Assessment of Aspergillus Kinases As Targets for Antifungal Drug Discovery

Home , Ibrexafungerp, Omoconazole, Talaromycosis

ASSESSMENT OF ASPERGILLUS KINASES AS TARGETS FOR ANTIFUNGAL DRUG DISCOVERY

A thesis submitted to The University of Manchester for the degree of Doctor of Philosophy in the Faculty of Biology, Medicine and Health

2019

NARJES CHYAD ALFURAIJI

School of Biological Sciences Infection, Immunity and Respiratory Medicine Manchester Fungal Infection Group

List of Contents

List of Contents ...... 2

List of Tables ...... 10

List of Figures ...... 12

List of Abbreviations ...... 16

Declaration ...... 19

Dedication ...... 20

Acknowledgments ...... 21

Chapter 1 : Fungi and Fungal Infections ...... 23 1.1 Introduction to fungi ...... 23 1.1.1Polyploidy and fitness ...... 24 1.1.2 Fungal biofilms ...... 26 1.1.3.Importance of fungi ...... 27 1.2 Fungal infections (Mycoses) ...... 28 1.2.1 Superficial mycoses ...... 30 1.2.2 Subcutaneous mycoses ...... 31 1.2.3 Systemic mycoses ...... 32 1.2.3.1 Systemic mycoses caused by primary pathogens...... 32 1.2.3.2 Systemic mycoses caused by opportunistic pathogens ...... 33 1.2.3.2.1 Candidiasis ...... 33 1.2.3.2.2 Aspergillosis ...... 34 1.2.3.2.3 Mucormycosis (zygomycosis) ...... 35 1.2.3.2.4 Pneumocystosis ...... 36 1.2.3.2.5 Fusariosis ...... 36 1.3 Antifungal pharmacology ...... 37 1.3.1 Polyene antifungal drugs ...... 37 1.3.2 Azole antifungals ...... 39 1.3.2.1 Topical azoles ...... 40 1.3.2.2 Oral Azoles ...... 42 1.3.3. Echinocandins ...... 44 1.3.4 Pyrimidine analogues ...... 45 2

1.3.5 Allylamines, thiocarbamate and morpholines ...... 46 1.3.6 Griseofulvin ...... 47 1.3.7 Developmental antifungal drug ...... 47 1.3.8 Combination therapy ...... 48 1.4 Resistance to antifungal drugs ...... 50 1.4.1 Resistance to Polyenes ...... 51 1.4.2 Resistance to echinocandins ...... 51 1.4.3 Resistance to flucytosine ...... 52 1.4.4 Resistance to Azoles ...... 53 1.4.4.1 Mechanisms of azole resistance ...... 54 1.4.4.1.1 Increase of drug efflux ...... 54 1.4.4.1.2 Target mutation ...... 55 1.4.4.1.3 Target expression deregulation ...... 55 1.4.4.1.4 Ergosterol biosynthesis pathway alteration ...... 55 1.5 Protein Kinases are fundamental for all living organisms...... 58 1.5.1. Background ...... 58 1.5.1 Classification of protein kinase ...... 62 1.5.1.1 Conventional protein kinases ...... 62 1.5.1.2 Atypical protein kinase ...... 63 1.5.2 Human protein kinase ...... 64 1.5.3 Kinases as drug target ...... 66 1.6 Project overview ...... 69 1.6.1 Hypothesis ...... 69 1.6.2 Study plan ...... 70 1.6.3 Aims of the project ...... 70

Chapter 2 : Materials and Methods ...... 71 2.1 Bioinformatics ...... 71 2.1.1 Data search to identify the predicted kinases ...... 71 2.1.2 Phylogenetic analysis ...... 72 2.1.3 Kinases cluster and heat map analysis of kinases ...... 72 2.2 Gene knockout ...... 72 2.2.1 A. fumigatus strain, culture and growth conditions ...... 72 2.2.1.1 Handling of samples ...... 73 2.2.1.2 A. fumigatus strains used in this study ...... 73 2.2.1.3 Growth of A. fumigatus, spore harvesting and storage ...... 73

2.2.1.4 Preparing PK mutants’ pool for competitive fitness study ...... 74 2.2.1.5 Harvesting of fungal mycelia from pooled growth ...... 74 2.2.1.6 Growth of A. fumigatus A1160+ and PK mutants for total RNA extraction ...... 74 2.2.1.7 Growth of A. fumigatus A1160+ and PK mutants for genomic DNA extraction ...... 75 2.2.2 Extraction of nucleic acids ...... 75 2.2.2.1 DNA extraction ...... 75 2.2.2.1.1 DNA extraction from fungal spores using Cetyl Trimethyl Ammonium Bromide and glass beads ...... 75 2.2.2.1.2 DNA extraction from fungal mycelia ...... 76 2.2.2.1.3 DNA extraction from infected lungs tissues ...... 76 2.2.2.1.4 DNA extraction from infected macrophage ...... 77 2.2.2.2 RNA extraction ...... 77 2.2.3 Separation of DNA fragments by agarose gel electrophoresis ...... 78 2.2.4 Generation of knockouts in A. fumigatus ...... 78 2.2.5 Design of oligonucleotide primers for the generation of the gene knockout cassette ...... 78 2.2.6 DNA barcodes ...... 80 2.2.6.1 Generating the DNA barcode ...... 80 2.2.6.2 Quality control of designed DNA barcodes ...... 81 2.2.7 Polymerase chain reaction...... 81 2.2.7.1 Amplification of the upstream and downstream flanking sequence ...... 81 2.2.7.2 Amplification of marker cassette for fusion constructs ...... 82 2.2.7.3 Fusion PCR ...... 82 2.2.7.4 Generation knockout of the control strain for competitive fitness analysis . 83 2.2.7.5 PCR set up for qPCR ...... 83 2.2.8 Transformation ...... 84 2.2.8.1 Transformation of PK gene ...... 84 2.2.8.2 Transformation of the control strain for the competitive fitness study ...... 85 2.2.9 Purification of protein kinase gene knockouts and the transposon knockout mutants...... 85 2.2.10 Validation of protein kinase mutants and the transposon mutant ...... 85 2.3 Functional analysis of the A. fumigatus kinome ...... 86 2.3.1 Screening PK mutants’ response to temperatures ...... 86

2.3.2 Screening PK mutants’ response to oxidative stress using H2O2 ...... 86 2.3.3 Screening PK mutants’ response to screening PK mutants’ response to PH ...... 86 2.3.4 Screening PK mutants’ response 1M sucrose ...... 87 2.3.5 Screening PK mutants’ response to iron starvation and excess ...... 87 2.3.6 Growth of PK mutants on Aspergillus complete media and Aspergillus minimal media ...... 87 2.3.7 Growth of PK mutants on RPMI media ...... 87 2.3.8 Growth of PK mutants on Vogel’s media ...... 87 2.4 Next generation sequencing NGS ...... 87 2.4.1 Designing primers for NGS ...... 88 2.4.2 Amplification of PK mutants’ specific barcodes ...... 90 2.4.3 Purification of PCR products for sequencing ...... 90 2.4.4 DNA quantification for sequencing ...... 91 2.4.5 Ion Personal Genome Machine system preparation ...... 91 2.4.5.1 OneTouch 2 system (Amplification of ion sphere particles) ...... 91 2.4.5.2 Recovering the template positive ISPs ...... 91 2.4.5.3 Enrichment of the template: positive ISPs ...... 92 2.4.5.4 Quality control of the template-positive ISPs (Qubit 3.0) ...... 92 2.4.5.5 Ion torrent setup ...... 92 2.4.5.6 NGS data processing ...... 93 2.5 Screening for itraconazole-resistant mutants ...... 93 2.5.1 Determination of minimal inhibitory concentration (MIC) of itraconazole for WT ...... 93 2.5.2 Screening of pooled PK mutant’s sensitivity to itraconazole ...... 94 2.5.3 Validation of sensitivity to itraconazole using individual strain (PK mutants and their reconstituted strains) ...... 94 2.5.4 Studying the expression of cyp51A, cyp51B and cdr1B ...... 94 2.6 Virulence studies using animal models to determine the virulence of PK knockouts library ...... 95 2.6.1 Using pooled PK mutants to infect the THP-1 cells ...... 95 2.6.2 Studying the virulence of WT A1160+ and transposon mutant using larvae of G. mellonella ...... 95 2.6.3 Infection study using pooled PK mutants to infect the larvae ...... 96 2.6.4 Infection study using larvae to validate the NGS results ...... 96 2.6.5 Infection of mice with pooled PK mutants ...... 96

Chapter 3 : Analysis of the Kinome of A. fumigatus Reveals Potential Antifungal Drug Targets ...... 98 3.1 Introduction ...... 98 3.2 Results ...... 99 3.2.1 Data search to identify the protein kinome ...... 99 3.2.2 Classification of kinases revealing that the kinomes of filamentous fungi have members from all previously defined ePK families ...... 102 3.2.3 Phylogenetic conservation within the kinome of A. fumigatus reveals a set of highly conserved filamentous fungal-specific kinases ...... 103 3.2.4 Comparative analysis of the A. fumigatus and human kinomes reveals a group of CMGC kinases with the potential to be drug targets ...... 107 3.2.5 Clustering and heat map analysis of protein kinases highlighting the potential antifungal targets in A. fumigatus A1163 ...... 108 3.3 Discussion ...... 111

Chapter 4 : Generation of a Library of Protein Kinase Null Mutants in A. fumigatus A1163 ...... 114 4.1 Introduction ...... 114 4.2 Results ...... 115 4.2.1 A fusion PCR approach enabled successful amplification of 115 kinase knockout cassettes ...... 115 4.2.2 Knockouts generated ...... 117 4.2.3 Generation of a collection of validated kinase knockout mutants in A. fumigatus A1160+...... 118 4.2.4 Validation of protein kinase null mutants ...... 121 4.2.5 Comparative analysis of protein kinases essential for viability in A. fumigatus A1163 and other fungi ...... 124 4.3 Discussion ...... 129

Chapter 5 : Functional Analysis of the A. fumigatus Kinome ...... 132 5.1 Introduction ...... 132 5.2 Results ...... 132 5.2.1 Generation of a control isolate that should phenocopy the wild-type isolate for use in competitive fitness studies ...... 132 5.2.2 Checking the growth of the transposon mutant ...... 135 5.2.3 Optimisation of the PCR reaction to amplify the DNA barcodes from the knockout mutants ...... 136 6

5.2.4 Competitive fitness analysis is highly reproducible in A. fumigatus ...... 137 5.3 Competitive fitness analysis ...... 142

5.3.1 Checking WT response to oxidative stress using H2O2 ...... 148 5.3.2. Screening PK mutants’ response to different stress conditions ...... 149 5.4 Parallel fitness can be used to predict the fitness of some strains grown individually in liquid culture ...... 161 5.5 Phenotypic clustering analysis reveals PK mutants with common phenotypes ...... 163 5.6 Discussion ...... 165

Chapter 6: Assessment of the Role of Protein Kinases in Itraconazole Susceptibility ...... 168 6.1 Introduction ...... 168 6.2 Competitive fitness profiling of the protein kinase mutant library enables identification of key regulators associated with azole resistance and sensitivity ...... 169 6.3 Validation of competitive fitness analysis: Assessment of strains identified with altered azole tolerance using a radial growth assay ...... 173 6.4 Reconstitution of the wild-type genotype in ssn3 and fus3 null mutants ...... 174 6.5 Evaluating the mechanistic basis of altered azole susceptibility in ssn3, yak1 and fus3 null mutants ...... 178 6.6 Discussion ...... 181

Chapter 7: Studying the Virulence of the PK Knockout Library in Galleria mellonella Larvae and Mice ...... 186 7.1 Introduction ...... 186 7.2 Studying the virulence of the PK knockout library using macrophage as an infection model ...... 187 7.3 Optimising a dosing regimen for a G. mellonella infection model ...... 191 7.4 Competitive fitness profiling in G. mellonella larvae ...... 192 7.5 Studying the virulence of some PK knockout mutants using G. mellonella to validate the results of competitive fitness ...... 197 7.6 Studying the virulence of the PK knockout library using murine as an animal mode ...... 198 7.7 Discussion ...... 205

Chapter 8: Conclusion and Future Work ...... 210 8.1 Conclusion ...... 210 8.2 Future Work ...... 217

References ...... 218

Appendices ...... 267

Appendix 1: Media, Plasmids and Antibiotics ...... 267 A: Growth Media ...... 267 D- Antibiotics ...... 271

Appendix 2: Buffers and Solutions ...... 272

Appendix 3: List of strains used in the project. Red colour indicate the putative essential genes among the PK knockout collection, the remaining are the viable PK knockouts. ... 280

Appendix 4: 96-well plate orientation with 95 corresponding PK gene, which was used in PCR reactions. The last well (H12) was used as a negative control...... 281

Appendix 5: Primers designed manually by using Primer3 to amplify the upstream and downstream flanking sequences in first step of fusion PCR, and nested primers P5 and P6 to aid in fusion PCR reaction ...... 282

Appendix 6: Primers that were redesigned manually using Primer 3 to amplify the upstream flanking sequences and downstream flanking sequences that failed to amplify the corresponding region PCR (step 1) ...... 283

Appendix 7: Primers designed manually by using Primer 3 to amplify the upstream flanking and downstream flanking sequences of the transposon control strain, and the nested primers P5 and P6, which were used in the fusion PCR ...... 283

Appendix 8: Primers 1 and 2, which were used to amplify the upstream flanking sequences ...... 284

Appendix 9: Primers 3 and 4, which were used to amplify downstream flanking sequences ...... 289

Appendix 10: Primers 5 and 6, which were used to aid in the fusion PCR reaction (nested primers) upstream flanking sequences ...... 294

Appendix 11: DNA barcodes sequences which were used to amplify hph cassette in accordance with 96-well plate orientation ...... 297

Appendix 12: Analysis of DNA barcodes that were designed using DNA barcode generator to amplify hygromycin marker cassettes with the corresponding analysis to ensure that these barcodes do not self-anneal. Hairpin shows the margin melting temperature and Heterop Dimer shows the free energy represented by Delta G that required annealing the secondary sequence (hph primer)...... 302

Appendix 13: Primers designed manually using Primer 3 to amplify the corresponding genes in QPCR ...... 309

Appendix 14: Phylogenetic tree of kinases in A. fumigatus and human, constructed using T-rex. Shows the divergent PKs in A. fumigatus A1163 ...... 309

Appendix 15: Phylogenetic tree of STE kinases in A. fumigatus and human, constructed using MEGA6. The highlighted group, are those with <40% similarity compared to human PKs ...... 310

Appendix 16: Phylogenetic tree of humans TKs kinases and A. fumigatus kinases, constructed using MEGA6. The highlighted group, are those with <40% similarity compared to human PKs ...... 311

Appendix 17: Layout of the gDNA consolidated into one 96-well plate ...... 312

Appendix 18: Gel electrophoresis of validation PCR for 5’ flanking fragment insertion check using P1+ Rv1 (expected amplicon size: about 1.6kb) ...... 313

Appendix 19: Gel electrophoresis of validation PCR for 3’ flanking fragment insertion check using Fw hph2+ P4 (expected amplicon size: about 1.4 kb) ...... 313

Appendix 20: Repeat of PCR validation: 5’ amplification checking PCR - P1+ Rv1 (expected amplicon size: about 1.6 kb) on left side; 3’ PCR check - Fw2+ P4 (expected amplicon size: about 1.4 kb) on the right side ...... 314

Appendix 21: Gel electrophoresis image of PCR validation using P1-P4 (expected amplicon size: about 5 kb) ...... 315

Appendix 22: Gel electrophoresis image of the repeat of PCR validation using P1-P4 (expected amplicon size: about 5 kb) ...... 315

Appendix 23: Summary of PK knockouts validation by conducting PCR to ensure the integration of the selectable marker and the corresponding flanking region, and to ensure the purity of strains by conducting P1-P4 PCR validation...... 316

Total word count: 57,868 (excluding references and appendices)

List of Tables

Table 1.1: The burden of fungal infections (Adapted from Bongomin et al., 2017)...... 29 Table 1.2: List of current available oral antifungal drugs that was approved by FDA and their antifungal spectrum...... 38 Table 2.1: PCR condition for amplification of upstream and downstream flanking fragments, and hygromycin selective marker cassettes...... 81 Table 2.2: PCR condition of fusion PCR, where upstream and downstream flanking fragments, and hygromycin selective marker cassettes are combined to get the knockout construct...... 83 Table 2.3: The unique primers, designed to use in NGS. The forward sequencing primers were linked to a unique ion express identifier with 10 bp followed by the hphF primer...... 89 Table 2.4: PCR conditions for amplifying the PK mutants’ specific barcode...... 90 Table 2.5: Endpoints in the infection study and the expected symptoms to be observed. ... 97 Table 3.1: The total number of protein kinases in Aspergillus spp classified using the Kinomer v.1.0. HMM Library...... 101 Table 3.2: The ID% of protein blast results of A. nidulans Ffks against A. fumigatus A1163 protein kinase...... 105 Table 3.3: Kinases highly conserved in the aspergilli and significantly different to human kinases...... 110 Table 4.1: The number of transformants for each PK gene, obtained after transforming the PK fusion cassettes into A. fumigatus A1160+...... 120 Table 4.2: The putative essential PK knockouts genes in A. fumigatus A1163 and their orthologs in A. nidulans and S. cerevisiae...... 126 Table 4.3: The ID% between the putative essential PK in A. fumigatus A1163 and human PK...... 128 Table 5.1: Total sequence of reads and total mapped reads obtained using Ion PGM sequencing machine for 4 biological replicates in corresponding to their culture condition...... 141 Table 5.2: List of PK mutants identified with significant increase or reduced abundance from the competitive fitness analysis of the PK knockout library 20 hr post- inoculation on liquid fRPMI...... 144 Table 5.3: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI at 48°C for 20 hr...... 151 10

Table 5.4: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI at 30°C for 20 hr...... 152 Table 5.5: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI supplemented with 1M sucrose at 37°C for 20 hr...... 153 Table 5.6: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI deficient with iron at 37°C for 20 hr...... 155 Table 5.7: List of PK mutants identified with significant increase or decrease in relative

fitness (Deseq2 analysis) when grown on fRPMI supplemented with 2mM H2O2 at 37°C for 20 hr...... 157 Table 5.8: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI at pH8 at 37°C for 20 hr...... 159 Table 5.9: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI at pH4 at 37°C for 20 hr...... 160 Table 5.10: PK mutant’s that show similar phenotype on competitive and individual growth...... 161 Table 6.1: Strains identified as outliers from competitive fitness analysis of protein kinase null mutants exposed to itraconazole...... 172 Table 7.1: Strains identified as outliers from the competitive fitness analysis of the PK knockout library 16 hr post-infection with 1x106 spores/ml using the THP1 cell line...... 190 Table 7.2: Strains identified as outliers from the competitive fitness analysis of the PK knockout library 5 days post-infection using 10 µl of 1x106 spores/ml in G. mellonella larvae...... 195 Table 7.3: Strains identified as outliers from competitive fitness analysis of the PK knockout library 5 days post-infection using 10 µl of 5x106 spores/ml in G. mellonella larvae...... 196 Table 7.4: Primers that were used to amplify strain specific barcodes using Illumina MiSeq system...... 200 Table 7.5: Strains identified as outliers from competitive fitness analysis of the PK knockout library in each pool 7 days post-infection using mice as the infection model...... 202

List of Figures

Figure 1.1: Classification of fungi adapted from (de Pauw, 2011)...... 23 Figure 1.2: Mechanisms of resistance to antifungal drugs that target the cell membrane (the azoles and polyenes)...... 57 Figure 1.3: Protein kinases within the enzyme classification hierarchy...... 60 Figure 1.4: Phosphorylation is a reversible post-translational modification (PTM), which regulates protein function...... 61 Figure 1.5: A key event in cell regulation is reversible protein phosphorylation. A protein kinase moves a phosphate group from ATP to the protein...... 62 Figure 1.6: Recognised kinase drug targets undergoing medical studies...... 65 Figure 2.1: Schematic figure of the PCR approach used to generate gene knockouts...... 79 Figure 2.2: Schematic figure of the DNA barcode’s insertion, which was used to tag the PK mutants...... 80 Figure 2.3: The validation PCR across the 5’ flanking region and selective marker gene . 85 Figure 2.4: Design the NGS primers targeting the DNA barcodes, where both hphF and hph R were included to amplify the strain-specific barcode...... 88 Figure 3.1: Schematic flow chart describing the kinase identification and classification process...... 100 Figure 3.2: The total predicted kinases in the Aspergillus species (A. fumigatus A1163, A. fumigatus AF293, A. nidulans, A. niger, A. oryzae, A. terreus, N. fischeri and A. flavus) using the Kinomer v.1.0 HMM Library, as well as Pfam and InterPro analyses...... 101 Figure 3.3: Distribution of the predicted protein kinases in the Aspergillus species based on the Kinomer v.1.0 HMM Library, Pfam and InterPro classification...... 102 Figure 3.4: Phylogenetic tree for the predicted conventional kinases in A. fumigatus A1163...... 104 Figure 3.5: Phylogenetic tree for the predicted Ffks in A. fumigatus A1163 and A. nidulans...... 106 Figure 3.6: Phylogenetic tree of A. fumigatus A1163 and human kinases constructed using T-REX, showing the expansion of the group of kinases in A. fumigatus A1163 compared to human kinases...... 107 Figure 3.7: Heat map showing the percentage similarity between the kinases in A. fumigatus A1163 and other Aspergillus species, as well as N. crassa, S. cerevisiae and human...... 108

Figure 4.1: A: Pooled PCR products of amplified upstream and downstream flanking sequence after purification ...... 116 Figure 4.2: Agarose gel electrophoresis of fusion PCR products for 20 PK knockout cassettes...... 117 Figure 4.3: Hygromycin-resistant colonies obtained by transforming A. fumigatus A1160p+ after 3 days of incubation at 37°C on YPS/200 µg/ml hygromycin...... 119 Figure 4.4: Schematic representation of validation PCR conducted to validate the PK knockout mutants...... 121 Figure 4.5: Validation of gene knockout by PCR for 12 PK mutants...... 122 Figure 4.6: Schematic presentation summarising the generation of the PK knockouts in A. fumigatus A1163...... 123 Figure 4.7: Growth of three independent transformants for 10 putative essential PK genes following incubation at 37°C for 48 hr...... 125 Figure 5.1: Agarose gel electrophoresis of fusion PCR products for transposon knockout cassettes (6 replicates)...... 133 Figure 5.2: A: schematic representation of PCR validation for the Aft4 knockout...... 134 Figure 5.3: Radial growth of wild type A1160P+ isolate and the Δ transposon after 4 days of incubation at 37˚C...... 135 Figure 5.4: A: Schematic presentation of strain-specific barcode amplification, where Hph- F was used in this amplification, plus hph-R. B: Gel electrophoresis of the DNA barcode amplification of the PK mutant DNA using three different sets of primers (labelled 1–3)...... 136 Figure 5.5: A: Schematic representation of the competitive fitness profiling process, where BC refers to the barcode and CR refers to the common region in the amplified knockout cassette. B: Gel electrophoresis of amplified products from DNA extracted from the pooled PK mutants’ spores (treatment point zero) in triplicates (labelled 1 - 3). C: The PCR product in triplicates (labelled as 1–3) for T0 after purification . .... 138 Figure 5.6: Pairwise comparison of two technical replicates (1 and 2) from the pooled PK mutants’ spores at T0...... 139 Figure 5.7: Scatter dot plot of log2 relative fitness of the PK knockouts library obtained from comparing their relative frequency at T0 to their relative frequency from growth at 37°C (fq37°C/fqT0)...... 142 Figure 5.8: Radial growth of PK mutants after 48 hr on RPMI solid media. The Aft4 transposon mutant was included as a control...... 145 Figure 5.9: Growth of PK mutants on ACM solid media after 48 hr at 37°C...... 146

Figure 5.10: Growth of PK knockout mutants after 48 hr at 37°C on Vogel’s solid media...... 147 Figure 5.11: Dried weight of fungal biomass in response to different concentrations of

H2O2...... 148 Figure 5.12: Log2 relative fitness of PK knockout mutants obtained from comparing their growth at 48°C, 30°C, 1M sucrose, without iron and with a final concentration of 2

mM H2O2, respectively, to their growth at 37°C...... 149 Figure 5.13: Log2 relative fitness of PK knockout mutants obtained from comparing their growth at pH 8 and pH 4 to their growth at 37°C...... 158 Figure 5.14: Relative fitness of PK knockout mutants obtained from comparing their OD after 48 hr of growth on liquid fRPMI at 48°C, 30°C, 1M sucrose, without iron and

with final concentration of 2 mM H2O2, respectively, to their OD when grown at 37°C...... 162 Figure 5.15: Hierarchical clustering of the competitive fitness outputs from this study. .. 164 Figure 6.1: Reduction in dried weight of fungal biomass in response to itraconazole treatment after 24 hr of incubation at 37°C and 200 rpm...... 170 Figure 6.2: Gel electrophoresis image of PCR products of replicates after purification. .. 171 Figure 6.3: Log2 relative fitness of protein kinase null mutants exposed to 0.02-mg/L itraconazole...... 173 Figure 6.4: Radial growth of 3 PK mutants and wild type A1160P+ isolate with itraconazole after 4 days of incubation at 37˚C...... 174 Figure 6.5: Reconstitution of Δssn3 and Δ fus3 genes...... 174 Figure 6.6: Gel electrophoresis: A: PCR products of pooled upstream and downstream flanking region for ssn3 and fus3 genes after purification. B: PCR products of the marker cassette (ptrA) after purification (product size is 2kb). C: Fusion PCR products for ssn3 and fus3 genes respectively...... 176 Figure 6.7: A: Gel electrophoresis from validation of reconstituted fus3 gene. B: Gel electrophoresis from validation of in reconstituted ssn3 gene...... 177 Figure 6.8: Schematic representation of validation PCR of reconstituted genes (ssn3 and fus3)...... 177 Figure 6.9: Fold change in the expression of the target genes (cyp51A, cyp51B and cdr1B) in Δfus3, Δssn3 and Δyak1 without itraconazole in relative to WT...... 179 Figure 6.10: Fold change in the expression of the target genes (cyp51A, cyp51B and cdr1B) in Δfus3, Δssn3 and Δyak1 after 4 hr of exposure to itraconazole in relative to WT...... 180

Figure 7.1: Schematic representation describing the experimental setup for the competitive fitness profiling of the PK knockout library in the presence of the THP-1 macrophage cell line...... 187 Figure 7.2: DNA gel electrophoresis of PCR product amplified from DNA from the biological replicates (1–4) of infected THP1- cells 16 hr post-infection, and pooled PK knockout library spores (T0, 1–3)...... 188 Figure 7.3: Log2 competitive fitness of the PK knockout library 16 hr post-infection using the THP1 cell line...... 189 Figure 7.4: Survival rate of the WT A1160+ and the ΔAft4 isolate post-infection of G. mellonella larvae with 10 µl of the pooled PK knockout library at concentration of 1x106 spores/ml...... 191 Figure 7.5: Gel electrophoresis of the PCR products after purification. The DNA of G. mellonella larvae (4 replicates per group) was injected with 10 µl of 1x106 and 5x106 spores/ml, used in this reaction to amplify the strain-specific barcodes...... 192 Figure 7.6: Competitive fitness of PK knockout library after 5 days of infection using high dose (5x104 spores/ml) and low dose (1x104 spores/ml) to infect the G. mellonella larvae...... 194 Figure 7.7: Survival rate of the PK knockout mutants that appeared with reduced fitness in competitive fitness analysis 8 days post-infection with 10 µl of 1x106 spores/ml in G. mellonella larvae...... 197 Figure 7.8: Survival rate of the PK knockout mutants that appeared with increased fitness in competitive fitness analysis 8 days post-infection with 10 µl of 1x106 spores/ml in G. mellonella larvae...... 198 Figure 7.9: Schematic representation showing the experimental setup for competitive fitness profiling of the PK knockout library using mice as an infection model...... 199 Figure 7.10: DNA gel electrophoresis image of DNA extracted from 10 lungs (1–10) in each pool...... 199 Figure 7.11: Gel electrophoresis image of the PCR products after purification from amplifying the DNA of infected lungs from each pool (A–D) 7 days post-infection...... 200 Figure 7.12: Log2 competitive fitness of the PK knockout mutants obtained from replicates of each pool using mice as an infection model...... 203 Figure 7.13: Log2 fitness from the infected lung per each corresponding pool, showing the PK mutants with apparent increased fitness, and those that appeared with reduced fitness...... 204

List of Abbreviations

5-FC Flucytosine

ABC1 ABC1 domain-containing kinases ABPA Allergic bronchopulmonary aspergillosis AD Alzheimer’s disease AGC A,G and C family AIDS Acquired immune deficiency syndrome aPKs “atypical” Protein kinases AspGD Aspergillus Genome Database BRD Bromodomain-containing kinases BSC Biological safety cabinet CADRE The Central Aspergillus REsource CAMKs Chronic cavitary pulmonary aspergillosis CFPA Chronic fibrosing pulmonary aspergillosis CK Casein kinase family CMGC Cyclin dependent kinase (CDK), mitogen activated protein kinase (MAPK), glycogen synthase kinase (GSK) and CDC like kinase CPA Chronic pulmonary aspergillosis CTAB Cetyl trimethyl ammonium bromide CWAP Cell wall-associated protein EBI European Bioinformatics Institution ePKs “conventional” Protein kinases FDA The U.S. Food & Drug Administration FGSC Fungal Genetic Stock Centre GIO Gene of interest H2O Water hph Hygromycin HMMs Hidden Markov models HSCT Haematopoietic stem cell transplantation IV Invasive aspergillosis JTT Jones–Taylor–Thornton MAP Mitogenic activated protein kinase MAPKK Mitogenic activated protein kinase kinase

MAPKKK Mitogenic activated protein kinase kinase kinase MAPKKKK Kinase (Map4K) MFIG Manchester Fungal Infection Group P1–P6 Primers PBS Phosphate buffered saline PCR Polymerase chain reaction PD Parkinson’s disease Pfam Protein Families Database Ph+CML Philadelphia chromosome-positive chronic myeloid leukaemia PHDK Pyruvate dehydrogenase kinases PIKK Phosphatidyl inositol 3′ kinase-related kinases PJP Pneumocystis jirovecii pneumonia PK Protein kinase PFK Protein-tyrosine kinases RA Rheumatoid arthritis RGC Receptor guanylate cyclase family RIO Right open reading frame RT Room temperature SAB Sabouraud STE Serine Threonine

TIF1 Transcriptional intermediary factor 1 TK Tyrosine kinases family TKL Tyrosine kinase-like kinases family YPS Yeast peptone sucrose media

Abstract The University of Manchester Narjes Chyad Alfuraiji A thesis for the degree of Doctor of Philosophy, 2019 Assessment of Aspergillus Kinases as targets for antifungal drug discovery

Fungi cause a wide range of infections with a significant increment in annual burden, and a total estimate of 1.5 million deaths per year, with over a billion being at risk was reported recently. Fungal treatments are limited to certain classes of drugs. However, undesirable side effects, drug– drug interactions and the toxicity associated with some classes and the emergence of resistance with others has limited their use; thus, new antifungal drugs are needed. Protein kinases constitute the second most common group of drug targets after G-protein-coupled receptors, since they regulate most aspects of cell life by phosphorylation. In this project, we aimed to explore the druggability of kinases in the human pathogen A. fumigatus, in an attempt to address the scarcity of known antifungal agents through the validation of completely novel drug targets that can overcome the problems associated with current antifungal drugs, particularly provoking the increasing emergence of antifungal drug resistance.

Through a process of identification of protein kinases, 175 kinases were annotated in A. fumigatus A1163, identifying 15 filamentous fungal specific kinases, and other highly conserved kinases that share less than 50% similarity with humans, and they represent prospective antifungal targets. A high-throughput gene knockout strategy enabled the disruption of 115 PK genes, with 39 genes being identified as indispensable for viability; nine out of the total share <40% sequence similarity with human PKs, while two are Ffks. These findings heighten the potential of developing successful selective kinase inhibitors and maximises the potential therapeutic efficacy while potentially minimising human effects.

We developed a competitive fitness study in this project, utilising a PK knockout library of 65 mutants. This study enabled identification of two itraconazole-resistant strains (Δssn3 and Δfus3) and one sensitive strain (Δyak1). Resistance was associated with upregulation of cyp51A, cyp51B and cdr1B by 1.8-, 4.8- and 1.95-fold, respectively, in Δssn3 compared to the wild type, which provide some insight into molecular mechanism of resistance. Ssn3 is highly conserved among Aspergillus species with >90% sequence similarity, while it shares <50% sequence similarity with human, which maximise its potential as an antifungal drug target. Virulence studies identified three PK mutants that shown reduced fitness: ΔPSK1 in the larval model, ΔRim15 in the larval model, and Δkin4 in the macrophage cell line and larvae models. Interestingly, their orthologs appeared to be contributing to virulence. These findings serve to underscore the importance of these PK genes and their requirement for pathogenicity and to be a potential antifungal target. The current results provide greater insight into the role of PKs in A. fumigatus 1163 and heighten their potential as novel antifungal drugs.

Declaration

No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning

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Dedication

To my son Taha, for the strength and determination he provides, to my son Mustafa, who has shown such maturity, compassion and understanding, and of course to my husband for his unconditional love and support, I owe you all a great debt for your patience and belief in me while I have completed this PhD journey.

Acknowledgments

I would like to take this opportunity to extend my deepest gratitude to my enthusiastic supervisor, Dr Michael Bromley, who I thank wholeheartedly for his continued support, encouragement and expertise. Despite my Ph.D. journey proving challenging at times, his guidance and feedback have been a strong motivational factor in ensuring the successful completion of this work. Similarly, profound gratitude goes to Dr Paul Bowyer; I am particularly indebted for his support and advice.

Special thank are also offered to my advisor Dr Geof Robson for the advice and support that he offered, to Christine Burn for her continuous support and encouragement, as well as to Joy Stuart and Carolyn Glynn. I am also grateful to Dr Elaine Bignell for her tremendous support and encouragement.

My deep appreciation to the current and old members of the Manchester Fungal Infection Group, especially Dr Nicola Overton for her advice and support; as well as Dr Jane Gilsenan for help and support with Bioinformatics adding to her constructive feedback. To my colleagues (Dr Bromley’s group previous members) for their support in overcoming numerous obstacles I have faced through my research, particularly Paul Carr, Baharat Rash, Dr Anna Johns, Dr Darel Macdonald, and Dr Fabio Gsaller. Mention also extends to Dr Bromley’s group current members, especially to Dr Can Zhao for being supportive and for providing constructive feedback

Special mention to Dr Sara Gago for help in multi-tasks including Macrophage study; Dr Margarita Bertuzzi for her continuous and kind support; Lea Gregson for help with virulence study in mice; Marcin Fraczek for his help with illumina sequencing; Pavlos Geranios for doing phenotypic photography; as well as Dr Darren Thombson, Dr Takanori Furukawa, Norman Van Rhijn, and Sayema Khan.

Mention is extended to Faten Alwathiqi and Uju Icheoku, my fellow doctoral students, for their feedback and assistance, which was beyond the boundaries, and of course for their friendship. This also extends to Ahalm Alanazi, Esraa Almutawa, Hajer Alshammri and Najwa Ben Ghazi. Meanwhile, I extend my gratitude to the Student Services Centre at the University of Manchester and all other staff.

I am also most grateful to the Ministry of Higher Education and Scientific Research in Iraq for funding this project and its representative the Iraqi cultural attaché in London; of course 21 this mention extends to Karbala University particularly college of Medicine. Special thank to Mr Laith Mashkour/ Iraqi cultural attaché in London, for his continuous support throughout my study. This also extends to all other staff in the attaché for their kindness and hospitality.

Last but not least, I would like to thank Mrs Alaa Al-Thabit who was my sister abroad for all the kindness and the incredible support that she has offered to my family and me. May God please you in regards, brighten your life and make all your dreams come true. My appreciation extends to Dr Nihaya Khalaf, Mrs Huda and their lovely family for their tremendous support. Special mention to my friends back home and my family: my mum, my brothers and sisters for supporting me spiritually throughout this journey.

Chapter 1 : Fungi and Fungal Infections

1.1 Introduction to fungi

Fungi are eukaryotes with diverse nutritional and reproductive modes, featuring a multifactorial defensive cell wall consisting of chitin, a polysaccharides frame of glucan tolerant to microbial invasion and interweaved galactomannan polymers covered with cell wall-associated protein and entrenched pigment (Latgé et al., 2005). Due to its specific composition, the fungal cell wall and its fundamental plasma membrane are distinctive targets for the specific discovery of new antifungal agents active against pathogenic fungal species (Tada et al., 2013), with it functioning as a primary partition that protects the fungus from antagonistic environments confronted by the fungus (Latgé and Calderone, 2005). The cell wall retains several hydrolytic and toxic particles that are necessary for the fungi to attack their environmental niche. Moreover, its robust structure supports its ability to penetrate the tough substrates that it inhabits or invades (Latgé et al., 2005).

Fungi are vegetative organisms, which lack the ability to synthesize chlorophyll. They range from microscopic yeasts and moulds to giant puffballs mushrooms. The main components of microscopic fungi are various forms of spores and hyphae or pseudohyphae. An overview of fungal classification is summarised in Figure 1.1 (de Pauw, 2011). A complex of hyphal filaments, hyphal branches and any related spores holding structure form is known as a mycelium (Odds et al., 1996)

Figure 1.1: Classification of fungi adapted from (de Pauw, 2011).

Fungi reproduce by mitosis (asexual cell division), however most fungi are capable to do so by meiosis (sexual reproduction). Mating may occur between mother and daughter yeast cells, between different cell units within a hypha or between two different mould strains forming a sexual spore (de Pauw, 2011). Fungal spores, which are produced sexually or asexually, play an important role in the dispersion of fungi in the atmosphere.

1.1.1Polyploidy and fitness

Fungal cells typically harbour a pair of each chromosome (diploid), however, they also can harbour only one (haploid) or many sets (polyploid) of chromosomes. Some diploid fungi are capable of sexual reproduction via production of haploid cells. Once these fuse, new genetic recombinations can evolve. Polyploids can be classified into euploids and aneuploid based on their chromosomal composition, however tetraploidy is considered as the most common form of euploidy (Comai, 2005). Aneuploids have extra or missing chromosomes (one or more) from the total number of chromosomes that usually characterise the ploidy of the species.

Pathogenic fungi use polyploidy to adapt to host stresses. Cryptococcus neoformans increases cell size/genome content during human lung infection where resistance to phagocytosis by immune cells was driven by Titan cells (Zaragoza et al., 2010; Okagaki and Nielsen, 2012). Polyploidy may contribute to the development of antifungal resistance. However, in the absence of drugs, cells usually revert back to their standard ploidy. For example, Candida albicans can become polyploid in response to fluconazole antifungal treatment (Harrison et al., 2014). This is a result of chromosome mis-segregation, creating a fluconazole resistant aneuploid progeny with minor reduction in fitness due to the negative attributes of aneuploidy.

Cell ploidy changes can be introduced by endoreduplication that occur in Candida albicans, Cryptococcus neoformans, and some mammalian cell types. In endoreduplication genome content is doubles by DNA replication without subsequent segregation of chromosomes (Puig et al., 2008; Ullah et al., 2009; Zielke et al. 2013; Bennett et al., 2014). In parasexual reproduction, ploidy reduction occurs via a nonmeiotic mechanism, whereas in sexual reproduction mating occurs between haploid cells of opposite mating type forming diploid cells that consequently undergo meiosis, which is accompanied by sporulation and the formation of haploid progeny (Bennett & Turgeon, 2016).

In Aspergillus nidulans, sporadic anastomosis can happen upon fusion of hyphae leading to the formation of a heterokaryon. Fusion of haploid nuclei forms relatively stable diploid cells that produce haploid recombinants by mitotic crossing-over and loss of whole chromosomes where nonsexual exchange of genetic material occur. This can also occur in A. fumigatus. There is a potential benefits of a parasexual cycle which have been indicated by Schoustra et al, who revealed that extended culturing of A. nidulans diploid strains caused higher fitness compared to culturing of haploid strains (Schoustra et al., 2007). Diploids appeared to accumulate recessive deleterious mutations that became beneficial when exposed in recombinant haploid populations. This explains the sign epistasis, where mutations that are individually neutral or deleterious can be beneficial when present in combination. Diploid cells act as a reservoir of such recessive mutations and consequent reduction to haploidy that allows unmasking of these mutations with enhancing overall fitness. Thus parasexual reproduction and its ability to mediate changes in ploidy, promote adaptation even in the absence of a sexual one.

Mating and meiosis regulated by genes encoded at the mating-type (MAT) locus that contains transcription factors, which regulates cell-type specification, ensuring that only haploid cells of opposite mating type undergo conjugation and that only diploid cells are competent to undergo meiosis. In Aspergillus fumigatus, heterothallic mating between opposite mating types takes place, which is strictly regulated by nutritional cues. Heterothallic mating between MAT1-1 and MAT1-2cells results in the formation of sexual fruiting bodies (cleistothecia) containing ascospores. Progeny analysis showed extensive genetic recombination, a hallmark of conventional meiosis (O’Gorman et al. 2008; Sugui et al. 2011). Ploidy change is a potent modulator of cell behaviour and pathogenesis. In C. neoformans haploid cells were more virulent compared to diploid cells in a murine inhalation model of cryptococcosis, whereas in Aspergillus nidulans, a close relative of A. fumigatus, diploid strains found to be more virulent in mice than the corresponding haploid strains (Paurnell & Martin 1973; Lin et al., 2008).

Nonetheless, there is very little information about the importance of genomic stability on drug resistance, population structure and virulence of Aspergillus fumigatus. Additionally, not enough studies have addressed the impact of genomic stability or DNA repair on A. fumigatus azole-resistance and virulence despite the dramatic increase in the prevalence of azole resistance. Recent study by dos Reis et al, revealed that AtmA (Ataxia-telangiectasia mutated, ATM) and AtrA kinases collaborate for A. fumigatus genomic stability, where (ΔatmAand ΔatrA) mutants maintained their virulence accompanied by the evolution of voriconazole resistance to higher levels compared to wild-type strain (dos Reis et al., 25

2018). Cyp51A mutations or overexpression of cyp51 and/or cdr1B efflux transporter shown no involvement in voriconazole-resistant strains, indicating that other mechanisms underlying the evolution of drug resistance occur in these genetically unstable strains.

In non-pathogenic Saccharomyces cerevisiae, polyploid-induced genome change leads to selection of beneficial genotypes in response to stress conditions. Aneuploidy in yeast that results from a triploid parent drives proteomic changes beyond genes located on the aberrant chromosomes and these aneuploidies may result in fitness benefits in response to a sequence of stressful growth conditions. Under evolutionary pressure, both aneuploidy and an increased mutation rate can benefit polyploid cells. One in vitro evolution study found that tetraploid S. cerevisiae shows more rapid adaptation compared to diploids in response to growth on a depleted carbon source owing to more frequent beneficial mutations and improved fitness (Pavelka et al., 2010; Selmecki et al., 2015).

Fitness generally refers to the ability of microorganism to grow in normal conditions or its ability to adapt under stress condition. Intrinsic fitness differs between haploids and diploids, since the later tend to have larger cell sizes than haploids. Evolution studies of ploidy have assumed that the fitness effects of new mutations are equal in haploids and homozygous diploids; however, with different mutational effects, the overall intrinsic fitness of a haploid would not be equivalent to that of a diploid after a series of substitution events (Scott and Rescan, 2017).

1.1.2 Fungal biofilms

Biofilms are complex microbial communities entrenched in a self-formed extracellular matrix (ECM). Biofilms have distinct phenotypes different from planktonic growth forms and constitute a fundamental form of microbial growth providing protective environmental niches (Fanning, S., and Mitchell, A. P, 2012). Biofilms are critical to the development of clinical infection and are involved in resistance to antifungal drugs.

Many pathogenic fungi produce biofilms, including Candida, Aspergillus, Trichosporon, Cryptococcus, Pneumocystis and Coccidioides species. Biofilm cell communities are more resistant to antifungal drugs compared to planktonic cells, although this is drug and species dependent. Biofilm structural complexity, metabolic heterogeneity intrinsic to biofilms, biofilm-associated up-regulation of efflux pump genes and presence of extracellular matrix (ECM) are contributing factors (Fanning and Mitchell, 2012).

C. albicans biofilms consist of yeast and hyphal cells. Biofilms form in a sequential process-involving adherence to a substrate, which can be either an abiotic or mucosal 26 surface, followed by proliferation of yeast cells over the surface, and initiation of hyphal formation. ECM accumulates after biofilm maturation and found to be contributing to cohesion (Al-Fattani & Douglas, 2006). Biofilms can form on biotic and many abiotic surfaces, in denture stomatitis, as well as on combination of biotic mucosal and abiotic surface; however, C. tropicalis, C. parapsilosis, and C. glabrata do not produce true hyphae while form ECM-containing biofilms (Andes et al., 2004; Harriott et al., 2010; Nett et al., 2010; Silva et al., 2011). Aspergillus biofilms form on abiotic and biotic surfaces where the conidia initially adhere to the substrate while mycelia develop when the biofilm matures, or when spores get trapped into cavities such as nasal sinuses and germination leads to development of a ball of intertwined hyphae called aspergilloma (Mowat et al., 2009; Loussert et al., 2010). A. fumigatus and C. albicans hyphae can form channels or pores through biotic surfaces (Singhal et al., 2011; Lermann & Morschhauser, 2008). The emerging fungal pathogen T. asahii forms biofilms composed of yeast and hyphal cells entrenched in matrix as well as in Coccidioides immitis. In C. neoformans biofilms, consist of yeast cells on many abiotic substrates and ECM formed by the shedding of capsular polysaccharide; however no hyphae have been observed in C. neoformans biofilms as well as Pneumocystis species. This indicates hyphal formation is not a uniform feature of fungal biofilms (Fanning, S., and Mitchell, A. P, 2012).

1.1.3.Importance of fungi

Fungi are important for decomposition, and perform a significant role in carbon and nitrogen reprocessing, the production of certain antibacterial compounds such as Penicillin (a broad spectrum antibiotic) and Griseofulvin (an antifungal). They also use in the production of food flavourings and other industrial compounds such as glutamic acid, citric acid, food preservatives, food colouring agents, cheese, bread, soy sauce, beer, wine, cider, etc (Abbey, 1983; Hawksworth, 1991; Khachatourians, 2003). The total number of fungal species is estimated to be in the region of 5 million, however, only few of these (<500) are found to be responsible for causing disease in humans (Blackwell, 2011; Richardson & Warnock, 2012). In the majority of cases, fungi are opportunistic pathogens and are able to infect humans upon exposure to high dose or when the immune system is weakened or disrupted (Carvalho et al., 2008).

1.2 Fungal infections (Mycoses)

Emerging fungal infectious diseases are progressively known to present a significant risk to food safety and animal health ( Fischer et al., 2012; Brown et al., 2012). Moreover, the spread and development of fungal diseases are enhanced by human activity, principally via global trade which lacks adequate biosecurity measures, and are aggravated by the influence of climatic shifts (Brasier, 2008; Fisher et al., 2012).

The prevalence of fungal infections has significantly increased in the last two decades, as the number of people with suppressed immune systems has increased (e.g. through HIV infection, organ transplantation, cancer chemotherapy, immunosuppression), as well as those who being administered broad spectrum antibiotics which are reported to alter the normal microflora in the intestine, or others who have been exposed to invasive operations such as fitting catheters and prosthetic devices (Wisplinghoff et al., 2003). More than 1.5 million people are killed by fungal infection annually, with over a billion being affected (Bongomin et al., 2017), (Table 1.1).

Table 1.1: The burden of fungal infections (Adapted from Bongomin et al., 2017).

Annual Global Burden Comments Fungal Disease Incidence

Superficial Skin, hair, nail ~1,000,000,000 Fungal keratitis ~1,000,000 Mucosal Oral candidiasis ~2,000,000 HIV only, 90% of those not on ARVs HIV only, 20% on those with CD4 counts Oesophageal candidiasis ~1,300,000 <200 and 5% of those on ARVs

Vulvovaginal candidiasis 75% affected in their lifetime at least once episode 2,790,000,000

Recurrent vulvovaginal 138,000,000 ~372,000,000 6% in their lifetime experience candidiasis Allergic Allergic bronchopulmonary ~4,800,000 Adults only, rare in children aspergillosis in asthma Allergic bronchopulmonary ~6675 Adults only, starts from age 4 aspergillosis in cystic fibrosis Severe asthma with fungal Adults only, probably uncommon in ~6,500,000 sensitisation children Fungal rhinosinusitis ~12,000,000 Chronic severe Chronic pulmonary ~3,000,000 aspergillosis Mycetoma ~9000 1950–2013 case reports, NTD Chromoblastomycosis >10,000 Limited data and uncommon, NTD Coccidioidomycosis ~25,000 Paracoccidioidmycosis ~4000 Blastomycosis ~3000 Most of the new infections are Histoplasma infection ~500,000 ~25,000 asymptomatic based on skin testing Very limited global data. Very common in Sporotrichosis >40,000 hyper endemic regions of Peru, Brazil and Mexico Acute invasive Includes 60,000–100,000 cases of intra- Invasive candidiasis ~750,000 abdominal candidiasis Invasive aspergillosis >300,000 From about 10 million at risk annually Pneumocystis jirovecii pneumonia in AIDS ~500,000 and non-AIDS Cryptococcosis in AIDS ~223,000 HIV-related, up to another 10% non-HIV Based on French data = 4200. Mucormycosis >10,000 Based on Indian data = 910,000 Disseminated histoplasmosis ~100,000 No reliable estimates Talaromycosis * ~8000 SE Asia only

The fungi cause a broad range of diseases that can be classified in to 3 main types, as described below: 29

1.2.1 Superficial mycoses

Superficial mycoses are infections with an approximate global prevalence reaching 25% of world’s population (Havlickova et al., 2008). In superficial mycoses, the infection is limited to the external surface of the hair, nail and skin or the mucous membrane. The main infections include dermatophytoses and the superficial forms of candidiasis (Richardson & Warnock, 2012). Dermatophytoses are limited to the keratinized tissues of the epidermis, hair and nail with the fungi attacking the keratin layer. It is of great public health importance, with an estimate of affecting 10% to 15% of world’s population (Pires et al., 2014).

Dermatophytoses primarily caused by Microsporum, Trichophyton and Epidermophyton species. These fungi damage the underlying epithelial layer leading to inflammation as a result of their growth as invading saprobes on the cutis and its complements (Moore et al., 2011). T. rubrum is the most common pathogen occurring in highly developed countries and some urban areas, which causes tinea pedis, tinea corporis, tinea cruris, and tinea unguium (Borman et al., 2007). M. audouinii results in ringworm and commonly infects young children in underdeveloped countries, with high global prevalence. It is characterised by a ring formation on the skin as the growth of mycelia resembles the colonies growing in petri dishes and the redness results from the inflammation due to reaction to the fungal protein.

Tinea pedis (athletes’ foot) is most commonly caused by T. interdigitale (previously known as T. mentagrophytes and T. rubrum) and infects adults with a high prevalence worldwide. Its emergence is a consequence of modern lifestyles, such as the use of public swimming facilities and occlusive footwear (Havlickova et al., 2008). Onychomycosis refers to infections of the finger and toenails, which is caused by some ‘dermatophytes’ (T. rubrum, E. floccosum), and none dermatophyte spp such as C. albicans (yeast, Ascomycota) and Scopulariopsis brevicaulis (filamentous, Ascomycota) (Moore et al., 2011). Onychomycosis presents with a low incidence worldwide and appears to increase with age, reaching 5% of 70 year and older populations (Havlickova et al., 2008; Leelavathi and Noorlaily, 2014).

Candida species most frequently cause superficial infection on the mucous membranes of the mouth and vagina. The oral form of infection (thrush) exists on the gums, tongue, and the buccal mucosa. Impairment of salivary gland function can be predisposing factor. Antimicrobial proteins in the saliva like lactoferrin, sialoperoxidase, histidine-rich polypeptides, lysozyme, and specific anti-candida antibodies, interact with the oral mucosa 30 preventing overgrowth of Candida. Oral candidiasis frequently occurs in babies, denture wearers reaching to 65% of elderly people wearing full upper dentures, dentures predispose to infection with Candida by producing a microenvironment conducive to its growth (low oxygen, low pH and an anaerobic environment) (Singh et al., 2014). Asthmatic individuals who use steroid inhalers shown to have increase risk of oral candidiasis by possibly suppressing cellular immunity and phagocytosis, which is normally, reverts to normal upon discontinuation of the inhaler. Oral candidiasis occurs in leukemic and transplant patients, and in people with head and neck malignancies who have received radiotherapy, as well as in a high carbohydrate diet, which enhance its adherence to oral epithelial cells (Singh et al., 2014).

Vulvovaginal candidiasis affects women of childbearing age and it is the second most common infection after bacterial vaginitis in the UK with ≈75% of women having at least one episode per year and ≈6% having 4 or more episodes per year. Symptoms include itching and burning of the vulva and lower vagina (Sobel, 2007). Pregnancy and the use of oral contraceptives have been reported to increase the risk for infection (Sobel 2007; Denning et al., 2018). Incidence of vulvovaginal candidiasis in HIV-infected women and non-HIV-infected women is similar. Vulvovaginal candidiasis responds well to topical treatment or to oral antifungal drugs, however recurrent vulvovaginal infection may require long-term suppression with antifungals.

Piedraia hortae is a filamentous fungus of the Ascomycota which infects hair and causes a disease known as black piedra, and is characterised by brownish to black nodes on the hair of the scalp (De Hoog & Guého 1998). Trichosporon cutaneum is a yeast belonging to the Basidiomycota which infects the external part of the skin, scalp and pubic region causing white piedra disease (Nakagawa et al., 2000). This fungus appears to be an opportunistic pathogen that manifests in immunocompromised individuals.

1.2.2 Subcutaneous mycoses

Subcutaneous mycoses are primarily caused by saprobes, which infect people who typically walk barefoot, penetrating the skin through wounds. Madura foot (human mycetoma) is a localised infection caused by Madurella grisea and M. mycetomatis (filamentous, Ascomycota), which forms invasive abscesses resembling those resulting from tumours (Van de Sande, 2013; Reis and Reis-Filho, 2018). There are signs of long- lasting tenderness, leading to swelling, distortion and ulceration of the affected part. Pathogenic fungi might develop for many ages in the cutaneous and subcutaneous tissues,

31 before disseminating to underlying tissues and bones, with surgery proving to be the only solution for Mycetomas as it shows resistance to chemotherapy (Moore et al., 2011). Sporotrichosis is a localised fungal infection caused by Sporothrix schenckii; it is also referred to as ‘rose handler’s disease’ (Barros et al., 2011; Chakrabarti et al., 2015). The infection initiates by entering wounded skin and then disseminating through the lymphatics, to thus infect bones, lungs and joints, while also causing meningitis, endophthalmitis (inflammation of the internal layers of the eye), and invasive sinusitis (Moore et al., 2011b).

1.2.3 Systemic mycoses

Systemic mycoses refer to the infections that spread into the bloodstream and may disseminate to internal organs from the primary site of infection (Moore et al., 2011; Salzer et al., 2018, Pound et al., 2011). Systemic mycoses are complicated infections with high mortality. For systemic aspergillosis, the typical source is the lungs and for Candida, the gastrointestinal tract. Some fungi may enter the body through injured skin or paranasal sinuses. Immuno-compromised patients are at higher risk of getting systemic mycoses, however, infection may also develop in immunocompetent patients. There are two main types of systemic mycosis: those caused by virulent primary pathogens (endemic mycoses) and those caused by opportunistic pathogens (Brandt et al., 2011). Opportunistic systemic mycoses are the infections caused by opportunistic pathogenic fungus and only result in sick people or immune-compromised patients. These infections include candidiasis, aspergillosis, mucormycosis (zygomycosis) and cryptococcosis. Endemic respiratory infections develop in both healthy and immune-compromised individuals and include blastomycosis, histoplasmosis, penicilliosis, and paracoccidiodomycosis (Ramos-e-Silva 2012; Salzer et al., 2018).

1.2.3.1 Systemic mycoses caused by primary pathogens

Systemic mycoses caused by primary pathogens normally emerge after inhalation of the conidia either from soil or related bedrocks, whereby the infection targets the lungs principally, before disseminating into other tissues. Four fungal genera are known to cause primary systemic mycosis: Blastomyces dermatitidis, causing blastomycosis, which is prevalent in the Midwest of the United States (López-Martínez and Méndéz-Tovar, 2012); Coccidioides, which causes Coccidioidomycosis (Brown et al., 2013); Cryptococcus, causing cryptococcosis (Aguiar et al., 2017; George et al., 2017); and Histoplasma, which causes histoplasmosis (Scully and Baddley, 2018).

1.2.3.2 Systemic mycoses caused by opportunistic pathogens

This type of infection is commonly the result of low virulence fungi that are present in any environment. The pathogens typically infect immunocompromised individuals. Candida, filamentous Aspergillus and Zygomycete spp are the most common fungal pathogens isolated from immunocompromised individuals.

1.2.3.2.1 Candidiasis

Candida infection emerges once human immunity impaired or when the amount of Candida exceeds the capacity of the immune defences. Candida albicans is a component of the normal microflora of the gut, coexisting with other microbes in a balanced manner. Candida species are considered to be one of the most common fungal pathogens involved in life-threatening invasive infections. Steroids, immunosuppressive drugs and antibacterial agents inhibit bacterial microflora, consequently increasing the opportunities for infection as this facilitates the adhesion of Candida to the intestinal epithelium, which reflects fungus pathogenicity (Pfaller, and Diekema, 2007; Pappas et al., 2018)

Candida species are known to cause life-threatening invasive infections after long periods of hospitalisation in immunocompromised individuals, those undergoing intensive medical intervention, and those suffering from major trauma. Hospital acquired blood stream infections are the fourth most prevalent infection to be reported with Candida spp in the USA (Wisplinghoff et al., 2004).

The dissemination of Candida infections has been associated with the use of catheters, neonatal intensive care units, intestinal operations and hepatic transplants. The number of reported pathogenic Candida spp is 15, of which C. albicans is found in most infections (Pfaller et al., 2009). The mortality rate for disseminated Candida infection is significantly high in comparison with the most destructive types of bacterial and viral sepsis, and reaches 42% (Morgan, 2005). However, worldwide progressive shifts from C. albicans to non-albicans Candida spp. have been detected (Lamoth et al., 2018).

1.2.3.2.2 Aspergillosis

Aspergillosis infection emerges after inhaling Aspergillus conidia. The primary site of the infection is the lung, however it can affect other organs after dissemination of infection. The Aspergillus genus contains close to 200 species; however, few are considered pathogenic. The species that cause aspergillosis are, in order of how frequently they result in infection, Aspergillus fumigatus, A. flavus, A. niger, A. nidulans, A. terreus and A. glaucus (Moore et al., 2011). Aspergillosis occurs in mammals, birds, and has been found to be endemic in chickens raised under crowded conditions.

A. fumigatus is omnipresent, and several hundred spores are estimated to be inhaled daily by humans, with these being eradicated by the innate immune system (Jones and Cookson, 1983; Balloy and Chignard, 2009; Svirshchevskaya et al., 2012). The small size of the spores (2–3µm diameter), facilitates their invasion of the lung alveoli (Luther et al., 2007; Thomas, 2013). A. fumigatus was first described as a human pathogen by the physician John H. Bennet, who reported the presence of the fungus in the lung of a dead person with pneumothorax; fifty years later, A. fumigatus was highlighted as the initial cause of infection.

There are three forms of Aspergillosis, these are:

1.2.3.2.2.1 Allergic bronchopulmonary aspergillosis

Allergic bronchopulmonary aspergillosis (ABPA) is an infection which develops from allergic reaction to the spores of Aspergillus fumigatus (Patterson, 1998;Shah and Panjabi, 2014; Shah and Panjabi, 2016 ). It affects immunocompetent and atopic individuals, and frequently occurs in individuals with cystic fibrosis (15%) or asthmatics, although it sporadically occurs apart from these conditions (Denning et al., 2003); (Stevens et al., 2003); (Knutsen et al., 2012). Symptoms associated with ABPA include wheezing, uncontrolled asthmatic, and expectoration of mucus plugs, lethargy and gasping (Dani, 2005; Knutsen and Slavin, 2011).

1.2.3.2.2.2 Chronic Pulmonary Aspergillosis

Chronic pulmonary aspergillosis is a disease of the lungs, affecting susceptible individuals who have previously suffered from other lung diseases such as tuberculosis, sarcoidosis, or pneumothorax. There are three forms of infection: aspergilloma, chronic cavitary pulmonary aspergillosis (CCPA) and chronic fibrosing pulmonary aspergillosis (Denning

34 et al., 2003; Smith and Denning, 2011). In individuals with tuberculosis, the growing hyphae colonise within the pre-existing lung cavities and form fungal balls, or noninvasive aspergilloma comprising of clusters of mycelium, inflammatory cells, fibrin, mucus, and tissue debris (Soubani and Chandrasekar, 2002). CCPA develops when these cavities increase in number and size over time (Denning et al., 2003), with the clinical symptoms including emaciation, chronic cough, lethargy, shortness of breath and haemoptysis. Chronic fibrosing pulmonary aspergillosis progresses in untreated cases, with the presence of chronic inflammation inside the walls of these cavities along with the infiltration of inflammatory cells such as neutrophils, eosinophils or granulomas (Denning et al., 2003; Riscili and Wood, 2009; Bongomin et al., 2017).

1.2.3.2.2.3 Invasive Aspergillosis

Invasive aspergillosis is a devastating form of aspergillosis, commonly occurring due to constant exposure to fungal spores, and primarily affecting individuals with weakened immune function; for example, neutropenic individuals, solid organ transplant receivers, and those receiving immunosuppressant drugs such as high-dose steroids. Invasive aspergillosis disperses from the lung to various tissues and organs. High risk groups for invasive aspergillosis include those individuals with chronic obstructive pulmonary disease (COPD), leukemic individuals and those undergoing transplantation, with over 200,000 cases annually (Kousha et al., 2011; Brown et al., 2012) Despite therapy, the mortality rate for invasive aspergillosis is still considerable (Patterson et al., 2000; Soubani and Chandrasekar, 2002). Fifty percent of patients with invasive aspergillosis die post-therapy, while this statistic exceeds 90% in cases of missed and delayed diagnosis (Brown et al., 2012).

1.2.3.2.3 Mucormycosis (zygomycosis)

Mucormycosis is the third most common opportunistic fungal infection after Candidiasis and Aspergillosis, with a high incidence in transplant receivers, commonly caused by Absidia corymbifera, Rhizomucor pusillus and Rhizopus arrhizus (Nucci et al., 2005; Anaparthy and Deepika, 2014). The infection is associated with diabetic’s ketoacidosis, emaciated children, severely burned individuals, leukaemia, lymphoma, HIV infection and immunosuppressive treatment (Kolekar, 2015). Mucormycosis affects skin, the gastrointestinal tract, lungs and the rhino–facial–cranial area (Serris et al., 2019). Death from rhinocerebral zygomycosis in diabetic individuals occurs within a matter of days.

1.2.3.2.4 Pneumocystosis

Pneumocystis jirovecii pneumonia (PJP or PCP) is an infection caused by P. jirovecii (Carmona and Limper, 2011) with four clinical manifestations of infection: asymptomatic infections, infantile pneumonia, pneumonia in immunocompromised patients, and extrapulmonary infections. Infected individuals with pneumocystosis, who carry significant amounts of fungus, are the main source of contagion. Opportunistic infection due to Pneumocystis is common in immunocompromised patients, individuals with AIDS, and those receiving steroids or immunosuppressant drugs to treat malignancies or to prevent transplant refusal (Morris et al., 2008; Martin et al., 2013). Extrapulmonary pneumocystosis is a key AIDS-related disease, which emerges from the spread of infection from the lungs into other organs (Hammer, 2005; Sax 2001; Matos et al., 2017).

1.2.3.2.5 Fusariosis

Fusariosis is commonly caused by Fusarium solani, a recognised plant pathogen that is found widely dispersed on plants, including crops and in the soil. Fusarium species cause severe infections in patients with hematologic malignancies, which range from infections of eye, nail and skin in immunocompetent host to invasive and disseminated infections in mainly immunocompromised patients (Ozkocaman et al., 2015; Nucci et al., 2015). Infection was reported with individuals undergoing haematopoietic stem cell transplantation (HSCT), which usually conducted to treat different malignancies including bone marrow conditions; this highlights concerns regarding the increase in prevalence of infection (Nucci et al., 2005; Al-Hatmi et al., 2016).

1.3 Antifungal pharmacology

There are four principal groups of drugs that have been used either to supress or inhibit fungal infections: The Polyenes, Azoles, Echinocandins and Pyrimidine analogues (Pianalto and Alspaugh, 2016), (Table 1.2).

1.3.1 Polyene antifungal drugs

The first agent developed to treat fungal diseases was amphotericin B-deoxycholate, which was launched in 1958 by Squibb Laboratories following extensive trials to produce orally bioavailable forms of more than 200 polyene macrolide antibacterial from Streptomyces sp. (Dutcher, 1968). Polyenes include amphotericin B (AmpB), nystatin and natamycin.

AmpB is used for systemic invasive fungal infections, and it is effective against Cryptococcus, Candida and Aspergillus species (Lemek et al., 2005; Sanglard et al., 2009). Due to their slow absorption both nystatin and natamycin are used for topical infections (Vandeputte et al., 2012). Nystatin is frequently used for the treatment of cutaneous (as cream), vaginal (as pessaries), and esophageal candidiasis, whereas natamycin can be used for the treatment of fungal keratosis or corneal infection.

Polyene drugs targets and attaches to ergosterol (a main constituent in the fungal cell membrane), producing a composite that forms pores and leads to the efflux of intracellular materials (Brajtburg et al., 1990). Recent studies suggested other possible mechanism of action such as binding directly to ergosterol leading to electron transfer in the cell membrane, thus oxidative stress and reactive oxygen species are created (Gray et al., 2012; Kovacic and Cooksy, 2012). This compound has specificity to fungal ergosterol, rather than the closely-related human cholesterol (Odds et al., 2003).

Amphotericin B is toxic to mammalian cells, particularly causing severe damage to the distal tubular membrane of the kidney and nephrotoxicity (Odds et al., 2003). This effect appears to be dose-related, and as a result of the accumulation of the drug at higher concentrations (Groll et al., 1998). Infusion-related reactions (hyperpyrexia, shivering) are a further side effect and result from the enhanced liberation of certain pro-inflammatory cytokines ( Rogers et al., 1998; Cleary et al., 2003). These side effects encouraged the development of novel lipid medication formulations, which have fewer infusion-associated reactions and lower nephrotoxicity (Saliba and Dupont, 2008; Hamill, 2013).

Table 1.2: List of current available oral antifungal drugs that was approved by FDA and their antifungal spectrum. Green (good activity), yellow (some activity), red (poor activity), white (not known), * indicate resistance is common. Adapted from Nett et al 2016 and Chang et al., 2017.

Antifungal

Members

group

oplasmosis

fumigatus neoformans

parapsilosis tropicalis krusei lusitaniae guillermondi

. .

. immitis .

A C C.albicans glabrata C. C. C. C. C. C. C Fusariumspp Zygomycetes Blastomycosis His Dermatophytes Fluconazole ++ +* ++ ++ -* ++ ++ - ++ ++ - - + + - Itraconazole ++ +* ++ ++ + ++ ++ + ++ ++ + + ++ ++ ++ Azoles Voriconazole ++ ++* ++ ++ ++ ++ ++ ++ ++ ++ ++ - ++ ++ ++ Posaconazole ++ ++* ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ Isavuconazole ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ Caspofungin ++ + ++ ++ ++ ++ ++ + ------Echinocandins Micafungin ++ + ++ ++ ++ ++ ++ + ------Anidulafungin ++ + ++ ++ ++ ++ ++ + ------

Polyenes Amphotericin B ++ ++ ++ ++ ++ -* + ++ ++ ++ +* ++* ++ ++ + Pyrimidine Flucytocine + +* ++* ++* ++* - ++* ++* - ++* ------analogues Allylamines Terbinafine ++ ++ ++

Liposomal and lipid complex Amphotericin B is used to treat a broad spectrum of severe fungal infections. Despite its efficacy in suppressing serious Aspergillus infection for over four decades, the severe side effects and related nephrotoxicity, even in the newly formulated liposomal formulations, adding to the lack of availability in oral formulation limited its use.

A novel study reported the possibility of manufacturing less toxic amphotericin B derivatives with a deletion of C’2 hydroxyl group, which provides specificity to bind to fungal ergosterol and not mammalian cholesterol. Therapeutic and toxicity experiments in vivo, using a systemic candidiasis murine model revealed that amphotericin B methyl urea (AmBMU) was less toxic and more effective compared to amphotericin B deoxycholate formulation. These finding add to the current advance in antifungal drug development related to the gold standard antifungal amphotericin B (Wilcock et al., 2013; Davis et al., 2015).

Nystatin has a narrow spectrum of activity compared to amphotericin B, however it is active against Candida, Histoplasma, Blastomyces, Cryptococcus, and the dermatophytes (Epidermophyton, Trichophyton, and Microsporum) (No-Hee et al., 2017). Similarly to amphotericin B, nystatin is either fungistatic or fungicidal depending on the concentration of the drug present, nature of the infecting organism and the pH of the surrounding medium. Nystatin is used to treat Candida infection either systemically to treat intestinal tract infection, or topically to treat infections of the mucosa, skin, and vagina. Topical nystatin is a drug of choice for the treatment of the oral cavity infection caused by Candida and has also been used in immunocompromised patients prophylactically.

1.3.2 Azole antifungals

Azole antifungals constitute the primary drugs that have been prescribed for the treatment of different types of fungal infections for more than twenty years (Lewis, 2011). They are classified into two main groups imidazoles and triazoles based on the number of nitrogen atoms in the five-membered azole ring. The imidazole group of drugs have two nitrogen atoms, consists of oxiconazole, econazole, tioconazole, clotrimazole, miconazole and ketoconazole. This group of drugs are mainly used for the mucosal fungal infection. The triazoles group (with three nitrogen atoms) includes fluconazole, itraconazole, voriconazole, posaconazole, isavuconazole and newly developed drugs such as ravuconazole and albaconazole (which are undergoing clinical trials, yet they are not

39 licenced) (Pasqualotto and Denning, 2008). This group of drugs are used for both systemic and mucosal fungal infection.

The azole group of compounds exert their therapeutic effect through inhibiting lanosterol 14 α demethylase, a fungal cytochrome P450 dependent enzyme (Odds et al., 2003; Mazu et al., 2016). Inhibition results in the reduction of fungal cell membrane ergosterol, disruption of the membrane permeability and leads to the accumulation of toxic metabolic intermediate (14 alpha methylated sterols), with consequential growth cessation and cell death (Sheehan et al., 1999; Mazu et al., 2016). Unfortunately, this inhibition is not restricted to fungal cells but also extends to include human enzymes, resulting in drug– drug interactions (Mazu et al., 2016). The most common side effects of this group are nausea, vomiting, diarrhoea, hepatotoxicity and rash.

1.3.2.1 Topical azoles

Clotrimazole, econazole, and Miconazole drugs became available for treatment of fungal infection in late 1960s; however, their high toxicity restricted their use to external application only.

Miconazole was the first antifungal available for parenteral injection in 1968, but its high toxicity accompanied with narrow spectrum of activity among fungal species decreased and limit its use with no longer availability in the market (Patrick et al., 2012).

Ketoconazole was approved as antifungal in 1981by FDA and was the only available drug to treat systemic fungal infections caused by yeasts till 1991. There were many drawbacks associated with its usage. Although, it is applied topically and absorbed locally, considerable amount of drug reach the systemic circulation.

Clotrimazole is an imidazole antifungal that used topically for superficial candidiasis, pityriasis versicolor, and dermatophytosis (Christine, 2007). A vaginal cream and suppository are available for the treatment of vaginal candidiasis, and an oral lozenge is used for treatment and prophylaxis of oropharyngeal candidiasis. Absorption following topical application is poor and erratic. It induces hepatic microsomal enzyme activity, accelerating its own catabolism. Skin irritation, a burning sensation, and contact allergic dermatitis are common side effects.

Econazole nitrate is an imidazole antifungal used topically for the treatment of superficial candidiasis, dermatophytosis, pityriasis versicolor and for the treatment of intravaginal candidiasis (Carl and Clifford, 2019). Following topical administration 90% of the applied dose of a 1% econazole cream remains on the skin surface for 0.5 to 18 hours and more 40 than 98% is protein bound and is extensively metabolized to more than 20 metabolites. Side effects include erythema, rash, burning, itching and stinging. Econazole nitrate cream is contraindicated in those with known allergy to the formulation.

Oxiconazole nitrate is an imidazole antifungal applied topically as cream or lotion for the treatment of fungal infection of skin such as tinea pedis, tinea cruris, and tinea corporis (Carl and Clifford, 2019). Although it is applied topically, very little is absorbed systemically. Local reactions to topical application of oxiconazole include burning and itching (Christine, 2007).

Omoconazole is an imidazole antifungal used for the treatment of superficial fungal infections, including dermatophytosis, pityriasis versicolor, molds, and cutaneous and vaginal candidiasis. It has bacteriostatic activity against Staphylococcus aureus and Group A and D Streptococci. Omoconazole should be used with caution in children under 5 years of age, during pregnancy, during nursing, in people with known allergy to it and application prohibited on injured skin. Common side effects include skin irritation (redness, burning, and itching) and allergic reactions to the benzoic acid preservative may in rare cases skin (Christine, 2007).

Sertraconazole is an imidazole antifungal used for the treatment of dermatophytosis, superficial candidiasis, and pityriasis versicolor (No-Hee et al., 2017). The cutaneous absorption of sertaconazole, reach 72% after 24 hours of administration. Sertaconazole is contraindicated in people with known allergy to it. Common side effects include headache, drowsiness, pruritus, erythema, dermatitis and isolated instances of serum aminotransferases (Noble et al., 2002).

Sulconazole is an imidazole antifungal used for the treatment of skin infections including dermatophyte infections, pityriasis versicolor, and candidiasis. It is contraindicated in people with known hypersensitivity to it, with no data available regarding use with caution in pregnant women or nursing mothers, however it should be used during pregnancy and lactation only if the benefit outweigh the potential perinatal risk (Carl and Clifford, 2019). Side effects include local reactions (burning, blistering, itching, and erythema) (Sweetman, 2003).

Tioconazole is a broad spectrum imidazole antifungal active against dermatophytes, Malassezia furfur, and Candida albicans. Following topical administration, very little amount is absorbed systemically. Tioconazole is contraindicated in people with known

41 hypersensitive to it or to other imidazole antifungals. Common side effects include burning, itching, and erythema (No-Hee et al., 2017).

Terconazole is broad-spectrum triazoles that have structure similarities with ketoconazole. It is effective in treatment of vulvovaginal candidiasis caused by Candida albicans and in the treatment of non-Candida albicans species (Cooper and McGinnis, 1996; Sood et al., 2000). Terconazole is a fast acting, highly effective, and well-tolerated therapy for treating vulvovaginal candidiasis (Kjaeldgaard, 1986). It is available as a vaginal cream or as a vaginal suppository. Headache, burning, itching and irritation and an influenza-like symptom are the main reported side effects. Although Terconazole is effective in treating vaginal candidiasis in pregnancy, it is in the pregnancy safety category C, which limit its use in the first trimester, yet there is no adequate studies to confirm it crossing the human placenta (Carl and Clifford, 2019).

Efinaconazole nitrate is a triazole drug that used for the treatment of onychomycosis and other superficial fungal infections. Efinaconazole has broad-spectrum antifungal activity extend to include Candida and Aspergillus (No-Hee et al., 2017).

1.3.2.2 Oral Azoles

Fluconazole was launched in 1990. The pharmacokinetic characteristics of fluconazole contribute to its clinical applications. For example, low affinity protein binding permits high concentrations of the drug to pass the blood–brain barrier, and therefore fluconazole is an effective agent for the treatment of central nervous system infections (Góralska et al., 2018). It is highly water-soluble; available in both oral and intravenous formulations, with high, reliable bioavailability and minimal variation in absorption (Girmenia, 2009). The prolonged half-life of fluconazole reduces the need for frequent administration, and enables once or twice daily (Lewis, 2011).

Fluconazole active against many Candida species, however C. krusei and some strains of C. glabrata are inherently resistant. It is the treatment of choice for Candida urinary tract infections as it is excreted unchanged in the urine. It is highly active against Cryptococcus neoformans, and lack of activity against Aspergillus, Mucorales, and Fusarium species. Fluconazole is frequently used as prophylactic against Candida infections in high-risk patients, while its activity is not as effective as second-generation triazoles against aspergillosis (Chang et al., 2017).

Itraconazole has a broad-spectrum antifungal activity against invasive fungal infections. Unlike fluconazole, itraconazole has lower bioavailability and significant variation in

42 absorption with poor CNS penetration. Furthermore, itraconazole urinary metabolites are inactive. It is available in two formulations: oral capsule and oral solution. The oral solution is with improved bioavailability that makes it superior to capsule, however it is not tolerated as well as the later (Barone et al., 1998).

Itraconazole has a much wider spectrum of activity compared to fluconazole. This extends to include fluconazole susceptible Candida species, Cryptococcus, and many dimorphic fungi including Coccidioides, with some activity against Aspergillus, however it is not active against Fusarium species or Zygomycetes (Chang et al., 2017). Itraconazole constitute the second-line treatment for invasive candidiasis with better antifungal activity compared to fluconazole against some Candida species (Brad et al., 2006; Ashbee et al., 2013).

For clinical uses, it is replaced by second-generation triazoles (Voriconazole and posaconazole) due to its pharmacokinetics prosperities and most prominent drug- drug interaction.

Voriconazole, the production of the broad-spectrum triazoles voriconazole (2002) improved the treatment of invasive fungal infection in immunocompromised individuals. This drug has chemical structure similar to fluconazole, with excellent CNS penetration as same as fluconazole. Voriconazole has poor solubility in water, which required addition of cyclodextrin to improve solubility of intravenous solutions. Cyclodextrin component accumulates in patients with renal failure, as voriconazole is not eliminated by kidney (Girmenia, 2009). Variation in blood levels is an issue with voriconazole due to individual variations in metabolism.

Voriconazole is available in both intravenous and oral forms. The later forms have excellent bioavailability. It has a substantially broader spectrum of activity than fluconazole and itraconazole. This extends to include Candida species, Cryptococcus, dimorphic fungi, Fusarium, and Scedosporium, and most importantly, it has very high activity against most Aspergillus species to be the first line treatment for invasive aspergillosis (Nett and Andes, 2016). It is used as prophylactic agents to prevent yeast and molds infections in immunocompromised individuals, however it is not protect against Zygomycetes ( Ullmann et al., 2007; Wingard et al., 2010)

Posaconazole was authorised by the FDA in 2006. It has chemical structure similar to itraconazole, with poor water solubility. Absorption of oral suspension is highly variable 43 from the GIT and the bioavailability is depending on food intake. The delayed release oral tablet has higher azole plasma levels, better absorption and improved bioavailability compared to oral suspension (Cumpston et al., 2015). FDA approved intravenous formulation in 2014 for patients who are unable to take oral medications and the phase 1B trial results showed that the intravenous formulation of posaconazole was well tolerated in patients at high risk for IFIs (Maertens et al., 2014).

Posaconazole has broad spectrum antifungal activity which extends to include A. fumigatus, Cryptococcus, Candida, Zygomycetes, and other opportunistic microorganisms of medical importance such as Mucorales and Fusarium spp (Pfaller et al., 2004; Kauffman et al., 2007; Pfaller et al., 2009). It also has better efficacy than fluconazole as prophylactic against systemic fungal infections. Since posaconazole does not penetrate into the CSF well, thus might limit his suitability for treatment of invasive Aspergillus infections and disseminated candidiasis (Nett and Andes, 2016).

Isavuconazonium sulfate is a water-soluble pro-drug of isavuconazole, after intravenous administration (IV), the pro drug metabolized into isavuconazole by plasma esterases, while the oral capsule forms hydrolyses and convert to the active form in the gut lumen (Rybak et al., 2015). Studies showed that high bioavailability of oral capsules is minimally affected by food intake and suggest that blood levels are substantially more consistent compared to voriconazole or posaconazole. Furthermore, it has high affinity to protein bound, which decrease the drug levels in the CSF, however it may reach the brain parenchyma at clinically useful concentrations (Pettit and Carver, 2015). It has high water solubility compared to voriconazole and posaconazole, and active isavuconazole is not excreted in the urine.

Isavuconazole is a broad-spectrum triazole active against molds, yeasts and dimorphic fungi, with fewer drug-drug interactions. It is active against A. fumigatus and Candida spp (Miceli and Kauffman, 2015), with similar potency to Amphotericin B, itraconazole and voriconazole against Candida and Cryptococcus, and a superior effect to fluconazole and flucytocine (Girmenia 2009; Natesan and Chandrasekar, 2016).

1.3.3. Echinocandins

The echinocandin class of compounds were identified as antifungal drugs in 1970s, however anidulafungin, caspofungin and micafungin entered clinical trial in late 1990 (Bossche, 2002).

The echinocandins are synthetically altered lipopeptides that have been developed from the secondary metabolites isolated from a range of fungi (Wiederhold and Lewis, 2003). They include Caspofungin, which is synthesised from pneumocandin B isolated from Glarea lozoyensis (Vicente et al., 2003), micafungin derived from echinocadin B and made by Coleophoma empedri (Jarvis et al., 2004), and anidulafungin derived from echinocadin B and synthesised by A. nidulans (Vazquez, 2005; Dowell et al., 2004).

This group of drugs act by disrupting the synthesis of β-1, 3-d-glucan in fungal cell walls, resulting in the destruction of the cell wall (Onishi et al., 2000); Vazquez, 2005). The absence of glucan in mammalian cells increases the effectiveness of this drug while minimising the expected side effects in individuals with invasive candidiasis (Mora et al., 2002; Pappas et al., 2007) and Aspergillus infection (Maertens et al., 2004).

Although Echinocandins are highly active against Candida, they possess fungistatic activity against Aspergillus species (Nami et al., 2019). They are used as alternative or second line treatment against invasive aspergillosis while they are not effective against Cryptococcus, dimorphic fungi or zygomycetes. Furthermore, they are active against the cyst form not the vegetative form of Pneumocystis jirovecii (Nett and Andes 2016).

Echinocandins are generally very safe, however arrhythmia and cardiac failure occurred in some patients after the administration of caspofungin and it has been shown that both caspofungin and anidulafungin decrease left ventricle contractility in vivo (Stover et al., 2014). Thus echinocandins should be used with cautions in patients with pre-existing cardiac diseases.

1.3.4 Pyrimidine analogues

Flucytosine (5-FC) has been employed as an anti-cancer drug since 1957 (Heidelberger et al., 1957). Grunberg et al discovered antifungal activity of 5-FC against cryptococcosis and candidiasis in 1963 and successfully used for the treatment of systemic candidiasis and of cryptococcal meningitis five years later (Patrick et al., 2012).

The pharmacological effect of this drug relates to its active metabolites: 5-fluorouridine triphosphate (5-FUTP) and 5-fluoro-2’-deoxyuridine-5’-monophosphate (5-FdUMP) (Derissen et al., 2016). 5-FUTP disrupts protein synthesis by interacting with RNA, while 5-FdUMP inhibits thymidiate synthetase (Pound et al., 2011). Gastrointestinal disturbances (such as nausea, vomiting and diarrhoea), myelosupression and liver toxicity are the main associated adverse symptoms noticeable with 5-FC therapy (Rogler, 2010). The emergence of rapid resistance to 5-FC therapy highlights the necessity for combination therapy and 45 rationalises the use of 5-FC with other drugs like amphotericin B to treat cryptococcal meningitis (Tenforde et al., 2018). This combination has been recorded as being superior to using amphotericin B alone (Brouwer et al., 2004; Tenforde et al., 2018).

1.3.5 Allylamines, thiocarbamate and morpholines

The allylamines and thiocarbamates are synthetic antifungal with fungicidal activity. They are reversible, non-competitive inhibitors of squalene epoxidase, which converts squalene to lanosterol. Thus, the conversion of lanosterol to ergosterol is prevented, leading to substantial effects on fungal cell membrane structure and function due to ergosterol depletion (Groll et al., 1998). There are two allylamines antifungal agents, naftifine (topical preparation) and terbinafine (oral preparation), and one thiocarbamate, tolnaftate. The morpholines such as amorolfine act by inhibiting two different enzymes of the ergosterol biosynthesis pathway, the C14-reductase (encoded by ERG2) and the Δ7,8- isomerase (encoded by ERG24) (Vandeputte et al., 2012). Although they possess wide spectrum of activity, these antifungal agents are essentially used to treat dermatophyte infections such as tinea capitis, tinea pedis, and Onychomycosis (Sanglard et al., 2009).

Terbinafine and naftifine are used topically for the treatment of dermatophyte infections of the nails and has good in-vitro activity against Aspergillus spp., Fusarium spp. and other filamentous fungi, with variable activity against yeasts. Although it has not been very effective against invasive aspergillosis, systemic sporotrichosis, systemic candidiasis or pulmonary cryptococcosis in vivo, it has been shown to be effective against some strains of Aspergillus spp., Candida spp., including triazole-resistant strains, and P. boydii, in vitro when combined with azoles or amphotericin B. Combination with amphotericin B improved his activity against aspergillosis in vivo (Vandeputte et al., 2012). Few side effects are accompanied oral administration these include; nausea, anorexia, taste disturbances, abdominal discomfort, headache, rashes (occasionally sever), fatigue, arthralgia and myalgia (Derek et al., 2018).

Tolnaftate, a thiocarbamate drug that used topically to treat mild to moderate superficial dermatophyte fungal infection in skin and toenails (tinea pedis, tinea corporis, tinea cruris, tinea versicolor and tinea manuum. Tolnaftate is not effective against C. albicans. Side effects are generally mild, allergic contact dermatitis might occur (No-Hee et al., 2017).

1.3.6 Griseofulvin

Griseofulvin is a fungistatic antibiotic exerts its effect by inhibits fungal cell division through disrupting the mitotic spindle structure and might interfere with DNA production. Griseofulvin used in treating various tinea infections (tinea capitis, tinea cruris, tinea corporis, tinea pedis, and tinea unguium), however it is not effective against Candida albicans. It is an orally effective antifungal against dermatophytes that its systemic absorption leads to deposition in keratin precursor cells, where it exerts its antifungal effects. Although, Griseofulvin, used for infections involving the scalp, hair, nails and skin that do not respond to topical treatment, infections of the palms of the hands, the soles of the feet, and the nails respond slowly to Griseofulvin. Other drugs to treat dermatophytes replace griseofulvin now. Griseofulvin is contraindicated in pregnancy and in people suffering from hepatocellular failure or orphyria. Griseofulvin should be used with caution in the lupus, anticoagulant therapy, and the use of oral contraceptives and in the presence of photosensitivity. Headache, skin rashes and urticaria, gastrointestinal disturbances, dry mouth an altered sensation of taste are common side effects (Mark, 2016).

1.3.7 Developmental antifungal drug

Nikkomycin Z (NIK) is a competitive inhibitor of chitin-synthase with specificity to disrupt the fungal cell wall since human cells lack the cell wall. It is one of the newly introduced anti-fungal drugs against Candida spp, however evaluations of the sensitivity and resistance to this drug are still under consideration (Nami et al., 2019). The current approach in, antimicrobial drugs focus on blocking the main reactions rather than interfering with the function of virulence factors. Studying the virulence factors has led to approaches to developing new antifungal agents.

Two promising antifungal candidates are currently being evaluated in clinical trials these are VT-1161 and SCY-078 (formerly MK-3118) (Chang et al., 2017). VT-1161 is a novel ergosterol synthesis inhibitor targeting fungal cyp51A (lanosterol 14 α-demethylase) that has been in phase 2 clinical trials for treatment of vaginal candidiasis since 2013. It has been shown that it inhibits cellular function by binding to C. albicans cyp51A with minimum affinity toward inhibition of human enzymes such as CYP2C9, CYP2C19, and CYP3A4, which ultimately result in fewer unfavourable drug-drug interactions (Chang et al., 2017). Furthermore, it has high in vitro potency against several C. albicans isolates that are clinically fluconazole-resistant, where it shows equivalent effect to fluconazole for treatment of vaginitis (in vivo model of vaginal candidiasis) due to fluconazole-susceptible 47

C. albicans with significant superior effect toward the treatment of vaginal candidiasis due to fluconazole-resistant isolates. These finding are promising to be a potential efficacious and safe antifungal agent.

SCY-078 Ibrexafungerp is a prospective candidate as oral glucan synthase inhibitor that is currently in phase 2 clinical trials. Its mechanism of action is similar to that of echinocandins with excellent oral bioavailability (Chang et al., 2017). It has broad- spectrum antifungal activity against several Candida species extends to some echinocandin-resistant isolates. Adding to its activity against Aspergillus fumigatus, Paecilomyces variotii, and Scedosporium prolificans. The 1-log kill doses of oral treatment with SCY-078 were numerically lower than those of conventional intravenous in a neutropenic murine model of disseminated candidiasis, and a clinical phase 1 study showed that SCY-078 was generally well tolerated (Chang et al., 2017). Thus, indicating that SCY-078 is a promising antifungal agent.

1.3.8 Combination therapy

Combining several antifungal drugs with different mechanisms of action has often been considered to overcome the emergence of drug-resistant isolates and the limitation of monotherapy efficacy, to treat potentially life-threatening IFIs. Invasive Candida infections can be treated with azoles, echinocandins, or amphotericin B monotherapy. Although studies revealed using combination therapy, there are no indication necessitate combination therapy for treatment of candidiasis.

Invasive aspergillosis is usually treated following the clinical practice guidelines using voriconazole as a primary therapy, and amphotericin B, itraconazole, posaconazole, caspofungin, and micafungin as alternative therapies. Combination therapy is required if patients are refractory to primary therapy or predicted to fail monotherapy (Walsh et al., 2008). A comparison of voriconazole monotherapy versus combination therapy with voriconazole and anidulafungin showed no significant improvement in overall survival compare to monotherapy (Marr et al., 2015).

Most studies revealed an increase in adverse drug effects with combination therapy. Based on a retrospective chart review, combination therapy of voriconazole and caspofungin vs. voriconazole alone did not enhance the survival rate of patients compared with monotherapy, however adverse effects were higher (Raad et al., 2015). In another study,

48 patients who failed to respond to primary therapy had benefit from combination therapy using posaconazole and caspofungin, however no comparison data for monotherapy were provided (Lellek et al., 2011). Despite all these fact about the benefit of combination therapy; the potential dire outcome of invasive aspergillosis is driven the use of combination therapy by clinician.

According to the clinical practice guidelines for invasive Cryptococcus management, Co- administration of amphotericin B and flucytosine is more efficacious than amphotericin B alone and it has been shown that using amphotericin B plus flucytosine for cryptococcal meningitis was more effective than using amphotericin B alone or with fluconazole (Perfect et al., 2010; Day et al., 2013). This accompanied by decreased mortality and high rate of clearance of yeast in CSF, thus this combination constitute an excellent therapeutic strategy against cryptococcosis and is the standard of care for induction therapy. Treatment with amphotericin B and flucytosine requires a high level of supportive medical care, which limits its use in countries with insufficient medical resources (Loyse et al., 2012). Moreover FC has significant toxicity with limited availability and high cost even in the United States.

Fluconazole and amphotericin B combination constitutes a good alternative for treating Cryptococcus infection when flucytosine is not available or not tolerated by the patient and this supported by Loyse et al finding on cryptococcal meningitis in HIV patients were no significant difference retrieved in the early fungicidal activity of amphotericin B in combination with flucytosine, fluconazole, or voriconazole (Loyse et al., 2012). Combination therapy for treatment of mucormycosis infections revealed no significant difference compared to monotherapy in two retrospective clinical studies, and there were difficulties associated with interpreting the data that obtained from these retrospective studies, which highlight the quandary faced by clinicians (Reed et al., 2008; Kyvernitakis et al., 2016). Caspofungin and fluconazole combination therapy for Coccidioides revealed good efficacy compared to monotherapy with amphotericin B (Park et al., 2006). Voriconazole and caspofungin co therapy was successful in paediatric patients with Coccidioides infection (Levy et al., 2013). Warrant consideration of combination treatment might be considered for refractory cases.

Prophylaxis in high-risk patients (immunocompromised, neutropenic, organ transplant, and chemotherapy patients) is important. Fluconazole, posaconazole, voriconazole, and

49 micafungin are effective prophylactic agents against IFIs. It is possible that combination therapy would confer better protection from disease while decreasing the development of drug resistance. For example; micafungin and fluconazole was well tolerated up to one month after transplant in a randomized, double-blinded dose study in immunocompromised bone marrow/stem cell transplant receivers (Hiemenz et al., 2005). Posaconazole plus micafungin was well tolerated in healthy volunteers without affecting their pharmacokinetics, while a few patients in the combination prophylaxis group developed a suspected fungal infection (Krishna et al., 2011). Thus more trials are needed to outweigh the benefit of combination prophylaxis over the risks to indicate the feasibility of successful combination prophylactic therapy with posaconazole and micafungin.

1.4 Resistance to antifungal drugs

Resistance to antifungal drugs is either associated with micro‐organisms (microbiological resistance) or with host (clinical resistance) (Selmecki et al., 2006; Cowen et al., 2015). Genomic plasticity is high in yeast, particularly among C neoformans and C albicans, which leads to loss of heterozygosity, increased chromosomal copy number (polyploidy), isochromosome formation, or aneuploidy that affect the expression of the azole target or drug pumps, or both causing azole resistance (Selmecki et al., 2006).

In microbiological resistance, the micro‐organism lacks the susceptibility to an antimicrobial due to genetic or metabolic resistance mechanisms. It can be intrinsic when the drug cannot bind or act on its target due to genetic factors, or extrinsic, which usually develops after repeated exposure to a drug and leads to altered gene expression (Tobudic et al., 2012).

Clinical resistance is defined as the failure to respond to treatment either completely or partially (Tobudic et al., 2012). Clinical resistance is not always caused by microbiological resistance. Successful therapy is a result of a combination of many factors, including host's immune system, absorption and distribution of the drug, and adherence to the correct therapy (Cowen et al., 2015). Understanding of the mechanism of resistance and how fungal adapts to drugs is crucial to develop new antifungal targets, since the emergence of resistance in most pathogenic fungi limited the treatment options and necessitate the need for novel antifungal targets (Campoy and Adrio 2017).

Resistance to antifungal drugs is also associated with biofilm formation, which is a universal mechanism that affects azole and other systemic antifungal drugs. Biofilms are universal, complex, interdependent communities of surface-associated microorganisms,

50 where they enclosed in an exopolysaccharide matrix arising on any surface such as epithelial surfaces, teeth, or artificial devices including indwelling catheters and heart valves (Davey & O’toole, 2000). They composed of population raised from a single species or a community of multiple microbial species, which of advantage by protecting the microorganism from the environment and stress conditions (Davey & O’toole, 2000). Biofilm formation also contributes to changes in metabolic activity of the developing biofilm, which can increase drug tolerance (Chandra et al., 2001). The biofilm effectively decreases the drug concentration by trapping it in a glucan-rich matrix polymer, where the mature biofilms display complex architecture with heterogeneous cell types enmeshed in extracellular matrix (Nett et al., 2010).

1.4.1 Resistance to Polyenes

The resistance to amphotericin B among Candida spp is rare, however an increase of MIC has been reported in C. glabrata and C. krusei (Pfaller et al., 2004). Filamentous fungi seem to be less susceptible to amphotericin compared to yeast. A. terreus is generally resistant to amphotericin B (Sabatelli et al., 2006). Resistance to amphotericin B is reported in A. flavus and A. fumigatus (Reichert-Lima et al., 2018). Fusarium species, S. apiospermum, and S. prolificans are also resistant to amphotericin B (Pfaller et al., 2004). Resistance to amphotericin B is associated with mutation in the ERG3 gene that is involved in ergosterol biosynthesis, leading to accumulation of other sterols in the fungal membrane (Sanglard et al., 2003; Alcazar-Fuoli and Mellado, 2013).

1.4.2 Resistance to echinocandins

Resistance to echinocandins has been recorded in patients with Candida infections due to C. glabrata, C. albicans, C. krusei, and C. parapsilosis (Hernandez, 2004; Moudgal et al., 2005; Hakki et al., 2006; Perlin, 2015). This has been associated with treatment failure and has been developed during therapy. The resistance to echinocandins is related to the point mutations in the Fks1 gene of the β-1,3-D-glucan synthase complex in Candida spp, and in A.fumigatus (Palashov et al., 2006; Jiménez-Ortigosa et al., 2017), efflux pumps in C. glabrata (Bhattacharya and Fries, 2018).

In diverse fungal species β-1,3 glucan synthases are encoded by FKS genes. Echinocandins resistance has been associated with specific mutations of FKS genes leading to amino acid substitutions in two different regions (Hot spot 1 and 2 or HS1 and HS2). In C. albicans, FKS1 mutations have been reported in these two regions (HS1: region 640–650 and HS2: 1345–1365) and equivalent mutations in FKS1 in Candida lusitaniae, C. tropicalis, and C. 51 krusei and in the HS1 of FKS2 (an homolog to FKS1) in C. glabrata, have been reported. Some Candida species (the Candida parapsilosis family) display intrinsic low susceptibility to echinocandins, where FKS1 genes have a natural polymorphism (P660A at the 3′-extremity of HS1) that enabled decrease affinity of the β-1,3 glucan synthase to echinocandins. Natural FKS1 polymorphism of Candida parapsilosis family has less impact on resistance compared to those acquired by mutations and this presented by well respond to echinocandins (Sanglard, 2016).

Some species appear to be inherently resistant to the echinocandins. For example, C. guilliermondii and C. parapsilosis have MIC values significantly in excess of what is reported for C. albicans isolates (Mora et al., 2002). In vitro, echinocandins-susceptible Candida and Aspergillus isolates appeared to grow at concentrations exceeding the MICs of caspofungin (Stevens et al., 2004). This paradoxical growth is strain-dependent and it is associated with up-regulation of chitin synthesis in the fungal cell wall (Stevens et al., 2006). However, this phenomenon has not been observed in vivo fusing the highest treatment doses of echinocandins (Clemons et al., 2006).

1.4.3 Resistance to flucytosine

To enter C. albicans cells, 5-fluorocytosine takes advantage of the host permeases (FCA1, FCY2, FCY22, and FCY23) (Hope et al., 2004). Some yeast strains are intrinsically resistant to flucytosine (Pfaller, 2002; Hope et al., 2004). Acquired resistance to flucytosine usually results from impairment of cellular transport and uptake of FC or its metabolism, through certain mutations that results in a deficiency of cytosine deaminase or uracil phosphoribosyl transferase or might results from increased synthesis of pyrimidines that compete with the fluorinated antimetabolites of 5-FC and thus diminish its antimycotic activity (Vermes et al., 2000).

In C. albicans, a mutation in the gene FUR1 encoding uracil phosphoribosyltransferase decreases the conversion of 5-FU into a toxic metabolite (5-FC monophosphate). Thus, the toxic effect of 5-FC cannot be exerted (Sanglard, 2016). The prevalence of primary resistance to flucytosine is relatively low, <2% among C. neoformans (Price et al., 1994; Brandt et al., 2001) and 1%–2% among Candida isolates. The speed in developing resistance to flucytosine in yeast, necessitate its usage in combination with other antifungal drugs, mainly amphotericin B (Hospenthal & Bennett 1998).

1.4.4 Resistance to Azoles

The extensive use of triazole antifungal drugs has led to increase the resistance to these drugs (Nascimento et al., 2003). Resistance to Azole in Candida species has been driven by widespread use of itraconazole and fluconazole (Goldman et al., 2000). Resistance to fluconazole in oral candidiasis reaches 33% in patients with AIDS (Law et al., 1994), and this prevalence is lessened in vaginal candidiasis (Ribeiro et al., 2005) and candidemia (Sanglard et al., 2002).

The rates of azole resistance in invasive Candida species seems to be low, ranging from 1.0%–2.1% in C. albicans to 1.4%–6.6% in Candida tropicalis (Pfaller et al., 2005); however, it is not the case with C. glabrata, where the incidence has increased by 5% in 2004 compared to 2001 (Pfaller et al., 2006). In vitro and in vivo resistance to itraconazole (Denning et al., 1997; Dannaoui et al., 2001), and to voriconazole (Manavathu et al., 1999; Bueid et al., 2010), has been reported in A. fumigatus.

Itraconazole resistance in Aspergillus fumigatus is global issue, with a significant difference in frequency. The highest percentages, 5 and 6% are recorded in Manchester (UK) and Nijmegen (the Netherlands), respectively, with significant increase in azole resistance (Howard and Arendrup, 2011).

In a particular genome, phenotypic resistance depends on the genetic variation that has occurred. A detrimental effect of mutation on the fitness of fungi in the absence of the drug is frequently accompanied the development of resistance. The heat shock protein 90 (Hsp90) is essential for remodelling the relationship between phenotype and genotype in distant species, however its role in antifungal drug resistance has been underscored recently. Hsp90 acts as a capacitor for accumulation of genetic variation, upon compromising its function by genetic alteration, environmental stress or pharmacological inhibitors, genetic variations will be revealed leading to alteration of the relationship between genotype and phenotype. Hsp90 acts through calcineurin, thus any inhibitor of calcineurin or Hsp90 would possibly act synergistically with the antifungal agent (Chakrabarti, 2008).

Although, drug-resistance mechanisms provide a clear fitness advantage for the pathogen in the presence of drug, their maintenance in the absence of drug selection is reliant on minimal fitness costs of resistance (Gagneux et al., 2006, MacLean et al., 2010). Resistance mechanisms frequently modify key cellular functions; so are often associated with a fitness cost in the absence of drug and this has been suggested to slow the spread of resistance (Andersson and Hughes, 2010). Drug resistance is not necessarily costly, for 53 example, an isochromosome (i5L) of Candida albicans confers resistance to the antifungal azoles and is not deleterious in the absence of drug (Selmecki et al., 2009). Understanding whether drug resistance is costly in fitness terms, is crucial to evaluating whether resistance will persist in clinically relevant environments.

1.4.4.1 Mechanisms of azole resistance

Resistance is common with azoles-based antifungals (Pfaller, 2012). There are 4 main mechanisms attribute to resistance to azoles, these include; increase in drug efflux, target expression deregulation, target mutation, and ergosterol biosynthesis pathway alteration (Tobudic et al., 2012).

1.4.4.1.1 Increase of drug efflux

The activation of membrane‐associated efflux pumps leads to expelling of the drug out of the cell, with subsequent decrease in intracellular concentrations and drug shortage at the site of action (Pfaller, 2012). ATP‐Binding Cassette (ABC) superfamily and the Major Facilitator Superfamily (MFS), are responsible for azoles removal from the cytoplasm, and azole resistance is found to be associated with overexpression of genes encoding these transport proteins (Tobudic et al., 2012). In C. albicans, CDR1 and CDR2 are the two major ABC transporters genes that linked to azole resistance, in contrast to CgCDR1, CgCDR2 and CgSNQ2 in C. glabrata, and CnAFR1 in C. neoformans (Vandeputte et al., 2012; Cowen et al., 2014; Sanglard, 2016). In C. krusei, ABC1 (homologous genes for ABC transporters) is involved in intrinsic resistance to azoles and was upregulated in cultures treated with imidazole (Lamping et al., 2009; Scorzoni et al., 2017).

In C. albicans, overexpression of the CDR genes can render resistant to different azoles in contrast to MDR1 gene that encodes MFS transporters specifically for fluconazole and is not linked with cross‐resistance to other azoles. The efflux pump was associated with azoles resistance in C. neoformans and C. gattii (Basso et al., 2015; Scorzoni et al., 2017).

In Aspergillus spp., resistance to azole, particularly itraconazole was linked with overexpression of Afr1 (ABC family) and mdr3 and mdr4 (MFS family) (Vandeputte et al., 2012; Cowen et al., 2014; Scorzoni et al., 2017). In dermatophytes, upregulation of ABC transporters genes TruMDR1 and TruMDR2 after exposure to azoles appeared to be the leading mechanism of resistance (Scorzoni et al., 2017).

1.4.4.1.2 Target mutation

Point mutations in ERG11 gene in yeast altered the azoles binding site and subsequently decrease the affinity to bind drug enzyme (Pfaller, 2012). In A. fumigatus, cyp51A A and cyp51B encode two distinct forms of 14α-demethylase. Mutation in cyp51A, constitute the most important mechanism linked to azole resistance in clinical isolates, with over 30 amino acid substitution mutations being identified, mostly at codons 54 and 220 of cyp51A (Cowen et al., 2014). In H. capsulatum reduced susceptibility to fluconazole and voriconazole was associated with Y136F substitution in cyp51A p (VOZ) (Scorzoni et al., 2017). In Aspergillus fumigatus, the substitutions of leucine 98 for histidine (L98H) with presence of two copies of a 34-bp sequence tandem in the promoter of the cyp51A gene was linked to azole cross-resistance and the increased levels of cyp51A expression. Modification of cyp51A and its promoter (TR34/L98H, G448S; TR46/TY121F/T289A) constitute the most common mechanism of azole resistance found in both environmental and clinical isolates due to widespread use of fungicides in agriculture (Verweij et al., 2013; Perlin et al., 2017).

According to Ren et al, acquisition of mutation in the azole target by the environmental strains result in cross-resistance between the azole antifungals in the environment and the clinic (Ren et al., 2017). In his study, 5.8% of 144 environmental samples that were treated with non clinical azoles exhibit resistance to medical azoles. Two out of total were resistant to voriconazole with most frequent mutations (TR46/Y121F/T289A) while mutation TR34/L98H/S297T/F495I appeared in one itraconazole resistant sample, with TR34/L98H being the most common mutation in both environmental and clinical samples (Rivero-Menedez et al., 2016; Verweij et al., 2016; Ren et al., 2017). Furthermore, mutation TR53 was reported in environmental isolates in Columbia (Le et al., 2016).

1.4.4.1.3 Target expression deregulation

Overexpression of the ERG11 gene, after exposure to azoles antifungal in C. albicans, result in increased synthesis of lanosterol 14α‐demethylase thus more drug is required for inhibition contributing to the resistance (Tobudic et al., 2012), (Vandeputte et al., 2012).

1.4.4.1.4 Ergosterol biosynthesis pathway alteration

The exposure to azoles, result in decreasing the fungal membrane ergosterol leading to accumulation of the fungal growth inhibitor 14α‐methyl‐3,6‐diol. Mutation in the ERG3

55 gene inhibits the 14α‐methyl‐3,6‐diol from the 14α‐methyl‐fecosterol formation with subsequent accumulation of the precursors, which replace the cellular ergosterol (Tobudic et al., 2012). The ERG3 mutations were also associated with cross‐resistance to polyenes, as a result of target’s ergosterol depletion (Cowen et al., 2014). Reduced susceptibility to azoles might be associated with mutations in other genes involved in ergosterol biosynthesis, such as ERG2, ERG6 and ERG24 (Scorzoni et al., 2017).

Figure 1.2: Mechanisms of resistance to antifungal drugs that target the cell membrane (the azoles and polyenes). (A) Azole resistance is linked to multiple mechanisms including upregulation or alteration of the drug target, overexpression of drug transporters, or cellular changes that decrease drug toxicity or permit tolerance of drug-induced stress (The coloured circles indicate ergosterol biosynthesis intermediates). (B) Polyene resistance is associated with depletion of the target ergosterol attributable to loss-of-function mutations in ergosterol biosynthetic genes. Bullet points denote mechanism of resistance in C. albicans, Cryptococcus neoformans, and Aspergillus fumigatus. Dimmed images represent mechanisms that do not play a key role in resistance to the indicated drug class (Cowen et al., 2015).

1.5 Protein Kinases are fundamental for all living organisms.

1.5.1. Background

A protein kinase is an enzyme, which constitutes the leading subclass among the phosphotransferases family. Phosphotransferases are a category of enzymes (EC number 2.7) that catalyse phosphorylation reactions (Fig.1.3). For the greater part of cellular processes, series of phosphorylating events are indispensable and necessary to occur. Kinases perform the activity of phosphorylation by transferring phosphoryl group onto their target proteins, which eventually alters the target protein’s activity that can now be termed phosphorylated protein (Corcoran & Cotter 2013).

Antagonistic action of phosphorylation is performed by phosphatases, which dephosphorylates the target proteins. Cellular signal transduction pathways are substantially dependent on activation of proteins, which is conducted through phosphorylation. A great number of biological processes are dependent on phosphorylating activity of protein kinases through which cellular signals are transmitted within and out of the cell. Cell signalling mechanisms governed by phosphorylation regulates the crucial processes of cell differentiation, functions, growth, development and apoptosis (Arranz et al., 2012).

Extensive investigations have been conducted, studying the catalytic subunit of protein kinase and most comprehensive structural data has been provided for porcine, by providing the crystal structure of protein kinase (Francis et al., 2010). Up till now, the primary, secondary and tertiary structures of various protein kinases have been analysed and reported. The catalytic conformation with bound substrate and nucleotide has also been evaluated and examined (Fry et al., 2012). These studies are essential for development of drugs targeting the various distinct domains of protein kinases in pathological conditions. Protein kinases possess large rotational movement abilities, are highly flexible and contain conserved kinase lobes. Moreover, flexible loops and domains further facilitate binding of substrate, auto inhibitory domains, and cofactors, thus facilitating the interaction capabilities of kinase with other protein during regulation or catalysis (Arranz et al., 2012). The active site of enzyme is located within the mouth region of deep cleft and the bilobal conformation of enzyme facilitates its functions. The cleft of protein kinases accommodates the ATP molecule required for its activity while orienting y-phosphate

58 outwards. Active site of protein kinases contains most of the highly conserved residues (Taylor & Kornev 2011).

For the activity of protein kinases, a divalent cation is required such as Mg2+. According to certain in vitro investigations, the activity of enzymes is reported to be more efficient with Mn2+ than Mg2+, however, the cellular concentration of Mg2+ is greater and therefore governs the activity and function of the enzyme (Corcoran & Cotter 2013).

Threonine and serine in the enzyme contain alcoholic side chain while a phenolic side chain is attached with tyrosine residue. Of these proteins’ tyrosine kinases, 32 are non- receptor proteins while 58 are receptor based functioning proteins (Taylor & Kornev 2011). The category is classified as depicted in the figure below (1.3).

■ EC 2.7.1 Phosphotransferases (alcohol group as acceptor)

■ EC 2.7.2 Phosphotransferases (carboxyl group as acceptor)

■ EC 2.7.3 Phosphotransferases (nitrogenous group as acceptor)

■ EC 2.7.4 Phosphotransferases (phosphate group as acceptor)

■ EC 2.7.9 Phosphotransferases (paired acceptors)

■ EC 2.7.8 Transferases (other substituted phosphate groups)

Figure 1.3: Protein kinases within the enzyme classification hierarchy. The figure depicts the active classes of protein kinases in human genome with their respective references with Enzyme Nomenclature Hierarchy (NC-IUBMB). The enzymatic activities are determined through literature review due to which the numbers presented in figure differ from the numbers generated in phylogenetic tree. The four major families within which protein kinases are grouped are shown in the figure (Rask et al., 2014).

Figure 1.4: Phosphorylation is a reversible post-translational modification (PTM), which regulates protein function. Left panel: PK mediates phosphorylation at serine, threonine and tyrosine side chains; whereas phosphatases reverse the reaction by hydrolysing the phosphate group. Right panel: Phosphorylation results in conformational changes in proteins that either activate (top) or inactivate (bottom) protein function. Used with permission of https://www.thermofisher.com/us/en/home/life-science/protein-biology/protein-biology- learning-center/protein-biology-resource-library/pierce-protein-methods/phosphorylation.html

Phosphorylation is the main and crucial process catalysed by Protein Kinases (PKs) by transfer of phosphate group either form ATP or GTP to protein substrates. The reverse process of phosphorylation is dephosphorylation performed by protein phosphatase. Dephosphorylation results in transfer of phosphate group to a water molecule from a phosphoprotein (Fig 1.4). Another process related to phosphorylation is reversible dephosphorylation in which a γ-phosphate of ATP is transferred by kinases to hydroxyl groups of different amino acids, sugars, and lipids. The process of reversible phosphorylation has equally an important role in influencing cellular processes such as cellular signalling pathways, apoptosis, cell growth and compromising cell cycle rule. The process of phosphorylation by kinases of target protein’s threonine, serine or tyrosine moieties results in a conformational shift, thus affecting the overall activity or function of target/substrate protein (Fig 1.5).

Figure 1.5: A key event in cell regulation is reversible protein phosphorylation. A protein kinase moves a phosphate group from ATP to the protein. Its shape and function are altered. A protein phosphatase removes the phosphate and the protein reverts to its original state. Nobelprize, Reversible protein phosphorylation regulates most aspects of cell life. Available at: https://www.nobelprize.org/prizes/medicine/1992.

1.5.1 Classification of protein kinase

Protein kinases in eukaryotes have been classified into major two comprehensive groups “atypical” protein kinases (aPKs) and “conventional” protein kinases (ePKs). ePKs form the largest group of protein kinases. ePKs have been further sub-classified into major eight families on the basis of their recognised modes of regulation and catalytic domain sequence similarity.

1.5.1.1 Conventional protein kinases

The major ePKs sub-classified into eight classes are as follows. The eight-ePK families are as follows:

• The AGC family (including cyclic-nucleotide and calcium-phospholipid-dependent kinases, ribosomal S6-phosphorylating kinases, G protein-coupled kinases, together with all close relatives of these groups)

• CAMKs (calmodulin-regulated kinases)

• The CK1 family (casein kinase 1, and close relatives)

• The CMGC family (including mitogen-activated protein kinases, cyclin-dependent, kinases, glycogen synthase kinase and CDK-like kinases)

• The RGC family (receptor guanylate cyclase kinases, which are also similar to tyrosine kinases TKs in domain sequence)

• The STE family (consisting of several kinases working in MAP kinase cascades)

• The TK family (tyrosine kinases)

• The TKL family (tyrosine kinase-like kinases (TKLs), a varied group that are similar to TK but which are in fact serine-threonine kinases)

1.5.1.2 Atypical protein kinase

Atypical protein kinases (aPKs) include a small set of protein kinases without any obvious sequence similarity with ePKs, but which appear to have protein kinase activity experimentally (Manning et al., 2002) and include the following families:

• Alpha (e.g. myosin heavy chain kinase of Dictyostelium discoideum)

• PIKK (phosphatidyl inositol 3′ kinase-related kinases)

• PHDK (pyruvate dehydrogenase kinases)

• RIO (“right open reading frame” as it was one of two neighbouring (adjacent) genes that appeared to be transcribed (copied) divergently from the same intergenic region7)

• BRD (bromodomain-containing kinases)

• ABC1 (ABC1 domain-containing kinases)

• TIF1 (transcriptional intermediary factor 1)

Enzymes that phosphorylate both serine and threonine are known as dual-specificity kinases; such enzymes can also phosphorylate tyrosine (Tyr) residues. Tyrosine kinases (TKs) are a typical group that especially catalyse the phosphorylation of Tyr residues in proteins. In animals, TKs are important for crucial cellular process such as organ development, cell proliferation and differentiation, and immune response, (Hanks and Hunter.T., 1995; Dhanasekarana and Premkumar, 1998; Rudrabhatla et al., 2006).

1.5.2 Human protein kinase

Among the various macromolecular structures, protein kinases constitute one of the most important and largest structures in protein families. According to genome analysis reports of eukaryotic species, protein kinases form up to ~2% of all genes of eukaryotic genomes (Kooij et al., 2010). The cellular activity of up to 30% proteins is altered and modified by protein kinases. Moreover, phosphorylation by protein kinases also alters the location and affinities of phosphorylated proteins, thereby directing most cellular processes. Approximately 53 distinct kinase functions have been reported and many of the signal transduction pathways by protein kinases have been conserved. Their subfamilies are conserved between nematodes, yeasts, vertebrates and insects while 91 other subfamilies of protein kinases have been observed in metazoan genomes. These investigations allow flexibility to comparative studies and are an important source through which physiological state of the cell can be examined (Wilson et al., 2018).

The portion of genome comprised of protein kinases is termed the kinome and in approximately 535 protein kinases are encoded by the human kinome. Among these, 479 protein kinases in the human genome are recognised to contain ePK catalytic domain (Wilson et al., 2018). On the basis of primary sequences, ePK have been classified further into eight classes as mentioned earlier. Cells often contain approximately 300 distinct kinases, which have been revealed by deep proteomic analysis of 23 different mammalian cell lines. Major researches and investigations focused on tyrosine kinases, their location and activity thereby analysing the role in signal transduction and various other cellular mechanisms (Kooij et al., 2010).

Formation of groups, families and subfamilies of all kinases has been ensured by evolutionary biologists to compare and contrast the properties of homology and analogy between human kinases and model organisms (Davis et al., 2011). Kinases were primarily classified on the basis of sequence comparison of their catalytic domain however, later classification parameters shifted towards domain structure, known biological functions and sequence similarity (Wilson et al., 2018). Several domains of ePK lack kinase activity as observed through experiments. These moieties have been reported to act as scaffolds for assembly of signalling complexes and as kinase substrates. According to human kinome analysis, at least one conserved catalytic residue is lacked by 50 human kinases. The lacking domain therefore shifts the kinase activity making it enzymatically inactive. Similar kinome analysis reported 28 inactive kinases belonging from different families of

64 human kinases and comparative analysis indicates inactive forms of these kinases in fly, yeast and worm (Davis et al., 2011).

Figure 1.6: Recognised kinase drug targets undergoing medical studies. Phylogenetic tree of the human kinome, adapted from Manning and colleagues (Manning et al., 2002). Recognised drug targets and targets undergoing medical studies are coloured with green and yellow, respectively. Gene names were changed to chosen gene names according to UniProtKB/SwissProt. Acronyms: AGC - containing PKA, PKG, and PKC families; CMGC - consisting of CDK, MAPK, GSK3, and CLK families; STE - homologs to yeast Sterile 7, Sterile 11, and Sterile 20 kinases (Rask et al., 2014). As kinases are involved in a various range of biological processes, it is unsurprising that they are related to a number of human illnesses comprising malignant diseases (Hanahan and Weinberg, 2011; Hers et al., 2011), cardiovascular (Geraldes and King, 2010) and central nervous system conditions, Parkinson’s disease (PD), and Alzheimer’s disease

(AD) (Kim and Choi, 2010; Bové et al., 2011), and rheumatoid arthritis (Mcinnes and Schett, 2011).

In biopharma and biomedical studies, kinase deregulation in disease has been very well studied and considered a major aspect in investigating the aetiological factors associated with diseases (Davis et al., 2011). However, in individual patients, specific kinases as biomarkers for intensively studied pathological areas still require investigations through human trials to confirm the efficacy of diagnostic basis kinases as molecular markers. The kinase modulators have significantly improved the treatment outcomes for many patients thereby contributing in excellent progress for various diseases and illnesses (Wilson et al., 2018).

In the human genome, phosphotransferases constitute one of the major enzyme categories. Approximately 950 genes have been recognised to possess activity of phosphotransferases enzyme (UniProt, 2013). Among the various protein kinases classes of enzyme, serine/threonine kinases (SPKs) and protein-tyrosine kinases (PTK) are observed to be most abundant. Interestingly, there is only one member of the protein-histidine kinases in the human genome.

1.5.3 Kinases as drug target

The role of various human lipid and protein kinases has been well studied in cellular physiology, concerning the normal and abnormal growth patterns of cell (Holohan et al., 2013). The area has been intensively researched in pharmaceutical sciences and academic arenas while the payoff for pharmaceutical efforts and industries has remained substantial (Davis et al., 2011). However, the full potential of human kinome has not been yet exploited fully for therapeutic purposes. Approximately 50% of all human kinases are still uncharacterised and more than 100 kinases have still unknown functions (Davis et al., 2011). Thus, investigations are required for the identification of functions of these kinases in order to improve human health by developing modulators that may control their activities (Molina et al., 2013).

Currently, 30 small molecules have been designed and reported to possess kinase inhibiting activity and are under more than 150 preclinical trials. Receptor tyrosine kinase is also evidenced as an effective target for several monoclonal antibodies, thus making tremendous penetration into immunotherapeutic methods for cancer treatment (Holohan et al., 2013). Monoclonal antibodies include Bevacizumab, Cetuximab and Herceptin. Only 53 protein kinases are targeted by majority of clinical trials and FDA has provided 66 approval for about 50% of these inhibitors targeting the signal transduction activity of kinases (Strebhardt, 2010).

Thirty-eight further pharmacological agents that target 50 distinct human protein kinases are authorised by the FDA for the treatment of various potential diseases (Rask et al., 2014), (Figure 1.6). These comprises kinases activators and inhibitors, with at least 313 agents that target protein kinases are currently at various stages in the drug development pipeline, including agents that inhibit the action of 91 novel kinase targets (Rask et al., 2014). Most approved drugs that target kinases are reported to be active against more than one type of cancer however; only few have been recommended and approved for non- oncological treatment purposes (Rask et al., 2011). ROCK½ inhibitor was approved for non-oncological purpose in 1995 as a kinase inhibitor Fasudil for treatment of pulmonary arterial hypertension and cerebral vasospasm. Recently, FDA has approved JAK3 inhibitor facitinab for rheumatoid arthritis (Holohan et al., 2013).

In fungi, protein kinases require more studies to enable development of successful drug target. The mitogen activated protein kinases (MAPK) have been studied extensively for development of potential antifungal drugs. The mitogen-activated protein kinase (MAPK) pathway is a network of signalling pathways that is well conserved from yeasts to mammals and play important role in adaptation to environmental changes (Román et al., 2009). The MAPK (MAPKK) represents the final kinase in the relay; upstream of that respectively are the MAPKK kinase (MAP3KKK) and the MAPKKK kinase (Map4K). The MAPK phosphorylates transcription factors and other target proteins (May et al., 2005).

Cell wall integrity signaling pathway (CWI), which is composed of three mitogen activated protein kinase that are required for the organisation of various cellular reactions in eukaryotes, constitutes a potential antifungal target candidate. KP-372-1 is phosphoinositide-dependent kinase 1 (PDK1) inhibitor (Baxter et al., 2011). It is a potent antifungal molecule active against yeast and fungal biofilms. Other, two molecules, OSU- 03012 and UCN-01, also possess antifungal activity against C. albicans, C. neoformans and C. albicans biofilms. UCN-01 is a more selective derivative of staurosporine (protein kinase inhibitor), which has been used synergistically with fluconazole, and might be approved clinically in combination therapy (Baxter et al., 2011). OSU-0312,,still in early- phase clinical trials (Booth et al., 2015).

The HOG pathway, which is the main signal transduction system, that is responsible for cellular stress responses, is believed to be a promising as a potential target. In Saccharomyces cerevisiae, 2 compounds (4- and 5-substituted 1,2,3-triazoles) have been identified that selectively inhibit Hog1 (Dinér et al., 2011). Ambruticin (family of cyclopropyl-pyran acids with broad antifungal activity) is another compound that target the HOG pathway (Knauth P and Reichenbach H 2000). Furthermore, the heat shock protein 90 (Hsp90) is an essential molecular chaperone in eukaryotes and was found to be a key regulator of antifungal resistance to both azole and echinocandins antifungals in C. albicans and A. fumigatus and found to be a potential target (Lamoth et al., 2014; Lamoth et al., 2016). Although these studies have been conducted in fungi, employing important pathways to identify a new antifungal target, nevertheless, no comprehensive study is conducted screening the genome of human fungal pathogen A.fumigatus to explore protein kinase druggability.

1.6 Project overview

The emergence of resistance to the available antifungal drugs and toxicity associated with some classes necessitates the exploration of novel pharmacologically effective antifungal drugs. Protein phosphorylation and dephosphorylation by protein kinases and phosphatases, respectively, is believed to impact all areas of cellular activity (Cohen 2000; Nanthapisal et al., 2017). Atypical levels of phosphorylation has been recognised as being responsible for a range of human diseases (Cohen, 2001; Yuan et al., 2018); therefore, kinases and phosphatases have attracted significant attention as drug targets to treat human illnesses. A number of highly active drugs have been successfully developed that inhibit the action of human enzymes, validating their ‘druggability’; however, no work has been undertaken to assess in a comprehensive manner, the viability of targeting kinases in fungal pathogens.

1.6.1 Hypothesis

In this project, it is hypothesised that fungal kinases are potentially valuable drug targets. Identification of essential genes for viability in A. fumigatus A1163 knockout collection will highlight some potential candidates. Testing, the sensitivity of the PK knockout collection toward Itraconazole might reveal resistant and/or susceptible mutants that might be of interest to study the underlying mechanism. Through assessing the fitness of PK knockout collection in animal models, it is possible to detect avirulent mutants that can be used later to validate their druggability.

An ideal antifungal kinase target should be: 1) Essential for viability or pathogenicity 2) sufficiently conserved in other pathogenic fungi to permit the development of broad spectrum agents 3) sufficiently distinct from the nearest mammalian homologue to allow for drug specificity and 4) Druggable.

1.6.2 Study plan

In this project, de novo annotation of the protein kinases of Aspergillus fumigatus will be performed using the protein kinase prediction and classification software Kinomer. This annotation will be supported with homology searches of known protein kinases from closely related organisms. Phylogenetic analysis will be conducted to find how protein kinases are distributed in a number of fungal species A. fumigatus. Furthermore, blast analysis will be conducted to look at the similarity between protein kinases in A. fumigatus, and human. This is important to identify the kinases that share low similarity to be potential antifungal candidates.

The identified set of kinases will be used to generate a genetically barcoded library of PK null mutants. This will allow the identification of the essential kinase cohort and those kinases that a critical for virulence. These genes will constitute a potential antifungal drug targets. The viable PK knockout will be used in competitive fitness study (growth of all PK mutant strain under conditions of direct competition with each other and control strain) to assess associations between loss of function and phenotypes associated with virulence defects. This will include preparing one pool of all the viable PK mutants, in addition to the non-functional transposon of A. fumigatus as a control strain.

1.6.3 Aims of the project

Aim 1: Identification and classification of the predicated Protein kinases (PK) in A. fumigatus A1163 and conducting phylogenetic analysis to identify those with low sequence similarities compared to human kinases.

Aim 2: Generating a PK knockout library in A. fumigatus A1163, with barcoding each mutant to facilitate parallel fitness study.

Aim 3: Employing a comparative fitness analysis utilising the non-essential PK Knockout mutants, to screen their response to different stress conditions in shaking culture.

Aim 4: Employing a comparative fitness analysis, utilising the non-essential PK mutants after exposure to Itraconazole drug, in attempt to identify resistance and sensitive strains, and understand the mechanism associated with resistance or susceptibility.

Aim 5: Employing a comparative fitness analysis utilising the non-essential PK mutants in vivo (using larval infection, macrophage cell lines and murine models), to identify PK mutants that are involved in pathogenicity of A. fumigatus.

Chapter 2 : Materials and Methods

2.1 Bioinformatics

2.1.1 Data search to identify the predicted kinases

To identify all the predicted protein kinases in a range of fungal genomes, a deterministic search was carried out to identify those kinases that had been previously annotated using the following databases: CADRE (www.cadre-genomes.org.uk/index.html) (Mabey et al., 2012), AspGD (www.aspergillusgenome.org) (Arnaud et al., 2010), Ensemble Fungi (www.fungi.ensembl.org) and EBI (www.ebi.ac.uk) (McWilliam et al., 2013). The retrieved results were transferred into a separate worksheet, before being organised and sorted manually to exclude any potential for duplication.

Kinomer v.1.0 (Martin et al., 2009) program was employed to remove non-protein kinases or incorrectly annotated kinases and to classify the protein kinases identified in the search. Kinomer v.1.0 is a multi-level library of hidden Markov models (HMMs), where each protein kinase group is represented by a number of HMMs. Sequences of proteins of interest were inputted in FASTA format. For each search, the best match was selected among all the hits within the Kinomer threshold.

According to this analysis, the highest observed E-value for each kinase group was taken as the cut-off for confident assignment, these being AGC (2.7e−7), CAMK (3.2e−14), CK1 (3.2e−5), CMGC (1.2e−7), RGC (4.8e−5), STE (1.4e−6), TK (1.1e−9), TKL (1.7e−12), Alpha (8.5e−66), PDHK (2.7e−10), PIKK (8.4e−6) and RIO (2.3e−3).

PK domains with E-values above the threshold were classified as the ‘Other’ group. Proteins whose scores fell below the threshold were analysed using Pfam (www.pfam.xfam.org) (Finn et al., 2014) and InterPro (protein sequence analysis and classification) http://www.ebi.ac.uk/interpro/ (Hunter et al., 2009; Mitchell et al., 2014) for identification of kinases that were not detected by Kinomer. InterPro is a database of protein families, domains and functional sites. Newly identified protein sequences were characterised using InterPro by comparison with known proteins.

2.1.2 Phylogenetic analysis

In order to build phylogenetic trees for the predicted kinase in A. fumigatus A1163, multiple sequence alignment was constructed via CLUSTAL W v.2.0 (Thompson et al., 1994; Larkin et al., 2007). The multiple sequence alignments were then harnessed to construct phylogenetic trees using MEGA6.06 (Tamura et al., 2013) with maximum likelihood applying the Jones–Taylor–Thornton (JTT) model (Sullivan, 2005) of amino acid evolution, with MEGA6.06 being used to achieve phylogenetic visualisation.

Phylogenetic analysis between A. fumigatus A1163 kinases and human kinases was conducted in order to identify genes with low similarities compared to their human orthologous. Phylogenetic trees were employed for this purpose and were constructed using T-REX (Boc et al., 2012).

2.1.3 Kinases cluster and heat map analysis of kinases

Heat map analysis was conducted to investigate the similarity (identical percentage [ID%]) between human kinases and fungal kinases from various species including A. fumigatus A1163, A. fumigatus Af293, A. flavus, A. niger, A. clavatus, A. oryzae, N. fischeri, Neuroepora. crassa, and Saccharomyces cerevisiae. Blast was employed with a cut-off threshold at 1e-10 to retrieve the ID% for this analysis against human kinases. The ID% for A. fumigatus A1163 kinases against their fungal orthologues were calculated using Ensemble fungi (http://fungi.ensembl.org/tools.html), Whereas the ID% for A. fumigatus A1163 kinases against human kinases were calculated using NCBI protein blast (http://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins).

2.2 Gene knockout

2.2.1 A. fumigatus strain, culture and growth conditions

(For media preparation, buffers and solutions, and the list of strains used, please see the appendices1-3). In total 115 strains plus the control strain was used originally in the knockout study, however only 65 validated viable one plus control strain were used later for subsequent studies. Three replicates per each were used for phenotypic screening.

2.2.1.1 Handling of samples

Safety precautions were taken when working with A. fumigatus spores in order to prevent cross-contamination between samples. Moreover, all work associated with A. fumigatus spore stock was carried out in a class II biological safety cabinet. The work surface of the cabinet was sterilised before and after use with 70% ethanol or 2% ChemGen. Precautions were also applied when engaging with chemical reagents: laboratory coat, gloves, and goggles were worn when required to prevent skin contact and any other hazardous accidents that might occur.

For long-term storage of the A. fumigatus A1160+ spores and PK mutant spores, spore suspension was mixed at 1:1 ratio with 50% sterile glycerol and stored at -80°C. Furthermore, the genomic DNA, plasmid (pAN7-1), dNTP’s, polymerase, buffer mix and all PCR products were stored at -20°C. Finally, all used primers were stored at 4°C for short-term use.

2.2.1.2 A. fumigatus strains used in this study

The A. fumigatus A1160+ was used in this experiment. The parent strain was kindly provided by Dr Bromley’s Group, and was utilised for the genetic knockout. A1160+ is derivative of Δ Ku80 isolate (generated by De Silva, 2006 by replacing ku80 gene by A.fumigatus pyrG) where the A.fumigatus pyrG was randomly mutated using 5-FOA (A1160-) to reintroduce the native A.fumigatus pyrG to the native locus that previously contained another mutated copy of pyrG.

2.2.1.3 Growth of A. fumigatus, spore harvesting and storage

In order to grow the A. fumigatus A1160+ strain, spores were inoculated on plates containing either SAB agar (Oxoid), in SAB broth (Oxoid) or on Yeast Peptone Sucrose (YPS) media (See the appendices for the media preparation).

SAB agar was streak inoculated with A. fumigatus A1160P+ spores in petri dishes, and then incubated at 37°C overnight to thus isolate single colonies. The spores of the growing colonies were inoculated in vented tissue culture flasks containing SAB media and incubated at 37°C for 3 days to harvest the spores.

A. fumigatus A1160+ spores were harvested using 10 ml of 1% PBS Tween 20, which was introduced to the tissue culture flask containing the spores and mixed for 30 sec. The spores were harvested by pouring the PBS Tween 20/ spores solution through a sterile

glass wool filter to remove the agar, hyphae and any other contaminants, before being stored at 4°C for short-term use. The spores were enumerated using a haemocytometer.

2.2.1.4 Preparing PK mutants’ pool for competitive fitness study

With a view to performing competitive fitness study, sixty-five (65) strains plus the control strain at concentration of 5x106 spores per ml were used to prepare the PK mutants’ pool, where all strains were put together in a 50 ml falcon tube and mixed well by pipetting up and down. Centrifuge was applied at 4°C, with 3,000 rpm for 10 min to pellet the mixture, the supernatant was discarded and the pellet was re-suspended with 50% Glycerol + 0.01 PBS using the following equation: A= n x 10µl x X (where n=number of strains and X the number of pools), distributed in 2 ml cryogenic tubes and stored at -80°C. For each experiment, 100 µl from the pooled strains was then inoculated in tissue culture flask containing 50 ml of liquid fungal RPMI (fRPMI) media and incubated at 200 rpm and 37C° for 20 hr, and the fungal biomass was harvested subsequently. This experiment was conducted once for the purpose of phenotypic screening and one more time for virulence study using larvae of G.mellonella as infection model.

2.2.1.5 Harvesting of fungal mycelia from pooled growth

Fungal mycelia from the pooled growth were harvested by filtration through autoclaved funnels with sterile 0.3 mm filter paper attached under vacuum. Sterile inoculating loops (Brand) were used to collect the dry biomass. The biomass was freeze-dried using liquid nitrogen and transferred into sterile Eppendorf tubes to be used for subsequent DNA extraction. This experiment was conducted once employing 4 biological replicates.

2.2.1.6 Growth of A. fumigatus A1160+ and PK mutants for total RNA extraction

WT and the other strains were grown on tissue culture flasks (containing 50 ml of AMM) in triplicates, where the final inoculum level was 106 spores/ml. The fungal biomass was harvested after 18 hr of incubation at 37°C and 180 rpm using sterile Miraclothe (Calbiochem)). Approximately 1 gm of wet biomass was re-inoculated on RPMI media with 0.5 µg / ml of itraconazole and without itraconazole in triplicates for each strain and incubated at 37°C and 180 rpm for 4 hr. Fungal biomass was harvested as described earlier in section 2.2.1.6 and used for subsequent RNA extraction. This experiment was performed 4 times in total (done once and repeated three times) employing 3 biological replicates.

2.2.1.7 Growth of A. fumigatus A1160+ and PK mutants for genomic DNA extraction

A. fumigatus A1160+ spores were inoculated on tissue culture flask containing SAB agar and incubated at 37°C for 3–4 days. Spores were harvested accordingly as described in section 2.2.1.5 and used for DNA extraction. PK mutants were grown on tissue culture flasks containing SAB media supplemented with Hygromycin 200 μg/ml for selection and incubated at 37°C for 5 days. Spores were harvested as described in section 2.2.1.5 and used for DNA extraction. This experiment was conducted once employing 3 replicates, however, repeating was required for some PK mutants upon failure to validate the integration of marker cassette, which often required repeating the transformation.

2.2.2 Extraction of nucleic acids

2.2.2.1 DNA extraction

2.2.2.1.1 DNA extraction from fungal spores using Cetyl Trimethyl Ammonium Bromide and glass beads

A. fumigatus spores, harvested as described in section 2.2.1.5, were vortexed for 15 sec to obtain an even suspension. One millilitre of spore suspension was transferred to a sterile 1.5 ml Eppendorf tube. The tubes were centrifuged at 13,400 rpm for 1 min. After removing the supernatant, the pellet was re-suspended in 1 ml DNA extraction buffer (Park et al., 2014) (composed of 10 mM EDTA, 100 mM tris HCl (pH8), 2% cetyl trimethyl ammonium bromide (CTAB) and 1.4 M NaCl). The mixture was transferred into a 2 ml screwcap microtubes containing approximately 200 μl equivalents of acid-washed 212–300 mm glass beads (Sigma). The cells were lysed by vigorous agitation for 20 sec at 45/m using a FastPrep (MP Bio), then incubated for 10 min at 60°C using a heat block. After incubation, the cell debris was pelleted by centrifugation at 13,400 rpm for 2 min at room temperature, and then 700 μl of the supernatant was transferred into a sterile 1.5 ml Eppendorf tube. To remove the RNA, 4 μl RNase (100 mg/ml RNase) was added and incubated at 37°C for 15 min.

To remove the protein, 700 μl of chloroform–isomyl alcohol 24:1 was then added to the tubes. The tube was inverted to mix for several times and then centrifuged for 2 min at 13,400 rpm. The upper aqueous phase was transferred to new 1.5 ml Eppendorf tubes and 0.6 volume of isopropanol was added to precipitate the DNA. The DNA was re-pelleted by centrifugation at 13,400 rpm for 5 min. To remove the excess salt from the DNA, the pellets were washed with 0.5 ml of 70% ethanol before the DNA was re-concentrated by 75

centrifugation for 2 min at 13,400 rpm.

The pellets were air-dried for approximately 15 min and then dissolved in 900 μl of molecular biology grade water. For quality control, 3 μl of extracted DNA sample was used for agarose gel electrophoresis (method as described in section 2.2.3). The DNA was visualised on an AUV visualiser (Image Lab Bio-Rad). In general, one single extract per strain was used. DNA extraction was repeated upon failure to validate the integration of marker cassette with the first extract.

2.2.2.1.2 DNA extraction from fungal mycelia

Fungal mycelia were harvested and snap frozen in liquid nitrogen and then quickly transferred into a sterile mortar containing a small amount of liquid nitrogen. The frozen mycelia were quickly ground into a fine powder with the presence of liquid nitrogen (using a pestle and mortar) and transferred into a safe lock 2 ml Eppendorf tube. Approximately 1 gm of the mycelia powder was re-suspended in 1 ml of DNA extraction buffer and incubated for 10 min at 65°C. DNA extraction was performed following the protocol described in section 2.2.2.1.1, except for the Fast prepping step being no longer required. This experiment was conducted once employing 4 biological replicates.

2.2.2.1.3 DNA extraction from infected lungs tissues

DNA extraction from the infected lung was conducted using a DNeasy Blood and Tissue Kit (Qiagen), following the manufacturer’s instructions. In general, lung tissue (0.3–0.5 gm) was homogenised with an equal volume of sterile normal saline using a tissue homogeniser, and 180 µl of ATL buffer was then added to the homogenised products (~250 µl) and mixed well, followed by adding 20 µl of proteinase K. The mixture was incubated at 56°C for 1 hr. The AL buffer from the kit was then added to the mixture, followed by a further 10 min incubation at 56°C. Purification of the DNA was carried out as per the manufacturer’s instructions. This experiment was conducted twice employing 4 replicates per each infected pool. Repeating of DNA extraction was required after experienced contamination with the sequences, which necessitate conduction of all the related work in an independent research facility by Dr Marcin.

2.2.2.1.4 DNA extraction from infected macrophage

A cell scraper was used to detach the cells, which were attached to the bottom of the flask. This was followed by centrifugation at 14,000 rpm for 1 min to precipitate the pellet. The pellet was then transferred into a Brad tube containing 1 ml of DNA extraction buffer, and 3 cycles of vigorous agitation (for 20 sec at 45/m) on a FastPrep (MP Bio) were performed to lyse the cells. At this point, DNA extraction was conducted following the extraction method described in section 2.2.2.1.1. This experiment was conducted once employing 4 replicates per each group.

2.2.2.2 RNA extraction

Fungal biomass was harvested using 0.3 mm Whiteman filter paper, where the dried biomass was transferred into 2 ml safe lock Eppendorf tubes containing glass beads. Accordingly, 1 ml of Triazole (Sigma) was added to each sample before applying 3 cycles of FastPrep (setting 1, 6,800, 30 sec x 3 times), followed by centrifugation at 4°C, 12,000rpm for 1 min. The supernatant was then transferred into sterile 2 ml tubes containing 200 µl of chloroform and mixed well by pipetting up and down. Centrifuge was applied at room temperature, and approximately 500 µl of the supernatant was transferred into new RNase free tubes containing 250 µl of 0.8 M sodium-citrate-1.2M NaCl and 250 µl of isopropanol. The samples were vortexed and left to settle at room temperature for 10 min, followed by centrifugation at 4°C, 12,000 rpm for 15 min, and then the supernatant was discarded. The pellet was washed with 300 μl of 100% ethanol and centrifuged at 4°C, 12,000 rpm for 5 min and the supernatant was discarded. The pellet was air dried at room temperature for 15 min and re-suspended by adding 100 µl of RNase free water. Fifty µl of the un-purified RNA sample was stored at -80°C, and the rest was used for subsequent purification.

Before purifying the RNA, DNAase treatment of the RNA solution was conducted using a PROMEGA kit following the manufacturer’s instructions, where 50 µL RNA solution was mixed with 6 µl of x10 buffer, 4 µl of DNase solution and incubated at room temperature for 1 hr. After 1 hr of incubation, 1 µl of RQI stop solution was added.

Purification of the RNA/DNAase treated solution was conducted using an RNase mini purification kit (QIAGEN) following the manufacturer’s instructions. The purified RNA concentration was measured using a Nano drop and stored at -80°C until required.

2.2.3 Separation of DNA fragments by agarose gel electrophoresis

To separate the DNA according to its molecular weight, agarose gel electrophoresis was performed using either 1% w/v or 2% w/v molecular biology grade agarose (Melford, UK) dissolved in 1 x Tris/Acetic acid/EDTA, and stained with Safe View (0.5 μg/ml) (Lee et al., 2012). To facilitate the loading of the DNA samples, a blue loading dye (consisting of 6 gm of 30% bromophenol blue, 30 ml glycerol and 70 ml H2O) was mixed with the DNA (1:5). In order to estimate the size of the DNA samples, 1kb DNA marker (Biolabs, UK) or 100 bp marker (Bioline) were loaded on the gel. The gel was run at 80 V (Bio-Rad, UK) in 1 x TAE buffer for 40 min, and the DNA was visualised on a UV visualiser (Image Lab Bio-Rad).

2.2.4 Generation of knockouts in A. fumigatus

The fusion PCR based method of Aspergillus gene replacement was adapted from Oakley’s group (Szewczyk et al., 2007). In this study, a generic linker sequence was introduced to the central selectable marker fragment (Hygromycin cassette, hph) and the flank fragments. Gene knockout cassettes were designed to include a PCR amplifiable genetic barcode and a class III restriction enzyme site at the selectable marker–gene junction. pAN7-1 (3876 pb) (Punt et al., 1987) (Gene Bank Z32698.1) was used as a template to amplify the hygromycin marker cassettes. pAN7-1 contains the hph gene from E. coli (Punt et al., 1987) joined to the A. nidulans glyceraldehyde-3-phosphate dehydrogenase promoter (Pgpd) and the transcriptional terminator of a tryptophan synthase structural gene (trpC) (PgpdA-hph-TrpC) cassette, which was amplified and subsequently combined with the gene flanking regions. This cassette was employed as a selectable marker in the A. fumigatus transformation experiments. This experiment was repeated at least 10 times upon require repeating the transformation for about 25 PK genes, where the marker cassettes integration validation could not be confirmed employing the validation PCR.

2.2.5 Design of oligonucleotide primers for the generation of the gene knockout cassette

In this study, we attempted to disrupt 115 protein kinase genes. Eight primers were required for each to generate the knockout cassette: two to amplify the 5’ flank region, two to amplify the 3’ flank region, two to amplify the selectable marker, and finally two nested primers were necessary to amplify the final product, as shown in the schematic in Figure 2.1. 78

An automated program based on primer 3 parameters (Rozen and Skaletsky, 1999) (run and edited by Dr Jane Glisenan) was used to generate all the required primers (P1–P6). Primers 1 and 2 were generated in order to amplify the upstream flanking region, with primers 3 and 4 being generated to amplify the downstream flanking region, and primers 5 and 6 generated for use in the stage 2 PCR fusion. The gene of interest was identified on the central Aspergillus data repository (CADRE).

Figure 2.1: Schematic figure of the PCR approach used to generate gene knockouts. Oligonucleotide primers P1 & P2 were used to amplify the 5’ flanking region (I), while primers P3 & P4 were used to amplify the 3’ flanking region (II) of a gene of interest. Hygromycin marker cassette was amplified in a separate reaction from the plasmid template (pAN7-1). Post-amplification products have overlapping linkers, which permit fusion in the second stage of the PCR. Nested primers were employed to increase the specificity of the reaction and aid amplification. All gene knockout products are approximately the same size (4.7 kb).

For those genes where the automated software failed to identify appropriate primers, a manual method (Primer 3) was used (http://bioinfo.ut.ee/primer3-0.4.0/). The coding sequence, together with the 1,200 bp flanking sequence for the gene of interest, were identified from CADRE before being extracted and used to design P1–P4 (CADRE, 2011) and inserted into a Microsoft Word document. The upstream flanking sequence or 79

downstream flanking was inputted into Primer 3 to retrieve the corresponding primers.

The criteria for designing these primers were as follows: product size 1,000–1,200 bp (with some exception when ranged 900–966 bp to avoid the clutch in the sequence), the minimum primer size was 18 bp and the maximum size was 22 bp; minimum melting temperature TM was 58°C, and the maximum was 62°C; the minimum GC content was 40 and the maximum was 60. Consequently, both primers 5 and 6 were designed by selecting all the sequences located between P1 and P4. Word counting in the Microsoft Word application was employed to identify the number of bases and the same process was repeated, but the product size was changed to be the product size minus 200. All the required primers for this experiment were synthesised by Eurofins Genomics (Germany).

2.2.6 DNA barcodes

2.2.6.1 Generating the DNA barcode

With a view to employing competitive fitness analysis utilising the generated PK knockout mutants, a unique strain specific DNA barcode is required. To design the unique barcodes to be embedded in the selective marker cassette, DNA barcode generator v.2.8 http://www.comailab.genomecenter.ucdavis.edu/index.php/Barcode_generator (Bystrykh, 2012) was employed. The python scripts were edited by Dr Jane Mabey (MFIG) to compile the generic linker and the hph forward primer, alongside the designed barcode, as shown in Figure 2.2.

Figure 2.2: Schematic figure of the DNA barcode’s insertion, which was used to tag the PK mutants. This ultimately will be the unique identifier in the parallel fitness experiment.

In total, 115 unique barcodes were designed (see Appendix 11). Each of the barcodes was of 18 bp in length, with a minimum genetic distance of 2bp. The minimum GC contents were 40, the maximum was 60 and the cycles of random attempts were 1,000,000.

2.2.6.2 Quality control of designed DNA barcodes

To ensure the barcodes generated did not lead to self-annealing, the OligoAnalyzer v.3.1 (Owczarzy et al., 2008) was used. The Hairpin function was employed to predict the likelihood of the presence of oligo secondary structures using the mFold algorithm, whereas the Hetero-Dimer function scanned for the potential annealing of target sequences by this oligo, as shown in Appendix 12.

2.2.7 Polymerase chain reaction

The polymerase chain reaction (PCR) was utilised to amplify upstream/downstream flanking sequence and hygromycin marker cassettes for the generation of gene knockout cassettes (as shown in the schematic in Figure 2.1). The PCR mixtures were prepared on ice using 96-well plates and sealed with sealing aluminium foil (Star Laboratories, USA). The PCR was carried out on either QB-96 or S-96 thermal cyclers (Quanta Biotech, UK). PCR mixture without DNA template was employed as a negative control. Agarose gel electrophoresis was performed to assess the PCR product, as described in section 2.2.3.

All the PCR products were purified using the Qiagen PCR purification kit (Qiagen, UK) following the manufacturer’s instructions, before setting up stage 2 fusion PCR.

2.2.7.1 Amplification of the upstream and downstream flanking sequence

To amplify the either upstream or downstream flanking fragments, reaction mixtures were prepared by mixing1 μl of each primer (5 pmol/ul), with 23 μl of prepared master mix containing 1 μl of (100 ng/ul) genomic DNA, 0.5 μ of 2.5 u/μl LongAmp polymerase (New England Biolabs), 5 μl of 5x buffer mix (NEB), 1 μl of (20Mm) d NTP (Bioline) and 15.5 μl of molecular biology grade H2O. P1 and P2 amplified the upstream flanking sequences (see Appendix 8), whereas P3 and P4 were used to amplify the downstream flanking sequences (see Appendix 9). The PCR condition used is shown in Table 2.1.

Table 2.1: PCR condition for amplification of upstream and downstream flanking fragments, and hygromycin selective marker cassettes.

STEP TEMPERATURE (°C) TIME CYCLE Initial Denaturation 95 2 min 1 Denaturation 95 30 sec Annealing 55 30 sec 30 Extension 68 1 min 30 sec Final Extension 68 10 min 1 81

2.2.7.2 Amplification of marker cassette for fusion constructs

The fungal hygromycin cassettes (hph) were amplified using the pAN7-1 as a plasmid template, as described above. The PCR reaction mixtures were prepared by mixing1 μl of each primer (hph forward + DNA barcode & reverse primer) 5 pml/ml, with 23 μl of prepared master mix containing 1 μl of 1 μl of pAN7-1, 0.5 μl of (2.5 U/μl) LongAmp polymerase, 5 μl of (5x) buffer mix, 0.5 μl of (20 mM) dNTP’s and 16 μl of molecular biology grade (H2O). The PCR condition used is shown in Table 2.1. The pyrithiamine resistance cassette (ptrA) used as a selectable marker in the knockout experiment (where the reconstitution of genes was performed) was similarly amplified from PTRII. Amplification of the marker cassettes was conducted once and the purified PCR products were stored at -20°C and used when required to generate the knockouts cassette.

2.2.7.3 Fusion PCR

Fusion PCR was performed to combine the upstream and downstream fragments with hph marker cassette, as shown in the schematic in Figure 2.1. The nested primers P5 and P6 (see Appendix 10) were used in the fusion PCR reaction in order to increase the specificity and efficiency of the reaction. The PCR was performed in the S-96 thermal cycler. The reaction mixtures were prepared by mixing 2 μl of pooled upstream and downstream fragments (not quantified), 1 μl of hph fragment and 2 μl of P5 and P6 (5 pmol/ml), with 45 μl of prepared master mix containing 10 μl of (5x) buffer mix, 2 μl of (20 mM) dNTP’s, 2 μl of (2.5 U/μl) LongAmp polymerase and 31 molecular biology grade H2O. The PCR condition used is shown in Table 2.2.

Table 2.2: PCR condition of fusion PCR, where upstream and downstream flanking fragments, and hygromycin selective marker cassettes are combined to get the knockout construct.

STEP TEMPERATURE (°C) TIME CYCLE

Initial Denaturation 94 2 min 1 Denaturation 94 20 sec 70 1 sec 10 Annealing 55 30 sec Extension 68 5 min Initial Denaturation 94 20 sec 5 70 1 sec Annealing 55 30 sec Extension 68 5 min 5 sec Denaturation 94 20 sec 5 70 1 sec Annealing 55 30 sec Extension 68 5 min 20 sec Final Extension 68 10 min 1

Fusion PCR was conducted once, but repeated if the transformation of the PK mutants was repeated.

2.2.7.4 Generation knockout of the control strain for competitive fitness analysis

Wild type A1160P+ non-functional transposon was selected as a control for competitive fitness study. Wild type DNA was extracted as described in section 2.2.2. Norman Van Raj designed primers that used in amplification of the upstream and downstream flanking region, while the primers used to amplify the hph cassette were designed as described in 2.2.6. The PCR was utilised to amplify the upstream and downstream flanking sequence and hygromycin marker cassettes for the generation of the transposon knockout, as described in section 2.2.7.1. Fusion PCR was conducted as described in 2.2.7.3.

2.2.7.5 PCR set up for qPCR

QRT-PCR reactions were performed in 10 μl reaction volumes using 30 ng of A. fumigatus RNA, 0.1 μM of each forward and reverse primer for each of the genes cyp51A, cyp51B, beta-tubulin, GDPA and cdr1B (see Appendix 13), and 2X SensiFASTTM SYPER® No-

Rox One-Step Master Mix (Bioline, UK). PCR was carried out under the following conditions: 45°C for 10 min (for reverse transcriptase), then 95°C for 2 min, followed by 40 cycles at 95°C for 15 sec, 60°C for 1 min and then extension at 72°C for 30 sec. The data were extracted in Excel format and subsequently analysed using changes in the cycle threshold (CT) values. This experiment was conducted 4 times in total using triplicates.

2.2.8 Transformation

2.2.8.1 Transformation of PK gene

Cell culture was set up on the day before the transformation by inoculating 125 μl of 4 x

108 conidia/ml of A. fumigatus A1160+ into a sterile flask containing 50 ml of liquid SAB broth medium (final concentration 106 spores/ml); then the inoculum was split into three Petri dishes (approximately 15 ml in each) and wrapped with parafilm to prevent contamination. Subsequently, the cultures were incubated at 37°C overnight. After incubation (normally 14 hr), the mycelia biomass was harvested by filtering through a sterile J-cloth attached to a funnel. The biomass was transferred into a sterile 50 ml falcon tube containing 20 ml of 5% Glucanex (Novozymes) (prepared on the day of the experiment, dissolved in KCl/CaCl2 (600mMKcl, 50mM CaCl2) and sterilised by filtering through a 0.22 nm filter), then shaken thoroughly and incubated at 30°C in a shaking incubator for 3 hr. The protoplasts were harvested by filtering them through a sterile lens tissue into a sterile 50 ml falcon tube, before being pelleted by centrifugation at 4°C for 20 min and re-suspended in 1 ml ice-cold 600 mM KCl/50mMCaCl2, and the excess liquid removed.

The protoplasts were enumerated using a haemocytometer and diluted to 1 x 107 in 10 ml of cold 600 mM KCl/50 mM CaCl2. To each well of the 96-well plate, 20 μl of 60% PEG 4000 and 20 μl of fusion cassettes were added. Then, 100 μl of protoplasts solution (107) was added to each well and mixed by carefully pipetting using a multi-channel pipette and then incubated on ice for 30 min. After incubation, 200 μl of 60% PEG 4000 was added to the mixture and then incubated at room temperature for 5 min. The transformation mixture was plated on YPS selective media containing 200-μg/ml hygromycin.

A positive control included a fusion cassette of a validated gene knockout, provided by Dr Bromley’s Group, and a negative control plate (protoplast only was plated) were also included in each transformation. The mixture was transferred onto YPS selective plate and

spread using a sterile spreader. All plates were incubated for 1 hr at room temperature and then at 37°C for 3-5 days.

Hph resistant colonies were obtained after 3 days from the positive fusion cassette and the PK cassettes, while no colonies were obtained for the negative control plate excluding contamination. Transformation was repeated as required up to10 times.

2.2.8.2 Transformation of the control strain for the competitive fitness study

Transformation of the transposon cassette was conducted, as described in section 2.2.8.1.

2.2.9 Purification of protein kinase gene knockouts and the transposon knockout mutants.

In order to differentiate the hygromycin resistant strains from other grown strains, spores from a single colony were streaked to single colony again on SAB media containing Hygromycin B 200 μg/ml, for at least two times. Purification of the transposon knockout mutant, which generated to be used as a control in the competitive fitness study, was performed similarly. This experiment conducted employing 3 replicates.

2.2.10 Validation of protein kinase mutants and the transposon mutant

PCR reactions were used to ensure insertion of the selective marker gene into the correct location. The extracted DNA from the purified transformants was used in these PCR reactions, which involved two amplifications across the 5’ flanking region and selective marker gene (using P1 + hphR); and the 3’ flanking region and selective marker gene (using P4 + hphF), as shown in Figure 2.3.

Figure 2.3: The validation PCR across the 5’ flanking region and selective marker gene (using P1 + hphR) giving a product size of 2.4 kb; and the 3’ flanking region and selective marker gene (using P4 + hphF + DNA barcode) giving a product size of 3 kb. P1–P4 validation gives a product size of 5kb.

Another validation PCR across of the gene using P1–P4 was conducted to exclude the contamination with WT and to ensure the strain is pure. Repeating of validation was required upon frailer to validate the marker cassette integration in some PK mutants, however at least 3 attempts were achieved.

2.3 Functional analysis of the A. fumigatus kinome

This experiment was conducted once employing 4 biological replicates per each condition.

2.3.1 Screening PK mutants’ response to temperatures

Pooled PK mutants were cultured at 30°C and 48°C, respectively, where other conditions remained the same as described in section 2.2.1.4; this is to screen for temperature- sensitive mutants.

2.3.2 Screening PK mutants’ response to oxidative stress using H2O2

Before conducting this experiment using pooled PK mutants, the WT response to different concentrations of H2O2 was checked. Serial dilution of H2O2 at final concentrations of 0.5, 1, 2, 4, 6 and 8 mM were used for this purpose, where the WT strain was inoculated at concentration of 5x108 spores/ml on tissue culture flasks containing 50 ml fRPMI (3 biological replicates were used). The incubation condition was as described in section 2.3.4. Following the results obtained from this study, two concentrations including 2 mM

H2O2 and 4 mM H2O2 were selected to check the response of pooled PK mutants to oxidative response, applying the same conditions as per section 2.2.1.4

2.3.3 Screening PK mutants’ response to screening PK mutants’ response to PH

Pooled PK mutants were inoculated on 50 ml tissue culture flasks containing 1 X fRPMI; PH was adjusted to 8 using 10M NAOH. For PH 4, MOPS-NAOH buffer was replaced by a citric-acid–Na2HPO4 buffer solution. The latter was prepared according to the Sigma- Aldrich Buffer Reference (www.sigmaaldrich.com/life-science/core- bioreagents/biological-buffers/learning-center/buffer-reference-center.html). The pooled PK mutants’ strains’ inoculation and incubation conditions were as described in section 2.2.1.4.

2.3.4 Screening PK mutants’ response 1M sucrose

Two times fRPMI media at pH 7 was mixed with 2M sucrose at 1:1 ratio to achieve a final concentration of 1M sucrose. The inoculum level of the PK mutants and the incubation conditions were as described in section 2.2.1.4.

2.3.5 Screening PK mutants’ response to iron starvation and excess

PK pooled mutants were inoculated on tissue culture flasks as described in 2.2.1.4. For iron starvation conditions, fRPMI without ferrous with 100 µM BPS chelator was used instead of complete fRPMI.

2.3.6 Growth of PK mutants on Aspergillus complete media and Aspergillus minimal media

PK mutants’ growth alongside wild type strain was checked in triplicates on liquid Aspergillus complete media (ACM) and Aspergillus minimal media (AMM), respectively. To check the growth of PK mutants on the ACM and AMM liquid media, 5 µl of each strain at inoculum level of 4x105 was added to 195 µl of media in 96-well plates using multichannel pipettes and mixed well. The plates were incubated at 37°C for 48 hr, and the optical density (OD) at 600 was read after 12 hr, at 6 hr intervals. Furthermore, the growth of PK mutants was also checked on ACM and AMM solid media, where 2.5 µl of each strain at inoculum levels of 4x105 was inoculated on solid ACM and AMM plates and incubated at 37°C for 48 hr, with photographs taken at this point.

2.3.7 Growth of PK mutants on RPMI media

Growth of PK mutants and wild type strain on liquid RPMI 1640 was checked using the same method described in section 2.3.6.

2.3.8 Growth of PK mutants on Vogel’s media

PK mutants and WT were grown on solid Vogel’s media, where 1,000 spores were inoculated on Vogel’s media and incubated at 37˚C for 48 hr.

2.4 Next generation sequencing NGS

This experiment was conducted 3 times without repeat employing screening the sensitivity of PK mutants toward itraconazole antifungal, for functional analysis and for virulence study using larvae of G.mellonella as infection model. 87

2.4.1 Designing primers for NGS

Primers were designed to amplify the DNA barcode, as shown in Figure 2.4. The designed primers include the hph F and the hph R primer sequence and also unique barcoding sequences enabling downstream decoding and differentiation each individual strain.

Figure 2.4: Design the NGS primers targeting the DNA barcodes, where both hphF and hph R were included to amplify the strain-specific barcode.

Forward Ion PGM sequencing primer was attached to a unique Ion express index (that identifies the condition of the experiment and facilitates pooling of the DNA from multiple cultures), as shown in Table 2.3, followed by the hph F primer that will be utilised to amplify strain-specific barcodes alongside the universal hph R primer, which was linked to the sequencing reverse adaptor, as shown below:

5’-CCTCTCTATGGGCAGTCGGTGAT GGTCGTTGTAGGGGCTGTAT

Table 2.3: The unique primers, designed to use in NGS. The forward sequencing primers were linked to a unique ion express identifier with 10 bp followed by the hphF primer.

ID Forward sequencing adaptor Ion express sequence hphF primer

Ion express 001 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTAAGGTAACGATCCGGCTCGGTAACAGAACTA Ion express 002 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGTAAGGAGAACGATCCGGCTCGGTAACAGAACTA Ion express 003 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGAAGAGGATTCGATCCGGCTCGGTAACAGAACTA Ion express 004 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGTACCAAGATCGATCCGGCTCGGTAACAGAACTA Ion express 005 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGCAGAAGGAACGATCCGGCTCGGTAACAGAACTA Ion express 006 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTGCAAGTTCGATCCGGCTCGGTAACAGAACTA Ion express 007 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTCGTGATTCGATCCGGCTCGGTAACAGAACTA Ion express 008 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTCCGATAACGATCCGGCTCGGTAACAGAACTA Ion express 009 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGTGAGCGGAACGATCCGGCTCGGTAACAGAACTA Ion express 010 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGCTGACCGAACGATCCGGCTCGGTAACAGAACTA Ion express 011 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCCTCGAATCGATCCGGCTCGGTAACAGAACTA Ion express 012 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGTAGGTGGTTCGATCCGGCTCGGTAACAGAACTA Ion express 013 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCTAACGGACGATCCGGCTCGGTAACAGAACTA Ion express 014 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGTTGGAGTGTCGATCCGGCTCGGTAACAGAACTA Ion express 015 5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGTCTAGAGGTCGATCCGGCTCGGTAACAGAACTA Ion express 016 5’- CCATCTCATCCCTGCGTGTCTCCGACTCAGTCTGGATGACGATCCGGCTCGGTAACAGAACTA

2.4.2 Amplification of PK mutants’ specific barcodes

PCR was conducted to amplify the DNA extracted from the pooled PK mutants at different screening conditions. The PCR reactions were prepared on ice using 8 well PCR strips (Star Laboratories, USA). The reaction mixtures were prepared by mixing 2 μl from each primer (P1 and P2), 5 µL DNA with 16 µL of prepared master mix containing 12.5 µL of 2X fusion Polymerase and 3.5 µL of molecular biology grade H2O. The PCR condition was as shown in Table 2.4.

Table 2.4: PCR conditions for amplifying the PK mutants’ specific barcode.

STEP TEMPERATURE (°C) TIME CYCLE

Initial Denaturation 95 2 min 1 Denaturation 95 15 sec 35 Annealing 57 20 sec Extension 68 1 min 30 sec Final Extension 68 5 min 1

The PCR was carried out on QB-96 cyclers. A negative control containing the same reaction components without a DNA template was employed to exclude the potential for contamination. Post amplification products were checked by running gel electrophoresis using 2% agarose, as described in section 2.2.3,

2.4.3 Purification of PCR products for sequencing

The PCR products were purified using AMPure beads (AgenCout). The PCR products were diluted with AMPure beads at a 1:8 ratio using 1.5 Eppendorf tubes, which were labelled beforehand reflecting the samples’ ID. Tubes were inverted several times to mix the DNA with the beads and then transferred onto a magnet rack. After being settled for about 1 min, the supernatant was discarded carefully. To wash the beads, 125 μl of 70% ethanol was added to each sample and mixed well by pipetting up and down, being then transferred into the magnetic rack after 5 min of settlement. The supernatant was discarded. The wash was repeated one more time using 150 μl of 70% ethanol. After discarding the supernatant, the tubes were left open to air dry the pellets for about 15 min to allow the evaporation of ethanol. To elute the DNA, 50 μl of nuclease free water was added to the beads and incubated at room temperature for 15 min. After that, centrifugation

was carried out at 14,000 rpm for 3 sec at room temperature to precipitate the beads. All the tubes were returned to the magnetic rack, and the clear supernatant transferred to new sterilised Eppendorf tubes.

2.4.4 DNA quantification for sequencing

To quantify the amount of DNA in each PCR product, 10 μl of each sample was loaded on 2% agarose gel, alongside 10 μl of 100 bp ladder. To achieve a good separation, the gel was run 70 v for 1 hr. The gel image was visualised using the Bio Rad Image Lab. The amount of DNA in each sample was quantified manually using absolute band size detection corresponding to the standard amount of 100 bp ladder bands’ size. The quantified amount of DNA was adjusted to ng/μl by dividing the net amount by 10. Then, DNA samples were diluted in molecular grade water to the lowest amount obtained in each experiment before pooling together. The final DNA concentration was adjusted to 0.035 ng/μl. The purified and pooled barcoded libraries were stored at -20°C until required.

2.4.5 Ion Personal Genome Machine system preparation

2.4.5.1 OneTouch 2 system (Amplification of ion sphere particles)

The manufacturer’s instruction was followed for the machine setup. To the reagent mixture, 25 µl nuclease free water, 50 µl ion Personal Genome Machine (PGM) enzyme mix, 25 µl quantified pooled DNA (0.035 ng/ml) and 100 µl Ion PGM ion sphere particles (ISPs) were added, respectively. The mixture was pipetted many times for homogenisation, and vortexed for 5 sec before loading 1,000 µl into the filter. Then, a total of 1,700 µl was added accordingly in two steps with changing tips. The filter was placed on the Ion OneTouch system machine with the corresponding orientation, followed by inserting the recovery tubes and the router to the OneTouch centrifuge with 150 µl of break solution added to each tube. When started, the machine run lasted for 5 hr, followed by spinning for 10 min.

2.4.5.2 Recovering the template positive ISPs

To recover the ion sphere particle (ISPs), supernatant from the previous process was discarded; leaving 100 µl to be processed. To wash the ISP pellet, 500 μl of ion touch wash solution was added to each recovery tube and mixed by pipetting several times to re- suspend the pellet. After that, the mixture was transferred into sterile Eppendorf tube and incubated for 2 min at 50°C, followed by centrifuging at 14,000 rpm for 2 min and 30 sec.

After that, 100 µl of the supernatant was transferred into sterile 0.5 µl Eppendorf tube, of which 2 µl was kept for quality control (QC) analysis.

2.4.5.3 Enrichment of the template: positive ISPs

Clonal amplification was conducted using OneTouch 2 system, amplifying the ISP many times, where the template DNA is bound to the ISP on one side (template-positive ISPs) and biotinylated on the other side. During enrichment the biotinylated side will bind to magnetic beads that are coated with Streptavidin, thus ISPs that do not contain DNA fragment will be washed away. In this step, ION PGM Template OT2 200 Kit (Life TechnologiesTM), Dynabeads and an Ion OneTouch ES machine are used according to the manufacturers’ guidelines. The ISPs were mixed well by pipetting before being loaded into well 1 of the 8 well strips for enrichment. Ion one touch solution (300 µl) was added to wells 3, 4 and 5, respectively. Melt of solution (300 µl) was added to well 7, the beads’ wash solution (130 µl, which was prepared previously following the manufacturer’s instructions) was added to well 2, and a 0.2 ml tube containing 10 µ neutralisation solution was placed before starting the run. This usually lasted for 35 min, and after completion the ISPs were stored at 4°C until required.

2.4.5.4 Quality control of the template-positive ISPs (Qubit 3.0)

The positive ISPs was quality controlled using an ION PGM Quality Control Kit (Life TechnologiesTM), where a Qubit Fluorometer was used to assess the percentage of template ISPs according to the manufacturer’s instructions. The ratio of Alexa Fluor 647 fluorescence (which binds to the attached DNA fragment) to Alexa Fluor 488 fluorescence (which binds to all ISPs) gives the total percentage of template ISPs. The optimum level of the template ISPs is 10–30%.

2.4.5.5 Ion torrent setup

The Ion PGM system was cleaned and initialised using an ION PGM Sequencing 200 Solutions Kit (Life TechnologiesTM), ION PGM Sequencing Reagents 200 Kit (Life TechnologiesTM) and a Cleaning Chip (Life TechnologiesTM), according to the manufacturers’ guidelines. The template ISPs were mixed thoroughly by pipetting, followed by spinning for 1.5 min at maximum speed, while precaution was taken to avoid disturbance of the pellet in order to remove the supernatant, leaving only 15 µl. Twelve µl of the sequencings’ primer was added and mixed well by pipetting and placed on a thermocycler for 2 min at 95°C, followed by 37°C for 2 min, and stored at room temperature. Three µl of PGM sequencing polymerase (Life TechnologiesTM) was added 92

(while the 318 chip V2 check was running) and incubated at room temperature for 5 min. Before loading the chip, any extra fluid was pipetted out by holding the chip at 45°C, followed by centrifugation for 5 sec with the face down. The chip was loaded with 30 µl of ISPs solution slowly by dialling the pipette, with approximately 1 µl of the solution left to avoid inducing bubbles. To distribute the ISPs, the chip was put back in the centrifuge with the nodule facing inward and spun 30 sec. In order to mix the ISPs, approximately 25 µl was pipetted in and out three times without removing the tip. Centrifuge was carried out for 30 sec with the tip pointing out. This process was repeated one more time with the tip pointing in. This was followed by holding the chip at 45°C and pipetting out most of the liquid and placing it on the sequencer to proceed with chip calibrations, before starting the targeted sequencing with 500 flows to sequence the DNA.

2.4.5.6 NGS data processing

Sequenced reads were sorted according to the 10 bp unique index, with the FASTQ sequences exported. The FASTX-Toolkit FASTQ/A Trimmer (http://hannonlab.cshl.edu/fastx_toolkit/) was used to trim away the adaptors, leaving only the first 18 bp of the unique gene identifier (DNA barcodes as shown in the Appendix, Table 11). The sequence reads were mapped to the set of unique gene identifiers using Bowtie. The counts for each gene identifier were calculated using a bespoke bash script (courtesy of Dr Jane Gilsenen) after which the data were normalised to the total number of sequenced reads per condition. The pairwise comparisons, Pearson correlation and coefficient of determination were calculated between each technical and biological replicate using the Microsoft Excel software to evaluate the reproducibility of each experiment. Counts for each strain were subsequently normalised to counts from T=0, and then the Log2 values were calculated using Microsoft Excel and plotted into an aligned scatter graph using the GraphPad Prism7 software.

2.5 Screening for itraconazole-resistant mutants

2.5.1 Determination of minimal inhibitory concentration (MIC) of itraconazole for WT

To start with, and in order to check the sensitivity of the PK mutants to itraconazole, the MIC of WT in duplicates was conducted using 96-well plates, as shown below. Itraconazole stock 1,600 mg/L was diluted first at 1:10 using distilled water. Then, serial dilution at 1:1 was applied using 10% DMSO. The highest concentration was 16 mg/L (started at well 1, and the lowest concentration was 0.03125 mg/L at well 10. Well 11 was 93

used for positive control RPMI media + spores + 10% DMSO, and well 12 was used as the negative control, where only RPMI media + 10% DMSO were added). The plate was incubated at 37°C for 48 hr and the OD reading at 600 was taken, in addition to visual inspection.

2.5.2 Screening of pooled PK mutant’s sensitivity to itraconazole

This experiment was conducted in an attempt to identify either itraconazole resistant or sensitive PK mutant in pooled growth. Four biological replicates were used for the tested concentration alongside four biological replicates without drug (itraconazole) as the positive control. One hundred µl of 5x108 from pooled PK mutants was added to the corresponding tissue culture flask containing 50 ml of liquid fRPMI with or without drug. Sub MIC concentration of 0.02 mg/L was used in this experiment. To achieve the required drug concentration from the itraconazole stock of 1600 mg/L in 100% DMSO, the drug stock was diluted 1:10 in dH2O, and the equation C1V1=C2V2 was used to calculate the required amount of the drug. The incubation condition was as described in section 2.2.1.4.

2.5.3 Validation of sensitivity to itraconazole using individual strain (PK mutants and their reconstituted strains)

To consider the results obtained from testing the sensitivity of the pooled PK mutants to itraconazole and to validate the output of NGS from the previous study, the radial growth of two predicted resistant and one predicted sensitive strain was assessed in triplicates on RPMI solid media using the wild type strain as the control. Radial growth was also assessed in triplicates for the two resistant strains and their reconstituted one, in an attempt to prove that the knockout of these two genes was responsible for the resistance, since reconstitution of strains will return the phenotype.

2.5.4 Studying the expression of cyp51A, cyp51B and cdr1B

The gene expressions of cyp51A, cyp51B and cdr1B were analysed in WT, PK null sensitive and PK null resistance strains. The difference in the genes’ expression with and without itraconazole was investigated in triplicates using quantitative real-time RT-PCR. Changing the cycle threshold values was used to calculate the fold change in expression of each target gene with and without drug in reference to the control strain. QPCR was performed in a 7500 Fast Real-Time PCR System (Applied Biosystems) using the SensiFASTTM SYPER® No-Rox One-Step RT-PCR kit with SYBR Green. The primers

used for RT-qPCR analysis are listed in Appendix 13. Amplification reactions were performed in a final volume of 10 μl, and the amplification condition was as described in section 2.2.7.4.

2.6 Virulence studies using animal models to determine the virulence of PK knockouts library

Studying the virulence of the PK mutants was necessary, since one of the key factors for developing new antifungal drugs will be based on choosing the less virulent strain to start with. However, choosing the right inoculum level to start with is crucial to avoid high mortality rate and give enough time for the pooled PK mutants to be inside the host. This experiment was conducted once per each infection model employing 4 replicates per each group including the control groups, however for purpose of validation, it was conducted once using 3 replicates per each group including the control groups.

2.6.1 Using pooled PK mutants to infect the THP-1 cells

This experiment was conducted by Dr Sara Gago (MFIG), using the THP1 cell line, where 5 million THP1 cells were seeded in 4 replicates using 75-T flasks, and a 1:2000 dilution of PMA was added to the RPMI supplemented media (FBS + PS, PMA 0.5 µM final concentration) and incubated overnight at 37°C. On the following day, the media was replaced with fresh RPMI supplemented media and incubated further for 24 hr. After that, the media was replaced with 10 ml of RPMI media containing 1 million pooled PK mutants. Five µl of 2.5x108 of spores’ stock was added to 10 ml of supplemented RPMI and incubated for 16 hr. Cells were harvested and preceded with DNA extraction for NGS.

2.6.2 Studying the virulence of WT A1160+ and transposon mutant using larvae of G. mellonella

This study was conducted to find out whether the transposon mutant virulence is the same as that of WT A1160+. The larvae used in this study were ordered from live food stock, weighing 0.3–0.37 gm and distributed randomly between the groups. Four groups were used with 5 larvae for each in triplicates. Larvae were injected in the proleg area with 10 µl of 1x106 spores/ml from each corresponding isolate. Two control groups were used: a pierced group where a needle was used empty for injection in the proleg area to exclude any technical area that might be causing the death, while in the other group the larvae were injected with 10 µl of PBS only as a negative control. The survival rate was recorded for 7 days; statistical analysis was employed using GraphPad Prism7. 95

2.6.3 Infection study using pooled PK mutants to infect the larvae

Ten µl of pooled PK mutants at concentration of 1x106 and 5x106 spores/ml were used to infect the larvae in an attempt to identify virulent and avirulent strains. For each inoculum level, 4 replicates were used with 10 larvae in each. Two control groups were used in 4 replicates with 10 larvae in each: a pierced group where nothing was injected in the proleg area and a PBS group where 10 µl of PBS was injected in the proleg area. Survival rate was recorded for 5 days, where dead larvae were excluded from the study. On the fifth day, the larvae were snapped frozen in liquid nitrogen to proceed with DNA extraction for NGS using Ion PGM system.

2.6.4 Infection study using larvae to validate the NGS results

This experiment was conducted to validate the results obtained from the infection study described previously (section 2.6.3), to exclude the effect of the pool and to ensure that the PK mutants would behave similarly when used individually. Here, 10 µl from the strains of interest (both virulent and avirulent) at concentration of 106 spores/ml were used alongside the transposon mutant (which was used as a control in the previous experiment) to show the difference. Ten larvae per strain were used in triplicates. A negative control was conducted using PBS for injection in the proleg area. The survival rate was monitored for 7 days.

2.6.5 Infection of mice with pooled PK mutants Forty leukopenic mice (CD1 male, weighing 23–24 gm) were used in this experiment. Upon arrival, they were left for one week for adaptation and to overcome any stress related to transportation. This experiment conducted by Lea Gregson (MFIG infection technician).

After the adaptation period, the mice were weighed, and immunosuppression started three days before the infection. Cyclophosphamide (Baxter 2638B3847) was used for this purpose at a dose of 150 mg/kg and was administered intraperitoneally (IP) using a 27- gauge needle. This was performed one day prior to infection, and every third day until completion of the study. A single dose of cortisone-acetate (Sigma C3130) (25 mg/mL) was administered subcutaneously (SC) using a 23-gauge needle one day prior to infection.

The mice were given 40 l of spores’ suspension (pooled PK mutants of 18 strains, at concentration of 1.2x105 spores) intranasally following induction of a deep plane of anaesthesia by exposing the mice to 2–3% isoflurane (Sigma CDS019936) for 5–10 min. The mice were observed for anaesthetic effect when the pedal reflex was no longer present, 96

indicating the mice were ready for intranasal infection. The mice were observed twice a day at 9 am and 5 pm. Weight loss was recorded carefully during the infection period until the end point was reached (Please refer to Table 2.5 for the expected symptoms).

Table 2.5: Endpoints in the infection study and the expected symptoms to be observed.

FEATURE EXPERIMENTAL

Body weight (EP1) Weight loss 20% weight loss from date of infection Standalone end-point Posture Persistently hunched or onset of torticolis Appearance (EP2) Piloerection Poor grooming, marked staring coat

Intermittent abnormal pattern (systemic infection) Clinical Signs (EP3) Respiration Noisy laboured breathing with in-drawing of rib cage (pulmonary infection) Unprovoked behaviour Socialisation No peer interaction (in combination with other signs) (EP4) Response to stimulus Provoked Subdued response with physical stimulation (in (EP5) behaviour combination with other signs)

At the end of experiments, the sick mice were culled by administering Pentobarbitone (Vet drug Pentoject; 0.1 ml/mouse IP) intraperitoneally to collect the infected lung. The collected lungs were stored at -80°C until required for DNA extraction. Subsequent amplification of PK mutant’s specific barcodes was employed as described in section 2.4.2 and followed by purification as described in section 2.4.3. NGS was performed using Illumina MiSeq using the Sequencing Facility at the University of Manchester.

Chapter 3 : Analysis of the Kinome of A. fumigatus Reveals Potential Antifungal Drug Targets

3.1 Introduction

In eukaryotes, protein kinases with the exception of the histidine kinases are primarily distributed into two large groups: conventional protein kinases (ePKs), constituting the largest group; and atypical protein kinases (aPKs), which although lacking sequences that define ePKs are still biologically functional (Hanks and Hunter, 1995; Manning et al., 2002). The ePKs include the AGC, CMGS, CAMC, CK1, OTHER, STE, TK, TKL, and RGC groups, which are allocated by their sequence identity to core members of the group.

Recent advances in sequencing technologies have enabled increases in the number of sequenced fungal genomes (Xu et al., 2006; Wang et al., 2011), resulting in an expansion of the primary classification of kinases (Hanks and Hunter, 1995). The kinome of the filamentous fungus Aspergillus nidulans, a model system for the Aspergilli, has been characterised using the Kinomer bioinformatic search algorithm (De Souza et al., 2013) in order to identify and study the functional role of 128 protein kinases.

A novel subset of fungal-specific linage protein kinases (FslK) identified by Zhao et al., (2014) in Agaricomycetes. These kinases are closely related to tyrosine kinases (TKs), but lack the key amino acid residues that characterise TK (Zhao et al., 2014). Similarly, in A. nidulans, 11 members of this filamentous fungal-specific protein kinase group were also identified (De Souza et al., 2013). These subsets of kinases have the potential to represent useful targets for antifungal therapy due to their lack of mammalian orthologues.

A comparative analysis of fungal protein kinases and associated domains previously conducted by Kosti et al., (2010) that included an annotation of the kinome of A. fumigatus revealed a number of interesting and somewhat unusual results. Notably, there were significant differences in the number and type of protein kinase genes between very closely related species of Aspergillus, including between A. fumigatus (n=85) and Neosartoria fisheri (n=170).

In an attempt to interrogate the results from previous studies and to provide a basis for our evaluation of protein kinases as drug targets, this chapter describes a comprehensive bioinformatic re-evaluation of the genomes of several Aspergillus species. Subsequent classification of the identified kinases was carried out employing the Kinomer v1.0 HMM 98

Library (Miranda-Saavedra and Barton, 2007), conducting an evaluation of the InterPro signatures (Hunter et al., 2009) and Pfam-based analysis (Finn et al., 2014). Significant differences between our data and that previously published by Kosti et al., (2010) were revealed.

3.2 Results

3.2.1 Data search to identify the protein kinome

While Kosti et all’s (2010) study indicated that 85 kinases were encoded within the A. fumigatus genome, information regarding the genes identified by the group was unfortunately not published in their manuscript. As a prelude to generating a kinome knockout library, we conducted our own assessment of the A. fumigatus genome following the pipeline outlined in Figure 3.1.

In order to identify all the predicted kinases in the Aspergillus species (A. fumigatus A1163, A. fumigatus AF293, A. nidulans, A. niger, A. oryzae, A. terreus, N. fischeri and A. flavus), the search string ‘kinase’ was applied to retrieve all previously annotated kinases from CADRE, AspGD, Ensemble Fungi and EBI, as described in section 2.1.1. In total, 293, 314, 260, 234, 292, 241, 255, 333 and 302 search results were returned for A. fumigatus A1163, A. fumigatus AF293, A. nidulans, A. niger, A. oryzae, A. terreus, N. fischeri, A. clavatus and A. flavus, respectively. Since this cohort of genes will be expected to include both protein kinases and small molecule kinases, each kinase was assessed further.

Identification of the protein kinases from these genes was achieved through analysing all of the putative kinases using Kinomer v.1.0 (Martin et al., 2008). Kinomer allows the identification of predicted protein kinases according to specific molecular motifs, as defined in the Kinomer HMM Library (Miranda-Saavedra and Barton, 2007). The output of Kinomer enables the classification of each kinase as either AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL, Alpha, PDHK, PIKK or RIO based on the highest observed E-value and threshold scores, as defined in the Materials and Methods chapter, section 2.1.2.

By employing the Kinomer v.1.0 Library for the kinases annotated in CADRE, AspGD, Ensemble Fungi and EBI, we were able to classify 115 predicted protein kinases in A. fumigatus A1163, 113 in A. fumigatus AF293, 118 in N. fischeri, 128 in A. niger and 114 in A. clavatus. Meanwhile, A. terreus appeared to have 106 predicted kinases, A. nidulans 104, A. oryzae 107, and A. flavus 105, as shown in Table 3.1. Kinases where no significant 99

assignation could be made were classified as non-classified kinases (Table 3.1), and subjected to further assessment, as seen in the schematic Figure 3.1.

Figure 3.1: Schematic flow chart describing the kinase identification and classification process.

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Table 3.1: The total number of protein kinases in Aspergillus spp classified using the Kinomer v.1.0. HMM Library and those where no match was found (non-classified kinases).

Number of predicted PK Number of non-classified Species kinases

Aspergillus fumigatus A1163 115 178

Aspergillus fumigatus AF293 113 201

Aspergillus terreus 106 135

Aspergillus nidulans 104 156

Aspergillus niger 128 106

Aspergillus oryza 107 185

Aspergillus clavatus 114 219

Aspergillus flavus 105 197

Neosartorya fischeri 118 137

Figure 3.2: The total predicted kinases in the Aspergillus species (A. fumigatus A1163, A. fumigatus AF293, A. nidulans, A. niger, A. oryzae, A. terreus, N. fischeri and A. flavus) using the Kinomer v.1.0 HMM Library, as well as Pfam and InterPro analyses.

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The Kinomer v.1.0 HMM Library software is somewhat limited, as it is unable to classify proteins with fewer than 180 amino acids; therefore, we interrogated those kinases without a specific assignation using the Pfam and InterPro database searches (Finn et al., 2014). InterPro offers a powerful classification in a single search engine as it combines protein information from collaborative databases, enabling the functional analysis of proteins by predicting domains and important sites. This analysis via the Pfam and InterPro searches resulted in an increased total number of kinases identified as PKs (Figure 3.2).

3.2.2 Classification of kinases revealing that the kinomes of filamentous fungi have members from all previously defined ePK families

The classification of the identified predicted protein kinases has based on the optimum match for each predicted kinase sequence with 180 or more residues (Miranda-Saavedra and Barton, 2007) using the Kinomer v.1.0 HMM Library (Martin et al., 2008). For those kinases with sequences less than 180 residues, the Pfam and InterPro protein families’ matches were selected. The distribution of the predicted protein kinases in the Aspergillus species for each PK group was as shown in Figure 3.3.

CMGC CAMK AGC N. fischeri STE A. clavatus RGC A. flavus TKL A. terreus TK A. oryzae CKI A. nidulans PIKK A. niger PDHK A. fumigatus Af 293 RIO A. fumigatus A1163 Histidine kinase PK domain

0 10 20 30 40 50 60

Figure 3.3: Distribution of the predicted protein kinases in the Aspergillus species based on the Kinomer v.1.0 HMM Library, Pfam and InterPro classification. The ePKs groups are shown at the top of this chart (CMGC-CK1), while the aPKs groups are shown below the dashed line. Histidine kinases were considered as a separate categorisation as they do not fit in either of these groups, while the PK domains refers to the non- classified kinases with no match from both analyses.

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The majority of the remaining kinases identified in our initial text search were classified as proteins with kinase-like domains belonging to the phosphotransferases family, while the others were small molecules that were classified as pyruvate kinase, adenylate kinase, glycerol kinase and other non-protein kinases.

3.2.3 Phylogenetic conservation within the kinome of A. fumigatus reveals a set of highly conserved filamentous fungal-specific kinases

To evaluate whether each kinase had been accurately allocated into each classification group, a phylogenetic tree was constructed, including only the predicted conventional kinases from A. fumigatus A1163. Multiple sequence alignments of this subset of the kinome were constructed by employing ClustalW (Thompson et al., 1994). An estimation for the optimum model for constructing the phylogenitc tree was determined using the ‘find best model’ algorithm in MEGA 6.06, while a phylogenetic tree employing this model was also constructed using the software (Sullivan, 2005; Cho, 2012).

As seen in Figure 3.4, the tree highlights that the ePKs are distributed over 6 distinct groups that matched the CMGC, AGC, CGC, TK, CAMK, and STE designations identified by Kinomer.

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Figure 3.4: Phylogenetic tree for the predicted conventional kinases in A. fumigatus A1163. The tree was constructed using MEGA 6.06, while the protein sequences were aligned using ClustalW. The main kinase groups are highlighted in different colours.

Our analysis revealed that each kinase had to be accurately grouped, with the exception of a group of 16 protein kinases that clustered together (highlighted in red in Figure 3.4) and were phylogenetically distinct from the other kinase classification groups. We hypothesised they were members of the filamentous fungal kinase group (Ffks) defined by De Souza et al (2013). In order to assess whether this was indeed the case, reciprocal BLAST analysis was performed to identify the orthologues of the putative Ffks from A. fumigatus in A. nidulans. In each case, the reciprocal BLAST hit for this new group of kinases identified in A. fumigatus matched a Ffks from A. nidulans (Table 3.2).

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Table 3.2: The ID% of protein blast results of A. nidulans Ffks against A. fumigatus A1163 protein kinase in order to identify the orthologues, with the results obtained from performing the analysis via EnsemblFungi.

ID%A.fimugatus A. fumigatus A1163 A. nidulans Ffks orthologues best Reciprocal blast orthologues’ best hits A. nidulans hits hit? ANIA_07986(FfkA) AFUB_095050 56 ANIA_07986 Yes ANIA_10819(FfkB) AFUB_045710 61 ANIA_10819 Yes ANIA_02373(FfkC) AFUB_081770 44.4 ANIA_03775 No

ANIA_01789(FfkD) AFUB_030290 80 ANIA_01789 Yes ANIA_10869(FfkE) AFUB_030290 56 ANIA_10869 Yes ANIA_04196(FfkF) AFUB_006780 55 ANIA_04238 No ANIA_06192(FfkG) AFUB_027480 84 ANIA_06192 Yes ANIA_05511(FfkH) AFUB_089280 43 ANIA_04279 No

ANIA_06768(FfkI) AFUB_030290 35 AN6959 No ANIA_06758(FfkJ) AFUB_093160 52 ANIA_06508 No ANIA_09302(FfkK) AFUB_041010 61 ANIA_02927 No

To further assess the interrelatedness of each of the Ffks, phylogenetic analysis incorporating the predicted Ffks in A. fumigatus A1163 and A. nidulans was performed, and a phylogenetic tree was constructed (Figure 3.5).

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Figure 3.5: Phylogenetic tree for the predicted Ffks in A. fumigatus A1163 and A. nidulans. The tree was constructed using MEGA 6.06; with the protein sequences aligned using ClustalW. It shows the presence of closely related kinases and the divergent ones. The scale number (1) refers to the branch length.

This analysis revealed that the majority of the A. fumigatus Ffks had orthologues in A. nidulans; however, a number of key differences were identified. Specifically, AFUB_045710 is closely related with ANIA_10819, AFUB_030290 is closely related with ANIA10869 and ANIA01789, AUB_044400 is closely related with ANIA04196, and AFUB_027480 is closely related with ANIA06192. However, this phylogenetic analysis revealed that other kinases that were sub clustered within this group might also be an Ffks.

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3.2.4 Comparative analysis of the A. fumigatus and human kinomes reveals a group of CMGC kinases with the potential to be drug targets

To consider any fungal protein as a potential antifungal target, it is necessary for it to be significantly different from any human orthologue. The kinomes of A. fumigatus and humans were therefore compared through evolutionary phylogenetic analysis in order to identify the genes of interest. Human protein kinases sequences were extracted from KinBase (http://kinase.com/kinbase). The protein sequences were aligned using ClustalW, and a phylogenetic tree of A. fumigatus (A1163) and human kinases was constructed using T-REX (www.trex.uqam.ca).

Although it is not possible to display the entire phylogenetic tree in this thesis, a subset of the CMGC kinases is presented in Figure 3.6 as an exemplar. In this example there is a clear expansion of the orthologous of the human Hsap type CMGC kinases in A. fumigatus, where the functional significance of these kinases will be explored in Chapter 4.

Figure 3.6: Phylogenetic tree of A. fumigatus A1163 and human kinases constructed using T-REX, showing the expansion of the group of kinases in A. fumigatus A1163 compared to human kinases. The test UPGMA and test minimum evolutionary trees were constructed using MEGA 6.06, while the protein sequences were aligned using ClustalW. The scale number (1) refers to the branch length. 107

3.2.5 Clustering and heat map analysis of protein kinases highlighting the potential antifungal targets in A. fumigatus A1163

Protein BLAST analysis was performed for each kinase identified in A. fumigatus in order to identify those kinases with low levels of sequence identity with human kinases. Comparisons were also made with the genomes of A. fumigatus AF293, N. fisheri, A. nidulans, A. flavus, A. oryzae, A. terreus, A. clavatus, N. crassa and S. cerevisiae to assess the useful spectrum of any target identified.

To enable rapid visualisation of the protein identity data, a heat map showing percentage identity was generated using Plotly (https://plot.ly/python/heatmaps/). The heatmap was ordered by relative sequence identity to the closest human kinase, as seen in Figure 3.7.

Figure 3.7: Heat map showing the percentage similarity between the kinases in A. fumigatus A1163 and other Aspergillus species, as well as N. crassa, S. cerevisiae and human. The heat map was constructed using Plotly. The identities were coded by increasing the colour saturation, with bright red denoting the highest degree of similarity. 108

The kinase cluster and heat map analysis in Figure 3.7 revealed the presence of several kinases that are highly conserved in the aspergilli (>80% sequence identity) and are significantly different to human kinases (<50% sequence identity) (Table 3.3). The impact of disrupting the function of the kinases highlighted in Table 3.3 amongst other kinases in the A. fumigatus genome will be assessed in the chapters that follow.

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Table 3.3: Kinases highly conserved in the aspergilli and significantly different to human kinases.

Gene ID A.fumigatus Af 293 N. fischeri A. clavatus A. flavus A. nidulans A. niger A. oryza A. terreus N. crassa S. cerevisiae Human AFUB_010510 100 100 98 92 93 94 92 100 94 64 40 AFUB_089280 100 97 91 91 84 86 88 90 80 75 39 AFUB_059090 100 99 98 96 96 96 96 97 91 80 39 AFUB_010360 100 100 97 93 92 89 93 93 86 71 38 AFUB_059390 100 98 96 91 100 85 91 95 78 70 38 AFUB_077210 100 96 94 94 98 92 56 96 82 71 37 AFUB_054310 100 99 91 87 91 81 87 82 71 75 37 AFUB_038630 100 98 94 88 94 93 89 85 70 73 36 AFUB_030570 100 96 91 86 91 83 86 82 69 59 35 AFUB_029820 100 97 82 83 85 82 83 86 71 71 35 AFUB_071600 81 86 81 89 65 86 90 84 56 56 35 AFUB_054020 100 95 84 91 91 88 91 91 70 55 34 AFUB_041920 100 98 92 88 83 89 88 91 60 52 33 AFUB_053500 100 100 94 98 95 98 94 100 87 76 32 AFUB_036630 100 99 95 95 95 94 94 95 66 55 32 AFUB_066030 100 99 94 85 83 85 85 85 67 60 30 AFUB_027480 100 98 95 85 84 85 85 89 71 1 30 AFUB_020650 100 100 99 98 97 98 98 100 88 62 26 AFUB_036640 100 100 96 95 93 96 95 90 65 62 25 AFUB_064490 100 96 83 86 84 88 86 83 58 47 1 AFUB_001940 100 94 96 85 87 92 85 94 55 52 1

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3.3 Discussion

Protein kinases play an important role in all living cells. Reversible phosphorylation, in which kinases transmit the γ-phosphate of ATP to hydroxyl groups of different substrates such as sugar, amino acids or lipids, play a vital role in cellular signalling pathways, cell cycle, growth and apoptosis. Specifically, the phosphorylation of serine, threonine, or tyrosine moieties on target proteins, catalysed by protein kinases, leads to a conformational shift that alters the activity of the substrate protein (Cohen, 2000). Abnormal phosphorylation has been associated with many diseases, mostly tumours (Ardito et al., 2017).

By employing our protein kinase discovery pipeline (Figure 3.1) we have been able to identify 175 predicted kinase in A. fumigatus A1163, 162 predicted kinases in A. fumigatus 293, 143 in N. fischeri, 171 in A. niger and 150 in A. clavatus, while A. terreus appeared to have 146, A. nidulans 142, A. oryzae 154, and A. flavus 145, respectively. This finding reveals that Aspergillus species have similar numbers and distribution of protein kinases.

These finding is in disagreement with a previous study where Kosti et al., (2010) showed that A. clavatus, A. niger, and N. fischeri appear to have double the number of kinases when compared to A. fumigatus, for which they only identified 85 protein kinases. This likely reflects differences in the pipeline that we used to identify our collection of protein kinases. Specifically, we used slightly different threshold values in determining protein kinase identity, and in addition to the output of kinomer we employed Pfam and InterPro to expand the number of the identified predicted kinases significantly in all Aspergillus species.

The predicted kinases, which identified in this study were classified into 11 different kinase groups by HMMer using the Kinomer v.1.0 HMM Library. The most predominant groups were CMGC, STE, AGC and CAMK. The minority of kinases were distributed over PIKK, PDHK, CK1, and TK; whereas the TKL representatives were present only in A. niger, A. terreus, A. flavus and N. fischeri. Unlike previous studies we identified 2 RGC kinases, one in each of the genomes of A. nidulans and A. terreus (Figure 3.3). According to the results of Kosti et al., (2010), applying the Kinomer HMM Library did not retrieve any RGC representative in fungal species when they conducted their study, and this study’s findings might have benefitted from updates to the genome library.

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In this study, the predicted conventional kinases were clearly clustered into seven different groups in the phylogenetic tree. These groups are CAMK, AGC, STE, CMGC, CK1, TK and the predicted Ffks.

In the TK group there were two predicted kinases being classified by the Kinomer v.1.0 HMM Library according to the sequence similarity and were clustered together alongside the other identified kinases with protein kinase domain. These kinases are more likely to be TKL, as the fungi appeared to feature all the different classes of kinases except TKs (Miranda and Barton, 2007), and possess tyrosine kinase-like kinases (TKLs) (Kosti et al., 2010), a kinases group that shares marked sequence resemblance with tyrosine kinases, but functions principally as serine-threonine kinases. Furthermore, a recent study by Zhao et al., (2014) has revealed that while fungi miss animal tyrosine kinases orthologs, they possess a precise protein kinase lineage, which is strictly associated to tyrosine kinases (Zhao et al., 2014).

The importance of tyrosine kinases as vital components of cellular signalling pathways (Lemmon and Schlessinger, 2010; Pathi et al., 2016) highlights their importance as a prospective antifungal target, which might overcome the problems associated with current antifungal drugs, most notably resistance, drug–drug interaction and the toxicity associated with some of the currently available drug.

The fungal cell wall, which is essential in fungi, prevents the cell from lysing and protects the fungus against environmental stress conditions (Valiante et al., 2015). Unlike fungi, human lack the cell wall, thus the fungal cell wall is a valuable antifungal drugs target, which heightens species specificity and reduces the likelihood of undesirable side effects.

A. fumigatus appeared to have four MAP kinases (May et al., 2005). In eukaryotes, celluar responses to environmental changes are regulated by MAP kinases, which constitute the last protein kinase in a kinase cascade. MAPK cascades typically include three kinases acting sequently. These are named MAPKKK, MAPKK and a MAPK (May et al., 2005). Our analysis of A. fumigatus A1163 highlighted these previously identified kinases among the current predicted kinases. In fungal pathogens, MAPKs are of exceptional importance due to their prospective influence on pathogenicity, and have been studied extensively as potential antifungal drug targets (Bruneau et al., 2001).

A recent study by De Souza et al., (2013) revealed the presence of 11 Filamentous fungal kinases (Ffks) in A. nidulans, with the majority of these kinases having no orthologs in

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Aspergillus species. Interestingly, A. fumigatus (A1163) appeared to have 15 Ffks that cluster as a single group in our phylogenetic analysis (Figure 3.4). At least one of these kinases, encoded by AFUB_024580, is conserved across a broad range of fungi, sharing more than 85% amino acid sequence identity with Aspergillus species, 70% with N. crassa and with no significant similarity in S. cerevisiae or H. sapiens. Other highly conserved kinases were identified in the genome of A. fumigatus that share less than 50% similarity with H. sapiens. They represent prospective antifungal targets and further detailed studies are carried out on the role of these genes in the upcoming sections.

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Chapter 4 : Generation of a Library of Protein Kinase Null Mutants in A. fumigatus A1163

4.1 Introduction

Resistance to the currently available anti-fungal drugs, alongside the toxicity associated with certain classes of drugs, necessitates the exploration of novel pharmacologically effective antifungal drugs. Kinases and phosphatases have attracted significant attention as drug targets to treat human diseases, since protein phosphorylation and dephosphorylation by protein kinases (PKs) and phosphatases, respectively, are believed to impact all areas of cellular activity (Cohen, 2000), while and atypical levels of phosphorylation have been recognised as being responsible for a range of human diseases (Cohen, 2001).

PKs constitute one of the most important protein categories for pharmaceutical drug discovery, while a large number of PK-interacting agents have been discovered (Rask et al., 2014). The first selective kinase inhibitor to be developed was Imatinib, now marketed as Gleevec by Novartis International AG, which was authorised by the US Food and Drug Administration (FDA) in 2001 to treat multiple cancers. This drug specifically inhibits the active mutant tyrosine kinase, BCR-ABL, a hallmark oncogene specific for Philadelphia chromosome-positive chronic myeloid leukaemia (Ph+CML) (Capdeville et al., 2002). Although a number of highly active drugs have been successfully developed that inhibit the action of human kinases, validating their “druggability”, no work has been undertaken to comprehensively assess the viability of targeting kinases in fungal pathogens.

In order to determine the role of A. fumigatus PKs on pathogenicity, and hence to determine their potential suitability as drug targets, each of the PK genes identified by Kinomer (see Chapter 3) were disrupted via gene knockout. The current chapter describes the work undertaken to generate this library of knockout mutants, how each strain has been genetically barcoded to enable competitive fitness analysis (Robinson et al., 2013; Payen et al., 2016), and the process of validation. Through this process we have been able to isolate validated homokaryotic mutants for 65 kinase genes. The remaining genes represent a set of protein kinase encoding genes that maybe essential for the viability of A. fumigatus, and hence could be candidate drug targets.

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4.2 Results

4.2.1 A fusion PCR approach enabled successful amplification of 115 kinase knockout cassettes

To assess the role of PKs in A. fumigatus A1160+, an attempt was made to generate null mutants for all genes that encode PK, as defined in section 2.2.8. The 115 genes identified in the initial Kinomer search were chosen as the first candidates for gene knockout. A two- step fusion PCR approach was conducted to produce constructs comprising a selection cassette flanked by C. 1.2 kb regions immediately up and downstream of the gene of interest.

Oligonucleotide primers were designed to amplify a C. 1.2 kb region immediately (within 100 bp of the ATG) upstream of the coding sequence of the gene of interest (named P1 and P2), a C. 1.2 kb region downstream of the gene of interest (within 100 bp of the stop codon, and named P3 and P4) and a set of nested primers to facilitate fusion of the gene fragments, as shown in the schematic Figure 2.1. In order to allow fusion of the upstream and downstream fragments to a hygromycin resistance cassette, the primers P2 and P3 incorporated linkers homologous to the ends of the primers employed to amplify the hygromycin cassette. To permit the competitive fitness analysis of multiple strains, each knockout construct was barcoded, with the barcodes introduced on the primers utilised to amplify the hygromycin resistance cassettes as per section 2.2.6 (see Appendix 11).

These primers were designed employing an automated pipeline incorporating Primer 3, as described in the Methods chapter. The pipeline was successful in designing primers for 674/690 priming sites (115 knockout cassettes, each requiring 6 primers), representing a failure rate of 2.3%. The remaining 16 primers were designed manually.

At the first step, PCR reactions were performed to amplify the upstream flanking sequence of each gene using primers P1 and P2 (Appendix 9), and downstream flanking sequences using primers P3 and P4 (Appendix 9). The PCR reactions were successful in amplifying both flanks for 112 genes, representing a 97% success rate (see the exemplar data for 20 genes in Figure 4.1A). Amplification of the flanks for the 3 other genes was successful upon redesign of the primers, while amplification of the barcoded hygromycin marker cassettes was conducted as described in section 2.2.7.2, with a success rate of 100% (see examples in Figure 4.1B).

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Figure 4.1: A: Pooled PCR products of amplified upstream and downstream flanking sequence after purification, where the product size is ~1kb. B: Post purification PCR product of hygromycin marker cassettes, where the product size is 2878 bp. A1–B8 refer to the corresponding well number in a 96-well plate. (See Appendix 4, which is the key for the CADRE gene identifier.)

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4.2.2 Knockouts generated

Fusion of the upstream, downstream and hygromycin marker was successful for all knockout cassettes, as confirmed by the presence of a C. 4.8 kb amplicon post-PCR (see examples in Figure 4.2).

Figure 4.2: Agarose gel electrophoresis of fusion PCR products for 20 PK knockout cassettes. A 1kb DNA ladder was loaded on the gel alongside the sample to estimate the size of the product. A1–B8 refers to the corresponding well number in a 96-well plate. (Please refer to Appendix 4 for the gene ID.)

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4.2.3 Generation of a collection of validated kinase knockout mutants in A. fumigatus A1160+

The knockout cassettes generated in section 4.2.2 were used to transform A. fumigatus A1160+ protoplasts. Positive transformants (hygromycin resistant colonies) were identified after 3 days of incubation at 37°C (YPS media containing 200 mg/L hygromycin). An exemplar output for this transformation procedure is shown in Figure 4.3. Transformants colonies were identified for all 115-fusion cassettes. The number of transformants ranged from 9 for gene AFUB_035220 to 1 for 14 genes (Table 4.1).

When transforming A. fumigatus multi-nucleate protoplast, resistant colonies are typically heterokaryotic, where each cell harbours a wild type and transformed nucleus. In an attempt to purify the transformants, spores from each colony were streaked subsequently twice onto SAB media containing 200 μg/ml hygromycin B for selection (this allowed only hph-resistant colonies to grow) and incubated at 37°C overnight. We isolated growing colonies for 76 of the 115 gene knockouts attempted upon passage on selective media.

We were unable to obtain hygromycin resistant progeny upon subsequent passage of transformants for 39 knockout cassettes. This result is consistent with the disruption of genes that are essential for viability. This occurs as mono-nucleate spores derived from heterokaryon transformants either lacks the selectable marker, or a gene required for viability. To assess these genes in more detail, where possible at least three independent transformants from each knockout cassette were assessed for viability on selective media.

Of the 39-protein kinase encoding genes for which we were unable to obtain a knockout on our first attempt, we were able to obtain at least 3 independent transformants for 25, and 2 for 13. Only one was obtained for AFUB_002120. In all cases, transformants failed to propagate upon passage to selective media, indicating that the gene in question encodes a protein kinase that is essential for viability (examples for 10 of these are given in Figure 4.7). PCR validation for integration of the knockout cassette in the heterokaryon was attempted for these isolates. Amplification of both upstream and downstream flanking regions (using the strategy outlined in Figure 4.4) was successful for 21 of the 39 loci (Table 4.2).

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Figure 4.3: Hygromycin-resistant colonies obtained by transforming A. fumigatus A1160p+ after 3 days of incubation at 37°C on YPS/200 µg/ml hygromycin. A: Transformants obtained from positive control cassette (validated gene knockout cassette for AFUB_013810. B: No DNA negative control plate, C: Transformants obtained by transforming the knockout cassettes for AFUB_032300, D: Transformants obtained by transforming the knockout cassettes for AFUB_052450.

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Table 4.1: The number of transformants for each PK gene, obtained after transforming the PK fusion cassettes into A. fumigatus A1160+.

transformants transformants transformants transformants trans transformants

No of No of No of No of No of No of

formants Gene ID Gene ID Gene ID Gene ID Gene ID Gene ID

AFUB_045810 2 AFUB_041010 2 AFUB_016170 1 AFUB_015480 6 AFUB_030660 2 AFUB_090090 2

AFUB_072800 2 AFUB_043130 2 AFUB_048440 1 AFUB_026080 2 AFUB_052450 3 AFUB_078920 3

AFUB_021710 1 AFUB_010510 3 AFUB_013090 3 AFUB_029820 4 AFUB_007300 3 AFUB_078980 2

AFUB_053300 2 AFUB_029710 2 AFUB_019630 5 AFUB_027640 2 AFUB_014350 4 AFUB_053960 3

AFUB_029290 3 AFUB_018770 1 AFUB_006780 2 AFUB_038060 4 AFUB_035220 9 AFUB_070630 2

AFUB_012420 2 AFUB_020560 1 AFUB_053440 2 AFUB_001600 2 AFUB_019930 3 AFUB_096030 2

AFUB_032300 3 AFUB_029320 2 AFUB_039620 3 AFUB_053500 2 AFUB_017750 1 AFUB_096080 4

AFUB_026400 3 AFUB_025560 2 AFUB_038640 2 AFUB_018600 2 AFUB_011380 3 AFUB_090090 2

AFUB_044560 2 AFUB_038630 4 AFUB_045840 2 AFUB_023600 3 AFUB_066150 3 AFUB_006190 6

AFUB_043460 5 AFUB_051670 3 AFUB_030570 3 AFUB_054310 3 AFUB_007300 2 AFUB_089280 2

AFUB_028770 2 AFUB_023730 4 AFUB_053520 3 AFUB_098230 2 AFUB_060320 2 AFUB_050750 2

AFUB_036500 3 AFUB_027480 1 AFUB_039100 1 AFUB_099170 1 AFUB_081870 5 AFUB_068890 2

AFUB_079830 3 AFUB_072800 3 AFUB_087120 2 AFUB_055480 1 AFUB_052630 3 AFUB_072800 2

AFUB_072000 4 AFUB_054020 3 AFUB_074550 4 AFUB_099990 4 AFUB_095640 2 AFUB_059390 4

AFUB_029240 2 AFUB_059540 2 AFUB_053070 4 AFUB_063820 2 AFUB_010360 1 AFUB_081540 4

AFUB_002120 1 AFUB_059090 1 AFUB_051750 3 AFUB_077790 2 AFUB_044400 3 AFUB_072650 3

AFUB_027890 4 AFUB_095460 2 AFUB_071620 3 AFUB_066030 3 AFUB_073970 4 AFUB_071600 3

AFUB_051100 3 AFUB_079830 3 AFUB_056110 3 AFUB_095720 2 AFUB_078810 6 AFUB_075210 2

AFUB_056020 1 AFUB_077210 2 AFUB_082830 3 AFUB_054290 3 AFUB_093160 2

AFUB_089870 2 AFUB_075350 2 AFUB_082700 4 AFUB_056640 5 AFUB_089250 3

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4.2.4 Validation of protein kinase null mutants

Diagnostic PCR reactions were used to confirm integration of the 5’ and 3’ flanks of the knockout cassettes for the 76 mutants for whom we were able to isolate growing colonies upon passage on selective media. Two validation PCR reactions were attempted for this purpose. Amplification across the 5’ flanking region and hph marker cassette was conducted using P1 and hph_R, while amplification across the 3’ flanking fragment and hph marker cassette was conducted using P4 and hph_F, as shown in Figure 4.4. A further validation PCR reaction was performed to amplify across the entire knockout cassette using primers P1 to P4. This final validation procedure was employed to confirm strain purity.

Figure 4.4: Schematic representation of validation PCR conducted to validate the PK knockout mutants. Upstream and downstream flanking check was conducted using the corresponding primers. Strain purity with one amplicon was checked via P1–P4.

An example output from the verification of the 5’ and 3’ flanks is presented in Figure 4.5A and 4.5B, respectively, while the remaining validation data can be found in appendices 18, 19 and 20, respectively. Validated transformants were defined as those where the amplification was successful for both flanking regions, and a single amplified product of the expected size (C. 4.8 kb) was identified using primers P1–P4 (see appendices 21 and 22). For a summary of the validation results please refer to Figure 4.6 and Appendix 23. In total, 65 of the 76 gene knockout mutants were confirmed using this process.

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Figure 4.5: Validation of gene knockout by PCR for 12 PK mutants. A: Amplification across the 3’ flanking region and selective marker gene (hph_F_ + P4). B: Amplification across the 5’ flanking region and selective marker gene (hph_R + P1), with a product size of ~3.7kb. C: A negative control was conducted using d H2O (carried out for both PCR reactions, with an example given here for hph_R+P1), where no products were amplified excluding presence of contamination.

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Figure 4.6: Schematic presentation summarising the generation of the PK knockouts in A. fumigatus A1163.

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4.2.5 Comparative analysis of protein kinases essential for viability in A. fumigatus A1163 and other fungi

To assess if the essential protein kinases identified in this study are also required for viability in other fungi, putative orthologs were identified in A. nidulans and S. cerevisiae using BLAST (Table 4.2).

Of the 39 putative essential kinases we identified in A. fumigatus A1163, 22 are essential in A. nidulans (De Souza et al., 2013), whereas 18 are essential in S. cerevisiae (SGD). It is noteworthy that 3 genes are essential in A. fumigatus A1163 and S. cerevisiae, but not in A. nidulans.

In total, 25 essential kinases have been identified in A. nidulans. The 3 essential kinases from A. nidulans that were not identified in our screen were not in the original gene set identified by Kinomer and hence knockout was not attempted for these genes. Interestingly, 13 of the genes that we have identified as being essential for viability in A. fumigatus A1163 are dispensable in both S. cerevisiae and A. nidulans.

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Figure 4.7: Growth of three independent transformants for 10 putative essential PK genes following incubation at 37°C for 48 hr. A: Growth after streaking on non-selective media (SAB only). B: No growth observed after streaking on selective media (SAB/Hygromycin).

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Table 4.2: The putative essential PK knockouts genes in A. fumigatus A1163 and their orthologs in A. nidulans and S. cerevisiae. The phenotype of A. nidulans genes was obtained from the Aspergillus Genome Database (AspGD), and for S. cerevisiae from the Saccharomyces Genome Database (SGD). Those genes highlighted in grey are not essential in either S. cerevisiae or A. nidulans.

Large-scale S. Large-scale Survey Number of transformants that failed to Putative function A. fumigatus A. nidulans A1163 survey null cerevisiae null propagate on selective media AFUB_089250 ANIA_00576 Inviable KIN28 Inviable 3 Serine/threonine protein kinase (Kin28)

AFUB_038640 ANIA_04936 Inviable YAK1 Viable 3 Serine/threonine protein kinase (Prp4),

AFUB_023600 ANIA_05815 Inviable IPL1 Inviable 3 Serine/threonine protein kinase (Ark1). AFUB_043460 ANIA_03450 Inviable CDC7 Inviable 5 Meiosis induction protein kinase (Ime2).

AFUB_059540 ANIA_00106 Inviable PKC1 Inviable 2 (1x heterokaryon was validated by PCR). pkcA Protein kinase C

AFUB_019630 ANIA_04563 Inviable HRR25 Inviable 5 Casein kinase I, putative AFUB_063820 ANIA_04385 Inviable CDC15 Inviable 2 (1x heterokaryon was validated by PCR). Serine-threonine kinase SepH

AFUB_053070 ANIA_08865 Inviable/ Viable SGV1 Inviable 3 Cyclin-dependent protein kinase Sgv1. AFUB_029290 ANIA_06339 Inviable KIC1 Inviable 3 Serine/threonin protein kinase.

AFUB_077210 ANIA_00576 Inviable VPS15 Viable 2 (1x heterokaryon was validated by PCR). Protein kinase (VPS15), putative

AFUB_002120 ANIA_00235 Inviable IRE1 Viable 1 (1x heterokaryon was validated by PCR). Protein kinase and ribonuclease Ire1. AFUB_082830 ANIA_10193 NA ENV7 Viable 3 Serine/threonine protein kinase.

AFUB_015480 ANIA_00931 Viable PBS2 Viable 3 MAP kinase kinase (Pbs2), putative AFUB_051670 ANIA_08190 Viable CTK1 Viable / inviable 3 (1x heterokaryon was validated by PCR). Protein kinase, putative

AFUB_054290 ANIA_10895 Viable SKY1 Viable 3 Protein kinase, putative Ortholog(s) have histone acetyltransferase AFUB_051100 ANIA_12431 Viable TRA1 Inviable 3 (1x heterokaryon was validated by PCR). activity AFUB_077790 ANIA_10895 Viable SKY1 Viable 2 (1x heterokaryon was validated by PCR). Protein kinase, putative

AFUB_081870 ANIA_01560 Inviable/ Viable CDC5 Inviable 3 Serine/threonine-protein kinase

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AFUB_079830 ANIA_10082 Viable SKY1 Viable 3 Putative protein kinase

AFUB_072800 ANIA_05757 Viable YCK2 Viable 3 Casein kinase I homolog, putative

AFUB_054310 ANIA_02246 Viable GCN2 Viable 3 CpcC, Protein kinase (Gcn2) putative

AFUB_071600 ANIA_10082 Viable SKY1 Viable 3 Serine/threonine-protein kinase

AFUB_050750 ANIA_11032 Viable KIC1 Viable 2 (1x heterokaryon was validated by PCR). Mst3-like protein kinase, putative

AFUB_095460 ANIA_08751 Viable DBF20 Viable/inviable 2 (1x heterokaryon was validated by PCR). Serine/threonine protein kinase.

AFUB_089870 ANIA_05674 Viable KIC1 Viable 3 Ste20-like serine/threonine AFUB_026400 ANIA_05973 Inviable YPK1 Viable 3 (1x heterokaryon was validated by PCR). Serine/threonine protein kinase (YPK1).

AFUB_041010 ANIA_02927 Inviable MPS1 Inviable 2 (1x heterokaryon was validated by PCR). Checkpoint protein kinase, putative

AFUB_068890 ANIA_05529 Inviable CBK1 Inviable 2 (1x heterokaryon was validated by PCR). Putative serine/threonine kinase AFUB_095640 ANIA_09504 Inviable KIN3 Viable 3 G2-specific protein kinase NimA.

AFUB_029710 ANIA_06363 Inviable RIO1 Inviable 2 (1x heterokaryon was validated by PCR). Serine/threonine-protein kinase RIO

AFUB_013090 ANIA_01177 NA SEC26 Inviable 3 Cell division protein kinase (Ctk1).

AFUB_026080 ANIA_05982 Inviable TOR2 Inviable 2 (1x heterokaryon was validated by PCR). Serine/threonine-PK, TOR.

AFUB_036500 ANIA_03110 Inviable PKH2 Viable 3 (1x heterokaryon was validated by PCR). Serine/threonine protein kinase.

AFUB_023730 ANIA_05822 Inviable SWE1 Viable 4 (1x heterokaryon was validated by PCR). Protein kinase, putative

AFUB_052630 ANIA_08261 Viable PHO85 Viable 2 (1x heterokaryon was validated by PCR). Cyclin-dependent PK, PhoA AFUB_082700 ANIA_01485 Inviable CKA2 Viable 4 (1x heterokaryon was validated by PCR). Casein kinase, putative

AFUB_073970 ANIA_04182(nimX) Inviable CDC28 Inviable 4 (1x heterokaryon was validated by PCR). Cell division control protein kinase.

AFUB_093160 ANIA_06508 Viable RIM11 Viable 2 (1x heterokaryon was validated by PCR). Glycogen synthase kinase (Skp1). AFUB_053440 ANIA_08836 Viable CLA4 Viable 2 (1x heterokaryon was validated by PCR). Protein kinase (Chm1), putative

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Essential kinases that have low sequence similarity (<40%) with human PKs are clearly of interest (Table 4.3).

Table 4.3: The ID% between the putative essential PK in A. fumigatus A1163 and human PK. These percentages were obtained by employing protein blast using the National Centre of Biotechnology Information (NCBI) website.

A. fumigatus A1163 ID% A. fumigatus A1163 ID%

AFUB_077790 31 AFUB_068890 50

AFUB_023730 34 AFUB_026400 53

AFUB_071600 35 AFUB_043460 54

AFUB_054290 35 AFUB_010390 55

AFUB_079830 36 AFUB_029710 55

AFUB_054310 37 AFUB_059540 55

AFUB_077210 37 AFUB_036500 56

AFUB_051100 38 AFUB_038640 57

AFUB_029290 38 AFUB_052630 60

AFUB_002120 41 AFUB_023600 60

AFUB_095460 41 AFUB_053440 62

AFUB_041010 43 AFUB_073970 65

AFUB_082830 45 AFUB_072800 66

AFUB_063820 46 AFUB_050750 67

AFUB_095640 46 AFUB_089870 68

AFUB_089250 46 AFUB_026080 70

AFUB_081870 47 AFUB_093160 71

AFUB_051670 48 AFUB_082700 72

AFUB_015480 48 AFUB_019630 79

AFUB_053070 49

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4.3 Discussion

Antifungal drug innovation has exploited the substantial progress that has unfolded in the genomics field over the last years. Since classical medication studies on current targets have yet to provide the desired powerful therapeutic effect, significant progress will be achieved by employing original genetics and genomics-centred approaches in order to facilitate discovery of new antifungal medications.

The existing pattern in antifungal drug targeting detection essentially focuses on the identification of those genes essential for viability and those that are involved in pathogenicity (De Backer and Van Dijck, 2003). Moreover, preferred targets should be conserved in a range of fungal pathogenic species such as Cryptococcus, Candida and Aspergillus.

The identification of essential genes in A. fumigatus is challenging, as many of the molecular genetic techniques employed to survey the genomes of other organisms are not readily transferrable to filamentous fungi (Ninomiya et al., 2004; Silva et al., 2006). There have been some limited examples where series of essential genes have been identified. Notably, Carr et al., (2010), employed an inducible transposable mutagenesis system to identifying 96 essential loci, of which two were putative noncoding RNAs (ncRNAs). Many of these genes are considered to be potential targets for valuable innovative antifungal medications.

In this study we aimed to generate a kinase knockout library in A. fumigatus A1163, which will subsequently be utilised in competitive fitness analysis in attempt to identify potential drug targets. An attempt was made to disrupt 115 protein kinases using a high-throughput gene knockout strategy. Each strain was barcoded to facilitate upcoming competitive fitness studies. PK knockouts were validated by PCR reactions, with the exception of the predicted essential genes. This study conducted between March 2015 and February 2016 including many repeats (at least 10 attempts) in an attempt to get knockout compromising the whole 115 PK genes. However, most these attempts were to obtain sufficient transformants for those genes with micro colonies or those where no transformation was obtained at all.

Thirty-nine putative essential genes were identified in the current collection due to the inability of their transformation colonies to grow on selective media (SAB / hph). Three independent transformants were considered where possible to identify the predicted essential genes. In most instances, the genes that were identified also appeared to be

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essential for viability in either one or both of A. nidulans and S. cerevisiae, as shown in Table 4.2. However, our study has identified 13 genes that appeared to be essential for viability in A. fumigatus when they were not in A. nidulans or S. cerevisiae.

In the current collection, 22 out of 25 essential genes that have been identified in A. nidulans by De Souza et al (2013), have orthologs in A. fumigatus 1163 PK, however the knockout was not attempted for remaining, as they did not identified by Kinomer. Despite the similarity between this finding and other group finding, we found that 2 of the identified orthologs appeared to be viable in this study, since these were involved in cell wall integrity pathway, and shown to be viable by Valiante (2009). These mutants lack AFUB_006190 (mkk2) and AFUB_038060 (bck1), respectively.

Of the 13 genes identified as being essential for viability in A. fumigatus A1163, but dispensible in S. cerevisiae and A. nidulans, 3 were homologues of S. cerevisiae SKY1. In S. cerevisiae, the Sky1 regulates proteins that involved in mRNA metabolism and cation homeostasis, while null mutant has a significantly decreased growth rate; small cell size and displays decreased competitive fitness on various growth media (Jorgensen et al., 2002). Despite an apparent duplication of this gene in A. fumigatus, it is surprising that these genes are not redundant with respect to each other. This is likely to mark an expansion in the importance of these kinases in the genome of A. fumigatus.

In S. cerevisiae, Gcn2 and Pbs2 mutants are an inositol’s auxotrophs (Villa-García et al., 2011). If these genes carry out similar functions in A. fumigatus, adding inositol to our growth media would likely reverse the apparent essential phenotype we observed for the knockouts of these genes. This experiment has yet to be completed.

Interestingly, the orthologue of AFUB_072800 in S. cerevisiae, Yck2 has been duplicated in S. cerevisiae. The Yck1/Yck2 double knockout is inviable (Sun et al., 2003) supporting our result. Interestingly the A. nidulans orthologue did not appear to be essential. Similarly, the orthologue of AFUB_093160 in S. cerevisiae, Rim11 has also been duplicated (giving Mrk1) in S. cerevisiae, and the Rim11/Mrk1 double knockout has been shown to have strong growth defect in S. cerevisiae, (Byrne and Wolfe, 2005).

The orthologue of AFUB_052630 in S. cerevisiae is PHO85. The Pho85 in S. cerevisiae shows a slow growth rate and reduced competitive fitness in range of different media, (Jorgensen et al., 2002). This suggests that role of Pho85 is more fundamental to growth in A. fumigatus than S. cerevisiae. CLA4 has a paralog (SKM1), which resulted from whole

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genome duplication. Growth defect was observed with the cla4 skm1 ste20 triple mutant, and with Kic1 null mutant, as well as with Env7 null mutants (Jin et al., 2007).

Two of the Ffks highlighted in the previous chapter are amongst the essential kinome and maybe interesting in developing novel antifungal agents, whereas, null mutants were generated for AFUB_027480, AFUB_011380 and AFUB_078980 respectively. These null mutants will be used in competitive fitness study.

PK genes that found to have low similarity with human kinase or lacking the conserved domain (i.e AFUB_044560, AFUB_045810, AFUB_071620, AFUB_096080, AFUB_053300, AFUB_077790, AFUB_095720 and AFUB_081540) will be a potential antifungal drug target, since the new drug will address the drug- drug interaction and minimise the likelihood of serious side effects. These PKs were an expansion of the CMGC group, with dual serine threonine activity as defined in previous chapter. These PKs will be a valuable antifungal drug targets, upon identification of their importance in A. fumigatus in subsequent studies.

Essential PK genes can be validated using conditional gene expression based on the utilization of regulatable promoters under environmental conditions that turn on and off gene expression. The tightly regulated promoter of the alcohol dehydrogenase I gene (alcAp) in Aspergillus nidulans permits easy confirmation through simple phenotypic analysis in defined media allowing induction or complete repression of a gene product (Felenbok et al., 2001; Romero et al., 2003). Here, using the promoter replacement technique, the expression of the essential gene will be controlled by the A.nidulans alcAp. If it is inviable, no growth will be expected on 3% glucose MM or YEPD medium, conditions that completely repress the expression of the A. nidulans alcA gene. Inducible promoters do not work effectively for all targets. Although regulation by alcAp is well controlled, it is also known to be somewhat leaky. If levels of expression of an essential gene are quite low, using the alcAp will not always completely down regulate the target sufficiently to abolish function (Romero et al., 2003)

The validated PK mutants will be utilised in competitive fitness study to investigate the role of PKs in A. fumigatus in response to different stress conditions, in response to the antifungal Itraconazole and for virulence.

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Chapter 5 : Functional Analysis of the A. fumigatus Kinome

5.1 Introduction

The previous chapter described the generation of a library of 65 protein kinase null mutants. Phenotypic characterisation of large collections of mutant isolates, such as the library generated in this research, is exceptionally time consuming and expensive. The evaluation of large libraries generated in yeast model organisms is typically performed using competitive fitness profiling methodologies, and recent evidence from our laboratory suggests that similar systems may be used to evaluate the relative fitness of large pools of A. fumigatus mutants (Macdonald et al., 2019). The published competitive fitness study carried out by members of our laboratory in A. fumigatus used a non-genetically barcoded library of mutants and required multiple processing steps to deliver a fitness output. As each strain in the library generated in this study incorporates a genetic barcode, evaluation of competitive fitness should be more straightforward. This chapter will describe the successful development of competitive fitness profiling methodologies using 65 non- essential PK knockout mutants. To facilitate this evaluation, the generation of a control isolate will be described, where we replaced a non-functional transposable element with a barcoded hygromycin cassette. It is predicted that this isolate will have a phenotype consistent with that of a wild-type isolate.

5.2 Results

5.2.1 Generation of a control isolate that should phenocopy the wild-type isolate for use in competitive fitness studies

As the wild-type isolate does not contain a genetic barcode, a control isolate was needed that would be able to act in a similar way to a genetically unmodified isolate. To generate this control isolate, a null mutant was generated by replacing a non-functional Aft4 transposon, with a barcoded hygromycin resistance marker in a similar way to that described for each of the PK null isolates. Primers were designed by Norman van Rhijn to amplify a knockout cassette spanning genomic region DS499601: 1809839-1813271, with the aim of removing the transposon sequence located at DS499601: 1810859-1812192. A knockout cassette was constructed as for the PK cassettes (Figure 5.1).

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Figure 5.1: Agarose gel electrophoresis of fusion PCR products for transposon knockout cassettes (6 replicates). A 1kb DNA ladder was loaded on the gel alongside the sample to estimate the size of the product.

Hygromycin-resistant isolates were detected three days post-transformation. No growth was observed for mock transformation reactions lacking the knockout cassette. Purification of the transformants was carried out as previously described. Validation of the transposon knockout mutants was conducted for 1 purified transformant using primers designed to amplify across the 5’ and 3’ boundaries of the knockout cassette (Figure 5.2.A), using primers P1 and hph_R (5’), and P4 and hph_F (Figure 5.2.B).

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Figure 5.2: A: schematic representation of PCR validation for the Aft4 knockout. B: Gel electrophoresis of PCR validation for both upstream flanking fragments (using P1 and hph-R, lanes 1 and 2), and downstream flanking fragment using P4 and hph-F (lanes 3 and 4). M refers to the 1kb DNA ladder used to estimate the product size.

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5.2.2 Checking the growth of the transposon mutant

Before employing the competitive fitness analysis using the PK knockouts library, it was crucial to check the growth of the transposon mutant to ensure it behaved like a WT A1160+ isolate. Growth of A1160+ and the transposon mutant (Δaft4) was assessed in triplicates on RPMI-1640 solid agar. Inoculum level of 103 spores was used, while the plates were incubated at 37°C, growth was assessed after 4 days by measuring the horizontal and vertical diameters, and the data were plotted using GraphPad Prism7 (Figure 5.3).

The results of this study revealed that there is no difference between the growth on RPMI- 1640 media of A1160+ isolate and the transposon mutant (P > 0.05) when statistical analysis was conducted using the student t-test.

Figure 5.3: Radial growth of wild type A1160P+ isolate and the Δ transposon after 4 days of incubation at 37˚C. It shows no significant difference in the growth between the two isolates. (P > 0.05 applying the student t-test).

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5.2.3 Optimisation of the PCR reaction to amplify the DNA barcodes from the knockout mutants

Amplification of the DNA barcodes was required to facilitate mutant identification in competitive fitness studies using a multiplex barcoding approach (Smith et al., 2010). Primers were designed to anneal at regions immediately flanking the barcode to enable the amplification of small fragments that could be sequenced on the ion PGM. The hphF primer, used in the knockout validation procedure, was used as the forward primer, while three reverse primers were designed to amplify fragments of 112 bp (R_1), 144 bp (R_2) and 181 bp (R_3), as shown in the schematic Figure 5.4A. Gradient PCR was conducted to optimise the annealing temperature for amplification using these primers (data not shown). The amplification from the DNA of one PK null mutant (AFUB_012420), using the optimum annealing temperature (55°C) for the 3 PCR reactions is shown in Figure 5.4B. Using these conditions, no amplification is seen when gDNA from the isotype strain A1160+ is used as a template. As the product for the primer set hph_F, hph_R2 seemed to amplify most efficiently; this primer set was taken forward for subsequent study.

Figure 5.4: A: Schematic presentation of strain-specific barcode amplification, where Hph-F was used in this amplification, plus hph-R. B: Gel electrophoresis of the DNA barcode amplification of the PK mutant DNA using three different sets of primers (labelled 1–3), where 1 refers to hph-F plus hph_R1, 2 refers to hph-F plus hph_R2, and 3 refers to hph-F plus hph_R3. WT A1160+ DNA was used as a control. M refers to a 100 bp DNA ladder.

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5.2.4 Competitive fitness analysis is highly reproducible in A. fumigatus

To assess whether competitive fitness profiling could be used to assess the fitness of the PK mutants generated in Chapter 4, it was necessary to assess the reproducibility of the barcoding amplification and counting procedure (see schematic Figure 5.5A). To explore this, DNA was extracted from a pool containing spores from all PK mutants’ 65 strains. The barcodes were amplified from 3 technical replicates (i.e. 3 different DNA extractions from the same pool; Figures 5.5B and 5.5C) and sequenced on the Ion PGM. Reproducibility assessed by comparing the number of barcodes counted from each replicate.

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Figure 5.5: A: Schematic representation of the competitive fitness profiling process, where BC refers to the barcode and CR refers to the common region in the amplified knockout cassette. B: Gel electrophoresis of amplified products from DNA extracted from the pooled PK mutants’ spores (treatment point zero) in triplicates (labelled 1 - 3). M refers to the 1kb DNA ladder used to estimate the product size. C: The PCR product in triplicates (labelled as 1–3) for T0 after purification: Negative control in triplicates (1–3) for PCR reaction using d H2O. .

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A total of 74,355, 82,622, and 63,273 reads were generated upon sequencing the 3 technical replicates, respectively. The reads were trimmed in silico to remove all sequences, with the exception of the genetic barcode. The barcodes were matched to the strain from which they were derived using Bowtie2 and counted using a bespoke Perl script (kindly written by Dr Jane Gilsenan). From this analysis 45,108, 48,649, and 20,999 reads were respectively mapped uniquely to a PK null strain. The counts for each strain were normalised to account for the differences in the total number of sequenced reads per condition sequencing reaction and compared for reproducibility. The replicates revealed high levels of reproducibility with the coefficient of determination (Pearson correlation coefficient) of 0.98 (replicate 1 vs replicate 2), 0.93 (replicate 3 vs replicate 2) and 0.87 (replicate 1 vs replicate 3) (Figure 5.6). The average coefficient of variation for all strains in the technical replicates was 0.016 and ranged from 0.006 to 0.083. This information provided confidence to assess the relative fitness of each strain when grown under culture conditions.

Figure 5.6: Pairwise comparison of two technical replicates (1 and 2) from the pooled PK mutants’ spores at T0. Each dot represents an individual PK mutant. R2 refers to the coefficient of determination.

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To evaluate the relative fitness of each strain, 100 μl of the pooled PK mutants at concentration of 5x108 spores/ml were inoculated into 4 separate tissue culture flasks containing 50 ml of fRPMI media. The DNA was extracted from fungal mycelia harvested after 20 hr (at 37°C with 200 rpm), and the barcodes were amplified, sequenced and quantified as previously described (please refer to Table 5.1 for total sequence reads and corresponding mapped reads). Again, the data from the replicates was highly reproducible with an average coefficient of determination between the samples of 0.85 and an average coefficient of 0.028.

The results of sequencing revealed some variability between the total reads and the mapped reads. This usually occur due to failure of reads to map unambiguously to known sequences as a result of setting a high threshold that been selected for filtering the reads. However, in most cases data were processed when achieved minimum 50% of mapped reads.

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Table 5.1: Total sequence of reads and total mapped reads obtained using Ion PGM sequencing machine for 4 biological replicates in corresponding to their culture condition.

Total sequence reads from IonPGM sequencing Total mapped reads after processing Condition Replicate 1 Replicate 2 Replicate 3 Replicate 4 Replicate 1 Replicate 2 Replicate 3 Replicate 4

37°C 107,848 237,280 106,659 86,805 94,265 185,741 75,337 64,732

48°C 80,511 86,594 78,495 105,746 52,272 50,866 52,724 52,520 30°C 82,854 75,049 69,980 115,189 50,734 43,985 32,371 49,288 1M sucrose 177,708 256,353 192,702 200,302 161,262 237,523 180,122 187,948

2mM H2O2 207,816 204,985 305,719 263,652 191,383 185,200 273,418 243,043 pH4 47,130 26,776 47,214 11,206 39,155 22,492 34,792 9,345 pH8 67,841 72,405 72,854 61,859 1,068 32,964 39,568 23,232 Fe - 218,086 217,384 247,668 154,129 201,785 199,494 224,030 142,258

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5.3 Competitive fitness analysis

For our first competitive fitness analysis we assessed the responses of the PK null strains at 37°C for 20 hr in fRPMI. The number of mapped reads (mr) for each mutant (Tabernero et al.,, 2013) was normalised to account for differences in the total number of reads (tr) obtained for each experimental replicate mri/tr. Replicates were averaged to provide the relative frequency of each null mutant in the pool (e.g. fq AFUB_006190fq, AFUB_038060fq, etc). To calculate the relative fitness of a mutant at 37°C in fRPMI and the relative frequency in the pool at 37°C, fq AFUB_006190 was divided by the relative frequency in the pool at the start of the experiment (t0; t0fqAFUB_006190). Log2 values were calculated and plotted using the GraphPad 7 software (Figure 5.7).

Figure 5.7: Scatter dot plot of log2 relative fitness of the PK knockouts library obtained from comparing their relative frequency at T0 to their relative frequency from growth at 37°C (fq37°C/fqT0). The aft4 transposon knockout mutant (ΔTransposon) is shown in yellow. The mean with SD is represented by a horizontal line highlighting the standard deviation in fitness for the whole pool (n=65). For the gene IDs, please refer to Table 5.2. 142

These results indicated that 8 mutants were less fit than the other strains in the pool (ΔAFUB_038060 (bck1), ΔAFUB_070630 (mpkA), ΔAFUB_006190 (Mkk2), ΔAFUB_027890, ΔAFUB_059390, ΔAFUB_066150, ΔAFUB_030660, and ΔAFUB_010360), whereas 2 strains appeared to be more fit (ΔAFUB_053520 and AFUB_096030). To apply more rigour to the analysis and to evaluate the statistical significance of the data, we processed the read numbers using Deseq2, which is frequently used to assess statistical significance in reads from RNAseq data. The relative fitness calculated by Deseq2 differed little from the previous calculations. In all cases, the differences in fitness were deemed to be statistically significant (Table 5.2).

The Deseq2 analysis (Table 5.2) highlighted PK mutants with increased and reduced fitness. The results showed that PK mutants lacking AFUB_096030, AFUB_053520, AFUB_055480, and AFUB_090090 have increased fitness at 37°C, whereas PK mutants lacking ΔAFUB_038060, ΔAFUB_070630, ΔAFUB_059390, ΔAFUB_066150, ΔAFUB_030660, ΔAFUB_010360, ΔAFUB_006190 and ΔAFUB_027890 have reduced fitness in competitive growth compared to the transposon control.

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Table 5.2: List of PK mutants identified with significant increase or reduced abundance from the competitive fitness analysis of the PK knockout library 20 hr post-inoculation on liquid fRPMI. Each strain here was allocated an ID number corresponding to its representation in Figure 5.7.

Relative log2 fitness Relative fitness as Benjamin-Hochberg ID number Strain ID: ∆ P value Description Common name ratio calculated by Deseq2 corrected P values Serine/threonine protein kinase, 1 AFUB_096030 2.699562975 2.721729702 6.08E-28 2.82E-27 Kcc4 putative Calcium/calmodulin dependent 2 AFUB_053520 2.229606808 2.220696958 1.14E-12 3.21E-12 protein kinase, putative Serine/threonine protein kinase, 3 AFUB_055480 1.834369 1.851680742 2.77E-42 2.19E-41 putative Serine/threonine protein kinase, 4 AFUB_090090 1.828308 1.839679407 5.73E-12 1.56E-11 putative MAP kinase kinase kinase, 5 AFUB_038060 -2.929002259 -2.861515524 1.94E-15 6.14E-15 Bck1 putative Mitogen-activated protein 6 AFUB_070630 -3.534810236 -3.418679209 9.82E-35 6.47E-34 MpkA kinase

7 AFUB_059390 -4.315416306 -4.107712106 5.92E-62 9.35E-61 Protein kinase, putative

Serine threonine protein kinase, 8 AFUB_066150 -4.66408982 -4.605552376 1.66E-82 4.38E-81 Atg1 putative Serine threonine protein kinase, 9 AFUB_030660 -5.364766628 -5.24133775 4.26E-34 2.59E-33 RIM15 putative MAP kinase kinase kinase, 10 AFUB_010360 -6.219389295 -5.548987685 3.60E-83 1.42E-81 putative SskB

MAP kinase kinase, putative 11 AFUB_006190 -7.519760681 -6.019463059 1.36E-29 7.19E-29 Mkk2

cAMP-dependent protein kinase 12 AFUB_027890 -6.761191147 -6.581743072 1.04E-61 1.37E-60 PkaC1 catalytic subunit

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To assess if the growth rates of these mutants differed in isolated culture conditions, 7 of the strains were grown independently on RPMI-1640 solid agar (n=7) and the radial growth assessed after 48 hr at 37°C. Significant differences in growth (P < 0.05) were observed for all tested mutants (AFUB_038060, ΔAFUB_070630, ΔAFUB_006190, ΔAFUB_027890, ΔAFUB_053520, AFUB_096030, and ΔAFUB_059390), Figure 5.8.

Figure 5.8: Radial growth of PK mutants after 48 hr on RPMI solid media. The Aft4 transposon mutant was included as a control. The error bars represent standard deviations; strains with growth statistically significant from the control are highlighted (one-way ANOVA-test ****P < 0.0001, **P < 0.01).

The growth of the PK mutants was also assessed on Aspergillus Complete agar and Vogel’s agar media (see Figure 5.9 and Figure 5.10, respectively). Clear phenotypes were observed for a number of isolates, notably the mutants that lacked the kinases involved in the cell wall integrity pathway (AFUB_070630 (mpkA), AFUB_0308060 (bck1) and AFUB_006190 (mkk2)). These results is consistent with previously reported data for these three isolates (Valiante et al., 2015).

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Figure 5.9: Growth of PK mutants on ACM solid media after 48 hr at 37°C. WT A1160+ was included as a control. 146

Figure 5.10: Growth of PK knockout mutants after 48 hr at 37°C on Vogel’s solid media. WT A1160+ was included as a control. 147

5.3.1 Checking WT response to oxidative stress using H2O2

Optimisation of some of the growth conditions that were assessed was not necessary as similar work had previously been performed within Dr Bromley’s group; however, the group had not previously assessed the response of A. fumigatus to H2O2. Optimisation was therefore necessary to check the response of our isogenic WT isolate to oxidative stress using different concentrations of H2O2 in RPMI1640 media for 20 hr. The growth of

A1160+ was restricted even with the lowest concentration of H2O2 assessed (14% reduction in dry weight at 1 mM H2O2 (P < 0.05), reaching 31% at 6 mM (P < 0.05)) (Figure 5.11).

Figure 5.11: Dried weight of fungal biomass in response to different concentrations of H2O2. The error bars represent the standard deviation, with significant differences (one-way ANOVA) from untreated cultures (**P < 0.01, ****P < 0.0001).

From this result, we decided to assess the impact of 2 mM H2O2 on the growth of our pooled isolates.

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5.3.2. Screening PK mutants’ response to different stress conditions

Growth of PK mutants (pooled PK knockout library) was assessed in response to iron starvation, 1M sucrose, Ph4, Ph8, 30°C and 48°C. The culture conditions for these analyses are given in the Materials and Methods chapter; however, all cultures were grown in fRPMI in 50 ml flasks with shaking.

To calculate the relative fitness of the mutant at each condition in fRPMI, the relative frequency of each strain in the pool at each condition (Fe -fqi AFUB_006190) was divided by the frequency in the pool at 37°C in fRPMI. Log2 values were calculated using the GraphPad 7 software and plotted as previously (Figure 5.12).

Figure 5.12: Log2 relative fitness of PK knockout mutants obtained from comparing their growth at 48°C, 30°C, 1M sucrose, without iron and with a final concentration of 2 mM H2O2, respectively, to their growth at 37°C. The cell wall integrity pathway mutants are denoted in green, blue, and red. The transposon control is highlighted in yellow for reference. A horizontal line represents the mean. Any points falling outside of the standard deviation range were identified as potential outliers. For the gene IDs, please refer to Tables 5.3, 5.4, 5.5 and 5.6 respectively. 149

Our competitive fitness analysis revealed some consistent and compelling results, particularly with reference to the cell wall integrity pathway mutants (Figure 5.12). Consistent with previously published data, strains lacking mpkA, bck1 and mkk2 are temperature sensitive (i.e. they have reduced fitness at 48°C and increased fitness at 30°C, compared to that at 37°C) and have increased fitness in high osmotic conditions. Interestingly, our data suggest that these mutants also have increased fitness in low-iron- containing media. Although it has been shown that cell wall integrity pathway mutants have increased siderophore production, this could not be linked to any changes in growth rate in previous studies (Jain et al., 2011). This suggests that competitive fitness analysis may be more sensitive to standard growth rate analyses. As with the previous analysis, the data were also assessed using Deseq2 (Tables 5.3–5.9).

Six PK mutants were identified as outliers, with significant increased fitness at 48°C: ΔAFUB_010510, ΔAFUB_044400, ΔAFUB_021710, ΔAFUB_027890, ΔAFUB_027480, and ΔAFUB_059090. Also, 7 PK mutants, including the cell wall integrity pathway mutants, were identified as outliers, appearing with significant reduction in fitness (Table 5.3).

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Table 5.3: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI at 48°C for 20 hr. Each strain here was allocated an ID number corresponding to its representation in Figure 5.12.

Relative log2 Relative fitness as Benjamin-Hochberg Strain ID: ∆ P value Description Common name ID number fitness ratio calculated by Deseq2 corrected P values

1 AFUB_010510 1.340076 1.551758507 0.000281 0.001479 Predicted serine/threonine protein kinase Kin1

2 AFUB_044400 1.096638 1.369514234 4.13E-09 6.79E-08 Uncharacterised protein

3 AFUB_021710 1.161554 1.368850388 2.72E-05 0.000239187 Serine/threonine kinase Ste20 pakA cAMP-dependent protein kinase catalytic 4 AFUB_027890 1.220777 1.23812008 0.003563676 0.011214001 PkaC1 subunit 5 AFUB_027480 0.834082 1.133086758 0.000125073 0.000988078 Protein kinase-containing domain GIN4

6 AFUB_059090 0.688814 0.884450354 0.001087 0.004296 Serine/threonine protein kinase, putative Nrc-2

7 AFUB_019930 -1.64478 -1.313672372 2.10E-10 5.52E-09 Serine/threonine protein kinase, putative PSK1

8 AFUB_099990 -1.918310 -1.540298313 4.30E-09 6.79E-08 Serine protein kinase, putative Sky1

9 AFUB_038060 -2.003950 -1.46789 0.001035 0.004296 MAP kinase kinase kinase, putative Bck1

10 AFUB_071620 -2.249700 -1.7004334 0.000498 0.002457 Hypothetical protein SKY1

11 AFUB_006190 -2.988920 -1.038801101 0.098248 0.17248 MAP kinase kinase, putative Mkk2

12 AFUB_070630 -3.096950 -1.87868 0.00119 0.004478 Mitogen-activated protein kinase MpkA Calcium/calmodulin dependent PK, 13 AFUB_053520 -3.320600 -2.742734016 1.19E-11 4.71E-10 TOS3 putative

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Five PK mutants, including the cell wall integrity pathway mutant, were identified with significant increase in fitness at 30°C (Table 5.4). Interestingly, Δsky1 appeared with increased fitness in contrast to its growth at 48°C. Significant increase in relative fitness was observed with ΔAFUB_059390. Two mutants, ΔAFUB_053520 and ΔAFUB_071620, which appeared with reduced fitness at 48°C, also showed significant reduction in relative fitness at 30°C. These findings suggest that these two mutants are temperature sensitive.

Table 5.4: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI at 30°C for 20 hr. Each strain here was allocated an ID number corresponding to its representation in Figure 5.12.

Relative log2 Relative fitness as calculated Benjamin-Hochberg ID number Strain ID: ∆ P value Description Common name fitness ratio by Deseq2 corrected P values

12 AFUB_070630 4.837451 4.519976246 2.05E-66 1.62E-64 Mitogen-activated protein kinase MpkA

9 AFUB_038060 3.68276 3.451886827 2.17E-33 5.72E-32 MAP kinase kinase kinase, putative Bck1

11 AFUB_006190 2.863271 2.246182583 1.17E-06 4.85E-06 MAP kinase kinase putative Mkk2

14 AFUB_059390 1.996539 1.895779951 8.76E-12 7.69E-11 Protein kinase, putative HRK1

8 AFUB_099990 1.406674 1.420387896 1.84E-20 3.64E-19 Serine protein kinase, putative Sky1

Calcium/calmodulin-dependent 13 AFUB_053520 -3.823800 -3.562711156 2.25E-42 8.90E-41 TOS3 protein kinase, putative

10 AFUB_071620 -1.564430 -1.469246746 2.53E-16 3.33E-15 Hypothetical protein SKY1

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Adding to the cell wall integrity pathway mutants, significant increase in the relative fitness of ΔpakA, ΔAFUB_044560 was observed in high osmotic conditions, while reduced fitness was observed with ΔAFUB_039100 and ΔAFUB_006780, as shown in Table 5.5.

Table 5.5: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI supplemented with 1M sucrose at 37°C for 20 hr. Each strain here was allocated an ID number corresponding to its representation in Figure 5.12.

Relative log2 Relative fitness as Benjamin-Hochberg ID number Strain ID: ∆ P value Description Common name fitness ratio calculated by Deseq2 corrected P values 12 AFUB_070630 5.231728 4.974209846 3.13E-61 2.47E-59 Mitogen-activated protein kinase MpkA

11 AFUB_006190 4.062235 3.435585409 8.55E-13 1.13E-11 MAP kinase kinase, putative Mkk2

9 AFUB_038060 2.460246 2.390826672 2.18E-13 4.25E-12 MAP kinase kinase kinase, putative Bck1

3 AFUB_021710 1.664336 1.668453527 1.50E-06 7.88E-06 Serine/threonine kinase Ste20 pakA

15 AFUB_044560 1.534446 1.590620395 5.95E-09 5.22E-08 Protein kinase, putative SKY1

Serine/threonine protein kinase, 16 AFUB_039100 -1.16911 -1.038928946 2.69E-13 4.25E-12 YPK3 putative cAMP-dependent protein kinase- 17 AFUB_006780 -1.36404 -1.241038929 1.76E-15 4.63E-14 schA like, putative

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In addition to the cell wall integrity pathway mutants, there were 7 mutants that appeared with increased siderophore production (Table 5.6). Among these, ΔpakA appeared with increased fitness at 48°C and in high osmotic condition (number 3 on the graph), ΔSKY1 appeared with increased fitness in high osmotic condition (number 15 on graph), and ΔPkaC1 and ΔNrc-2 appeared with increased fitness at 48°C (numbers 4 and 6 on the graph, respectively). Other mutants include ΔAFUB_030660, ΔAFUB_045810, and ΔAFUB_095720, respectively. Two PK mutants were identified with significant reduced fitness: ΔAHRK1, which appeared with increased fitness at 30°C, and ΔAFUB_099170 (yak1).

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Table 5.6: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI deficient with iron at 37°C for 20 hr. Each strain here was allocated an ID number corresponding to its representation in Figure 5.12.

Relative log2 Relative fitness as Benjamin-Hochberg ID number Strain ID: ∆ P value Description Common name fitness ratio calculated by Deseq2 corrected P values

11 AFUB_006190 2.474569 -2.599382525 0.000167068 0.001885483 MAP kinase kinase, putative Mkk2

15 AFUB_044560 1.840044 -1.930485978 3.84189E-06 0.00010117 Protein kinase, putative SKY1

9 AFUB_038060 1.694374 -1.772613887 1.19213E-06 4.70893E-05 MAP kinase kinase kinase, putative Bck1

3 AFUB_021710 1.643769 -1.729691322 0.001315874 0.010460508 Serine/threonine kinase Ste20 pakA

12 AFUB_070630 1.206975 -1.293453953 0.001000884 0.009883727 Mitogen-activated protein kinase MpkA

6 AFUB_059090 0.913584 -0.999312249 0.007287809 0.033866878 Serine/threonine PK, putative Nrc-2

18 AFUB_030660 1.356993 -1.507530344 0.014977665 0.053783433 Serine threonine PK, putative RIM15

19 AFUB_045810 1.294577 -1.318477165 0.014977665 0.053783433 Putative protein kinase SKY1

20 AFUB_095720 0.93111 -1.0228632 0.022248776 0.072123645 Protein kinase, putative SKY1

14 AFUB_059390 -1.33257 1.219369407 0.021642012 0.072123645 Protein kinase, putative HRK1

21 AFUB_099170 -1.93924 1.831069497 4.27848E-11 3.38E-09 Protein kinase, putative Yak1

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In response to oxidative stress, 7 mutants were identified with significant increase in fitness (Table 5.7). Among them was the cell wall integrity pathway mutant mkk2; however, ΔmpkA and Δbck1 appeared with reduced fitness, even though they did not fall as outliers. Also, ΔpakA (number 3 on the graph), which appeared with increased fitness in previous conditions (48°C, high osmotic condition and iron starvation) showed increased fitness here. ΔNrc-2 (number 6 on the graph) was identified with increased fitness here, as well as at 48°C and in response to iron starvation. This was also the case with ΔSKY1 (number 15 on the graph), which appeared with increased fitness at high osmotic condition and iron starvation. Moreover, ΔAFUB_044560 (number 2 on the graph) and ΔSky1 (number 20 on the graph) also appeared with increased fitness, as seen with 48°C and iron starvation, respectively. The ΔKcc4 mutant (number 22 on the graph) appeared with increased fitness in response to oxidative stress. Three PK mutants were identified with significant reduced fitness. Among them, ΔPSK1 and ΔSky1 (numbers 7 and 8 on the graph), which appeared with reduced fitness at 48°C and in an iron starvation environment, respectively. ΔHog1 (number 23 on the graph) was also identified with reduced fitness in oxidative stress.

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Table 5.7: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI supplemented with 2mM H2O2 at 37°C for 20 hr. Each strain here was allocated an ID number corresponding to its representation in Figure 5.12.

Relative log2 Relative fitness as Benjamin-Hochberg ID number Strain ID: ∆ P value Description Common name fitness ratio calculated by Deseq2 corrected P values

11 AFUB_006190 2.366852 -2.367581961 0.001574391 0.010233541 MAP kinase kinase, putative Mkk2

15 AFUB_044560 1.85728 -1.958774545 2.91991E-06 0.000227753 Protein kinase, putative SKY1

3 AFUB_021710 1.65427 -1.743470225 0.000749294 0.005313175 Serine/threonine kinase Ste20 pakA

Serine/threonine protein kinase, 6 AFUB_059090 1.187856 -1.278222653 0.000541239 0.005313175 Nrc-2 putative

22 AFUB_096030 1.15256 -1.250975827 1.49653E-05 0.000583645 Serine/threonine protein kinase Kcc4

20 AFUB_095720 0.917965 -1.018039588 0.015610672 0.081175495 Protein kinase, putative SKY1

2 AFUB_044400 0.823475 -0.939964192 7.69665E-05 0.001500847 Uncharacterised protein

8 AFUB_099990 -1.17865 1.086750968 0.000737348 0.005313175 Serine protein kinase, putative Sky1

-1.06071 7 AFUB_019930 0.971443761 0.001731016 0.010386099 Serine/threonine protein kinase PSK1

AFUB_012420 -1.06448 Mitogen-activated protein 23 0.962410381 0.000556234 0.005313175 Hog1 kinase

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The result of testing the response of the PK mutants’ response to two different pH level revealed high reproducibility, where most of the mutants showed a similar response at both levels (Figure 5.13, Table 5.8 and Table 5.9). Cell wall integrity pathway mutants appeared with reduced fitness at pH 8, while they showed a similar response at pH 4, but were not recognised as outliers as other mutants masked them. Some mutants appeared to be sensitive toward pH 8 (e.g. ΔPkaC1 and ΔHRK1), while Δyak1 (number 21 on the graph) appeared with reduced fitness at pH 8, in contrast to the response observed at pH 4.

Figure 5.13: Log2 relative fitness of PK knockout mutants obtained from comparing their growth at pH 8 and pH 4 to their growth at 37°C. The cell wall integrity pathway mutants are denoted in green, blue, and red. The transposon control is highlighted in yellow for reference. A horizontal line represents the mean. Any points falling outside of the standard deviation range were identified as potential outliers. For the gene IDs, please refer to Table 5.8 and Table 5.9. .

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Table 5.8: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI at pH8 at 37°C for 20 hr. Each strain here was allocated an ID number corresponding to its representation in Figure 5.13

Relative log2 Relative fitness as Benjamin-Hochberg ID number Strain ID: ∆ P value Description Common name fitness ratio calculated by Deseq2 corrected P values 15 AFUB_044560 2.18 2.176947905 3.00E-10 1.85E-09 Protein kinase, putative SKY1 24 AFUB_048440 1.99 2.125821288 9.59E-48 7.67E-46 Protein kinase, putative SKY1 25 AFUB_010360 1.34 1.308950363 0.003302101 0.007547659 MAP kinase kinase kinase, putative SskB 3 AFUB_021710 1.26 1.210443688 0.003129776 0.007364179 Serine/threonine kinase Ste20 pakA 18 AFUB_030660 1.20 1.516083713 0.007825617 0.016474983 Serine threonine protein kinase, putative RIM15 20 AFUB_095720 1.17 1.423357297 2.91E-05 0.000105737 Protein kinase, putative SKY1 22 AFUB_096030 1.17 1.203263638 1.06E-06 4.70E-06 Serine/threonine protein kinase Kcc4 2 AFUB_044400 0.93 0.932286028 0.000353031 0.001046019 Uncharacterised protein 11 AFUB_006190 -1.55 -1.673029863 0.065886785 0.11214772 MAP kinase kinase, putative Mkk2 27 AFUB_017750 -1.75 -1.633974984 1.01E-11 9.01E-11 Protein kinase, putative HOG1 21 AFUB_099170 -1.82 -1.535193502 2.62E-10 1.75E-09 Protein kinase, putative Yak1 26 AFUB_014350 -1.89 -1.256613475 1.98E-06 8.35E-06 Serine/threonine protein kinase, putative Kin4 28 AFUB_053300 -1.95 -2.092393625 6.37E-34 2.55E-32 Protein kinase, putative SKY1 7 AFUB_019930 -2.68 -2.224244488 8.22E-09 4.11E-08 Serine/threonine protein kinase, PSK1 8 AFUB_099990 -2.69 -1.959312754 1.95E-10 1.42E-09 Serine protein kinase, putative Sky1 29 AFUB_025560 -3.81 -2.346501792 2.56E-07 1.21E-06 Protein kinase, putative NpkA

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Table 5.9: List of PK mutants identified with significant increase or decrease in relative fitness (Deseq2 analysis) when grown on fRPMI at pH4 at 37°C for 20 hr. Each strain here was allocated an ID number corresponding to its representation in Figure 5.13.

Relative log2 Relative fitness as Benjamin-Hochberg ID number Strain ID: ∆ P value Description Common name fitness ratio calculated by Deseq2 corrected P values 15 AFUB_044560 2.85 3.12145176 4.63E-14 6.17E-13 Protein kinase, putative SKY1 3 AFUB_021710 2.45 2.777519802 2.38E-10 1.91E-09 Serine/threonine kinase Ste20 pakA 20 AFUB_095720 1.51 1.881002453 5.26E-07 2.21E-06 Protein kinase, putative SKY1 21 AFUB_099170 1.31 1.695256796 1.90E-25 1.52E-23 Protein kinase, putative Yak1 18 AFUB_030660 1.25 1.533055239 0.003829 0.00851 Serine threonine protein kinase, putative RIM15 6 AFUB_059090 1.24 1.617849819 4.39E-07 1.95E-06 Serine/threonine protein kinase, putative Nrc-2 22 AFUB_096030 1.20 1.59312369 3.13E-07 1.47E-06 Serine/threonine protein kinase Kcc4 8 AFUB_099990 -2.46 -2.025979103 1.89E-10 1.68E-09 Serine protein kinase, putative Sky1 29 AFUB_025560 -2.65 -2.638610078 1.43E-08 8.18E-08 Protein kinase, putative NpkA 13 AFUB_053520 -3.02 -2.442333048 4.40E-06 1.76E-05 Calcium/calmodulin dependent PK, putative TOS3 27 AFUB_017750 -3.05 -2.38078093 1.30E-09 9.40E-09 Protein kinase, putative HOG1 26 AFUB_014350 -3.22 -2.443588504 1.92E-12 2.20E-11 Serine/threonine protein kinase, putative Kin4 14 AFUB_059390 -3.14 -2.619555197 0.000114725 0.000327787 Protein kinase, putative HRK1 4 AFUB_027890 -3.44 -2.303130969 0.002713 0.006383 cAMP- dependent PK catalytic subunit PkaC1 7 AFUB_019930 -3.61 -3.032428741 4.88E-12 4.88E-11 Serine/threonine protein kinase PSK1

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5.4 Parallel fitness can be used to predict the fitness of some strains grown individually in liquid culture

To assess whether parallel fitness could be used to predict the fitness of strains when grown individually, each outlier strain from each data set was grown on liquid fRPMI media using similar conditions. Strains were grown in 96-well format and optical density (OD) readings were taken after 48 hr. Relative fitness was calculated by dividing the blanked OD values for the condition of interest with the OD values obtained for the strain at 37°C. For most strains, the results obtained in this analysis were constant with that of the competitive fitness study (Table 5.10, Figure 5.14).

The cell wall integrity mutants behaved as expected at 48°C, 30°C, on 1M sucrose- containing media and in iron-limiting conditions. However, strain like ∆AFUB_044560 did retain the given phenotype on competitive fitness and this might reflect the discrepancy that driven by the pool effect or it might indicate that the OD method of assessing fitness is less sensitive than the competitive fitness method.

Table 5.10: PK mutant’s that show similar phenotype on competitive and individual growth.

Strain ID: ∆ 48°C 30°C IM sucrose Fe- 2mM H2O2 Common Name

AFUB_070630 Yes Yes Yes Yes N/A Mpk A AFUB_006190 Yes Yes Yes Yes Yes Mkk2 AFUB_038060 Yes Yes Yes Yes N/A Bck1 AFUB_027890 Yes N/A N/A N/A N/A PkaC1 AFUB_053520 Yes Yes N/A N/A N/A Tos3 AFUB_071620 N/A Yes N/A N/A N/A Sky1 AFUB_044560 N/A N/A No Yes No Sky1 AFUB_099170 N/A N/A N/A Yes N/A Yak1 AFUB_059390 N/A Yes Yes N/A Yes HRK1 AFUB_099990 Yes N/A N/A N/A Yes Sky1 AFUB_021710 N/A N/A Yes N/A Yes pakA AFUB_030660 N/A N/A N/A Yes N/A Rim15

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Figure 5.14: Relative fitness of PK knockout mutants obtained from comparing their OD after 48 hr of growth on liquid fRPMI at 48°C, 30°C, 1M sucrose, without iron and with final concentration of 2 mM H2O2, respectively, to their OD when grown at 37°C. Error bars represent standard deviations, ***P < 0.001, **P <0.01, *P < 0.05.

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5.5 Phenotypic clustering analysis reveals PK mutants with common phenotypes

To assess if the phenotypic profiling could reveal strains that phenocopy each other and could therefore be part of the kinase signalling cascades, clustering analysis was performed using the data obtained for the competitive fitness study. Hierarchical clustering of the relative fitness data for all mutant isolates was performed using Gene Cluster 3.0. The resulting tree was viewed using treeview (Figure 5.15). As expected, the cell wall integrity pathway mutants clustered together (see CWI in Figure 5.15). Interestingly, a number of other distinct clusters were identified including AFUB_053520 and AFUB_072650; AFUB_021710 and AFUB_072000; AFUB_044560 and AFUB_059720; as well as AFUB_039620 and AFUB_055480. Remarkably, those genes that clustered together in this way were also members of the same family of protein kinases.

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Figure 5.15: Hierarchical clustering of the competitive fitness outputs from this study. The PK mutants involved in CWI pathway are clustered together, and the same was observed for PK mutants belonging to same group from AGC, CMGC and TK, suggesting that they are functionally associated. 164

5.6 Discussion

Advances in next generation sequencing have significantly improved our ability to undertake functional genomic screens, even in species that are not considered to be model organisms. One of the few examples of how functional genomics can be used to identify drug targets in pathogenic fungi has been carried out in C. albicans with the Candida albicans fitness test (CaFT) (Xu et al., 2007), which employed strains with gene-specific barcodes to enable differentiation of fitness for heterozygote isolates and enabled their multiplex screening for haploinsufficiency growth phenotypes with the presence of antifungal agents. Tn–Seq is another technique for the analysis of transposon mutant libraries that enabled the identification of the mutants’ fitness (Van et al., 2009; van O T & Camilli A, 2013;Van et al., 2015; Lourdault et al., 2017). The changes in frequency of mutants after sequencing the flanking region en masse were used to calculate the fitness and have been successful in determining the gene fitness in Streptococcus pneumonia.

In this chapter, we have shown that the competitive fitness model could be used to predict the fitness of some individual strains when grown individually on liquid culture, highlighting the bias of the pool effect for the mutants that showed discrepancy between the two studies (Amorim-Vaz al., 2015). However, we were unable to reproduce the outcome of competitive growth on solid media for many considerations, including using different culture media, and the short duration in which the radial growth was assessed. Thus, it will be interesting to look at the radial growth rate of PK mutants that appeared as outlier in this screening, to find whether they can retain the given phenotype from competitive fitness study or not.

Current study involved developing competitive fitness study technique in Aspergillus fumigatus, which can be utilised to screen large pool of mutants against different compound in parallel on shaking culture. This technique can be used also in virulence screening utilising either microphage cell line or larvae of Galleria mellonella.

Competitive fitness profiling study can be used to assess the fitness of large number mutant strains. Xu et al, utilising the CaFT, pool containing 2,868 strains was used to screen different compounds successfully (Xu et al., 2007). Tn-Seq technique was successful in inferring the fitness of 25,000 transposon mutant libraries (Van et al., 2009; Van et al., 2015). Although competitive fitness in this study was limited to 65 mutants, there is clearly a potential to increase the library size to thousands (>3000).

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Our results showed that strains lacking mpkA, bck1, and mkk2 are temperature sensitive (i.e. they have reduced fitness at 48°C and increased fitness at 30°C, compared to that at 37°C) and have increased fitness in high osmotic conditions. Moreover, these mutants also have increased fitness in low-iron-containing media. These results match the findings from published data (Jain et al., 2011). Mpk A was shown to be controlling cell wall integrity, the response to oxidative stress, iron adaptation and gliotoxin production in A. fumigatus, however, no growth adaptation has been observed on iron-depleted media in their study (Jain et al., 2011). The current finding obtained from the competitive fitness analysis and from individual growth on liquid culture, in contrast to their finding suggests that growth adaptation occur in both competitive fitness analysis and on individual growth on liquid culture.

Competitive fitness analysis as performed in this study uses an indirect measurement of growth rate through measuring nuclear copy number (DNA barcodes). It should be noted that a number of factors might result in delinking of growth rate and nuclear copy number. For instance, if a mutation increases nuclear division rate it would appear as it were affecting growth as a result of increased barcode reads. Competitive fitness profiling may not always be useful in spotting growth defects, especially if these defects can be complemented by other strains growing in the pool. For instance, growing siderophore producing strains and siderophore deficient strains competitively, would lead to compensation of siderophore from the efficient strain that mask the identification of the incompetent one.

Activation of the MAP cascade under stress conditions has led to the phosphorylation of MAPKKK (AFUB_0308060), which phosphorylates MAPKK (AFUB_006190) and the later phosphorylate MAPK (AFUB_070630) given the observed results, thus supporting the hypothesis of the controlling cell wall integrity being influenced by MAPK (Mpk A).

Mitogen activated protein kinase (MAPK) pathways are crucial to regulate the biosynthesis of the cell wall and responses to extracellular signals in A. fumigatus (Rispail et al., 2009). Four MAPK genes are present in A. fumigatus: sakA/hogA, mpkA, mpkB, and mpkC (May et al., 2005). MpkA is essential for the cell wall integrity pathway of A. fumigatus (Valiante et al., 2008; Valiante et al., 2009; Jain et al., 2011). SakA affects the conidial germination in response to different conditions such as nitrogen and carbon source depletion hypertonic conditions, heat shock and ROS (Xue et al., 2004), while its involvement in A. fumigatus virulence has been shown (Bruder Nascimento et al., 2016). 166

MpkC is involved in carbon source utilisation (Reyes et al., 2006) and in A. fumigatus virulence (Bruder Nascimento et al., 2016), while MpkB is still uncharacterised.

In response to oxidative stress, our result has shown increased sensitivity with MpkA, and increased tolerance with MKK2; however, this was not the case in another study that showed increased tolerance to H2O2 was recorded with MpkA (Valiante et al., 2008). PK genes involved in the cell wall integrity pathways (mpkA, bck1 and Mkk2) were grouped together on clustal analysis (Figure 5.15), which showed their correlation in response to different stress conditions.

In this chapter, the AFUB_021710 gene (null mutant), an AGC member that is similar to Ste20, was shown to be heat sensitive with an increase in relative fitness in response to other stress conditions including iron starvation and oxidative stress. Phenotypic analysis of the MpkA mutant has shown that it grows as well as WT on ACM, with a slight reduction in growth on VMM, as shown in the result section, with this observation being recorded previously when replacing VMM with AMM (Valiante et al., 2008). MAPKK (Mkk2) and MAPKKK (bck1) showed significant reduction in growth on complete media compared to the wild type and this was consistent with minimal media VMM.

In this study, few other PK knockouts mutants appeared to grow slower than the wild type on complete and minimal media, and it would be interesting to look at specific PK genes such as AFUB_099170, whose orthologs seem to be with serine/threonine kinase activity and protein tyrosine kinase activity (Cerqueira et al., 2014). This PK appeared to have reduced fitness with the absence of iron, and with the presence of sucrose. Its orthologs in S. Cerviciae yak1 constitute part of the glucose sensing system, where the presence of glucose results in growth inhibition. Moreover, it regulates mRNA as a result of Pop2p phosphorylates in the lack of nutrition (Moriya et al., 2001).

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Chapter 6: Assessment of the Role of Protein Kinases in Itraconazole Susceptibility

6.1 Introduction

The azole class of antifungals represents the first line of therapeutics to treat the various forms of aspergillosis (Patterson et al., 2016); however, the emergence of azole-resistant isolates of A. fumigatus is changing the way clinicians are treating disease (Verweij, 2015). In some centers the frequency of azole resistance exceeds 20% (Verweij, 2015), forcing clinicians to rely on alternative or combination therapies that use compounds that can be poorly tolerated. Mortality rates in patients that have azole-resistant isolates exceed 80%. Therefore, early detection of resistance is critical to ensure patients are given the most appropriate therapy.

Resistance in A. fumigatus is associated with exposure to azole fungicides in the environment (Snelders et al., 2009; Dunne et al., 2017), and with long-term treatment with azoles. The mechanisms of resistance that dominate appear to be different depending on the drivers of resistance. Most environmentally acquired resistant isolates have a well- defined mechanism of resistance. Specifically, they carry modifications to the cyp51A, a gene that encodes the molecular target of the azoles, lanosterol demethylase. The most frequent mechanism of resistance in environmentally acquired isolates of A. fumigatus is involved with a tandem repeat in the cyp51A promoter (TR34) and a single base SNP resulting in an L to H substitution at position 98 (L98H) (Chowdhary et al., 2013). Similar mechanisms have also been observed such as TR46/Y121F/T289A (Zoll et al., 2013).

Mechanisms of resistance resulting from therapeutic exposure appear to be more diverse. SNPs giving rise to substitution at glycine 54 (G54) and 138 (G138) leads to cross- resistance to itraconazole and posaconazole, whereas mutation at glycine 448 (G448S) resulted in voriconazole (VRC) resistance with some reduction in itraconazole and posaconazole susceptibility. Reduced susceptibility for triazoles is also linked to amino acid substitution at methionine 220 (M220) (Mann et al., 2003; Mellado et al., 2004). G54E/R/V and M220I/V/T/K substitutions have also been reported in patients with chronic aspergillosis receiving long-term azole therapy (Susan et al., 2009). Infrequent point mutations, such as P216L, F219C, F219I, A284T, Y431C, G432S, and G434C, have also been reported (Susan et al., 2009; Albarrag et al., 2011).

Non-cyp51 mechanisms of resistance have been reported in 43% of A. fumigatus resistant

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isolates (Bueid et al., 2010). Overexpression of multidrug resistance efflux transporter genes of the ATP-binding cassette and the major facilitator superfamily classes was correlated with failure to accumulate such antifungal agents (Tobin et al., 1997; Vanden et al, 1998), and have been described in azole-susceptible and azole-resistant isolates, with or without azole therapy (Fraczek et al., 2013; Paul et al., 2017). Resistance in A. fumigatus is conferred by mutation P88L in HapE, a key subunit of the CCAAT-binding transcription factor complex (Camps et al., 2012).

If we are to develop adequate diagnostics to detect resistance rapidly, we need to understand what mechanisms can cause azole resistance. As protein kinases are global regulators of protein function, it is reasonable to assume that kinases will play some role in the way that Aspergillus can tolerate azoles.

In this chapter, the competitive fitness study developed in Chapter 5 will be used to assess the role of protein kinases in azole susceptibility in a competitive manner.

6.2 Competitive fitness profiling of the protein kinase mutant library enables identification of key regulators associated with azole resistance and sensitivity

Minimal inhibitory concentration for the isogenic wild type A1160+ was determined in vitro before conducting the main study using the PK null library. Our results revealed that the MIC of WT was 0.025mg/L. This level is with an agreement with that described for CEA10, from which A1160+ was derived (Lass et al., 2006).

Optimisation was required to check the response of our isogenic WT isolate to itraconazole using different concentrations on RPMI1640 media for 24 hr. A1160+ showed a 30% reduction in dry weight at 0.01 mg/L (P < 0.05), reaching 40% at 0.02 mg/L (P < 0.05; one-way ANOVA: Figure 6.1). When concentrations above 0.02 mg/L were used, growth rates did not continue to fall as we expected, suggesting potential problems using higher drug levels in shake culture. From this result, we decided to assess the impact of 0.02 mg/L on the growth of our pooled isolates (P < 0.05).

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Figure 6.1: Reduction in dried weight of fungal biomass in response to itraconazole treatment after 24 hr of incubation at 37°C and 200 rpm. Significant inhibition of fungal biomass was observed with 0.02mg/L. The error bars represent standard deviations * P < 0.05 when statistical analysis was employed using one-way ANOVA. To assess if any of the protein kinase mutants exhibited differential sensitivity to itraconazole, competitive fitness profiling was undertaken using the 65-member library. Spores for each strain in the library were freshly harvested from 3-day old Sabouraud dextrose agar flasks, enumerated, normalised and pooled so that each strain was approximately equally represented in the starting pool.

The pool was used to inoculate liquid fRPMI shake flasks either lacking itraconazole or containing sub-MIC levels of the drug (0.02 mg/L). Four replicates for each experimental condition were used. DNA was extracted from fungal biomass harvested following incubation for 24 hr at 37˚C. For T0 samples (i.e. the inoculum), DNA was extracted from pooled spores in triplicate. Barcodes were amplified from the pools (Figure 6.2) and sequenced as described previously. Relative competitive fitness was calculated by comparing the fitness of each strain in the presence and absence of drug in a similar manner to that described for conditional fitness in Chapter 5. In this case, statistical significance for each data point was calculated using Deseq2 to obtain Benjamin-Hochberg corrected p values.

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Figure 6.2: Gel electrophoresis image of PCR products of replicates after purification. A: PCR products from DNA extracted from pooled PK mutant spores (T0) (replicates 1–3). B: PCR products from untreated growth after 24 hr at 37˚C and 200 rpm (replicates 1–4), and PCR from treated growth with 0.02 mg/L of itraconazole after 24 hr at 37˚C and 200 rpm (replicates 1-4). M refers to the 100 bp ladder used to estimate the size of PCR product.

In this competitive growth experiment, it would be expected that strains that exhibit resistance to itraconazole would increase in relative frequency and vice versa for those that are hyper-susceptible. Profiling the data on a box-whisker plot, 9 outliers were identified that exhibited azole sensitivity phenotypes (log2 fitness ratios <-2) and 3 where the pooled fitness data suggested an azole resistance phenotype (log2 fitness ratios >2; [Figure 6.3 and Table 6.1]). All 12 isolates exhibited statistically significant differences when the data were analysed using Deseq2 (Table 6.1).

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Table 6.1: Strains identified as outliers from competitive fitness analysis of protein kinase null mutants exposed to itraconazole.

Relative log2 Relative fitness as Benjamin-Hochberg Strain ID: ∆ P value Description Common name fitness ratio calculated by Deseq2 corrected P values AFUB_035220 6.71 8.48 4.44E-52 1.84E-50 Cyclin-dependent protein kinase ssn3

AFUB_078810 3.54 5.21 7.13E-41 1.97E-39 MAP kinase MpkB/Fus3 fus3

AFUB_066150 2.37 3.97 5.99E-27 9.93E-26 Serine/threonine protein kinase (Pdd7p) apg1

AFUB_025560 -4.65 -3.24 0.000738 0.00204 Protein kinase (NpkA) kin28

AFUB_010510 -4.96 -3.31 1.51E-16 1.57E-15 Serine/threonine protein kinase (Kin1) kin1

AFUB_014350 -5.01 -3.40 1.71E-08 9.47E-08 Serine/threonine protein kinase (Kin4) kin4

AFUB_059090 -5.61 -4.10 1.92E-18 2.28E-17 Serine/threonine protein kinase (Nrc-2) kad5

AFUB_059390 -5.70 -4.22 7.65E-07 3.17E-06 Protein kinase, putative cAMP-dependent protein kinase catalytic AFUB_027890 -5.78 -4.26 6.9E-125 5.7E-123 pkac1 subunit PkaC1 AFUB_030570 -6.20 -4.55 1.64E-10 1.13E-09 Protein kinase

AFUB_099170 -7.58 -7.37 5.47E-11 4.12E-10 Protein kinase Yak1 yak1

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Figure 6.3: Log2 relative fitness of protein kinase null mutants exposed to 0.02-mg/L itraconazole. Median is represented by central vertical line, while any mutants fall below or above the whiskers and have log2 fitness values of >2 or <-2 are identified as outliers.

6.3 Validation of competitive fitness analysis: Assessment of strains identified with altered azole tolerance using a radial growth assay

To assess if the PK knockouts identified with altered azole tolerance in our competitive fitness mutants did have an azole susceptibility defect in monoculture, kinase mutants were analysed in triplicate on solid RPMI agar in the presence of increasing levels of itraconazole (0.03 mg/L up to 2 mg/L). Plates were incubated at 37°C, and growth was assessed after 4 days by measuring the horizontal and vertical diameter, and the data normalised to log2 values and plotted using GraphPad Prism7 (Figure 6.4).

We were unable to identify an azole susceptibility defect for 8 of the 11 isolates we had identified from our sequencing data; however, for the three strains that exhibited the biggest relative fitness defect we were able to confirm the results of the competitive fitness experiment. Specifically, we were able to demonstrate that two strains lacking genes – AFUB_035220 (ssn3) (P = <0.0001) and AFUB_078810 (fus3) (P = <0.0001) – were more resistant than the wild-type and one strain lacking AFUB_099170 (yak1) (P = <0.0001) was hyper-sensitive to itraconazole when two way ANOVA analysis was conducted. It is notable that despite the very high relative fitness defect observed for ∆AFUB_099170, the defect observed in solid culture was relatively small (radial growth 1.4 cm vs 2.1 cm, which equates to a 70% reduction in radial growth).

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Figure 6.4: Radial growth of 3 PK mutants and wild type A1160P+ isolate with itraconazole after 4 days of incubation at 37˚C. The data reveals that two strains lacking genes AFUB_035220, and AFUB_078810 have increased resistance to itraconazole; whereas one isolate lacking AFUB_099170 is more sensitive.

6.4 Reconstitution of the wild-type genotype in ssn3 and fus3 null mutants

In an attempt confirm a role for ssn3 and fus3 in azole tolerance; the genes were transformed back into the mutant backgrounds. Specifically, the hygromycin selectable marker in the mutant strain is to be replaced with the native gene. To select for integration, replacement cassettes were constructed to incorporate a pyrithiamine as a selectable marker (Figure 6.5).

Figure 6.5: Reconstitution of Δssn3 and Δ fus3 genes. Pyrithiamine as a selectable marker has replaced hph gene, where it was amplified independently using ptrA_F and ptrA_R (product size is 2kb). The upstream flanking region of the native genes was amplified using the corresponding primers (P1 and P2, product size about 3 kb). Downstream flanking region was amplified using the corresponding primers (P3 and P4, product size about 651 bp). To aid in amplification during fusion PCR, the nested primers P5 and P6 were utilised. 174

Independent amplification of the upstream (using primers 1 and 2) and downstream flanking regions of the original gene (using primers 3 and 4) along with the pyrithiamine resistance cassette (ptrA) (using primers ptrA_ F & ptrA_ R) was successful (Figure 6.6A, B). All three fragments were used to construct the final knock-in cassette using fusion PCR, as previously described in the Method chapter (Figure 6.6C).

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Figure 6.6: Gel electrophoresis: A: PCR products of pooled upstream and downstream flanking region for ssn3 and fus3 genes after purification. B: PCR products of the marker cassette (ptrA) after purification (product size is 2kb). C: Fusion PCR products for ssn3 and fus3 genes respectively, product size about 5 kb.

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Transformation of the knock-in cassettes was performed as previously described, however using pyrithiamine selection on VM. Successful transformation was confirmed after 3 days by detecting the presence of growing colonies on transformation plates and lack of growth on mock transformation plates. The transformants underwent two rounds of single colony purification to exclude the presence of heterokaryons.

DNA was extracted from two independent transformants and the presence of the native gene was confirmed by PCR (Figure 6.7; schematic Figure 6.8). A light band was noted for the upstream flanking region in the reconstituted AFUB_035220 gene (ssn3), and this was consistent even after many repeats. The presence of non-specific bands was obtained from the upstream flanking region in the reconstituted AFUB_078810 gene (fus3).

Figure 6.7: A: Gel electrophoresis from validation of reconstituted fus3 gene. B: Gel electrophoresis from validation of in reconstituted ssn3 gene.

Figure 6.8: Schematic representation of validation PCR of reconstituted genes (ssn3 and fus3). Validation of upstream flanking insertion will be employed utilising P1+ptA_R_, where a product size of 2.6kb will be expected. Validation of downstream flanking insertion will be employed utilising P4+ptrA_F, where a product size of 625bp-1.5 kb is expected. After reintroduction of the native genes, the reconstituted strains for ssn3 and fus3 were assessed to see if they retained resistance to itraconazole. The wild type, ∆ssn3, ∆fus3 and their reconstituted strains were analysed in triplicate on solid RPMI agar in the presence of increasing levels of itraconazole (0.03 mg/L up to 2 mg/L). Plates were incubated at 37°C, 177

and growth was assessed daily by measuring the horizontal and vertical diameter. Unfortunately, the reconstituted strains retained the phenotype of the mutant and hence no conclusions could be drawn with respect to the cause of the azole resistance in these strains. Due to time limitations, no further attempts to assess this were undertaken.

6.5 Evaluating the mechanistic basis of altered azole susceptibility in ssn3, yak1 and fus3 null mutants

There are many possible explanations for altered azole resistance in the three mutant strains identified in this screen; however, one of the most obvious is a change in the transcriptional expression of the genes involved in azole resistance. Therefore, we have assessed the relative expression of cyp51A, cyp51B and cdr1B by quantitative real-time PCR in wild-type and ssn3, yak1 and fus3 null mutants. To obtain sufficient biomass for this study, mycelia were grown for 20 hr in AMM media with shaking at 37˚C. Biomass for each strain was harvested and pooled before equal quantities of biomass (0.5 g wet weight) were transferred into fRPMI liquid cultures with or without itraconazole (0.05 mg/L) and incubated for 4 hr at 37˚C with shaking before harvesting and RNA extraction. Three independent replicate samples from each PK mutant genes were used.

In this experiment, the quantification is performed based on the expression levels of the target genes with and without itraconazole in PK null mutants (fus3, ssn3 and yak1) versus their expression in the wild type utilising housekeeping gene (beta-tubulin). The QRT-PCR

-∆∆Ct results were analysed following the 2 method.

The results have shown that in the absence of itraconazole, no statistically significant differences in transcripts were seen, with the exception of a 2.1-fold increase in cyp51B expression in the yak1 null mutant compared to wild type strain (Figure 6.9).

In the presence of itraconazole, no significant differences in expression of cyp51A, cyp51B or cdr1B were observed in the fus3 null mutant (Figure 6.10). However, for the ssn3 null mutant, in keeping with our observed increase in azole resistance in this isolate, cyp51A, cyp51B and cdr1B were upregulated by 1.8-, 4.8- and 1.95-fold, respectively, when compared to the wild-type (Figure 6.10). In contrast to expectations, however, the expressions of all three genes were also upregulated in the yak1 null mutant (Figure 6.10).

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Figure 6.9: Fold change in the expression of the target genes (cyp51A, cyp51B and cdr1B) in Δfus3, Δssn3 and Δyak1 without itraconazole in relative to WT. No significant difference was obtained with Δfus and Δssn3. However, significant difference was observed in cyp51B expression in Δyak1. *P < 0.05

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Figure 6.10: Fold change in the expression of the target genes (cyp51A, cyp51B and cdr1B) in Δfus3, Δssn3 and Δyak1 after 4 hr of exposure to itraconazole in relative to WT. No significant difference was obtained with Δfus3 (P > 0.05), while a significant difference was observed in cyp51A, cyp51B and cdr1B expression in Δ ssn3 and Δyak1 (P<0.05), **P<0.01, ***P<0.001 and ****P<0.0001. . 180

6.6 Discussion

Resistant to antifungal drug is of special concern and constitutes a threat to the lives of human beings, particularly vulnerable groups. This explains the high efforts that have been motivating scientists and researchers to search for a novel antifungal target with high sensitivity and specificity. Moreover, efforts have been made to understand the mechanism of resistance at the molecular level.

Insufficient research about the role of protein kinases, if any, in response to itraconazole treatment in vitro in filamentous fungus encouraged the conducting of this study, benefiting from multiplex barcoding applying next generation sequencing in an attempt to identify any resistant or sensitive strain to itraconazole in competitive growth.

Competitive fitness study using the PK knockout library identified two PK mutants lacking AUB_078810 (fus3) and AFUB_035220 (ssn3) with elevated MIC and reduced susceptibility to itraconazole and identified one sensitive mutant lacking AFUB_099170 (yak1), a serine threonine kinase, as shown in Figure 6.3.

Ssn3 is important for sexual reproduction and pathogenesis in Fusarium graminearm (Cao et al., 2016), and appeared to be involved in phosphorylation of the RNA polymerase II C- terminal domain and in glucose repression in S. cerevisiae (Balciunas et al., 1995; Kuchin et al., 1995). Ssn3 and ssn8 mutants of S. cerevisiae act synergistically with the MIG1 mutant repressor protein to relieve glucose repression. Both ssn3 and ssn8 function as a cdk-cyclin pair and the ssn3–ssn8 complex play an important role in transcriptional repression of different genes’ regulations, including the induction of the GAL promoter (Kuchin et al., 1995).

Increased resistance to chemicals was observed with ssn3 in S. cerevisiae, where it showed resistance to Miconazole at concentration 1000 ug/ml (Vandenbosch et al., 2013), and to other chemicals including cycloheximide (Huang et al., 2013). In C. albicans, increased resistance to the inhibitory effects of PYO on biofilm formation was observed with the ssn3 mutant which shown to be hyperbiofilm former (Lindsay et al., 2014). Transcriptional independent for Sfu1 in the direct inhibition of Sef1 was reported in C. albicans (Chen and Noble, 2012), where ssn3 formed alternative complex with Sef1 under iron starvation leading to phosphorylation, nuclear localisation, and transcriptional activity. These post- transcriptional regulatory mechanisms are a key element of C. albicans virulence in a mammalian model of disseminated candidiasis (Chen and Noble, 2012). More studies

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reveal the involvement of ssn3 in resistance to fluconazole in C. albicans (Liu and Myers, 2017).

In a recent study, it has been shown that deletion of Afssn3 forms two “barrier” layers between the intracellular to extracellular spaces, resulting in decreased itraconazole penetration into the cell in A. fumigatus (Long et al., 2018). Loss of ssn3 in A. fumigatus results in increased absorption and utilisation of glucose and amino acids. Acceleration of extracellular polysaccharide formation is driven by absorption and the utilisation of glucose and increased sphingolipid pathway intermediates’ accumulation is driven by the utilisation of the amino acid serine, threonine and glycine, respectively (Long et al., 2018). Moreover, induced efflux pump proteins activity upon the absence of Afssn3 was observed. Resistance to azoles in ΔAfssn3 is associated with mature biofilm, which is driven by previously described factors and constitutes the major mechanisms of resistance in A. fumigatus (Long et al., 2018). These finding support our results and bring insight to the role of ssn3 in A. fumigatus.

Mitogen-activated protein kinases (MAPKs) are known for their important role in regulating the fungal cell physiology in response to different stress conditions such as temperature shock, oxidative stress and hypertonic shock (Gustin et al., 1998; Lengeler et al., 2000; Xue et al., 2004). They are also involved in regulating cell morphology, in addition to their involvement in sexual and asexual development (Lengeler et al., 2000; Xu et al., 2000). Studies have shown that decreased virulence of the fungus in a host was due to the defects in MAPK signalling pathways. In S. cerevisiae, fus3 MAPK was shown to be resistant to Miconazole at concentration of 1000 ug/ml (Vandenbosch et al., 2013). Recent study revealed that the MAP kinase (fus3) is required by the major facilitator superfamily transporter mediated resistance to oxidative stress and fungicides in the fungal pathogen Alternaria alternata (Chen et al., 2017).

AFUB_099170 (yak1) is a member of the dual-specificity tyrosine phosphorylation- regulated protein kinases. Its orthologs in S. cerevisiae show susceptibility to 32 μg/ml neomycin (Zhu et al., 2015), 30 ng/ml caspofungin (García et al., 2015), and Miconazole at concentration of 1000 ug/ml (Vandenbosch et al., 2013). In Botrytis cinerea, ΔBcYak1 mutant displays less virulence, less conidia and sclerotium formation, and exhibits more sensitivity to H2O2 and triadimefon, an ergosterol biosynthesis inhibitor (Yang et al., 2018).

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Understanding the molecular mechanism of drug resistance is vital for drug discovery to identify the prospective drug target, and it can be hypothesised that it is associated with the over expression of either cyp51A, cyp51B or drug efflux transporters cdr1B.

Quantitative PCR of the expression level of cyp51A, cyp51B and cdr1B reveals upregulation in WT, when the CT values were normalised to that of GDPA. Other laboratory members have observed these results, when RNA seq experiment was conducted to look at their expression in WT with the presence of itraconazole at 0.5 mg/L. These consistent observations indicate that cyp51A, cyp51B and cdr1B are upregulated in WT.

Beta-tubulin was used as the housekeeping gene to normalize expression in the data sets described in figures 6.9 and 6.10. This housekeeping gene was chosen as previous RNAseq data suggested it’s expression was unaffected by various concentrations of itraconazole ranging from 0.25 to 4x MIC. The gpdA (AFUB_050490) gene is also frequently used to normalize gene expression in experiments of this nature however; our RNAseq data suggests that it is downregulated in response to itraconazole.

In our study, upregulation in the relative expression of cyp51A and cyp51B was observed in ssn3 mutant. This finding suggests their role in itraconazole resistance phenotype in ssn3 mutants, which this may explain the increased fitness of this mutant in the presence of itraconazole in competitive and individual growth assays. Upregulation of cyp51A and cyp51B was observed by Albarrag et al., (2011), were they showed a 2-fold increment in their expression level in clinical isolates of A. fumigatus. Upregulation of cyp51A caused a wide range of resistance in clinical isolates according to Mellado et al., (2005).

In the presence of itraconazole, although there was increment trend in expression of either cyp51A, cyp51B in the fus3 null mutant, no statistical significance has obtained, P> 0.05. This result is in contrast to the finding that obtained for fus3 when grown competitively or individually. Aspergillus fumigatus appeared to adapt to itraconazole after short time of incubation with itraconazole through MAPKs cascades. Fus3 (MpkB) showed slight upregulation < 2 log2FC after 60 of exposure to itraconazole, however this was diminished after 4 hours (Hokken et al., 2019). This might explain the finding that obtained in this study.

Cyp51A and cyp51B appeared with >1-fold change in relative expression in yak1 mutant when CT values were normalised to B tubulin CT values; This in contrast to our finding, as

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yak1 appeared with reduced fitness to itraconazole when grown competitively or individually, and we expect downregulation in either one or both genes to consolidate our findings and bring insight to the mechanism that is involved in the given phenotype.

The drug efflux transporters play an important role in fungal survival and in drug resistance in various fungal species (Perlin et al., 2014; Paul et al., 2017). These transporters include ATP-binding cassette transporters (ABC-transporter) and a Major Facilitator Superfamily transporter (MFS-transporter). Interestingly the genome of A. fumigatus is predicted to contain at least 49 ABC family transporter genes and 278 MFS genes. Two genes (AfuMDR1 and AfuMDR2) were identified in A. fumigatus encoding proteins of the ATP-binding cassette superfamily (Tobin et al., 1997). The ABC family transporter atrF has been shown to be upregulated in response to sub-MIC levels of itraconazole however no direct link between overexpression and resistance has been confirmed. Similarly of the MFS family transporters AfumDMR3 and / or AfumDMR4 transporters, have also been shown to be upregulated in response to azole treatment (Nascimento et al., 2003).

Studies have shown azole resistance in Candida albicans, and Candida is associated with the overexpression of the drug efflux transporters CDR1, CDR2, which supports the finding in this study that the cdr1B efflux transporter is found to be involved in non- cyp51A-itraconazole resistance in A. fumigatus ( Fraczek et al., 2013). In our study, cdr1B appeared to have a >2-fold relative expression compared to WT in ssn3 null mutant and appeared with a >1-fold in yak1 null mutant, which might result from the increment of intracellular level of the itraconazole drug. These results are consistent with the observations that have been obtained from both competitive and individual growth. There was a clear variation in the fold change in fus3, corresponding to different housekeeping.

The current result reveals that competitive fitness analysis can be used to infer the phenotypic susceptibility (increased and reduced) in individual strains. It also reveals the role of cyp51A and cyp51B in two PK mutants – ssn3 and MAP kinase (fus3) that appeared with elevated MIC and reduced susceptibility to itraconazole – and involvement of the efflux drug transporter cdr1B.

It should be noted that although we have identified mutants with increased resistance to itraconazole, this increase does not result in levels of resistance that exceed clinical breakpoints (MIC ≥ 4mg/L). As there are significant strain to strain variations in the ability of A. fumigatus isolates to tolerate azoles (Ref required), it is possible that mutation in 184

either ssn3 or fus3 could, in combination with other mutations, lead to clinically relevant levels of drug resistance. The results obtained in this study are promising move toward the mechanism of reduced susceptibility to itraconazole in two PK mutants – the ssn3 and the MAP kinase gene – both of which are of special interest due to their biological importance and their role in cell signalling and transcriptional regulation.

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Chapter 7: Studying the Virulence of the PK Knockout Library in Galleria mellonella Larvae and Mice

7.1 Introduction

Aspergillus fumigatus is a harmful pathogen that affects immunosuppressed patients, causing aspergillosis with high mortality rates reaching 90% in untreated cases (Brakhage and Axel, 2005). Although A. fumigatus is the most frequent cause of aspergillosis, it is also caused by other species of Aspergillus such as A. terreus, A. flavus and A. niger (Walsh and Groll, 2001). Only a small number of factors contributing towards what makes A. fumigatus such a successful pathogen has been uncovered. Secondary metabolites are vital for the pathogenicity of many pathogens, including A. fumigatus, where they are required for the adaptation to the host environment and constitute part of the pathogens' defence system. PptA, a critical metabolic mediator of all non-ribosomal peptide synthase, and polyketide synthase secondary metabolite biosynthesis are also required for growth under iron-restricted conditions, and are essential for the virulence of A. fumigatus (Johns et al., 2017). In A. fumigatus, siderophore-mediated iron sequestration and uptake are vital for virulence and are required to initiate an infection in iron-restricted environments (Schrettl et al., 2004). The dihydroxy naphthalene (DHN)-melanin in A. fumigatus is necessary to inhibit acidification of the phagolysosomes in the host (Thywißen et al., 2011), while it has been shown that the subsequently produced melanin serves to protect the fungus from the host’s defence mechanisms (Bayry et al., 2014). Gliotoxin (glip) is an epipolythiodioxopiperazine toxin that is secreted by A. fumigatus and has been associated with virulence (Sugui et al., 2007).

Treatment options are limited to four classes of antifungal drugs, and since many of these are toxic or have severe drug–drug interactions this restricts their usage. Most importantly, resistance to the currently available antifungal drugs is emerging; therefore, new drugs are required (Lewis, 2011; Scorzoni et al., 2017).

With kinases representing a druggable class of enzymes, there is significant value in assessing the virulence of the kinase KO library in virulence. However, assessing the virulence of all of the mutants in the library via standard methods would be time consuming due to the large number of strains in the collection, and thus the number of mice required for a study of this nature would likely be deemed unethical. This chapter evaluates the feasibility of performing competitive fitness profiling in an infection setting. 186

Although competitive fitness profiling has been successfully undertaken in bacteria, the attempts to assess the virulence of A. fumigatus in a high throughput manner in mice had limited success (Brown et al., 2000). This chapter presents the work performed to study the virulence of PK mutants employing a macrophage cell line, G. mellonella larvae and mice.

7.2 Studying the virulence of the PK knockout library using macrophage as an infection model

With a view to studying the behaviour of the library in a macrophage infection model, and to ensure we had a means of comparing the fitness of each mutant, we included the Δaft4 isolate and a strain lacking AFUB_027890 (pkcA), which has previously been demonstrated to have a virulence defect in mice (Rocha et al., 2015). Five million THP1 cells were infected with 1x106 spores from a freshly prepared pool of 63 strains from the PK knockout library and ΔAft4. The co-culture was incubated for 16 hr at 37°C with 5%

CO2. Cells were harvested as described in the methods section to extract the DNA. For T0, DNA was extracted from the starting inoculum of PK knockouts (see the schematic Figure 7.1).

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Strain-specific barcodes were amplified using primers designed for the Illumina MiSeq, as shown in Figure 7.2.

Figure 7.2: DNA gel electrophoresis of PCR product amplified from DNA from the biological replicates (1– 4) of infected THP1- cells 16 hr post-infection, and pooled PK knockout library spores (T0, 1–3). M refers to the 1kb DNA ladder used to estimate the size of the PCR product.

Sequencing was performed on the Illumina MiSeq platform at the University of Manchester’s Sequencing Facility, with the data analysed utilising our existing bioinformatics pipeline. As expected, our ΔAFUB_027890 (ΔpkaC) virulence control isolate had reduced fitness when compared with the majority of the other isolates in the collection (Table 7.2). The cell wall integrity pathway mutant ΔmpkA (AFUB_070630, No. 3 in the graph in Figure 7.3) also showed statistically significant decreases in fitness in the macrophage model (Note that Δbck1 saw significant reduction in fitness but was not identified as an outlier, while the Δmkk2 mutant, the other member of the cell wall integrity pathway, was omitted from this study). Defects in fitness were also observed for ΔAFUB_014350, ΔAFUB_087120, ΔAFUB_59390, ΔAFUB_053500, ΔAFUB_099170, ΔAFUB_021710, ΔAFUB_048440, ΔAFUB_099990, and ΔAFUB_066150. Only one strain with an apparent increased fitness was detected in our study ΔAFUB_053520 (Figure 7.3).

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Figure 7.3: Log2 competitive fitness of the PK knockout library 16 hr post-infection using the THP1 cell line. Transposon (ΔAft4) and ΔpkaC are highlighted as the control strains. The PK mutant that appeared to exhibit increased fitness is labelled 1, while those that appeared to exhibit reduced fitness are labelled 2-11. For the corresponding gene ID, please refer to Table 7.1.

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Table 7.1: Strains identified as outliers from the competitive fitness analysis of the PK knockout library 16 hr post-infection with 1x106 spores/ml using the THP1 cell line. Each strain here was allocated an ID number corresponding to its representation in Figure 7.3. The highlighted PK mutants are those that appeared to have similar phenotype using different infection models.

Relative log2 Relative fitness as Benjamin-Hochberg ID number Strain ID: ∆ P value Description Common name fitness ratio calculated by Deseq2 corrected P value

Calcium/calmodulin-dependent protein 1 AFUB_053520 1.277884 1.09381173 0.002834291 0.006397 kinase 2 AFUB_014350 -0.51 -0.698972355 1.15E-18 7.02E-18 Serine/threonine protein kinase Kin4

3 AFUB_070630 -0.54243 -0.727781449 1.33E-19 9.56E-19 MpkA, Mitogen-activated protein kinase

4 AFUB_087120 -0.58805 -0.768774642 2.14E-34 2.11E-33 NACHT and Ankyrin domain protein

5 AFUB_059390 -0.67666 -0.864720087 3.82E-78 7.54E-77 Protein kinase, putative

6 AFUB_053500 -0.6907 -0.870909824 3.33E-36 3.76E-35 Serine/threonine protein kinase, putative 5.37E-24 4.72E-23 7 AFUB_099170 -0.71074 -0.884640329 Protein kinase, putative Yak1 8 AFUB_021710 -0.72202 -0.915415042 8.19E-12 4.04E-11 Serine/threonine kinase Ste20 pakA

9 AFUB_048440 -0.80721 -0.994229621 2.14E-50 3.38E-49 Protein kinase, putative

10 AFUB_099990 -1.06441 -1.254707868 1.56E-96 4.11E-95 Serine protein kinase, putative Sky1

11 AFUB_066150 -1.09228 -1.270785485 3.43E-46 4.52E-45 Serine/threonine kinase, putative atg1 cAMP-dependent protein kinase catalytic Control strain AFUB_027890 -2.19739 -2.36991671 1.06E-171 8.41E-170 PkaC1 subunit

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7.3 Optimising a dosing regimen for a G. mellonella infection model

Prior to performing a virulence study of the PK knockout library in G. mellonella, it was crucial to assess the virulence of the isotype host strain utilised to generate the knockout library (A1160+). Previous data from our laboratory had indicated that an inoculum of 1×104 spores would be sufficent to cause mortality in larvae after 4 days (Johns et al., 2017). To ensure our barcoded control isolate behaved in the same manner, a comparison between the isotype strain and the ΔAft4 (transposon control strain) was conducted.

Ten larvae in each group were injected in the last pro leg with 10 µl of the pooled PK knockout library spore suspension at concentration of 1x106 spores/ml. Two control groups that were either injected with PBS or just pierced were also included. The larvae were incubated at 37°C, and the survival rate was measured every 24 hr until reaching the end point in the infected groups.

The result of this study showed no significant difference in terms of the virulence between the A1160+ strain and the ΔAft4 (P > 0.05, Figure 7.4).

Figure 7.4: Survival rate of the WT A1160+ and the ΔAft4 isolate post-infection of G. mellonella larvae with 10 µl of the pooled PK knockout library at concentration of 1x106 spores/ml. No significant difference was detected (applying the Log-rank (Mantel-Cox) test and Gehan-Breslow-Wilcoxon test) in the survival rate of the larvae infected with the two strains (P > 0.05). Figure created using GraphPad Prism7.

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7.4 Competitive fitness profiling in G. mellonella larvae

To assess if competitive fitness profiling could reveal defects in the virulence for strains within the protein kinase library, a pooled spore suspension containing 65 strains from the PK knockout library and the Δaft4 was generated as described previously and used to infect the larvae. To avoid high mortality rate at the early stages of the experiment, so as to provide the pooled library with sufficient time to grow in the host, the larvae were infected using 10 µl of pooled PK knockouts library spore suspension at concentrations of 1x106 and 5x106 spores/ml. The infection experiment was conducted at 37°C and allowed to proceed for 5 days. Post-infection, the dead larvae were excluded and only the surviving larvae were utilised in the fitness experiment. PCR and sequencing were carried out as previously described (Figure 7.5). The competitive fitness was calculated by comparing the fitness of each strain post-infection (in vivo) and at T0 in a similar manner to that described for the conditional fitness in Chapter 5. The statistical significance for each data point was calculated using Deseq2 to obtain the Benjamin-Hochberg corrected P values (See Table 7.2 and Table 7.3 for the corresponding infected group).

Figure 7.5: Gel electrophoresis of the PCR products after purification. The DNA of G. mellonella larvae (4 replicates per group) was injected with 10 µl of 1x106 and 5x106 spores/ml, used in this reaction to amplify the strain-specific barcodes. M refers to the100bp DNA ladder used to estimate the size of the PCR product.

Log2 fitness was plotted using the GraphPad Prism7 software (Figure 7.6), with the results showing that the transposon null Δaft4 displayed similar fitness to the majority of stains in the collection, as expected (Figure 7.6). Similarly, the ΔAFUB_027890 (ΔpkaC) virulence control strain had reduced fitness compared to the majority of the other PK strains in the collection. The cell wall integrity pathway mutant ΔmpkA exhibited reduced fitness only

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in the low dose model (No, 8 on the graph), while the Δmkk2 isolate showed statistically significant decreases in fitness in the higher dose larvae model (No, 24 on the graph). Interestingly, the Δbck1 isolate appeared to show increased fitness in the low dose model. Recurrent defects in fitness (i.e. in both the high and low dose models) were observed for ΔAFUB_039100, ΔAFUB_059390, ΔAFUB_014350, ΔAFUB_099990, and ΔAFUB_030660; while reduced fitness was also observed for ΔAFUB_039100, and ΔAFUB_052450 in the low dose model, and for ΔAFUB_025560, ΔAFUB_051750, ΔAFUB_029320, and ΔAFUB_053500 in the high dose model.

A number of strains with an apparent hyper virulent phenotype were detected in our study. Notably, ΔAFUB_043130, AFUB_096030, ΔAFUB_ 012420 and ΔAFUB_044560, which were recurrent in larvae infected with the low dose (Table 7.2) and high dose and (Table 7.3), (Figure 7.6). Another two mutants (ΔAFUB_081540 and ΔAFUB_001600) appeared to have increased fitness in response to infection with low dose in the larvae (Table 7.2). Other mutants including ΔAFUB_095720, ΔAFUB_090090, ΔAFUB_044440, and ΔAFUB_055480 also appeared with increased fitness in response to infection with the high dose (Table 7.3).

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Figure 7.6: Competitive fitness of PK knockout library after 5 days of infection using high dose (5x104 spores/ml) and low dose (1x104 spores/ml) to infect the G. mellonella larvae. The control strains, transposon and ΔpkaC are denoted in different colours as a reference. The PK mutants that exhibited similar fitness in both infection studies are given the same no. For the gene IDs, please refer to Table 7.2 and Table 7.3.

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Table 7.2: Strains identified as outliers from the competitive fitness analysis of the PK knockout library 5 days post-infection using 10 µl of 1x106 spores/ml in G. mellonella larvae. The highlighted genes are those that appeared with enhanced or reduced fitness in the competitive fitness analysis in larvae in response to infection with low and high doses.

Relative log2 Relative fitness as Benjamin-Hochberg ID no Strain ID: ∆ P value Description Common name fitness ratio calculated by Deseq2 corrected P value

1 AFUB_043130 4.747634 6.065459416 5.09E-06 8.52E-06 MAP kinase kinase Ste7

2 AFUB_096030 3.843909 4.471295629 7.19E-45 1.04E-43 Serine/threonine protein kinase, putative Kcc4

3 AFUB_012420 3.533924 4.129133203 1.01E-25 7.32E-25 Mitogen-activated protein kinase Hog1 4 AFUB_044560 0.181728 2.866740243 1.44E-31 1.15E-30 Protein kinase, putative 5 AFUB_081540 2.139935 2.776714966 8.51E-47 1.36E-45 Protein kinase, putative

6 AFUB_001600 1.721709 2.371345194 4.79E-81 1.91E-79 Serine/threonine protein kinase, putative 7 AFUB_039100 -3.56308 -2.895500916 2.31E-23 1.32E-22 Serine/threonine protein kinase, putative

8 AFUB_070630 -3.54603 -2.864478237 6.68E-08 1.41E-07 Mitogen-activated protein kinase MpkA 9 AFUB_099990 -3.96324 -3.273053919 6.14E-16 1.97E-15 Serine protein kinase, putative Sky1 10 AFUB_030660 -4.15723 -3.39232545 6.05E-08 1.31E-07 Serine threonine protein kinase, putative 11 AFUB_019930 -4.24409 -3.515494576 2.34E-12 6.68E-12 Serine/threonine protein kinase, putative 12 AFUB_014350 -4.37726 -3.696062726 4.01E-33 3.57E-32 Serine/threonine protein kinase, putative Kin4 13 AFUB_096080 -5.0134 -3.310126544 0.01833 0.02222 Serine/threonine-protein kinase, putative 14 AFUB_059390 -4.93884 -4.194612095 7.79E-25 4.80E-24 Protein kinase, putative 15 AFUB_052450 -5.80455 -4.985828048 2.25E-19 9.48E-19 Protein kinase, putative 16 AFUB_029820 -6.21326 -5.391668374 4.53E-28 3.29E-27 Protein kinase, putative

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Table 7.3: Strains identified as outliers from competitive fitness analysis of the PK knockout library 5 days post-infection using 10 µl of 5x106 spores/ml in G. mellonella larvae. The highlighted genes are those that appeared with enhanced or reduced fitness in competitive fitness analysis in larvae in response to infection with low and high doses.

Relative log2 Relative fitness as Benjamin-Hochberg ID no. Strain ID: ∆ P value Description Common name fitness ratio calculated by Deseq2 corrected P value 1 AFUB_043130 5.657314 7.218522471 1.47E-06 3.05E-06 MAP kinase kinase Ste7 2 AFUB_096030 3.242185 4.18326568 5.49E-91 1.10E-89 Serine/threonine protein kinase, putative Kcc4 3 AFUB_012420 3.913577 4.852220918 1.73E-190 1.39E-188 Mitogen-activated protein kinase Hog1 4 AFUB_044560 3.470225 4.398834329 2.64E-93 7.05E-92 Protein kinase, putative 17 AFUB_038060 2.541006 3.4663643 3.09E-86 4.95E-85 MAP kinase kinase kinase putative Bck1 18 AFUB_095720 2.318494 3.23912694 6.20E-42 5.51E-41 Protein kinase, putative 19 AFUB_090090 2.157829 3.075505989 9.67E-29 7.04E-28 Serine/threonine protein kinase, putative 20 AFUB_044400 1.978069 2.90090647 8.67E-95 3.47E-93 Uncharacterised protein 21 AFUB_055480 1.624795 2.5521677 1.47E-32 1.18E-31 Serine/threonine protein kinase, putative 12 AFUB_014350 -3.85356 -2.859077599 2.85E-08 6.52E-08 Serine/threonine protein kinase, putative Kin4 9 AFUB_099990 -3.931 -2.91352672 5.90E-08 1.31E-07 Serine protein kinase, putative Sky1 22 AFUB_025560 -3.87838 -2.928114507 2.09E-07 4.39E-07 Protein kinase, putative NpkA 13 AFUB_096080 -3.82332 -2.739503963 0.001839 0.003002792 Serine/threonine-protein kinase, putative 23 AFUB_051750 -4.10182 -3.092510571 2.51E-12 7.17E-12 Pyruvate dehydrogenase kinase, putative 10 AFUB_030660 -4.16224 -2.994890143 0.0003050 0.0005423 Serine threonine protein kinase, putative 24 AFUB_006190 -4.20544 -3.063726787 0.0001564 0.00029104 MAP kinase kinase, putative Mkk2 Calcium/calmodulin-dependent protein 25 AFUB_029320 -4.31378 -3.325085852 2.27E-19 9.57E-19 kinase, putative 26 AFUB_053500 -4.16562 -3.196790346 1.12E-22 5.99E-22 Serine/threonine protein kinase, putative 11 AFUB_019930 -6.37257 -5.414545567 1.60E-49 1.83E-48 Serine/threonine protein kinase, putative

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7.5 Studying the virulence of some PK knockout mutants using G. mellonella to validate the results of competitive fitness

In order to confirm the results from the competitive fitness study, and to exclude the bias of the pool effect, a virulence study using G. mellonella larvae as an infection model was conducted to include the PK mutants that showed enhanced or reduced fitness in both larval models when compared to the rest of the pool in response to infection in both doses (i.e. high and low). Ten larvae per group in triplicates were used in this study. Δaft4 was utilised as a control strain, with PBS and pierced groups included as controls. Incubation was carried out at 37°C, with survival assessed every 24 hr for 7 days.

In keeping with the results from the competitive fitness analysis, the PK mutants (ΔAFUB_030660, ΔAFUB_019930, and ΔAFUB_014350) that exhibited reduced fitness showed similar behaviour (P < 0.05, applying the log-rank test using GraphPad Prism7). Meanwhile, a reduction trend was observed for ΔAFUB_096080 and ΔAFUB_099990 (Figure 7.7).

Figure 7.7: Survival rate of the PK knockout mutants that appeared with reduced fitness in competitive fitness analysis 8 days post-infection with 10 µl of 1x106 spores/ml in G. mellonella larvae. Figure created using GraphPad Prism7.

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A virulence study was also conducted for those PK mutants that showed enhanced fitness in larvae in comparison to the rest of the pool, where ΔAFUB_044560 retained the same response and there was a trend observed with ΔAFUB_043130. In contrast to the competitive fitness assay, no statistical difference was observed between the control (Δaft4) and ΔAFUB_096030 and ΔAFUB_012420 (hog1) (Figure 7.8).

Figure 7.8: Survival rate of the PK knockout mutants that appeared with increased fitness in competitive fitness analysis 8 days post-infection with 10 µl of 1x106 spores/ml in G. mellonella larvae. Figure created using GraphPad Prism7. * Refers to the statistical significance (P < 0.05).

Given the evidence obtained from this experiment, we were encouraged to extend our competitive fitness profiling experiment to explore the virulence of the pool in a mouse model.

7.6 Studying the virulence of the PK knockout library using murine as an animal mode

To assess the competitive fitness of each strain in the library in a murine model of infection, pools of kinase knockout mutants were generated in a similar manner to that described above. In contrast to our previous study, but with the aim of maximising the potential for experimental success, each pool was limited to 16 isolates (Figure 7.9). 198

To ensure we had a means to compare the data between pools, we included the Δaft4 isolate and a strain lacking AFUB_027890 (pkcA) that had previously been demonstrated to have a virulence defect in mice (Rocha et al., 2015). Neutropenic mice were infected intranasally with 40 µl of spore suspension (pooled PK mutants of 18 strains) at concentration of 1.2x105 spores/ml. Ten replicate mice were used for each pool. After 7 days, the lungs from each mouse were collected and the DNA extracted independently. DNA was also collected from each of the mutant pools (Figure 7.10).

Figure 7.9: Schematic representation showing the experimental setup for competitive fitness profiling of the PK knockout library using mice as an infection model.

Figure 7.10: DNA gel electrophoresis image of DNA extracted from 10 lungs (1–10) in each pool. M refers to the 1kb ladder (HyperLadder, Bioline). 199

As with the macrophage experiment, strain-specific barcodes were amplified using primers designed for the Illumina MiSeq system (For the primer sequences, see Table 7.4) (Figure 7.11).

Table 7.4: Primers that were used to amplify strain specific barcodes using Illumina MiSeq system. Nextera adaptor sequences are denoted in yellow. Hph primers sequences where denoted in different colour, hph_F (red) and hph_R (purple).

Primer ID Nextera adaptor sequence hph primer sequence

Forward TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG CCGGCTCGGTAACAG AACTA

GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGTCG TTG TAG GGG CTG TAT Reverse

Figure 7.11: Gel electrophoresis image of the PCR products after purification from amplifying the DNA of infected lungs from each pool (A–D) 7 days post-infection. Numbers 1–10 refer to the mice used in each pool. M refers to the 1kb ladder (HyperLadder, Bioline).

Sequencing was performed on the Illumina MiSeq platform at the University of Manchester’s Sequencing Facility. The data were analysed using our existing bioinformatics pipeline. Due to the contamination of several of the samples during the processing, we were only able to retrieve data from one replicate for Pool A. Nevertheless, sufficient data were obtained from 4/10 (Pool B), 8/10 (Pool C) and 8/10 (Pool D) mice for analysis. Although significant variability in fitness was observed between replicates (Figure 7.12, Table 7.5), clear and statistically significant differences in fitness were observed for a number of isolates. As expected, our virulence control isolate, ΔAFUB_027890 (ΔpkaC), had reduced fitness in each pool (Table 7.5).

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Interestingly, the cell wall integrity pathway mutants ΔmpkA (which appeared in Pool C and Pool D), and bck1 (Pool B) also showed statistically significant decreases in fitness in the mouse model. Unfortunately, the Δmkk2 mutant, that is another kinase in the cell wall integrity pathway, was not included in this competitive fitness study. Defects in fitness were also observed for ΔAFUB_59390 (Pool C). A number of strains with an apparent hypervirulent phenotype were detected in our study: ΔAFUB_071620 (Pool C) and ΔAFUB_096030 (Pool D) (gin4) (Figure 7.13).

Interestingly, the cell wall integrity pathway mutants ΔmpkA and Δbck1 appeared to show reduced fitness in the macrophage cell line, larvae model and in mice, which underscores its phenotype. ΔAFUB_059390 appeared with reduced fitness in all used virulence models, while ΔAFUB_096030 showed increased fitness in the larvae model and the mice model. Unfortunately, it was not possible to completely validate these fitness defects in isolated mouse model experiments due to time constraints.

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Table 7.5: Strains identified as outliers from competitive fitness analysis of the PK knockout library in each pool 7 days post-infection using mice as the infection model.

Relative log2 fitness Relative fitness as Benjamin-Hochberg Strain ID: ∆ P value Description Common name ratio calculated by Deseq2 corrected P value

AFUB_045810 2.632087 N/A N/A N/A Putative protein kinase

cAMP-dependent protein AFUB_027890 -7.41098 N/A N/A N/A PkaC1

Pool A Pool kinase catalytic subunit MAP kinase kinase kinase, AFUB_038060 -6.51786 -4.292634914 1.08E-07 7.91E-07 Bck1

putative

cAMP-dependent protein AFUB_027890 -7.73145 -5.104228941 2.13E-10 2.34E-09 PkaC1

Pool B Pool kinase catalytic subunit AFUB_071620 3.624031 3.01385984 0.002943036 0.021582263 hypothetical protein

AFUB_059390 -3.57307 -2.746781846 0.013271867 0.049619175 Protein kinase, putative

Mitogen-activated protein AFUB_070630 -4.59716 -2.361555705 0.013532502 0.049619175 MpkA

kinase

cAMP-dependent protein AFUB_027890 -5.23982 -2.432639642 0.006132668 0.033729673 PkaC1

Pool C Pool kinase catalytic subunit Serine/threonine protein kinase, AFUB_096030 1.603382 1.985391577 0.000913636 0.006699998 Kcc4 putative

Mitogen-activated protein AFUB_070630 -7.47183 -2.152916873 0.007455693 0.033547282 MpkA

kinase

cAMP-dependent protein AFUB_027890 -3.74102 -1.696288747 0.045009883 0.165036236 PkaC1

Pool D Pool kinase catalytic subunit

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Figure 7.12: Log2 competitive fitness of the PK knockout mutants obtained from replicates of each pool using mice as an infection model. Each point represents the relative fitness of the assigned mutant from one mouse.

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Figure 7.13: Log2 fitness from the infected lung per each corresponding pool, showing the PK mutants with apparent increased fitness, and those that appeared with reduced fitness. The control strain Δ transposon, and Δ pkaC are denoted in different colours as a reference.

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7.7 Discussion

Although there are approximately 200 species of Aspergillus, few are considered pathogenic. Nevertheless, A. fumigatus is one of the most prevalent species that infects immunocompromised patients with AIDS, and those receiving chemotherapy and with increased incidence of fatal invasive aspergillosis, with high mortality rates reaching 90% in some cases. The severity of the disease is influenced by a range of factors including the site of infection, the host’s immune status, and the regimen of treatment (Sax, 2001). An enhanced understanding of the fungus–host interaction is required to improve our understanding of the mechanisms that drive pathogenicity and to identify novel therapeutic targets for this devastating disease.

Virulence is vital for pathogenicity, and there are numerous factors that might be attributed to the virulence of A. fumigatus. It has been shown that protease secretion is involved in nutrient uptake, which is required for conidia germination inside the lung. Bertuzzi et al., (2014) showed that the pH-responsive transcription factor PacC is involved in the regulation of both protease secretion and invasion of the lung epithelial cells (Bertuzzi et al., 2014).

Micronutrients such as zinc are required for enzyme activity, and it has been shown that the zrfC gene is involved in the virulence of A. fumigatus (Amich et al., 2014). Inactivation of the nucleolar ribosomal biogenesis protein CgrA results in growth impairment at 37°C (Bhabhra et al., 2008). However, gliP has been shown to be important due to its involvement in gliotoxin secretion, and no reduction in virulence was observed in the isogenic mutant for neutropenic mice (Askew, 2008). Reduced virulence in A. fumigatus was associated with disruption of the IDH gene in immunosuppressed mice (Zheng et al., 2012).

Adaptation to low-iron environments is a key factor for fungal virulence; therefore, mutants that are deficient in either siderophore synthesis or secretion could be virulent. Yasmin et al., (2012) found an interesting relation between siderophore and ergosterol biosynthesis (Yasmin et al., 2012). Aspergillus infections appear to be modulated by the pulmonary microbiome (Kolwijck and van de Veerdonk, 2014), although further study is required to understand how the microbiome can be affected by the antimicrobial and its consequences on colonisation.

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Experimentally, different animal models have been utilised to study the virulence of A. fumigatus, such as immunocompetent and immunosuppressed animals (Clemons and Stevens, 2005). Immunosuppressant drugs are administered before and after injecting or inhaling the conidia to produce an inflammatory response to the model system or to enhance the growth of conidia inside the lung for pulmonary aspergillosis. A. fumigatus conidia can be challenged with a macrophage cell line from susceptible animal models such as guinea pigs, rabbits, birds, and mice or rats, in order to study the virulence (Clemons and Stevens, 2005).

Larvae of G. mellonella – the greater wax moth – were found to be an improved model system to study the virulence of pathogens of medical importance (Mukherjee et al., 2010). It is an ethically acceptable model that is able to grow at 37°C, the temperature at which human pathogens are adapted to and thus crucial for the synthesis of many pathogenicity or virulence factors. They are of low nurturing cost, with convenient and feasible injection (Mukherjee et al., 2011). All these advantages make the larvae of G. mellonella suitable for preliminary infection studies, and they were used to evaluate the virulence in a range of fungal and bacterial pathogens in correlation with their virulence in mice (Renwick et al., 2006; Kavanagh and Fallon, 2010) in an attempt to reduce the high cost and the effort that accompanies the use of mice. They have also been used to study the virulence of Candida albicans (Brennan et al., 2002), Cryptococcus neoformans (Mylonakis et al., 2005), A. fumigatus mutants (Maerker et al., 2005; Maurer et al., 2015; Ballard et al., 2018), and A. nidulans (Fernandes et al., 2017).

In this chapter, we competitively assessed the fitness of the PK knockouts library through using macrophage cell lines, G. mellonella larvae and mice. A pkaC mutant, the control strain in our experiments, showed reduced fitness, which is in agreement with Fuller et al., (2011).

In this study, two different approaches were used to sequence the PK knockout library. These are Ion PGM sequencing and Illumina MiSeq platform. Ion PGM sequencing was conducted to sequence PK knockouts library that were obtained from virulence study utilising G. mellonella larvae as infection model. Illumina MiSeq platform was employed as alternative due to contamination issues with PK knockout library, which was obtained from virulence study using mice.

Although high consideration were taken during running these experiments, with zero likelihood of contamination with PK knockout library by conducting all the PCR related 206

works in DNA free lab, contamination does happened during processing the first pool (pool A). Unfortunately, analysing the sequencer output revealed the presence of whole PK knockout library (n=64) in contrast to our expectation for pool A (n=18). This was happened, as the amount of fungal DNA that was extracted from infected lung was small compared to the animal DNA. Ultimately, small level of contamination, which probably happened while loading the chip, will be maximised by sequencing. Considering the risk that might accompany processing other samples, adding to the cost associated with each run, altogether, necessitate processing the remaining samples using Illumina MiSeq platform.

The results of current study showed that some PK mutants had reduced fitness across all different infection models. The cell wall integrity pathway mutant mkk2 showed significant reduction in fitness in larvae using low and high doses; however, it appeared as an outlier only with the high dose model. A similar result was observed with the macrophage model. It has been shown that deleting the genes involved in cell signalling will decrease the virulence of mutants (Fuller et al., 2011), while the mkk2 mutant showed increased susceptibility to antifungal drugs and decreased the adherence and virulence in mice (Dirr et al., 2010).

MpkA, mutant another cell wall integrity pathway member, exhibited reduced fitness in all infection models. Decreased virulence was reported with MpkA (Valiante, 2017). Δbck1 showed significant reduction in fitness in the mice model, and a similar response was observed in the macrophage cell line. However, the apparent increase in fitness was observed in larvae infected with the high dose. Δbck1 orthologs in Fusarium oxysporum f. sp. cubense (Foc) resulted in reduced fungal virulence (Ding et al., 2015), and have been shown to be essential for pathogenicity in Colletotrichum gloeosporioides (Fang et al., 2018).

The mitogen-activated protein kinase cascade mediates the cell wall integrity-signalling pathway in A. fumigatus, and the latter has been shown to be necessary for virulence (Valiante et al., 2009; Valiante et al., 2015). Protein kinases are involved in the cell wall integrity pathway, which promotes the connection between the cell wall and the cytoplasmic membrane with glycoprotein receptors (Valiante et al., 2015), and is associated with the Ras proteins localised to the cell membrane (Al Abdallah and Fortwendel, 2015). Our finding suggests that MAP kinases might be involved in the virulence of A. fumigatus, and that they may ultimately be valuable antifungal drug targets.

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However, reconstitution of the native genes is required to validate the output of this study. Additionally, in vivo studies utilising MAPKs mutant and their reconstituted strains are required to confirm the observed phenotypes and to assess their role in virulence.

In our study, three PK mutants that exhibited reduced fitness in the competitive fitness analysis in larvae retained their phenotype when used to infect larvae individually: ΔAFUB_019930, ΔAFUB_030660 (Rim15), and ΔAFUB_014350 (kin4). Although not statistically significant, ΔAFUB_096080 and ΔAFUB_099990 (SKY1) appeared to have a trend towards reduced virulence in larvae. The Kin4 orthologues appeared to be associated with reduced virulence in C. neoformans (Kim et al., 2015). Rim15 was found to be associated with early meiotic gene expression in yeast (Su and Mitchell, 1993), while SKY1 was found to be similar to human (Forment et al., 2002) and associated with salt tolerance, membrane potential and the Trk1,2 potassium transporter in yeast. These findings serve to underscore the importance of these PK genes and their requirement for pathogenicity.

ΔAFUB_044560 is a serine-arginine protein kinase 1 SRPK1-like Kinase in yeast and similar to that in humans (Forment et al., 2002) appeared in our study with enhanced fitness in larvae using two different doses. The human orthologue SPRK is of special interest as it has been found to be a stimulator for tumour cell growth by modulating the small nucleolar RNA expression in gastric cancer (Li et al., 2019), and as novel target to treat the early stages of glioma. High SRPK1 expression was found to be associated with poor prognosis in breast cancer with lung and brain metastasis. In two separate studies using murine animal, SRPK1 knockdown suppressed metastasis to other organs including the liver, lung, and spleen, in addition to the inhibition of foci adhesion reformation (Rosmalin et al., 2015). These findings suggest that the SPARK1 in fungus behaves differently or they might reflect the difference due to divergency between the human and fungus.

ΔAFUB_012420 encodes the high osmolarity glycerol (hog1) mitogen-activated protein kinase (MAPK), which has been found to be essential for the virulence in some fungal pathogens and controls the adaptation to environmental stress (Ma and Li, R 2013). It has been shown that a hog1 mutant is a virulent in Candida auris (Day et al., 2018). The Δhog1 appeared with increased fitness in competitive fitness analysis using two different inoculums when G. mellonella was utilised as an infection model. However, when using an individual infection model, Δhog1 appeared to have minor reduction in virulence, although this was not statistically significant. Replication of this study is required using Δhog1 208

utilising larvae of G. mellonella. This to be followed by reconstitution of the native gene before conducting in vivo studies using Δhog1 and its reconstituted strain utilising mice as infection model to validate the its phenotype and to assess its role in virulence

The results obtained in this study have shown that competitive fitness analysis could be used to identify the phenotype of certain mutants when conducting virulence studies using large cohorts of strains, and thus enabling a reduction in time, cost and the ethical impact of carrying out these experiments. However, the PK knockout mutants that were shown to have increased or decreased fitness require further validation by the reconstitution of the original genes to confirm their phenotypes.

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Chapter 8: Conclusion and Future Work

8.1 Conclusion

The annual burden of fungal infections has increased in recent decades, especially in immunocompromised patients (Chang et al., 2017). A total estimate of 1.5 million deaths per year, with over one billion affected was reported (Bongomin et al., 2017). Asthma, HIV infection, organ transplantation, cancer and long-term corticosteroid therapies were found to be risk factors that aggravate fungal disease (Lionakis and Kontoyiannis, 2003; Shoham and Marr, 2012; Sipsas and Kontoyiannis, 2012; Denning et al., 2014; Limper et al., 2017).

Four fungi – C. albicans, C. neoformans, A. fumigatus, and H. capsulatum – constitute the main threat for human health (Kim, 2016). Successful eradication of fungal infection is associated with early diagnosis accompanied by early treatment; however, treatment failure was associated with the emergence of resistant strains subsequent to the widespread use of antifungal drugs (Pfaller, 2012).

Fungal treatments are limited to certain classes of drugs including azoles, echinocandins, polyenes, allylamines and pyrimidine analogues (Flevari et al., 2013). However, undesirable side effects, drug–drug interactions and toxicity associated with some classes have limited their use, and thus new antifungal drugs are needed (Lewis, 2011; Pianalto and Alspaugh, 2016).

Protein kinases constitute the second most common group of drug targets, after G-protein- coupled receptors, since they regulate most aspects of cell life by phosphorylation. Abnormal phosphorylation is linked with many diseases including cancers (Cohen, 2002; Nanthapisal et al., 2017). Many drugs that target kinases have been approved for clinical use to treat malignancies and inflammatory diseases (Cohen and Alessi, 2013; Patterson et al., 2014), breast cancer (Yuan et al., 2018). However, in this project we aimed to explore the druggability of kinases in the human pathogen A. fumigatus.

A potential antifungal target should be specific to the fungi rather than the host to reduce drug–drug interactions and host toxicity, and it should also be essential for viability or involved in the pathogenicity of fungi while being conserved across fungal species.

In this project we aimed to assess the Aspergillus fumigatus protein kinases in an attempt to identify a potential antifungal candidate. Bioinformatic analysis of the protein kinases in

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A. fumigatus, enabled identification of 175 protein kinases, comprising the conventional and atypical group kinases. This annotation adds 90 protein kinases to those previously annotated by Kosti et al 2010.Thus; we propose that A. fumigatus may have more protein kinases. According to Aspergillus genome database, only 4.95% of Aspergillus protein- coding genes are annotated as of June 2019. Conducting similar analysis to the whole genome might uncover a new set of kinases.

Interestingly, our finding reveals a presence of two tyrosine kinases. These kinases might be a prospective antifungal targets candidate, as they are more likely to be tyrosine kinase- like kinases (Miranda and Barton, 2007; Kosti et al., 2010). According to Zhao et al., fungi lack animal tyrosine kinase orthologs, but possess a protein kinase lineage (Zhao et al., 2014). This heightens the potential specificity to fungi and may suggest their suitability as a new target’s candidate, which can be investigated further to evaluate their druggability.

Fungal filamentous kinases (Ffks) are providing new candidates to be explored as antifungal drugs. Ffks were previously identified in A. nidulans (De Souza et al., 2013). A. fumigatus appeared to have 15 Ffks, which showed divergence from other kinases and most importantly sharing low sequence similarity with human kinome. Among these kinases, AFUB_024580 shared more than 85% sequence similarity with other Aspergillus species but showed no significant similarity with human kinases. Further studies are required to explore their biological function and to validate their druggability. Other highly conserved kinases were identified in the genome of A. fumigatus that share less than 50% similarity with human. Thus, may represent a promising antifungal targets candidate to be explored for druggability.

Essential genes that are required for fungal growth and viability have the potential to be drug target candidates. Many techniques have been used to identify essential genes including the heterokaryons rescue technique as we describe in this study, conditional promotor replacement (CPR) technology using the nitrogen regulated NiiA promoter, using Aspergillus nidulans alcA promoter (alcAp) to tightly regulate gene expression and transposon mutagenesis (Romero et al., 2003; Hu et al., 2007; Carr et al., 2010). Multiple lines of evidence that support the assertion that genes are essential for viability are required before drug development studies are carried out.

Employing a high-throughput gene knockout strategy, 115 PK genes were disrupted, with knockout strains being barcoded to facilitate competitive fitness studies. Thirty-nine genes were identified as indispensable for viability, while most of their orthologs were also 211

indispensable in A. nidulans and S. cerevisiae, which indicate their requirement for viability.

Essential genes constitute potential antifungal target candidates, of special interest those kinases that share low sequence similarity (< 40%) with human PKs as previously shown in Table 4.3. Interestingly, two are Ffks, which heightens the potential of specificity to fungi. Therefore, they could be prospective candidates to develop successful selective kinase inhibitors that maximises the potential therapeutic efficacy while potentially minimising human harm.

Competitive fitness studies enabled compounds screening of large libraries generated in yeast ( Xu et al., 2007; Hietpas et al., 2012); however, no work has been conducted to assess the competitive fitness of a large library in A. fumigatus. The current study has benefited from work presented in MacDonald et al., (2019), which suggests that a similar system to that used in yeast could be feasible in A. fumigatus (Macdonald et al., 2019). Thus, this study is first study to be conducted in A. fumigatus applying competitive fitness analysis to enable large library screening.

Our findings indicate that competitive fitness can be used to accurately assess the fitness of A. fumigatus strains under different conditions. Through assessing competitive fitness of the non-essential PK knockout mutants in vitro utilising NGS (Ion PGM System), high reproducibility was obtained with biological and technical replicates. In this study, we have also shown the success of the A. fumigatus competitive fitness system to determine the fitness of majority of individual strains, whereas in other organisms there is an impact of the “pool effect” for mutants that showed a discrepancy between competitive fitness and growth defects in individual strains (Amorim-Vaz et al., 2015).

Although, we were able to test the response of the PK mutants in parallel in liquid culture as shown in chapter 5, we were unable to conduct the same experiment on sold media due to time limitation. Given the aforementioned discrepancies that may be observed between competitive and parallel fitness studies it would be sensible to assess the protein kinase null mutant library on solid media

The A. fumigatus cell wall has been searched extensively as a possible antifungal drugs’ target. MAPK has a significant role in maintaining cell wall integrity (MpkA) (Bruneau et al., 2001). The canonical (MAPK) signalling pathways involve a relay of kinases that are required for the organisation of various cellular reactions in eukaryotes. Studies have

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shown that the cell wall integrity pathway is directly involved in responses to oxidative stress, iron adaptation and gliotoxin production in A. fumigatus, whereas deletion of these genes enhances tolerance towards H2O2 (Valiante et al., 2008; Jain et al., 2011).

Phosphoproteomic and transcriptomic analysis of MpkA in A. nidulans revealed its role in multiple functions independent of cell wall stress (Chelius et al., 2019).

Our study implicated the MpkA regulated pathway, which involves MKK2 and Bck1, in fitness during iron acquisition, adaptability to temperature and tolerance to H2O2 and osmotic stress. Current results suggest that the control of cell wall integrity is influenced by MAPK (Mpk A), and the activation of the MpkA MAP kinase cascade via Bck1, which phosphorylates MAPKK, which subsequently phosphorylates MpkA. As shown previously, the cell wall integrity pathway mutants (mpkA, bck1 and Mkk2) were clustered together in a phylogram and exhibited correlation in response to different stress conditions.

Phosphoproteome study could be conducted utilising the cell wall integrity pathway mutants and the wild type strain as a control to look at their role at cellular level, explore the characteristics of these mutants and to explore any connected pathways of importance to fungi.

Biofilms are a fundamental form of microbial growth protecting the fungi from the environment and play an important role in development of clinical infection and were associated with resistance to antifungal drugs (Fanning et al., 2012). Many fugal genera produce biofilms. For example, Candida, Aspergillus, Cryptococcus, Coccidioides, Pneumocystis and Trichosporon.

The azole antifungals constitute one the most frequently used classes in therapy, while resistance is a major drawback (Pfaller, 2012). Mechanisms of resistance have been associated with increased drug efflux, target mutation, target expression deregulation and ergosterol biosynthesis pathway alteration (Tobudic et al., 2012). Non-cyp51 mechanisms of resistance have been reported in A. fumigatus-resistant isolates and was associated with overexpression of cdr1B efflux transporter (Fraczek, et al., 2013). Understanding the molecular mechanisms for antifungal resistance might uncover new facts that may lead to identifying new drug targets.

In this study, competitive fitness profiling was used to evaluate the relative fitness of the PK knockout library in sub-MIC levels of itraconazole, and NGS was used to measure the change in relative abundance of each strain with and without itraconazole. Although the

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level of itraconazole tested was low compared to the standard level that set for resistance, results revealed that two PK mutants (ssn3 and fus3) appeared with elevated MIC and reduced susceptibility to itraconazole on competitive fitness and individual growth. Additionally, one PK mutant (yak1) appeared with increased susceptibility to itraconazole. These results have shown that competitive fitness profiling can be used to identify the fitness of strains to enable study of the mechanism involved in resistance to antifungal drugs at the molecular level.

Biofilm formation was associated with resistant to antifungal in ssn3 (Lindsay et al., 2014). Deletion of Afssn3 is believed to form two “barrier” layers between the intracellular-to- extracellular spaces, resulting in decreased itraconazole penetration into the cell in A. fumigatus (Long et al., 2018). Additionally, deletion of ssn3 results in increased absorption and utilisation of glucose and amino acids, which drives the acceleration of extracellular polysaccharide formation. Increased sphingolipid pathway intermediate accumulation is also driven by the utilisation of the amino acids serine, threonine and glycine, respectively, and induced efflux pump proteins activity upon the absence of ssn3 (Long et al., 2018).

MAP kinase (fus3) was shown to be required by the major facilitator superfamily transporter mediated resistance to oxidative stress and fungicides in the fungal pathogen

Alternaria alternata (Chen et al., 2017). Yak1, exhibited increased sensitivity to H2O2 and triadimefon, an ergosterol biosynthesis inhibitor and revealed attenuated virulence, less conidia and sclerotium formation (Yang et al., 2018).

Assessment of expression of cyp51A, cyp51B and cdr1B gene in response to itraconazole in PK mutants (ssn3, fus3, and yak1) and WT, revealed that in the presence of itraconazole, no significant differences in expression of cyp51A, cyp51B or cdr1B were observed in the fus3 null mutant, whereas for the ssn3 null mutant, cyp51A, cyp51B and cdr1B were upregulated 1.8-, 4.8- and 1.95-fold, respectively, compared to the wild-type. In contrast, expression of all three genes was also upregulated in the yak1 null mutant.

How yak1, snn3 and fus3 regulate the genome of A. fumigatus is not clear. A comparative evaluation of the phosphoproteome between the wild type and null mutants may reveal which proteins are differentially phosphorylated and hence may give further clues to why the strains have different levels of susceptibility to itraconazole.

Experimentally, different models have been utilised to study the virulence of A. fumigatus. These include the challenge of conidia using macrophage cell lines, the infection of G.

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mellonella larvae, and the infection of mice (Clemons and Stevens, 2005; Kowalski et al., 2016; Ballard et al., 2018). In the current study, virulence of A. fumigatus was evaluated using different host models (macrophage cell line challenge, infection of larvae of G. mellonella and infection of mice) by applying competitive fitness profiling. The results revealed high reproducibility across different host models. The virulence control strain pkca1 showed a reproducible decrease in fitness in macrophage cell lines, the larvae of G. mellonella and in mice.

The cell wall integrity-signalling pathway in A. fumigatus has been shown to be indispensable for virulence (Valiante et al., 2009; Valiante et al., 2015). Deleting the genes involved in cell signalling may attenuate the virulence of mutants (Fuller et al., 2011; Valiante, 2017).

In current study, mkk2 and mpkA, the cell wall integrity pathway mutants showed significant reduction in fitness in larvae, and with the macrophage model, while Δbck1, another cell wall integrity pathway mutant, showed significant reduction in fitness in the mouse model, and in the macrophage cell line. In contrast, increased fitness was observed in larvae infected with high dose of inoculum (spore suspension), which might reflect different host response. Δbck1 is essential for virulence in Fusarium oxysporum (Ding et al., 2015). These finding suggest that MAP kinases might be involved in the virulence of A. fumigatus, however more studies are required to validate the results.

Three PK mutants exhibited reduced fitness: ΔPSK1 in the larval model, ΔRim15 in the larval model, and Δkin4 in macrophage cell line and larvae models. Interestingly, their orthologs appeared to be essential for virulence (Su and Mitchell, 1993; Forment et al., 2002; Kim et al., 2015). These findings may serve to underscore the importance of these PK genes and their requirement for pathogenicity. However, reconstitution of these mutants is required before conducting in vivo studies to confirm the given phenotype and their involvement in pathogenicity of A. fumigatus.

ΔAFUB_096080 was shown to have reduced fitness in virulence study using larvae as infection model. This mutant of clear interest, as it is an expansion of CMGC GROUP, and might constitute a potential drug targets candidate as a result of divergence with human kinases.

The high osmolarity glycerol (hog1) mutant has been found to control the adaptation to environmental stress, and is essential for virulence in A. fumigatus and Candida auris

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(Alonso-Monge et al., 2003; Bruder Nascimento et al., 2016; Day et al., 2018). In the current study, Δhog1 appeared to have increased fitness when G. mellonella was utilised as an infection model, while reduced virulence was observed when it was used individually in the same host. We suggest this discrepancy is due to the use of different pathogenicity models, and replication is required to validate these observations.

ΔAFUB_045810 (in mouse infection models), ΔAFUB_071620 (in mouse infection models), ΔAFUB_095720 (in larval infection models), and ΔAFUB_081540 (in larval infection models), appeared to have enhanced fitness in virulence studies, which render the original avirulent. These kinases are among the extended group of CMGC, which have low similarity with human kinases that might heightens their potential as antifungal targets candidate upon using kinase activator to enhance their activity.

This study enables us to focus on protein kinases that are likely to be required for virulence and essentiality. Further validation is required to uncover the role of kinases in the virulence of A. fumigatus via reconstitution of the native genes and conducting in vivo studies before moving forward with kinase inhibitor library screening. Protein kinases are valuable target, the potential antifungal drug candidates may have the potential to overcome undesirable side effects, drug–drug interactions and host toxicity resulting from the similarity of human and fungal drug target proteins, by providing protein targets specific to fungal pathogen cells.

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8.2 Future Work

For any drug development it is crucial to pass pre-clinical studies before submitting Investigational New Drug (IND) Application to enter the clinical trial (phase1-phase3) about 6-7years. If the new drug candidate fulfils the therapeutic efficacy and safety requirements, FDA and EMEA will approve it.

In this project, we highlighted some potential PK mutants that could be prospective antifungal target candidate. Of special interest the essential PK genes, PK mutants that exhibited reduced fitness in virulence studies and other PK genes within Ffks group. The later, could provide specificity toward fungal cells, however any potential PK target candidates should pass the pre-clinical study before moving forward. Here, we recommend the following for future studies:

1. Essential genes: Solve the crystal structure of the protein to enable in silico modelling to identify initial enzyme inhibitors through library screening against known inhibitor molecules. This to be followed by in vitro studies to confirm its efficacy and to test its range of antifungal spectrum before entering clinical trials. 2. The cell wall integrity MAPK pathway: utilising gel kinase assays, isotope- labelled ATP protein labelling, phospho-specific MAPK antibodies and phosphoproteome analysis to investigate their network signalling. 3. CWI pathway mutants: In order to visualise changes in the cell wall resulting from the loss of wall integrity kinase, electronic microscopy could be used to investigate the changes in the cell wall integrity pathway mutants under various stress conditions. 4. PK mutants that showed reduced fitness in virulence studies: Reconstitution of the native is required before conducting virulence study in vivo utilising mice as infection model including both the mutants and their native genes. This to be followed by solving the crystal structure and process as described with essential genes. 5. A Southern blot will be required as final validation for the PK mutants of interest to prove the correct gene has been knocked out. This to ensure that a single knockout cassette was inserted into the generated PK mutant.

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Appendices

Appendix 1: Media, Plasmids and Antibiotics

A: Growth Media

All media were prepared in dH2O in loosely capped bottles and then autoclaved at 121°C for 20 min. They were cooled to 50°C before adding anything and poured into sterile Petri dishes inside a laminar flow hood. 1. Sabouraud dextrose agar (Oxiod) Mycological peptone 10 gm Glucose 40 gm Agar 15 gm To prepare Sabouraud agar solid media, 26 g Sabouraud dextrose agar powder was dissolved in 400ml-distilled water, mixed well and boiled to dissolve completely. Media was autoclaved at 121°C for 20 min and stored at room temperature to be used later.

2. Sabouraud dextrose broth (Oxiod) Dextrose 20 gm Pancreatic digest of casein 5 gm Peptic digest of animal tissues 5 gm To prepare this media, 12 gm Sabouraud dextrose broth was dissolved in 200ml-distilled water and mixed well. The final volume was adjusted to 400 ml and then autoclaved at 121°C for 20 min and stored at room temperature.

3. Aspergillus Minimal Media (MM) To prepare 1L of liquid media, the following ingredients were added to 800ml of d H2O 20 ml Aspergillus salt solution 20 ml Glucose (from 50% glucose stock) pH 6.5 with 10 M NaOH 10 ml Ammonium tartrate (from 500mM stock) 10 ml Biotin (from stock) Volume was adjusted to 1 L using d H2O. For solid media, 1% (w/v) agar No 1 was added and autoclaved at 121°C for 20 min and stored at room temperature

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4. Aspergillus Complete Media (ACM) To prepare 1L of liquid media; the following ingredients were added to 800 ml 0.075 gm adenine (add it first it takes a while to dissolve) 10 gm Glucose 1 gm Yeast extract 2 gm Bacteriological Peptone 1 gm Casamino acids 10 ml Vitamin Solution 20 ml Salt Solution 10 ml Ammonium tartrate (from 500mM stock) pH 6.5 with 10 M NaOH The volume was adjusted to 1 L using d H2O. For solid media 1% of Agar No 3 was added and autoclaved at 121°C for 20 min and stored at room temperature.

5. RPMI Media 1640 To prepare 500 ml of 2X RPMI 1640, the following ingredients was added to 400ml of d H2O (10.4 gm RPMI 1640, 18 gm Glucose and 34.534 gm MOPS [morpholinepropanesulfonic acid]. pH was adjusted to 7.0 using 5M NaOH, and the final volume was adjusted to 500 ml using d H2O. Media was sterilised by filtration the vacuum pump and stored at 4°C. For Solid media, bacteriological Agar was prepared by dissolving 7.5 gm in 500ml distilled water and autoclave at 121°C for 20 min. To prepare 1X RPM1 1640 solid media, 500 mL of warm bacteriological agar was added to the 500 ml of 2x RPMI 1640 in the class II fungal hood, mixed and poured on plates.

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6. Fungal RPMI To prepare 1L of fungal RPMI, the following ingredients from the stock’s solutions were mixed;

Stock Solutions Volume (mL) MilliQ water 500 X10 MOPS-NaOH (pH7.0) 100 X100 Amino acids 10 X50 glutamine 20 X100 Fungal RPMI vitamin stock 10 solution X10 Fungal salts stock solution 100 X100 CaCl2 10 X100 MgSO4 10 X10 D-glucose 100 X100 Ammonium tartrate 10 X1000 glutathione 1.0 X1000 trace elements 1.0 sub total volume (mL) 872

The pH was adjusted to 7.0 using 10M NaOH, media was sterilised using 0.2 µm filter membrane (Corning® 500mL Vacuum Filter/Storage Bottle System) and stored at 4°C.

7. Glycerol nutrient broth This nutrient broth has been used to store fungal spores in -80freezer; it composed of 50% glycerol. It was autoclaved at 121°C for 20 min and stored at room temperature.

B. Transformation Media The Aspergillus strain (A1160), was prepared for transformation by culturing in liquid SAB media at 37°C for 16 hr.

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1. Yeast Peptone Sucrose (YPS) media This media was used for gene knock-out constructs. It contains 2% Yeast extracts 8 gm 0.5 % Peptone 2 gm 10mm Tris citrate (MW 121.14) 0.242 1 M Sucrose 136.92.4 gm Agar 6 gm These ingredients were dissolved in 200 ml, mixed with stir bar to dissolve completely. Then the volume was adjusted just below 400ml, and the pH was adjusted to 6 with 10% HCl and then autoclaved at 121°C for 20 min. Subsequently 200μg/ml hygromycine was added to the cooled medium at 50°C.

2. Vogel’s Media To prepare 500 ml of media, the following ingredients were added:

50X Vogel’s Salt 10 ml

Sucrose 10 gm Final conc. 2%

Agar 10 gm

dH2O to 500 ml

Autoclaved at 121°C for 20 min, and biotin was added before use.

C- Vectors

Plasmids used for gene knock-out and for reconstitution.

1. pPTRII pPTRII (10.0kb) (Kubodera et al., 2002), was kindly provided by Dr Nick Read (MFIG). It was used as a template to amplify a 2.0 kb PtrA cassette. This cassette was combined with gene upstream and downstream flanking regions and used in A. fumigatus transformation as a gene knock-out construct.

2. pAN7-1

PAN7-1 (6756 bp; Punt et al., 1987; Gene Bank Z32698.1), was kindly provided by Dr Bromley group. This cassette was used as a selectable marker in A. fumigatus transformation experiments.

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D- Antibiotics

1. Hygromycin B (Melford)

A stock solution of 50 mg/ml was obtained from manufacturer and stored at 4°C). Only 200 μg/ml of hygromycin B was used in A. fumigatus transformation experiments to select transformed colonies of A. fumigatus.

2. Pyrithiamine (Sigma-Aldrich Ltd)

A stock powder of 1.0 mg Pyrithiamine was dissolved in 1 ml dH2O in a dark vial and stored at 4°C until required. It was used as a selectable marker in A. fumigatus transformation experiments at a final selective concentration of 0.1 μg/ml.

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Appendix 2: Buffers and Solutions All buffers and solutions were prepared in (distilled water) dH2O and sterilised either by autoclaving at 121°C for 20 min or by filtration using a 0.22 μM filter

1. Phosphate Buffered Saline (PBS) NaCl 4.00 gm K2 HPO2 0.605 g KH2PO4 0.17 gm All the ingredients were dissolved in 400ml of distilled water. The PBS buffer was autoclaved at 121°C for 20min.

2. Phosphate Buffered Saline (PBS) with 0.1% Tween 20(Sigma) 500μl of Tween 20 was added to 500 ml PBS buffer. The PBS-tween 20 was autoclaved at 121°C for 20 min.

3. TAE buffer (1L, 50X) Tris-base (MW= 121.14) 242 gm 0.5 M EDTA 100 ml Glacial Acetic acid 57.1 ml Tris has been added to 600 ml of d H2O, mixed with stir bar to dissolve and then added the EDTA and acetic acid. Final volume was then adjusted up to 1L with dH2O. The final working concentration is (1X).

4. Kcl CaCl2 solution Dissolve the following ingredients in 300 ml dH2O:

0.6 M Kcl 22.36 gm CaCl2 2.7744 gm Volume was adjusted to 500 ml with dH2O, autoclaved at 121°C for 20 min and stored at room temperature.

5. CTAB extraction buffer The following ingredients were dissolved in 200 ml dH2O 100mMTris-HCL 3.152 gm 1.4M NaCL gm

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10mM EDTA gm 2% CTAB 2.00gm Volume was then adjusted to400 ml with dH2O, pH adjusted to 8.0, and autoclaved at 121°C for 20 min.

6. Biotin stock solution

100x Stock 100 ug/ml final concentration Add 10 mg to 100ml H2O Autoclave Store at 4 °C Working stock Make this by 1/100 dilution from 100x stock. Use this one for adding to minimal media and ACM.

7. Glucose stock solution 50 % 50% w/v glucose Autoclave Store at 4°C

8. Aspergillus Salt Solution

To prepare 1 L of media, the following ingredients were added to 800 ml of d H2O;

• 26 gm KCl (Potassium chloride) • 26 gm MgSO4. 7H2O (Magnessium dihydrogen heptahidrate) • 76 gm KH2PO4 (Potassium dihydrogen orthophosphate) • 50 mL Trace element solution Final volume was adjusted using d H2O, while 1.5 ml/l Chloroform was added as a preservative and stored at 4°C.

9. Ammonium tartrate stock (500 mM)

To prepare 500 ml, 46g ammonium tartrate was added, autoclaved at 120C for 20 min and stored at 4°C.

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10. Vitamin stock solution

To prepare 1L, the following ingredients were added to 800 ml of d H2O; • 400 mg PABA (4-aminobenzoic acid) • 50 mg aneurin (thiamine) • 1 mg biotin (or 10ml of biotin stock-stock 0.1mg/ml) • 24 gm inositol • 100 mg nicotinic acid • 200 mg Panto (DL-phantothenic acid) • 250 mg pyridoxine • 100 mg riboflavin • 1.4 gm choline chloride Final volume was adjusted using d H2O, aluminium foil was used to wrap the bottle, autoclaved and stored at 4 °C.

11. Trace Elements Solution

To prepare 1L, the following ingredients were added to 800 ml of d H2O; • 40 mg Na2B4O7.10H2O (di-sodium tetraborate 10H2O) • 400 mg CuSO4 .5H2O (cupric sulphate 5H2O) • 800 mg FePO4. 2H2O (ferric orthophosphate dihydrate) • 800 mg MnSO4.2H2O (manganese sulphate dihydrate) • 800 mg Na2MoO4.2H2O (sodium molybdate 2H2O) • 8g ZnSO4. 7H2O (zinc sulphate 7H2O) Autoclaved and stored at 4 °C.

12. Tris-HCl (1 M) pH 7.2 To prepare 1L of stock solution, 121.14 g was added to 1L of d H2O. PH was adjusted to 7.2 with concentrated HCl, autoclaved and stored at 4°C.

13. Tris-HCl (1 M) pH 8.0

To prepare 1L, 121.14 gm was added to d H2O. Ph was adjusted to 8 using concentrated HCl, autoclaved and stored at 4°C.

274

14. Amino Acids stock solution X100 (store at -20˚C)

Final concentration in fRPMI ver. 3 Final concentration in X100 stock solution Components Molecular Weight Concentration (mg/L) mM Concentration (mg/L) Glycine 75.0 10.0 0.133 1000 L-Arginine 174.0 200.0 1.149 20000 L-Asparagine 132.0 50.0 0.379 5000 L-Aspartic acid 133.0 20.0 0.150 2000 L-Cystine 2HCl 313.0 65.0 0.208 6500 L-Glutamic Acid 147.0 20.0 0.136 2000 L-Histidine 155.0 15.0 0.097 1500 L-Hydroxyproline 131.0 20.0 0.153 2000 L-Isoleucine 131.0 50.0 0.382 5000 L-Leucine 131.0 50.0 0.382 5000 L-Lysine hydrochloride 183.0 40.0 0.219 4000 L-Methionine 149.0 15.0 0.101 1500 L-Phenylalanine 165.0 15.0 0.091 1500 L-Proline 115.0 20.0 0.174 2000 L-Serine 105.0 30.0 0.286 3000 L-Threonine 119.0 20.0 0.168 2000 L-Tryptophan 204.0 5.0 0.025 500 L-Tyrosine disodium salt dihydrate 261.0 29.0 0.111 2900 L-Valine 117.0 20.0 0.171 2000

275

15. X50 L-glutamine stock solution (store at -20˚C)

Final concentration in fRPMI ver. 3 Final concentration in X50 stock solution

Components Molecular Weight Concentration (mg/L) mM Concentration (mg/L)

L-Glutamine 146.0 300.0 2.055 15000

16. X100 Fungal RPMI Vitamin stock solution (store at 4˚C)

Final concentration in X100 stock Final concentration in fRPMI ver. 3 solution Components Molecular Weight Concentration (mg/L) mM Concentration (mg/L) Vitamins Biotin 244.0 0.20 0.0008 20.00 Choline chloride 140.0 3.00 0.0214 300.00 D-Calcium pantothenate 477.0 0.25 0.0005 25.00 Folic Acid 441.0 1.00 0.0023 100.00 Niacinamide 122.0 1.00 0.0082 100.00 Para-Aminobenzoic Acid 137.0 1.00 0.0073 100.00 Pyridoxine hydrochloride 206.0 1.00 0.0049 100.00 Riboflavin 376.0 0.20 0.0005 20.00 Thiamine hydrochloride 337.0 1.00 0.0030 100.00 Vitamin B12 1355.0 0.01 0.0000 0.50 i-Inositol 180.0 35.00 0.1944 3500.00 276

17. X10 Fungal Salts stock solution (store at room temperature ˚C)

Final concentration in fRPMI ver. 3 Final concentration in X10 stock solution

Components Molecular Weight Concentration (mg/L) mM Concentration (mg/L) Potassium Chloride (KCl) 75 400 5.33 4000

Sodium Bicarbonate (NaHCO3) 84 2000 23.81 20000

Sodium Chloride (NaCl) 58 6000 103.45 60000

Sodium Phosphate dibasic (Na2HPO4) anhydrous 142 800 5.63 8000

18. X100 CaCl2 stock solution (store at room temperature ˚C)

Final concentration in fRPMI ver. 3 Final concentration in X100 stock solution Components Molecular Weight Concentration (mg/L) mM Concentration (mg/L) Calcium chloride (CaCl2) 110.98 46.6 0.42 4660

19. X100 MgSO4 stock solution (store at room temperature ˚C)

Final concentration in fRPMI ver. 3 Final concentration in X100 stock solution Components Molecular Weight Concentration (mg/L) mM Concentration (mg/L) Magnesium Sulfate (MgSO4) (anhydrous) 120 48.84 0.41 4884 277

20. X10 MOPS-NaOH (pH7.0) stock solution (store at room temperature ˚C)

Final concentration in fRPMI ver. 3 Final concentration in X10 stock solution

Components Molecular Weight Concentration (mg/L) mM Concentration (mg/L)

MOPS 209.26 34,530 165 345300

21. X10 D-glucose stock solution (20% glucose) (store at room temperature ˚C)

Final concentration in fRPMI ver. 3 Final concentration in X10 stock solution Components Molecular Weight Concentration (mg/L) mM Concentration (mg/L) D-Glucose (Dextrose) 180 20000 111.1000 200000

22. X1000 Glutathione (store at -20˚C)

Final concentration in fRPMI ver. 3 Final concentration in X1000 stock solution Components Molecular Weight Concentration (mg/L) mM Concentration (mg/L) Glutathione (reduced) 307 1 0.0033 1000

278

23. X100 Ammonium tartrate (500 mM ammonium tartrate) (store at room temperature ˚C)

Final concentration in fRPMI ver. 3 Final concentration in X1000 stock solution Components Molecular Weight Concentration (mg/L) mM Concentration (mg/L) Ammonium tartrate 184.148 920.74 5.00 92074

24. X1000 trace elements solution (store at room temperature ˚C)

Final concentration in fRPMI ver. 3 Final concentration in X1000 stock solution Components Molecular Weight Concentration (mg/L) mM Concentration (mg/L) Na2B4O7.10H2O 381 0.04 1.05E-03 40 CuSO4. 5H2O 250 0.40 1.60E-03 400 FeCl3.6H2O 270 1.16 4.28E-03 1157 MnSO4. 2H2O 187 0.80 4.28E-03 800 Na2MoO4. 2H2O 430 0.80 1.86E-03 800 ZnSO4. 7H2O 288 8.00 2.78E-02 8000

279

Appendix 3: List of strains used in the project. Red colour indicate the putative essential genes among the PK knockout collection, the remaining are the viable PK knockouts.

No ID Strain Δ No ID Strain Δ No ID Strain Δ

1 AFUB_045810 39 AFUB_001600 77 AFUB_073970 2 AFUB_050750 40 AFUB_053500 78 AFUB_078810 3 AFUB_021710 41 AFUB_018600 79 AFUB_093160 4 AFUB_053300 42 AFUB_023600 80 AFUB_089250 5 AFUB_029290 43 AFUB_010360 81 AFUB_075210 6 AFUB_012420 44 AFUB_044400 82 AFUB_090090 7 AFUB_032300 45 AFUB_043460 83 AFUB_078920 8 AFUB_026400 46 AFUB_028770 84 AFUB_077210 9 AFUB_044560 47 AFUB_036500 85 AFUB_066030 10 AFUB_030660 48 AFUB_051670 86 AFUB_095720 11 AFUB_052450 49 AFUB_023730 87 AFUB_054290 12 AFUB_041010 50 AFUB_027480 88 AFUB_060320 13 AFUB_043130 51 AFUB_030570 89 AFUB_081870 14 AFUB_010510 52 AFUB_053520 90 AFUB_095460 15 AFUB_029710 53 AFUB_039100 91 AFUB_087320 16 AFUB_018770 54 AFUB_007300 92 AFUB_079830 17 AFUB_020560 55 AFUB_014350 93 AFUB_078980 18 AFUB_029320 56 AFUB_006320 94 AFUB_053960 19 AFUB_025560 57 AFUB_052630 95 AFUB_070630 20 AFUB_038630 58 AFUB_035220 96 AFUB_072800 21 AFUB_029240 59 AFUB_019930 97 AFUB_068890 22 AFUB_002120 60 AFUB_017750 98 AFUB_072000 23 AFUB_016170 61 AFUB_011380 99 AFUB_095640 24 AFUB_048440 62 AFUB_027890 100 AFUB_087120 25 AFUB_013090 63 AFUB_051100 101 AFUB_075350 26 AFUB_019630 64 AFUB_056020 102 AFUB_054020 27 AFUB_006780 65 AFUB_089870 103 AFUB_081540 28 AFUB_053440 66 AFUB_059390 104 AFUB_055480 29 AFUB_039620 67 AFUB_059540 105 AFUB_056640 30 AFUB_038640 68 AFUB_059090 106 AFUB_074550 31 AFUB_045840 69 AFUB_066150 107 AFUB_071600 32 AFUB_053070 70 AFUB_071620 108 AFUB_054310 33 AFUB_051750 71 AFUB_056110 109 AFUB_098230 34 AFUB_015480 72 AFUB_082830 110 AFUB_099170 35 AFUB_026080 73 AFUB_082700 111 AFUB_096030 36 AFUB_029820 74 AFUB_072650 112 AFUB_096080 37 AFUB_027640 75 AFUB_063820 113 AFUB_099990 38 AFUB_038060 76 AFUB_077790 114 AFUB_006190 115 AFUB_089280

280

Appendix 4: 96-well plate orientation with 95 corresponding PK gene, which was used in PCR reactions. The last well (H12) was used as a negative control.

Plate 1

1 2 3 4 5 6 7 8 9 10 11 12

A AFUB_045810 AFUB_050750 AFUB_021710 AFUB_053300 AFUB_029290 AFUB_012420 AFUB_032300 AFUB_026400 AFUB_044560 AFUB_030660 AFUB_052450 AFUB_041010

B AFUB_043130 AFUB_010510 AFUB_029710 AFUB_018770 AFUB_020560 AFUB_029320 AFUB_025560 AFUB_038630 AFUB_029240 AFUB_002120 AFUB_016170 AFUB_048440

C AFUB_013090 AFUB_019630 AFUB_006780 AFUB_053440 AFUB_039620 AFUB_038640 AFUB_045840 AFUB_053070 AFUB_051750 AFUB_015480 AFUB_026080 AFUB_029820

D AFUB_027640 AFUB_038060 AFUB_001600 AFUB_053500 AFUB_018600 AFUB_023600 AFUB_010360 AFUB_044400 AFUB_043460 AFUB_028770 AFUB_036500 AFUB_051670

E AFUB_023730 AFUB_027480 AFUB_030570 AFUB_053520 AFUB_039100 AFUB_007300 AFUB_014350 AFUB_006320 AFUB_052630 AFUB_035220 AFUB_019930 AFUB_017750

F AFUB_011380 AFUB_027890 AFUB_05110 AFUB_056020 AFUB_089870 AFUB_059390 AFUB_059540 AFUB_059090 AFUB_066150 AFUB_071620 AFUB_056110 AFUB_082830

G AFUB_08270 AFUB_072650 AFUB_063820 AFUB_077790 AFUB_073970 AFUB_078810 AFUB_093160 AFUB_089250 AFUB_075210 AFUB_090090 AFUB_078920 AFUB_077210

H AFUB_066030 AFUB_095720 AFUB_054290 AFUB_060320 AFUB_081870 AFUB_095460 AFUB_087320 AFUB_079830 AFUB_078980 AFUB_053960 AFUB_070630 Empty

Plate 2

A AFUB_072800 AFUB_068890 AFUB_072000 AFUB_095640 AFUB_087120 AFUB_075350 AFUB_054020 AFUB_081540 AFUB_055480 AFUB_056640 AFUB_072800 AFUB_068890

B AFUB_054310 AFUB_098230 AFUB_099170 AFUB_096030 AFUB_096080 AFUB_099990 AFUB_006190 AFUB_089280 Empty Empty AFUB_054310 AFUB_098230

281

Gene ID P1 P2 TAGTTCTGTTACCGAGCCGG AFUB_006190 CTCCCCACTTGGAGATGGTA CCTTGAACGAGAAGGTCGAG

Gene ID P3 P4 GCTCTGAACGATATGCTCCC AFUB_010510 TGGATTTGGTCTACGGATGA CGTGGAGCATGAAGCATTTA

GCTCTGAACGATATGCTCCC AFUB_096080 AGTTAAAAACGCCGGGAGA GAGCCAAGCGCTTAGAAGTC T

GCTCTGAACGATATGCTCCC AFUB_006190 GCCTGTCAACCGATCGTAAT TCTGCAGA TTTCAGCCTTCA

Gene ID P5 P6 GAAGCACACATTACGCATG AFUB_098230 AAGCAGTGGACCTCCAAGAA G

AFUB_051100 GAACGCCGTATCGTTTTTGT TATCGAGGAGACGGAACAGG

AFUB_006190 CTGGGTATGATGGTGACGTG CCTATGATGACATGGCGTTG

AFUB_060320 TCATGAAAGCGCATAATTGC TACGATTGACTGCCATGCTC

282

Gene ID P1 P2

AFUB_018600 TCATCCATCATGCCCTAAGC TAGTTCTGTTACCGAGCCGG CACTGAATTTCCCAGCCTTC AFUB_025560 CCCATCACCTTAGCCTTCAA TAGTTCTGTTACCGAGCCGGAAGTCGGGAAGGAGGTGGTA AFUB_070630 TGGAAACCTACATCTGGGTGA TAGTTCTGTTACCGAGCCGG AATTCGAATCGCAATGCTTC

Gene ID P3 P4

AFUB_027640 GCTCTGAACGATATGCTCCC GCGAAACAGGGAAGTTTCT GTCCATGAGACGGACGATG

AFUB_029240 GCTCTGAACGATATGCTCCC CTCCAGCATTGCCACTACAA CGGCCTAACAGAGGAAAAGA

AFUB_075350 GCTCTGAACGATATGCTCCC GGGGAGAAAGGGGTCTACA GAAAATGGCTCGACGGTTTA

P1 P2 TGGAATCCTGTTCCATCTCC TAGTTCTGTTACCGAGCCGGATACACCAGCGCAGAACTCC

P3 P4

GCTCTGAACGATATGCTCCAACAAGCCGCAATGTAACTAGGG CGAAAATCTGGGTTGATGG

P5 P6

TGGAGATCCTTCATCACACG TGACAGAATGACCCGTAGCC 283

Appendix 8: Primers 1 and 2, which were used to amplify the upstream flanking sequences

Gene ID P1 P2 AFUB_045810 GGTAACGAGGGTCAACTGGT TAGTTCTGTTACCGAGCCGGAGATCCCGAGGGCTTTCTCT AFUB_050750 GATGGTGTTCTGGCCCAAGA TAGTTCTGTTACCGAGCCGGGATCCGAGGCTGATACGCAA AFUB_021710 TCTCTGGATGGAGACCTCAAGA TAGTTCTGTTACCGAGCCGGGATGTGCGATTAGGAGGGGG AFUB_053300 TATAGAGAGCCACTCCGGGG TAGTTCTGTTACCGAGCCGGCTCACTGGCCACTATAGCGT AFUB_029290 CGGCTGACACTGATACCCTC TAGTTCTGTTACCGAGCCGGCGGACTCAAAACGTCCTCGA AFUB_012420 AGTTGCCCCATTGGTGACAT TAGTTCTGTTACCGAGCCGGGGGCAGCTTTCTAACGGTCT AFUB_032300 TTCACTGACGGTGTCCACTG TAGTTCTGTTACCGAGCCGGGGGTGGATTTCACGGCTACA AFUB_026400 ACGCCCATCGATTGTGAGTT TAGTTCTGTTACCGAGCCGGGAGGTTTAAGGAGAGGGGCG AFUB_044560 CCATGGTACGGATCCTCACG TAGTTCTGTTACCGAGCCGGTTACTTCCCGTAAGCCGACG AFUB_030660 TCCAGAAGAGTTGGGGAAGGA TAGTTCTGTTACCGAGCCGGTCAAGGGAGCGAGATGAACC AFUB_052450 GATGACGACGACGATGACGA TAGTTCTGTTACCGAGCCGGTAATGTCGTGGACCTGGTGG AFUB_041010 AACTGTGAGGATGGAGCCAC TAGTTCTGTTACCGAGCCGGATCATCGGATGCCCCAATGG AFUB_043130 CCACCGCCTTCCAACTTGTA TAGTTCTGTTACCGAGCCGGGTGGGGTGAAACAGAGTGCT AFUB_010510 GGCGCCCAGGTACCTTTTAA TAGTTCTGTTACCGAGCCGGCAAGGAGATCGTGGGAAAGGA AFUB_029710 GCAGTTCGTCCTTCTCCTCC TAGTTCTGTTACCGAGCCGGTCGAGTTGGTAACCGTGGTG AFUB_018770 GCCTCCACATCTGTGCCTTA TAGTTCTGTTACCGAGCCGGGAGGACGGACAAGACAACGT AFUB_020560 CAGCGTGGGAGGTATGAAGG TAGTTCTGTTACCGAGCCGGGGAGTGGGAAGAGCACAACA AFUB_029320 TGAGAGACGGTTGACACCAC TAGTTCTGTTACCGAGCCGGTCCCATGGGAATGCTCGAAC AFUB_025560 AATCAGCTCCCAAATCGCCA TAGTTCTGTTACCGAGCCGGCCAAAGCCAGTAGATTTTGCCG AFUB_038630 AATCCGCGCTCGTTATTTGC TAGTTCTGTTACCGAGCCGGGCACAGGGATATGCAGCCTT AFUB_029240 CTGCCGCAACAGTCAAGAAC TAGTTCTGTTACCGAGCCGGGCTTGTGGTTGCCATCATGG AFUB_002120 CGCCTCCGAAATTGCGATTG TAGTTCTGTTACCGAGCCGGCGGCAAAACGATGAGGCTTT 284

AFUB_016170 TGTTCCACGGTGGTGTTGAA TAGTTCTGTTACCGAGCCGGGCTGTAGGAGTGTCGGATGG AFUB_048440 CCCAGTTCCACTAGTCACGG TAGTTCTGTTACCGAGCCGGTTAAGAGACCTTCGCTCGCC AFUB_013090 GGAGCCAATCCGCGATATCC TAGTTCTGTTACCGAGCCGGGACGGTTTGATGACCCTGGT AFUB_019630 CCTTTCATGGCCGGGAATCT TAGTTCTGTTACCGAGCCGGTGTCGTCATCTTGACTGTCGA AFUB_006780 TCGGTAGTGGTCCATCTGGT TAGTTCTGTTACCGAGCCGGAGATCTGGCTTGGGAGTTGC AFUB_053440 GGAACATCCCAAAACGAACCA TAGTTCTGTTACCGAGCCGGCTGCGACAGAGGTCAGTCAA AFUB_039620 CGGTGCCCTTTCTGTCAAAC TAGTTCTGTTACCGAGCCGGGCGATCCGGATGACAGATGT AFUB_038640 CCTGTGCGCTATGGATCTGT TAGTTCTGTTACCGAGCCGGTAGAATGGAGCGTGCACGAG AFUB_045840 TGTCACAATAGGCGGTGCTT TAGTTCTGTTACCGAGCCGGGACTACGTTGCTGGAGCCTT AFUB_053070 TTCTTCCCCCTACTCTCCCG TAGTTCTGTTACCGAGCCGGAACGTCCGTGCTCCAATGAT AFUB_051750 GCGAACAACAGTCAGCACAG TAGTTCTGTTACCGAGCCGGGGATTTTGACTCAGCCGGGA AFUB_015480 AACAGCGACGAGAGCTTTCA TAGTTCTGTTACCGAGCCGGGAGGGTGCTGTTTCGGGTAA AFUB_026080 TGAGAGTGAACCTTTTCGGAGG TAGTTCTGTTACCGAGCCGGCCCCAGGGTGGACATTTGAA AFUB_029820 AAGACCCCTTGCTCTCTGGA TAGTTCTGTTACCGAGCCGGATTCTCACCGTGGGTTGTCC AFUB_027640 AAGGGTCAAAGGCCGATCAG TAGTTCTGTTACCGAGCCGGGTGCGCGCTTTATTAACCCA AFUB_038060 CCCTTTCAAGTTCCTCCCCC TAGTTCTGTTACCGAGCCGGGTCGTGGTCTGCCTATACCG AFUB_001600 ACTGTTGGTTGCAAGAACGC TAGTTCTGTTACCGAGCCGGTTAGAACAAGCCCAGCCAGG AFUB_053500 TGCCGCCATCAATAACAGGT TAGTTCTGTTACCGAGCCGGATGTGCGAACGGAGGACAAT AFUB_018600 GAGATGACGGGAATGGGCTC TAGTTCTGTTACCGAGCCGGGGAGCCCAGTGTCGTTTTCT AFUB_023600 CCTGGCGGACTCTTTCGTAA TAGTTCTGTTACCGAGCCGGGCTTTGGGAAGAGGAGAAGCA AFUB_010360 CCAAAGAGCAGATCCCCCAA TAGTTCTGTTACCGAGCCGGCCACACATCTACACGGCTGT AFUB_044400 GCTTTTCTCGATTGCACCCC TAGTTCTGTTACCGAGCCGGGCGACTGCGTTCTCCCTTAT AFUB_043460 AGAGACAGCCCGGACACTAT TAGTTCTGTTACCGAGCCGGGACCGAAGCTAAGTGAGGGG AFUB_028770 CATGGATCGGGTGTTTTGCG TAGTTCTGTTACCGAGCCGGACGGACTGATGATTGTGCCA AFUB_036500 GACTGTTTGTGGCGGATTCG TAGTTCTGTTACCGAGCCGGGACCCAGGTTGCAATGGAGA AFUB_051670 ACGTTCTCAGCGTCTCCATG TAGTTCTGTTACCGAGCCGGCCGACTGGAGAACGGATGAA 285

AFUB_023730 GCTCTGATCGCCCAGAACAA TAGTTCTGTTACCGAGCCGGTTATCGGCAACTGTGGTCGT AFUB_027480 GGCTGTTGCAAGTGAGCATT TAGTTCTGTTACCGAGCCGGAGGAACGACTTGAACACCCC AFUB_030570 CGCAGCACAGATCAAAGTACT TAGTTCTGTTACCGAGCCGGAAGGATGGCTCCCAAACGAG AFUB_053520 CGGATGTTCGACAGGTGACA TAGTTCTGTTACCGAGCCGGTATCATCGTGCTTGGGTGCG AFUB_039100 GGTCTTGAAGCGACCTCTCT TAGTTCTGTTACCGAGCCGGTCAGGTCAGAAAGCGCCAAT AFUB_007300 ACTGCACAAGTGTTGTTACCA TAGTTCTGTTACCGAGCCGGGGATGCTTTCCTCCCTGTCC AFUB_014350 TGCCTTTTCCTTCCTGTTTCA TAGTTCTGTTACCGAGCCGGACAGATTGAAGGTGCTGCGT AFUB_006320 AGTGGCTGTTGATTGGCTGA TAGTTCTGTTACCGAGCCGGAGGAGGGAGAGAACGACACA AFUB_052630 GCCTGGGTTGTTGAAAGCTG TAGTTCTGTTACCGAGCCGGCTCGGAGGTGATCTGATCGC AFUB_035220 GCGAAAGAGCAACATGTCCA TAGTTCTGTTACCGAGCCGGTCCGATGGCTTTGTAGACGC AFUB_019930 ACGACAAATGGCCTGTGGTT TAGTTCTGTTACCGAGCCGGGGACGACAAAGGACTCGGTT AFUB_017750 TTGTTAACCCACTGCGACGA TAGTTCTGTTACCGAGCCGGCCCACAACCCGGTATTTCGA AFUB_011380 TGTTTCGGCACATACCAGCT TAGTTCTGTTACCGAGCCGGGATGAGGCGACTACATGAGGG AFUB_027890 GGTGTACAAGTACCCCTGGC TAGTTCTGTTACCGAGCCGGGATTGGTGAGTGGCAGGGTA AFUB_051100 CGTTGAAACGTCCGTTTGCT TAGTTCTGTTACCGAGCCGGCGGTGGATCCCTGGAGAAAG AFUB_056020 GCGGACGAGATACCCACAAA TAGTTCTGTTACCGAGCCGGGGAGTAGTAGACTGATGGGGGT AFUB_089870 CAATTTAGAGGCGCTGCGAC TAGTTCTGTTACCGAGCCGGTGAGCAGCCGATCACAAGAA AFUB_059390 AAGATAGGGGCAGCTGACCT TAGTTCTGTTACCGAGCCGGGGTCGCTCCAGACGTCATAC AFUB_059540 GTGCCTGGAGAAGTCGTACG TAGTTCTGTTACCGAGCCGGCAGTAAACCCGGGGAAACGA AFUB_059090 TGTGATGGGGTTTATGGCCC TAGTTCTGTTACCGAGCCGGCGACCACTATGGAAGCGACA AFUB_066150 TCGATATCAAGAACGGCGCA TAGTTCTGTTACCGAGCCGGAACCAAGCACGTCCTGTCAA AFUB_071620 CGGATCCATGGTACGGATCG TAGTTCTGTTACCGAGCCGGAGCAAGGCGGAGAAACAAGA AFUB_056110 TATCGCGGTCTACGGTTTGG TAGTTCTGTTACCGAGCCGGTGTTGTGACGAGAACTGCGA AFUB_082830 CTCGAATTGACGAACGCGAC TAGTTCTGTTACCGAGCCGGTGATCTTTCCGTGATCCCCC AFUB_082700 GCCTCCTTACCGAGTTCCTG TAGTTCTGTTACCGAGCCGGCTGATTCGCAGCTTCCGAGT AFUB_072650 GCCCCCTGTTGGCTAGATAA TAGTTCTGTTACCGAGCCGGATTTGGCGAGCTATAGGGGC 286

AFUB_063820 TGCACACAATAGCCAGTCGG TAGTTCTGTTACCGAGCCGGCAGTTGTCTCGGACCAGGTG AFUB_077790 TGATACCTGGCCCCGAGTTA TAGTTCTGTTACCGAGCCGGCATGATGAGCGTGCGGAAAG AFUB_073970 TGCTCCGGGTTTGACAGATC TAGTTCTGTTACCGAGCCGGACTTTTGCTCGAAGGACGGT AFUB_078810 TACTGCCGATCATCAGCGAC TAGTTCTGTTACCGAGCCGGTCATAGTCAAGGTCACCCCC AFUB_093160 GCGGCCATAAACAGAAAGCC TAGTTCTGTTACCGAGCCGGGAGAGTATGGAGGGCTGCAC AFUB_089250 CCGTTCCTCGCTCCTCTTTT TAGTTCTGTTACCGAGCCGGTGTGAAGAGAGACGGCTTCG AFUB_075210 ACTATTCCGCAGCAGCTTCC TAGTTCTGTTACCGAGCCGGAGTATGGCGTTCGGTTTGGT AFUB_090090 CCTCAGCATGATAGCCGCAA TAGTTCTGTTACCGAGCCGGAAAAGACAAACAGCAGCGCC AFUB_078920 AAAAAGAGGCGCCGTGAAAC TAGTTCTGTTACCGAGCCGGGCGCCACGAATTTGATCGAG AFUB_077210 CGCAGAAGATCTCAGCTGCT TAGTTCTGTTACCGAGCCGGGCGACGACCTCATCTCAAGA AFUB_066030 GGCGAAGGTTCAACCCAAAC TAGTTCTGTTACCGAGCCGGGGACGCTAGTTCCACCGAAA AFUB_095720 ATTTCCACCTTGGCTTCCGT TAGTTCTGTTACCGAGCCGGCAGCTCTACTTTCCGCAGCT AFUB_054290 GGTTGATTGACTTGCGTGGC TAGTTCTGTTACCGAGCCGGGCGGCTAGTAGGGCATGTAG AFUB_060320 GATCTCCACGACGAATGCCA TAGTTCTGTTACCGAGCCGGAGCTCCGTTTATTCTGCTGGA AFUB_081870 ATGAGGAGTAGGAGCCGGTT TAGTTCTGTTACCGAGCCGGTCAATCGTGTGACCAGGCAA AFUB_095460 AGACATCACAAGCTTCCGCA TAGTTCTGTTACCGAGCCGGAGGAGGGAGAGGAGTCAACC AFUB_087320 GAGGAATTGGCAATGGCAGC TAGTTCTGTTACCGAGCCGGCGTAGGAAGCCGGACATTCA AFUB_079830 AGGAGCATACGGCCAACTTC TAGTTCTGTTACCGAGCCGGTAAAGGTGGCCCGGAAAAGG AFUB_078980 CTCTACTACACAGCGGCGAC TAGTTCTGTTACCGAGCCGGCCGGTAAATCAATTCCCACACG AFUB_053960 TCAGCATGGGGAAAGTTCGA TAGTTCTGTTACCGAGCCGGTCGTGCGGACGCTGATTTAT AFUB_070630 CAGGGTCTGTTCGACAGGTC TAGTTCTGTTACCGAGCCGGTCTTGTAATGGAGCGACCGG AFUB_089280 TCAAGGTACTATTGCCGCCTC TAGTTCTGTTACCGAGCCGGCGACAGAATCGCCGTTCTTG AFUB_072800 TCCAAACCTTGACAAAATCCAC TAGTTCTGTTACCGAGCCGGGACCGACTTCGAAGTGACGA AFUB_068890 ATTTTGAGACTGGCCCGAGG TAGTTCTGTTACCGAGCCGGGACAGATGTTGGGGGAGTGG AFUB_072000 ATGTGTGGGGAGCTCAACTG TAGTTCTGTTACCGAGCCGGACTATGTCGCACATCGCTCA AFUB_095640 AGAATGGCGCGTACCTTTCT TAGTTCTGTTACCGAGCCGGAGGAGGTGATGGGGTCAAGA 287

AFUB_087120 ACTCCATGTGATCCCATCGC TAGTTCTGTTACCGAGCCGGTGCGCAAAAGGAACCCATTG AFUB_075350 GCTCTGGACGAGTTGCAGTA TAGTTCTGTTACCGAGCCGGGCCGACCAGAGCAAATCGTA AFUB_054020 TCTCAACCTCAACTCGTTGCA TAGTTCTGTTACCGAGCCGGGATTTGGAGGTGACGTCCGT AFUB_081540 CCAGCGCCCAGAGGTATAAG TAGTTCTGTTACCGAGCCGGGTCTCCGTCCATCAGGTGTC AFUB_055480 AGCCACGCAAGATGAGCTTA TAGTTCTGTTACCGAGCCGGAATGATGACGGTTGGCTTGC AFUB_056640 AGCAGGGGTAGAGGAGAAGG TAGTTCTGTTACCGAGCCGGCGAGCGGTCAGGTATTGTGA AFUB_074550 CTAAGGAGGGCAAGGAAGGC TAGTTCTGTTACCGAGCCGGCACATCCAACCTCTGGCACT AFUB_071600 GCTAACCGTGGGTCTGTTCA TAGTTCTGTTACCGAGCCGGGGCATTTCGTTTCCTTGGGG AFUB_054310 TCGTTGGCTGTTAGTCGTCG TAGTTCTGTTACCGAGCCGGAGTGACAAAGATGCAGCCGA AFUB_098230 CACACATTACGCATGGCTCG TAGTTCTGTTACCGAGCCGGGCTTATCAAAGAGACGCGCC AFUB_099170 TGCGAGACCACTCATGAACC TAGTTCTGTTACCGAGCCGGGCTCAAGGATGGACGATCGT AFUB_096030 GTTGCAACAAGGCACAGGAG TAGTTCTGTTACCGAGCCGGGGCAAGCCCGGACTAATGAA AFUB_096080 TGAGCAAGCATTTGTCCCCA TAGTTCTGTTACCGAGCCGGACTCGAAGTCGGACGTGAAC AFUB_099990 CAGGCCTGTTAGTCGAAGCT TAGTTCTGTTACCGAGCCGGGATCTGGAGTAGGAGGGGCA AFUB_006190 CTCCCCACTTGGAGATGGTA TAGTTCTGTTACCGAGCCGGCCTTGAACGAGAAGGTCGAG AFUB_045810 GGTAACGAGGGTCAACTGGT TAGTTCTGTTACCGAGCCGGAGATCCCGAGGGCTTTCTCT

288

Appendix 9: Primers 3 and 4, which were used to amplify downstream flanking sequences

Gene ID P3 P4 AFUB_045810 GCTCTGAACGATATGCTCCAACTGTCCCTGGTCTAGCTGTCT CTAGACTTAGAGCGGCGTGG AFUB_050750 GCTCTGAACGATATGCTCCAACGAATCCCCACAGAGACCACG ATCCATTCAGGGGCACGATC AFUB_021710 GCTCTGAACGATATGCTCCAACCCCTAGATACCCCTTGCTCCT AGAGGGGAAGGGATATCGCA AFUB_053300 GCTCTGAACGATATGCTCCAACCTGTTGTGGTGTCCTTTGCG GCTGGCAGGTTTGTCCTCTA AFUB_029290 GCTCTGAACGATATGCTCCAACCCAAAGTTCTTGCGACAGGC CCACCAGCACTCCTAATCTCC AFUB_012420 GCTCTGAACGATATGCTCCAACGCACCTGAAATGAAGCGCAG CCTCTCCGATGCAGAGTTCG AFUB_032300 GCTCTGAACGATATGCTCCAACCACTATGCTCGGGCCTACAG TCGCCCCATTGACAACGTAA AFUB_026400 GCTCTGAACGATATGCTCCAACATGGCTTTCGGATTTCGGGT AAGTTCGAGCGATACCTCGG AFUB_044560 GCTCTGAACGATATGCTCCAACGGAACTGTAGGCAAATCGGC GCAAGAAATTCCGCAGCGAT AFUB_030660 GCTCTGAACGATATGCTCCAACTCCACGGTAATGCCAGTGAC GCCGTTCTACTGACTCCCAC AFUB_052450 GCTCTGAACGATATGCTCCAACCAGGACCCTTAGTGAGCTGC GATGGTTCACTCGACCGGTT AFUB_041010 GCTCTGAACGATATGCTCCAACATGACACGGCGTCCAAGTTC TGCTAATCTTCGTGCGCAGA AFUB_043130 GCTCTGAACGATATGCTCCAACCAGGCTTCAATGCATGTGGC TTTCCCAGGAGACAGGCCTA AFUB_010510 GCTCTGAACGATATGCTCCAACCGTGGAGCATGAAGCATTTA TGGATTTGGTCTACGGATGA AFUB_029710 GCTCTGAACGATATGCTCCAACAGTCGCGTCATCCTTTGCAT TCTCTTGGGTCAACACGCTT AFUB_018770 GCTCTGAACGATATGCTCCAACACGGAGTTCCTGCTTTTGGT TAATGTGAACAGACCGGGGC AFUB_020560 GCTCTGAACGATATGCTCCAACCCGTCTTCCGTGTTGCTTTG ACATCATGTACGGCGTCCTC AFUB_029320 GCTCTGAACGATATGCTCCAACCGATCGGTTTCCTCTCGTGT TCATTAGCGACGTCGATGCA AFUB_025560 GCTCTGAACGATATGCTCCAACGCCAGCATTTGTGTCTTGGG TCCTCTGTCTGCTCTCGTGA AFUB_038630 GCTCTGAACGATATGCTCCAACGGCGGACGAACGTGGTTATA TGGGGGTTTGCAGTCCTTAC AFUB_029240 GCTCTGAACGATATGCTCCAACAGGCAGTTTACCTCACCTGC AGGCTGCTACCAGATCAGGA 289

AFUB_002120 GCTCTGAACGATATGCTCCAACCGTGATCGTTCATGATGATTGC GCATATGGCGCTGGTTTCTG AFUB_016170 GCTCTGAACGATATGCTCCAACAAGTGGCAATGTCGCTGGTA TGGAGACGGACTCTCGGAAT AFUB_048440 GCTCTGAACGATATGCTCCAACCCAGATGGGGCACGTTTACT TGTCGGATCTCTTTGCGCTT AFUB_013090 GCTCTGAACGATATGCTCCAACTGTTGCGATCATCATGGCCA CGCTCTGATCCAGCTCACTA AFUB_019630 GCTCTGAACGATATGCTCCAACTCCTCTGCGTTTGGTGTTCA TCCGTCGCCAATTACCCAAA AFUB_006780 GCTCTGAACGATATGCTCCAACACGTCCTCAAGATGCATGACA GCTCACCATACCAAGCCCAA AFUB_053440 GCTCTGAACGATATGCTCCAACAATCCACTGTGCCAGCTTCA CTCTGACGCTCATTCCTCCC AFUB_039620 GCTCTGAACGATATGCTCCAACGGACTCTAGGGGCTCTACGT AGCCATCCACCATCCTACCT AFUB_038640 GCTCTGAACGATATGCTCCAACTGGCGCCGGTAGGTTTTTAT TGTGGACTCGAATGGAAAGGG AFUB_045840 GCTCTGAACGATATGCTCCAACACGCACCCTGTGTCTAGTTG AAGGGAACGATGCAGTCCAG AFUB_053070 GCTCTGAACGATATGCTCCAACTCGCCTAATGGAAGCTGTGG ACACATTGGAGGTAGCACCG AFUB_051750 GCTCTGAACGATATGCTCCAACCGACCCATAATCTGCGCCTA CGGTGAACCTCTCAAGCAGT AFUB_015480 GCTCTGAACGATATGCTCCAACATTCCCTATTGGACCGAGCG GCTACCACCACTACCACCAC AFUB_026080 GCTCTGAACGATATGCTCCAACATTCCCCCTTTCTTCCCCCT TAGCCGCCGTAATACGAAGC AFUB_029820 GCTCTGAACGATATGCTCCAACAGCTGGTCTCTTGCGCATT CGCATTCCCTTGGTACCCAT AFUB_027640 GCTCTGAACGATATGCTCCAACCCACTTCCCACAGACTGCAT CATCGTCCGTCCCATTGACA AFUB_038060 GCTCTGAACGATATGCTCCAACATTCGTCGCTGATTCCTCCC CGAGCGCTCAAATCTCAAGC AFUB_001600 GCTCTGAACGATATGCTCCAACCTTCAAACCCCGTCAACACG ACAAGGCGAAGATGGACCAG AFUB_053500 GCTCTGAACGATATGCTCCAACCACATCTGGGGAGGCATTGT TATCGGCGAGCATGCGAATA AFUB_018600 GCTCTGAACGATATGCTCCAACAACCACGAGGCCATTTGACT AAGCTGCGTCTGACACTTGA AFUB_023600 GCTCTGAACGATATGCTCCAACCCTCCTTTCCTTGTCAGCCA GCGGCAAGTCTGATAGTGGA AFUB_010360 GCTCTGAACGATATGCTCCAACAGCTCACTTTCCGTGCTTCA CGAGCTTCGAGGGCTTCTAT AFUB_044400 GCTCTGAACGATATGCTCCAACTATACTACACGGCCCCTCCA AAACACGTTGCTCACCTCCT AFUB_043460 GCTCTGAACGATATGCTCCAACTGTGTCAATCCAATGAGTCCAC CTGGGTTGACAGCGTACGAT AFUB_028770 GCTCTGAACGATATGCTCCAACACTCCCTTCTTCCCTCTCGA AAAGGACGCAATCGGGGTAG AFUB_036500 GCTCTGAACGATATGCTCCAACGTCTATGTCCCGCTCACTGG ACGACTGACTATGACGACGA 290

AFUB_051670 GCTCTGAACGATATGCTCCAACGACACTACATACGCGGCACT CCGCACCATCGTTTTTCCTG AFUB_023730 GCTCTGAACGATATGCTCCAACCGATGTTCGAACAGCACCAG GCTATCGCGCATGATTGGAC AFUB_027480 GCTCTGAACGATATGCTCCAACACCACCCAGCATTTCTTCTCA CTATACCCGCCGACAAGGTC AFUB_030570 GCTCTGAACGATATGCTCCAACGAGAGGGCACTTGGGTTAGG TCGGTGGAGTAAAACTGGCC AFUB_053520 GCTCTGAACGATATGCTCCAACGGGAAATGACCCTGGCCTAC GCATGGGTTGGGTTCTCACT AFUB_039100 GCTCTGAACGATATGCTCCAACGACGCCCGTTACTGGAGATC CGATCGAAAGAGCCGGAAGA AFUB_007300 GCTCTGAACGATATGCTCCAACTCGATTTAGAGTCTGCCGCC ATTTCCGAGACAACAGCGGT AFUB_014350 GCTCTGAACGATATGCTCCAACCTTTCGGTTTTCGCGTTCGT GCATCTTGGCTCAACCCTCT AFUB_006320 GCTCTGAACGATATGCTCCAACTAGACTTGGCTTGGCTTGGG CGAGGATCGCCGAATCCTTT AFUB_052630 GCTCTGAACGATATGCTCCAACCCAGGTGGGCACAATGAGAT AGCATGAAACGGTTCGGTCT AFUB_035220 GCTCTGAACGATATGCTCCAACCCGGTATTCGAGCCTAAGCA GCAGACCCTTTGAATGCGTC AFUB_019930 GCTCTGAACGATATGCTCCAACACAGCTGGGACAAACCTAGG CAGGACGACACTCCGTATCG AFUB_017750 GCTCTGAACGATATGCTCCAACTCTCCCCTGCAACTATCCCA GCCATCGAAAATCAAGCGCT AFUB_011380 GCTCTGAACGATATGCTCCAACCCGTCACTTTCCTACGCCTC GCATCATGAAGCGTGAGACG AFUB_027890 GCTCTGAACGATATGCTCCAACTGTTGACTGGAGATGGCCAC TTCGCAGTGCATGGAGAAGT AFUB_051100 GCTCTGAACGATATGCTCCAACCAAGGGGATGGCATCTGGTT CAAATTCAACGCGCGCAAAG AFUB_056020 GCTCTGAACGATATGCTCCAACTCAAGCCTCTTTCTGCGGAC CAGCGACCGATGACCCATTA AFUB_089870 GCTCTGAACGATATGCTCCAACTACTGCTGAAGACGCTCGAG TTATCCACGACACTCCGCAG AFUB_059390 GCTCTGAACGATATGCTCCAACGGTGAAACCGATTGCTACCG GGGATACAGCGAGCAATCGA AFUB_059540 GCTCTGAACGATATGCTCCAACGCCTTCGTTGCTCATTCGTC CGTCGTGTGTGTCTGTGGTA AFUB_059090 GCTCTGAACGATATGCTCCAACGCCTCCTGCGACAAAACAAG CGTCCAGACTTGTAGCACGT AFUB_066150 GCTCTGAACGATATGCTCCAACAAGGCATTCTGACCCTTCCG GTGGGCATCAATCGGAGAGG AFUB_071620 GCTCTGAACGATATGCTCCAACGGCAAATAATGATGGCCGTGG ATTGAGGGAAGATGGGCTGC AFUB_056110 GCTCTGAACGATATGCTCCAACCCAGTCCCTTCCCCCTGATA CTGTGGACAAGCCGACCTAG AFUB_082830 GCTCTGAACGATATGCTCCAACACCTGTCTTCCTTTTCGCGT CTGCGAAATTGGGGTCATCC AFUB_082700 GCTCTGAACGATATGCTCCAACATTGCGTTCTTGAACACCGC CGATGAGTCATGGCGCAATG 291

AFUB_072650 GCTCTGAACGATATGCTCCAACCAGTTCCATGTCAGGTGACCA CGCTGCGTCTATCACCCAAA AFUB_063820 GCTCTGAACGATATGCTCCAACACAAGATCGCCAGGACACTG ACTTGTCTTGCAGAGGGACG AFUB_077790 GCTCTGAACGATATGCTCCAACTCCAAAACACGCTCCACTCA AACGGCATCTTCCCTCGATG AFUB_073970 GCTCTGAACGATATGCTCCAACGGCTGACAGATGCCGAAAAC AGCGTTACACCCATCTCGTG AFUB_078810 GCTCTGAACGATATGCTCCAACATTCAGTGGCGTCTCTTGCA CAGAAACTTGCGCTTGCGAT AFUB_093160 GCTCTGAACGATATGCTCCAACCGTTACGAATCAAACGCCCC TGCTCAGGATGATGAAAAGGCT AFUB_089250 GCTCTGAACGATATGCTCCAACCCGCCTCTCATCATCCCATT TGAGCGTATGGACAGCTTGA AFUB_075210 GCTCTGAACGATATGCTCCAACTAGCTTCACAACAGGGCTGG GTGCTTGACAAGAACGCCAG AFUB_090090 GCTCTGAACGATATGCTCCAACCGGGTCCAGACACTGATCAG CGACCACGTATCTACCACCG AFUB_078920 GCTCTGAACGATATGCTCCAACCGGCAGGGCGAAGGAATAAA ACTGACTCACTGGCACATCA AFUB_077210 GCTCTGAACGATATGCTCCAACAAGCTGCTGATGCTCGATGA TGCAGTAAGCCGAATACACTCA AFUB_066030 GCTCTGAACGATATGCTCCAACCTCCTGCCAAAACACTTGCG AGTAGCGGTCCAAAGAAGGC AFUB_095720 GCTCTGAACGATATGCTCCAACTCGCGTTACACCCACCTTAC ATAAGAGCCCTGGTCCCTGT AFUB_054290 GCTCTGAACGATATGCTCCAACCTGTAAGCTCAGCGTGTGGA AGCTGTTCTCTTGCTGCTCT AFUB_060320 GCTCTGAACGATATGCTCCAACCTTGGAGGCCCTGCTGTATC TCCAGAGCTCGGAAGTACCA AFUB_081870 GCTCTGAACGATATGCTCCAACCGGCAACCTCTTCAGCTACA CGATCAGAGTCGACCTTGGG AFUB_095460 GCTCTGAACGATATGCTCCAACGGTCGTTCGGAACCATGTTC CTCGAGTATCAAGACCGCCC AFUB_087320 GCTCTGAACGATATGCTCCAACTCTTGTGCCACGGCTCATAG TCAGTCGTTTGTCGCGAGAA AFUB_079830 GCTCTGAACGATATGCTCCAACCGAGCGTCTACTGAGGAACG TATGTTGAAGGCGTACGGGG AFUB_078980 GCTCTGAACGATATGCTCCAACCGAACCCGGAAGAGGACAAA GCATGTGCATCAAGCCTGAG AFUB_053960 GCTCTGAACGATATGCTCCAACGTGTCCTACATAAGCGGGCA CACAGACATGGGAGTCGACC AFUB_070630 GCTCTGAACGATATGCTCCAACTGTCCATTGCCCACTGTTCA GGAACGGCATCTGCACTTTC AFUB_089280 GCTCTGAACGATATGCTCCAACGATCATCAAGTTCAGCCCGC TTTGGCAGCGACAATGCAAA AFUB_072800 GCTCTGAACGATATGCTCCAACTGGACTTTCCCCGTTTCAGG CCATGCCCAGCACTAGGAAA AFUB_068890 GCTCTGAACGATATGCTCCAACGATTGTGCACGAACCGAACC GCAAGCACCAGACAAACCTG AFUB_072000 GCTCTGAACGATATGCTCCAACACGCAGGAAGTTATCGGCAT AGCTTGGACATGGGAAAGCA 292

AFUB_095640 GCTCTGAACGATATGCTCCAACAGGCGCAATTGGCAAACAAA TATCAGGGAAGGAGAGGGCC AFUB_087120 GCTCTGAACGATATGCTCCAACTGGCGGATGAGTATGGGAGA CCAACCAGGGCTTTCTTGGA AFUB_075350 GCTCTGAACGATATGCTCCAACTACGCCAGTGCCATGTTCAA GTCCAAGACCTCGTCAACTGA AFUB_054020 GCTCTGAACGATATGCTCCAACCTTCCCGTTGTTCCGTCTCA TCAGTACATGGGCTTACCGC AFUB_081540 GCTCTGAACGATATGCTCCAACCGACTACCTAGGCACCTGGA CGCAATCAGATGGTTGAGCG AFUB_055480 GCTCTGAACGATATGCTCCAACAGGGAGACAAGAGAGGCCAA CAGCTAAACGCAGACGCATC AFUB_056640 GCTCTGAACGATATGCTCCAACTGCAGAAGGCTGATCCATGG GATCATGGAGGCTTACCGCA AFUB_074550 GCTCTGAACGATATGCTCCAACGCTGCAGTCAAGTCACGTTG GAAGGAGACGACAGTGTGGG AFUB_071600 GCTCTGAACGATATGCTCCAACTTTTGCTGAGCCTGGCTGTA CCTGTGAAGCTTCCCCCAAT AFUB_054310 GCTCTGAACGATATGCTCCAACAGGATCAACGAGTGACGAAG TGGTACAACAAGTGGTCCGG AFUB_098230 GCTCTGAACGATATGCTCCAACGTGCGGCTTTTCATGAGAGC GCCGCAACAAACATCGTTCT AFUB_099170 GCTCTGAACGATATGCTCCAACAGATCGAAACAGCCAGCCAA GCCTTCTGCACAAACATCCC AFUB_096030 GCTCTGAACGATATGCTCCAACGCGGCTTTCCTTGGTTTCTG GTTACAAGCGCTGGCATCAG AFUB_096080 GCTCTGAACGATATGCTCCAACAGTTAAAAACGCCGGGAGAT GAGCCAAGCGCTTAGAAGTC AFUB_099990 GCTCTGAACGATATGCTCCAACTCAAGCCAAGCGAAAACAGC TAGTTAAGATGCCCCGCTCC AFUB_006190 GCTCTGAACGATATGCTCCAACTCTGCAGATTTCAGCCTTCA GCCTGTCAACCGATCGTAAT AFUB_045810 GCTCTGAACGATATGCTCCAACTGTCCCTGGTCTAGCTGTCT CTAGACTTAGAGCGGCGTGG

293

Appendix 10: Primers 5 and 6, which were used to aid in the fusion PCR reaction (nested primers) upstream flanking sequences

Gene ID P5 P6

AFUB_045810 AGCAGGTGGGTTCCTCCATA CTTGCGGATCAAGGCCATTG AFUB_050750 TCTCTATTCCCCTGCCCGAA CAGGATGCCTTATTTGCGGC AFUB_021710 AAGAGTTTCCCTCGACTGGC GTCCGGTTCCGTAACACGAT AFUB_053300 AGATCGATGTGCTGTAGCGG ATCTCGGCGTTGCGGATTAA AFUB_029290 TGTTCTTCCGCGCTACCTTT CATCAGCAGCGATCATTGGC AFUB_012420 TACGTACCCTTGTTGGCCAC GCAGACAATGCGCACTATGG AFUB_032300 GCGATCGATGAGGACGACTT TCGCAACATCTGAACCAGCT AFUB_026400 AGCCGAGGTTCGTCAACAAA GAGTTCCGCTGGCCTTAAGT AFUB_044560 TCGACGGTTGTTCCATGGAG GGCATCATCCGCATTCAACC AFUB_030660 AATACATCGTGCCATCCGCT GCAGATCCGCACCATTTCAC AFUB_052450 TACGAAGCCGACGACAACAA CGGACCGATCCATGAAGAGG AFUB_041010 GGCAGAGACGGATAGTTCGG AAGTCCGCATTCTCGTTGGT AFUB_043130 GGGACTGTCCTTTCTTGGCA CGGTGGAAAGACGCCATCTA AFUB_010510 CCATCAGTGCCCAAAACACC AGCAGATAACAGCGCTGTCA AFUB_029710 CGCGCTTCATAGACCTGAGT TGATACCAGGCACAAGCTGG AFUB_018770 TCTTGGCTCAGCAACTGGAG CAGCGTCTTGAAGTCATGCG AFUB_020560 ACCTTCTGGTCTGGCGAAAT ATCTACGCCTCTGGTGGACT AFUB_029320 TGAGGTCAATAGCCCCTGGA AGAGCGGGTTGTCATCAAGG AFUB_025560 CCGGACAGTTGCCCAGTATT TCGTCAAGAGACTGCAGCAG AFUB_038630 CGCCTGCGACTATCTTGGAT TGTGAGTTAGCCATGCTGCA AFUB_029240 AACCAGATGCTCAACCCGAG ATTCGAGCAGGGTTTACCGG AFUB_002120 AGGCGCGGTGTTTCATTTTC ATGGGACTCACTCAGCGTTC AFUB_016170 GACATCTGTGGTCCTAGGCG TTGCTGCACCATAGAACGGT AFUB_048440 GCATAACAACGGCCTTGTCG GTCCAACGTCAACACCTTGC AFUB_013090 TACCAGCAGGGCAGGATACT CTGCCTTGTTGACATGCCAC AFUB_019630 CTGCCTAGCTGCTTCCTGAA AGAAGGACAACAGCAGAGGC AFUB_006780 CCTTTCCGACCCTTTCCTCC GCTTTCCGTGGGCTAAGACT AFUB_053440 ACAGCCAGAGAGGGATAGCA GATCGATGCTGGGTACTGGG AFUB_039620 TGATTGTCGGTTGGCAGACA TGTGTTTCGCATCCCGTACA AFUB_038640 TCAATGCCAACATGGATGCG GATGGGAAGACACCTGCCTC AFUB_045840 TGTCGAATGTTCCGCTTCGA CGATGTCTCTGACGGGTGAG AFUB_053070 GAATCTCGCCGCTCATTTCC TCGCAGCTGCTGAATTCTCA AFUB_051750 AGTGGCGGAGTTTCCAGAAC CATTGGGTCCGTACTGCTGT AFUB_015480 CGCGTCCCGATGGTAGTAAA TTGGGTTCGTCGATCTGCAA AFUB_026080 TGAATGGACTGGTGCAGGAC GGGAGTGCAAGATCGAGGAG AFUB_029820 TTGGCAGCGTACTACCGATC TGCCGTTGTACCAGACAGAC AFUB_027640 GGGTGTGAATTGCCAACCAC TCGGGGTCTAAGAAGAGGGG AFUB_038060 GTGCAGCGAATGAACGAGTC ATACCTTCTGGGAGCTCCGT AFUB_001600 GCGAACGGGGTGTTGTTTAC AGCCCTCTGACGTATGGAGT AFUB_053500 CACGACAGGCATGGTAACCT GTCCTGAGGTAATGCGGCTT 294

AFUB_018600 TTCTCCGCACAGAAGCACAT TATGCATCCTCGTCAAGGCC AFUB_023600 CTTGAGAGCAGCAGAAGCCT CGGGACAGATGACGGATGAG AFUB_010360 GGTTAGGGAGCTTGGCACAT ACCCTCGACTCTCTGCAGAT AFUB_044400 GATGGTTCTGGTCCTCTGCC GCTGGCGAGGCAATCCATTA AFUB_043460 GCTGGCGTTAAATGCTCGAG AACTGCTCGTGACCAGGAAG AFUB_028770 TACTCTGTACCCCCAGCCTC AGGCAATTGTGGGCTATGCA AFUB_036500 GCCATGGAGTCGATCTTCGT TTGTCCAACTCCCTGCAGTG AFUB_051670 GGCACGGGAAGAATCTGGAA CTGCGTATCGTTCTACCGCT AFUB_023730 TCCCCTCCCTTCGGATGATT TCAAGCTGTGGTGCCAAGAA AFUB_027480 ATCAGGCACATATCAGGCCG CGGGAAGTCTTCGAGATCCG AFUB_030570 CGGGAACTAGCCTTGTTGGT AAAGGGGACTGACTTTCGCC AFUB_053520 TGACGAAGCTGGGTGAACAA TCAGCAACGGCCTCAAATCA AFUB_039100 CGTGTTGGACCAGGATGTGA CAGCTGGCAGGATCAACAGA AFUB_007300 CACCAAAGTTCCCCACCACT TGTCTCGCTTTCTGCATGGT AFUB_014350 GACAAGACGACAGCTCACCA CATTGCAACTGTGGCAGACC AFUB_006320 CGATGCTCGTTTCCCTCAGT TCCCCATCGCAATCACTTCC AFUB_052630 AACCAATTCTCTCGCGCTGA CGGGGATGTTGATCTCGGAG AFUB_035220 GAGGGGGAAATTCAGGGACG ATCCCTACCTAACCCACCCC AFUB_019930 TGTCCGCTCTCTCTAGGCTT CCGTACGGACCCTGTAATCG AFUB_017750 CGGCTCAGCTCATCGTTTTG CAGGGGAGATTGCTAGAGCG AFUB_011380 CGTCATCGATGTTGCAGCAG TACAAGTACCGGCTCCCTGA AFUB_027890 ACAGGGAATAGTGCCGGTTG GAATCCAGCCTGTGAGCACT AFUB_051100 GAACGCCGTATCGTTTTTGT TATCGAGGAGACGGAACAGG AFUB_056020 GCAGGGAGAGACAGGCATAC GCTGTGTCTGGGTACTTGCT AFUB_089870 ACGCAAGCATGTCCCCTTAA CCGTACCTAGGAGACGTGGA AFUB_059390 CCTCCCTGTTCTCCGCAATA AGACTAGAGGCGCAGGGTAA AFUB_059540 AACATACCTGGCTGGATGGC TGCAAGGGAAGAGTGACTCG AFUB_059090 CACAGGTGTTCGGAAGGTCA GACTTCCTGCTCGAGATCCG AFUB_066150 GCCGCTTCTTGTCATTCGTC TGGACTACCGAAGAGAAGGGA AFUB_071620 CTGTCTGGCTGTGCTTGAGA AATAGCAGACCAAGAGGCCG AFUB_056110 AGTCACGCCATATCAAGCCC GATCCCTACTTACCCTGCGC AFUB_082830 GCAGAACAGACACGAGTGGA ACGATGACCACCCTTGAACC AFUB_082700 ACTTGACACAACGATCCCCC CAGAGGATGTTACTGCGGGG AFUB_072650 TTCTCCGGCAGATCCAACAG GAGTCTGTCTGCTTCCCCAC AFUB_063820 GGAATGCCATGGGTGAAAGC ATGCTACTGAGATGCTGGCC AFUB_077790 TTCTCCACAACAGGTTGCCA TATACGGCGGCGCAAGTATT AFUB_073970 GTCCTCATGTTCTCCGGCAA CCATTCTGGATTGCGCGTTT AFUB_078810 ATTGCTTTCTCCAGCCCCTC AACAGCGCCGCGATATCATA AFUB_093160 AACTGTCCAGGTGTGCAACA AGGAAAGGCAGCACGAAGAA AFUB_089250 GAGGGGTCTGGAGGGATCAT ACGAGCACAAGCACCATACA AFUB_075210 ACCTGCACTTTTCCCCTCTG GATGGTCTGTGGGAAGTCCG AFUB_090090 GTCACTTGCAACCGTGTGTC CTACGTCGCACCGATCTGTT AFUB_078920 CGTCAGGTACAAGGTCGCTT GAGCATCACGACGGGAATCT AFUB_077210 TCGGTAAGACAGGTGTTGCC AAACGCTGAAGGGAGTCTGG AFUB_066030 TGGCATCAAGAGCACTGGAG ATTGAAAAGCCAAACCCGCC

295

AFUB_095720 CTTTGCAGCCGATTTCGAGG GGTAGAAACGCGACCTTGGA AFUB_054290 CACATCGCAGGTAGGCTCAT CCCCGTCGTTTACTCCACAA AFUB_060320 TCATGAAAGCGCATAATTGC TACGATTGACTGCCATGCTC AFUB_081870 CCTCCTCTTCGGCTTTGGTT GCGCTGTATCGATTGCGTTT AFUB_095460 AGCACGCAGATAAGCCATCA ACCAGACACGTCATCAACCC AFUB_087320 GATACCCTCAGCTCGCACTC ATGACGCGCCATTTAAGGGA AFUB_079830 ATCAGATGCGCTCCATAGCC CTCGGATGGGTATCGACTGC AFUB_078980 TCCTCATCTCGGAATGCAGC AACATTGGTGGCTAGAGGGC AFUB_053960 CCACCAGCACTGATTCCACA TCCTTACCACAAGAACCGGC AFUB_070630 TGGCCATCAAGTCGAAGCAT ATGCAAGCACATGATCCCCA AFUB_089280 GTGCCAATCATCGGACATGC CAAGCACCCATGTTGACTGC AFUB_072800 ACCTCAGGCACTCAAGAAGC ACGTACGTCCCTACACCTCA AFUB_068890 CCTCTGTGTAGCTTCTGGCC CTATCAAGATCGGACGGCCC AFUB_072000 CAACCCACAACCAGGCTACT TGGTCCGTCCACTCGTAGAA AFUB_095640 AGTGCACCGGACTCAAGAAG CCTGCGGCAGTCAGAGTTAA AFUB_087120 CGCCATTAGTCGACTCGGTT CACGAGTAGCCAGCTTCACA AFUB_075350 GCAGTAACAGCGCTGGAATG AGCACCATTCGCGTACTCAA AFUB_054020 GTCTGCTTCGAGCTCCTCTC CTGTGCCTACAAATGCAGCC AFUB_081540 GTGATGGGGAGGTTTGGAGG GATAATGGGCCGATCTGGGG AFUB_055480 GCAGTGGTGGCAAAACAGAG AGGTTGCTCGACTGTCATGG AFUB_056640 GGCTGGACTGGAGGGAGATA GCGGACAATAGCGGTACAGT AFUB_074550 GAGGACGGCAAGGTCTTTGA CAACTGGTGACCCCGAGATC AFUB_071600 GACTGCCAACACAAGCAAGG TGCACGTATGGTTGGCTTCT AFUB_054310 GCAGTACCCGGTGTAACCAA TTCGGTCGGGACAAGTTCAG AFUB_098230 GAAGCACACATTACGCATGG AAGCAGTGGACCTCCAAGAA AFUB_099170 GATGGGAAGCCCTGCTAGAC CCGTGAGTGGCTGGACTTTA AFUB_096030 GGTTTGGGATCCTGGACCTG AATGCTGGCCTTGGGACTAC AFUB_096080 ATTGGGGCCACTTACTCTGC TCCAAAACGGAGCCAAGCG AFUB_099990 ACACCGTGCATACTCCAGTG CCCTTCTACTTGGCGAACGT AFUB_006190 CTGGGTATGATGGTGACGTG CCTATGATGACATGGCGTTG AFUB_045810 AGCAGGTGGGTTCCTCCATA CTTGCGGATCAAGGCCATTG

296

Appendix 11: DNA barcodes sequences which were used to amplify hph cassette in accordance with 96-well plate orientation

Plate Oligo name Sequence position A1 Hph1 CCGGCTCGGTAACAGAACTAaaaacccagatctctggaACGGCGTAACCAAAAGTCAC A2 Hph2 CCGGCTCGGTAACAGAACTAaaaagagggccttgagccACGGCGTAACCAAAAGTCAC A3 Hph3 CCGGCTCGGTAACAGAACTAaacagcaccctaacgcgtACGGCGTAACCAAAAGTCAC A4 Hph4 CCGGCTCGGTAACAGAACTAaacctgttcgccaccttcACGGCGTAACCAAAAGTCAC A5 Hph5 CCGGCTCGGTAACAGAACTAaacgactgtgatggtcgcACGGCGTAACCAAAAGTCAC A6 Hph6 CCGGCTCGGTAACAGAACTAaacgcaaactccttcaagACGGCGTAACCAAAAGTCAC A7 Hph7 CCGGCTCGGTAACAGAACTAaagaggctagcggcatgtACGGCGTAACCAAAAGTCAC A8 Hph8 CCGGCTCGGTAACAGAACTAaagctagactaaaggccgACGGCGTAACCAAAAGTCAC A9 Hph9 CCGGCTCGGTAACAGAACTAaatacgcaccgagggtagACGGCGTAACCAAAAGTCAC A10 Hph10 CCGGCTCGGTAACAGAACTAaattactaggcgaagcagACGGCGTAACCAAAAGTCAC A11 Hph11 CCGGCTCGGTAACAGAACTAacacgtgtcgtttagtccACGGCGTAACCAAAAGTCAC A12 Hph12 CCGGCTCGGTAACAGAACTAaccatgcgcaggtcatgtACGGCGTAACCAAAAGTCAC B1 Hph13 CCGGCTCGGTAACAGAACTAaccgcttagcatttccccACGGCGTAACCAAAAGTCAC B2 Hph14 CCGGCTCGGTAACAGAACTAacgcatcacgtagatgctACGGCGTAACCAAAAGTCAC B3 Hph15 CCGGCTCGGTAACAGAACTAacggaggacacatacctaACGGCGTAACCAAAAGTCAC B4 Hph16 CCGGCTCGGTAACAGAACTAacgtggtgcgaagttaccACGGCGTAACCAAAAGTCAC B5 Hph17 CCGGCTCGGTAACAGAACTAactagggggtataaacagACGGCGTAACCAAAAGTCAC B6 Hph18 CCGGCTCGGTAACAGAACTAactatctgagaaacggcaACGGCGTAACCAAAAGTCAC B7 Hph19 CCGGCTCGGTAACAGAACTAactgccccaagtagatctACGGCGTAACCAAAAGTCAC B8 Hph20 CCGGCTCGGTAACAGAACTAactgtttctactgcggccACGGCGTAACCAAAAGTCAC B9 Hph21 CCGGCTCGGTAACAGAACTAagcgcaagagtcacaaacACGGCGTAACCAAAAGTCAC 297

B10 Hph22 CCGGCTCGGTAACAGAACTAagcgcacactctagacctACGGCGTAACCAAAAGTCAC B11 Hph23 CCGGCTCGGTAACAGAACTAaggacacatctacacagaACGGCGTAACCAAAAGTCAC B12 Hph24 CCGGCTCGGTAACAGAACTAaggactctttgcttggttACGGCGTAACCAAAAGTCAC C1 Hph25 CCGGCTCGGTAACAGAACTAaggtgtgagttctagctaACGGCGTAACCAAAAGTCAC C2 Hph26 CCGGCTCGGTAACAGAACTAagtcctcgagcaaagagaACGGCGTAACCAAAAGTCAC C3 Hph27 CCGGCTCGGTAACAGAACTAataattccggagtacccaACGGCGTAACCAAAAGTCAC C4 Hph28 CCGGCTCGGTAACAGAACTAatacgttctgcttctccgACGGCGTAACCAAAAGTCAC C5 Hph29 CCGGCTCGGTAACAGAACTAatcaatcacgattcacgcACGGCGTAACCAAAAGTCAC C6 Hph30 CCGGCTCGGTAACAGAACTAatccacgaggcctgaaacACGGCGTAACCAAAAGTCAC C7 Hph31 CCGGCTCGGTAACAGAACTAatcggcaaactgggaagcACGGCGTAACCAAAAGTCAC C8 Hph32 CCGGCTCGGTAACAGAACTAatggatcacaccaacggaACGGCGTAACCAAAAGTCAC C9 Hph33 CCGGCTCGGTAACAGAACTAattactgtggcggtattcACGGCGTAACCAAAAGTCAC C10 Hph34 CCGGCTCGGTAACAGAACTAattggccctgtttttccgACGGCGTAACCAAAAGTCAC C11 Hph35 CCGGCTCGGTAACAGAACTAcaagcaggcacctatatcACGGCGTAACCAAAAGTCAC C12 Hph36 CCGGCTCGGTAACAGAACTAcacgctcgacggtagataACGGCGTAACCAAAAGTCAC D1 Hph37 CCGGCTCGGTAACAGAACTAcagtgctaacccagcgatACGGCGTAACCAAAAGTCAC D2 Hph38 CCGGCTCGGTAACAGAACTAcagttgactcggtggttcACGGCGTAACCAAAAGTCAC D3 Hph39 CCGGCTCGGTAACAGAACTAcatgacagtcttcagctgACGGCGTAACCAAAAGTCAC D4 Hph40 CCGGCTCGGTAACAGAACTAcatgcaagcaagcccaaaACGGCGTAACCAAAAGTCAC D5 Hph41 CCGGCTCGGTAACAGAACTAcatgctgctttagatgtgACGGCGTAACCAAAAGTCAC D6 Hph42 CCGGCTCGGTAACAGAACTAcatgtaacccggatgcacACGGCGTAACCAAAAGTCAC D7 Hph43 CCGGCTCGGTAACAGAACTAcgaatagttcgggtgcgtACGGCGTAACCAAAAGTCAC D8 Hph44 CCGGCTCGGTAACAGAACTAcgacgtggtggccttaatACGGCGTAACCAAAAGTCAC D9 Hph45 CCGGCTCGGTAACAGAACTAcgatgtaagccaaggcaaACGGCGTAACCAAAAGTCAC D10 Hph46 CCGGCTCGGTAACAGAACTAcgcacatctgtcaccactACGGCGTAACCAAAAGTCAC D11 Hph47 CCGGCTCGGTAACAGAACTAcgcttgcctcttagccatACGGCGTAACCAAAAGTCAC 298

D12 Hph48 CCGGCTCGGTAACAGAACTAcggaagtccgttctcactACGGCGTAACCAAAAGTCAC E1 Hph49 CCGGCTCGGTAACAGAACTAcgtatgcgaaggtgattaACGGCGTAACCAAAAGTCAC E2 Hph50 CCGGCTCGGTAACAGAACTAcgtttgatgccaaccgtcACGGCGTAACCAAAAGTCAC E3 Hph51 CCGGCTCGGTAACAGAACTActaaccgtgttcggactgACGGCGTAACCAAAAGTCAC E4 Hph52 CCGGCTCGGTAACAGAACTActagaagtcactgattccACGGCGTAACCAAAAGTCAC E5 Hph53 CCGGCTCGGTAACAGAACTActcttcaactaaagggtgACGGCGTAACCAAAAGTCAC E6 Hph54 CCGGCTCGGTAACAGAACTActgaaagatcatagcccgACGGCGTAACCAAAAGTCAC E7 Hph55 CCGGCTCGGTAACAGAACTActgcacccaatagaccagACGGCGTAACCAAAAGTCAC E8 Hph56 CCGGCTCGGTAACAGAACTActggcgcttgcaacatagACGGCGTAACCAAAAGTCAC E9 Hph57 CCGGCTCGGTAACAGAACTActggtgttgagccaagctACGGCGTAACCAAAAGTCAC E10 Hph58 CCGGCTCGGTAACAGAACTActgtggaggtttcggtctACGGCGTAACCAAAAGTCAC E11 Hph59 CCGGCTCGGTAACAGAACTActtctgataggctagcttACGGCGTAACCAAAAGTCAC E12 Hph60 CCGGCTCGGTAACAGAACTActttccggctcgaaagttACGGCGTAACCAAAAGTCAC F1 Hph61 CCGGCTCGGTAACAGAACTAgaaatcggcttttgacccACGGCGTAACCAAAAGTCAC F2 Hph62 CCGGCTCGGTAACAGAACTAgaacgaactgtatgatggACGGCGTAACCAAAAGTCAC F3 Hph63 CCGGCTCGGTAACAGAACTAgaacgtttacgaattccgACGGCGTAACCAAAAGTCAC F4 Hph64 CCGGCTCGGTAACAGAACTAgacagagatggctttgaaACGGCGTAACCAAAAGTCAC F5 Hph65 CCGGCTCGGTAACAGAACTAgacgaaaacttggagcttACGGCGTAACCAAAAGTCAC F6 Hph66 CCGGCTCGGTAACAGAACTAgagcgatacgacgacatcACGGCGTAACCAAAAGTCAC F7 Hph67 CCGGCTCGGTAACAGAACTAgagcgataggaaagtgttACGGCGTAACCAAAAGTCAC F8 Hph68 CCGGCTCGGTAACAGAACTAgagcttactgacccgtctACGGCGTAACCAAAAGTCAC F9 Hph69 CCGGCTCGGTAACAGAACTAgaggtctagcacctttaaACGGCGTAACCAAAAGTCAC F10 Hph70 CCGGCTCGGTAACAGAACTAgatttttaatcgggagccACGGCGTAACCAAAAGTCAC F11 Hph71 CCGGCTCGGTAACAGAACTAgccgttaagatgactaagACGGCGTAACCAAAAGTCAC F12 Hph72 CCGGCTCGGTAACAGAACTAgcctcaccctgatttggtACGGCGTAACCAAAAGTCAC G1 Hph73 CCGGCTCGGTAACAGAACTAgcgaagaacgctgaaataACGGCGTAACCAAAAGTCAC 299

G2 Hph74 CCGGCTCGGTAACAGAACTAgcgttaaggtctctgtcaACGGCGTAACCAAAAGTCAC G3 Hph75 CCGGCTCGGTAACAGAACTAgctgaacatcaggtctgcACGGCGTAACCAAAAGTCAC G4 Hph76 CCGGCTCGGTAACAGAACTAggagatgcgatcttcaccACGGCGTAACCAAAAGTCAC G5 Hph77 CCGGCTCGGTAACAGAACTAggattacgaaacggaagtACGGCGTAACCAAAAGTCAC G6 Hph78 CCGGCTCGGTAACAGAACTAggcaaggcaacatgatcaACGGCGTAACCAAAAGTCAC G7 Hph79 CCGGCTCGGTAACAGAACTAggcttacctagtttctctACGGCGTAACCAAAAGTCAC G8 Hph80 CCGGCTCGGTAACAGAACTAgggatcgccttttggtaaACGGCGTAACCAAAAGTCAC G9 Hph81 CCGGCTCGGTAACAGAACTAgtaagacatcgtcgaagcACGGCGTAACCAAAAGTCAC G10 Hph82 CCGGCTCGGTAACAGAACTAgtaatagttggctcgtgcACGGCGTAACCAAAAGTCAC G11 Hph83 CCGGCTCGGTAACAGAACTAgtaatcttaggttccggtACGGCGTAACCAAAAGTCAC G12 Hph84 CCGGCTCGGTAACAGAACTAgtcaacgcgactattatgACGGCGTAACCAAAAGTCAC H1 Hph85 CCGGCTCGGTAACAGAACTAgtccgtgactttttcatgACGGCGTAACCAAAAGTCAC H2 Hph86 CCGGCTCGGTAACAGAACTAgtgtttcgggttgcatttACGGCGTAACCAAAAGTCAC H3 Hph87 CCGGCTCGGTAACAGAACTAgttactctaatgttggggACGGCGTAACCAAAAGTCAC H4 Hph88 CCGGCTCGGTAACAGAACTAgttatacgaaatacgcggACGGCGTAACCAAAAGTCAC H5 Hph89 CCGGCTCGGTAACAGAACTAtaaccagcttcttctggcACGGCGTAACCAAAAGTCAC H6 Hph90 CCGGCTCGGTAACAGAACTAtacacaagtgggctcttgACGGCGTAACCAAAAGTCAC H7 Hph91 CCGGCTCGGTAACAGAACTAtaccacttggagctgctcACGGCGTAACCAAAAGTCAC H8 Hph92 CCGGCTCGGTAACAGAACTAtatagagcccctctttgaACGGCGTAACCAAAAGTCAC H9 Hph93 CCGGCTCGGTAACAGAACTAtatcaccgatgcttaccaACGGCGTAACCAAAAGTCAC H10 Hph94 CCGGCTCGGTAACAGAACTAtcatgcagagtcacctaaACGGCGTAACCAAAAGTCAC H11 Hph95 CCGGCTCGGTAACAGAACTAtccacggaaacgtggctaACGGCGTAACCAAAAGTCAC A1 Hph96 CCGGCTCGGTAACAGAACTAtcccttctgcgctaacagACGGCGTAACCAAAAGTCAC A2 Hph97 CCGGCTCGGTAACAGAACTAtccgggcgaaaaaaccacACGGCGTAACCAAAAGTCAC A3 Hph98 CCGGCTCGGTAACAGAACTAtcgtcttcgcaatgcgctACGGCGTAACCAAAAGTCAC A4 Hph99 CCGGCTCGGTAACAGAACTAtctgctcccattagctcaACGGCGTAACCAAAAGTCAC 300

A5 Hph100 CCGGCTCGGTAACAGAACTAtcttacaccttttgcacgACGGCGTAACCAAAAGTCAC A6 Hph101 CCGGCTCGGTAACAGAACTAtcttttcgctggccttcaACGGCGTAACCAAAAGTCAC A7 Hph102 CCGGCTCGGTAACAGAACTAtgacaattcccctgatcgACGGCGTAACCAAAAGTCAC A8 Hph103 CCGGCTCGGTAACAGAACTAtgagggaaccgtcagactACGGCGTAACCAAAAGTCAC A9 Hph104 CCGGCTCGGTAACAGAACTAtgatagttgcagcgcgacACGGCGTAACCAAAAGTCAC A10 Hph105 CCGGCTCGGTAACAGAACTAtgcaagctcacctactagACGGCGTAACCAAAAGTCAC A11 Hph106 CCGGCTCGGTAACAGAACTAtgcaagtcggcttaaggaACGGCGTAACCAAAAGTCAC A12 Hph107 CCGGCTCGGTAACAGAACTAtgcaggctggcgtaacaaACGGCGTAACCAAAAGTCAC B1 Hph108 CCGGCTCGGTAACAGAACTAtggagatagtacgtcgggACGGCGTAACCAAAAGTCAC B2 Hph109 CCGGCTCGGTAACAGAACTAtgtgctaggtgcgaaggaACGGCGTAACCAAAAGTCAC B3 Hph110 CCGGCTCGGTAACAGAACTAttcctaagacgcatccacACGGCGTAACCAAAAGTCAC B4 Hph111 CCGGCTCGGTAACAGAACTAttcgctagtagacactcgACGGCGTAACCAAAAGTCAC B5 Hph112 CCGGCTCGGTAACAGAACTAttcgtggccaacgagcatACGGCGTAACCAAAAGTCAC B6 Hph113 CCGGCTCGGTAACAGAACTAttctgctactttcgcacgACGGCGTAACCAAAAGTCAC B7 Hph114 CCGGCTCGGTAACAGAACTAttgcccaggttgttggttACGGCGTAACCAAAAGTCAC B8 Hph115 CCGGCTCGGTAACAGAACTAttggactaaccggcgttcACGGCGTAACCAAAAGTCAC

301

Hetero-Dimer DNA barcode Hetero-Dimer DNA barcodes sequences Hairpin/Tm Analysis GC contents Analysis Delta G Maximum Delta G

>adA2_aaaacccagatctctgga CCGGCTCGGTAACAGAACTAaaaacccagatctctggaACGGCGTAACCAAAAGTCAC 45.6 44.40% -5.13 kcal/mole -80.28 kcal/mole >adA2_aaaagagggccttgagcc CCGGCTCGGTAACAGAACTAaaaagagggccttgagccACGGCGTAACCAAAAGTCAC 45.5 55.60% -9.38 kcal/mole -80.28 kcal/mole >adA2_aacagcaccctaacgcgt CCGGCTCGGTAACAGAACTAaacagcaccctaacgcgtACGGCGTAACCAAAAGTCAC 55.8 55.60% -8.09 kcal/mole -80.28 kcal/mole >adA2_aacctgttcgccaccttc CCGGCTCGGTAACAGAACTAaacctgttcgccaccttcACGGCGTAACCAAAAGTCAC 45.2 55.60% -9.82 kcal/mole -80.28 kcal/mole >adA2_aacgactgtgatggtcgc CCGGCTCGGTAACAGAACTAaacgactgtgatggtcgcACGGCGTAACCAAAAGTCAC 44.5 55.60% -6.75 kcal/mole -80.28 kcal/mole >adA2_aacgcaaactccttcaag CCGGCTCGGTAACAGAACTAaacgcaaactccttcaagACGGCGTAACCAAAAGTCAC 46.2 44.40% -8.09 kcal/mole -80.28 kcal/mole >adA2_aagaggctagcggcatgt CCGGCTCGGTAACAGAACTAaagaggctagcggcatgtACGGCGTAACCAAAAGTCAC 39.5 55.60% -6.68 kcal/mole -80.28 kcal/mole >adA2_aagctagactaaaggccg CCGGCTCGGTAACAGAACTAaagctagactaaaggccgACGGCGTAACCAAAAGTCAC 42.9 50% -9.82 kcal/mole -80.28 kcal/mole >adA2_aatacgcaccgagggtag CCGGCTCGGTAACAGAACTAaatacgcaccgagggtagACGGCGTAACCAAAAGTCAC 48.3 55.60% -11.2 kcal/mole -80.28 kcal/mole >adA2_aattactaggcgaagcag CCGGCTCGGTAACAGAACTAaattactaggcgaagcagACGGCGTAACCAAAAGTCAC 39 44.40% -5.19 kcal/mole -80.28 kcal/mole >adA2_acacgtgtcgtttagtcc CCGGCTCGGTAACAGAACTAacacgtgtcgtttagtccACGGCGTAACCAAAAGTCAC 44 50% -6.9 kcal/mole -80.28 kcal/mole >adA2_accatgcgcaggtcatgt CCGGCTCGGTAACAGAACTAaccatgcgcaggtcatgtACGGCGTAACCAAAAGTCAC 39.6 55.60% -6.75 kcal/mole -80.28 kcal/mole >adA2_accgcttagcatttcccc CCGGCTCGGTAACAGAACTAaccgcttagcatttccccACGGCGTAACCAAAAGTCAC 40.3 55.60% -8.02 kcal/mole -80.28 kcal/mole 302

>adA2_acgcatcacgtagatgct CCGGCTCGGTAACAGAACTAacgcatcacgtagatgctACGGCGTAACCAAAAGTCAC 46.8 50% -8.09 kcal/mole -80.28 kcal/mole >adA2_acggaggacacataccta CCGGCTCGGTAACAGAACTAacggaggacacatacctaACGGCGTAACCAAAAGTCAC 35.6 50% -6.68 kcal/mole -80.28 kcal/mole >adA2_acgtggtgcgaagttacc CCGGCTCGGTAACAGAACTAacgtggtgcgaagttaccACGGCGTAACCAAAAGTCAC 53 55.60% -8.66 kcal/mole -80.28 kcal/mole >adA2_actagggggtataaacag CCGGCTCGGTAACAGAACTAactagggggtataaacagACGGCGTAACCAAAAGTCAC 31.3 44.40% -4.41 kcal/mole -80.28 kcal/mole >adA2_actatctgagaaacggca CCGGCTCGGTAACAGAACTAactatctgagaaacggcaACGGCGTAACCAAAAGTCAC 48 44.40% -6.68 kcal/mole -80.28 kcal/mole >adA2_actgccccaagtagatct CCGGCTCGGTAACAGAACTAactgccccaagtagatctACGGCGTAACCAAAAGTCAC 36.7 50% -6.21 kcal/mole -80.28 kcal/mole >adA2_actgtttctactgcggcc CCGGCTCGGTAACAGAACTAactgtttctactgcggccACGGCGTAACCAAAAGTCAC 42.3 55.60% -6.84 kcal/mole -80.28 kcal/mole >adA2_agcgcaagagtcacaaac CCGGCTCGGTAACAGAACTAagcgcaagagtcacaaacACGGCGTAACCAAAAGTCAC 40.1 50% -6.75 kcal/mole -80.28 kcal/mole >adA2_agcgcacactctagacct CCGGCTCGGTAACAGAACTAagcgcacactctagacctACGGCGTAACCAAAAGTCAC 40.1 55.60% -6.75 kcal/mole -80.28 kcal/mole >adA2_aggacacatctacacaga CCGGCTCGGTAACAGAACTAaggacacatctacacagaACGGCGTAACCAAAAGTCAC 37.2 44.40% -3.17 kcal/mole -80.28 kcal/mole >adA2_aggactctttgcttggtt CCGGCTCGGTAACAGAACTAaggactctttgcttggttACGGCGTAACCAAAAGTCAC 44.2 44.40% -10.25 kcal/mole -80.28 kcal/mole >adA2_aggtgtgagttctagcta CCGGCTCGGTAACAGAACTAaggtgtgagttctagctaACGGCGTAACCAAAAGTCAC 42 44.40% -8.06 kcal/mole -80.28 kcal/mole >adA2_agtcctcgagcaaagaga CCGGCTCGGTAACAGAACTAagtcctcgagcaaagagaACGGCGTAACCAAAAGTCAC 48.3 50% -9.92 kcal/mole -80.28 kcal/mole >adA2_ataattccggagtaccca CCGGCTCGGTAACAGAACTAataattccggagtacccaACGGCGTAACCAAAAGTCAC 42.9 44.40% -9.75 kcal/mole -80.28 kcal/mole >adA2_atacgttctgcttctccg CCGGCTCGGTAACAGAACTAatacgttctgcttctccgACGGCGTAACCAAAAGTCAC 44.5 50% -8.41 kcal/mole -80.28 kcal/mole >adA2_atcaatcacgattcacgc CCGGCTCGGTAACAGAACTAatcaatcacgattcacgcACGGCGTAACCAAAAGTCAC 42.1 44.40% -8.09 kcal/mole -80.28 kcal/mole >adA2_atccacgaggcctgaaac CCGGCTCGGTAACAGAACTAatccacgaggcctgaaacACGGCGTAACCAAAAGTCAC 40.4 55.60% -6.78 kcal/mole -80.28 kcal/mole 303

>adA2_atcggcaaactgggaagc CCGGCTCGGTAACAGAACTAatcggcaaactgggaagcACGGCGTAACCAAAAGTCAC 35.1 55.60% -6.68 kcal/mole -80.28 kcal/mole >adA2_atggatcacaccaacgga CCGGCTCGGTAACAGAACTAatggatcacaccaacggaACGGCGTAACCAAAAGTCAC 42.9 50% -6.68 kcal/mole -80.28 kcal/mole >adA2_attactgtggcggtattc CCGGCTCGGTAACAGAACTAattactgtggcggtattcACGGCGTAACCAAAAGTCAC 39.1 44.40% -6.68 kcal/mole -80.28 kcal/mole >adA2_attggccctgtttttccg CCGGCTCGGTAACAGAACTAattggccctgtttttccgACGGCGTAACCAAAAGTCAC 38.6 50% -6.97 kcal/mole -80.28 kcal/mole >adA2_caagcaggcacctatatc CCGGCTCGGTAACAGAACTAcaagcaggcacctatatcACGGCGTAACCAAAAGTCAC 37.2 50% -4.74 kcal/mole -80.28 kcal/mole >adA2_cacgctcgacggtagata CCGGCTCGGTAACAGAACTAcacgctcgacggtagataACGGCGTAACCAAAAGTCAC 38.7 55.60% -8.09 kcal/mole -80.28 kcal/mole >adA2_cagtgctaacccagcgat CCGGCTCGGTAACAGAACTAcagtgctaacccagcgatACGGCGTAACCAAAAGTCAC 38.8 55.60% -5.19 kcal/mole -80.28 kcal/mole >adA2_cagttgactcggtggttc CCGGCTCGGTAACAGAACTAcagttgactcggtggttcACGGCGTAACCAAAAGTCAC 41.3 55.60% -8.31 kcal/mole -80.28 kcal/mole >adA2_catgacagtcttcagctg CCGGCTCGGTAACAGAACTAcatgacagtcttcagctgACGGCGTAACCAAAAGTCAC 40.3 50% -4.87 kcal/mole -80.28 kcal/mole >adA2_catgcaagcaagcccaaa CCGGCTCGGTAACAGAACTAcatgcaagcaagcccaaaACGGCGTAACCAAAAGTCAC 39.1 50% -7.81 kcal/mole -80.28 kcal/mole >adA2_catgctgctttagatgtg CCGGCTCGGTAACAGAACTAcatgctgctttagatgtgACGGCGTAACCAAAAGTCAC 38.7 44.40% -5.49 kcal/mole -80.28 kcal/mole >adA2_catgtaacccggatgcac CCGGCTCGGTAACAGAACTAcatgtaacccggatgcacACGGCGTAACCAAAAGTCAC 40.6 55.60% -9.75 kcal/mole -80.28 kcal/mole >adA2_cgaatagttcgggtgcgt CCGGCTCGGTAACAGAACTAcgaatagttcgggtgcgtACGGCGTAACCAAAAGTCAC 43.7 55.60% -7.42 kcal/mole -80.28 kcal/mole >adA2_cgacgtggtggccttaat CCGGCTCGGTAACAGAACTAcgacgtggtggccttaatACGGCGTAACCAAAAGTCAC 49.9 55.60% -6.37 kcal/mole -80.28 kcal/mole >adA2_cgatgtaagccaaggcaa CCGGCTCGGTAACAGAACTAcgatgtaagccaaggcaaACGGCGTAACCAAAAGTCAC 40.5 50% -7.81 kcal/mole -80.28 kcal/mole >adA2_cgcacatctgtcaccact CCGGCTCGGTAACAGAACTAcgcacatctgtcaccactACGGCGTAACCAAAAGTCAC 45.1 55.60% -6.75 kcal/mole -80.28 kcal/mole >adA2_cgcttgcctcttagccat CCGGCTCGGTAACAGAACTAcgcttgcctcttagccatACGGCGTAACCAAAAGTCAC 44.7 55.60% -7.81 kcal/mole -80.28 kcal/mole 304

>adA2_cggaagtccgttctcact CCGGCTCGGTAACAGAACTAcggaagtccgttctcactACGGCGTAACCAAAAGTCAC 47.4 55.60% -9.97 kcal/mole -80.28 kcal/mole >adA2_cgtatgcgaaggtgatta CCGGCTCGGTAACAGAACTAcgtatgcgaaggtgattaACGGCGTAACCAAAAGTCAC 40.7 44.40% -5.19 kcal/mole -80.28 kcal/mole >adA2_cgtttgatgccaaccgtc CCGGCTCGGTAACAGAACTAcgtttgatgccaaccgtcACGGCGTAACCAAAAGTCAC 37.4 55.60% -8.02 kcal/mole -80.28 kcal/mole >adA2_ctaaccgtgttcggactg CCGGCTCGGTAACAGAACTActaaccgtgttcggactgACGGCGTAACCAAAAGTCAC 43.5 55.60% -8.02 kcal/mole -80.28 kcal/mole >adA2_ctagaagtcactgattcc CCGGCTCGGTAACAGAACTActagaagtcactgattccACGGCGTAACCAAAAGTCAC 36.6 44.40% -3.55 kcal/mole -80.28 kcal/mole >adA2_ctcttcaactaaagggtg CCGGCTCGGTAACAGAACTActcttcaactaaagggtgACGGCGTAACCAAAAGTCAC 42.1 44.40% -4.41 kcal/mole -80.28 kcal/mole >adA2_ctgaaagatcatagcccg CCGGCTCGGTAACAGAACTActgaaagatcatagcccgACGGCGTAACCAAAAGTCAC 45.7 50% -7.81 kcal/mole -80.28 kcal/mole >adA2_ctgcacccaatagaccag CCGGCTCGGTAACAGAACTActgcacccaatagaccagACGGCGTAACCAAAAGTCAC 39.7 55.60% -4.41 kcal/mole -80.28 kcal/mole >adA2_ctggcgcttgcaacatag CCGGCTCGGTAACAGAACTActggcgcttgcaacatagACGGCGTAACCAAAAGTCAC 41.7 55.60% -6.75 kcal/mole -80.28 kcal/mole >adA2_ctggtgttgagccaagct CCGGCTCGGTAACAGAACTActggtgttgagccaagctACGGCGTAACCAAAAGTCAC 46.3 55.60% -9.38 kcal/mole -80.28 kcal/mole >adA2_ctgtggaggtttcggtct CCGGCTCGGTAACAGAACTActgtggaggtttcggtctACGGCGTAACCAAAAGTCAC 44.6 55.60% -6.68 kcal/mole -80.28 kcal/mole >adA2_cttctgataggctagctt CCGGCTCGGTAACAGAACTActtctgataggctagcttACGGCGTAACCAAAAGTCAC 44.2 44.40% -7.07 kcal/mole -80.28 kcal/mole >adA2_ctttccggctcgaaagtt CCGGCTCGGTAACAGAACTActttccggctcgaaagttACGGCGTAACCAAAAGTCAC 42.2 50% -9.75 kcal/mole -80.28 kcal/mole >adA2_gaaatcggcttttgaccc CCGGCTCGGTAACAGAACTAgaaatcggcttttgacccACGGCGTAACCAAAAGTCAC 40.5 50% -9.39 kcal/mole -80.28 kcal/mole >adA2_gaacgaactgtatgatgg CCGGCTCGGTAACAGAACTAgaacgaactgtatgatggACGGCGTAACCAAAAGTCAC 40.5 44.40% -5.19 kcal/mole -80.28 kcal/mole >adA2_gaacgtttacgaattccg CCGGCTCGGTAACAGAACTAgaacgtttacgaattccgACGGCGTAACCAAAAGTCAC 41.1 44.40% -7.86 kcal/mole -80.28 kcal/mole >adA2_gacagagatggctttgaa CCGGCTCGGTAACAGAACTAgacagagatggctttgaaACGGCGTAACCAAAAGTCAC 41.7 44.40% -5.84 kcal/mole -80.28 kcal/mole 305

>adA2_gacgaaaacttggagctt CCGGCTCGGTAACAGAACTAgacgaaaacttggagcttACGGCGTAACCAAAAGTCAC 35.7 44.40% -6.97 kcal/mole -80.28 kcal/mole >adA2_gagcgatacgacgacatc CCGGCTCGGTAACAGAACTAgagcgatacgacgacatcACGGCGTAACCAAAAGTCAC 50.8 55.60% -6.31 kcal/mole -80.28 kcal/mole >adA2_gagcgataggaaagtgtt CCGGCTCGGTAACAGAACTAgagcgataggaaagtgttACGGCGTAACCAAAAGTCAC 40.8 44.40% -6.31 kcal/mole -80.28 kcal/mole >adA2_gagcttactgacccgtct CCGGCTCGGTAACAGAACTAgagcttactgacccgtctACGGCGTAACCAAAAGTCAC 47 55.60% -8.02 kcal/mole -80.28 kcal/mole >adA2_gaggtctagcacctttaa CCGGCTCGGTAACAGAACTAgaggtctagcacctttaaACGGCGTAACCAAAAGTCAC 38.9 44.40% -5.49 kcal/mole -80.28 kcal/mole >adA2_gatttttaatcgggagcc CCGGCTCGGTAACAGAACTAgatttttaatcgggagccACGGCGTAACCAAAAGTCAC 42.8 44.40% -9.38 kcal/mole -80.28 kcal/mole >adA2_gccgttaagatgactaag CCGGCTCGGTAACAGAACTAgccgttaagatgactaagACGGCGTAACCAAAAGTCAC 47.2 44.40% -14.07 kcal/mole -80.28 kcal/mole >adA2_gcctcaccctgatttggt CCGGCTCGGTAACAGAACTAgcctcaccctgatttggtACGGCGTAACCAAAAGTCAC 46.2 55.60% -10.25 kcal/mole -80.28 kcal/mole >adA2_gcgaagaacgctgaaata CCGGCTCGGTAACAGAACTAgcgaagaacgctgaaataACGGCGTAACCAAAAGTCAC 49.3 44.40% -8.09 kcal/mole -80.28 kcal/mole >adA2_gcgttaaggtctctgtca CCGGCTCGGTAACAGAACTAgcgttaaggtctctgtcaACGGCGTAACCAAAAGTCAC 37.1 50% -7.86 kcal/mole -80.28 kcal/mole >adA2_gctgaacatcaggtctgc CCGGCTCGGTAACAGAACTAgctgaacatcaggtctgcACGGCGTAACCAAAAGTCAC 38.9 55.60% -5.13 kcal/mole -80.28 kcal/mole >adA2_ggagatgcgatcttcacc CCGGCTCGGTAACAGAACTAggagatgcgatcttcaccACGGCGTAACCAAAAGTCAC 36.6 55.60% -5.19 kcal/mole -80.28 kcal/mole >adA2_ggattacgaaacggaagt CCGGCTCGGTAACAGAACTAggattacgaaacggaagtACGGCGTAACCAAAAGTCAC 38.1 44.40% -7.86 kcal/mole -80.28 kcal/mole >adA2_ggcaaggcaacatgatca CCGGCTCGGTAACAGAACTAggcaaggcaacatgatcaACGGCGTAACCAAAAGTCAC 34.4 47.10% -3.53 kcal/mole -80.28 kcal/mole >adA2_ggcttacctagtttctct CCGGCTCGGTAACAGAACTAggcttacctagtttctctACGGCGTAACCAAAAGTCAC 49.3 44.40% -7.32 kcal/mole -80.28 kcal/mole >adA2_gggatcgccttttggtaa CCGGCTCGGTAACAGAACTAgggatcgccttttggtaaACGGCGTAACCAAAAGTCAC 49.3 50% -13.8 kcal/mole -80.28 kcal/mole >adA2_gtaagacatcgtcgaagc CCGGCTCGGTAACAGAACTAgtaagacatcgtcgaagcACGGCGTAACCAAAAGTCAC 42.4 50% -5.19 kcal/mole -80.28 kcal/mole 306

>adA2_gtaatagttggctcgtgc CCGGCTCGGTAACAGAACTAgtaatagttggctcgtgcACGGCGTAACCAAAAGTCAC 44.4 50% -6.97 kcal/mole -80.28 kcal/mole >adA2_gtaatcttaggttccggt CCGGCTCGGTAACAGAACTAgtaatcttaggttccggtACGGCGTAACCAAAAGTCAC 46.8 44.40% -9.75 kcal/mole -80.28 kcal/mole >adA2_gtcaacgcgactattatg CCGGCTCGGTAACAGAACTAgtcaacgcgactattatgACGGCGTAACCAAAAGTCAC 41.2 44.40% -8.09 kcal/mole -80.28 kcal/mole >adA2_gtccgtgactttttcatg CCGGCTCGGTAACAGAACTAgtccgtgactttttcatgACGGCGTAACCAAAAGTCAC 46.8 44.40% -13.64 kcal/mole -80.28 kcal/mole >adA2_gtgtttcgggttgcattt CCGGCTCGGTAACAGAACTAgtgtttcgggttgcatttACGGCGTAACCAAAAGTCAC 36.8 44.40% -6.68 kcal/mole -80.28 kcal/mole >adA2_gttactctaatgttgggg CCGGCTCGGTAACAGAACTAgttactctaatgttggggACGGCGTAACCAAAAGTCAC 38.5 44.40% -6.97 kcal/mole -80.28 kcal/mole >adA2_gttatacgaaatacgcgg CCGGCTCGGTAACAGAACTAgttatacgaaatacgcggACGGCGTAACCAAAAGTCAC 54.6 44.40% -9.05 kcal/mole -80.28 kcal/mole >adA2_taaccagcttcttctggc CCGGCTCGGTAACAGAACTAtaaccagcttcttctggcACGGCGTAACCAAAAGTCAC 41.5 50% -7.07 kcal/mole -80.28 kcal/mole >adA2_tacacaagtgggctcttg CCGGCTCGGTAACAGAACTAtacacaagtgggctcttgACGGCGTAACCAAAAGTCAC 41.5 50% -5.02 kcal/mole -80.28 kcal/mole >adA2_taccacttggagctgctc CCGGCTCGGTAACAGAACTAtaccacttggagctgctcACGGCGTAACCAAAAGTCAC 39.7 55.60% -6.97 kcal/mole -80.28 kcal/mole >adA2_tatagagcccctctttga CCGGCTCGGTAACAGAACTAtatagagcccctctttgaACGGCGTAACCAAAAGTCAC 45.3 44.40% -9.38 kcal/mole -80.28 kcal/mole >adA2_tatcaccgatgcttacca CCGGCTCGGTAACAGAACTAtatcaccgatgcttaccaACGGCGTAACCAAAAGTCAC 38.6 44.40% -9.6 kcal/mole -80.28 kcal/mole >adA2_tcatgcagagtcacctaa CCGGCTCGGTAACAGAACTAtcatgcagagtcacctaaACGGCGTAACCAAAAGTCAC 33.4 44.40% -4.41 kcal/mole -80.28 kcal/mole >adA2_tccacggaaacgtggcta CCGGCTCGGTAACAGAACTAtccacggaaacgtggctaACGGCGTAACCAAAAGTCAC 54.1 55.60% -5.02 kcal/mole -80.28 kcal/mole >adA2_tcccttctgcgctaacag CCGGCTCGGTAACAGAACTAtcccttctgcgctaacagACGGCGTAACCAAAAGTCAC 39.9 55.60% -7.07 kcal/mole -80.28 kcal/mole >adA2_tccgggcgaaaaaaccac CCGGCTCGGTAACAGAACTAtccgggcgaaaaaaccacACGGCGTAACCAAAAGTCAC 42.2 55.60% -9.75 kcal/mole -80.28 kcal/mole >adA2_tcgtcttcgcaatgcgct CCGGCTCGGTAACAGAACTAtcgtcttcgcaatgcgctACGGCGTAACCAAAAGTCAC 43.2 55.60% -6.75 kcal/mole -80.28 kcal/mole 307

>adA2_tctgctcccattagctca CCGGCTCGGTAACAGAACTAtctgctcccattagctcaACGGCGTAACCAAAAGTCAC 41.3 50% -5.13 kcal/mole -80.28 kcal/mole >adA2_tcttacaccttttgcacg CCGGCTCGGTAACAGAACTAtcttacaccttttgcacgACGGCGTAACCAAAAGTCAC 38.7 44.40% -9.39 kcal/mole -80.28 kcal/mole >adA2_tcttttcgctggccttca CCGGCTCGGTAACAGAACTAtcttttcgctggccttcaACGGCGTAACCAAAAGTCAC 42.7 50% -7.43 kcal/mole -80.28 kcal/mole >adA2_tgacaattcccctgatcg CCGGCTCGGTAACAGAACTAtgacaattcccctgatcgACGGCGTAACCAAAAGTCAC 34.9 50% -4.87 kcal/mole -80.28 kcal/mole >adA2_tgagggaaccgtcagact CCGGCTCGGTAACAGAACTAtgagggaaccgtcagactACGGCGTAACCAAAAGTCAC 40.8 55.60% -8.02 kcal/mole -80.28 kcal/mole >adA2_tgatagttgcagcgcgac CCGGCTCGGTAACAGAACTAtgatagttgcagcgcgacACGGCGTAACCAAAAGTCAC 44.8 55.60% -6.75 kcal/mole -80.28 kcal/mole >adA2_tgcaagctcacctactag CCGGCTCGGTAACAGAACTAtgcaagctcacctactagACGGCGTAACCAAAAGTCAC 37.2 55.60% -4.74 kcal/mole -80.28 kcal/mole >adA2_tgcaagtcggcttaagga CCGGCTCGGTAACAGAACTAtgcaagtcggcttaaggaACGGCGTAACCAAAAGTCAC 35.2 50% -6.68 kcal/mole -80.28 kcal/mole >adA2_tgcaggctggcgtaacaa CCGGCTCGGTAACAGAACTAtgcaggctggcgtaacaaACGGCGTAACCAAAAGTCAC 48.4 55.60% -5.02 kcal/mole -80.28 kcal/mole >adA2_tggagatagtacgtcggg CCGGCTCGGTAACAGAACTAtggagatagtacgtcgggACGGCGTAACCAAAAGTCAC 48.6 55.60% -6.68 kcal/mole -80.28 kcal/mole >adA2_tgtgctaggtgcgaagga CCGGCTCGGTAACAGAACTAtgtgctaggtgcgaaggaACGGCGTAACCAAAAGTCAC 36.8 55.60% -5.19 kcal/mole -80.28 kcal/mole >adA2_ttcctaagacgcatccac CCGGCTCGGTAACAGAACTAttcctaagacgcatccacACGGCGTAACCAAAAGTCAC 49.7 50% -8.09 kcal/mole -80.28 kcal/mole >adA2_ttcgctagtagacactcg CCGGCTCGGTAACAGAACTAttcgctagtagacactcgACGGCGTAACCAAAAGTCAC 39.3 50% -3.9 kcal/mole -80.28 kcal/mole >adA2_ttcgtggccaacgagcat CCGGCTCGGTAACAGAACTAttcgtggccaacgagcatACGGCGTAACCAAAAGTCAC 47.3 55.60% -6.21 kcal/mole -80.28 kcal/mole >adA2_ttctgctactttcgcacg CCGGCTCGGTAACAGAACTAttctgctactttcgcacgACGGCGTAACCAAAAGTCAC 46.4 50% -7.07 kcal/mole -80.28 kcal/mole >adA2_ttgcccaggttgttggtt CCGGCTCGGTAACAGAACTAttgcccaggttgttggttACGGCGTAACCAAAAGTCAC 49.6 50% -10.25 kcal/mole -80.28 kcal/mole >adA2_ttggactaaccggcgttc CCGGCTCGGTAACAGAACTAttggactaaccggcgttcACGGCGTAACCAAAAGTCAC 51.6 55.60% -9.75 kcal/mole -80.28 kcal/mole 308

Appendix 13: Primers designed manually using Primer 3 to amplify the corresponding genes in QPCR

Gene ID Forward Reverse

cyp51A GTGCAGAGAAAAG*TATGGCG TCCGCATTGACATCCTTGAG

cyp51B GGAGCAGAAGAAG*TTCGTCA GGAACGCCGGAGAATTTTTG

cdr1B CCCAGAGTGCGTACGATGTAT ACGTTCCGGACATTCAAATC

GPDA GAGCTCAAAA*ACATCCTCGGC CGAAGTTGGGGTTGAGGGAG

Beta tubulin CGACAACGAGGCTCT CAACTTGCGCAGAGTCAGAGTTGAG

Appendix 14: Phylogenetic tree of kinases in A. fumigatus and human, constructed using T-rex. Shows the divergent PKs in A. fumigatus A1163

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Appendix 15: Phylogenetic tree of STE kinases in A. fumigatus and human, constructed using MEGA6. The highlighted group, are those with <40% similarity compared to human PKs

310

Appendix 16: Phylogenetic tree of humans TKs kinases and A. fumigatus kinases, constructed using MEGA6. The highlighted group, are those with <40% similarity compared to human PKs

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Appendix 17: Layout of the gDNA consolidated into one 96-well plate

312

Appendix 18: Gel electrophoresis of validation PCR for 5’ flanking fragment insertion check using P1+ Rv1 (expected amplicon size: about 1.6kb)

Appendix 19: Gel electrophoresis of validation PCR for 3’ flanking fragment insertion check using Fw hph2+ P4 (expected amplicon size: about 1.4 kb)

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Appendix 21: Gel electrophoresis image of PCR validation using P1-P4 (expected amplicon size: about 5 kb)

Appendix 22: Gel electrophoresis image of the repeat of PCR validation using P1-P4 (expected amplicon size: about 5 kb)

315

5' and 3' flanks ID StrainΔ 5' PCR P1+Rv1 3' PCR Fw2+P4 P1-P4 Comments validation AFUB_045810 ok ok TRUE ok ok AFUB_021710 ok ok TRUE ok ok AFUB_053300 ok ok TRUE ok ok AFUB_012420 ok ok TRUE ok ok AFUB_032300 ok ok TRUE ok ok AFUB_044560 ok ok TRUE ok ok AFUB_030660 ok ok TRUE ok ok AFUB_052450 ok ok TRUE ok ok AFUB_043130 ok ok TRUE ok ok AFUB_010510 ok ok TRUE ok ok AFUB_018770 ok ok TRUE ok ok AFUB_020560 ok ok TRUE ok ok AFUB_029320 ok ok TRUE ok ok AFUB_025560 ok ok TRUE ok ok AFUB_038630 ok ok TRUE ok ok AFUB_029240 ok ok TRUE ok ok AFUB_048440 ok ok TRUE ok ok AFUB_006780 ok ok TRUE ok ok AFUB_039620 ok ok TRUE ok ok AFUB_045840 ok ok TRUE ok ok AFUB_051750 No ok TRUE ok ok AFUB_029820 ok ok TRUE ok ok AFUB_027640 ok ok TRUE ok ok AFUB_038060 ok ok TRUE ok ok AFUB_001600 ok ok TRUE ok ok AFUB_053500 ok ok TRUE ok ok AFUB_010360 ok ok TRUE ok ok AFUB_044400 ok ok TRUE ok ok AFUB_027480 ok ok TRUE ok ok AFUB_030570 ok ok TRUE ok ok AFUB_053520 ok ok TRUE ok ok AFUB_039100 ok ok TRUE ok ok AFUB_014350 ok ok TRUE ok ok AFUB_006320 ok ok TRUE ok ok AFUB_035220 ok ok TRUE ok ok AFUB_019930 ok ok TRUE ok ok AFUB_017750 ok ok TRUE ok ok AFUB_027890 ok ok TRUE ok ok AFUB_056020 ok ok TRUE ok ok 316

AFUB_059390 ok ok TRUE ok ok AFUB_059090 ok ok TRUE ok ok AFUB_066150 ok ok TRUE ok ok AFUB_071620 ok ok TRUE ok ok AFUB_056110 ok ok TRUE ok ok AFUB_078810 ok ok TRUE ok ok AFUB_090090 ok ok TRUE ok ok AFUB_095720 ok ok TRUE ok ok AFUB_060320 ok ok TRUE ok ok AFUB_087320 ok ok TRUE ok ok AFUB_087320 ok ok TRUE ok ok AFUB_087320 ok ok TRUE ok ok AFUB_087320 ok ok TRUE ok ok AFUB_087120 ok ok TRUE ok ok AFUB_054020 ok ok TRUE ok ok AFUB_081540 ok ok TRUE ok ok AFUB_055480 ok ok TRUE ok ok AFUB_056640 ok ok TRUE ok ok AFUB_074550 ok ok TRUE ok ok ΔAFUB_098230 ok ok TRUE ok ok AFUB_099170 ok ok TRUE ok ok AFUB_096030 ok ok TRUE ok ok AFUB_096080 ok ok TRUE ok ok AFUB_099990 ok ok TRUE ok ok AFUB_006190 ok ok TRUE ok ok AFUB_089280 ok ok TRUE ok ok

317