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Apr 7, 2015 - exclude unlinked mutational effects, we generated more than two independent TF mutants for all 155 TFs, including 4 TFs (HXL1,. ATF1, MBS1 ...
ARTICLE Received 3 Dec 2014 | Accepted 24 Feb 2015 | Published 7 Apr 2015

DOI: 10.1038/ncomms7757

OPEN

Systematic functional profiling of transcription factor networks in Cryptococcus neoformans Kwang-Woo Jung1,*, Dong-Hoon Yang1,*, Shinae Maeng1, Kyung-Tae Lee1, Yee-Seul So1, Joohyeon Hong2, Jaeyoung Choi3, Hyo-Jeong Byun1, Hyelim Kim1, Soohyun Bang1, Min-Hee Song1, Jang-Won Lee1, Min Su Kim1, Seo-Young Kim1, Je-Hyun Ji1, Goun Park1, Hyojeong Kwon1, Suyeon Cha1, Gena Lee Meyers1, Li Li Wang1, Jooyoung Jang1, Guilhem Janbon4, Gloria Adedoyin5, Taeyup Kim5, Anna K. Averette5, Joseph Heitman5, Eunji Cheong2, Yong-Hwan Lee3, Yin-Won Lee3 & Yong-Sun Bahn1

Cryptococcus neoformans causes life-threatening meningoencephalitis in humans, but its overall biological and pathogenic regulatory circuits remain elusive, particularly due to the presence of an evolutionarily divergent set of transcription factors (TFs). Here, we report the construction of a high-quality library of 322 signature-tagged gene-deletion strains for 155 putative TF genes previously predicted using the DNA-binding domain TF database, and examine their in vitro and in vivo phenotypic traits under 32 distinct growth conditions. At least one phenotypic trait is exhibited by 145 out of 155 TF mutants (93%) and B85% of them (132/155) are functionally characterized for the first time in this study. The genotypic and phenotypic data for each TF are available in the C. neoformans TF phenome database (http://tf.cryptococcus.org). In conclusion, our phenome-based functional analysis of the C. neoformans TF mutant library provides key insights into transcriptional networks of basidiomycetous fungi and human fungal pathogens.

1 Department

of Biotechnology, Center for Fungal Pathogenesis, College of Life Science and Biotechnology, Yonsei University, Seoul 120-749, Korea. of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 120-749, Korea. 3 Department of Agricultural Biotechnology, Center for Fungal Pathogenesis, Seoul National University, Seoul 151-921, Korea. 4 Unite´ Biologie et Pathoge´nicite´ Fongiques, De´partement de Mycologie, Institut Pasteur, Paris F-75015, France. 5 Department of Molecular Genetics and Microbiology, Medicine, and Pharmacology and Cancer Biology, Duke University Medical Center, Durham, North Carolina 27710, USA. * These authors contributed equally to this work. Correspondence and requests for materials should be addressed to Y.-S.B. (email: [email protected]). 2 Department

NATURE COMMUNICATIONS | 6:6757 | DOI: 10.1038/ncomms7757 | www.nature.com/naturecommunications

& 2015 Macmillan Publishers Limited. All rights reserved.

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ARTICLE

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms7757

ryptococcus neoformans is a basidiomycete fungal pathogen that causes meningoencephalitis—mainly in immunocompromised populations—and is responsible for more than 600,000 deaths annually worldwide1. However, limited therapeutic options are available for treating cryptococcosis2, and a complete understanding of diverse biological aspects of Cryptococcus is urgently required for developing novel therapeutic targets and methods. To this end, the signalling cascades governing the general biology and pathogenicity of C. neoformans have been extensively studied over the past decades. This has allowed us to understand several key metabolic and signalling pathways in this pathogen, including those involving Ras, cAMP, Rim101, calcineurin, three MAPKs (Cpk1, Mpk1 and Hog1) and the unfolded protein response (UPR)3. Most of the aforementioned signalling cascades are composed of sensor and receptor-like proteins and kinases or phosphatases, and are often equipped with unique adaptor or scaffolding proteins to enhance the specificity of each signalling pathway to prevent aberrant crosstalk. Nevertheless, each signalling cascade ultimately activates or represses its effector proteins through transcription factor (TF) binding to specific promoters to regulate transcription. Repertoires of TFs are often more divergent among species than are those of other signalling components. This appears particularly true in the case of C. neoformans, as evident from recent genome analyses4. Therefore, C. neoformans appears to possess numerous evolutionarily conserved signalling cascades featuring divergent sets of TFs, which might govern the characteristics of C. neoformans that are unique compared with those of other fungi. To understand C. neoformans TF networks on a global scale, we constructed a high-quality gene-deletion collection through homologous recombination methods for 155 putative C. neoformans TFs previously predicted to contain DNA-binding domains (DBDs)5,6. The TF mutant strains are analysed for 30 distinct in vitro phenotypic traits, which cover growth, differentiation, stress responses, antifungal resistance and virulence-factor production. Moreover, we also performed a large-scale virulence test using an insect host model and signature-tagged mutagenesis (STM) scoring in a murine host model. This comprehensive phenotypic data set (phenome) of the TFs, which can be accessed online through the Cryptococcus Transcription Factor Phenome Database (http://tf.cryptococcus. org) provides a unique opportunity to understand general biological features of C. neoformans and identifies novel putative pathways that could be targeted for the treatment of cryptococcosis. This TF mutant collection and its phenome data are a valuable resource for those studying Cryptococcus and the general fungal research community.

C

Zn2-Cys6 DBD, and among these, 40 also harbour a fungal-specific TF domain. Several TFs contain more than two TF domains (Supplementary Data 1). To analyse the functions of the TFs, we deleted 155 putative TF genes out of 178 using homologous recombination. To perform a large-scale virulence test, dominant nourseothricin-resistance markers (NATs) containing a series of signature tags of distinct oligonucleotide sequences were employed (Supplementary Data 2). The genotypes of all TF mutants were confirmed by performing Southern blot analysis to verify both the gene deletion and the absence of any ectopic integration of each genedisruption cassette. To accurately validate the phenotype and exclude unlinked mutational effects, we generated more than two independent TF mutants for all 155 TFs, including 4 TFs (HXL1, ATF1, MBS1 and SKN7) that we previously reported7–9, and thus obtained a total of 322 strains. For parallel in vitro and in vivo phenotypic analysis, we deleted 53 TF genes, which were previously deleted in the CMO18 strain (a less virulent H99 strain)10, and derived more than two independent mutants. Certain known TFs, including RIM101, ADA2, CUF1, SXI1, SP-1/ CRZ1, NRG1, STE12, BWC2, SRE1, ZNF2 and HAP1/HAP2, were also independently deleted here to accurately compare phenotypes. When two independent TF mutants showed inconsistent phenotypes because of inter-isolate inconsistency, additional TF mutants were generated to exclude outlier mutants. We found that about 8% of gene knockouts (13 TFs) exhibited inconsistent phenotypes, potentially attributable to undetectable mutational artefacts or unexpected alterations in the genome (Supplementary Data 4). This level is highly similar to that reported in a similar study on the ascomycete fungal pathogen Candida albicans11. For the remaining 23 TFs, we could not generate TF mutants. Among these TFs, 6 (ESA1, CEF1, CDC39, RSC8, HSF1 and PZF1) are orthologous to yeast TFs that are essential for growth of Saccharomyces cerevisiae. The remaining 17 TF genes could not be deleted even after repeated attempts at gene disruption and thus they are presumed to be critical or essential for growth in C. neoformans. However, CIR1, MIG1, CNAG_06252 and CNAG_04798 have been successfully deleted previously10,12,13, suggesting that these TFs could be deleted through additional efforts in the future. In summary, we successfully constructed a C. neoformans TF mutant collection that covers 155 TFs and 322 TF mutant strains in total (Fig. 1b). Out of the 155 TFs whose mutants were constructed, 57 TF genes possess names designated in published studies or reserved by other researchers through registration in FungiDB (www. fungidb.org). For the remaining 98 TFs, we provided gene names by following the systematic genetic nomenclature flowchart in C. neoformans recently reported by Inglis et al.14 (Supplementary Data 1).

Results Cryptococcus transcription factor mutant collection. We first selected putative TFs using the published DBD TF prediction database (http://www.transcriptionfactor.org/)6. The C. neoformans H99 strain, a serotype A platform strain, contains 188 TFs (148 predicted from Pfam and 96 from SUPERFAMILY). Because these TFs were predicted based on the first version of the annotated H99 genome database, we updated this database with reference to the most recent version (ver.7) of the annotated H99 genome database4, which resulted in a final prediction of 178 TFs (Supplementary Data 1). Orthologue mapping based on the BLAST e-value matrix demonstrated that C. neoformans contains several evolutionarily distinct groups of TFs (Supplementary Data 3). The Cryptococcus DBD TFs were classified based on their DBDs (Fig. 1a). Nearly 44% of these TFs (78) contain a fungal

Phenotypic profiling of the Cryptococcus TF mutant library. For the 322 TF mutants constructed, we performed a series of phenotypic analyses for the following phenotypic classes: growth, differentiation and morphology, stress responses, antifungal drug resistance, virulence-factor production and in vivo virulence (Fig. 1b). This overall phenome data set generated for the TF mutant collection is illustrated together with a colour scale in the phenome heat map in Fig. 2 and Supplementary Data 4. Data for transcript levels of each TF measured by RNA sequencing analyses under six distinct growth conditions were obtained from a recent H99 genome analysis report4 and also demonstrated as a heat map (Fig. 2; Supplementary Data 4). The phenotypic analysis revealed that about 93% of the TF mutants (145/155) exhibited at least one discernable phenotype, suggesting a high functional coverage of this TF mutant collection. Almost 85% of the TFs

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NATURE COMMUNICATIONS | 6:6757 | DOI: 10.1038/ncomms7757 | www.nature.com/naturecommunications

& 2015 Macmillan Publishers Limited. All rights reserved.

ARTICLE

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms7757

(132/155) have not been functionally characterized before in C. neoformans. All of these phenome data are publicly available in the Cryptococcus neoformans TF database (http://tf. cryptococcus.org).

TFs governing growth and differentiation of C. neoformans. C. neoformans undergoes both saprobic and pathogenic life cycles in natural and animal host environments. Therefore, it must be capable of growing at temperatures ranging from ambient (25 °C)

SRF-like transcription factor domain (2; 1%) Other zinc fingers: C5HC2, MIZ, NF-X1 type (3; 2%) Miscellaneous (8; 4%) HSF-type DNA-binding domain (4; 2%) APSES domain (3; 2%) Homeobox domain (9; 5%) Basic region leucine zipper domain (12; 7%) Helix-turn-helix (HTH) domain (2; 1%) C2H2 zinc-finger domain (25; 14%) Helix-loop-helix (HLH) domain (9; 5%) Myb-like DNA-binding domain (4; 2%)

GRF zinc-finger domain (3; 2%)

Fork head domain (5; 3%)

GATA zinc-finger domain (11; 6%)

Fungal Zn2-Cys6 binuclear cluster domain (78: 44%) (40 of them also contain a fungal-specific transcription factor domain)

Selection of 178 putative transcription factors in Cryptococcus neoformans var. grubii (predicted by DBD database, http://www.transcriptionfactor.org/)

Construction of gene disruption cassettes by NAT-split marker double-joint PCR of overlap PCR (using plasmids having signature-tagged NAT marker)

Gene disruption by biolistic transformation (parental strain: H99α strain)

First screening of nourseothricin-resistant transformants by diagnostic PCR

Mutant verification by Southern blot analysis (155 TF genes: >2 independent strains; total 322 strains)

In vitro phenotypic analysis

In vitro phenotypic analysis

Growth Differentiation Virulence factor production Stress responses Antifungal drug resistance

Survival test : insect model (wax moth) Fungal burden assay : STM-based murine model

Cryptococcus TF phenome database (http://tf.cryptococcus.org)

Figure 1 | Overview of Cryptococcus neoformans transcription factors and strategies for their systematic deletion and phenome-based analysis. (a) Pie chart showing the class and distribution of C. neoformans TFs. Each TF was classified based on the DBDs predicted using Superfamily (http:// www.supfam.org/SUPERFAMILY/) and Pfam (http://pfam.xfam.org/) databases or Cryptococcus genome database (http://www.broadinstitute.org). Certain TFs contain multiple DBDs (Supplementary Data 1). (b) Flowchart of the construction of the C. neoformans TF mutant library and in vitro and in vivo phoneme-based analyses. NATURE COMMUNICATIONS | 6:6757 | DOI: 10.1038/ncomms7757 | www.nature.com/naturecommunications

& 2015 Macmillan Publishers Limited. All rights reserved.

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ARTICLE

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms7757

In vivo virulence data

4

H99 ID

Gene

02566 00791 01069 07464 03401 04588 00828 03561 06762 06276 05785 03132 01438 07593 04837 05093 05642 05431 04398 04878 03183 03710 03279 04637 06425 04345 04184 02774 00670 00068 05010 04090 06134 04630 07901 01173 03115 07924 07435 02555 04594 06188 05170 02241 06921 05186 00896 00039 07940 06814 01454 03527 05255 05112 01883 04353 05375 03998 00239 06871 00156 04268 05420 00018 03346 00514 05538 03409 06339 07011 07506 04807 02435 02364 03116 02877 00559 03914 00871 06483 07797 05019 05380 04518 05176 05861 00460 02305 03849 01014 01858 06719 04093 04176 01431 07443 04916 05392 02322 04012 00505 06751 04841 03212 01626 03894 02066 06818 05940 01948 04804 04352 03768 01977 04895 04583 01973 02603 04036 06156 03431 07922 00031 03018 04263 01708 00332 07724 03902 03366 05222 05049 02516 04774 03059 00193 03336 03086 03229 01841 02476 05153 02700 04586 02723 03741 04457 06283 07411 04836 00841 06223 00830 01551 04908

FKH2 HLH1 FZC11 MBS1 GAT203 ERT1 SIP401 FZC33 GAT204 CEP3 STB4 FZC5 MBS2 YAP4 MLN1 HOB6 FZC37 RIM101 ARO80 FZC1 FZC24 ECM22 CCD4 MBF1 PPR1 ARO8001 FZC47 MAL13 FZC12 MET32 ZFC7 ATF1 BZP1(HXL1)

YAP2 FZC29 PAN1 FZC46 MCM1 HAP2 SIP402 FZC27 FZC15 PIP2 HOB5 HOB4 GRF1 FZC34 FZC6 BZP5 SXI1alpha STE12 HEL2 FZC2 FZC42 GAT8 CLR1 HLH2 RLM1 YAP1 FZC41 SP1(CRZ1) APN2 USV101 FZC6 BAP4 GAT6 JJJ1 SKN7 FZC35 FZC22 FAP1 FZC8 BWC2 FZC19 HCM1 FZC51 BZP3 FZC14 CRL3 FZC25 CRL6 FZC21 FZC44 FZC5 HOB3 FKG101 LIV1 FZC245 ASG1 ZFC4 HOB2 FZC49 YRM103 HSF2 HOB1 HLH4 FZC16 ZAP104 FZC17 FZC18 FZC28 HLH3 FZC43 HCM101 ADA2 PDR802 FZC13 HAP1 ZFC3 FZC36 SRE1 ZAP103 FZC32 FZC39 FZC3 DDT1 ZFC2 ZFC1 HSF3 FZC7 FZC48 FZC4 MLR1 ASG101 BZP2 GAT7 SIP4 CUF1 RDS2 ZNF2 NRG1 PIP201 HLH5 FZC26 FZC9 GAT1 FZC50 FZC20 YOX101 GLN3 YRM101 GAT5 ZFC8 HOB7 FZC23 FZC31 FZC30 LIV4 RUM1 FZC10 FZC40 MIZ1 FZC38 GAT201 CLR4

TF class 25 30 37 39 MA ME CA UR NR KR SR NS KS SS HP TH MD DA MS HU TM DT CD CR CW SD FC AB FZ FX

FKH HLH FZC APS GAT FZC FZC FZC GAT FZC FZC FZC APS BZP HLH HOM FZC C2Z FZC FZC FZC FZC HOM HTH FZC FZC FZC FZC FZC C2Z C2Z BZP BZP BZP FZC P53 FZC SRF CCA FZC FZC FZC FZC HOM HOM GRF FZC C2Z BZP HOM C2Z C2Z FZC FZC GAT C2Z HLH SRF BZP FZC C2Z GRF C2Z FZC BZP GAT C2Z HSF FZC FZC NFX FZC GAT FZC FKH FZC BZP FZC BZP FZC FKH FZC FZC C2Z HOM FKH HLH FZC FZC C2Z HOM FZC FZC HSF HOM HOM FZC C2Z FZC FZC FZC HLH FZC FKH MYB FZC FZC FZC C2Z FZC HLH C2Z FZC FZC FZC DDT C2Z C2Z HSF FZC FZC FZC FZC FZC BZP GAT FZC CDB FZC C2Z C2Z FZC HLH FZC FZC GAT FZC FZC HOM GAT FZC GAT C2Z HOM FZC FZC FZC MYB PHZ FZC FZC MIZ FZC GAT BZP

Insect Virulence RMS

WT WT WT WT WT WT WT WT Reduced

WT WT WT WT WT WT WT WT WT WT Reduced

WT WT WT WT WT WT WT WT WT WT WT WT Reduced

WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT Reduced WT Reduced WT Reduced

WT WT WT WT WT WT WT WT WT Reduced

WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT Reduced

WT WT WT WT WT WT WT WT WT Reduced Reduced

WT WT WT WT Reduced

0.83 1.00 1.00 0.83 0.83 1.00 0.83 0.83 1.5 0.83 1.00 1.00 1.00 0.90 1.00 1.00 1.00 1.10 1.00 1.2 1.00 0.83 0.92 0.83 0.83 1.00 0.83 0.92 0.83 1.00 1.00 1.09 >1.67 0.83 0.92 1.00 1.00 1.00 1.20 1.00 1.00 1.00 1.00 1.00 1.20 1.00 1.00 1.17 1.00 1.00 0.90 0.83 0.83 0.83 0.88 0.83 0.83 0.83 1.15 1.00 1.37 1.00 1.50 1.00 0.83 0.99 1.00 1.17 0.92 0.83 1.00 0.83 1.50 0.83 1.00 0.92 0.92 1.00 1.00 1.20 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.03 1.00 1.17 1.00 1.46 1.00 1.00 1.00 0.85 1.00 1.20 1.00 1.00 1.02 >1.43 1.73 0.92 0.83 1.00 0.83 2.04

Mouse Expression data (RNAseq) Virulence STM score D30 D32 FCZ SDS D37 G30

WT Reduced

WT WT WT WT WT WT Enhanced

WT WT Reduced Reduced Enhanced

WT WT Reduced

WT WT Reduced

WT WT WT WT WT WT Reduced Reduced

WT Reduced WT WT Reduced

WT WT Reduced WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT Enhanced

WT WT WT Reduced

WT WT WT WT WT WT Reduced

WT WT Enhanced

WT WT WT Reduced

WT WT Reduced

WT WT WT WT WT WT WT WT WT WT WT Enhanced

WT Reduced

WT WT WT WT WT WT WT Reduced

WT WT Reduced Enhanced Enhanced

WT WT Reduced

WT WT Reduced

WT WT WT WT Reduced

0.95 –2.41 –1.33 –0.22 1.33 0.99 –0.60 –0.57 2.99 –0.88 0.67 –2.69 –2.44 2.64 –1.35 –0.75 –1.62 0.14 –0.48 –5.37 0.96 –0.62 0.46 –1.41 –0.59 0.67 –3.37 –5.22 0.32 –1.67 1.29 –0.90 –6.82 0.41 0.25 –1.88 –0.07 –0.78 –0.15 –0.15 0.50 0.80 –0.17 –1.36 0.14 1.48 0.01 0.23 0.12 0.41 0.58 2.49 –1.46 –1.33 0.17 –1.59 0.47 –0.59 0.81 0.86 –0.49 0.17 –4.68 1.00 0.60 1.52 –0.73 1.03 –0.71 –2.31 –0.58 0.38 –4.66 1.41 –0.85 0.39 –0.64 –0.35 0.32 0.03 0.15 –0.59 –0.88 –0.32 2.53 0.32 –3.88 0.22 –0.04 0.76 0.72 –0.81 –0.46 –0.50 –5.34 0.16 –0.35 –5.53 3.44 1.59 0.24 –0.51 –3.40 1.47 1.45 –7.21 –0.18 –0.26 0.44 0.22 –4.64

1.00 WT 0.18 0.83 WT 0.47 1.00 WT –1.16 1.10 WT 0.18 1.00 Reduced –4.83 Reduced 1.34 Reduced –3.55 WT 1.70 Enhanced 2.29 WT 1.12 Reduced –1.65 WT WT –0.28 1.40 0.29 WT WT 1.80 WT –0.58 WT 1.22 WT WT 0.08 1.13 0.30 WT WT 1.60 Reduced 1.58 1.29 WT WT WT –0.57 1.20 0.46 WT WT 1.20 WT 1.20 Enhanced 5.66 WT 0.18 WT 1.20 1.00 Enhanced 3.35 WT –6.73 Reduced 1.33 Reduced 1.00 0.52 WT WT –0.02 WT WT 1.00 1.10 –1.43 WT WT 1.08 –3.86 WT WT 0.85 WT WT 1.00 Reduced 1.47 –0.74 WT 0.92 –0.07 WT WT 0.96 1.35 WT WT 1.10 0.54 WT WT 0.96 –0.34 WT WT 1.00 Reduced –4.91 WT WT WT –0.53 1.00 WT WT –0.13 1.00 WT WT 0.41 1.00 Reduced 1.17 Reduced –4.33 WT 1.00 Reduced –1.95 1.09 WT –1.10 WT WT 0.23 WT 1.00 1.09 WT –0.10 WT WT 1.00 Enhanced 1.69 WT –0.16 WT 1.00 WT 1.00 Enhanced 2.17 Reduced Reduced –11.13 1.9 1.20 WT 1.36 WT WT WT WT WT WT

Transcription factor classes APS : APSES domain/Ankyrin repeat BZP : Basic region leucine zipper domain C2Z : C2H2 type zinc-finger domain CCA : CCAAT-binding TF subunit B FKH : Fork head domain FZC : Fungal Zn2/Cys6 DNA-binding domain GAT : GATA zinc-finger GRF : GRF zinc-finger HLH : Helix-loop-helix DNA binding domain HOM : Homeobox domain HSF : HSF-type DNA-binding domain HTH : Helix-turn-helix/Psq domain NFX : NF-X1 type zinc-finger P53 : p53-like/LAG1-like DNA-binding domain SRF : SRF-type TF MYB : Myb-like DNA binding domain

Phenome codes DA : diamide 25 : growth at 25 °C MS : methyl 30 : growth at 30 °C methanesulfonate 37 : growth at 37 °C HU : hydroxyurea 39 : growth at 39 °C TM : tunicamycin MA : mating ability DT : dithiothreitol ME : melanin CD : CdSO4 CA : capsule CR : Congo red UR : urease CW : calcofluor white NR : NaCI in YPD SD : sodium dodecyl KR : KCI in YPD sulfate SR : sorbitol in YPD FC : flucytosine NS : NaCI in YP AB : amphotericin B KS : KCI in YP FZ : fluconazole SS : sorbitol in YP HP : H2O2 FX : fludioxonil TH : tert-butyl hydroperoxide MD : menandione

Phenome heat map scales Strongly sensitive or defective (1,000-fold) Moderately sensitive or defective (100-fold) Weakly sensitive or defective (10-fold) Wild-type like Weakly resistant or enhanced (10-fold) Moderately resistant or enhanced (100-fold) Strongly resistant or enhanced (1,000-fold)

Galleria virulence heat map scales 1.8≤RMS ratio (P< 0.05) 1.4≤RMS ratio < 1.8 (P< 0.05) 10.05) 0.8≤RMS ratio