed cells were then plated on YPD media containing NAT, and grown at 30°C for 2 days. All C. albicans strains are listed in Supplementary Table 1.

Growth curves

Overnight cultures of C. albicans grown in YPD were diluted to an OD₆₀₀ of 0.05 in fresh YPD and added to a 96-well flat-bottomed plate with a total volume of 200 µl per well. Growth was measured at 30°C via optical density at 600 nm at 15 min intervals over the course of 24 h using an Infinite 200 PRO microplate reader (Tecan), and plates were shaken orbitally for 900 s at a 4-mm amplitude in between growth measurements.

Minimum inhibitory concentration assays

Fluconazole minimum inhibitory concentration (MIC) assays were performed in 96-well flat-bottomed plates. A suspension of 40 μg/ml fluconazole was prepared in water, of which 100 μl was added to the first column of each plate containing 100 μl of culture, to obtain a total volume of 200 µl per well. The first column was serially diluted 2-fold across the plate in water. The gradient of fluconazole, therefore, ranged from 20 to 0 μg/ml. Overnight cultures of C. albicans grown in YPD were diluted to an OD₆₀₀ of 0.1 in 2X RPMI-1640 with 40 g/l of added D-glucose and mixed into the plates in an equal volume such that the starting OD₆₀₀ values of each strain were 0.05 and the growth media was diluted to RPMI-1640 with 20 g/l of added D-glucose. Plates were incubated at 37°C at 900 RPM, and absorbance values at 600 nm were read after 24 h using an Infinite 200 PRO microplate reader (Tecan). The amphotericin B MIC assays were also performed in 96-well flat-bottomed plates similar to the protocol described above with a few modifications. A starting concentration of 5 μg/ml of amphotericin B was used to create a gradient ranging from 2.5 to 0 μg/ml. Strains were instead grown in YPD media using a starting OD₆₀₀ of 0.001. Plates were incubated at 37°C statically and absorbance values at 600 nm were read after 72 h using an Infinite 200 PRO microplate reader (Tecan).

Biofilm assays

RPMI-based biofilm assays were performed as previously described (Razzaq et al. 2021), with minor modifications. Overnight cultures of C. albicans grown in YPD were diluted to an OD₆₀₀ of 0.001 in 5 ml of RPMI-1640 with 20 g/l of supplemented D-glucose. 100µl of RPMI-1640 was mixed with 100 µl of each diluted overnight culture into a 96-well flat-bottomed plate (12 wells per strain). Plates were grown statically at 37°C for 24 h to allow biofilms to form. About 120 µl of planktonic cell supernatant was removed from each well and moved to a new 96-well flat-bottomed plate. Density of the planktonic cells was measured at 600 nm using an Infinite 200 PRO microplate reader (Tecan). Original plates from which planktonic cells were removed were washed by adding 200 µl 1X PBS to each well with an electronic multichannel pipette at a moderate dispense speed. 1X PBS was then discarded, and this wash step was repeated. Plates were then left to dry for 1–2 h. Once biofilms were dry, 90 µl of 1 mg/ml tetrazolium salt (XTT) (prepared in 1X PBS and centrifuged to remove sediment prior to use) and 10 µl of 0.32 mg/ml PMS (prepared in water) were added to each well. Plates were incubated statically at 30°C for 2 h to allow biofilms to reduce XTT, measured at 490 nm using an Infinite 200 PRO microplate reader (Tecan), and normalized to the growth of planktonic cells harvested previously.

For urine-based biofilm assays, biofilm formations were performed in 96-well flat-bottomed plates. Plates were coated with 150 µg/ml of fibrinogen and incubated overnight at 4°C. The C. albicans strains were cultured at 37°C with aeration in 5 ml of YPD broth. The inoculum was normalized to ∼1 × 10⁶ CFU/ml and then diluted (1:10) in human urine (female, pH ∼6.5). Urine was supplemented with 20 mg/ml of bovine serum albumin (BSA) for carbon and nitrogen sources mimicking the catheterized bladder environment. 200µl of the urine with inoculum was incubated in each well of the 96-well plate at 37°C for 48 h while static. Following the 48 h incubation, the cultures were removed and the wells were washed 3× with 200 µl 1X PBS to remove unbound fungi. Plates were incubated with 200 µl of 0.5% crystal violet for 15 min. Crystal violet was removed, and plates were washed with water and dried. 200µl of 33% acetic acid was added to the wells and 100 µl was transferred to a new 96-well plate. Absorbance was measured at 595 nm using a SpectraMax ABS Plus microplate reader (Molecular Devices).

RNA extraction and real-time quantitative PCR

To detect differences in gene expression, overnight cultures of C. albicans grown in YPD were diluted to an OD₆₀₀ of 0.05 in fresh YPD and grown to an OD₆₀₀ of >0.2 at 30°C. ALS3 CRISPRa strains were diluted to an OD₆₀₀ of 0.05 in fresh RPMI and grown for 5 h at 37°C as well to measure overexpression in conditions that favor high levels of basal expression. Cultures were pelleted and frozen at −80°C before RNA was extracted. RNA extractions were performed using either a Presto Mini RNA Yeast kit from FroggaBio (cat. RBYD050) or an RNeasy Mini kit from Qiagen (cat. 74104). To assess RNA integrity, an RNA ScreenTape assay was performed on all samples using the TapeStation 4150 system following manufacturer's instructions (Agilent Technologies). Only samples with RIN values of >5.0 were used for reverse-transcription quantitative PCR (RT-qPCR). Synthesis of cDNA from 1,000 ng (10 µl) RNA was performed using a High Capacity cDNA Reverse-Transcription kit from Applied BioSystems (cat. 4368814). Briefly, 20 μl reaction mixtures were prepared with 2 μl 10X reverse-transcription buffer, 0.8 μl of 25X dNTPs at a concentration of 100 mM, 2 μl of random primers, 1 μl of MultiScribe Reverse Transcriptase at 50 IU/μl, 4.2 μl of nuclease-free water, and 10 μl of RNA (1 μg). Reverse-transcription run conditions were as follows: 10 min at 25°C, 120 min at 37°C, 5 min at 85°C. Real-time PCR assays were conducted using a QuantStudio 7 Pro Real-Time PCR system from Thermo Fisher Scientific Inc. 20 μl reaction mixtures containing 10 μl 2X SsoAdvanced Universal Inhibitor-Tolerant SYBR supermix (Bio-Rad, cat: 172-5017), 0.8 μl of PCR forward and reverse primer mix at 5 μM (final concentration of primer at 200 nM), 4.2 μl of water, and 5 μl of diluted cDNA. The run conditions were as follows: 3 min at 98°C polymerase activation step, followed by 40 cycles of a two-step qPCR (10 s of 98°C denaturation, 30 s of 60°C combined annealing/extension). Primers are listed in Supplementary Table 1. Expression profiling calculations were performed according to the comparative C_T method (Schmittgen and Livak 2008). Briefly, expression values for the experimental gene of interest were compared with the housekeeping gene ACT1 to obtain a ΔC_T value within each strain. The ΔC_T values in the experimental CRISPRa strains were then compared with the nontargeting CRISPRa control strain to obtain a ΔΔC_T value and finally a fold difference in expression of the experimental gene, and are listed in Supplementary Table 3.

RNA sequencing

RNA preparation and sequencing (RNA-seq) were performed as previously described, with minor modifications (Razzaq et al. 2021). Strains were grown in YPD at 30°C overnight preceding RNA extraction. RNA extraction, sample QC, library preparations, and sequencing reactions were conducted at GENEWIZ, LLC./Azenta US, Inc. as follows: Total RNA was extracted using Qiagen RNeasy Plus Universal kit following manufacturer's instructions (Qiagen). RNA samples were quantified using Qubit 2.0 Fluorometer (Thermo Fisher Scientific) and RNA integrity was checked with 4200 TapeStation (Agilent Technologies). ERCC RNA Spike-In Mix kit (cat. 4456740) from Thermo Fisher Scientific was added to normalized total RNA prior to library preparation following the manufacturer's protocol. The RNA-seq library was prepared using the NEBNext Ultra II RNA Library Prep kit for Illumina using the manufacturer's instructions (New England Biolabs). Briefly, mRNAs were initially enriched with Oligod(T) beads. Enriched mRNAs were fragmented for 15 min at 94°C. First-strand and second-strand cDNAs were subsequently synthesized. cDNA fragments were end-repaired and adenylated at 3′ ends, and universal adapters were ligated to cDNA fragments, followed by index addition and library enrichment by PCR with limited cycles. The sequencing library was validated on the Agilent TapeStation (Agilent Technologies), and quantified by using Qubit 2.0 Fluorometer (Thermo Fisher Scientific) as well as by quantitative PCR (KAPA Biosystems). The sequencing libraries were multiplexed and clustered onto a flowcell. After clustering, the flowcell was loaded onto the Illumina HiSeq 4000 or equivalent instrument according to the manufacturer's instructions. The samples were sequenced using a 2 × 150 bp paired end configuration. Image analysis and base calling were conducted by the Illumina Control Software.

Raw sequence data (.bcl files) generated from the Illumina instrument was converted into fastq files and de-multiplexed using Illumina bcl2fastq 2.20 software. One mis-match was allowed for index sequence identification. Sequence reads were trimmed to remove possible adapter sequences and nucleotides with poor quality using Trimmomatic v.0.36, and trimmed reads were mapped to the candida_albicans reference genome available on ENSEMBL using the STAR aligner v.2.5.2b. The STAR aligner is a splice aligner that detects splice junctions and incorporates them to help align the entire read sequences. BAM files were generated as a result of this step. Unique gene hit counts were then calculated by using featureCounts from the Subread package v.1.5.2. The hit counts were summarized and reported using the gene_id feature in the annotation file. Only unique reads that fell within exon regions were counted. After extraction of gene hit counts, the gene hit counts table was used for downstream differential expression analysis. Using DESeq2, a comparison of gene expression between the control and experimental groups of samples was performed. The Wald test was then used to generate P-values and log2-fold changes. Genes with an adjusted P-value <0.05 and absolute log2-fold change >1 were called as differentially expressed genes for each comparison. The data have been deposited in NCBI's Gene Expression Omnibus (GEO) (Edgar et al. 2002) and are accessible through GEO Series accession number GSE201904 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201904).

Results

Development of a single-plasmid CRISPRa tool in C. albicans

First, we designed a single-plasmid CRISPRa system for targeted gene activation, optimized for C. albicans. We exploited an integrating plasmid backbone, which we previously used for CRISPRi in C. albicans, containing a codon-optimized dCas9 and a Golden Gate assembly compatible sgRNA cloning site (Wensing et al. 2019). This plasmid can be linearized to integrate at the C. albicans NEUT5L locus (a large intergenic region whose disruption does not affect fungal fitness; Gerami-Nejad et al. 2013), and contains a dual SapI cloning site for facile and highly efficient Golden Gate cloning of the sgRNA targeting sequence. This plasmid also contains a dominant NAT resistance cassette, to enable strain generation in a diversity of fungal strains, including clinical isolates.

To generate a version of dCas9 that could lead to strong constitutive activation of genes of interest, we generated a plasmid with dCas9 fused to the tripartite activator complex, VPR (Fig. 1, a and b). VPR consists of 3 previously described transcriptional activators: (1) VP64, a viral transcriptional activator domain composed of 4 tandem copies of VP16 (Herpes simplex viral protein 16), which has been used extensively in CRISPRa systems (La Russa and Qi 2015); (2) p65, the NF-κB subunit responsible for the strong transcription activation in mammalian cells (Schmitz and Baeuerle 1991); and (3) Rta, the Epstein–Barr virus R transactivator, with strong transcriptional activation properties (Hardwick et al. 1992; Fig. 1, a and b). Together the VPR activator complex fused to dCas9 and targeted to the promoter region of genes has previously been demonstrated to generate robust gene activation in S. cerevisiae (Chavez et al. 2015). Exploiting the ability of dCas9 to bind and be targeted to genomic loci via sgRNAs enables us to design sgRNAs that will target dCas9 along with these transcriptional activators to any gene promoter of interest.

Fig. 1.

Design of a CRISPRa system for Candida albicans. a) The single-plasmid system contains a catalytically inactive (“dead”) dCas9 protein, paired with the VPR complex of activator domains to cause upregulation at a target locus. The sgRNA region can be subjected to Golden Gate cloning with a custom N20 sequence in order to target the dCas9-VPR complex to a desired region of DNA through complementary base-pairing. The PacI enzyme is used to linearize the plasmid at the NEUT5L locus, which allows the plasmid to be integrated into the cell's chromosome and remain in the cell following cell division. Panel created with BioRender.com. b) Once the sgRNA recognizes and binds to a target sequence of DNA, the dCas9-VPR complex will cause the recruitment of transcription initiation complex factors to the adjacent genetic material, thus increasing gene expression from that region. Panel created with BioRender.com. c) Wild-type C. albicans cells and cells containing a nontargeting CRISPRa plasmid were grown in YPD at 30°C to compare fitness under standard laboratory conditions. Growth was recorded via optical density at 600 nm at 15 min intervals over the course of 24 h. Data points show the mean and standard deviation at each time point (n = 32).

This plasmid was generated by homology-based cloning and validated via Sanger sequencing, and has been deposited to Addgene (Supplementary Table 1, Addgene plasmid #182707). To confirm if the expression of the dCas9-VPR construct had an impact on fungal fitness, we compared the growth of a wild-type C. albicans strain with a strain transformed with the dCas9-VPR CRISPRa plasmid (expressing a nontargeting sgRNA: a random sequence of nucleotides that have no predicted complementarity to any region in the C. albicans genome). We found no difference in growth between these strains (Fig. 1c), suggesting no significant impact of the CRISPRa system on C. albicans fitness.

Validation and sgRNA design principles for CRISPRa overexpression in C. albicans

Next, we aimed to validate that the CRISPRa system could successfully overexpress genes in C. albicans, and sought to determine the optimal sgRNA design strategy to maximize genetic regulation. Our previous C. albicans CRISPRi system exploited sgRNAs ∼50–150 bp upstream of the start codon to functionally repress genes of interest (Wensing et al. 2019). However, CRISPRa systems are known to have different optimal sgRNA targeting regions (Jensen 2018; Sanson et al. 2018), and our previous design did not take into account the transcription start site (TSS), which is known to be an important predictor of regulatory efficiency for CRISPRi/a systems (Smith et al. 2016; Jensen 2018), but which had not been robustly characterized across all C. albicans genes at the time. More recently, the TSS for many C. albicans genes has been predicted with single-nucleotide resolution using nAnT-iCAGE technology, which specifically identifies the 5′ end of RNA fragments (Lu and Lin 2021). Thus, we sought to determine the optimal design for CRISPRa sgRNAs based on the start codon and the TSS. We identified 3 C. albicans genes with a predicted TSS that was particularly far upstream of the start codon compared with all other genes in the genome (801–952 bp upstream): KSP1, ALS3, and FGR28. We designed 6 sgRNAs for each gene, targeting upstream of the start codon (as previously designed for CRISPRi; Wensing et al. 2019), as well as upstream of the TSS (based on predicted TSS; Lu and Lin 2021).

We generated CRISPRa plasmids based on these sgRNAs, used these to create C. albicans strains, and monitored overexpression of each gene via RT-qPCR. This analysis validated our novel CRISPRa system and its ability to overexpress C. albicans genes ∼2- to 12-fold, based on different sgRNA designs and target genes (Fig. 2). We found that in some instances, such as for KSP1, the TSS was in fact a better predictor of CRISPRa overexpression, as the most robust overexpression of this gene was observed with sgRNAs targeted upstream of the TSS, while no overexpression was observed based on targeting upstream of the start codon (Fig. 2a). In other instances, such as for ALS3, sgRNA targeting upstream of the start codon led to very high levels of overexpression, while sgRNAs targeted to the TSS region led to moderate overexpression (Fig. 2b). For FGR28, modest overexpression (∼2-fold) was achieved with sgRNAs targeted to either the start codon or the TSS upstream regions (Fig. 2c), together suggesting that optimal sgRNA design can be predicted based on targeting the TSS and/or start codon, but varies by gene. This data also suggest that our CRISPRa tool may have additional applications in the functional assessment of key transcriptional regulatory regions in C. albicans. These differences in sgRNA targeting may be based on differences in overall sgRNA efficiency or may be attributed to inaccurate TSS predictions, or complex promoter dynamics and the preferential use of different TSS regions in different conditions (McMillan et al. 2019).

Fig. 2.

Determining optimal guide-design principles for CRISPRa overexpression in Candida albicans. CRISPRa strains were created using 6 different sgRNA molecules designed to target different positions in the promoter region of each of a) KSP1, b) ALS3, and c) FGR28 upstream of either the start codon or the predicted dominant TSS. Fold change in the expression of the target gene relative to the housekeeping gene ACT1 in the experimental strains and the nontargeting CRISPRa control strain was measured with RT-qPCR and compared. Data points represent the mean fold difference and standard deviation in expression of the respective target gene in the CRISPRa strains compared with the nontargeting CRISPRa control (n = 3). d) Fold change in the basal expression level of ALS3 relative to the housekeeping gene ACT1 in the nontargeting CRISPRa control strain grown in either standard laboratory conditions (YPD at 30°C) or conditions known to favor a high level of ALS3 expression (RPMI at 37°C) was measured with RT-qPCR. Data represent the mean fold difference and standard deviation in expression of ALS3 in the nontargeting CRISPRa control strain in both conditions normalized to growth in YPD at 30°C (n = 3). e) Fold change in the expression of ALS3 relative to the housekeeping gene ACT1 in two CRISPRa strains targeting ALS3 for overexpression as well as the nontargeting CRISPRa control strain grown in either YPD at 30°C or RPMI at 37°C was measured with RT-qPCR. Data represent the mean fold difference and standard deviation in expression of ALS3 in all strains in both conditions normalized to the nontargeting CRISPRa control strain (WT; n = 3).

Given that our CRISPRa system has the benefit of exploiting a gene's endogenous promoter for overexpression, we next wanted to determine if our CRISPRa system could be used to promote enhanced expression of genes under conditions that already favored high gene expression. We focused on ALS3, where expression levels are known to be significantly enhanced upon growth in conditions that favor filamentous or biofilm growth, such as growth in RPMI media at 37°C (Bahn et al. 2007; Ruben et al. 2020; Deng et al. 2021). We compared basal ALS3 transcript levels of the C. albicans control strain grown in YPD at 30°C compared with RPMI at 37°C via RT-qPCR, and confirmed that ALS3 exhibits ∼100-fold higher expression in RPMI at 37°C (Fig. 2d). To determine whether our CRISPRa system could promote enhanced overexpression of ALS3 in these conditions, we monitored ALS3 expression in two CRISPRa ALS3 strains (from Fig. 2b) in RPMI at 37°C. We found ALS3 transcript levels in the CRISPRa strain were further overexpressed ∼4-fold in RPMI at 37°C, despite the existing high level of ALS3 under these conditions (Fig. 2e). However, while ALS3 is overexpressed in RPMI via CRISPRa, the degree of overexpression is relatively lower than what occurs in YPD conditions (Fig. 2e). Together this suggests that our CRISPRa system can effectively be used to increase transcription under conditions that already favor high levels of expression of a target gene of interest, but that there may be a maximal level of overexpression possible under these conditions.

Use of CRISPRa to study antifungal drug susceptibility phenotypes

To validate that the CRISPRa system could recapitulate phenotypes associated with C. albicans gene overexpression, we targeted genes with known phenotypes associated with high levels of expression. Initially, we focused on CDR1, a well-characterized C. albicans gene, which encodes a drug efflux pump (Prasad et al. 2015), and whose overexpression is known to play a role in resistance to azole antifungals in laboratory and clinical isolates (Prasad et al. 1995; Sanglard et al. 1995; White 1997; Marr et al. 1998, 2001; Niimi et al. 2004; Manoharlal et al. 2008). To determine an optimal range in which to target our sgRNA in the CDR1 promoter, we designed 3 sgRNAs targeting loci ∼90, 200, and 290 bp upstream of the CDR1 TSS. These sgRNAs were each cloned into our CRISPRa dCas9-VPR backbone and transformed into C. albicans. Next, we quantified the expression of CDR1 in the 3 different fungal strains via RT-qPCR, compared with a control strain containing the CRISPRa plasmid with a nontargeting sgRNA. We found that targeting the dCas9-VPR construct to the CDR1 promoter enhanced transcription by 2.9- to 5.6-fold (Fig. 3a), indicating that this CRISPRa system is able to drive enhanced levels of gene expression from this target gene of interest. For CDR1, we found the highest level of overexpression was obtained by targeting the dCas9-VPR construct ∼200 bp upstream of the TSS (Fig. 3a), which helped further inform the design of sgRNAs for subsequent target genes.

Fig. 3.

Using CRISPRa to validate antifungal drug susceptibility phenotypes. a) Three C. albicans CRISPRa strains were created targeting the drug transporter gene CDR1 for overexpression. Fold change in the expression of CDR1 relative to the housekeeping gene ACT1 in the 3 experimental strains and the nontargeting CRISPRa control strain was measured with RT-qPCR. All 3 experimental strains are represented by the position of their sgRNA in the promoter region upstream of the proposed dominant CDR1 transcriptional start site. Data represent the mean fold difference and standard deviation in expression of CDR1 in the CRISPRa strains compared with the nontargeting CRISPRa control (WT; n = 3). b) The fitness of all 3 CRISPRa strains targeting CDR1 for overexpression was measured in the presence of the antifungal drug fluconazole with a minimum inhibitory concentration assay. The OD₆₀₀ value of each strain in each concentration of fluconazole was measured to represent growth in differing conditions, where OD₆₀₀ values approaching 1.0 reflect the highest fitness. c) Fold change in the expression of YAK1 relative to the housekeeping gene ACT1 in the experimental strain and the nontargeting CRISPRa control strain was measured with RT-qPCR. Data represent the mean fold difference and standard deviation in expression of YAK1 in the CRISPRa strains compared with the nontargeting CRISPRa control (WT; n = 3). d) The fitness of the CRISPRa strain targeting YAK1 for overexpression was measured in the presence of the antifungal drug amphotericin B with a minimum inhibitory concentration assay. The OD₆₀₀ value of each strain in each concentration of amphotericin B was measured to represent growth in differing conditions, where OD₆₀₀ values approaching 1.0 reflect the highest fitness. ND: no drug.

In order to determine if CDR1-overexpressing strains had measurable changes in antifungal drug susceptibility phenotypes, we performed MIC assays with these strains in the presence of the antifungal drug fluconazole. We found that strains overexpressing CDR1 exhibited decreased sensitivity to fluconazole compared with the control strain and that higher levels of overexpression corresponded with lower levels of susceptibility (Fig. 3b). Together, this confirms that our CRISPRa system can be exploited to functionally overexpress target genes of interest, and recapitulate phenotypes associated with high levels of gene expression. Further, targeting dCas9-VPR to different promoter loci leads to variable levels of overexpression, which could be used to study correlations between expression levels and associated phenotypes.

We next sought to determine the ability of our CRISPRa system to be employed to characterize a gene whose overexpression has never been profiled with regard to antifungal drug susceptibility, to our knowledge. We selected the gene YAK1 as a target for overexpression with CRISPRa. Candida albicans YAK1 is a predicted serine-threonine protein kinase, inactivation of which renders cells more sensitive to the antifungal amphotericin B (Chen et al. 2018). While YAK1 overexpression has been shown to promote filamentation in C. albicans (MacAlpine et al. 2021), and there is emerging evidence for a direct relationship between the 2 phenotypes (Sharma et al. 2019), the role of overexpressing this factor in antifungal drug susceptibility has not previously been established. We generated a CRISPRa strain overexpressing YAK1 by ∼2-fold (Fig. 3c) and profiled the ability of this strain to grow in the presence of amphotericin B. We found that the YAK1 CRISPRa strain was less sensitive to amphotericin B based on MIC testing on this strain compared with the control strain (Fig. 3d). This highlights our capacity to exploit this CRISPRa system to investigate novel phenotypes in C. albicans.

Validation of CRISPRa overexpression targeting fungal pathogenesis traits

Next, we aimed to validate the CRISPRa system as a means to assess C. albicans genes involved in fungal pathogenesis traits. We focused on ALS1, a well-characterized C. albicans adhesin gene, with important roles in adherence, biofilm formation, and virulence (Fu et al. 2002; Kamai et al. 2002; Alberti-Segui et al. 2004; Zhao et al. 2004; Nobile et al. 2008; Rosiana et al. 2021). Previous work has demonstrated that overexpression of ALS1 via promoter replacement increases fungal adherence (Fu et al. 2002); therefore, we chose to overexpress this gene using our CRISPRa system. We designed and cloned 2 sgRNAs targeting ∼130 and 260 bp upstream of the ALS1 TSS, and generated C. albicans ALS1 CRISPRa strains with each of these constructs. We performed RT-qPCR to confirm the overexpression of these strains, which enhanced the expression of ALS1 by ∼25- and ∼6-fold, respectively (Fig. 4a).To confirm phenotypes associated with overexpression of ALS1, we performed two distinct biofilm growth assays that compared the ALS1 CRISPRa strain to the CRISPRa control strain. In the first assay, we allowed biofilms to establish over 24 h in flat-bottom 96-well polystyrene plates, removed planktonic cells, and quantified the metabolic activity of the biofilm via XTT reduction, relative to the planktonic cell population. We found that the ALS1 CRISPRa strain formed significantly more robust biofilms, relative to the control strain (P < 0.0005; Fig. 4b). Interestingly, biofilm growth was not correlated with ALS1 overexpression levels, as the strain with ∼25-fold ALS1 overexpression formed less robust biofilms compared with the strain expressing ∼6-fold more ALS1. This suggests that very high levels of ALS1 may ultimately impair biofilm formation, or that the level of ALS1 expression varies in the conditions used for RT-qPCR analysis compared with biofilm growth, perhaps due to condition-specific repositioning in the TSS.

Fig. 4.

Validation of CRISPRa overexpression and target specificity through targeting fungal pathogenesis traits. a) Two C. albicans CRISPRa strains targeting different positions in the promoter region of the adhesin protein gene ALS1 were created. Fold change in the expression of ALS1 relative to the housekeeping gene ACT1 in both experimental strains and the nontargeting CRISPRa control strain was measured with RT-qPCR. Experimental strains are represented by the position of their sgRNA in the promoter region upstream of the proposed dominant ALS1 transcriptional start site. Data points represent the mean fold difference and standard deviation in expression of ALS1 in the CRISPRa strains compared with the nontargeting CRISPRa control (WT; n = 3). b) Two CRISPRa strains targeting ALS1 and a nontargeting CRISPRa control strain were grown in the biofilm-inducing media RPMI and left to grow statically at 37°C for 24 h to allow biofilms to form. The robustness of biofilms formed by all strains was elucidated through an XTT reduction assay and measuring of the resulting OD₄₉₀ values before normalizing final values to nonbiofilm-forming planktonic cells. Data points represent the mean and the standard error of the mean (n = 72). Differences between groups were tested for significance using Dunnett's multiple comparison test. ****P < 0.0001. ***P < 0.001. c) A C. albicans CRISPRa strain overexpressing ALS1 ∼6-fold and a nontargeting CRISPRa strain were grown in human urine supplemented with fibrinogen and BSA at 37°C for 48 h to simulate a catheterized bladder environment. The robustness of biofilms formed by both strains was measured by reading the OD₅₉₅ value following a crystal violet assay. Data points are combined from 3 independent experiments where n = 32 for the nontargeting CRISPRa control and n = 48 for the CRISPRa ALS1 overexpression strain. Differences between groups were tested for significance using the Mann–Whitney U test. ****P < 0.0001. d) Global gene expression of a CRISPRa strain overexpressing ALS1 ∼25-fold, as well as a nontargeting CRISPRa control strain, was measured using RNA-seq. Differential gene expression analysis showed that ALS1 had a log2-fold change of ∼3.3 in the ALS1 overexpression strain compared with the CRISPRa control strain, with an adjusted P-value of 3.07E-142, and showed no additional transcripts that had differential expression with an adjusted P-value of <0.03.

Next, to mimic conditions associated with fungal biofilm formation in catheter-associated urinary tract infections (CAUTIs), we used a urine-based assay and analyzed biofilm formation of a C. albicans strain overexpressing ALS1, relative to its control strain, using crystal violet staining. For this assay, we followed up on the ALS1 CRISPRa strain with ∼6-fold overexpression of ALS1, which had more robust biofilm growth than the control strain (Fig. 4, a and b). The C. albicans strains were incubated for 48 h in human urine supplemented with BSA in fibrinogen-coated 96-well plates. Fibrinogen has been shown to be a critical protein for pathogen binding in CAUTIs, by providing a scaffold for biofilm formation and a nutritional source for pathogens (Flores-Mireles et al. 2016; La Bella et al. 2021; Andersen et al. 2022). The ALS1 CRISPRa overexpression strain showed significantly greater biofilm formation compared with the corresponding wild-type strain (P < 0.0001; Fig. 4c). This difference in biofilm formation suggests that ALS1 promotes binding to fibrinogen and plays a vital role in biofilm formation in urine.

To assess the specificity of this CRISPRa system and ensure that there were no off-target effects from the dCas9–sgRNA complex, we monitored global changes in transcription in the ALS1 CRISPRa strain via RNA-seq. Since ALS1 is a member of the large agglutinin-like sequence (ALS) gene family (Hoyer et al. 2008; Hoyer and Cota 2016), it may be a likely candidate for any potential off-target effects. We also considered ALS1 as a strong candidate for this RNA-seq off-target profiling since ALS1 is not known to be a transcriptional regulator, and therefore its overexpression is unlikely to lead to direct downstream transcriptional alterations that would obscure our analysis of CRISPRa off-target effects. Therefore, we performed RNA-seq on the validated CRISPRa strain overexpressing ALS1 by ∼25-fold as well as on a control strain containing the dCas9 cassette and a nontargeting sgRNA when grown in standard laboratory conditions. This analysis confirmed the specific overexpression of ALS1 (log2-fold change ∼3.3 via RNA-seq analysis, adjusted P-value 3.07E-142; Fig. 4d, Supplementary Table 2). Five additional transcripts were identified as significantly modulated (adjusted P-value < 0.05, absolute log2-fold change > 1; Supplementary Table 2). However, their altered expression was much less significant than ALS1 (Fig. 4d), and none of them had an adjusted P-value of <0.03, differing from the adjusted P-value of 3E-142 for ALS1. We suspect these additional transcripts’ altered expression is likely unrelated to the CRISPRa system based on the fact that 4/5 genes were down-regulated instead of up-regulated, and none of the genes are in proximity to the ALS1 locus, nor do they appear to share any sgRNA target similarities in their promoter regions. Together this suggests that this CRISPRa system is likely specific with regards to gene targeting.

Discussion

Here, we present the development, validation, and application of a single-plasmid-based CRISPRa platform for efficient genetic overexpression in C. albicans. We demonstrate the ability of this system to overexpress genes ∼2- to 20-fold, and recapitulate important biological phenotypes associated with genetic overexpression, including biofilm formation and antifungal drug susceptibility. In addition, we assess sgRNA design principles based on predicted TSS, and validate the specificity of this system by monitoring off-target effects via RNA-seq. Together, this work demonstrates an effective, efficient, and specific tool for genetic overexpression in C. albicans to enhance the available techniques for genetic modulation in this important fungal pathogen. We believe this tool will have practical application for many researchers, and can be easily applied in other laboratories by following plasmid and strain construction protocols for C. albicans–based dCas9 strategies, which we previously described in detail (Wensing and Shapiro 2022). Based on our results, we advise designing and testing 2-3 sgRNAs in regions −100 to −350 bp upstream of both the start codon (ATG) and TSS of the target gene of interest to achieve high levels of overexpression.

Studying genetic overexpression is an important strategy for functional genetic analysis, as overexpressing genes can mimic natural biological states where genes are expressed at high levels. In C. albicans, there are numerous scenarios in which the overexpression of genes occurs, including mutations that modify the function of transcriptional regulators and promote the expression of downstream genes (Coste et al. 2004, 2006), as well as aneuploidies such as trisomies (Selmecki et al. 2006; Forche et al. 2009, 2018; Ford et al. 2015; Li et al. 2015; Anderson et al. 2017; Ene et al. 2018), isochromosome formation (Selmecki et al. 2006, 2008), and copy number variations (Ford et al. 2015; Todd and Selmecki 2020). These overexpression events are frequently detected among C. albicans clinical isolates (Selmecki et al. 2006; Ford et al. 2015; Anderson et al. 2017) and can play an important role in drug resistance (Selmecki et al. 2006, 2008; Li et al. 2015; Todd and Selmecki 2020), host adaptation (Forche et al. 2018, 2019), and other stress tolerance phenotypes (Yang et al. 2019, 2021). For example, amplification of part or all of chromosome 5, via trisomy or isochromosome formation has been tightly linked to the development of resistance to azole antifungals in C. albicans via the increased copy number of genes encoding the azole target, Erg11, and the transcriptional regulator of drug efflux, Tac1 (Selmecki et al. 2008). The prevalence and importance of these phenomena associated with increased gene expression highlight the relevance of developing new tools to targetedly assess phenotypes associated with genetic overexpression in C. albicans.

Harnessing the cell's natural transcriptional machinery to increase gene expression with CRISPRa also leads to a unique set of obstacles. While our RNA-seq results suggest that our CRISPRa system is specific when paired with one sgRNA targeted to ALS1, there remains the possibility of off-target effects occurring in different CRISPRa constructs (Zhang et al. 2015). Using complementary genetic tools to confirm phenotypes observed from CRISPRa can help mitigate this. One phenomenon shared among many different CRISPRa activation domains, including VPR, is a reduced capacity to induce overexpression in genes that are already expressed at a high levels (Chavez et al. 2016). This may explain why we achieved only modest overexpression of YAK1, the gene with the highest basal expression level of all genes tested, but also why a ∼2-fold change in transcript abundance still resulted in a clear decrease in susceptibility to amphotericin B (Fig. 3d). Incidentally, the gene that we achieved the lowest overexpression with across all of our testing, FGR28, had the lowest basal expression (Supplementary Table 3). However, efficiency of the sgRNA molecule and its exact position in the target region of the gene of interest, as well as a host of promoter dynamics, can also influence CRISPR-Cas9 efficacy (Radzisheuskaya et al. 2016; Cámara et al. 2020; Lu and Lin 2021). This illustrates why multiple sgRNAs need to be designed when targeting a gene with CRISPRa to maximize successful overexpression of the target gene. While the rapid and inexpensive nature of CRISPRa strain construction justifies the need to design several sgRNA molecules for a given gene, limitations resulting from the specific “NGG” PAM recognition site by Streptococcus pyogenes Cas9 proteins leads to the restraint that, on rare occasions, it may not be possible to produce any efficient sgRNA molecules in a suitable region of the promoter (Uthayakumar et al. 2021). It may therefore be prudent to further expand the CRISPR toolbox in C. albicans by introducing novel Cas9 variants with differing PAM recognition sites, or by constructing novel CRISPR systems exploiting alternative Cas proteins with different and broader PAM recognition capacities, offering the ability to target regions in the genome that cannot be manipulated with current CRISPR-Cas9 tools (Hu et al. 2018; Ciurkot et al. 2021).

Alternative overexpression strategies in C. albicans that involve introducing an extra copy of a gene of interest or introducing an alternative promoter have been used to construct and screen libraries composed of hundreds and even thousands of protein-coding genes (Rai et al. 2021). These studies have revealed novel insights into gene function related to morphogenesis (Chauvel et al. 2012), cell cycle progression (Jaitly et al. 2022), drug tolerance (Delarze et al. 2020), and biofilm formation (Cabral et al. 2014). Previous comparisons between CRISPRa and ORF-based overexpression libraries in other species have highlighted the potential for these complementary strategies to both confirm findings and unveil distinct genetic hits (Sanson et al. 2018). CRISPRa offers the advantage of being high throughput and easily scalable into a large-scale pooled format, and mutant libraries in different strain backgrounds can therefore be quickly generated (Sanjana 2017; Momen-Roknabadi et al. 2020). In contrast, ORF-based overexpression strategies may be preferable if attempting to overexpress only a single transcript variant or isoform (La Russa and Qi 2015), and promoter replacement strategies may be more valuable to overexpress diverse genes to similar levels. Both of these strategies would be advantageous as well in rare instances when a target sequence lacks a PAM site. Non-CRISPR overexpression libraries previously assembled in C. albicans have the potential weakness of missing phenotypes if the level of expression achieved in mutant strains is too low (Znaidi et al. 2018). The ability of our system to cause upwards of 25-fold overexpression of gene targets is thus an important feature, and future iterations of this CRISPRa system could also exploit an inducible dCas9-VPR construct for tunability of gene expression. Furthermore, phenotypes in mutant strains may only be observable if the level of overexpression closely mimics the level of upregulation that naturally occurs in the cell in a given condition (Znaidi et al. 2018). That our CRISPRa system exploits the endogenous promoter to drive upregulation may thus be critical in understanding natural overexpression mechanisms and regulatory circuitry that other systems are not capable of studying.

While large-scale CRISPRa libraries have not been generated in C. albicans, their application in other organisms, namely mammalian cell lines, has enabled important discoveries in cancer biology, stem cell reprogramming, cellular fitness, and drug resistance (Gilbert et al. 2014; Konermann et al. 2015; Horlbeck et al. 2016; Joung et al. 2017; Jost et al. 2017; Kampmann 2018; Sanson et al. 2018; Yang et al. 2019). The development of CRISPRa systems in C. albicans could enable the development of larger overexpression libraries for functional genomic screens to complement existing fungal gene deletion and depletion libraries (Roemer et al. 2003; Homann et al. 2009; Blankenship et al. 2010; Noble et al. 2010), that have been subject to robust screening and analysis under diverse conditions (Gervais et al. 2021) to reveal critical new insight into fungal biology (Lohse et al. 2016; Hossain et al. 2020), pathogenesis (Noble et al. 2010; Nobile et al. 2012; Ryan et al. 2012; O’Meara et al. 2015; Lee et al. 2016; Hossain et al. 2020), and mechanisms of drug and stress tolerance (Motaung et al. 2017; Mount et al. 2018; Caplan et al. 2018; Fu et al. 2021). The application of similar CRISPRa systems to other fungal organisms would lend further insight into fungal biology across this kingdom. Many CRISPR tools have been developed in a diversity of fungal organisms (Muñoz et al. 2019; Cai et al. 2019; Román, Prieto, et al. 2019; Schuster and Kahmann 2019; Morio et al. 2020; Ouedraogo and Tsang 2020; Shan et al. 2021), including numerous non-albicans Candida species (Enkler et al. 2016; Lombardi et al. 2017, 2019; Zoppo et al. 2019; Maroc and Fairhead 2019; Uthayakumar et al. 2021; Ennis et al. 2021; Santana and O’Meara 2021), and other prominent pathogens such as Cryptococcus neoformans (Fan and Lin 2018; Wang 2018; Huang et al. 2021) and Aspergillus fumigatus (Fuller et al. 2015; Zhang et al. 2016; Al Abdallah et al. 2018; van Rhijn et al. 2020). These CRISPR platforms could be adapted to similar CRISPRa tools in these organisms, with many possible applications for the analysis of pathogen biology or drug target identification (Li et al. 2004; Luesch et al. 2005; Smith et al. 2010; Arnoldo et al. 2014; Begolo et al. 2014).

Genetic overexpression systems also have many useful applications for industrially important fungi, as targeted genetic overexpression can be exploited for metabolic engineering to enhance the production of commercially important metabolites (Donohoue et al. 2018; Zhao et al. 2018; Ouedraogo and Tsang 2020; Jiang et al. 2021). CRISPR techniques have already been adapted to numerous industrially relevant fungi (Nødvig et al. 2015; Kwon et al. 2019; Jiang et al. 2021; Wilson and Harrison 2021), and CRISPRa has recently been optimized for use in the filamentous fungi Aspergillus nidulans and Penicillium rubens (Roux et al. 2020; Mózsik et al. 2021). These CRISPRa systems were applied to activate transcriptionally silent biosynthetic gene clusters in these filamentous fungi, with important applications for the discovery of novel bioactive molecules (Roux et al. 2020; Mózsik et al. 2021). As many Candida species play important industrial roles in manufacturing metabolites for food and pharmaceutical industries, as well as remediating wastewater via hydrocarbon degradation (Joo et al. 2008; Gargouri et al. 2015; Kieliszek et al. 2017; Payen and Thompson 2019; García-Béjar et al. 2020), species-specific adaptations to the CRISPRa tool presented here could produce optimized Candida strains for valuable biomanufacturing and bioremediation applications.

Data availability

The C. albicans CRISPRa plasmid backbone was deposited to Addgene as plasmid number #182707. RNA-seq data were deposited in NCBI GEO repository (accession number GSE201904; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201904).

Supplemental material available at G3 online.

Funding

This work was supported by an Natural Sciences and Engineering Research Council of Canada (NSERC Discovery) Grant (RGPIN-2018-4914), an Ontario Early Research Award, and a CIFAR Azrieli Global Scholar award to R.S.S. N.C.G, L.F.W., and M.F. are EvoFunPath Fellows (NSERC CREATE), L.F.W. is supported by an Ontario Graduate Scholarship, M.F. is supported by an NSERC-CGS-M scholarship, and R.O.C.L was supported by a MITACS Globalink Research Internship. A.C. is supported by a Career Award for Medical Scientists from the Burroughs Wellcome Fund. Work by A.L.F.M. and F.S.T. was supported by institutional funds from the University of Notre Dame as well as National Institute of Health grant R01-DK128805 (to A.L.F.M. and A.A.L.B.), and the Arthur J. Schmitt Presidential Leadership Foundation (to A.A.L.B.).

 

Communicating editor: J. Heitman