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Uncontrolled transposition following RNAi loss causes hypermutation and antifungal drug resistance in clinical isolates of Cryptococcus neoformans

Nature Microbiology volume 7, pages 1239–1251 (2022)Cite this article

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Abstract

Cryptococcus neoformans infections cause approximately 15% of AIDS-related deaths owing to a combination of limited antifungal therapies and drug resistance. A collection of clinical and environmental C. neoformans isolates were assayed for increased mutation rates via fluctuation analysis, and we identified two hypermutator C. neoformans clinical isolates with increased mutation rates when exposed to the combination of rapamycin and FK506. Sequencing of drug target genes found that Cnl1 transposon insertions conferred the majority of resistance to rapamycin and FK506 and could also independently cause resistance to 5-fluoroorotic acid and the clinically relevant antifungal 5-flucytosine. Whole-genome sequencing revealed both hypermutator genomes harbour a nonsense mutation in the RNA-interference component ZNF3 and hundreds of Cnl1 elements organized into massive subtelomeric arrays on each of the fourteen chromosomes. Quantitative trait locus mapping in 28 progeny derived from a cross between a hypermutator and wild-type identified a locus associated with hypermutation that included znf3. CRISPR editing of the znf3 nonsense mutation abolished hypermutation and restored small-interfering-RNA production. We conclude that hypermutation and drug resistance in these clinical isolates result from RNA-interference loss and accumulation of Cnl1 elements.

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Fig. 1: Hypermutation in Bt65 and Bt81 is driven primarily by the insertion of Cnl1 into FRR1.
Fig. 2: QTL analysis of the hypermutator phenotype.
Fig. 3: Retrotransposon content in the genomes of H99, Bt65, and Bt81.
Fig. 4: Genetic recombination sites and Cnl1 distribution in Bt65 × H99 crg1Δ F1 progeny.
Fig. 5: ZNF3 complementation in Bt65 significantly reduces mutation rates and restores siRNA production.

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Data availability

Data generated in this study are available under BioProject PRJNA749953. The BioProject accession numbers for each sample are provided in Supplementary Table 7. The publicly available datasets utilized in this study are: H99 genome (genome assembly ASM301198v1), RepBaseRepeatMaskerEdition-20181026 libraries, RepBase EMBL database (v26.04), H99, and Bt65 Illumina reads were used from published datasets (SRR642222 and SRR647805 for H99; SRR836876, SRR836877, SRR836878, SRR836880, SRR836884, and SRR836885 for Bt65) and the H99 reference genome (FungiDB-46_CneoformansH99_Genome.fasta). Source data are provided with this paper.

Code availability

Genetic variant filtering, QTL mapping, and SNP-effect prediction were conducted in Python (anaconda 3.7.3) via custom scripts available in GitHub (https://github.com/magwenelab/Hypermutator_QTL). All custom Perl scripts reported in Methods for sRNA analysis are also available in GitHub (https://github.com/timdahlmann/smallRNA).

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Acknowledgements

We thank B. Billmyre for initial project guidance, S. Clancey for instruction in conducting fluctuation assays and dsRNA enrichment protocols, J. Granek for preliminary analyses of hypermutator genomes, Z. Chang for assistance with the sRNA isolation, K. Sylvester for assistance with the screening of C. neoformans isolates, and the laboratory of C. Holley at Duke University for the use of their Nanodrop and BioAnalyzer equipment for preliminary sRNA analyses. We thank M. Farman and M. Rahnama for stimulating discussion on the impacts of transposons on telomere dynamics. We thank K. Zhu for assistance with the generation of figures. We also thank S. Sun, B. Billmyre, A. Alspaugh, S. Jinks-Robertson, A. Gusa, and K. Sylvester for critical reading and comments on the manuscript. This work was funded by NIH/NIAID F31 Fellowship 1F31AI143136-02A1 awarded to S.J.P., NIH/NIAID R37 MERIT award AI39115-23 and R01 grant AI50113-16 awarded to J.H., and R01 grant AI33654-04 awarded to P.M.M. and J.H. These studies were supported by a Visiting Professor travel grant awarded by Ruhr-Universität Bochum, Germany to J.H. J.H. is co-director and Fellow of the CIFAR program Fungal Kingdom: Threats and Opportunities. We also thank the Madhani laboratory and NIH grant R01 AI100272 for the KN99α msh2Δ deletion strain. T.A.D. and U.K. are funded by the German Research Foundation (DFG; Bonn Bad Godesberg, Germany; grant no. KU 517/15-1).

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Author notes

  1. These authors contributed equally: Vikas Yadav, Cullen Roth.

Authors and Affiliations

  1. Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC, USA

    Shelby J. Priest, Vikas Yadav & Joseph Heitman

  2. Department of Biology, Duke University, Durham, NC, USA

    Cullen Roth & Paul M. Magwene

  3. University Program in Genetics and Genomics, Duke University, Durham, NC, USA

    Cullen Roth

  4. Allgemeine und Molekulare Botanik, Ruhr-Universität Bochum, Bochum, Germany

    Tim A. Dahlmann & Ulrich Kück

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Contributions

S.J.P., V.Y., C.R., T.A.D., U.K., P.M.M., and J.H. designed experiments, interpreted data, and wrote the paper. S.J.P. performed experiments and analysed the fluctuation assay and Sanger sequencing data. V.Y. conducted the Nanopore sequencing and analysed all of the resulting data. C.R. and P.M.M. analysed the sequencing data from Bt65 × H99 crg1Δ F1 progeny and conducted QTL mapping and analysis. T.A.D. and U.K. analysed the sRNA sequencing data. S.J.P., U.K., P.M.M., and J.H. provided resources.

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Correspondence to Joseph Heitman.

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Extended data

Extended Data Fig. 1 Bt65 and Bt81 do not display a hypermutator phenotype on 5-FC or 5-FOA.

Mutation rates of closely related VNBII strains and controls on (A) YNB + 5-FC and (B) YNB + 5-FOA media. Heights of bars represent the mutation rate and error bars represent 95% confidence intervals; mutation rates represent the number of mutations per cell per generation. Fluctuation analysis was conducted once for all strains in each experiment, with n = 10 biologically independent cultures per strain. Schematic depicts the phylogenetic relationships of all strains included in fluctuation analyses based on Desjardins et al. 201713.

Source data

Extended Data Fig. 2 Growth at elevated temperature does not result in increased mutation rates in C. neoformans strains.

Fluctuation assays were used to quantify the mutation rates of strains grown overnight at 30 °C or 37 °C and plated on YPD medium with R + F. Heights of bars indicate mean mutation rate and error bars indicate 95% confidence intervals. Mutation rates represent the number of mutations per cell per generation. Fluctuation analysis was conducted once for all strains, with n = 10 biologically independent cultures per strain.

Source data

Extended Data Fig. 3 Gel electrophoresis of FRR1, URA5, and FUR1 PCR products from resistant colonies.

Gel electrophoresis of FRR1 PCR products from (A) all H99 R + FR colonies and a subset of (B) Bt65 and Bt81 R + FR colonies sequenced in Fig. 1D. PCR amplification of wild-type FRR1 in C. neoformans produces a 1,165 bp electrophoretic species (primers ZC7/8). Gel electrophoresis of a subset of (C) URA5 PCR products from H99, Bt65, and Bt81 5-FOAR colonies and (D) FUR1 and (E) UXS1 PCR products from 5-FCR colonies of Bt65 and Bt81.

Source data

Extended Data Fig. 4 Protein length differences of genes within QTL.

In the upper panels, points mark the strength of association (y-axis) between bi-allelic SNP sites and hypermutation for Chr3 and Chr11 (top left and right, respectively). Grey dashed lines depict the 95% confidence intervals (CI) of the two QTL. For the bi-allelic SNPs within the two QTL 95% CIs, P = 1.46868 × 10−5 (Kruskal–Wallis H-test). Lower panels show the predicted differences in lengths of proteins (y-axis) encoded by annotated genes in Bt65 compared to H99 within each 95% CI of the QTL (x-axis) on Chr3 and Chr11 (bottom left and right, respectively). The name of each gene with a predicted nonsense mutation is annotated. Blue and red colours denote the gene orientation.

Source data

Extended Data Fig. 5 QTL associated with the hypermutator phenotype span a chromosomal translocation.

(A) Nanopore whole-genome sequencing followed by synteny analysis was used to identify all indicated genomic rearrangements with respect to the reference strain H99. There is a chromosomal translocation between Chr3 and Chr11 unique to H99, and a translocation between H99 Chr1 and Chr13 unique to Bt65 and Bt81. Phylogenetic relationships of these strains are depicted in the top schematic, telomeric repeat sequences accurately identified in genomic assemblies are indicated by black half circles, and centromeres are indicated by white circles. (B) Haplotype maps of Bt65 x H99 crg1Δ F1 progeny utilized for QTL mapping. For QTLs on Chr3 and Chr11, the haplotypes (x-axis) are inferred by SNP data per segregant (y-axis) and coloured blue or orange if inherited from H99 crg1Δ or Bt65, respectively. Segregants are sorted along the y-axis by their mutation rate; largest to smallest, top to bottom. Vertical red lines display boundaries of the QTL(s). Vertical black lines depict approximate location of the translocation between H99 and Bt65. Boundaries of the QTG, ZNF3, are depicted by vertical green lines. Vertical white spaces indicate approximate locations of centromeres.

Extended Data Fig. 6 Mutation rates of Bt81 x H99 crg1Δ F1 progeny.

Fluctuation analysis was used to quantify the mutation rates of the indicated strains on YPD + rapamycin + FK506 medium (y-axis) – sorted smallest to largest, left to right – for F1 progeny and the parental strains, H99 crg1Δα and Bt81 (x-axis). Heights of bars indicate the mean mutation rate and error bars represent 95% confidence intervals. Mutation rates represent the number of mutations per cell per generation. Inheritance of the Bt81 znf3 allele or H99 crg1Δ ZNF3 allele in the F1 progeny is indicated above mutation rates. Fluctuation analysis was conducted once for all strains, with n = 10 biologically independent cultures per strain.

Source data

Extended Data Fig. 7 Subtelomeric and centromeric retrotransposons in Bt89 and Bt133.

Distributions of the Tcn1–Tcn6 LTR retrotransposons and the Cnl1 non-LTR retrotransposon in the genomes of (A) Bt89 and (B) Bt133. 50 kb of subtelomeric regions as well as centromeric regions are displayed for both strains. Shading corresponds to the lengths of the Cnl1 elements, and gene arrowheads indicate the direction of transcription for all retrotransposons.

Extended Data Fig. 8 Centromere lengths do not significantly differ among H99, Bt65, Bt81, Bt89, and Bt133.

The length of each centromere (y-axis) is plotted for each strain (x-axis). The thin horizontal black line indicates average centromere length and the thicker black error bars indicate the standard error of the mean. No significant difference was found between the average centromere length of each strain (one-way analysis of variance, P = 0.153).

Extended Data Fig. 9 Distribution of Cnl1 among Bt65 x H99 crg1Δ F1 progeny and parental strains.

The Cnl1 non-LTR elements identified in the Nanopore-based whole-genome assemblies are depicted for H99, Bt65, three hypermutator F1 progeny (P02, P08, and P34, all on the left), and three non-hypermutator F1 progeny (P14, P18, and P20, all on the right). Blue and orange bars under the subtelomeric region of each chromosome indicate which parental strain the region was inherited from (orange for Bt65, blue for H99 crg1Δ). Red asterisks indicate invasion of Cnl1 into an H99 crg1Δ subtelomeric region that previously had zero Cnl1 copies/fragments. Accurate assembly of telomeric repeat sequences at the end of each chromosome is indicated by a black half circle. Cnl1 length is also indicated by the shade of black for each element.

Extended Data Fig. 10 Enrichment for dsRNA does not identify any fragments likely to be dsRNA mycoviruses.

Pictured on the left are RNA samples following LiCl enrichment for dsRNA run on a 1% agarose gel. Total RNA prior to dsRNA enrichment is pictured on the right on a 1% agarose gel. Ms + is a Malassezia sympodialis strain that harbours a dsRNA virus, and Ms- is a congenic virus-cleared strain69. Two biological replicates for all samples are shown and labelled (1) and (2). The TriDye 1 kb DNA ladder (NEB) was used to estimate RNA fragment sizes.

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Supplementary Tables 1–7, Supplementary Figs. 1–10 and Source Data Supplementary Figs. 9,10.

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Source Data Fig. 1

Raw colony counts and mutation frequencies from fluctuation analysis and Sanger sequencing results.

Source Data Fig. 2

Raw colony counts and mutation frequencies from fluctuation analysis.

Source Data Fig. 5

Raw colony counts and mutation frequencies from fluctuation analysis, differential expression analysis and quantification of sense and antisense sRNA reads corresponding to transposable elements.

Source Data Extended Data Fig. 1

Raw colony counts and mutation frequencies from fluctuation analysis.

Source Data Extended Data Fig. 2

Raw colony counts and mutation frequencies from fluctuation analysis.

Source Data Extended Data Fig. 3

Original gel images.

Source Data Extended Data Fig. 4

Genetic variants and predicted changes in genes within the QTL between H99 and Bt65.

Source Data Extended Data Fig. 6

Raw colony counts and mutation frequencies from fluctuation analysis.

Source Data Extended Data Fig. 10

Original gel images.

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Priest, S.J., Yadav, V., Roth, C. et al. Uncontrolled transposition following RNAi loss causes hypermutation and antifungal drug resistance in clinical isolates of Cryptococcus neoformans. Nat Microbiol 7, 1239–1251 (2022). https://doi.org/10.1038/s41564-022-01183-z

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