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Pooled CRISPRi screening reveals fungal-specific drug target candidates

Nature Microbiology (2026) Cite this article

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Abstract

The rising rate of drug-resistant fungal infections and the emergence of intrinsically resistant pathogens pose growing clinical challenges. Because fungi are closely related to mammals, developing antifungals without toxic off-target effects is difficult. Targeted gene repression can model drug-mediated inhibition and reveal gene dosage sensitivity, but traditional approaches in the fungal pathogen Candida albicans are labour intensive and low throughput. Here we adapt pooled CRISPR interference (CRISPRi) screening in C. albicans to enable large-scale functional genomic analysis. We assess repression sensitivity of 130 essential genes conserved in fungi without close homologues in humans and identify highly dosage-sensitive genes across multiple pathways. Screening across ten environmental conditions reveals environment-dependent effects on gene sensitivity. Extending these experiments to two drug-resistant clinical isolates shows that many fitness defects are conserved across genetic backgrounds. Thus, CRISPRi pooled screening enables rapid, large-scale functional genomics across diverse genetic backgrounds in C. albicans.

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Fig. 1: Regulatable CRISPRi for pooled screening in Candida albicans.
The alternative text for this image may have been generated using AI.
Fig. 2: Pooled CRISPRi screening of essential genes in C. albicans identifies repression-sensitivity phenotypes.
The alternative text for this image may have been generated using AI.
Fig. 3: Parallel screening identifies condition-independent repression-sensitive genes.
The alternative text for this image may have been generated using AI.
Fig. 4: Portable CRISPRi screening of C. albicans essential genes identifies a core set of repression-sensitive targets.
The alternative text for this image may have been generated using AI.

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Data availability

The sequencing data for pooled competition assays and whole-genome sequencing were deposited on the NCBI SRA (accession number PRJNA1381690). Read counts and log2FC values of sgRNAs for each screen are provided as Supplementary Tables 6–8. Source data for growth curve, qPCR assays and whole-genome sequencing are available in GitHub at https://github.com/TheShapiroLab/Calbicans_Pooled_CRISPRi_2025/ (ref. 67). Source data are provided with this paper.

Code availability

Data analysis was performed using custom Python 3.12.7 notebooks and scripts, available at https://github.com/TheShapiroLab/Calbicans_Pooled_CRISPRi_2025/ (ref. 67).

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Acknowledgements

This research was enabled in part by support provided by SHARCNET (sharcnet.ca), Calcul Québec (calculquebec.ca) and the Digital Research Alliance of Canada (alliance can.ca). We thank C. Landry for comments on the manuscript. P.C.D. was supported by a Fonds de Recherche du Québec - Santé (FRQS) postdoctoral fellowship (https://doi.org/10.69777/376319). L.F.W., M.F. and N.C.G. were supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) through PGS-D awards. This work was supported by the Canadian Institutes of Health Research (CIHR) through grant PJT 162195 to R.S.S., the Canadian Institute for Advanced Research (CIFAR) through a grant from the Fungal Kingdom: Threats & Opportunities programme to both C.A.C. and R.S.S., and the National Institute of Allergy and Infectious Diseases through grant U19 AI110818, and an NSERC Discovery Grant RGPIN-2019-05867 to A.C.G. R.S.S. holds the Canada Research Chair in Microbial Functional Genomics and Synthetic Biology. R.S.S and A.C.G received funding through the CIFAR Azrieli Global Scholars programme.

Author information

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  1. These authors contributed equally: Lauren F. Wensing, Philippe C. Després.

Authors and Affiliations

  1. Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario, Canada

    Lauren F. Wensing, Philippe C. Després, Desiree Francis, Meea Fogal, Anthony Hendriks, Nicholas C. Gervais, Clara Fikry & Rebecca S. Shapiro

  2. Infectious Disease and Microbiome Program, Broad Institute, Cambridge, MA, USA

    Philippe C. Després & Christina A. Cuomo

  3. Department of Molecular Microbiology and Immunology, Brown University, Providence, RI, USA

    Philippe C. Després & Christina A. Cuomo

  4. Department of Microbiology, Faculty of Science, University of Manitoba, Winnipeg, Manitoba, Canada

    Abdul-Rahman Adamu Bukari & Aleeza C. Gerstein

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Contributions

L.F.W., P.C.D., C.A.C. and R.S.S. designed the research. L.F.W., P.C.D., D.F., M.F., A.H., N.C.G. and C.F. performed experiments. P.C.D., L.F.W., A.-R.A.B. and A.C.G. analysed the data. P.C.D., L.F.W. and R.S.S. wrote the manuscript, with input from all authors.

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Correspondence to Rebecca S. Shapiro.

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Extended data

Extended Data Fig. 1 Replicate correlation in the time-course competition assay.

a) Log2FC in CRISPRi ON conditions after the third passage across replicates. Randomized sgRNAs (n = 29) are shown in green, and those targeting essential gene promoters (n = 438) are in black. The correlation between values was measured using two-sided Spearman’s rank correlation. b) Spearman’s rank correlation matrix across all time-course samples (n = 469 sgRNAs per comparison).

Source data

Extended Data Fig. 2 Distribution of abundance changes across passages.

In all plots, the dotted red line shows the significance threshold for depletion when comparing library and randomized sgRNAs. a) Abundance changes for CRISPRi-ON (media without ATc) samples. b) Abundance change for CRISPRi-OFF samples (media with ATc) c) Distributions of CRISPRi-ON/OFF Log2FC differences for the different passages.

Source data

Extended Data Fig. 3 Repression sensitive hit genes encoding mitochondrial ribosomal subunits do not cluster in space.

The structure shown is the Saccharomyces cerevisiae mitochondrial subunit (pdb: 5MRC68, with orthologs corresponding to putative repression sensitive genes in C. albicans: MRPL33 (S.c. MRPL33, red), C1_12610W (S.c.: MRPL15, yellow), C2_07680W (S.c.: MRPL7, purple), CR_01370C (S.c.: MRPS28, blue), and CR_04140W (S.c.: NAM9, green).

Extended Data Fig. 4 Inter-replicate and inter-condition correlation in the multi-condition screen.

Spearman’s rank correlation matrix across all samples (n = 469 sgRNAs per comparison) for CRISPRi-OFF and CRISPRi-ON samples.

Source data

Extended Data Fig. 5 The tunicamycin condition has lower signal to noise ratio, resulting in downstream bottlenecks when comparing conditions.

a) The number of hit sgRNAs for a specific condition is negatively correlated with signal to noise ratio. For each condition, we computed the difference between the 5% FDR Log2FC threshold and the median Log2FC of the five most depleted sgRNAs. Correlation was measured using two-sided Spearman’s rank correlation (n = 11 conditions). Data points are colored by condition type as shown on the left. b) Number of conditions with significant fitness decreases for hit genes in the experiment, with or without including the tunicamycin condition. c) Number of depleted sgRNAs per gene in each condition, with tunicamycin highlighted in green. Only genes which had hits in at least two conditions are shown.

Extended Data Fig. 6 Genes with constant repression sensitivity phenotypes.

Overall distribution of Log2FC for library sgRNAs is shown as a violin plot, with an inner dark gray boxplot representing the upper and lower quartiles of the data, and whiskers extending to 1.5 times the interquartile range (Q3–Q1) at most. Randomized sgRNAs are shown individually in green. The black lines indicate the significance threshold for each condition. Error bars show the 95% confidence interval of the three biological replicates around the mean Log2FC. a) MRPL33 b) SPT23 c) C2_07680W d) C2_03560C.

Source data

Extended Data Fig. 7 Pooled CRISPRi screening is reproducible in different genetic backgrounds.

a) Inter-replicate correlation for CRISPRi-ON samples of the wild-type strain library (R1 vs R2: n = 475, R1 vs R3: n = 473, R2 vs R3: n = 474, two-sided Spearman’s rank correlation). b) Inter-replicate correlation for CRISPRi-ON samples of the FLUR strain library (R1 vs R2: n = 526, R1 vs R3: n = 515, R2 vs R3: n = 515, two-sided Spearman’s rank correlation). c) Inter-replicate correlation for CRISPRi-ON samples of the CASPR strain library (R1 vs R2: n = 521, R1 vs R3: n = 519, R2 vs R3: n = 519, two-sided Spearman’s rank correlation). d) Two-sided Spearman’s rank correlation matrix across all backgrounds (n = 436 sgRNAs per comparison) for CRISPRi-OFF and CRISPRi-ON samples.

Source data

Extended Data Fig. 8 Fitness effects of depleted sgRNAs can be validated across backgrounds.

Fitness effects of sgRNAs in the pooled assay (top) and in small-scale growth curve assays (bottom). Overall distribution of Log2FC for library sgRNAs is shown as a violin plot, with a inner dark gray boxplot representing the upper and lower quartiles of the data, and whiskers extending to 1.5 times the interquartile range (Q3–Q1) at most. Randomized sgRNAs are shown individually in green. a) Effects for MRPL33-2 (yellow) and MRPL33-3 (purple) Grey bars show the mean of two biological replicates; error bars correspond to the 95% confidence interval of three technical replicates. b) Effects for ASK1-2 (yellow) and ASK1-3 (purple). Grey bars show the mean of two biological replicates; error bars correspond to the 95% confidence interval of three technical replicates.

Source data

Extended Data Fig. 9 Few sgRNAs are affected by heterozygous or homozygous variants.

a) Single nucleotide polymorphisms (SNPs) detected by whole genome sequencing for the three strains when compared to the SC5314 reference genome. b) Overlap between sgRNAs with target binding regions affected by SNPs. The lower left histogram shows the total number of sgRNAs affected for each strain.

Source data

Extended Data Fig. 10 CRISPRi antagonism or synergy with caspofungin in the FLUR genetic background.

Interaction between CRISPRi repression and Caspofungin treatment. Only genes with at least one sgRNA showing antagonism or synergy of sufficient magnitude and statistical significance (Benjamini-Hochberg corrected p-value < 0.05, two-sided Welch’s t-test) are shown as triangles and colored. The direction of the triangle indicates the nature of the interaction, as indicated by the compass: left: synergy in WT (QCR8); right: antagonism in WT (MRPL33, SPT23); down: synergy in CASPR (none); up: antagonism in CASPR (MMM1, MRPL33, QCR8, RSM22, SPT23); upper right: antagonism in both strains (C2_035560C). The black lines show the thresholds for the magnitude of z-score change (see methods). Genes and sgRNAs not showing synergy or antagonism are shown in grey.

Source data

Supplementary information

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Supplementary Figs. 1–4 and Note.

Reporting Summary (download PDF )

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Supplementary Table (download XLSX )

Supplementary Tables 1–8 contains (1) functional annotations of the 130 fungal specific essential genes targeted in the CRISPRi strain library, (2) strains used in this study, (3) plasmids used in this study, (4) oligonucleotides used in this study, (5) media recipes, (6) counts and log2FC values for the timecourse assay, (7) counts and log2FC values for the environmental assay and (8) counts and log2FC values for the clinical isolates assay.

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Wensing, L.F., Després, P.C., Francis, D. et al. Pooled CRISPRi screening reveals fungal-specific drug target candidates. Nat Microbiol (2026). https://doi.org/10.1038/s41564-026-02356-w

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