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Harnessing environmental and culture conditions to alter fungal ‘omics’

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Harnessing environmental and culture conditions to alter fungal ‘omics’

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HARNESSING ENVIRONMENTAL AND CULTURE
CONDITIONS TO ALTER FUNGAL ‘OMICS’

by
Jillian Romsdahl

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(MOLECULAR PHARMACOLOGY AND TOXICOLOGY)

December 2018

Copyright 2018 Jillian Romsdahl
ii
DEDICATION

This work is dedicated to my mother, Kari Romsdahl, who for the past 5 years has patiently
listened to every mundane experimental detail as my research projects gradually transformed into
the final product that encompasses this thesis. Thank you for your unwavering support and
continuous encouragement.
iii
ACKNOWLEDGEMENTS

I would like to express my utmost gratitude to my advisor, Dr. Clay Wang. In addition to
constantly providing valuable guidance and expertise, he made this process a truly remarkable
journey. When I began graduate school, I could have never anticipated that I would have the
opportunity to watch my research project launch to the International Space Station and work with
NASA to accelerate our understanding of how fungi adapt to space. Thank you for being an
incredibly innovative professor. Because of you, I will depart graduate school with unforgettable
experiences and exposure to a broad range of technical and analytical approaches.

I am also thankful for the contributions of the professors who served on my committees,
including Dr. Curtis Okamoto, Dr. Wei-Chiang Shen, Dr. Ian Haworth, and Dr. Travis Williams.
Thank you all for your valuable feedback. Significant credit is due to our JPL collaborator, Dr.
Kasthuri Venkateswaran, who has provided invaluable expertise throughout this process. I also
would not have been able to navigate the Unix landscape to process and interpret whole genome
sequencing data without the patient guidance of Dr. Jason Stajich.

I am especially grateful for Adriana Blachowicz, who has worked closely with me on the fungal
space projects for the past three years. Thank you for being an excellent partner and an even
better scientist. Thank you to Dr. Yi-Ming Chiang for your constant ingenious insights. And
finally, thank you to the rest of my lab and its former members, Mike Praseuth, Dr. Junko
Yaegashi, Dr. Jim Sanchez, Dr. Weiwen Sun, Dr. Johannes Van Dijk, Dr. Kevin Lin, Michelle
Grau, Yi-En Liao, Chris Rabot, Eva Yuan, and Patrick Lehman. Thank you for your support and
feedback. It has been a pleasure to work with each one of you.
iv
Chapter 2 is incorporated from Romsdahl, J., Blachowicz, A., Chiang, A.J., Chiang, Y.M.,
Masonjones, S., Yaegashi, J., Countryman, S., Karouia, F., Kalkum, M., Stajich J.E.,
Venkateswaran K., Wang, C.C.C., International Space Station Conditions Alter Fungal ‘Omics’
in Aspergillus nidulans, which is being prepared for submission to Applied Microbiology and
Biotechnology.

Chapter 3 is incorporated from Romsdahl, J., Blachowicz, A., Chiang, A.J., Singh, N., Stajich
J.E., Kalkum, M., Venkateswaran K., Wang, C.C.C., (2018). Characterization of Aspergillus
niger Isolated from the International Space Station, mSystems, 3:e00112-18. DOI:
10.1128/mSystems.00112-18.

Chapter 4 is incorporated from Romsdahl, J., Blachowicz, A., Chiang Y.M., Venkateswaran K.,
Wang, C.C.C., Metabolomic Analysis of Aspergillus niger Isolated from the International Space
Station Reveals the Radiation Resistance Potential of Pyranonigrin A, which is being prepared
for submission to Fungal Genetics and Biology.

Chapter 6 is adapted from Yaegashi, J., Romsdahl, J., Chiang, Y.M., Wang, C.C.C., (2015).
Genome Mining and Molecular Characterization of the Biosynthetic Gene Cluster of a
Diterpenic Meroterpenoid, 15-Deoxyoxalicine B, in Penicillium canescens, Chemical Science,
6(11):6537-44. DOI: 10.1039/C5SC01965F. All genetic manipulations described in Chapters 5
and 6 were generated by Dr. Junko Yaegashi.

v
TABLE OF CONTENTS

DEDICATION ii

ACKNOWLEDGEMENTS iii

LIST OF TABLES viii

LIST OF FIGURES ix

ABSTRACT xi

CHAPTER I: Introduction
1.1 Adaptability of fungi to various environments 1
1.1.1 Mechanisms of adaptation 2
1.1.2 Methods to evaluate adaptation 3
1.2 Biotechnological applications of fungi 5
1.2.1 Secondary metabolites 5
1.2.2 Enzymes 6
1.3 Utilization of environmental and culture conditions to study fungal adaptation
and biotechnology applications 6
1.3.1 International Space Station 7
1.3.2 Culture conditions 8

CHAPTER II: International Space Station conditions alter fungal ‘omics’ in Aspergillus
nidulans
2.1 Abstract 10
2.2 Introduction 11
2.3 Results 14
2.3.1 Genomic variation among ISS-grown samples 14
2.3.2 Proteomic profiling of ISS-grown A. nidulans 17
2.3.3 Secondary metabolome alterations of ISS-grown A. nidulans 21
2.4 Discussion 25
2.5 Materials and Methods 30
2.5.1 Strains, media, and growth conditions 30
2.5.2 Genomic DNA extraction, library preparation, and genome sequencing 32
2.5.3 Genetic mutation identification 32
2.5.4 Protein extraction 33
2.5.5 Tandem mass tag (TMT) labeling 33
2.5.6 LC-MS/MS analysis 34
2.5.7 Quantitative proteomics analysis 35
2.5.8 Secondary metabolite extraction and analysis 37
vi
2.5.9 Data availability 37

CHAPTER III: Characterization of Aspergillus niger isolated from the International Space
Station
3.1 Abstract 38
3.2 Introduction 39
3.3 Results 42
3.3.1 Identification of A. niger sampled from the ISS 42
3.3.2 Visual characterization and growth rates of JSC-093350089 in vitro 43
3.3.3 Overview of proteome analysis 44
3.3.4 Differential abundance of cell wall modulation proteins 48
3.3.5 Differential abundance of stress response proteins 49
3.4 Discussion 51
3.5 Materials and Methods 56
3.5.1 Isolation and identification of the ISS A. niger isolate 56
3.5.2 Genome sequencing, assembly, and annotation 56
3.5.3 Phylogenetic analysis 57
3.5.4 Growth conditions 59
3.5.5 Physiological analysis 59
3.5.6 Protein extraction 59
3.5.7 Tandem mass tag (TMT) labeling 60
3.5.8 LC-MS/MS analysis 61
3.5.9 Proteome data analysis 62
3.5.10 Data availability 63

CHAPTER IV: Metabolomic analysis of Aspergillus niger isolated from the International
Space Station reveals the radiation resistance potential of pyranonigrin A
4.1 Abstract 64
4.2 Introduction 65
4.3 Results 68
4.3.1 Secondary metabolite analysis of JSC-093350089 68
4.3.2 Analysis of the potential gene clusters responsible for production
pyranonigrin A in silico 70
4.3.3 Development of an efficient gene targeting system in JSC-093350089
and identification of the PKS-NRPS hybrid responsible for pyranonigrin
A biosynthesis 71
4.3.4 Identification of pyranonigrin A biosynthesis gene cluster boundaries 72
4.3.5 Assessment of the UV resistance potential of pyranonigrin A 74
4.4 Discussion 76
4.5 Materials and Methods 80
4.5.1 Secondary metabolite extraction and analysis 80
vii
4.5.2 Strains and molecular manipulations 80
4.5.3 Radiation resistance analysis 87

CHAPTER V: Utilization of various culture conditions facilitates molecular genetic mining
of the Penicillium canescens metabolome
5.1 Abstract 88
5.2 Introduction 89
5.3 Results and Discussion 91
5.3.1 Identification of putative PKS and NRPS genes within the P. canescens
genome 92
5.3.2 Construction of genome-wide PKS and NRPS deletion library 92
5.3.3 Verification of mutant generation 95
5.3.4 Selection of culture media to maximize SM production 96
5.3.5 Characterization of genome-wide PKS and NRPS gene deletion library 97
5.4 Materials and Methods 101
5.4.1 Strains and molecular genetic manipulations 101
5.4.2 Fermentation and LC-MS analysis 102
5.4.3 Isolation and characterization of metabolites 102

CHAPTER VI: Molecular characterization of the biosynthetic gene cluster of a diterpenic
meroterpenoid, 15-deoxyoxalicine B, in Penicillium canescens
6.1 Abstract 112
6.2 Introduction 113
6.3 Results and Discussion 116
6.3.1 Identification of genes involved in 15-deoxyoxalicine B biosynthesis 116
6.3.2 Identification of 15-deoxyoxalicine B biosynthetic intermediates and
shunt products 118
6.3.3 Elucidation of the biosynthetic pathway of 15-deoxyoxalicine B 121
6.4 Materials and Methods 125
6.4.1 Strains and molecular genetic manipulations 125
6.4.2 Cultivation and LC-MS analysis 126
6.4.3 Isolation and purification of secondary metabolites 127
6.4.4 Compound structure identification with NMR analysis 128
6.5 Supporting Information 129

CHAPTER VII: The big picture
7.1 Conclusions and perspectives for fungal space research 133
7.2 Conclusions and perspectives for secondary metabolite research 136

BIBLIOGRAPHY 138

viii
LIST OF TABLES

Table 2-1. A. nidulans stains used in this study 12

Table 2-2. Features of SNPs and INDELs 14
Table 2-3. Comparative analysis of non-synonymous SNPs occurring during spaceflight 15
Table 2-4. Differentially expressed proteins by strain and biological process 20
Table 2-5. Primers used in this study (5’ à 3’) 30
Table 3-1. Relative abundance of cell wall modulation proteins 47

Table 3-2. Relative abundance of stress response proteins 50
Table 4-1. Comparison of A. niger PKS-NRPSs to pyranonigrin E-producing PKS-NRPS 70

Table 4-2. Putative function of genes within the pyranonigrin A gene cluster and their homologs
in Penicillium thymicola 72

Table 4-3. Aspergillus niger strains used in this study 81

Table 4-4. Primers used in this study (5’ à 3’) 81

Table 5-1. List of all putative NRPS and PKS genes in P. canescens 91

Table 5-2. Primers used in this study (5’ à 3’) 103

Table 6-1. Putative function of genes within the 15-deoxyoxalicine B gene cluster and their
homologs in A. fumigatus 117

Table 6-2. Penicillium canescens strains used in this study 125

Table S6-1.
1
H-NMR Data for Compounds 7 and 8 129

Table S6-2.
13
C-NMR data for compounds 7 and 8 130

Table S6-3. Spectral data of compounds 132

ix
LIST OF FIGURES

Figure 2-1. Schematic overview of A. nidulans ISS experiment 13

Figure 2-2. Overview of proteomic analysis 17

Figure 2-3. LC-MS profiles of SMs identified in A. nidulans strains 22

Figure 2-4. Secondary metabolite quantification showing percent change for ISS-grown samples
in relation to Earth-grown counterparts 23

Figure 2-5. Asperthecin production in ISS-grown LO1362 and CW12001 24

Figure 2-6. Hardware used in A. nidulans ISS flight experiment 31
Figure 3-1. Phylogenetic characterization of JSC-093350089 displaying its relative placement
within the A. niger/welwitschiae/lacticoffeatus clade 42

Figure 3-2. In vitro growth of JSC-093350089 compared to ATCC 1015 44

Figure 3-3. Biological process GO Slim categories of differentially expressed proteins 46

Figure 4-1. Secondary metabolite production in JSC-093350089 relative to ATCC 1015 68

Figure 4-2. DAD total scan and MS total ion current (TIC) HPLC profiles of extracts from JSC-
093350089 WT and mutant strains pynA- and pyrA- 71

Figure 4-3. Schematic representation of pyr cluster 73

Figure 4-4. Percent survival following exposure to varying doses of UVC radiation for JSC-
093350089 WT and JSC-093350089 pyrA- (CW12005) 75

Figure 4-5. Results of diagnostic PCR for JSC-093350089 mutant strains 84

Figure 4-6. Strategy for gene deletion via selection marker replacement 85

Figure 4-7. Strategy for pyrG deletion 86

Figure 4-8. Strategy kusA reintegration 87

Figure 5-1. Structures of compounds identified in this study 90

Figure 5-2. Strategy for ku70 deletion in P. canescens using hygromycin resistance marker
(hph) 93

x
Figure 5-3. Strategy for pyrG deletion 94

Figure 5-4. Morphological difference between control (top wells) and mutants carrying Protein
ID 366620 deletion (bottom wells) 95

Figure 5-5. UV-Vis and ESIMS spectra of compounds 1 to 3 96

Figure 5-6. HPLC profiles of extracts from control and mutant strains 97

Figure 5-7.
1
H-NMR spectrum of 15-deoxyoxalicine B (2) in CDCl
3
98

Figure 5-8.
1
H-NMR spectrum of amauromine (3) in CDCl
3
99

Figure 5-9. Proposed pathway for the biosynthesis of amauromine in P. canescens 100

Figure 6-1. Structurally related fungal meroterpenoids 114

Figure 6-2. HPLS profiles of extracts from parental strain and olcA- strain 116

Figure 6-3. Orientation of the genes surrounding the PKS olcA involved in 15-deoxyxoalicine B
biosynthesis and HPLC extracts of cluster deletion strains 118

Figure 6-4. Key gHMBC correlations of predecaturin E and decaturin G and selective 1D
NOESY correlations of decaturin G 120

Figure 6-5. Proposed biosynthetic pathway for 15-deoxyoxalicine B and related shunt
products 122

Figure 6-6. Proposed mechanism of oxidative rearrangement catalyzed by cytochrome P450
OlcB 124

Figure S6-1. Results of diagnostic PCR for deletion strains 131

xi
ABSTRACT

Fungi are ubiquitous in nature. This is partially due to their phenotypic plasticity and ability to
readily undergo adaptive alterations that enable survival in a vast array of ecological niches. For
example, fungi produce various bioactive secondary metabolites (SMs) in response to external
stimuli, which confer selective advantage despite not being directly required for survival. Other
mechanisms of fungal persistence include melanin production in high radiation environments
and the liberation of carbon from cell wall polymers during starvation. Recent advances in
‘omics’ technologies, which include genomic, proteomic, and metabolomic techniques, have
revolutionized our ability to characterize the biological state of the cell. Additionally, such
studies have the potential to reveal novel biotechnological opportunities due to the various
therapeutic and industrial applications of fungal SMs and enzymes. The work herein utilizes
environmental and culture conditions to study fungal adaptation and plasticity mechanisms and
explore any relevant biotechnological applications. More specifically, it characterizes the ‘omics’
of Aspergillus species exposed to International Space Station (ISS) conditions and harnesses
culture conditions to investigate the Penicillium canescens metabolome.

The influence of ISS conditions on fungal ‘omics’ on Aspergillus nidulans is described in
Chapter 2. The results revealed that specific and localized genetic alterations bestow selective
advantage during growth in the ISS environment. Although the observed proteomic differences
were minimal, especially in the wild type strain, alterations in proteins involved in carbohydrate
metabolic processes, stress response, and SM biosynthesis were observed. Additionally,
increased production of the pigment asperthecin was observed in ISS-grown mutant strains,
perhaps to confer resistance to the high levels of radiation present on the ISS. The ‘omics’ of
xii
Aspergillus niger isolated from the ISS were investigated in Chapters 3 and 4. The data revealed
that although the ISS isolate was within the genetic variance of other sequenced A. niger strains,
it exhibited a unique proteome when compared to a terrestrial strain, which suggested an
enhanced ability to acquire nutrients and increased radiation and oxidative stress resistance.
Additionally, high production levels of the antioxidant pyranonigrin A were observed, which
presented a potential biotechnological application for its use as a radiation resistance molecule.
To explore this finding, we generated targeted gene deletions to confirm its radiation resistance
ability. The Penicillium canescens metabolome is explored in Chapters 5 and 6. Initially, various
culture conditions were utilized to optimize yield and diversity of SMs produced. Following
condition selection, a genome-wide polyketide synthase (PKS) and nonribosomal peptide
synthetase (NRPS) deletion library was screened, which enabled three SMs to be linked to their
core biosynthesis genes. One such compound, 15-deoxyoxalicine B, belongs to a unique class of
compounds that exhibits activity against the fall armyworm, which has devastated corn fields.
We therefore used targeted gene deletions to elucidate the biosynthetic pathway of 15-
deoxyoxalicine B. When taken together, this thesis reveals the significance and various
biotechnological applications associated with the characterization of fungal ‘omics’ following
growth in or exposure to different conditions.

1
CHAPTER I: Introduction

1.1 Adaptability of fungi to various environments

It has been well documented that fungal populations persist in a vast array of conditions,
including various temperatures and pH (Amaral Zettler et al., 2002; de Crecy et al., 2009;
Gunde-Cimerman et al., 2009; López-Archilla et al., 2001; Onofri et al., 2007), desiccation
(Barnard et al., 2013), ionizing radiation (Dadachova and Casadevall, 2008; Tkavc et al., 2018),
and the spacecraft environment (Checinska et al., 2015; Onofri et al., 2008). Fungi have been
isolated from a myriad of extreme environments on Earth, including Antarctic ice sheets
(Gonçalves et al., 2017; Ruisi et al., 2007), mountain peaks (Selbmann et al., 2008), acid mine
drainages (Baker et al., 2004), extremely cold and dry deserts (Selbmann et al., 2005), caves
(Bastian et al., 2010; Man et al., 2015), geothermal soils (Redman et al., 1999), and nuclear
accident sites (Dadachova and Casadevall, 2008). Fungi also reside on and inside the human
body, including the skin (Findley et al., 2013), nasal and oral cavities (Ghannoum et al., 2010;
Sellart-Altisent et al., 2007), the gut, lungs, and urogenital tracts (Cui et al., 2013; Seed, 2015),
thereby playing a fundamental role in maintenance of the human microbiome. The ubiquity of
fungi is due, in part, to an immense capacity to sense and respond to external stimuli through
physiological and morphological alterations (Selbmann et al., 2013). This phenotypic plasticity
enables fungi to colonize unfamiliar environments, utilize novel resources, and readily adapt to
their surroundings, enabling survival in a wide variety of ecological niches (Checinska et al.,
2015; de Crecy et al., 2009; Dadachova and Casadevall, 2008; Onofri et al., 2007, 2008).

2
1.1.1 Mechanisms of adaptation
One remarkable characteristic of fungi is their ability to biosynthesize a myriad of bioactive
secondary metabolites (SMs). SMs are organic small molecules that confer some type of
selective advantage to the organism despite not being directly required for survival. The ability
of fungi to produce SMs is likely the result of adaptation to various external stressors and distinct
environmental niches. SMs can function as competitive weapons to eliminate neighboring
competition, chemical symbols in microbial cell communication, agents of symbiosis and
transportation, sexual hormones, or differentiation effectors (Demain and Fang, 2000). Although
some SMs are constitutively expressed, many are only produced in response to specific external
stimuli, which is likely an effort to minimize energy expenditures. Examples include antibiotic
production following growth in the presence of bacteria, antifungal production following growth
in the presence of other fungal species, and siderophore production when cultured in the absence
of iron (Bader et al., 2010). Another example is the SM terrein, which is produced by the soil-
dwelling fungus Aspergillus terreus in response to nitrogen and iron starvation and the presence
of methionine, which are conditions typical of areas surrounding plant roots. Terrein then acts as
both a chemical signal and weapon to enhance iron uptake and inhibit growth of nearby plants
and microbes, thereby increasing fitness and minimizing competition (Haas, 2015). SMs
therefore play a fundamental role in the adaptability of fungi to various environments.

In addition to SM production, fungi have developed a myriad of other mechanisms to ensure
survival in a wide range of stressful environments. One such example is the production of
biological macromolecule melanin, which confers protection against radiation and extreme
temperatures (Sterflinger, 2006). Accordingly, fungi isolated from highly irradiated
3
environments, such as the Chernobyl nuclear power plant disaster (Dadachova and Casadevall,
2008), ‘Evolution Canyon’ in Israel (Singaravelan et al., 2008), and the Antarctic deserts (Onofri
et al., 2008) often exhibit high melanin production and demonstrate immense resistance to
radiation and temperature under experimental conditions (Dadachova and Casadevall, 2008;
Onofri et al., 2008). Many fungi adapt to low-nutrient environments by altering carbon
metabolism for energy acquisition. Examples include isocitrate lyase, whose expression is
induced in nutrient-deficient immune cells (Barelle et al., 2006), and glycoside hydrolases
capable of liberating carbon from cell wall polymers during starvation (Nitsche et al., 2012).
Fungal pathogens have been reported to adapt to volatile pH levels within the human body using
the transcription factors PacC and Rim101 (Bignell et al., 2005; Davis et al., 2000).
Thermotolerance of human pathogenic fungi is achieved in a variety of ways, including a
transition between dimorphic states (Boyce and Andrianopoulos, 2015) or the activation of
specific survival factors, such as the THTA gene in Aspergillus fumigatus (Chang et al., 2004).

1.1.2 Methods to evaluate adaptation
Recent advances in high-throughput technologies have revolutionized investigations into fungal
phenotypic plasticity and adaptation mechanisms. ‘Omics’ technologies, which include
genomics, proteomics, and metabolomics, each explore a different aspect of the subject that,
when combined, provide a holistic approach to characterizing the biological state of the cell.
Genomics is the elucidation of an organism’s DNA sequences and provides information about
genomic element structure and function. Since the first eukaryotic genome was sequenced in
1996 (Johnston, 1996), whole-genome sequencing (WGS) of fungal species has increased
exponentially. This is due, in part, to large-scale sequencing initiatives, such as the U.S.
4
Department of Energy Joint Genome Institute’s (JGI’s) 1000 Fungal Genomes project (Grigoriev
et al., 2014a). Further, significant decreases in WGS costs have enabled research groups to
independently sequence samples for investigations. As of March 2018, there were 1,532 publicly
available fungal whole-genome sequences (Kew, 2018), the majority of which are on JGI’s
MycoCosm database (Grigoriev et al., 2014a). These efforts have provided invaluable
information regarding the underpinning genes that define molecular pathways and interactions.
Still, the amount of useful information extracted from genomic sequences is maximized when
interpreted alongside other ‘omics’ datasets, as DNA sequences do not depict the cell’s dynamic
metabolic state.

Proteomics involves the characterization of protein sequence, structure, function, abundance, and
interactions for all proteins within the cell. Although the proteome is dynamic, analysis of the
proteome provides a snapshot of the molecular phenotype of a cell at any given moment. Recent
advances in high-throughput analytical instruments and techniques (Domon and Aebersold,
2006; Gevaert et al., 2007) combined with the availability of extensive proteomic databases, such
as the UniProt Consortium (UniProt Consortium, 2017) and PRIDE (Jones et al., 2006), have led
to a wealth of generated data (Doyle, 2011). Such information has accelerated our understanding
of various biological processes defined by proteins. Further, it enables in-depth characterization
of molecular adaptations and plasticity alterations. Similarly, metabolomics provides a snapshot
of the small molecule metabolites present within a cell or its immediate external environment,
which as discussed previously, can be triggered in response to external stimuli (Bode et al.,
2002). Although the metabolome consists of both primary and secondary metabolites, this thesis
5
will focus solely on SMs due to their various biotechnological applications, which is discussed
thoroughly in the following sections.

1.2 Biotechnological applications of fungi
The utilization of ‘omics’ technologies to study fungal plasticity and adaptation mechanisms
provides substantial economic potential due to the various therapeutic and industrial applications
of fungi. Noteworthy examples are fungal SMs and enzymes, which have made momentous
contributions to pharmaceuticals and biotechnology (Bennett, 1998; Newman and Cragg, 2012).

1.2.1 Secondary metabolites
Fungal SMs have had a tremendous impact on human health because of their diverse
bioactivities, which range from therapeutic to toxic (Newman and Cragg, 2012; Segal, 2009;
Sharpe et al., 2015). They possess enormous structural and chemical diversity due to the
enzymatic nature of their biosynthesis, which facilitates many reactions that are not possible
synthetically. SMs often feature more chiral centers and increased steric complexity than
synthetic molecules. Also, because SMs have evolved within a biological setting, they usually
possess many favorable drug-like properties. For these reasons, many fungal SMs, such as the
antibiotic penicillin and the cholesterol-lowering statin lovastatin, have been exploited by
humans as pharmaceutical drugs. Other notable examples include the immunosuppressant
cyclosporine, which has played a major role in preventing donor organ rejection (Colombo and
Ammirati, 2011), and the antitumor agent paclitaxel, which is widely used in cancer clinics
(Flores-Bustamante et al., 2010). In fact, the majority of small-molecule drugs introduced
between 1981 and 2010 were either SMs, SM derivatives, SM mimics, or possessed a SM
6
pharmacophore. Further, approximately 49% of all anticancer drugs are SMs or were inspired by
SMs (Newman and Cragg, 2012). Other industrial applications of fungal SMs include pigment
production for various purposes, including food dyes, wool fabric dyes, and heavy metal
detection dyes (Narsing Rao et al., 2017).

1.2.2 Enzymes
Fungi are potent producers of enzymes that have a wide variety of industrial applications due to
their diverse catalytic properties and ability to carry out industrial chemical processes under mild
conditions (Kirk et al., 2002). Applications include the utilization of cellulase, hemicellulase,
lipase, and xylanase enzymes to increase the efficiency of pulping processes in paper
manufacturing (Green and Beezhold, 2011; Sigoillot et al., 2005), the addition of amylase,
cellulose, lipase, and protease to textile detergents to soften and brighten fabric (Green and
Beezhold, 2011; Sajith et al., 2015), and the use of amylase, cellulose, glucoamylase, protease,
and xylanase to generate ethanol biofuel from complex biomass (Coyne et al., 2013). Other
applications include pollutant bioremediation (Karigar and Rao, 2011), food and leather
processing (Poutanen, 1997; Thanikaivelan et al., 2004), and beer and wine production (Green
and Beezhold, 2011). In fact, approximately 60% of industrially used enzymes come from fungi
(Kew, 2018).

1.3 Utilization of environmental and culture conditions to study fungal adaptation and
biotechnology applications
This thesis aims to harness environmental and culture conditions to enhance scientific
understanding of fungal adaptation and plasticity mechanisms and explore associated
7
biotechnological applications. More specifically, it investigates the multi-omic characteristics of
Aspergillus species following growth in the spacecraft environment using the International Space
Station (ISS) as a research platform and utilizes various culture conditions to alter the
Penicillium canescens metabolome. Genomic, proteomic, and metabolomic investigations into
fungi exposed to extreme environments may reveal previously unrealized adaptation biomarkers
as well as countermeasures to eradicate unfavorable microbes. Proteomic or metabolomic
alterations that occur in response to a specific environment can provide information regarding
the function of specific proteins or SMs. Further, certain conditions may naturally optimize
production yields of biotechnologically-relevant SMs or proteins, thereby reducing the costs
associated with laborious laboratory-based optimization. Additionally, it enables further
characterization of the genes involved in the biosynthesis of therapeutically or industrially useful
SMs, thereby facilitating genetic engineering efforts to optimize compound yield or generate
second generation molecules.

1.3.1 International Space Station
Every 90 minutes, a truly unique research laboratory circles the Earth at an average altitude of
249 miles. The ISS was created through a collaborative effort between American, European,
Japanese, Russian, and Canadian space agencies. Its environment is characterized by
microgravity, or weightlessness, increased exposure to high-energy radiation as a result of being
outside Earth’s protective atmosphere, and constant temperature and humidity (Mora et al.,
2016a). We are on the cusp of significant advances in human interplanetary space exploration, as
NASA aims to send humans to Mars in the 2030s. To successfully achieve this goal, a thorough
understanding of how fungi respond and adapt to the various stimuli encountered during
8
spaceflight is imperative for the health of crew and presents many economic benefits. Although
the ISS has been utilized to investigate the influence of the spacecraft environment on
microorganisms (Benoit et al., 2006), plants (Driss-Ecole et al., 2008; Kittang et al., 2014),
animals (Ijiri, 2003; Tavella et al., 2012), and humans (Van Ombergen et al., 2017; Williams et
al., 2009), there is an unmet need for studies focused on fungi, as most microbial studies have
concentrated on alterations occurring within bacteria or microbial community composition
(Checinska et al., 2015; Huang et al., 2018; Mora et al., 2016b; Tixador et al., 1985; Wilson et
al., 2008). Such studies may provide valuable insights into the adaptive mechanisms of fungi in
extreme environments which can help NASA’s Human Research Program maintain a habitat
healthy for crew during long-term manned space missions. Further, recent advances in space
microbial research present the prospect of radiation resistant SMs that may have extensive
applications in space programs or cancer therapies (Gabani and Singh, 2013).

1.3.2 Culture conditions
Due to the immense phenotypic plasticity of fungi, culturing fungi in different conditions alters
fungal color, morphology, sporulation, and SM production (VanderMolen et al., 2013).
Methodical modification of growth temperature and media composition to alter fungal SM
production has been referred to as the OSMAC (one strain, many compounds) approach (Bode et
al., 2002; Scherlach and Hertweck, 2006; VanderMolen et al., 2013), as variations in nutrient and
environment have a significant impact on the diversity and abundance of SMs produced (Bills et
al., 2012; Bode et al., 2002; Miao et al., 2006; Mohanty and Prakash, 2009; Shang et al., 2012).
Using this technique, the fungal metabolome can be altered to study a variety of SMs that are
produced in response to different external stimuli. Further, it provides a means for conditional
9
optimization of specific SMs of therapeutic or industrial relevance, thus enabling subsequent
investigation into their biosynthetic mechanisms.

10
CHAPTER II: International Space Station conditions alter fungal ‘omics’ in Aspergillus
nidulans

2.1 Abstract

The impact of International Space Station (ISS) conditions on the genome, proteome, and
secondary metabolome of Aspergillus nidulans is reported. The investigation included the A.
nidulans wild-type and 3 mutant strains, two of which were genetically engineered to enhance
secondary metabolite (SM) production. Whole genome sequencing (WGS) revealed that ISS
conditions altered the A. nidulans genome in specific regions. In strain CW12001, which features
overexpression of the SM global regulator laeA, ISS conditions induced the loss of the laeA stop
codon. Differential expression of proteins involved in stress response, carbohydrate metabolic
processes, and SM biosynthesis was observed. ISS conditions significantly decreased prenyl
xanthone production in the wild-type strain and increased asperthecin production in LO1362 and
CW12001, which are deficient in a major DNA repair mechanism. These data provide valuable
insights into the adaptation mechanism of A. nidulans to spacecraft environments and present
many economic benefits.

11
2.2 Introduction
Fungi are omnipresent and have been reported to persist in extreme conditions, including the
spacecraft environment (Checinska et al., 2015; Onofri et al., 2008). Their ability to survive in a
wide variety of ecological niches is largely due to their capacity to sense, respond, and adapt to
external stimuli (Selbmann et al., 2013). These microorganisms can be both beneficial and
detrimental to human health, as fungi produce a myriad of secondary metabolites (SMs) in
response to environmental stressors with activities ranging from therapeutic to toxic (Keller et
al., 2005; Newman and Cragg, 2012). Fungi are also potent producers of enzymes, and therefore
have various industrial applications (MacCabe et al., 2002; Vries and Visser, 2001). As we enter
the era of human interplanetary exploration, a thorough understanding of how fungi respond and
adapt to the various stimuli encountered while in space is crucial for the success of future
missions and introduces economic benefits. Further, such studies will play an important role in
evaluating fungi as drug production hosts during these missions, as fungi currently play an
indispensable role in pharmaceutical biotechnology.

Fungi residing on the International Space Station (ISS) are exposed to increased levels of high-
energy radiation, microgravity, and constant temperature and and humidity (Horneck et al.,
2010). Radiation alters biological processes by acting as a promoter of mutagenesis, which may
result in an increased rate of biological evolution leading to the development of adaptive
responses (Horneck et al., 2006). Microgravity is thought to decrease the transfer of extracellular
nutrients and metabolic by-products, which may alter the chemical environment that the cell is
exposed to (Horneck et al., 2010). Although it has been established that fungi are ubiquitous in
spacecraft environments (Checinska et al., 2015; Novikova et al., 2006), our understanding of
12
how fungi respond and adapt to the various conditions encountered during spaceflight remains in
its infancy (Council, 2011). The objective of this study was to investigate the changes
encountered in various aspects of fungal “omics” under ISS conditions using the well-
characterized organism, Aspergillus nidulans. Of 30 distinct fungal species retrieved from ISS
habitat surfaces during one microbial monitoring study, Aspergillus was the dominant genus,
featuring a diverse population of 13 species, with A. nidulans being one of four fungal species
isolated from both surfaces and air in the ISS (Novikova et al., 2006). Of particular interest is
how the space environment alters secondary metabolism, as fungal SM production is highly
variable and dependent on external stimuli (Haas, 2015). A. nidulans is an extensively studied
model organism and in the last decade many of the genes and regulatory networks involved in
SM formation have been revealed (Yaegashi et al., 2014). This new information enabled a
comprehensive investigation into genomic, proteomic, and SM production alterations in response
to ISS conditions.

Table 2-1. A. nidulans stains used in this study.

Herein, the multi-omic characterization of wild-type (WT) A. nidulans, FGSC A4, and three
mutant strains, displayed in Table 2-1, following 4 and 7 days of growth on the ISS and
compared to ground counterparts is reported (Figure 2-1). The first mutant strain, LO1362, is the
Fungal
name
Strain # Genotype Significance
WT FGSC A4 WT Well-characterized model fungus
nkuA- LO1362 pyroA4, riboB2, pyrG89, nkuA::argB Essential gene for NHEJ DNA repair
mcrA- LO8158 pyroA4, riboB2, pyrG89, nkuA::argB (mcrA)::AfpyroA Negative regulator of secondary metabolism
oe:laeA CW12001 pyroA4, riboB2, pyrG89, nkuA::argB AfpyrG-gpdA(p)laeA Positive regulator of secondary metabolism

13
A. nidulans nkuA deletion strain, which is a homolog of the human KU70 gene. These genes are
crucial for non-homologous end joining of DNA double strand break repair, and therefore
deletion of nkuA disrupts a major DNA repair mechanism in A. nidulans. The second mutant
strain, LO8158, is deficient in mcrA, which is a negative regulator of at least 10 SM gene clusters
(Oakley et al., 2017). Deletion of this gene stimulates SM production while impairing fungal
growth. The final mutant strain used in this study was an A. nidulans laeA overexpression
mutant, CW12001. LaeA is a global positive regulator of secondary metabolism, and therefore
overexpression of laeA increases the production levels of a number of SMs (Bok and Keller,
2004).

Figure 2-1. Schematic overview of A. nidulans ISS experiment. The A. nidulans wild-type
(FGSC A4) and three mutant strains (LO1362, LO8158, and CW12001) were seeded onto GMM
agar Omnitray plates and integrated into PHAB systems. Samples were transported to the ISS at
4
o
C and subjected to growth in SABL systems at 37
o
C for either 4 or 7 days. Earth-grown
PHABs were simultaneously transferred to on-ground containers mimicking ISS SABL systems.
Following growth, all samples were subjected to 4
o
C and ISS-grown samples were transported to
Earth. All samples were subjected to genomic, proteomic, and metabolomics analysis.
LO1362
(nkuA-)
FGSC A4
(wild-type)
CW12001
(nkuA-, oe:laeA)
LO8158
(nkuA-, mcrA-)
In cold bags at 4
o
C
ISS-grown samples
Earth-grown samples
In cold bags at 4
o
C
PHAB C PHAB D
PHAB A PHAB B PHAB E PHAB F
PHAB G PHAB H
7days at 37
o
C 4days at 37
o
C
SABL 1
SABL 2
Genomics
Proteomics
Metabolomics
PHAB 3 PHAB 4
PHAB 1 PHAB 2 PHAB 5 PHAB 6
PHAB 7 PHAB 8
7days at 37
o
C 4days at 37
o
C
Seeded on GMM agar Omnitraysand
integrated into PHABs
OH
OH
HO
O
O
OH
OH
OH
14
2.3 Results

Table 2-2. Features of SNPs and INDELs.

2.3.1 Genome variation among ISS-grown samples
To identify genomic alterations occurring in A. nidulans strains in response to ISS conditions,
whole genome paired-end sequencing (WGS) was performed on ISS-grown samples and ground
controls. Reads were aligned to the FGSC A4 reference genome and any single nucleotide
polymorphisms (SNPs) present in ground controls were removed from each strain’s sample set.
This revealed 129, 136, 108, and 106 SNPs, and 36, 39, 41, and 31 INDELs in ISS-grown FGSC
A4, LO1362, LO8158, and CW12001, respectively, when compared to ground controls, the
features of which are displayed in Table 2-2. The number of missense mutations in ISS-grown
strains ranged from 9 to 15. Interestingly, missense mutations were observed in only 5 genes,
with the same mutation often present in multiple samples (Table 2-3). A total of 13 unique
missense base mutations were observed within AN5254, which encodes a protein containing
domains predicted to be involved in RNA binding and RNA-directed DNA polymerase activity.
Table 1. Features of SNPs and INDELs
Strain ID FGSC A4 LO1362 LO8158 CW12001
No. of SNPs 129 137 108 106
Intergenic 111 116 84 88
Missense 10 11 15 9
Synonymous 4 3 6 3
Intron 0 4 2 2
UTR 1 2 0 2
Stop gained 2 1 1 1
Stop lost 0 0 0 1
No. of INDELs 36 38 41 31
Intergenic 36 34 37 29
Frameshift 0 2 1 1
UTR 0 2 3 1
Stop gained 0 1 1 0
15
Table 2-3. Comparative analysis of non-synonymous SNPs occurring during spaceflight.

Table 2. Comparative analysis non-synonymous SNPs occurring during spaceflight
Gene Base mutation Type of mutation 4d 7d 4d 7d 4d 7d 4d 7d
ChrV_A3367369G Missense + - - - + + - -
ChrV_C3367409T Missense + - - + - - - -
ChrV_A3367453G Missense - + - - - - - -
ChrV_C3367733T Missense - - - + - - - -
ChrV_G3367916A Missense + + - - - - - -
ChrV_T3367958C Missense - - - - + + - -
ChrV_C3367973T Missense - - - - + + - -
ChrV_C3368005T Missense - - - - + + + +
ChrV_C3368023T Stop gained - - - - + + + +
ChrV_T3368023C Stop gained - + - - - - - -
ChrV_A3368024G Missense - - - - + + + +
ChrV_G3368024A Missense - + - - - - - -
ChrV_T3368096C Missense - + - - - - + -
ChrV_T3368312C Missense - - + + - - - -
ChrV_C3368312T Missense - - - - - - + -
AN0807 ChrVIII_G2423110A Stop lost - - - - - - + +
ChrVIII_A3254138C Missense - - - + - - - -
ChrVIII_T3254236C Missense - - + - - - - -
ChrVIII_C3254236T Missense - - - - + + - -
ChrVIII_A3255566G Missense - - - - - + - -
ChrVIII_C3255576T Missense - - - - - - + -
ChrVIII_T3255576C Missense - - - - - + - -
ChrVIII_G3255581A Missense - - - - - - + -
ChrVIII_A3255581G Missense - - - - - + - -
ChrVIII_T3255781C Missense + + - + - + - -
ChrVIII_C3255975A Splice region - - - - - - + +
ChrVIII_G3256068A Missense - - + - - - - -
ChrVIII_A3256068G Missense - - - - - + - -
ChrVIII_A3259230G Stop gained - + + + - - - -
ChrVIII_A 3259256G Missense - + + + - - - -
ChrVIII_C3259257T Missense - + + + - - - -
ChrVIII_T3259346C Missense - - - - + - - -
ChrVIII_C3259346T Missense + + - - - - - -
ChrVIII_G3259467C Missense - - + - + + - -
ChrVIII_G3259508C Missense - - - - + + - -
ChrVIII_C3259563T Missense - - - - - - - +
ChrV_C3267200T Missense - - - - - - - +
ChrV_T3267230C Missense - - + + + + - -
CW12001
AN5254
AN0538
AN0537
+ indicates presence of point mutation; - indicates absence of point mutation
AN0535
AN0532
FGSC A4 LO1362 LO8158
16
Two unique stop-gain mutations were also observed within AN5254, one of which occurred in
both the 4- and 7-day LO8158 and CW12001 ISS-grown samples. The remaining missense
mutations occurred within AN0532, AN0535, AN0537, and AN0538, which are clustered
together in the genome. AN0532 encodes a predicted DDE1 transposable element gene, while
the products of AN05235, AN0537, and AN0538 are uncharacterized. Interestingly, in both
CW12001 ISS-grown samples, identical stop-lost mutations were observed within the laeA
(AN0807) gene.

For all strains, most SNPs (>77%) and INDELs (>89%) occurred in intergenic regions. Most
intergenic SNPs were clustered nearby several specific genes and did not appear to be strain
specific. Among all strains, many intergenic SNPs occurred near genes involved in transcription
and translation, including the putative C6 transcription factor AN4972, the transcription
elongation factor AN11131, the U3 small nucleolar ribonucleoprotein AN4298, the S-
adenosylmethionine-dependent methyltransferase AN10829 with a predicted role in translational
read-through, and AN6968 which is predicted to have RNA-directed DNA polymerase activity.
Intergenic SNPs were also clustered nearby the putative alanine-tRNA ligase AN9419, the
putative C4 sterol methyl oxidase AN6973 which has a predicted role in sterol metabolism, and
AN9410 which has a predicted role in lipid metabolic processes. Interestingly, intergenic SNPs
were also observed near AN0538 and AN0539, which are within the uncharacterized cluster of
genes that were reported above to possess high numbers of missense mutations. Most of the
remaining intergenic SNPs occurred near AN6972, AN7848, AN10328, and AN11577, the
products of which remain uncharacterized.

17
2.3.2 Proteomic profiling of ISS-grown A. nidulans

Figure 2-2. Overview of proteomic analysis. (A) Number of up- and down-regulated proteins in
ISS-grown strains (FC >½2½, P < 0.05) compared to ground-grown counterparts. (B-D)
Biological process GO Slim categories of differentially expressed proteins. Differentially
expressed proteins in LO1362 (B), LO8158 (C), and CW12001 (D) were mapped to terms
representing various biological processes using AspGD Gene Ontology (GO) Slim Mapper.

To investigate alterations in the proteome of A. nidulans strains following growth on the ISS,
total protein was extracted from two biological replicates of each ISS-grown sample and Earth-
grown counterpart. All samples were subjected to tandem mass tag (TMT) labeling and LC-MS
analysis. The resulting MS data were analyzed using Proteome Discoverer with the Sequest-HT
search engine against the A. nidulans FGSC A4 protein database (NCBI). The abundance ratios
for all ISS-grown samples were normalized to their Earth-grown counterparts, which led to the
A
C
B
D
WT LO1362 LO8158 CW12001
4d 7d 4d 7d 4d 7d 4d 7d
Up-regulated 0 2 17 8 8 28 4 22
Down-regulated 0 0 10 8 13 77 2 13
4d up-regulated
7d up-regulated
4d down-regulated
7d down-regulated
8 7 6 5 4 3 2 1 1 2 3 4 5 6 7 8 0
4 3 2 1 1 2 3 4 0 5 5
5 4 3 2 1 1 2 3 4 5 0
LO8158
CW12001
LO1362
18
identification of up- and down-regulated proteins (fold-change (FC) >½2½, P < 0.05) in response
to the ISS environment (Figure 2-2A). Interestingly, only two proteins, QutC (AN1140), which
is involved in quinic acid utilization, and AN2704, a putative aryl-alcohol oxidase-related
protein, were up-regulated in A. nidulans WT strain. The number of proteins up-regulated and
down-regulated in the three ISS-grown mutant strains ranged from 4 to 28, and 2 to 77,
respectively. Distribution of AspGD Gene Ontology (GO) Slim terms among the differentially
expressed proteins for ISS-grown mutant strains is displayed in Figure 2-2B-D. The GO Slim
categories that possessed the highest number of differentially expressed proteins in LO1362 were
stress response and carbohydrate metabolic processes. Similarly, in both LO8158 and CW12001,
most differentially expressed proteins were involved in carbohydrate metabolism.

Our study revealed differential abundance of proteins involved in the A. nidulans stress response
following growth on the ISS (Table 2-4). The heat shock protein Hsp20 (AN10507) was highly
affected by ISS conditions, displaying a twofold and fourfold increase in protein abundance in
LO1362 and CW12001, respectively, and a twofold decrease in protein abundance in LO8158.
Induction of Hsp20 has been reported following exposure to osmotic stress in A. nidulans (Wu et
al., 2016). Conversely, the osmotic stress response protein CipB (AN7895) was down-regulated
in LO1362 and the chaperone/heat shock protein Awh11 (AN3725) was down-regulated in
LO1362 and LO8158. Differential abundance of proteins involved in oxidative stress response
was observed among all ISS-grown strains relative to Earth-grown counterparts. The glutathione
S-transferase GstB (AN6024), which has been reported to be significantly induced in response to
menadione-induced oxidative stress (Pusztahelyi et al., 2011), was up-regulated over twofold in
LO8158, but down-regulated 1.7-fold and 1.8-fold in LO1362 and CW12001, respectively, after
19
7 days of growth on the ISS. Two catalase, CatA (AN8637) and AN8553, exhibited decreased
protein abundance in LO8158 and increased protein abundance in CW12001. Similarly, both the
nitrosative stress response protein AN2470 and the menadione stress-induced protein AN5564
were down-regulated approximately threefold in LO8158 and up-regulated approximately 1.7-
fold in CW12001. Up-regulation was observed for phosphatidylinositol phospholipase C
AN3636 whose ortholog plays a major role in responding to nutrient deprivation in Candida
albicans (Uhl et al., 2003). Additionally, the GPI-anchored protein EcmA (AN4390) whose
ortholog plays a major role in cell wall integrity, morphogenesis, and virulence, was up-regulated
twofold in LO8158 after 7 days of growth on the ISS (Martinez-Lopez et al., 2004).

Among all strains, proteins involved in secondary metabolism were significantly differentially
expressed in response to ISS conditions (Table 2-4). The polyketide synthase AptA (AN6000),
which is required for biosynthesis of the spore pigment asperthecin ([CSL STYLE ERROR:
reference with no printed form.]), was up-reguated over twofold in CW12001 and nearly twofold
in FGSC A4 strain after 7 days of growth on the ISS. LO1362 also exhibited up-regulation of the
monooxygenase MdpD (AN0147) after 7 days of growth. The MdpD protein is the product of a
gene in the prenyl xanthone gene cluster, which is responsible for the production of
monodictyphenone, emericellin, shamixanthone, and epishamixanthone (Sanchez et al., 2011).
Interestingly, the opposite was observed in CW12001, as MdpJ (AN10038), which is the product
of another gene in the prenyl xanthone gene cluster, exhibited decreased protein abundance in
ISS-grown samples after 4 days. A similar trend was observed for proteins involved in the
biosynthesis of the potent carcinogenic mycotoxin sterigmatocystin (ST) (Brown et al., 1996).
20
ST gene cluster products AN7817 and StcN (AN7812) exhibited increased protein abundance in
LO8158, while StcN was down-regulated in CW12001, following growth in ISS conditions.

Table 2-4. Differentially expressed proteins by strain and biological process.

Table 3. Differentially expressed proteins by strain and biological process
Biological process ORF Protein 4d 7d 4d 7d 4d 7d 4d 7d
AN2470 -0.04 0.01 0.72 0.51 0.42 -1.56 -0.24 0.82
AN3636 0.10 1.02 0.09 -0.42
AN4891 Asf1 0.42 1.05 -0.33 0.22
AN5564 -0.15 0.08 -0.04 0.14 0.07 -1.49 -0.12 0.75
AN3725 Awh11 -0.04 -0.16 -1.27 -0.68 -0.43 -1.07 0.22 -0.02
AN8637 CatA -0.21 -0.24 -0.64 -0.41 -0.35 -1.65 0.31 0.90
AN8553 -0.24 -0.36 -0.30 0.03 0.04 -1.53 0.00 0.68
AN7895 CipB -0.02 0.09 -0.65 -1.21 0.25 0.72 -0.39 -0.53
AN4390 EcmA 0.13 0.24 -0.38 -0.17 0.30 1.23 -0.02 -0.72
AN1216 GppA 0.02 0.11 1.07 0.33 0.15 -0.69 -0.33 0.38
AN6024 GstB -0.04 0.10 -0.81 -0.75 0.11 1.26 -0.19 -0.85
AN10507 Hsp20 0.60 0.69 1.03 0.99 -0.48 -1.38 0.99 2.20
AN5217 PilA 0.30 0.01 0.31 0.41 0.00 -1.04 0.05 0.75
AN6000 AptA -0.02 0.90 0.24 -0.42 -0.25 -0.61 -0.15 1.06
AN0147 MdpD -0.11 1.24 -0.04 0.37
AN10038 MdpJ 0.23 0.89 -0.19 -1.06
AN7911 OrsB -0.24 0.01 -0.66 -0.72 -1.01 0.39 0.03 -0.61
AN7812 StcN -0.05 0.07 -0.69 -0.74 0.19 0.96 -0.18 -1.15
AN7817 0.43 -0.37 0.32 0.32 1.24 0.07 0.22 0.33
AN0567 0.08 0.23 -0.51 -0.62 -0.24 1.37 0.41 -0.65
AN10124 0.37 0.44 -0.71 -0.72 1.14 0.32 0.15 0.13
AN1715 -0.18 -0.16 -0.52 -0.39 -0.20 -1.49 0.26 0.92
AN2334 -0.05 0.08 -0.01 -0.49 -0.05 1.13 0.11 -0.69
AN6035 0.00 -0.20 -0.72 -0.68 0.55 1.58 0.17 -0.85
AN8068 0.32 -0.25 -0.91 -0.79 -0.11 1.53 -0.05 -1.03
AN9443 0.55 -0.32 1.14 -0.58
AN1277 AbfC -0.20 -0.04 -0.76 -0.72 -0.22 1.09 0.05 -0.71
AN5634 AcuD -0.17 -0.03 -0.19 0.09 0.02 -1.57 0.30 0.91
AN1918 AcuF -0.22 -0.26 -0.03 -0.16 -0.01 -1.38 0.17 0.90
AN7345 AgdC/AgdD -0.09 0.13 -0.38 -0.52 -1.63 -0.14 0.49 0.10
AN7396 BglM 0.19 0.04 -0.72 -0.56 0.29 1.33 0.21 -0.46
AN0494 CbhB 0.55 -0.07 -1.02 -1.13 -0.52 0.43 0.22 -1.25
AN6792 GfdB 0.03 -0.02 0.46 0.21 0.26 -1.35 -0.07 1.03
AN0756 LacA 0.09 0.31 -0.16 -0.30 0.56 1.09 -0.15 -1.05
AN3368 MndB 0.13 0.55 -0.74 -0.82 1.00 0.38 -0.44 -0.07
AN7349 MutA -0.28 0.16 -0.50 -0.13 -1.26 -0.51 0.43 0.22
AN7135 RglA 0.33 0.35 -0.68 -0.60 0.32 1.48 -0.06 -1.08
AN5061 XgeB 0.22 -1.12 -0.07 -0.03
AN1818 XlnC -0.11 0.11 -0.85 -0.82 -0.08 1.01 -0.32 -0.88
AN7401 XlnE 0.12 -1.10 -0.36 -0.11
LO8158 CW12001
Response to
stress
Secondary
metabolism
Carbohydrate
metabolism
FGSC A4 LO1362
Values depict the log2 fold change of ISS-grown samples relative to ground-grown counterparts (P < 0.05).
21
The proteome of ISS-grown A. nidulans samples also revealed differential levels of proteins
involved in carbohydrate metabolism when compared to Earth-grown controls (Table 2-4). Many
glycoside hydrolases involved in carbohydrate degradation processes were up-regulated more
than twofold in ISS-grown LO8158 samples, including beta-1,4-endoglucanase AN8068 and
beta-glucosidase BglM (AN7396), both of which are involved in cellulose degradation, alpha-
arabinofuranosidase AbfC (AN1277), involved in pectin degradation, endo-1,4-beta-xylanase
XlnC (AN1818), involved in xylan degradation, and beta-galactosidase LacA (AN0756),
involved in xyloglucan, xylan, pectin, and galactomannan degradation. Interestingly, these
glycoside hydrolases also exhibited decreased protein abundance in CW12001 when compared
to ground counterparts. A similar trend was observed with alcohol oxidase AN0567, beta-
glycosidase AN10124, ketose-1,6-bisphosphate aldolase AN2334, dehydratase AN6035, and
rhamnogalacturonan lyase RglA (AN7135), which were up-regulated at least twofold in ISS-
grown LO8158 samples and down-regulated at least 1.5-fold in CW12001. Conversely,
mannose-6-phosphate isomerase AN1715, isocitrate lyase AcuD (AN5634),
phosphoenolpyruvate carboxykinase AcuF (AN1918), and NAD+ dependent glycerol 3-
phosphate dehydrogenase GfdB (AN6792) were down-regulated at least 2.5-fold in ISS-grown
LO8158 samples and up-regulated at least 1.8-fold in CW12001 samples.

2.3.3 Secondary metabolome alterations in ISS-grown A. nidulans
Alterations in SM production of A. nidulans in response to ISS conditions were assessed by
extracting SMs from three biological replicates of each ISS- Earth-grown counterpart, and
analyzing each sample using high-performance liquid chromatography coupled with diode-array
detection and electrospray ionization tandem mass spectrometry (HPLC-DAD-MS). All SMs
22
were identified based on mass, UV absorption, and retention time, which led to the identification
of austinol and dehydroaustinol, terrequinone, sterigmatocystin and its intermediate, nidulanin A
and its analogues, the emericellamides, the prenyl xanthones, and asperthecin (Sanchez et al.,
2012) (Figure 2-3). Relative differences in SM production levels of ISS-grown samples and
Earth-grown counterparts were quantified by integrating the area under each SM’s EIC trace
(Figure 2-4). ISS conditions induced asperthecin production in LO1362 and CW12001 after 7
days of growth, with production levels increased by over 300% and 150%, respectively (Figure
2-5). Production levels of prenyl xanthones decreased approximately fivefold in FGSC A4 ISS-
grown samples (Figure 2-4F). In LO8158 samples, emericellamide and terrequinone production
decreased (Figure 2-4B&E), while nidulanin and sterigmatocystin production increased in ISS
grown samples (Figure 2-4C&D).

Figure 2-3. LC-MS profiles of SMs identified in A. nidulans strains following growth on GMM
for 7 days at 37
o
C, as detected by DAD total scan and MS total ion current (TIC).
10 15 20 25 30 35 40 45 50
1. Asperthecin
2. Austinol
3. Dehydroaustinol
4. Nidulanin A – prenyl
5. Sterigmatocystin
6. Nidulanin A + O
7. Nidulanin A analogue
8. Terrequinone
9. Nidulanin A
10. Sterigmatocystinintermediate
11. Emericellin
12. Shamixanthone
13. Epishamixanthone
Not UV active
14. Emericellamide C
15. Emericellamide D
16. Emericellamide A
17. Emericellamide E
18. Emericellamide F
1 3
4
5
6
8,9,10
11,12
13 2 7
14,15,16
17
18
DAD (Total scan)
MS (Total ion current)
23

Figure 2-4. Secondary metabolite quantification showing percent change for ISS-grown samples
in relation to Earth-grown counterparts for (A) austinol and dehydroaustinol, (B) terrequinone,
(C) sterigmatocystin, (D) nidulanins, (E) emericellamides, and (F) prenyl xanthones.
Significance was determined using Welch’s t-test.

WT gr 4d
WT sp 4d
WT gr 7d
WT sp 7d
nkuA- gr 4d
nkuA- sp 4d
nkuA- gr 7d
nkuA- sp 7d
mcrA- gr 4d
mcrA- sp 4d
mcrA- gr 7d
mcrA- sp 7d
oe:laeA gr 4d
oe:laeA sp 4d
oe:laeA gr 7d
oe:laeA sp 7d
0
50
100
150
200
250
300
Percent of ground control
ST/ST intermediate w cell
**
A
WT gr 4d
WT sp 4d
WT gr 7d
WT sp 7d
nkuA- gr 4d
nkuA- sp 4d
nkuA- gr 7d
nkuA- sp 7d
mcrA- gr 4d
mcrA- sp 4d
mcrA- gr 7d
mcrA- sp 7d
oe:laeA gr 4d
oe:laeA sp 4d
oe:laeA gr 7d
oe:laeA sp 7d
0
20
40
60
80
100
120
140
160
Percent of ground control
Prenyl xanthones
** *
Austinol and dehydroaustinol Nidulanins
B
C
D
E
F
Terrequinone Emericellamides
Sterigmatocystin Prenylxanthone
24

Figure 2-5. Asperthecin production in ISS-grown LO1362 and CW12001. (A) LC-MS profiles
depicting asperthecin production after 7 days of growth on the ISS, as detected by UV total scan.
(B) Quantification of asperthecin production showing percent change for ISS-grown samples
relative to Earth-grown counterparts. Significance was determined using Welch’s t-test. (C)
Chemical structure of asperthecin.

nkuAΔ 4d
nkuAΔ 7d
oe:LaeA 4d
oe:LaeA 7d
0
100
200
300
400
Asperthecin (no cellophane)
% of production of control
Control
Space
*
**
HO
OH O
O OH
OH
OH
OH
nkuAΔ 4d
nkuAΔ 7d
oe:LaeA 4d
oe:LaeA 7d
0
100
200
300
400
Asperthecin (no cellophane)
% of production of control
Control
Space
*
** Control
ISS-grown
LO1362 7d control
LO1362 7d ISS-grown
CW12001 7d control
CW12001 7d ISS-grown
A B
C
100
200
300
400
Percent of ground control
10 15 20 25 30 35 40 45 50
Time (min)
25
2.4 Discussion
Although the persistence of fungi within space vessels is well-documented and unavoidable
(Checinska et al., 2015; Novikova et al., 2006), little is understood about how fungi respond and
adapt to spacecraft conditions, such as microgravity and enhanced radiation. To date, most
microbiological studies conducted in such environments have focused on changes occurring
within bacteria or microbial community composition (Checinska et al., 2015; Huang et al., 2018;
Mora et al., 2016b; Tixador et al., 1985; Wilson et al., 2008). Additionally, despite the various
therapeutic and industrial applications of SMs, few studies have analyzed the global influence of
space conditions on fungal secondary metabolism, as previous investigations have often focused
on the production of a single SM (Benoit et al., 2006; Lam et al., 1998, 2002). Therefore, with
the duration of space missions expected to increase, a major goal of this study was to investigate
space-induced alternations in fungal “omics” to identify specific adaptation biomarkers and
acquire insight into potential benefits of fungi that may not be discernable using traditional
methodology.

This study revealed that the spacecraft environment alters the A. nidulans genome in specific
regions. Five protein-coding genes displayed signatures of positive selection in the form of a
high ratio of non-synonymous to synonymous SNPs across all ISS-grown samples. These data
stand in agreement with a study that investigated genomic alterations occurring in
Staphylococcus aureus during spaceflight, in which missense mutations were clustered and
occurred within only 9 protein-coding genes (Guo et al., 2015). High numbers of intergenic
mutations were clustered near genes encoding transcriptional and translational machinery. One
gene with several non-synonymous and two unique stop gain mutation encodes a putative
26
retrotransposon, suggesting its suppression confers selective advantage during growth in
spacecraft environments. Other missense and intergenic mutations were clustered within and
around a specific region of the genome (AN0532-AN0538), suggesting that it underwent positive
selection and therefore plays a role in adapting to the space environment. One of these genes also
encoded an uncharacterized transposable element gene, underscoring the significance of
alterations in transposable element activity in response to growth in ISS conditions. These
findings are consistent with results from the aforementioned S. aureus study, as variations were
also observed within a putative transposase (Guo et al., 2015). Interestingly, transposable
element activity has been associated with stress response due to the novel variation it introduces
into the genome (Capy et al., 2000). Future work should focus on characterizing the activities of
the rest of these genes, as many of their functions remain unknown. Such knowledge may
provide information key to elucidating the genetic adaptation mechanisms of fungi residing in
spacecraft environments.

Correlations linking genomic and proteomic data were observed with the two strains genetically
engineered to increase SM production. ISS conditions appeared to curtail enhanced SM
production in the laeA overexpression strain through the introduction of a point mutation that
resulted in loss of the laeA stop codon, thereby activating nonstop decay degradation of laeA
mRNA (Hoof et al., 2002). The ISS-induced down-regulation of laeA was also observed in the
proteome of those samples, which exhibited an expression profile opposite that of the mcrA
deletion strain for many proteins. Proteins involved in SM biosynthesis pathways regulated by
laeA exhibited decreased and increased abundance in ISS-grown CW12001 and LO8158,
respectively. The extensively studied protein LaeA forms a nuclear complex with VeA and VelB
27
that coordinates secondary metabolism regulation with fungal development (Bayram et al.,
2010). LaeA is often referred to as a global regulator of secondary metabolism (Bok and Keller,
2016), and has been reported to also influence proteins involved in carbohydrate metabolism and
oxidative stress response (Lv et al., 2018). Accordingly, several proteins involved in
carbohydrate and antioxidant metabolic processes exhibited opposite protein expression profiles
in LO8158 and CW12001. Regulation by laeA has been reported for some of these proteins in A.
flavus, including endo-1,4-beta-xylanase, beta-glycoside, endo-beta-1,4-glucanase, and oxidative
stress response proteins CatA and GstB (Lv et al., 2018). These findings verify the extensive
regulatory role of laeA and highlight the complexity involved in identifying the laeA-controlled
processes responsible for conferring selective advantage of the observed laeA stop lost mutation.
This is compounded by reports that laeA also alters chromatin remodeling, cell growth and
metabolism, conidiogenesis, conidial-chain elongation, sporulation, pigmentation and colony
hydrophobicity in various fungal species (Brakhage, 2013; Chang et al., 2012; Kosalková et al.,
2009; Lv et al., 2018). It is also possible that curtailing laeA overexpression is favored in ISS
conditions to reduce energy expenditures in a stressful environment.

Spacecraft conditions significantly increased production of asperthecin, an anthraquinone
pigment, in LO1362 and CW12001. Both mutants are deficient in NkuA production, which
facilitates non-homologous end-joining (NHEJ) DNA repair, the favored DNA double strand
break repair pathway in filamentous fungi (Krappmann, 2007). We had anticipated that NkuA
deficient strains would be particularly interesting in flight studies due to impairment of a
preferred DNA repair pathway in a mutagenic, high-radiation environment. Additionally,
deletion of the nkuA homolog has been reported to increase sensitivity toward gamma irradiation
28
in other fungal species (Meyer et al., 2007). We hypothesize that asperthecin production was
induced in LO1362 during spaceflight to serve as an alternative protective mechanism from
high-energy radiation present on the ISS. Our observation stays in agreement with other reports
suggesting that pigment production is a key adaptive response of fungi exposed to similar
environments (Dadachova and Casadevall, 2008; Singaravelan et al., 2008; Volz and Dublin,
1973). It is therefore possible that asperthecin can be used to protect other forms of life present
on the spacecraft. Cultivating plants for food in space will be crucial for the success of future
space missions. However, space radiation can generate mutations in plant DNA, including base
substitutions, deletions, and chromosomal alterations, which can result in genetic changes in
seeds or tissue damage (Arena et al., 2014; Micco et al., 2011). The transformation of
asperthecin biosynthesis genes into plants may potentially minimize plant DNA damage, and
optimize plant and astronaut health. Future studies should be conducted to verify this hypothesis.
Interestingly, space conditions did not increase asperthecin production in LO8158, which also
possesses the nkuA- genetic background. Global regulation of SM production was altered to
increase SM production in both LO8158 and CW12001, but the genetic alteration was reversed
through a stop lost point mutation only in CW12001. It is therefore possible that alternative
metabolomic protective mechanisms were sufficient in LO8158, and therefore asperthecin
production was not enhanced in ISS conditions.

Only a small proportion of proteins were differentially expressed in ISS-grown A. nidulans
samples, which emphasizes the potential and safety of A. nidulans as a therapeutic production
host during outer space missions. This finding may not hold true across the Aspergillus genus, as
increased virulence has been reported in Aspergillus fumigatus strains isolated from the ISS
29
(Knox et al., 2016a). Currently, if pharmaceutical stocks in space are depleted, a small,
unmanned spacecraft is launched to restock crew supplies. In the era of long-term space travel,
the duration of future space missions is expected to drastically increase. The inability to deliver
required drugs to astronauts in a timely manner may result in serious complications. Through
heterologous expression of specific genes, A. nidulans introduces the ability to biosynthesize a
wide range of pharmaceutical drugs within a week, which could significantly improve
astronauts’ safety during long-term manned space missions.

In summary, this work has revealed the multi-omic response of the well-characterized model
filamentous fungus, A. nidulans, to spacecraft conditions. These findings illustrate the potential
of asperthecin to confer radiation resistance and of A. nidulans to be utilized as a small molecule
production host in space. Further, specific genetic mutations involved in the adaptive mechanism
of fungi in space environments were identified. Such knowledge will be indispensable to
NASA’s Space Biology and Human Research Program in planning for future outer space
explorations.

30
2.5 Materials and Methods
2.5.1 Strains, media and growth conditions
The WT A. nidulans FGSC A4 strain was obtained from the Fungal Genetics Stock Center.
LO1362 and LO8158 were obtained from previous studies (Oakley et al., 2017). The laeA
overexpression strain (CW12001) was generated using the constitutive gpdA promoter according
to standard protocol (Hunter et al., 1992). Protoplast production, construction of fusion PCR
products, and transformation were carried out as described previously (Szewczyk et al., 2006).
Primers used are listed in Table 2-5 and correct transformants were verified using diagnostic
PCR.

Table 2-5. Primers used in this study (5’ à 3’).

All strains were seeded onto Nunc OmniTray plates (Thermo Scientific) (Figure 2-6A)
containing 46 ml of solid GMM media (6g/L NaNO
3
, 0.52 g/L KCl, 0.52 g/L MgSO
4
*7H
2
O,
1.52 g/L KH
2
PO
4
, 10g/L D-glucose, 15 g/L agar supplemented with 1ml/L of Hutner’s trace
elements). Seeded Nunc OmniTrays were loaded into Plate Habitat (PHAB) systems (BioServe)
(Figure 2-6B), with 6 Omnitray plates in each PHAB, and immediately transferred to 4
o
C. The
AfpyrG_Fw CAATGCTCTTCACCCTCTTCG
AfpyrG_Rev CTGTCTGAGAGGAGGCACTG
PgpdA_Fw CAGTGCCTCCTCTCAGACAGTCGGAGAATATGGAGCTTCA
PgpdA_Rev TGTGATGTCTGCTCAAGCG
oe:laeA_PgpdA_P1 TCAAGCAAATGAATGGACGA
oe:laeA_PgpdA_P2 GGGCGTTGGGGATATATTTT
oe:laeA_PgpdA_P3 CGAAGAGGGTGAAGAGCATTGTTGACGATTGACAGGCTG AG
oe:laeA_PgpdA_P4 CGCTTGAGCAGACATCACAATGTTTGAGATGGGCCCGGT
oe:laeA_PgpdA_P5 CCAGCGATAGACACGACTGA
oe:laeA_PgpdA_P6 CGAGTTGCTGGATTCTCTCC
oe:laeA_PgpdA_P4_ver2 CGATCTTTGTACCCTGTTTCG

31
PHAB system is a growth platform for biological materials that allows for gas exchange. Each
PHAB was equipped with a temperature logger (HOBO) (Figure 2-6C) that accurately measured
the temperature throughout the duration of the experiment. PHABs containing cultures bound for
the ISS were transferred to 4
o
C cold bags and transported to the ISS on the SpaceX CRS-8
mission. On the ISS, PHABs were loaded into Space Automated Bioproduct Lab (SABL)
systems (Figure 2-6D), where they remained at 4
o
C. Ground culture PHABs were simultaneously
transferred to on-ground containers mimicking ISS SABL systems. To initiate growth, ISS and
ground cultures were subjected to 37
o
C, where they remained for either 4 or 7 days. Following
growth, all samples were subjected to 4
o
C, where they remained until ISS cultures were
transported back to Earth.

Figure 2-6. Hardware used in A. nidulans ISS flight experiment. (A) Omnitray plates, (B)
Omnitrays in Plate Habitat (PHAB) systems, (C) PHAB systems with temperature logger
(HOBO), (D) Space Automated Bioproduct Lab (SABL) system.

A C
B D
32
2.5.2 Genomic DNA extraction, library preparation and genome sequencing
Mycelia was collected from Earth-grown (7-day) and space-grown (4- and 7-day) GMM again
Omnitrays for all strains (FGSC A4, LO1362, LO8158, and CW12001), frozen with liquid
nitrogen, and ground using a mortar and pestle. DNA was extracted using the Mo Bio PowerMax
Soil DNA Isolation Kit (Mo Bio Laboratories, Carlsbad, California, USA) according to the
manufacturers protocol. Library preparation and whole genome sequencing were performed at
the Duke Center for Genomic and Computational Biology. DNA quality and quantity were
checked using the Agilent 2100 Bioanalyzer DNA assay and Qubit 2.0 Fluorometer. The library
was prepared for paired-end sequencing using the TruSeq Nano DNA Library Preparation Kit
(Illumina, San Diego, California, USA). Samples were sequenced using a HiSeq 4000 Illumina
Sequencer and 101 bp read lengths were generated.

2.5.3 Genetic mutation identification
Illumina sequence reads were trimmed using Trimmomatic and quality was checked using
FastQC and further processed according to the Broad Institute’s GATK Best Practices Pipeline.
The genome and annotation files for A. nidulans FGSC A4 (Galagan et al., 2005) were
downloaded from the FungiDB web portal (Stajich et al., 2012). Reads were mapped to the
FGSC A4 reference genome using the Burrows-Wheeler Aligner (BWA) software package v
0.7.12 (Li and Durbin, 2009), and further processed with SAMtools v 1.6 to generate sorted
BAM files (Li et al., 2009). SNPs and indels were identified using GATK v 3.7 (DePristo et al.,
2011), according the GATK Best Practices pipeline (https://software.broadinstitute.org/gatk/best-
practices/). Duplicates were marked using Picard-tools MarkDuplicates
(https://broadinstitute.github.io/picard/) to remove PCR artifacts. Sequence reads containing
33
putative indels were realigned using GATK’s IndelRealigner to generate an updated BAM file.
Variants within each sample were called using GATK’s Haplotype Caller, and the resulting
Variant Call Format (VCF) files were combined using GATK’s Genotype GVCFs so that there
was one VCF file for each strain (4 in total). GATK’s VariantFiltration was used to filter each
VCF file based on stringent cutoffs for quality and coverage {SNPs: QD <2.0, MQ <40.0,
QUAL <100, FS >60.0, MQRankSum <−12.5, SOR >4.0, ReadPosRankSum <−8.0; Indels: QD
<2.0, FS >200.0, MQRankSum <−12.5, SOR >4, InbreedingCoeff <-0.8, ReadPosRankSum
<−20.0}, so that only high-quality variants remained, and annotated using snpEff (Cingolani et
al., 2012).

2.5.4 Protein extraction
For protein extraction, the lysis buffer consisted of 100 mM triethylammonium bicarbonate
(TEAB) with 1X Halt Protease Inhibitor Cocktail (100X) (Thermo Fisher Scientific, Rockford,
IL) and 1mM phenylmethylsulfonyl fluoride (Sigma-Aldrich, St. Louis, MO). The frozen ground
mycelia were transferred and subjected to a Precellys 24 homogenizer (Bertin, Rockville, MD) in
which each sample was processed inside a 2-mL cryotube with 1.0 mm glass beads for three
times (at 4°C, 6500 rpm, 1 min., repeated 3 times with 15 sec pauses in between). The lysed cells
were centrifuged at 17,000 ´ g for 15 min. Protein concentrations in the supernatants were
measured by a Bradford assay (Bio-Rad Laboratories, Inc. Hercules, CA).

2.5.5 Tandem mass tag (TMT) labeling
About 200 µg proteins from each sample were precipitated in 20% trichloroacetic acid (TCA) at
4°C. Protein pellets were obtained by centrifugation (17,000 g), washed with ice-cold acetone,
34
and resuspended in 25µL TEAB (100 mM) and 25µL 2,2,2-trifluoroethanol (TFE). Proteins were
reduced by adding 1µL of tris(2-carboxyethyl)phosphine (TCEP, 500 mM) and incubated for 1
hour at 37 °C (10 mM final TCEP concentration). Proteins were alkylated in presence of
iodoacetamide (IAA, 30 mM) in the dark for 1 hour at room temperature. 2.5 µg per sample of
mass spec grade trypsin/lysC (Promega, Madison, WI) was used to digest the peptides overnight
at 37 °C.

The digested peptides were quantified using the Pierce Quantitative Colorimetric Peptide Assay
(Thermo Fisher Scientific). 40 µg of peptides from each sample was labeled with the Thermo
Scientific TMT 6-plex (TMT
6
) Isobaric Mass Tagging Kit according to the manufacturer’s
protocol. The TMT
6
-130 label was used as either 4 day or 7 day strains’ reference that contained
5 µg of peptides from each of the 8 strain. The TMT
6
-131 label was used as a total mixture
reference that contained 2.5 µg of peptides from each of the 16 strains. All six labeled-peptide
mixtures were combined into a single tube, mixed, and fractionated using the Pierce High pH
Reversed-Phase Peptide Fractionation Kit (Thermo Fisher Scientific). While this kit usually uses
only eight fractions with step elution of up to 50% acetonitrile, we added a ninths fraction eluting
at 100% acetonitrile. Nine fractionated samples were dried using a SpeedVac concentrator and
re-suspended in 1% formic acid prior to LC-MS/MS analysis.

2.5.6 LC-MS/MS analysis
The samples were analyzed on an Orbitrap Fusion Tribrid mass spectrometer with an EASY-
nLC 1000 Liquid Chromatograph, a 75 μm x 2 cm Acclaim PepMap100 C18 trapping column,
and a 75 μm x 25 cm PepMap RSLC C18 analytical column, and an Easy-Spray ion source
35
(Thermo Fisher Scientific). The column temperature was maintained at 45 °C and the peptides
were eluted at a flow rate of 300 nL/min over a 110 min gradient, from 3-30% solvent B (100
min), 30-50% solvent B (3 min), 50-90% solvent B (2 min), and 90% solvent B (2 min). The
solvent A was 0.1 % formic acid in water and the solvent B was 0.1% formic acid in acetonitrile.
The full MS survey scan (m/z 400-1500) was acquired in the Orbitrap at a resolution of 120,000
and with an automatic gain control (AGC) target of 2´10
5
. The maximum injection time for MS
scans was 50 ms. Monoisotopic precursor ions were selected with charge states 2-7 with a ±10
ppm mass window using a 70 s dynamic exclusion. The MS
2
scan (m/z 400-2000) was
performed using the linear ion trap with the CID collision energy set to 35 %. The ion trap scan
rate was set to “rapid”, with an AGC target of 4´10
3
, and a maximum injection time of 150 ms.
Ten fragment ions from each MS
2
experiment were subsequently simultaneously selected for an
MS
3
experiment. The MS
3
scan (m/z 100-500) was performed to generate the TMT reporter ions
in the linear ion trap using HCD at a collision energy setting of 55 %, a rapid scan rate and an
AGC target of 5´10
3
, and a maximum injection time of 250 ms.

2.5.7 Quantitative proteomics analysis
All MS/MS spectra were analyzed using the Proteome Discoverer (version 2.2.0.388, Thermo
Fisher Scientific) with the Sequest-HT searching engines against an Aspergillus nidulans FGSC
A4 database containing 10,525 protein sequences (NCBI). The search was performed with the
following parameters: 2 maximum missed cleavage sites, 6 minimum peptide length, 5 ppm
tolerance for precursor ion masses, and 0.6 Da tolerance for fragment ion masses. The static
modification settings included carbamidomethyl of cysteine residues, and dynamic modifications
included oxidation of methionine, TMT6plex modification of lysine ε-amino groups and peptide
36
N-termini, and acetyl modification of protein N-terminus. A false discovery rate (FDR) of 1%
for peptides and proteins was obtained using a target-decoy database search. The reporter ions
integration tolerance was 0.5 Da while the co-isolation threshold was 75%. The average signal-
to-noise threshold of all reporter peaks was greater than 10. The quantitative abundance of each
protein is determined from the total intensity of the detected reporter ions. The ratios between
reporter and the reference reporter ion (TMT
6
- 131) were used to estimate the abundance ratio of
each protein.

For the statistical analysis, technical triplicate measurements for each protein were averaged.
Only proteins that were identified and quantified with at least one peptide detected in all three
technical replicates were considered for the analysis. The normalization across two biological
sample sets in eight TMT experiments was carried out according to Plubell, D.L. et al. with
modifications (Plubell et al., 2017). Briefly, the data from the eight TMT experiments were first
corrected for small systematic differences resulting from sample loading variations and labeling
efficiency, by normalizing the reporter ion totals for each channel. The trimmed mean of M
values (TMM) normalization corrected the compositional bias by aligning the median of the
distribution of abundance intensities between samples (Robinson and Oshlack, 2010). Internal
reference scaling was used to adjust eight TMT data sets onto the same intensity scale. The
normalized data was then averaged and log2 transformed. One-way ANOVA was preformed to
identify proteins that were differentially expressed among strains in either 4 days or 7 days,
respectively (p-value ≤ 0.05). The identified proteins were also evaluated for up- and down-
regulation by setting a ±2-fold change cut-off.

37
2.5.8 Secondary metabolite extraction and analysis
Organic compounds were extracted by taking 3 plugs of fungal-grown agar and extracting with 3
ml methanol (MeOH), followed by 3 ml 1:1 MeOH-dichloromethane, each with 1 hour of
sonication and filtration. The extract was evaporated in vacuo, re-dissolved in 250 μl of 20%
dimethyl sulfoxide in MeOH, and a portion (10 μl) was examined by high performance liquid
chromatography-photodiode array detection-mass spectroscopy (HPLC-DAD-MS) analysis.
HPLC-MS was carried out using a ThermoFinnigan LCQ Advantage ion trap mass spectrometer
with a reverse-phase C18 column (3 μm; 2.1 by 100 μm; Alltech Prevail) at a flow rate of 125
μl/min. The solvent gradient for HPLC-DAD-MS was 95% MeCN/H
2
O (solvent B) in 5%
MeCN/H
2
O (solvent A) both containing 0.05% formic acid, as follows: 0% solvent B from 0 to 5
min, 0 to 100% solvent B from 5 min to 35 min, 100 to 0% solvent B from 40 to 45 min, and re-
equilibration with 0% solve B from 45 to 50 min. For quantification, positive-ion electrospray
ionization (ESI) was used for the detection of austinol, dehydroaustinol, sterigmatocystin,
nidulanin A and analogues, emericellamides A and C-F, emericellin, shamixanthone, and
epishamixanthone. Negative-ion ESI was used for the detection of asperthecin, terrequinone, and
sterigmatocystin intermediate. Relative production levels were quantified by integrating the area
under each SM’s ESI trace.

2.5.9 Data availability
Raw WGS data for ISS-grown A. nidulans strains and ground-grown counterparts are available
in the NCBI SRA under accession numbers SRR7724113- SRR7724124 and BioProject accession
number PRJNA486827. Proteomics data is accessible through the ProteomeXchange Consortium
via PRIDE with the dataset identifier PXD010778.
38
CHAPTER III: Characterization of Aspergillus niger isolated from the International Space
Station

3.1 Abstract
The initial characterization of the Aspergillus niger isolate, JSC-093350089, collected from U.S.
segment surfaces of the International Space Station (ISS) is reported, along with a comparison to
the extensively studied strain ATCC 1015. Whole-genome sequencing of the ISS isolate enabled
its phylogenetic placement within the A. niger/welwitschiae/lacticoffeatus clade and revealed
that the genome of JSC-093350089 is within the observed genetic variance of other sequenced A.
niger strains. The ISS isolate exhibited an increased rate of growth and pigment distribution
when compared to a terrestrial strain. Analysis of the isolate’s proteome revealed significant
differences in the molecular phenotype of JSC-093350089, including increased abundance of
proteins involved in the A. niger starvation response, oxidative stress resistance, cell wall
modulation, and nutrient acquisition. Together, these data reveal the existence of a distinct strain
of A. niger onboard the ISS and provide insight into the characteristics of melanized fungal
species inhabiting spacecraft environments.

39
3.2 Introduction
Throughout the history of human space exploration, filamentous fungi have traveled with us and
are omnipresent on spacecraft (Novikova et al., 2006; Pierson, 2001; Van Houdt et al., 2012).
Microorganisms have been reported to cause biodegradation of structural spacecraft components,
resulting in decreased integrity of spacecraft hardware (Pierson, 2001). Microbial infections also
constitute a major health risk for astronauts, especially in closed environments where the
combined stresses of sleep disruption, microgravity, and high levels of radiation may further
compromise the human immune system (Pierson, 2001; Sonnenfeld et al., 2003). Studies have
suggested that microbial virulence and antimicrobial resistance increase in response to spacecraft
environments (Tixador et al., 1985; Wilson et al., 2007, 2008). Other reports have associated the
abundance of filamentous fungus in indoor environments with allergies and invasive infections
(Chaudhary and Marr, 2011; Ward et al., 2010). Additionally, fungi produce a myriad of
bioactive secondary metabolites (SMs) in response to environmental stressors, and while many
SMs have diverse therapeutic and industrial applications, others are toxins and can have
detrimental effects on human health (Newman and Cragg, 2012). As we set our exploration
sights beyond low-Earth orbit, a thorough understanding of how fungi respond and adapt to the
various stimuli encountered during spaceflight is critical to the success of long-term space travel.

Microorganisms inhabiting the International Space Station (ISS) are exposed to microgravity and
increased exposure to high-energy radiation as a result of being outside Earth’s protective
atmosphere (Horneck et al., 2010). In general, it is thought that microgravity alters biological
processes by initially altering the physical forces acting on the cell and its environment. This
results in decreased transfer of extracellular nutrients and metabolic by-products, causing the cell
40
to be exposed to a completely different chemical environment (Horneck et al., 2010). The inside
cabin of the ISS is exposed to a complex radiation environment (Cucinotta et al., 2008), at levels
that aren’t fungicidal (Gomoiu et al., 2016), permitting fungi to thrive. Radiation primarily
interacts with biological systems through the ionization and excitation of electrons in molecules,
and its strong mutagenic properties result in an increased rate of biological evolution (Horneck et
al., 2006). Further, radiation can have many harmful effects on biological systems, which results
in the development of adaptive responses. Fungi inhabiting spacecraft are also forced to
acclimate to reduced nutrient availability, as the National Aeronautics and Space Administration
(NASA) routinely performs stringent microbial monitoring and remediation on the ISS
(Checinska et al., 2015).

Aspergillus niger was reported to be the predominant species isolated in one ISS microbial
monitoring study (Checinska et al., 2015), which is consistent with its frequent detection in built
environments (Pierson et al., 2013). A. niger is a melanized fungal species that is ubiquitous in
nature and commonly used in biotech industries as a production host for citric acid and enzymes
(E. et al., 2002). Despite the recurring detection of A. niger in spacecraft environments,
investigations into its genetic alteration and gene expression modulation in ISS conditions has
not been carried out. Although A. niger is less pathogenic to humans than other Aspergillus
species, such as A. fumigatus and A. flavus (E. et al., 2002), it has been associated with ear
infections and can cause invasive pulmonary aspergillosis in immunocompromised patients
(Person et al., 2010). This enhances the need for studies to understand how A. niger responds and
adapts to the environment of the ISS where microgravity might play a role in compromising the
human immune system (Pierson, 2001; Sonnenfeld et al., 2003). Additionally, melanized fungi
41
are highly resistant to ionizing radiation, and respond to radiation with enhanced growth and up-
regulation of many proteins (Dadachova and Casadevall, 2008; Robertson et al., 2012), some of
which may provide important insight into the adaptive evolutionary mechanism of melanized
fungal species.

The objective of this study was to investigate a strain of A. niger isolated from surfaces of the
ISS, with the aim to characterize its molecular phenotype. Although it has been well-established
that fungi are ubiquitous on spacecraft (Checinska et al., 2015; Novikova et al., 2006; Pierson,
2001; Van Houdt et al., 2012), very few studies have been conducted to characterize fungi
isolated from the ISS (Knox et al., 2016b). Given that melanin production in fungi is considered
an evolutionary-derived trait to confer radiation resistance (Bell and Wheeler, 1986; Dadachova
and Casadevall, 2008), the present study of a melanized fungus that has inhabited the ISS may
reveal important insights into the key traits necessary to withstand such environments. Our work
investigated differences of the ISS A. niger isolate compared to Earth isolates to better
understand the characteristics of strains isolated from the space station built environment. Due to
the significance of secondary metabolic processes in filamentous fungi (Keller et al., 2005), A.
niger ATCC 1015 was used as a terrestrial reference strain for physiologic and proteomic
analyses because its SM profile has been thoroughly characterized (Chiang et al., 2011), and we
aim to build on this work by investigating SM production in JSC-093350089.

42
3.3 Results

Figure 3-1. Phylogenetic characterization of JSC-093350089 displaying its relative placement
within the A. niger/welwitschiae/lacticoffeatus clade.

3.3.1 Identification of A. niger sampled from the ISS
Sampling of surfaces on the ISS during microbial monitoring surveys resulted in the isolation of
numerous bacterial and fungal strains (Checinska et al., 2015). A strain of A. niger, JSC-
093350089, identified by morphological characteristics and verified by internal transcribed
spacer (ITS) region sequencing, was used for this study. This strain was isolated by swabbing
surface materials on the U.S. segment of the ISS. Due to the nature of this sampling method, it is
0.3
Aspergillus niger ATCC 10864
Aspergillus niger CBS 513.88
Aspergillus niger SH-2
Aspergillus niger ATCC 1015 v4
Aspergillus niger FGSC A1279
Aspergillus niger L2
Aspergillus niger JSC-093350089
Aspergillus niger NRRL3
Aspergillus niger FDAARGOS_311
Aspergillus niger (lacticoffeatus ) CBS 101883
Aspergillus niger van Tieghem ATCC 13496
Aspergillus niger H915-1
Aspergillus niger (phoenicis)
Aspergillus welwitschiae CBS 139.54b
Aspergillus niger A1
1
1
0.87
1
1
0.99
0.73
1
0.91
1
0.6
0.96
43
impossible to know the exact duration of time that this strain was on the ISS. The 36.08 Mb
genome sequence of JSC-093350089 was generated using whole genome paired-end sequencing
(WGS), which was further improved to high-quality assemblies of 223 scaffolds possessing
12,532 coding sequences and 287 tRNA. The JSC-093350089 genome was similar in size to
other A. niger genomes, which typically range from 34.0-36.5 Mb (Andersen et al., 2011; Pel et
al., 2007; Yin et al., 2017). To further verify the identity of JSC-093350089 and place it into the
larger context of the A. niger/welwitschiae/lacticoffeatus clade, phylogeny was assessed using
maximum likelihood (Figure 3-1). Of the A. niger strains surveyed, the ISS isolate displayed the
closest phylogenetic relationship to A. niger (phoenicis). When compared to ATCC 1015, an
industrial strain used for citric acid production (Andersen et al., 2011), and CBS 513.88, an
ancestor of the A. niger strains used industrially for enzyme production (Pel et al., 2007), it
differed by 37,548 and 39,433 variants, respectively.

3.3.2 Visual characterization and growth rates of JSC-093350089 in vitro
The basic physiological phenotype of JSC-093350089 was investigated on glucose minimal
medium (GMM) agar plates. Visual characterization of centrally inoculated GMM plates
revealed differences in pigment distribution and colony diameter after 7 days of growth (Figure
3-2A). JSC-093350089 colony size appeared larger, and pigment had spread to the periphery of
the colony in a shorter time than ATCC 1015. Assessment of radial growth rates revealed that
the ISS strain grew at a significantly faster rate than ATCC 1015 after 3 days of growth (Figure
3-2B).
44

Figure 3-2. In vitro growth of JSC-093350089 compared to ATCC 1015. (A) Growth on GMM
at 30
o
C after 7 days, showing colony morphology and color. (B) Radial growth at 30
o
C on
GMM. Statistical analyses were performed by multiple t-tests, corrected for multiple
comparisons using the Holm-Sidak method.

3.3.3 Overview of proteome analysis
To investigate the differences in the proteome of JSC-093350089 and ATCC 1015, total protein
was extracted from each strain and subjected to tandem mass tag (TMT) labeling, followed by
LC-MS analysis. All MS data were analyzed using the Proteome Discoverer with the Sequest-
HT search engine against the A. niger CBS513.88 protein database (NCBI). The CBS 513.88
protein database was used because it has been extensively annotated and enabled subsequent
functional analysis using the AspGD Gene Ontology Slim Mapper tool. The abundance ratio for
all proteins were normalized to ATCC 1015, which resulted in the identification of 218 proteins
with increased abundance and 109 proteins with decreased abundance (fold-change (FC) >½2½,
P < 0.05), in JSC-093350089, relative to ATCC 1015 (Table S1). Distribution of AspGD GO
Slim terms among differentially expressed proteins is displayed in Figure 3-3. Many proteins that
exhibited increased abundance in JSC-093350089 were involved with carbohydrate metabolic
A B
ATCC 1015 JSC-093350089
Top
Bottom
JSC-093350089
ATCC 1015
45
processes (10.1% of all up-regulated proteins), response to stress (9.6%), organelle organization
(9.6%), and transport (8.7%). Proteins involved in cytoskeleton organization, protein folding,
secondary metabolic processes, and transcription were only associated with increased protein
abundance in JSC-093350089, while proteins involved in cellular homeostasis were only
associated with decreased protein abundance in JSC-093350089. GO Slim term enrichment
analysis was conducted using FungiDB (Stajich et al., 2012), which identified significantly over-
represented proteins that exhibited increased abundance in the proteome of JSC-093350089.
Significantly over-represented GO Slim terms included carbohydrate metabolic processes (4.7%
of background genes with this term), cellular component assembly (5.3%), catabolic processes
(4.1%), protein complex assembly (6.4%), and response to stress (3.2%).
46

Figure 3-3. Biological process GO Slim categories of differentially expressed proteins.
Differentially enriched proteins (FC >½2½, P < 0.05) were mapped to terms representing various
biological processes using AspGD Gene Ontology (GO) Slim Mapper.

25 20 15 10 5 0 5 10 15 20 25
Cellular homeostasis
Transcription
Sexual sporulation
Cytokinesis
Signal transduction
Secondary
metabolic process
Pathogenesis
Asexual sporulation
Cellular amino acid
metabolic process
Lipid metabolic process
Ribosome biogenesis
Protein folding
Cytoskeleton organization
Vesicle-mediated transport
DNA metabolic process
Developmental process
Filamentous growth
Translation
Cell cycle
Protein catabolic process
Cellular protein
modification process
RNA metabolic process
Response to chemical
Transport
Organelle organization
Response to stress
Carbohydrate
metabolic process
No. of differentially
expressed proteins
GO SLIM category
Protein down-regulated
Protein up-regulated
47
Table 3-1. Relative abundance of cell wall modulation proteins.

ORF Protein CAZy Family Description logFC
a

An16g02910

GH92 α-mannosidase 3.53
An02g09050 GelG GH72 β-1,3-glucanotransferase 2.99
An14g04240

GH92 α-1,2-mannosidase 2.25
An07g08640 AgnB GH71 α-1,3-glucanase 2.23
An13g01260

GH92 α-1,2-mannosidase 2.15
An11g03340 AamA GH13 Acid α-amylase 2.01
An11g06080

GH3 β-glucosidase 1.68
An06g01530 Scw4 GH17 β-glucanase 1.6
An01g11660 CbhB GH7 1,4-β glucan cellobiohydrolase 1.53
An02g13180 BgxB GH55 β-1,3-glucanase 1.48
An07g03340 Hyp1 Spore wall fungal hydrophobin 1.47
An01g09290 TraB GH37 Trehalase 1.27
An09g05730 AlbA Polyketide synthase 1.24
An08g11070 SucA GH32 Invertase 1.23
An08g08370

GH92 α-mannosidase 1.19
An14g04190 GbeA GH13 1,4-α-glucan branching enzyme 1.18
An01g09960 XlnD GH3 β-D-xylosidase 1.06
An14g05340 UrghB GH105 Rhamnogalacturonyl hydrolase -1
An10g00400 GelA GH72 β-1,3-glucanotransferase -1.04
An16g06800 EglB GH5 Endoglucanase -1.13
An09g03100 AgtA GH13
GPI-anchored α-
glucanosyltransferase
-1.2
An04g06930 AmyC GH13 α-amylase -1.22
An18g03570 BglA GH3 β-glucosidase -1.22
An01g12150 LacA GH35 β-galactosidase -1.4
An02g00610

GH2 β-glucuronidase -1.41
An12g08280 InuE GH32 Exo-inulinase -1.5
An11g02100

GH1 β-glucosidase -1.54
An14g01770

GH3 β-glucosidase -1.54
An11g00200

GH3 β-glucosidase -1.69
An07g08950 EglC GH5 Endoglucanase -1.82
An15g03550

GH43 Endo-arabinase -1.91
a
log2 fold change of JSC-093350089 compared to ATCC 1015 (P < 0.05).
48
3.3.4 Differential abundance of cell wall modulation proteins
The proteome of JSC-093350089 revealed differential levels of cell wall modulation proteins
(Table 3-1). Conidia of A. niger possess a relatively thick cell wall made of a network of
carbohydrates, including β-glucans, chitin, α-glucans, galactomannan and
galactosaminogalactan, with an outer cell wall layer consisting of complex melanin pigments
(Johnston, 1965). The polyketide synthase AlbA (An09g05730), which is required for the
production of 1,8-dihydroxynaphthalene-melanin (DHN-melanin) in A. niger (Chiang et al.,
2011), was over twofold more enriched in JSC-093350089 compared to ATCC 1015. Protein
abundance of hydrophobin Hyp1 (An07g03340) was nearly threefold ATCC 1015 levels. RodA,
the homologue of Hyp1 in A. nidulans, has been reported to play a role in biofilm formation and
efficient deconstruction of cell wall polysaccharides (Brown et al., 2016). Its homologue in A.
fumigatus was shown to enhance fungal virulence by masking Dectin-1 and Dectin-2 mediated
recognition of conidia in vivo (Carrion et al., 2013).

Differential expression was observed for a number of genes encoding glycoside hydrolases,
which were identified using the CAZy database (http://www.cazy.org/) (Lombard et al., 2014).
The starvation-induced cellobiohydrolase CbhB (An01g11660), which is regulated by XlnR
(Gielkens et al., 1999), exhibited levels nearly threefold in the proteome of JSC-093350089
relative to ATCC 1015 (van Munster et al., 2013). XlnR is a transcriptional activator that
regulates xylanolytic, endoglucanase, and cellobiohydrolase gene expression in A. niger
(Gielkens et al., 1999; van Peij et al., 1998). Increased protein abundance was observed for β-D-
xylosidase XlnD (An01g09960), which is also regulated by XlnR (van Peij et al., 1998).
Decreased protein abundance was observed for XlnR-regulated β-galactosidase LacA
49
(An01g12150), which is exclusively expressed on xyloglucan-derived substrates (Ferreira de
Oliveira et al., 2011). Similary, XlnR-regulated endoglucanase EglC (An07g08950), which
exhibits its greatest activity towards xyloglucan, also displayed decreased protein abundance
(Hasper et al., 2002). Other starvation-induced cell wall degradation glycoside hydrolases
enriched in JSC-093350089 included α-1,3-glucanase AgnB (An07g08640) and β-glucanase
Scw4 (An06g01530). Differential abundance of glycoside hydrolases involved in starch
utilization was also observed. Extracellular acid α-amylase AamA (An11g03340), which plays a
role in starch degradation and is regulated by starch degradation regulator AmyR (Petersen et al.,
1999), was present in JSC-093350089 at levels fourfold that of ATCC 1015. Four of the five
enzymes in the family of GH92, which consists of mannosidases, displayed increased abundance
in the proteome of JSC-093350089. These GH92 proteins included An08g08370, An13g01260,
An14g04240, and An16g02910.

3.3.5 Differential abundance of stress response proteins
Our study also revealed differential abundance of proteins involved in the stress response of A.
niger (Table 3-2). Heat shock proteins, including DnaK-type molecular chaperone Ssb2
(An16g09260) and An06g01610, were present in JSC-093350089 at levels twofold and fivefold
that of ATCC 1015, respectively. An06g01610 is very similar to LEA-like Hsp12 of
Saccharomyces cerevisiae and has been reported to stabilize the plasma membrane (Sales et al.,
2000). Increased protein abundance was observed for the serine/threonine protein kinase Srk1
(An07g07970) and the mitogen activated protein kinase SakA (An08g05850), which have been
reported to mediate cell cycle arrest and mitochondrial function in response to oxidative stress
(Jaimes-Arroyo et al., 2015). Other proteins that exhibited higher levels in JSC-093350089
50
included the oxidative stress protein Svf1 (An18g02900) and An02g07350, which encodes a
protein homologous to group 3 LEA proteins resposible for mitigating stress-induced damage,
such as protecting seeds from drought (Chakrabortee et al., 2007; Tompa and Kovacs, 2010).
The catalase An12g10720 was present at levels 13-fold that of ATCC 1015. Increased abundance
was also observed for the stress response nuclear envelope protein Ish1, whose expression has
been reported to increase in response to glucose starvation and osmotic stress (Taricani et al.,
2002).

Table 3-2. Relative abundance of stress response proteins.

ORF Protein Description logFC
a

An12g10720 Catalase 3.71
An06g01610 Heat shock protein 2.51
An02g07350 LEA domain protein 1.95
An16g04420 Ish1 Stress response protein 1.53
An08g05850 SakA MAP kinase 1.5
An18g02900 Svf1 Survival factor 1 1.43
An07g07970 Srk1 Serine/threonine protein kinase 1.21
An16g09260 Ssb2 Heat shock protein 1.09
a
log2 fold change of JSC-093350089 compared to ATCC 1015 (P < 0.05).

51
3.4 Discussion
In the current study, the molecular phenotype of a strain of A. niger isolated from the ISS was
characterized. Despite its frequent detection in built environments, this is the first investigation
into the “omic” differences of an ISS A. niger isolate compared to an Earth strain. As the
frequency and duration of manned space missions increase, investigations into how fungi
respond and adapt to various stimuli encountered during spaceflight is imperative for the health
of crew and presents many economic benefits. Further, such studies provide insight into the
adaptive evolutionary mechanism of melanized fungal species and the biological alterations of
microbes isolated from extreme spaceflight environments.

The genome of JSC-093350089 was within the genetic variation of other A. niger strains,
suggesting that its ability to survive and proliferate in a spacecraft environment is not contingent
on enhanced genetic variance. This finding is consistent with a previous report on the genetic
variance of ISS Aspergillus isolates (Knox et al., 2016b). To further understand the effect of
microgravity and enhanced irradiation on fungal genomics, future studies should investigate the
same strain grown under both space and ground conditions to quantify and identify any
mutations that may result from life on the ISS. Additional sequencing of terrestrial A. niger
strains will also be important to better identify the donor population of the strain and further
isolate the sequence variation that is specific to ISS-derived strains.

One characteristic of the ISS isolate was increased protein abundance of AlbA, a key
biosynthesis enzyme involved in the production of DHN-melanin in A. niger. While A. niger
historically has black conidia due to its high melanin content, the A. niger ΔalbA mutant was
52
reported to display a white or colorless conidial phenotype (Chiang et al., 2011). This is
consistent with reports that fungi isolated from high-radiation environments exhibit increased
melanin production. One study found that A. niger strains occupying the south-facing slope of
the “Evolution Canyon” in Israel, which receives 200-800% higher solar radiation than the north
slope, produced three times more melanin than strains isolated from the north-facing slope
(Singaravelan et al., 2008). It is reasonable to presume that increased melanin production is a key
adaptive response to the enhanced irradiation environment of the ISS, as there is considerable
evidence that melanized fungi are highly resistant to ionizing radiation under experimental
conditions (Dadachova and Casadevall, 2008; Saleh et al., 1988). In fact, it has been reported
that exposure of melanin to ionizing radiation alters its electronic properties, and melanized
fungal cells exhibit increased growth rates following exposure to ionizing radiation (Dadachova
et al., 2007).

Interestingly, the ISS isolate exhibited a more rapid growth rate than the terrestrial strain, which
is consistent with previous reports of enhanced growth in melanized fungi following radiation
exposure and Aspergillus and Penicillium species recovered from the ISS and Mir spacecraft
(Dadachova and Casadevall, 2008; Dadachova et al., 2007; Knox et al., 2016b; Novikova, 2004).
Although this finding cannot be definitively attributed to the isolation environment, it is
conceivable that rapid growth may confer selective advantage in environments operating under
strict microbial monitoring procedures. The reported increase in colony pigmentation distribution
may point to enhanced melanin production in the ISS isolate, as the AlbA protein was twofold
more enriched when compared to the terrestrial strain. However, the ability to rapidly spread
53
pigment to the periphery of the colony may offer additional modes of protection from high levels
of radiation present in spacecraft.

The ISS isolate displayed general hallmarks of carbon starvation. During starvation, A. niger
produces a myriad of glycoside hydrolases that facilitate the release of nutrients from
biopolymers and the recycling of cell wall components to generate energy and building blocks
that can be used for maintenance and conidiogenesis (Nitsche et al., 2012; Pandey et al., 1999).
The observed enrichment of starvation-induced nutrient acquisition enzymes may be the result of
adaptation to the low-nutrient environment that exists as a result of stringent microbial
monitoring and remediation by NASA (Checinska et al., 2015). The same may also be true for
the increased abundance of the glycoside hydrolase AamA. AamA is highly up-regulated in
growing hyphae at the periphery of mycelium (Vinck et al., 2011), and following secretion from
exploring hyphae, AamA degrades starch into small molecules that can be taken up by the
fungus to serve as nutrients. The significant enrichment of AamA suggests that JSC-093350089
can utilize starch encountered during colonization more efficiently that ATCC 1015, which may
have conferred selective advantage in the low-nutrient spacecraft environment.

During spaceflight, ionizing radiation can generate reactive oxygen species (ROS) via the
hydrolysis of intracellular water, which can result in oxidative damage to DNA, proteins, lipids,
and other cell components (Lehnert and Iyer, 2002). Accordingly, catalase was among the
highest up-regulated proteins in the ISS isolate, which degrade H
2
O
2
and therefore play a major
role in curtailing oxidative stress (Halliwell and Gutteridge, 2015; Kawasaki and Aguirre, 2001).
This is consistent with previous reports that spaceflight induces the expression of oxidative stress
54
resistance genes in microbes, animals, and astronauts (Baqai et al., 2009; Crabbé et al., 2013;
Gasch et al., 2001; Rizzo et al., 2012; Robertson et al., 2012; Stein, 2002; Watson, 2003), and
increased susceptibility to ionizing radiation has been observed in S. cerevisiae strains lacking
cytosolic catalase (Lee et al., 2001; Nishimoto et al., 2015). Similarly, increased abundance was
also observed for kinases that mediate key biological processes in response to oxidative stress
(Jaimes-Arroyo et al., 2015). The high levels of oxidative stress response proteins, as found in
this study, is consistent with the observed response of the melanized yeast Wangiella
dermatitidis following exposure to ionizing radiation (Robertson et al., 2012). On the other hand,
increased resistance to oxidative stress may be a response to microgravity, as low shear modeled
microgravity have induced such a response in bacteria (Crabbé et al., 2010).

This study has revealed the existence of a distinct strain of A. niger onboard the ISS that
exhibited differential growth and conidiation patterns compared to a terrestrial strain. Proteomic
analysis revealed significant differences in the phenotype of JSC-093350089 that included
enrichment of proteins involved in the A. niger starvation response, oxidative stress resistance,
cell wall modulation, and nutrient acquisition. Given the ubiquity of A. niger in nature along with
its genetic diversity among sequenced strains (Andersen et al., 2011; Yin et al., 2017), it is not
surprising that JSC-093350089 exhibited a distinct molecular phenotype, and more studies will
reveal if the observed phenotype is widespread for other A. niger strains isolated from the ISS.
Since most of the microgravity-induced response studies were carried out utilizing opportunistic
pathogens of bacteria and yeast, the “omics” characterization of ISS A. niger, a saprophyte, that
exhibited higher melanin content compared to Earth counterparts could be a model to elucidate
molecular mechanisms involved in microbial adaptation to the ISS environment. Developing
55
countermeasures to eradicate problematic microorganisms that adapt to unfavorable conditions
would help NASA’s Human Research Program in planning for long duration manned missions.
Additionally, such analyses will further our understanding of the molecular pathways that define
host-microbe interactions, thus enabling development of suitable cleaning strategies to maintain
the health of habitat and co-living crew for future missions.

56
3.5 Materials and Methods
3.5.1 Isolation and identification of the ISS A. niger isolate
Surface samples were collected from the U.S. Segment of the ISS using the Surface Sampling
Kit (SSK) (NASA, 2011). Microbes were removed from surfaces using a swab and sterile saline
solution (0.85% sodium chloride) and were transported to Earth for analyses. Materials retrieved
from the swabs were subsequently inoculated onto potato dextrose agar (PDA) supplemented
with chloramphenicol. The PDA plates were incubated at ambient cabin temperature (28
o
to
37
o
C) for 5 days. The fungal colonies that exhibited growth were further purified and stored at -
80
o
C in sterile glycerol stock until further analyses. When required, fungal isolates were revived
on PDA medium, and DNA from pure cultures was extracted (UltraPure DNA Kit [Mo Bio,
Carlsbad, CA]). An approximately 600 bp region consisting of ITS 1, 5.8 S, and ITS 2 of the
isolated fungal DNA were PCR amplified, using primers ITS1F (5’ TTG GTC ATT TAG AGG
AAG TAA 3’) and Tw13 (5’ GGT CCG TGT TTC AAG ACG 3’) (Lai et al., 2007) and
following the established protocol (Taylor and Bruns, 1999). The UNITE database was used to
determine the closest similarity to ITS sequences of fungal type strains (Abarenkov et al., 2010).
The identity of the ISS isolate was subsequently confirmed by WGS.

3.5.2 Genome sequencing, assembly, and annotation
Extracted DNA was sent to MACROGEN clinical laboratory (MACROGEN INC, Rockville,
USA) for WGS. Library preparation was carried out using Illumina Nextera kit (random
fragmentation, adapter ligation, and cluster generation) and quantified with Quant-iT dsDNA
High Sensitivity assays. Generated libraries were sequenced with 100 bp paired-end sequencing
protocols on the Illumina HiSeq. 2500 platform. Raw data images were produced utilizing HCS
57
(HiSeq Control Software v2.2.38) for system control and base calling (BCL) was done through
an integrated primary analysis using Real Time Analysis software v1.18.61.0. The BCL binary
was converted into FASTQ utilizing Illumina package bcl2fastq (v1.8.4). The NGS QC toolkit
version 2.3 (Patel and Jain, 2012) was used to filter the data for high-quality vector- and adaptor-
free reads for genome assembly (cutoff read length for high quality: 80%; cutoff quality score:
20), 22,769,466 vector filter reads were obtained after the quality check. High-quality vector-
filtered reads were used for de-novo assembly with the MaSuRCA genome assembler (k-mer
size - 70) (Zimin et al., 2013). The final assembly consisted of 223 scaffolds with a total size
36,079,011 bp (~100X). The N
50
scaffold length was 543,773 kb and the largest scaffold was
1,390.254 kb. There were no random “N” joining of the contigs to maintain high assembly
quality. Quality check of final assembly was performed using quality assessment tool for genome
assemblies (QUAST) (Gurevich et al., 2013). The number of N’s detected were less than 12 per
100Kb which represent very good assembly. The JSC-093350089 genome was annotated and
uploaded in the NCBI Genbank with accession #MSJD00000000. Genome annotation was
performed with funannotate (Palmer, 2018) (https://github.com/nextgenusfs/funannotate v1.3.0-
beta) which utilizes a combination of ab inito gene prediction tools (Stanke et al., 2006; Ter-
Hovhannisyan et al., 2008) and experimental evidence including proteins and RNAseq and a
consensus gene calling with EvidenceModeler (Haas et al., 2008).

3.5.3 Phylogenetic analysis
Phylogenetic analysis of A. niger strains was performed by identifying conserved protein coding
genes in available A. niger genomes and close relatives. These data were obtained by
downloading public sequence data from NCBI and the Department of Energy’s Joint Genome
58
Institute Mycocosm. The assemblies for strains A1, ATCC 10864, An76, FDAARGOS 311,
FGSC A1279, H915-1, L2, SH-2 were downloaded from NCBI Assembly Archive. The strains
FDAARGOS 311 and An76 already had deposited annotation and were downloaded directly. For
the remaining strains, gene prediction with Augustus (v 3.2.2) (Stanke et al., 2006) using the pre-
trained model ‘aspergillus_niger_jsc_093350089’ generated from the genome annotation
procedure. This parameter set is deposited in https://github.com/hyphaltip/fungi-gene-prediction-
params. Additional strains with annotation from JGI were downloaded (ATCC 1015, DSM 1,
CBS 513.88, NRRL 3) and related species (A. niger van Tieghem ATCC 13496, A. welwitschiae
CBS139.54b, A. phoenicis, A. lacticoffeatus CBS 101883, A. brasiliensis CBS 101740). Coding
sequences were obtained, translated into proteins and searched for a conserved set of 71 protein
coding gene markers “AFTOL_70” as part of the 1000 fungal genomes project
(https://github.com/1KFG/Phylogenomics_HMMs). These markers were searched using
PHYling (https://github.com/stajichlab/PHYling_unified), which first searches for conserved
markers using HMMsearch followed by extraction of best hits and concatenated alignment of all
the orthologous matches. A back-translated alignment of coding sequences was produced from
the input proteins in order to resolve the closely related strains in this dataset. A phylogenetic
tree was inferred from the coding sequence tree using IQTREE (v1.6.3) first by identifying a
partition scheme with (-m TESTMERGE -st CODON) parameters followed by a tree inference
using the options (-st CODON -bb 1000 -spp Partition.txt) to infer the tree and obtain branch
support with ultrafast bootstrapping on the reduced partition parameters under a codon model in
IQTREE (Chernomor et al., 2016; Hoang et al., 2018; Nguyen et al., 2015). To identify the
number of single nucleotide variations occurring between JSC-093350089 and both ATCC 1015
59
and CBS 513.88, variants were called using the Harvest suite’s Parsnp tool (Treangen et al.,
2014).

3.5.4 Growth conditions
JSC-093350089 and ATCC 1015 were cultivated on 10 cm Petri dishes containing 25 mL
glucose minimal medium (GMM) agar plates (6g/L NaNO
3
, 0.52 g/L KCl, 0.52 g/L
MgSO
4
*7H
2
O, 1.52 g/L KH
2
PO
4
, 10g/L D-glucose, 15 g/L agar supplemented with 1mL/L of
Hutner’s trace elements) with a cellophane membrane on top, on which the fungus was grown.
Unless otherwise specified, 1 x 10
7
conidia per Petri dish (D = 10 cm) were inoculated into each
medium and incubated at 30
o
C for 5 days.

3.5.5 Physiological analysis
Growth rates were assessed by centrally inoculating 1 x 10
4
conidia on GMM plates in replicates
of 5, and measuring radial growth at the same time each day. Statistical analyses were performed
using multiple t-tests, and corrected for multiple comparisons using the Holm-Sidak method.
Photos depicting morphological differences were taken after 7 days.

3.5.6 Protein extraction
Mycelia from GMM agar plates were collected and stored at -80
o
C prior to protein extraction.
For protein extraction, the lysis buffer consisted of 100 mM triethylammonium bicarbonate
(TEAB) with 1x Halt Protease Inhibitor Cocktail (100x), with the final concentration of each
component being 1mM AEBSF, 800 nM Aprotinin, 50 μM Bestatin, 15 μM E64, 20 μM
Leupeptin, and 10 μM Pepstatin A (Thermo Scientific, Rockford, IL), and 200 µg/ml
60
phenylmethylsulfonyl fluoride (Sigma-Aldrich, St. Louis, MO). Mycelia were homogenized
directly using Precellys 24 homogenizer (Bertin, Rockville, MD) in which each sample was
processed inside a 2-mL cryotube with 0.5 mm glass beads three times (at 4 °C, 6500 rpm, 1
min., repeated 3 times with 15 s pauses in between). The lysed fungi were centrifuged at 17,000
g for 15 min. Protein concentrations in the supernatants were measured by the Bradford assay
with albumin for the standard curve (Bio-Rad Laboratories, Inc. Hercules, CA).

3.5.7 Tandem mass tag (TMT) labeling
200 µg proteins from each sample were precipitated in 20% trichloroacetic acid (TCA) at 4 °C.
Protein pellets were obtained by centrifugation (17,000 g), washed with ice-cold acetone, and
resuspended in 25µL TEAB (50 mM final concentration) and 25µL 2,2,2-trifluoroethanol (TFE)
(50% final concentration). Proteins were reduced by adding 1µL of tris(2-
carboxyethyl)phosphine (TCEP, 500 mM) followed by incubation for 1 hour at 37 °C (10 mM
final TCEP concentration). Proteins were alkylated ion presence of iodoacetamide (IAA, 30 mM)
in the dark for 1 hour at room temperature. 2.5 µg per sample of mass spec grade trypsin/lysC
(Promega, Madison, WI) was used to digest the peptides overnight at 37 °C.

The digested peptides were quantified using the Pierce Quantitative Colorimetric Peptide Assay
(Thermo Scientific). 40 µg of peptides from each specific sample was labeled with the Thermo
Scientific TMTsixplex Isobaric Mass Tagging Kit (ATCC1015-GMM with TMT
6
-128, and JSC-
GMM with TMT
6
-129) according to the manufacturer’s protocol. The TMT
6
-130 and -131 labels
were used as a reference that contained an equal amount of the peptides from each of the four
samples. All labeled-peptide mixtures were combined into a single tube, mixed, and fractionated
61
using the Thermo Scientific Pierce High pH Reversed-Phase Peptide Fractionation Kit. Fractions
were dried using a SpeedVac concentrator and re-suspended in 1% formic acid prior to LC-
MS/MS analysis.

3.5.8 LC-MS/MS analysis
The samples were analyzed on an Orbitrap Fusion Tribrid mass spectrometer with an EASY-
nLC 1000 Liquid Chromatograph, a 75 μm x 2 cm Acclaim PepMap100 C18 trapping column,
and a 75 μm x 25 cm PepMap RSLC C18 analytical column, and an Easy-Spray ion source
(Thermo Scientific). The column temperature was maintained at 45 °C and the peptides were
eluted at a flow rate of 300 nL/min over a 110 min gradient, from 3-30% solvent B (100 min),
30-50% solvent B (3 min), 50-90% solvent B (2 min), and 90% solvent B (2 min). The solvent A
was 0.1 % formic acid in water and the solvent B was 0.1% formic acid in acetonitrile.
The full MS survey scan (m/z 400-1500) was acquired in the Orbitrap at a resolution of 120,000
and an automatic gain control (AGC) target of 2´10
5
. The maximum injection time for MS scans
was 50 ms. Monoisotopic precursor ions were selected with charge states 2-7, a ±10 ppm mass
window, and 70 s dynamic exclusion. The MS
2
scan (m/z 400-2000) was performed using the
linear ion trap with the CID collision energy set to 35 %. The ion trap scan rate was set to
“rapid”, with an AGC target of 4´10
3
, and a maximum injection time of 150 ms. Ten fragment
ions from each MS
2
experiment were subsequently selected for an MS
3
experiment. The MS
3

scan (m/z 100-500) was performed to generate the TMT reporter ions in the linear ion trap using
HCD at a collision energy setting of 55 %, a rapid scan rate and an AGC target of 5´10
3
, and a
maximum injection time of 250 ms.

62
3.5.9 Proteome data analysis
All MS spectra were searched using the Proteome Discoverer (version 2.1.0.81, Thermo
Scientific) with the Sequest-HT searching engines against an Aspergillus niger CBS513.88
database containing 10549 sequences (NCBI). The searches were performed with the following
parameters: 5 ppm tolerance for precursor ion masses and 0.6 Da tolerance for fragment ion
masses. The static modification settings included carbamidomethyl of cysteine residues, and
dynamic modifications included oxidation of methionine, TMT6plex modification of lysine ε-
amino groups and peptide N-termini, and acetyl modification of protein N-terminus. A target-
decoy database search was used to set a false discovery rate (FDR) of 1%. The reporter ions
integration tolerance was 0.5 Da. The co-isolation threshold was 75%. The average signal-to-
noise threshold of all reporter peaks were bigger than 10. The total intensity of a reporter ion for
a protein was calculated based on the sum of all detected reporter ions of associated peptides
from that protein. The ratios between reporter ions and the reference reporter ions (TMT
6
- 130 or
-131) were used to estimate the abundance ratio of each protein.

For statistical analysis, the sum of reporter ion intensities for each protein was log2 transformed
and the technical triplicates measurement for each protein was averaged. Only the proteins that
were identified with at least one peptide detected in each technical replicate, and quantified in all
three technical replicates, were considered for the analysis. Student’s t-test was performed to
identify proteins that are differentially expressed. Proteins with p-value < 0.05 were further
evaluated for increased or decreased abundance using a cut-off value of log2 fold change >½1½.

63
3.5.10 Data availability
WGS data for JSC-093350089 are available in the NCBI GenBank, under BioSample accession
number SAMN06076678 and BioProject accession number PRJNA355122. Raw WGS reads are
available in the NCBI SRA, under accession number SRP127978. Proteomics data is accessible
through the ProteomeXchange Consortium via PRIDE with the dataset identifier PXD008588.

64
CHAPTER IV: Metabolomic analysis of Aspergillus niger isolated from the International
Space Station reveals the radiation resistance potential of pyranonigrin A

4.1 Abstract
Secondary metabolite (SM) production of Aspergillus niger JSC-093350089, isolated from the
International Space Station (ISS), is reported, along with a comparison to the experimentally
established strain ATCC 1015. The analysis revealed enhanced production levels of naphtho-γ-
pyrones and therapeutically relevant SMs, including bicoumanigrin A, aurasperones A and B,
and the antioxidant pyranonigrin A. Using targeted gene deletions, the gene cluster responsible
for pyranonigrin A biosynthesis was identified, which consists of a hybrid nonribosomal peptide
synthetase/polyketide synthase (NRPS/PKS) adjacent to three contiguous tailoring enzymes.
UVC sensitivity assays enabled characterization of pyranonigrin A as a UV resistance agent in
the ISS isolate.

65
4.2 Introduction
Filamentous fungi are ubiquitous in spacecraft environments due to anthropogenic contamination
and an inability to completely sterilize the craft and cargo (Novikova et al., 2006; Pierson, 2001;
Van Houdt et al., 2012). Microbial infections are a major health risk for astronauts, and are
exacerbated by the combined stresses of microgravity, sleep disruption, alterations in food
intake, confined living space, and high levels of radiation that can compromise the immune
system (Pierson, 2001; Sonnenfeld et al., 2003). Additionally, several studies have indicated that
spacecraft environments increase microbial virulence and antimicrobial resistance (Tixador et al.,
1985; Wilson et al., 2007, 2008). As we make strides towards human interplanetary exploration,
investigations into the characteristics of filamentous fungi that reside in spacecraft environments
are critical for crew health. Additionally, such studies present diverse industrial and therapeutic
opportunities, as fungi produce a plethora of bioactive secondary metabolites (SMs) in response
to external stressors. These small molecules often kill or inhibit growth of other organisms,
enabling fungi to successfully compete within the complex ecosystem they reside in. While some
SMs are toxic to humans, others have diverse industrial and therapeutic applications, including
antibiotic, anticancer, antioxidant, immunosuppressant, and cholesterol-lowering activities
(Newman and Cragg, 2012). The remarkable structural and functional diversity of fungal SMs
arise from the combinatorial and modular nature of their biosynthesis, in which the SM core
backbone is often biosynthesized by either a polyketide synthase (PKS), a nonribosomal peptide
synthetase (NRPS), or a hybrid NRPS/PKS, and further diversified by a number of tailoring
enzymes that are clustered together within the genome (Fischbach and Walsh, 2006).

66
In order to understand the characteristics of microbes residing in the International Space Station
(ISS), National Aeronautics and Space Administration (NASA) has implemented a robust
microbial monitoring system (Pierson et al., 2013). In one study, the filamentous fungus
Aspergillus niger was reported to be the predominant isolate (Checinska et al., 2015). A. niger is
a melanized fungal species commonly used in the biotech industry as a production host for citric
acid and enzymes (Schuster et al., 2002). Melanized fungi are highly resistant to ionizing
radiation under experimental conditions (Mirchink et al., 1972; Saleh et al., 1988), and it has
been reported that the electronic properties of melanin change following exposure to ionizing
radiation (Dadachova et al., 2007). Additionally, highly-melanized fungal cells exhibit increased
growth compared to non-melanized fungal cells following exposure to ionizing radiation,
suggesting that melanin has the capacity to capture and utilize energy (Dadachova et al., 2007).
Several studies have reported the association of melanin production with fungal virulence
(Kwon-Chung et al., 1982; Nosanchuk et al., 1998; Wang et al., 1995), which enhances the need
for studies that assess the characteristics of melanized fungi inhabiting spacecraft environments.

Naphtho-γ-pyrones are the predominant class of SMs produced by A. niger, and they possess a
diverse array of reported biological properties, including anti-HIV, anti-hyperuricemia, anti-
tubercular, antimicrobial, antitumor, and antioxidant activities (Choque et al., 2015). Advances
in genome sequencing of A. niger has revealed its capacity to produce many other SMs in
addition to the naphtho-γ-pyrones, as the experimentally established A. niger ATCC 1015
genome harbors 33 PKSs, 15 NRPSs, and 9 NRPS-PKS hybrid genes (Sanchez et al., 2012).
Many of these SM biosynthesis genes are silent or expressed at very low levels in standard
laboratory conditions, which has resulted in a lack of studies on SMs that may have useful
67
industrial and therapeutic properties. Given that fungi produce an array of SMs in response to
environmental stress, investigations into the SM production of A. niger strains isolated from
space environments may reveal novel mechanisms of radiation resistance in melanized fungal
species. Further, such investigations may facilitate the identification of biosynthetic gene clusters
of SMs produced in low levels in “Earth” strains, but high levels in “space” strains. Additionally,
any SMs that confer radiation resistance can potentially be harnessed as radioprotective drugs,
which can have extensive benefits in space programs and protecting humans from harmful
radiation exposure.

Here, we report the SM production of JSC-093350089, a strain of A. niger isolated from surfaces
of the International Space Station (ISS) and previously described in Chapter 3 (Romsdahl et al.,
2018). Our analysis revealed that the ISS isolate produces high levels of the antioxidant
pyranonigrin A (Miyake et al., 2007) when compared to the experimentally established strain
ATCC 1015. Oxidative stress is implicated in many human diseases, including cancer, diabetes,
cardiovascular, and neurodegenerative diseases, and therefore antioxidants have significant
therapeutic potential (Rahman et al., 2012). Identification of the genes involved in SM
biosynthesis is crucial for optimization of compound yield and engineering of second generation
molecules. We therefore developed an efficient gene targeting system in JSC-093350089 and
used targeted gene deletions to identify the biosynthesis genes responsible for pyranonigrin A
production. Finally, the radiation resistance potential of pyranonigrin A was assessed, which
demonstrated its potential use as a radioprotective agent.

68
4.3 Results
4.3.1 Secondary metabolite analysis of JSC-093350089

Figure 4-1. (A) Secondary metabolite production in JSC-093350089 relative to ATCC 1015
following growth on GMM for 5 days, as detected by UV total scan. Each individual
metabolite’s production yield is reported as increased, decreased, or no change, compared to that
of ATCC 1015. (B) Quantification of secondary metabolites showing percent change for each
metabolite in JSC-093350089 when compared to ATCC 1015. Significance was determined
using Welch’s t-test.

SM profiles of JSC-093350089 and ATCC 1015 were examined after growth on GMM agar
medium using high-performance liquid chromatography coupled with diode-array detection and
electrospray ionization tandem mass spectrometry (HPLC-DAD-MS). SMs were identified based
on mass, UV-Vis absorption, and retention time, which were in good agreement with literature
(Chiang et al., 2011). The identity of pyranonigrin A was further verified by purchasing the pure
1. Nigragillin
2. PyranonigrinA
3. NigerazineB
4. PestalamideB
5. BicoumanigrinA
6. Unknown (albApathway)
7. AurasperoneC
8. AurasperoneB
9. Kotanin
10. FonsecinoneB
11. FonsecinoneC
12. FonecinoneC derivative
13. FonsecinoneA
14. AsperpyroneB
15. AurasperoneA
16. AsperpyroneC
10 15 20 25 30 40 35
Time (min)
1
1
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
11,12
10
12
13
15
13
14
15
16
ATCC 1015
JSC-093350089
A
B
Nigragillin PyranonigrinA NigerazineB PestalamideB
BicoumanigrinA Kotanin albApathway SMs
Increase compared to ATCC 1015
Decrease compared to ATCC 1015
No statistical difference compared
to ATCC 1015
ATCC 1015
JSC-093350089
69
compound from Enzo Life Sciences. The data revealed that each strain produced a distinct SM
profile, with the production yield of most SMs significantly altered (Figure 4-1). Production
yield analysis was carried out for each SM (Figure 4-1B). Compared to ATCC 1015, a
significant decrease in the production of nigragillin, an insecticide (Isogai et al., 1975), was
observed (P = 0.0001). The most significant difference was observed with the antioxidant
pyranonigrin A (Miyake et al., 2007), which exhibited a 6000% increase in production in JSC-
093350089 (P = 0.04), as it was produced at basal levels in ATCC 1015. Nigerazine B displayed
no statistical difference in production levels (P = 0.06). In the ISS strain, pestalamide B
production was approximately 2 times that of ATCC 1015 (P = 0.03), and biocoumanigrin A,
which was reported to have cytotoxic activity against human cancer cell lines (24), exhibited a
production yield 2.5 times that of ATCC 1015 (P = 0.01). Kotanin production in the ISS strain
was approximately 10 times that of ATCC 1015 (P = 0.03).

The majority of SMs produced were identified as naphtho-γ-pyrones, including aurasperone A, B
and C, fonsecinone A, B and C, a fonsecinone C derivative, asperpyrone B and C. These SMs,
highlighted in green in Figure 4-1A, are biosynthesized by the PKS AlbA (Chiang et al., 2011).
The molecular formula of the final SM was predicted using high-resolution mass spectrometry,
and a thorough literature search revealed that no known A. niger SM matched this formula. This
SM was later determined to also be biosynthesized by the albA pathway when A. niger devoid of
AlbA failed to produce the unknown compound. The combined production yields of albA
pathway SMs were approximately 2.5 times higher in the ISS stain compared to ATCC 1015 (P
= 0.03).

70
4.3.2 Analysis of the potential gene clusters responsible for production of pyranonigrin A in
silico
Considering the significant therapeutic and radioresistant potential of pyranonigrin A, we set out
to identify the genes involved in its biosynthesis. To identify the biosynthetic gene cluster
responsible for pyranonigrin A production, we searched for the core backbone synthase gene
involved its biosynthesis. The biosynthetic pathway of pyranonigrin E, a SM very similar to
pyranonigrin A, was recently proposed and pynA (An11g00250) was identified as the PKS-
NRPS hybrid involved in its biosynthesis (Awakawa et al., 2013). We hypothesized that
pyranonigrin A is either biosynthesized by the same cluster responsible for pyranonigrin E
production, or by a different cluster harboring a PKS-NRPS hybrid gene similar to pynA. The
genome of ATCC 1015 possesses 8 PKS-NRPS hybrid genes other than pynA. BLAST analysis
was performed using the Joint Genome Institute (JGI) MycoCosm database (Grigoriev et al.,
2014b) on the 8 remaining PKS-NRPS hybrids to determine which genes possessed high
sequence homology to pynA. The results revealed that An18g00520 possessed high sequence
similarity to pynA, with 53.4% sequence identity and 89.8% subject coverage (Table 4-1).

Table 4-1. Comparison of A. niger PKS-NRPSs to pyranonigrin E-producing PKS-NRPS

ATCC 1015 gene
(JGI Designation)
CBS 513.88 gene
(NCBI Designation)
% Identity % Subject Coverage
Aspni7:1128344 An18g00520 53.4 89.8
Aspni7:1188722 An08g03790 41.1 38.5
Aspni7:1112058 An11g06460 40.1 40.1
Aspni7:1170655 no homolog 41.2 36.6
Aspni7:1087173 no homolog 40.5 38.3
Aspni7:1122199 An02g08290 40.6 35.4
Aspni7:1099903 An14g01910 38.4 38.4
Aspni7:1115863 An14g04850 39.5 42.6

71
4.3.3 Development of an efficient gene targeting system in JSC-093350089 and
identification of the PKS-NRPS hybrid responsible for pyranonigrin A biosynthesis

Figure 4-2. DAD total scan and MS total ion current (TIC) HPLC profiles of extracts from JSC-
093350089 WT and mutant strains pynA- and pyrA-. Highlighted peaks indicate pyranonigrin A
production.

To determine which of the two putative PKS-NRPS hybrids is involved in the biosynthesis of
pyranonigrin A, a genetic system was developed in JSC-093350089. The kusA gene was first
deleted to decrease the rate of nonhomologous integration of transforming DNA fragments,
thereby improving gene targeting (Meyer et al., 2007). Next, the pyrG gene was deleted in the
kusA- background to generate CW12003, an auxotrophic mutant that requires uracil and uridine
supplementation (Arentshorst et al., 2015). CW12003 was then used to generate mutant strains
CW12004 and CW12005, which had the pynA gene and An18g00520 genes deleted,
respectively. JSC-093350089 and the two mutant strains were then cultured on GMM, and SMs
were extracted following 5 days of growth and subjected to HPLC-DAD-MS analysis.
Observation of SM traces as detected by UV-Vis total scan and mass spectroscopy in positive
ion mode [M+H]
+
m/z = 224 revealed an increase in production of pyranonigrin A in pynA-
(CW12004) and the complete elimination of pyranonigrin A in An18g00520- (CW12005),
DAD (Total scan) MS (Total ion current)
10 15 20 25 30 35 40
Time (min)
10 15 20 25 30 35 40
Time (min)
WT
pynA-
pyrA-
WT
pynA-
pyrA-
72
revealing that An18g00520 is responsible for the production of pyranonigrin A (Figure 4-2). This
finding was recently confirmed in Pencillium thymicola and reported while our work was being
completed (Tang et al., 2018).

Table 4-2. Putative function of genes within the pyranonigrin A gene cluster and their homologs
in Penicillium thymicola

4.3.4 Identification of pyranonigrin A biosynthesis gene cluster boundaries
Next, we aimed to identify additional genes involved in pyranonigrin A biosynthesis and any
detectable intermediates. To accomplish this, we identified genes surrounding pyrA (Table 4-2
and Figure 4-3A) as the genes involved in fungal SM biosynthesis are usually clustered in the
genome (Keller et al., 2005). Interestingly, when we compared the genes surrounding pyrA in A.
niger to their homologs in P. thymicola using the JGI MycoCosm database (Grigoriev et al.,
2014b), we noticed that the distribution of genes surrounding pyrA differed between the P.
thymicola and A. niger genomes (Tang et al., 2018). For example, pyrD, a hydrolase predicted to
be involved in pyranonigrin A biosynthesis in P. thymicola, is adjacent to pyrC in the P.
thymicola genome and has only two genes separating it from pyrA. However, its homolog within
the A. niger genome, An18g00470, is adjacent to An18g00480 and has four genes separating it
from pyrA. Similarly, although An18g00510 is adjacent to pyrA in the A. niger genome, its P.
thymicola homolog has three genes separating it from pyrA in the P. thymicola genome, and was
Pyranongrin A
biosynthesis gene
Gene Name
P. thymicola homolog
JGI Protein ID
Identity (%) Putative Function
An18g00470 180933 (pyrD) 148/256 (58%) Hydrolase
An18g00480 104760 386/554 (70%) Cycloheximide resistance protein
pyrC An18g00490 104735 190/269 (71%) FAD binding monooxygenase
pyrB An18g00500 104701 296/453 (65%) Cytochrome P450
pyrE An18g00510 168730 326/454 (72%) FAD binding oxidoreductase
pyrA An18g00520 168734 2371/3881 (61%) PKS-NRPS hybrid
An18g00530 Hypothetical protein
Table 1. Putative function of genes within the pyranonigrin A gene cluster and their homologs in Penicillium thymicola
73
predicted to not be involved in pyranonigrin A biosynthesis (Tang et al., 2018). To investigate
these observations, we generated a gene deletion library to identify the genes involved in
pyranonigrin A biosynthesis.

Figure 4-3. (A) Schematic representation of pyr cluster. Arrows indicate direction of
transcription and the relative size of each open reading frame (ORF). (B) DAD total scan and
MS total ion current (TIC) HPLC profiles of extracts from the pyr mutant library generated in
JSC-093350089 with the albA- genetic background. Highlighted peaks indicate pyranonigrin A
production.

A. niger produces large quantities of naphtho-γ-pyrone SMs (Chiang et al., 2011), which we
suspected may hinder our ability to detect any intermediate compounds in JSC-093350089
tailoring enzyme deletant strains. To circumvent this, we first generated CW12006, a JSC-
DAD (Total scan) MS (Total ion current)
10 15 20 25 30 35 40 10 15 20 25 30 35 40
Time (min) Time (min)
WT
albA-
An18g00480-, albA-
pyrC-, albA-
pyrB-, albA-
pyrE-, albA-
pyrA-, albA-
An18g00530-, albA-
WT
albA-
An18g00480-, albA-
pyrC-, albA-
pyrB-, albA-
pyrE-, albA-
pyrA-, albA-
An18g00530-, albA-
An18g00480
pyrC pyrB pyrE pyrA An18g00530
5 kb
A
B
An18g00470
74
093350089 mutant strain deficient in AlbA, and therefore also deficient in naphtho-γ-pyrone
production. The AfpyrG gene was then recycled to enable the generation of additional deletion
mutations in CW12007. Next, we used CW12007 to individually delete 5 genes surrounding
pyrA and generate a pyranonigrin A biosynthetic gene cluster mutant library with albA- genetic
background. The deletant strains were cultured in pyranonigrin A-producing conditions and their
SM production was analyzed using HPLC-DAD-MS (Figure 4-3B). Deletion strains CW12009,
CW12010, and CW12011, which had An18g00490, An18g00500, and An18g00510 deleted,
respectively, resulted in the complete elimination of pyranonigrin A, which confirmed their
involvement in its biosynthesis. Deletion strains CW12008 and CW12013, which had
An18g00480 and An18g00530 deleted, respectively, resulted in unchanged SM profiles, which
indicated that these two genes are not involved in pyranonigrin A biosynthesis and are outside
the gene cluster border. These results suggest that the pyranonigrin A biosynthetic gene cluster
consists of pyrA, pyrB, pyrC, and An18g00510, which we designated as pyrE (Table 4-2 and
Figure 4-3A). These finding are in contrast to the recent study conducted in P. thymicola, which
proposed that pyrD, rather than pyrE, is involved in pyranonigrin A biosynthesis (Tang et al.,
2018).

4.3.5 Assessment of the UV resistance potential of pyranonigrin A
We hypothesized that the enhanced production levels of pyranonigrin A in the ISS isolate played
a role in protecting the strain from the high levels of radiation present in the spacecraft. This
hypothesis was evaluated by comparing the UV sensitivity of pyranonigrin A-producing JSC-
093350089 to pyranonigrin A-deficient JSC-093350089. It has been reported that kusA deletion
significantly enhances the sensitivity of A. niger to UV exposure (Meyer et al., 2007). Therefore,
75
to investigate whether pyranonigrin A confers UV resistance to A. niger, the kusA gene was first
reintegrated into the pyrA- deletion strain (CW12005) to generate CW12015. Next, the JSC-
093350089 WT and pyrA- strains were exposed to varying doses of UVC radiation ranging from
5-25 mJ/cm
2
in triplicate. The results indicated that pyranonigrin A deficiency significantly
reduces the viability of UV-exposed strains at doses greater than 15 mJ/cm
2
(Fig. 4-4). The effect
became more pronounced as the radiation dose increased, with an approximate viability
reduction of 34% observed at 15 mJ/cm
2
(P = 0.005), 43% observed at 20 mJ/cm
2
(P = 0.005),
and 68% observed at 25 mJ/cm
2
(P = 0.0002).

Figure 4-4. Percent survival following exposure to varying doses of UVC radiation for JSC-
093350089 WT and JSC-093350089 pyrA- (CW12005).

0 5 10 15 20 25
0
20
40
60
80
100
120
UVC Dose [mJ/cm
2
]
Percent Viability
WT vs pygA-
WT
pygA-
**
*
*
0 5 10 15 20 25
0
20
40
60
80
100
120
UVC Dose [mJ/cm
2
]
Percent Viability
WT vs pygA-
WT
pygA-
**
*
*
WT
pyrA-
76
4.4 Discussion
Although the persistence of A. niger in spacecraft (Checinska et al., 2015; Pierson et al., 2013)
has been well-documented, few studies have investigated how spacecraft conditions alter its SM
production. Metabolomic characterization of A. niger strains that have inhabited spacecraft can
provide valuable information about SM-based adaptation mechanisms of fungi capable of
surviving such environments and provides many economic benefits. Despite a substantial effort
to map fungal SMs to their biosynthesis genes, most clusters remain unlinked to their final
product. In many cases, this is due to low production levels of many metabolites. There is
therefore significant potential in analyzing SM production in fungal species isolated from various
extreme environments, as such conditions may naturally optimize production yields of useful
SMs, and reduce the cost of laborious laboratory-based optimization.

Metabolomic analysis of the A. niger laboratory strain and ISS isolate revealed significant
differences in SM production levels. This finding is not surprising given that fungal SM
production is highly dependent on environmental conditions. Fungal SMs are often produced to
confer selective advantage through habitat defense, competitor inhibition, chemical signaling,
nutrient acquisition, or other self-defense mechanisms (Brakhage, 2013). It remains uncertain
whether the observed SM profile differences are the direct result of exposure to unfavorable ISS
conditions, such as microgravity, enhanced radiation, and low nutrient availability. Still, this
investigation provides valuable insight into the metabolomic fingerprint of melanized fungal
species capable of withstanding the ISS environment. Further, it reveals the existence of a strain
capable of producing enhanced levels of therapeutically relevant SMs, including the antioxidant
pyranonigrin A (Miyake et al., 2007), the human cancer cytotoxic agent bicoumanigrin A (Hiort
77
et al., 2004), the antimicrobial aurasperone A (Shaaban et al., 2012), and the antifungal and
antioxidant aurasperone B (Choque et al., 2015).

A cumulative increase in production was observed for naphtho-γ-pyrones, which are produced
by the same PKS responsible for 1,8-dihydroxynaphthalene-melanin (DHN-melanin)
biosynthesis. This stays in agreement with the observed enrichment of the AlbA in the proteome
of JSC-093350089, relative to ATCC 1015 (Romsdahl et al., 2018). Such observations suggest
that the ISS isolate produces enhanced levels of DHN-melanin, and is consistent with previous
reports that fungi inhabiting high-radiation environments produce enhanced levels of melanin
(Singaravelan et al., 2008). Melanin has significant radioprotective properties and appears to
play a role in energy transduction, as melanized fungi exhibit increased rates of growth following
exposure to radiation (Dadachova and Casadevall, 2008; Dadachova et al., 2007).

Perhaps the most significant observation was enhanced production of the antioxidant
pyranonigrin A in JSC-093350089 relative to ATCC 1015, which only produces the SM at basal
levels. On the ISS, radiation capable of penetrating the spacecraft generates reactive oxygen
species (ROS) within biological systems (Gabani and Singh, 2013). Oxidative stress occurs when
ROS overwhelm an organism’s antioxidant defense mechanisms, resulting in the generation of
oxidative damage among DNA, proteins, lipids, and other vital cell components (Gabani and
Singh, 2013; Lehnert and Iyer, 2002). Antioxidants have enormous therapeutic potential, as they
neutralize the harmful effects of ROS, which can cause or exacerbate a range of human diseases,
including cancer, diabetes, cardiovascular, and neurodegenerative diseases (Firuzi et al., 2011).
Examination of metabolic reserves of fungi isolated from enhanced radiation environments
78
therefore provides a means of identifying fungal strains that produce optimized levels of specific
therapeutics, as illustrated by this study.

It is reasonable to presume that enhanced production of oxidative stress resistance agents is a key
adaptive characteristic of fungi inhabiting high-radiation environments, as this has been observed
among other species (Baqai et al., 2009; Robinson et al., 2011). This may explain the enhanced
production levels of pyranonigrin A in the ISS isolate, and is supported by the UV resistance
study, which suggests that pyranonigrin A plays a significant role in conferring radiation
resistance in A. niger. This finding illustrates the potential for pyranonigrin A to be utilized as a
radiation protective agent for other organisms present on spacecraft, such as plants or humans.
One example is through the generation of transgenic plants capable of biosynthesizing
pyranonigrin A, which may minimize harmful mutations in plant DNA that can cause tissue
damage and genetic modifications in seeds (Arena et al., 2014). Additionally, such
radioprotective compounds can potentially have enormous applications for humans in space
programs or cancer therapies (Gabani and Singh, 2013).

Although fungi have immense capacity to produce therapeutically and industrially relevant SMs,
such as pyranonigrin A (Newman and Cragg, 2012), most SM clusters are silent under standard
laboratory conditions, hindering their full potential. Genetic modification techniques provide a
powerful way to activate silent cluster and optimize production levels of specific SMs of interest
(Brakhage and Schroeckh, 2011). However, these techniques usually require knowledge of the
specific genes involved in the biosynthesis of the molecule of interest. Therefore, to facilitate
product yield optimization and the generation of potentially useful second generation
79
compounds, we generated a gene cluster deletion library and identified the genes responsible for
pyranonigrin A biosynthesis. Future studies should aim to optimize pyranongrin A production
levels through genetic engineering techniques, as such investigations may play a substantial role
in reducing production costs and expand compound availability for future testing and
development.

In summary, this study harnessed the altered SM profile of an ISS A. niger isolate to facilitate
identification of the pyranonigrin A biosynthetic gene cluster. The potential for pyranonigrin A
to be utilized by space programs or chemotherapy recipients as a radiation-resistant therapy was
assessed. These findings illustrate the economic potential associated with investigating the
metabolite production of microbes isolated from extreme environments and provide a means for
the exploitation of pyranonigrin A in various applications.

80
4.5 Materials and Methods
4.5.1 Secondary metabolite extraction and analysis
JSC-093350089 and ATCC 1015 were cultivated at 28
o
C on GMM agar plates, starting with 1 x
10
7
spores per Petri dish (D = 10 cm). After 5 days, agar was chopped into small pieces and
extracted with 25 ml methanol (MeOH), followed by 25 ml 1:1 MeOH-dichloromethane, each
with 1 hour of sonication and filtration. The extract was evaporated in vacuo and re-dissolved in
2 ml of 20% dimethyl sulfoxide in MeOH and a portion (10 μl) was examined by high
performance liquid chromatography-photodiode array detection-mass spectroscopy (HPLC-
DAD-MS) analysis. HPLC-MS was carried out using a ThermoFinnigan LCQ Advantage ion
trap mass spectrometer with a reverse-phase C18 column (3 μm; 2.1 by 100 μm; Alltech Prevail)
at a flow rate of 125 μl/min. The solvent gradient for HPLC-DAD-MS was 95% MeCN/H
2
O
(solvent B) in 5% MeCN/H
2
O (solvent A) both containing 0.05% formic acid, as follows: 0%
solvent B from 0 to 5 min, 0 to 100% solvent B from 5 min to 35 min, 100 to 0% solvent B from
40 to 45 min, and re-equilibration with 0% solve B from 45 to 50 min.

4.5.2 Strains and molecular manipulations.
The A. niger wild-type and mutant strains used in this study are listed in Table 4-3, primers used
in this study are listed in Table 4-4, and diagnostic PCR results are shown in Figure 4-5. Deletion
cassettes were generated using the double joint PCR technique (Figure 4-6) (Yu et al., 2004).
DNA insertions into the A. niger genome were performed using protoplasts and standard PEG
transformation. To develop an efficient gene targeting system in JSC-093350089, the kusA gene
was first deleted by replacing it with the hygromycin resistance marker (hph). The two amplified
flanking sequences and the hygromycin phosphortransferase gene (hph) marker cassette
81
amplified from pCB1003 (Fungal Genetics Stock Center) were fused together into one construct
by fusion PCR using nested primers, and the mutation was selected for by growth on media
containing 100 μl/ml hygromycin. Diagnostic PCR was performed on the deletant strain using
external primers (P1 and P6) from the first round of PCR. The difference in size between the
gene replaced by the selection marker and the native gene allowed us to determine whether the
transformants carried the correct gene replacement.

Table 4-3. Aspergillus niger strains used in this study
Strain # Parent strain Introduced mutation Genotype
ATCC 1015 ATCC 1015 WT None (WT)
JSC-093350089 JSC-093350089 WT None (WT)
CW12002 JSC-093350089 kusA- (An15g02700) kusA::hph
CW12003 CW12002 pyrG- (An12g03570) kusA::hph; pyrG-
CW12004 CW12003 pynA- (An11g00250) kusA::hph; pyrG-; pynA::AfpyrG
CW12005 CW12003 pyrA- (An18g00520) kusA::hph; pyrG-; pyrA::AfpyrG
CW12006 CW12003 albA- (An09g05730) kusA::hph; pyrG-; albA::AfpyrG
CW12007 CW12006 AfpyrG- kusA::hph; pyrG-; albA-
CW12008 CW12007 An18g00480- kusA::hph; pyrG-; albA-; An18g00480::AfpyrG
CW12009 CW12007 pyrC- (An18g00490) kusA::hph; pyrG-; albA-; pyrC::AfpyrG
CW12010 CW12007 pyrB- (An18g00500) kusA::hph; pyrG-; albA-; pyrB::AfpyrG
CW12011 CW12007 pyrE- (An18g00510) kusA::hph; pyrG-; albA-; pyrE::AfpyrG
CW12012 CW12007 pyrA- (An18g00520) kusA::hph; pyrG-; albA-; pyrA::AfpyrG
CW12013 CW12007 An18g00530- kusA::hph; pyrG-; albA-; An18g00530::AfpyrG
CW12014 CW12005 AfpyrG- kusA::hph; pyrG-; pyrA-
CW12015 CW12014 + kusA pyrG-; pyrA-; AfpyrG-kusA

Table 4-4. Primers used in this study (5’ à 3’)
kusA (An15g02700) deletion construct
kusA_F1 GGCCGAGAACAAGAGAACCA
kusA_F2 CGTTTCCGTTTCCTCGCTTG
kusA_R3 CGGTGAGTTCAGGCTTTTTCATTAACCAGGAACAAGTGGGGC
kusA_F4 GTCCGAGGGCAAAGGAATAGGCCTGAGGACATGAGCTTGT
kusA_R5 GTAGTGGCCGTGTCATGGAA
kusA_R6 ACGACCACGAGAGGACTACA
kusA_DFw CATCACCGCATGCACTGTTG
kusA_DRev GCACGTGACGGAAGAAGTCT

82
hph gene
hph Fw GCTGGAGCTAGTGGAGGTC
hph Rev CGGTCGGCATCTACTCTATT

pyrG (An12g03570) deletion construct
PyrG_F1 TGTGCCAGTCAATTGTCCGA
PyrG_F2 CTCCTCATCCACCGTCATCG
PyrG_R3 CTTTGCAGGTGTGGCTGAACCGGTATTGATCCTGCAGGCT
PyrG_F4 GTTCAGCCACACCTGCAAAG
PyrG_R5 CTGTACCATCAGCGCTCCTC
PyrG_R6 GCAAGCGAAGTATGGCAGTG

Af_PyrG_Fw CAATGCTCTTCACCCTCTTCG
Af_PyrG_Rev CTGAGAGGAGGCACTGATGC

pynA (An11g00250) deletion constructs
pynA_F1 ATCGCAGCAATTTCCATGCC
pynA_F2 ACAAGGTGATGGTCCGGTTC
pynA_R3 CGAAGAGGGTGAAGAGCATTGTTTGGGCCAAATTGCGAACC
pynA_F4 GCATCAGTGCCTCCTCTCAGCTGAGGATGGGGGCAGAATC
pynA_R5 CATTGCCTTCTCGACCCTGT
pynA_R6 GGCTGCACTAAGCTGTGGTA

pyrA (An18g00520) deletion constructs
pyrA_F1 CATGTCCTATTCGACCCGGG
pyrA_F2 TCGGTTCAGACCCCGAGTA
pyrA_R3 CGAAGAGGGTGAAGAGCATTGGGGTGGTGAGGGAGGAAAAG
pyrA_F4 GCATCAGTGCCTCCTCTCAGAGCCTTGTTGTCGTCCACAA
pyrA_R5 TTCCACGACAGCCACATTGT
pyrA_R6
pyrA_R3_ PyrG_recycle
pyrA_F4_PyrG_recycle
CATCGGCCCATTTCTGCATG
TTGTGGACGACAACAAGGCTGGGTGGTGAGGGAGGAAAAG
AGCCTTGTTGTCGTCCACAA

albA (An09g05730) deletion constructs
albA_F1 AGTGCAGAGTCGAGTCGAAC
albA_F2 CAAATGAACCGGCCATGCTC
albA_R3 TGACCTCCACTAGCTCCAGCCCTTCCACATCCGTGTCGAT
albA_F4 AATAGAGTAGATGCCGACCGATCAGTGCCCATGCCCAATT
albA_R5 CCCTGAAACGGAAGGTCGAA
albA_R6 CATCGCTAGAACGCAAAGCC
alba_R3_PyrG_recycle AATTGGGCATGGGCACTGATCCTTCCACATCCGTGTCGAT
alba_F4_PyrG_recycle ATCAGTGCCCATGCCCAATT

An18g00480 deletion constructs
An18g00480_F1 TCGAACTGGACAGTGCTGAC
An18g00480_F2 GATGGGAGGACACTATGCCG
An18g00480_R3 CGAAGAGGGTGAAGAGCATTGCCGCTTCCTCCCAATTTCCT
An18g00480_F4 GCATCAGTGCCTCCTCTCAGATTGTGAGGCAGCCATTCGA
An18g00480_R5 CTTGCCTTCTCCTATGCCCC
83
An18g00480_R6 TTGAAGACGTGGGGGAGTTG

pyrC (An18g00490) deletion constructs
pyrC_F1 GGTGCTACCGCTGGTATACC
pyrC_F2 CGTATCCGAAGTACAGCGCT
pyrC_R3 CGAAGAGGGTGAAGAGCATTGGACCGGACTGATGGTGTGAG
pyrC_F4 GCATCAGTGCCTCCTCTCAGGCTTGATTGGGCTTTGGGTG
pyrC_R5 GGTCCCCGAAAACTGGGTAG
pyrC_R6 TCGGCAGTCATTCCAAACGA

pyrB (An18g00500) deletion constructs
pyrB_F1 CGGCCAAGAGGTGAGGATAC
pyrB_F2 GTTGGCGAATTGGGCTCATC
pyrB_R3 CGAAGAGGGTGAAGAGCATTGCAATGGCCCTTACCACCCTT
pyrB_F4 GCATCAGTGCCTCCTCTCAGAACGAGGGTTGAAGCGAGAG
pyrB_R5 TGACAGAGTGCGGAAAGACC
pyrB_R6 TGACAAGGCCCTTCTTCGAC

pyrE (An18g00510) deletion constructs
pyrE_F1 GAAGCCAACTACCAGCGAGT
pyrE_F2 GCTCACCTGACACTTCGACA
pyrE_R3 CGAAGAGGGTGAAGAGCATTGGACAAGGCCCTTCTTCGACA
pyrE_F4 GCATCAGTGCCTCCTCTCAGCCTCCCTCAAGTTACGCGTT
pyrE_R5 GTTGGTCTGGCGCATTCATC
pyrE_R6 CAGTCGTTGTTGGGGATCCA

An18g00530 deletion constructs
An18g00530_F1 CCCGTCAATTATCTCGCGGA
An18g00530_F2 TGCACGACACTACTCAACCC
An18g00530_R3 CGAAGAGGGTGAAGAGCATTGACGGTCGTTGTCTTGTCTCC
An18g00530_F4 GCATCAGTGCCTCCTCTCAGTTGCTTGGAGCGAAGACGAT
An18g00530_R5 CAAGTTGCACGGAGGTAGGT
An18g00530_R6 GCAAGACGCTGTTGACACTG

kusA (An15g02700) reintegration construct
kusA_reint_F1
AAAAGACCGTTCGTATCGCG
kusA_reint_R3
CGAAGAGGGTGAAGAGCATTGCTTCCTTTCGGCGCTCTCTT
kusA_reint_F4 GTCCGAGGGCAAAGGAATAGGCCTGAGGACATGAGCTTGT
kusA_reint_R6 ACGACCACGAGAGGACTACA

84

Figure 4-5. Results of diagnostic PCR for JSC-093350089 mutant strains.

3000
1000
2000
4000
6000
10000
1000
2000
3000
4000
6000
10000
CW12002
kusA DFw+ DRev
WT=4838, KO=3519
CW12003
pyrGP1+P6
WT=3759, KO=2475
WT KO WT KO
CW12004
5’: pynAP1 + AfpyrG rev; KO=3032
3’: AfpyrGFw + pynA P6; KO = 3261
1000
2000
3000
4000
6000
10000
WT WT KO KO
5’ 3’
CW12005
pygA P1+P6
WT=15083, KO=4709
WT KO
1500
3000
5000
7000
10000
20000
CW12006
albA P1+P6
WT=9306, KO=4321
1000
2000
3000
4000
6000
10000
WT KO
CW12007
albA P1+P6
WT=4321, KO=2439
WT KO
1000
2000
3000
4000
6000
10000
CW12008
5’: An18g00480 P1 + AfpyrG rev; KO=3378
3’: AfpyrGFw + An18g00480 P6; KO = 3347
WT WT KO KO
5’ 3’
1000
2000
3000
4000
10000
CW12009
5’: pygB P1 + AfpyrGrev; KO=3499
3’: AfpyrGFw + pygB P6; KO = 3172
WT WT KO KO
5’ 3’
1000
2000
3000
4000
10000
CW12010
5’: pygC P1 + AfpyrG rev; KO=3187
3’: AfpyrGFw + pygC P6; KO = 3578
CW12011
5’: pygD P1 + AfpyrGrev; KO=3011
3’: AfpyrGFw + pygD P6; KO = 3184
CW12012
5’: pygAP1 + AfpyrG rev; KO= 3267
3’: AfpyrGFw + pygA P6; KO = 3331
CW12013
5’: An18g00530 P1 + AfpyrG rev; KO=3044
3’: AfpyrGFw + An18g00530 P6; KO = 2992
WT WT KO KO
5’ 3’
WT WT KO KO
5’ 3’
1000
2000
3000
4000
10000
WT WT KO KO
5’ 3’
WT WT KO KO
5’ 3’
1000
2000
3000
4000
10000
1000
2000
3000
4000
10000
1000
2000
3000
4000
10000
CW12015
kusA DFw+ DRev
WT=3519, Correct=7324
WT Correct
1000
2000
3000
4000
10000
85

Figure 4-6. Strategy for gene deletion via selection marker replacement.

Next, an auxotrophic mutant in the kusA- background was generated by deleting the pyrG gene.
Two ~1500 base pair fragments upstream and downstream of pyrG were amplified and fused
together (Figure 4-7). The mutation was selected for by growth on media supplemented with 1.5
mg/ml of 5-fluoroorotic acid (5-FOA), as only cells lacking the pyrG gene can survive when 5-
FOA is present. The correct transformants were identified by performing diagnostic PCR on the
deletant strain using external primers (P1 and P6) from the first round of PCR. All other deletant
strains were generated by replacing each target gene with the A. fumigatus pyrG gene (AfpyrG).
Double deletion mutants were generated by recycling the AfpyrG gene in CW12006. This
involved amplifying two ~1500 base pair fragments upstream and downstream from the
alba::AfpyrG region of JSC-093350089 albAΔ genome, which were then fused together. The
F1
R3
3’-flanking
F4
R6
Target gene 5’-flanking
F2
R5
5’-flanking 3’-flanking
Selection marker
Fw
Rev
5’-flanking 3’-flanking
5’-flanking 3’-flanking
Selection marker
Selection marker
Target gene
Selection marker
86
mutation was selected for by growth on media supplemented with 1.5 mg/ml of 5-fluoroorotic
acid (5-FOA). Correct transformants were identified using diagnostic PCR with external and
internal primers (P1 and AfpyrG rev; AfpyrG Fw and P6).

Figure 4-7. Strategy for pyrG deletion.

To reintegrate the kusA gene into the genome of CW12005, the AfpyrG gene used to initially
delete pyrA was deleted according to the strategy described in Fig. S3, which generated
CW12014. The kusA gene was amplified from JSC-093350089 gDNA to include ~1500 base
pair fragment upstream and ~500 bp fragment downstream from kusA. This fragment was then
fused to the AfpyrG gene and the original 3’ region amplified for initial kusA deletion (Figure 4-
8). The kusA was reintegrated into the genome of CW12014 to generate CW12015. Correct
transformants were identified using diagnostic PCR with external primers (P1 and P6).

R3
3’-flanking
F4
R6
5’-flanking
F2
R5
5’-flanking 3’-flanking
5’-flanking 3’-flanking
5’-flanking 3’-flanking
pyrG
pyrG
87

Figure 4-8. Strategy for kusA reintegration.

4.5.3 Radiation resistance analysis.
Radiation resistance was assessed using JSC-093350089 WT and CW12015. Both strains were
cultivated at 28
o
C on GMM agar plates by seeding 1 x 10
7
spores per Petri dish (D = 10 cm).
Spores were collected after 5 days of growth and counted. An equal amount of spores were
resuspended in 5 mL of GMM agar and poured onto Petri dishes consisting of 20 mL GMM
agar. Mycelia-containing plates were exposed to varying doses of UVC radiation in triplicate
using a CL-1000 Ultraviolet Crosslinker (UVP, Inc.).

kusA 5’-flanking kusA 3’-flanking
F1
3’-flanking
F4
5’-flanking kusA (An15g02700)
R3
~500 bp
R6
AfpyrG
Fw
Rev
AfpyrG
5’-flanking kusA 500bp 3’-flanking 3’-flanking
5’-flanking kusA 500bp 3’-flanking 3’-flanking
AfpyrG
88
CHAPTER V: Utilization of various culture conditions facilitates molecular genetic mining
of the Penicillium canescens metabolome

5.1 Abstract
Penicillium canescens is a known producer of various bioactive secondary metabolites (SMs).
However, due to the lack of an efficient gene targeting system, none of its biosynthetic genes had
been linked to their downstream products. We therefore developed a gene targeting system in P.
canescens, which enabled the construction of a genome-wide polyketide synthase (PKS) and
nonribosomal peptide synthetase (NRPS) deletion library consisting of deletion strains for all 29
identified PKSs and 20 identified NRPSs. The library was screened using culture conditions
selected to optimize SM diversity and quantity, as culturing fungi in different conditions often
results in the production of different SMs. Using this method, we were successfully able to link
three SMs, griseofulvin (1), 15-deoxyoxalicine B (2), and amauromine (3), to the core genes
responsible for their biosynthesis.
89
5.2 Introduction
Fungal secondary metabolites (SMs) have proven to be a very successful source for potential
drug leads (Newman and Cragg, 2012). Blockbuster drugs produced by fungi include the
antibiotic penicillin and the cholesterol-lowering statin lovastatin. Recent advances in whole
genome sequencing (WGS) of filamentous fungi has provided us with a wealth of information
about fungal secondary metabolism. However, it has also illuminated our lack of knowledge
regarding the full pharmaceutical potential of fungal SMs. Fungi possess the capacity to produce
a far greater number of SMs than have been isolated (Sanchez et al., 2012). One major reason for
this is that the majority of genes involved in SM biosynthesis are either silent or expressed at
very low levels in standard laboratory conditions (Scherlach and Hertweck, 2009). Expression of
these genes often requires exposure to a specific condition, and therefore culturing fungi in
different conditions often results in the production of different SMs (Bode et al., 2002). The SMs
produced by these “cryptic” pathways offer a promising source for new drug discovery (Challis,
2008). Further, linking known therapeutically-relevant SMs to their biosynthetic genes facilitates
genetic manipulation efforts to optimize product yields of first-generation compounds and
engineer second-generation compounds.

Penicillium canescens is a known producer of various bioactive SMs (Frisvad et al., 2004;
Kozlovskiĭ et al., 2011, 2013). Examples include the anti-fungal griseofulvin, the antibiotic
canescin (Brian et al., 1953), and the bacterial DNA primase inhibitor Sch 642305 (Nicoletti et
al., 2007). Prior to this study and the study discussed in the following chapter, no SMs had been
linked to their biosynthetic gene clusters in P. canescens. This is largely due to a previous lack of
publicly available genome sequences for P. canescens, which significantly hindered the ability to
90
pursue genetic manipulation efforts. In 2013, the Joint Genome Institute sequenced the full
genome of P. canescens and released it to the public. One characteristic of fungal SM
biosynthesis genes is that they are often clustered within the genome (Keller et al., 2005). Within
these clusters is a core backbone synthase, such as a polyketide synthase (PKS) or a non-
ribosomal peptide synthetase (NRPS), and various tailoring enzymes, which can include
oxygenases, (de)hydratases, hydrolases, methylases, and others (Fischbach and Walsh, 2006).
Fungal PKSs and NRPSs contain several conserved protein domains, which enables their
identification within genome sequences.

In this study, we searched the P. canescens genome for putative PKS and NRPS genes and
generated gene knockout library of the identified core backbone synthase genes. Next, we
screened the mutant strains in various culture conditions to link SMs to their biosynthetic gene
clusters. Herein, we report that by using this method, we have successfully linked three SMs,
griseofulvin (1), 15-deoxyoxalicine B (2), and amauromine (3), to the core genes responsible for
their biosynthesis (Figure 5-1).

Figure 5-1. Structures of compounds identified in this study. The compounds are as follows:
griseofulvin, 1; 15-deoxyoxalicine B, 2; amauromine, 3.

1 2 3
91
5.3 Results and Discussion
Table 5-1. List of all putative NRPS and PKS genes in P. canescens

Protein ID Type Putative domain architecture Downstream product
316552 NRPS A-PCP-C
315960 NRPS A-PCP-C
369610 NRPS A-PCP-C
368421 NRPS A-PCP-C-PCP-C
344392 NRPS C-A-PCP-C-A-PCP-C
367730 NRPS A-C-A-PCP-C
369609 NRPS C-A-PCP-C
370383 NRPS A-PCP-C
371741 NRPS A-PCP-C-PCP-C amauromine
372572 NRPS A-C-A-PCP-C-A-PCP-E-C-A-PCP-C-A-PCP-C-PCP
372228 NRPS A-PCP-C-PCP-C
407019 NRPS A-PCP-C-A-PCP-C
435720 NRPS C-A-PCP-E-C-A-PCP-C-A-PCP-C-A-PCP-C
439034 NRPS A-PCP-C-A-PCP-C
367729 NRPS A-PCP-C
336181 NRPS A-PCP-C-A-NAD
368433 NRPS A-PCP-E-C-A-A-PCP-C-A-PCP-E-C-PCP-C
440327 NRPS A-PCP-C
369110 NRPS A-C-A-ACP-C-ACP-C-A-C-ACP-C-C
372566 NRPS AT-PCP-C-A-PCP-E-C-A-PCP-C-A-TD
243077 PKS KS-AT-ACP griseofulvin
317201 PKS KS-AT-ACP
395714 PKS KS-AT-ACP
248590 PKS KS-AT-ACP-TE
365444 PKS KS-AT-ACP-cMT-TD
328953 PKS KS-AT-ACP-cMT-NAD
308305 PKS KS-AT-ACP-ACP-TE
366620 PKS KS-AT-ACP-ACP-TE green conidial pigment
406411 PKS KS-AT-ACP-ACP-TE
427089 PKS KS-AT-ACP-ACP-TE
399280 PKS KS-AT-ACP-ACP-ACP-TE
371551 PKS KS-AT-DH-ACP-cMT
400488 PKS KS-AT-DH-cMT-KR 15-deoxyoxalicine B
352123 PKS KS-AT-DH-cMT-KR
435808 PKS KS-AT-DH-ER-KR
320000 PKS KS-AT-DH-ER-KR
352316 PKS KS-AT-DH-ER-KR-ACP
368325 PKS KS-AT-DH-ER-KR-ACP
358616 PKS KS-AT-DH-ER-KR-ACP
385201 PKS KS-AT-DH-ER-KR-ACP
401759 PKS KS-AT-DH-ER-KR-ACP
365434 PKS KS-AT-DH-cMT-ER-KR
364328 PKS KS-AT-DH-cMT-ER-KR
443472 PKS KS-AT-DH-cMT-ER-KR
378503 PKS KS-AT-DH-cMT-ER-KR
434561 PKS KS-AT-DH-cMT-ER-KR
362850 PKS KS-AT-DH-cMT-ER-KR
394144 PKS KS-AT-DH-cMT-ER-KR
391502 PKS KS-AT-DH-cMT-ER-KR-TD

92
5.3.1 Identification of putative PKS and NRPS genes within the P. canescens genome
To search for putative PKS genes within the P. canescens genome, bioinformatics analysis was
performed using the JGI database to identify genes containing putative beta-ketoacyl synthase
and acyl transferase domains. This resulted in the identification of 29 genes, and subsequent
domain structure analysis revealed that 15 of these genes were highly-reducing PKSs (HR-
PKSs), 12 were non-reducing PKSs (NR-PKSs), and 2 were partially reducing PKSs (PR-PKSs).
To find putative NRPS genes, genes containing putative condensation and adenylation domains
were identified. This revealed the existence of 20 putative NRPS genes within the P. canescens
genome. The complete list of putative PKS and NRPS genes, along with each gene’s domain
architecture, is display in Table 1.

5.3.2 Construction of genome-wide PKS and NRPS deletion library
To facilitate the generation of a PKS and NRPS gene deletion library in P. canescens, we first
developed an efficient gene targeting system. The first task required to accomplish this was the
establishment of an efficient protocol for protoplasting and cell regeneration. Protoplasting
involves digestion of the fungal cell wall to enable transforming DNA fragments to undergo
homologous recombination within the genome. To determine optimal protoplasting conditions,
we tested different digestive enzymes combinations, and found that Vino Taste Pro (Novozymes)
resulted in sufficient cell wall degradation. We also tested varying concentrations of the osmotic
stabilizer MgSO
4
, and concluded that 1.2 M MgSO
4
was optimal for maintaining protoplast
morphology. Similarly, regeneration conditions were determined by testing various
concentrations of the ionic osmotic stabilizer KCl and the organic osmotic stabilizer sorbitol in
93
GMM media. We determined that GMM containing 0.6 M KCl resulted in optimal growth and
sporulation in regenerating protoplasts.

Figure 5-2. Strategy for ku70 deletion in P. canescens using hygromycin resistance marker
(hph).

To decrease the rate of nonhomologous recombination, and thereby improve gene targeting
efficiency, we deleted the P. canescens ku70 gene. The gene was identified by performing
BLAST analysis, which revealed that Protein ID 369793 had 84% sequence similarity to NkuA,
which is the Ku70 homolog in A. nidulans. The ku70 gene was deleted in P. canescens ATCC
10419 by replacing it with a hygromycin resistance marker (hph), using the technique illustrated
in Figure 5-1. This involved amplifying approximately 2 kb fragments upstream and downstream
of the P. canescens ku70 gene, which were fused to an hph construct amplified from pCB1003
(Fungal Genetics Stock Center). The resulting fragment was transformed into P. canescens
94
ATCC 10419 according to the established A. niger transformation protocol with the
protoplasting and regeneration modifications previously discussed. Transformants were selected
for by growth on media supplemented with 0.1 mg/mL hygromycin, and correct transformants
were identified using diagnostic PCR. Next, the pyrG gene was deleted to generate an
auxotrophic mutant with the ku70- genetic background (Figure 5-2). This involved amplifying
~2000 bp fragments upstream and downstream of the P. canescens pyrG (PcanpyrG) gene by
PCR, and fusing the two fragments together. Transformants were selected for by growth on
media supplemented with 1.5 mg/ml of 5-fluoroorotic acid (5-FOA), as only cells lacking the
pyrG gene can survive when 5-FOA is present. Correct transformants were identified using
diagnostic PCR. Knockout constructs using PcanpyrG as a selectable marker were assembled for
all 29 putative PKS and 20 putative NRPS genes (Table 5-1) using fusion PCR. Transformants
carrying the correct replacement of the target gene were identified using diagnostic PCR.

Figure 5-3. Strategy for pyrG deletion.
95
5.3.3 Verification of mutant generation
The successful generation of mutants using the developed gene targeting system was verified by
searching for the PKS gene responsible for pigment production in P. canescens. This was
accomplished by performing BLAST analysis to identify homologs of the wA gene, which is
responsible for production of the green conidial pigment in A. nidulans (Fujii et al., 2001;
Watanabe et al., 1999). We found that Protein ID 366620 possesses 82% sequence similarity to
wA. As expected, the strain featuring deletion of Protein ID 366620 displayed white
condiospores, whereas the WT and all other mutants displayed condiospores with a green
pigment (Figure 5-4).

Figure 5-4. Morphological difference between control (top wells) and mutants carrying Protein
ID 366620 deletion (bottom wells).

Griseofulvin (1) production has been reported in P. canescens (Nicoletti et al., 2007), which we
confirmed by mass spectrometry (MS) (Figure 5-5), and its biosynthetic gene cluster was
identified in Penicillium aethiopicum (Chooi et al., 2010). We therefore used BLAST analysis to
identify its homolog in P. canescens, and found that Protein ID 243077 possessed 90% sequence
similarity to the griseofulvin-producing NR-PKS GsfA. To confirm that Protein ID 243077 was
96
responsible for griseofulvin production in P. canescens, we cultured that protein’s deletion
strain, which revealed that griseofulvin production was eliminated (Figure 5-6A).

Figure 5-5. UV-Vis and ESIMS spectra of compounds 1 to 3.

5.3.4 Selection of culture media to maximize SM production
It has been well-documented that filamentous fungi produce different SMs following growth in
different media and culture conditions (Bode et al., 2002; Davis et al., 1966; Pitt et al., 1983;
Scherlach and Hertweck, 2009). To maximize the number of SMs that we could link to their
biosynthetic genes, we screened P. canescens WT on ten different solid media compositions,
Czapek (CZA), Czapek Yeast Extract Agar (CYA), Yeast Agar Glucose (YAG), Yeast Extract
Sucrose (YES), Malt Extract Agar (MEA), Malt Extract Broth (MB), Tryptone Yeast Glucose
(TYG), Glucose Minimal Media (GMM), Lactose Minimal Media (LMM), and Lactose Dextrose
Minimal Media (LCMM), and compared the resulting organic extracts using liquid
chromatography-mass spectrometry (LC-MS). We found that P. canescens produced the most
1
2
3
97
abundant and diverse profiles of SMs following growth on CZA media, and therefore used this
medium for subsequent analysis of the genome-wide PKS and NRPS deletion library.

Figure 5-6. HPLC profiles of extracts from control strain and (A) Protein ID 243077 deletant as
detected by mass spectroscopy in positive ion mode [M+H]
+
m/z = 353 (top) and UV at 254 nm
(bottom), (B) Protein ID 400488 deletant as detected by mass spectroscopy in positive ion mode
[M+H]
+
m/z = 504 (top) and UV at 254 nm (bottom), and (C) Protein ID 371741 deletant as
detected by mass spectroscopy in positive ion mode [M+H]
+
m/z = 510 (top) and UV at 254 nm
(bottom).

5.3.5 Characterization of genome-wide PKS and NRPS gene deletion library
To characterize the genome-wide PKS and NRPS gene deletion library, wild type P. canescens
and 49 deletion strains were cultured on CZA media and organic compounds were extracted
following 5 days of growth. SM production in each mutant was analyzed by liquid
chromatography-mass spectrometry (LC-MS), which revealed that Protein ID 400488 is
responsible for production of a compound (2) that eluted at approximately 24 min (Figure 5-6B),
and that Protein ID 371741 is responsible for production of a compound (3) that eluted at
approximately 32.5 min (Figure 5-6C). To elucidate the identity of compounds 2 and 3, wild type
1
EIC 353
Control
Protein ID 243077Δ
15 20 25 30 35
Time (min)
2
15 20 25 30 35
Time (min)
15 20 25 30 35 40
3
C
Time (min)
Control
Protein ID 371741Δ
15 20 25 30 35
Time (min)
Control
Protein ID 243077Δ
UV 254 nm
1
15 20 25 30 35
Time (min)
UV 254 nm
EIC 510
B A
15 20 25 30 35 40
Time (min)
3
UV 254 nm
Control Control
Protein ID 308305Δ
Protein ID 371741Δ
2
Control
Protein ID 400488Δ
Control
2
Protein ID 400488Δ
EIC 504
98
P. canescens was cultured on CZA media in large scale, and both compounds were isolated by
flash column chromatography and subsequent high performance liquid chromatography (HPLC).
The pure compounds were analyzed by MS (Figure 5-5) and
1
H-NMR (Figure 5-7 and Figure 5-
8), which allowed us to identify compound 2 as 15-deoxyoxalicine B and compound 3 as
amauromine, as the NMR spectra were in good agreement with published data (Takase et al.,
1984a; Zhang et al., 2003a). Interestingly, 15-deoxyoxalicine B, which exhibits antiinsectan
activity against the fall armyworm (Spodoptera frugiperda) (Zhang et al., 2003a), belongs to a
rare skeletal class of meroterpenoids whose biosynthesis was not been molecularly characterized
yet. We therefore generated targeted gene deletions to elucidate the biosynthetic pathway of 15-
deoxyoxalicine B, which is described in detail in Chapter 6.

Figure 5-7.
1
H-NMR spectrum of 15-deoxyoxalicine B (2) in CDCl
3
.
99

Figure 5-8. H-NMR spectrum of amauromine (3) in CDCl
3
.

Amauromine is an antibiotic with calcium channel blocking and potent vasodilating activity
(Takase et al., 1984b). Its bioactivity and unique structure has resulted in investigations into its
total synthesis by synthetic chemists (Depew et al., 1999; Takase et al., 1986). To our
knowledge, this is the first time that the core biosynthetic gene responsible for amauromine
production has been reported, and its biosynthetic pathway has not been elucidated. Its chemical
structure revealed that it is a diketopiperazine with two reversely prenylated moieties (3’-(3’,3’)-
dimethylallyls (3’-DMAs)) and is derived from two tryptophan amino acids. SMs with similar
structural characteristics have been isolated from other fungal species, including
acetylaszonalenin from Neosartorya fischeri (Yin et al., 2009), aszonalenin and its isomers from
various Aspergillus species (Bhat et al., 1990, 1993; Kimura et al., 1982; Rank et al., 2006),
100
fructigenines A and B from P. fructigenum (Arai et al., 1989), and roquefortine C from P.
roquefortii (Barrow et al., 1979). The biosynthetic pathway of acetylaszonalenin was proposed in
N. fischeri, which revealed requirement of the NRPS AnaPS, the prenyltransferase AnaPT, and
the acetyltransferase AnaAT for its biosynthesis (Yin et al., 2009). We had previously
investigated this gene cluster through heterologous expression in A. terreus, and confirmed that
AnaPS converts starter units tryptophan and anthranilic acid to cyclic dipeptide (R)-
benzodiazepinedione (Guo and Wang, 2014). AnaPT then catalyzes the transfer of the
dimethylallyl moiety to the C3 position of the cyclic dipeptide indole ring, followed by
subsequent diketopiperazine ring system formation to form aszonalenin. Acetylaszonalenin is
then produced following acetylation by AnaAT.

Figure 5-9. Proposed pathway for the biosynthesis of amauromine in P. canescens.

To identify the remaining genes involved in amauromine biosynthesis, we searched for genes
neighboring Protein ID 371741, which we had previously identified as the NRPS involved in
amauromine biosynthesis. We identified Protein ID 371742, a prenyltransferase, which was
adjacent to Protein ID 371741 and possessed 48% sequence similarity to AnaPT. Based on the
biosynthesis of aszonalenin, which requires only the NRPS and the prenyltransferase, we
propose that Proteins ID 371741 and 371742 are sufficient for amauromine biosynthesis (Figure
5-9). It biosynthesis initiates with Protein ID 371741 utilizing two tryptophan amino acids as
substrates to form a cyclic dipeptide. Protein ID 371742 then catalyzes prenylation at the C3
positions and subsequent diketopiperazine ring system formation.
N
H
OH
O
NH
2
HN
NH
O
O
N
H
H
N
N
N
O
O
N
H
H
N
2
Protein ID
371741
Protein ID
371742
101
5.4 Materials and Methods
5.4.1 Strains and molecular genetic manipulations
The P. canescens wild type and mutant strains used in this study are listed in Table 5-2. All DNA
insertions into the P. canescens genome were performed using protoplasts and standard PEG
transformation. To develop an efficient gene targeting system in P. canescens ATCC 10419, the
homolog of ku70 was first deleted by replacing it with the hygromycin resistance marker (hph)
(Figure 5-2). The two amplified flanking sequences and the hygromycin phosphortransferase
gene (hph) marker cassette amplified from pCB1003 (Fungal Genetics Stock Center) were fused
together into one construct by fusion PCR using nested primers, and the mutation was selected
for by growth on media containing 100 μl/ml hygromycin. Diagnostic PCR was performed on the
deletant strain using external primers (P1 and P6) from the first round of PCR. The difference in size
between the gene replaced by the selection marker and the native gene allowed us to determine whether
the transformants carried the correct gene replacement.

Next, we created an auxotrophic mutant in the ku70∆ background by deleting the pyrG gene
(Figure 5-3). Two ~2000 base pair fragments upstream and downstream of pyrG were amplified
and fused together. The mutation was selected for by growth on media supplemented with 1.5
mg/ml of 5-fluoroorotic acid (5-FOA), as only cells lacking the pyrG gene can survive when 5-
FOA is present. The correct transformants were identified by performing diagnostic PCR on the
deletant strain using external primers (P1 and P6) from the first round of PCR. All other deletant
strains were generated by replacing each targeted gene with the P. canescens pyrG gene
(PcanpyrG) in the kus70∆, pyrG∆ background strain of P. canescens. Construction of double
joint fusion PCR products, protoplast generation, and transformation were carried out according
to previous procedures with 1.2 M MgSO4 used for protoplasting osmotic stabilizer and 0.6 M
102
KCl used for protoplast regeneration osmotic stabilizer. Diagnostic PCR of the mutant strains
was carried out using the external primers from the first round of PCR.

5.4.2 Fermentation and LC-MS analysis
Wild type P. canescens ATCC 10419 and mutant strains were cultivated at 26
o
C on Czapek’s
agar plates (complete medium; 3 g NaNO
3
/L, 0.5 g KCl/L, 0.5 g MgSO
4
·7H
2
O/L, 0.01 g
FeSO
4
·7H
2
O/L, 1 g K
2
HPO
4
/L, 30 g sucrose/L, and agar 15 g/L) starting with 1 x 10
7
spores per
Petri dish (D = 10 cm). After 5 days of cultivation, agar was chopped into small pieces and
extracted by 80 ml MeOH followed by 80 ml 1:1 CH
2
Cl
2
/MeOH, each with 1 hour of sonication.
The extract was evaporated in vacuo to yield a water residue, which was suspended in 50 ml H
2
O
and partitioned with 50 ml EtOAc. The EtOAc layer was evaporated in vacuo, re-dissolved in 1
ml of 20% DMSO in MeOH, and a portion (10 μl) was examined by high performance liquid
chromatography-photodiode array detection-mass spectroscopy (HPLC-DAD-MS) analysis.
HPLC-MS was carried out using a ThermoFinnigan LCQ Advantage ion trap mass spectrometer
with a RP C18 column (Alltech Prevail C18 3 mm 2.1 x 100 mm) at a flow rate of 125 μl/min.
The solvent gradient for HPLC-DAD-MS was 95% MeCN/H
2
O (solvent B) in 5% MeCN/H
2
O
(solvent A), both containing 0.05% formic acid, as follows: 0% solvent B from 0 to 5 min, 0 to
100% solvent B from 5 min to 35 min, 100 to 0% solvent B from 40 to 45 min, and re-
equilibration with 0% solvent B from 45 to 50 min.

5.4.3 Isolation and characterization of metabolites
For structure elucidation, the P. canescens ATCC 10419 was cultivated on ~80 Czapek’s agar
plates (~25 mL of medium per plate, D = 10 cm) at 1 x 10
7
spores per plate at 26
o
C for 6 days.
103
Extraction was performed in the same manner as described above. The crude material was
subjected to flash chromatography and further separated via semi-preparative reverse phase
HPLC (Phenomenex Luna 5 μm C18 (2), 250 x 10 mm) with a flow rate of 5.0 ml/min and
monitored by a UV detector at 254 nm. NMR spectra were collected on a Varian VNMRS-600
spectrometer. High-resolution electrospray ionization mass spectrum (HRESI-MS) was obtained
with an Agilent Technologies 1200 series high-resolution mass spectrometer.

Table 5-2. Primers used in this study (5’ à 3’).
ku70 deletion
construct
ku70_P1 GTTGTGATCCCGAGGCTTG
ku70_P2 AGGCTTGGCAAGGTCAGAT
ku70_P3 TGACCTCCACTAGCTCCAGTGAAGCAGTGGGAGAGTGAA
ku70_p4 AATAGAGTAGATGCCGACCGTCCAGCACTTTGGCCATT
ku70_p5 AACTTTCACCCCGGCTTC
ku70_p6 CAAAGCGGCCCCTAACTT

hph gene from
pCB1003 (outside
EcoRI site)

hph Fw GTTGTAAAACGACGGCCAGT
hph Rev GCAGGTCGACTCTAGAGGATC

pyrG deletion
construct

PyrG_P1 AAGACGGCCGAATTGACA
PyrG_P2 TTGACACCCGACGGAGTT
PyrG_P3 GCATACGGATCACCTACATGCGGGGTGAAGAAGTGGTG
PyrG_P4 CATGTAGGTGATCCGTATGC
PyrG_P5 GCGTCGTGGGAGTGTTTC
PyrG_P6 CGGTTCGTCGATTCATCC

P.can_PyrG_Fw
CAATGCTCTTCACCCTCTTCG
P.can_PyrG_Rev CTGTCTGAGAGGAGGCACTG

PKS deletion
constructs

243077-P1 ACTTTCGCCGGAGGAGAC
104
243077-P2 AGGGCTCTGTCGTGATGG
243077-P3 CGAAGAGGGTGAAGAGCATTGTCTTTGATCGCCCTCTGG
243077-P4 CAGTGCCTCCTCTCAGACAGATTTTGGCGGGCTTTTTG
243077-P5 GGGCATTCCAGCAAGATG
243077-P6 CAAAGGAGACGGCGAGTC

317201-P1

TCCCCTGGCTTCTCTGTG
317201-P2 CCTCCTGGCAAACGCTTA
317201-P3 CGAAGAGGGTGAAGAGCATTGCGAGCCTGGTGTCGTTCT
317201-P4 CAGTGCCTCCTCTCAGACAGCAAACCACTGCCCCACTC
317201-P5 CACACGCGAAGGACATTG
317201-P6 ACGCGACTTCCAGTCCAC

371551-P1

TTCGGATGATCGGCCATA
371551-P2 CGGCTGTGAGCTTGTCG
371551-P3 CGAAGAGGGTGAAGAGCATTGAGGCGCGTATTGATTCCA
371551-P4 CAGTGCCTCCTCTCAGACAGcccttagccttccgcact
371551-P5 aggcaaccaacccacctt
371551-P6 CGCGAGGGGTCAGAGTAA

400488-P1

AACGACCCGCATACTGGA
400488-P2 CTCAGGCCACGAATACGC
400488-P3 CGAAGAGGGTGAAGAGCATTGACGGAACTGGTGGGGAAC
400488-P4 CAGTGCCTCCTCTCAGACAGTCAAGCCCACTTCCAAGG
400488-P5 CCCAGAGTTGTCCGATGC
400488-P6 GGTTGTCCCATCGTCCAG

352123-P1

ACAATTTCGGGCATCTGG
352123-P2 GGCATCTGGGGAGGAAAC
352123-P3 CGAAGAGGGTGAAGAGCATTGTCGAGTGTGGTGGACGAG
352123-P4 CAGTGCCTCCTCTCAGACAGCCAGATCGTGCCCTCAGT
352123-P5 TGGCCAACACAGGCATC
352123-P6 GGCCCAGTCTCCGTCATA

308305-P1

AACGCGGCTGGTACTGAC
308305-P2 CGGCCCAGTCGTTCATAC
308305-P3 CGAAGAGGGTGAAGAGCATTGCGTTCCATTCCGTTTTCC
308305-P4 CAGTGCCTCCTCTCAGACAGTTAATCGCTTGGCGTTGG
308305-P5 GCCCATCTTTTGCATTCG
308305-P6 AACACGCCCATCTTTTGC

352316-P1

GCCGTCGTATTTGCCAAG
352316-P2 TCCGGTCTAGCGAAATGG
352316-P3 CGAAGAGGGTGAAGAGCATTGTGTGGAGCCCCAAATCTC
352316-P4 CAGTGCCTCCTCTCAGACAGTGCCCCCAATAGTTCGTG
352316-P5 GGCCACACCGTTCTTCTC
105
352316-P6 TGGTCCCTACGGAATTGG

378503-P1

TCAGAGGCACCGACAATG
378503-P2 CAGTGGCCGGGTGAATAG
378503-P3 CGAAGAGGGTGAAGAGCATTGGCGTGCTGATCCTTGGTC
378503-P4 CAGTGCCTCCTCTCAGACAGCCCACTTTCAGCCGTTGT
378503-P5 TGGGATCTTCAGCGAGGT
378503-P6 GGGTTTGGCTGACATGGA

366620-P1

TTCCGCAAGCCACAATCT
366620-P2 GCGTTCGGGAAAATCGTA
366620-P3 CGAAGAGGGTGAAGAGCATTGTGGCTCGGGTAATGCTGT
366620-P4 CAGTGCCTCCTCTCAGACAGTGCAGGTCCACACCACAC
366620-P5 TCAACGAAGCCTCCAAGC
366620-P6 CCCACAGACCCAGAAACG

368325-P1

ACAAAGGTGACCGCCAAG
368325-P2 CGAGCTGCCTCTGGAGAA
368325-P3 CGAAGAGGGTGAAGAGCATTGTTCGGCCCACATTAGCTC
368325-P4 CAGTGCCTCCTCTCAGACAGCGGTGGGCCTACAAAAGA
368325-P5 TGTCGGCAATTTGGGATT
368325-P6 CGCATACCGCTTTCCATC

395714-P1

ATATCCAAGGGGCGCATC
395714-P2 TTCTGCGTCCCGGTATCT
395714-P3 CGAAGAGGGTGAAGAGCATTGGGAAAGGTGAGCCCGAAT
395714-P4 CAGTGCCTCCTCTCAGACAGTCGCCTCTCACTCGCTTC
395714-P5 TCGATGCTTTTGCTTGTCC
395714-P6 GGTCGGCATCTTTCACGA

248590-P1

TATGTGACGCGTGGGATG
248590-P2 AGCCGACTATGCCCTTCC
248590-P3 CGAAGAGGGTGAAGAGCATTGCCGAAGCGTCTCCTTTGA
248590-P4 CAGTGCCTCCTCTCAGACAGTGTTTGCGACCTTTGTTGG
248590-P5 GGAGCTGCTGGTTTGGTG
248590-P6 AGACGGCGGACATGATTC

365444-P1

TGATTAGCGCCGAGAAGC
365444-P2 CTTTCGGGTGGTCGAGTG
365444-P3 CGAAGAGGGTGAAGAGCATTGCATCGGAGATTGGCCTTG
365444-P4 CAGTGCCTCCTCTCAGACAGCGACTTGGTTCGGATTGG
365444-P5 GCGAAGGACTGGTTGTGG
365444-P6 CCTCCGTCGCTTTTCTTG

328953-P1

AGTCTGTACTGCGGGTCCA
328953-P2 CTGCGGGTCCATTGACTT
106
328953-P3 CGAAGAGGGTGAAGAGCATTGTACGGCGTCCAAGGAGAC
328953-P4 CAGTGCCTCCTCTCAGACAGCGCCACCTCGTATTGGTG
328953-P5 AGAGCGGCATCCACTTTG
328953-P6 TGAAGGAGCGGTGTGTTG

406411-P1

GACCACCCCCTGAAGCTC
406411-P2 TTGCCTGGACCAACTTCC
406411-P3 CGAAGAGGGTGAAGAGCATTGGTAGGCTGGACGCCATTG
406411-P4 CAGTGCCTCCTCTCAGACAGTTCGTCGCCAACCCTAAG
406411-P5 GGTGCGTTGCTGTCGAG
406411-P6 TGTTGAGAGGCCACTTCG

427089-P1

ATGCGAAAGCTCGACAGC
427089-P2 CGACCTTTGATGCGTTCC
427089-P3 CGAAGAGGGTGAAGAGCATTGGGTCATACGCACGGCTTT
427089-P4 CAGTGCCTCCTCTCAGACAGCTTGTCCTCCCCCTCACA
427089-P5 GCGCAGAATCGCAGTAGC
427089-P6 GCGGCAATGCCACATTA

399280-P1

CTTGCAAGGTGCTTGCTG
399280-P2 GGTTACCAAGCCGTTGTCA
399280-P3 CGAAGAGGGTGAAGAGCATTGACGGTCCCCGATAGAGGT
399280-P4 CAGTGCCTCCTCTCAGACAGCATGGCTCAAGGGATACAGG
399280-P5 CCAGGGCTCACTAGACTTCC
399280-P6 TGTTTGGGTTGCTAACTTCG

435808-P1

GACCGAGGAATGGTGTGG
435808-P2 TCCGGGTCACACACATTG
435808-P3 CGAAGAGGGTGAAGAGCATTGTGGCGAGAACGAGAAAGG
435808-P4 CAGTGCCTCCTCTCAGACAGTGAAACACACCAGCTCCATC
435808-P5 AATCAACCAGCACACCTTCC
435808-P6 GCAGTGGCCATATCAGTGAC

320000-P1

CGCTGTTCGGTCTCATGG
320000-P2 CATGGCGCATTTGATCTG
320000-P3 CGAAGAGGGTGAAGAGCATTGAAGGCCAACAGGAGTTTGG
320000-P4 CAGTGCCTCCTCTCAGACAGTGCTCACGCAGCTCATTC
320000-P5 TGGGAGTCTCTGGGAACG
320000-P6 AGCTGCTGGGGTCAGATG

358616-P1

AGCGGGCCTATGAAGAGC
358616-P2 AATCGGCCATGCACTACG
358616-P3 CGAAGAGGGTGAAGAGCATTGGGTTGGTGCGTCGATCTC
358616-P4 CAGTGCCTCCTCTCAGACAGCTTGCTGCGCTTTGTATCC
358616-P5 GCAGGCCATCTTTGTTCC
358616-P6 GCGATTTCACGTTCTTTCG
107

385201-P1

AGCGCAAAGTGCAATCG
385201-P2 TCCGGCAAATGACTGTCC
385201-P3 CGAAGAGGGTGAAGAGCATTGGTATCTCGGGTCCGGTAGC
385201-P4 CAGTGCCTCCTCTCAGACAGTGAGCAACGGTAGTGACCTC
385201-P5 GAAGGCAGGGAGACAAGTGA
385201-P6 TGTGGAGTGAGATGGTCTGG

401759-P1

TCTGTGCGACGATTCACC
401759-P2 CATAGTCGGCGGGTTTCA
401759-P3 CGAAGAGGGTGAAGAGCATTGCGTTTCCGTTCCACAACC
401759-P4 CAGTGCCTCCTCTCAGACAGTTTGCCCCCTACATGTCC
401759-P5 CGTCATGGCGCTTTCTG
401759-P6 GTTTCAGAGCCCGTGACC

365434-P1

TGAATCTCCTGGCCCTTG
365434-P2 CTGGCTCGGCTGGTTAAG
365434-P3 CGAAGAGGGTGAAGAGCATTGGAAGCAGCGTCGGAAGAG
365434-P4 CAGTGCCTCCTCTCAGACAGCGCCTTGACCATTTGTCG
365434-P5 TTGGCCTGGTGATTTCG
365434-P6 GCCGAGGGTGTTAGAGACG

364328-P1

GCTTGCCATCTCCATTCG
364328-P2 TGCGATGATGACGAGTGG
364328-P3 CGAAGAGGGTGAAGAGCATTGTATGAGCGGGGTGAAACC
364328-P4 CAGTGCCTCCTCTCAGACAGCCGCGCTAGTCTTGCTTC
364328-P5 GCTGTGTTGCGCTCGATA
364328-P6 CCTCGTTGAAAGGCTCCA

443472-P1

TGTGCAGTTGACGTGTGC
443472-P2 TCACGACAGTGGCTTTGC
443472-P3 CGAAGAGGGTGAAGAGCATTGGGCTGGAAGAAAGGATGG
443472-P4 CAGTGCCTCCTCTCAGACAGTCAGGGATGGAAGACAGAGG
443472-P5 TCAGCTGAGGAGCAGAAAGC
443472-P6 CCTTCATTCCGCTTTATAGGC

434561-P1

GTACCCAGCCAGCTTTCG
434561-P2 TGATGAAGGCGTGACAGG
434561-P3 CGAAGAGGGTGAAGAGCATTGGGCAAGGAGGTGGTATCG
434561-P4 CAGTGCCTCCTCTCAGACAGATTTGGTTGCGGACATGG
434561-P5 AATCCTGCTCGCTGTTGC
434561-P6 CCACCGATCTTTCCAAGC

362850-P1

CTTGAGCTGCCAGAGTATCG
362850-P2 TCAGCATCAAGGCTGTCAAC
362850-P3 CGAAGAGGGTGAAGAGCATTGACCGCTACAGCATCAGTTCC
108
362850-P4 CAGTGCCTCCTCTCAGACAGATTTGGATTGCTCCATCAGG
362850-P5 GGTCGAGACTTCAAGGCAAC
362850-P6 CCGGACTGCTTGAAAGATTG

394144-P1

TGTGGCGTTGGATTGAAA
394144-P2 TGAAACGTCGCTCAGTGG
394144-P3 CGAAGAGGGTGAAGAGCATTGGTTGGAATCCCGTTGTCG
394144-P4 CAGTGCCTCCTCTCAGACAGTTGCTGCGTCTTCTTTGC
394144-P5 TGCTAAAGGCCGTCAACC
394144-P6 GGCTCAATGTTGGTGTTGG

391502-P1

TGGCGACACCATTGAGAA
391502-P2 TCTCATCCCAGGCTGTCC
391502-P3 CGAAGAGGGTGAAGAGCATTGTTTTGGCTGACGGAGAGG
391502-P4 CAGTGCCTCCTCTCAGACAGTCCAATGCGAACAACGTG
391502-P5 TGCTTGTCGTCCGTTGAA
391502-P6 CCGTTCCATTGGTTGGAG

NRPS deletion
constructs

316552-P1 CCTTTGCTCCCAATGCTG
316552-P2 CAATGCTGGCGGAAAAAG
316552-P3 GAAGAGGGTGAAGAGCATTGGGCGTTCGACAATTGAGC
316552-P4 CAGTGCCTCCTCTCAGACAGCCGCCTTCCCTTATAGCC
316552-P5 TACTGCCACACGCTGGAA
316552-P6 TCACTGAGCTGCCGTCTG

315960-P1

GGCCCTCATGCTCAATGT
315960-P2 CCCTTTCCTTCCCTCGTC
315960-P3 CGAAGAGGGTGAAGAGCATTGCCTTGCGTGAATGTGCAA
315960-P4 CAGTGCCTCCTCTCAGACAGTGCGCATGTTCAGGTAGC
315960-P5 TACCATGCGGCAGTGATG
315960-P6 CGAGTCGGGAATTGTTGG

369610-P1

CCGGGACCATGTAAGCTG
369610-P2 GCTGGGGCATTCATCTTC
369610-P3 CGAAGAGGGTGAAGAGCATTGGAGTCGGCAGGTCGAGTG
369610-P4 CAGTGCCTCCTCTCAGACAGCTCGCCTGTAGCCAGACC
369610-P5 GGGTAGCGAGGTGATTGG
369610-P6 TTCGGCATTGCCTAGGAG

368421-P1

ACGTGGTTGGTGGAGACG
368421-P2 AGACGGGGATGGGTTTTC
368421-P3 CGAAGAGGGTGAAGAGCATTGCTAAGAACCGCCGAGGTG
368421-P4 CAGTGCCTCCTCTCAGACAGTGCCCGTTTCTGGTATCC
368421-P5 AGCGGGTCGTTAACATGC
109
368421-P6 GTCGGTGCCAAACAGGAG

344392-P1

ATCGCGCAGACAAAGGTC
344392-P2 TCCGTCGAGCTCAAGTCC
344392-P3 CGAAGAGGGTGAAGAGCATTGGGTGATGTCGCAGGTTCC
344392-P4 CAGTGCCTCCTCTCAGACAGATGAACCCGACCCAAAGC
344392-P5 TGCCCTCTTCCCTTGTTG
344392-P6 GCACTTATGACGGCAGCA

367730-P1

TGGCTTCGGCAAGAGAAC
367730-P2 AGCCACCCATCACGAGAC
367730-P3 CGAAGAGGGTGAAGAGCATTGGCAGCTGTGGCTGAAGGT
367730-P4 CAGTGCCTCCTCTCAGACAGCGGCGGCGTCTATAACTG
367730-P5 CGAACACCACCAGCACAG
367730-P6 CCTCGGTGCACTTGGTTC

371741-P1

CGACATGGGCGAAGAGAC
371741-P2 TGGCAAAGGTCACCGACT
371741-P3 CGAAGAGGGTGAAGAGCATTGTGCCGCAGTTGAATGAGA
371741-P4 CAGTGCCTCCTCTCAGACAGTTTCCCAGTCGATTGAACG
371741-P5 AGTCGGATGGGGGAGAAG
371741-P6 CACGGAATTGCCCAGAAC

372572-1

GGCGGCAAAGAGTCAATG
372572-2 TCCCGAGAGATGGATTGC
372572-3 CGAAGAGGGTGAAGAGCATTGAAACCCGGGAGTTGTATCG
372572-4 CAGTGCCTCCTCTCAGACAGATGGGACCTCCACTGCTG
372572-5 AGTCCGGCACGAAGTGTC
372572-6 ATCCACAAATGCGTGCTG

372228-1

GCCCCTCGGTGAAAACTC
372228-2 TTCACGTTGCACCTCAGC
372228-3 CGAAGAGGGTGAAGAGCATTGCACGGTTCTGTCGCATTG
372228-4 CAGTGCCTCCTCTCAGACAGCGCAGCCTTCTCAAGACC
372228-5 CCCTTTTGCTTGGTGCTG
372228-6 CTCATCGCCGCCATTATC

407019-P1

GACGGTTGCTCGAGGGTA
407019-P2 TGATGTGCTGGCCATGTT
407019-P3 CGAAGAGGGTGAAGAGCATTGGGGAAGCAGACGCAAAGA
407019-P4 CAGTGCCTCCTCTCAGACAGGGGCCTCCTTTTGTCGAT
407019-P5 TGGGTAAGTTCGGGCATAAG
407019-P6 GCGATCTTAGCTAGGGCTTTC

435720-P1

CGTCCAATCGGCTTATCG
435720-P2 TGCGGCAGATGCGTATAA
110
435720-P3 CGAAGAGGGTGAAGAGCATTGGAGCATGGTTGGGAATCG
435720-P4 CAGTGCCTCCTCTCAGACAGCCCGTCAAGGTTTCAAGG
435720-P5 ATTTGGACTTTCGGCAAGG
435720-P6 GACTTCGGTGGTCACATCG

439034-P1

TGGCTTTCCTCCGTGGTA
439034-P2 CGGCGTCCTTAGGGTTCT
439034-P3 CGAAGAGGGTGAAGAGCATTGTATCCCACGGCTCGGTTA
439034-P4 CAGTGCCTCCTCTCAGACAGaagccctgaagcgattatacc
439034-P5 CTCCAGTATGCTTGGGTTGG
439034-P6 CTACGCTATCTGGGCTCTCG

367729-P1

GCATTCCGGTCTTGCTATGT
367729-P2 AGTCTCGCCTCCTAGCACAA
367729-P3 CGAAGAGGGTGAAGAGCATTGGTGATCGTCGCTGCATTCTA
367729-P4 CAGTGCCTCCTCTCAGACAGATCCACCCTAACACGAGTGC
367729-P5 CTTAGGGCCCAAGATAATCG
367729-P6 TAAGCAGGCCTAACCCTTCC

336181-P1

GGTGCACGGATATTTGTCTG
336181-P2 GAGTTCTCTCGCCACCAAAC
336181-P3 CGAAGAGGGTGAAGAGCATTGAAGGAACGCATGAAATCTGG
336181-P4 CAGTGCCTCCTCTCAGACAGCTTCCTTCCACCCCTCTACC
336181-P5 GCCTTAATGTTGTCGTGCAG
336181-P6 AGGAAGGCAATCTGAGCATC

368433-P1

AGATGAAGGGCGTGATGC
368433-P2 CCTTGAGGACCCTTGCAG
368433-P3 CGAAGAGGGTGAAGAGCATTGTTTCCTGCGGTCGAAGAG
368433-P4 CAGTGCCTCCTCTCAGACAGTTTCCGTTTGGTCTTGATCC
368433-P5 CAGCCTTTCCAATCGTCTTC
368433-P6 TCTCAACCCCAATCTGCTTC

440327-P1

ctggctaggaggtcgaacag
440327-P2 gctcgaaaaaggtctgatgc
440327-P3 CGAAGAGGGTGAAGAGCATTGTGAAGACTTTAGCGGGCAGT
440327-P4 CAGTGCCTCCTCTCAGACAGAAGTTGGAAACCGTCAATGG
440327-P5 AGGGTCGTGGTACATGGAAT
440327-P6 TGGGCCACTTCTCACAATTA

369110-P1

TAGGCCGCAACAGAGACC
369110-P2 AGAGCAGGGATGCGTGAC
369110-P3 CGAAGAGGGTGAAGAGCATTGGGGACTGCCTGTCACCTG
369110-P4 CAGTGCCTCCTCTCAGACAGTTCCCACGGCGATTGTAT
369110-P5 CGCGATAGTTGGGTCCAC
369110-P6 GATCAGGGCGATGTTTGG
111

372566-P1

CACGGGATCCTTCCGATT
372566-P2 CACATGCATACCCGAGCA
372566-P3 CGAAGAGGGTGAAGAGCATTGATTGCCTGCGAAATCAGC
372566-P4 CAGTGCCTCCTCTCAGACAGAATAGAACGCCGGTCAACAG
372566-P5 GTCGTTGTCGATGGAAGGAT
372566-P6 ACTCATTGTTCCGAGCCAAA

369609-P1

CTGGCTCGTCAATCAAACAA
369609-P2 GCAGCGTCATACCAGTCAAC
369609-P3 CGAAGAGGGTGAAGAGCATTGATTCCCAAGAACCACTGTGC
369609-P4 CAGTGCCTCCTCTCAGACAGGTTTTCGCAACCCATTGACT
369609-P5 AAGTGCGACGACCTTGTACC
369609-P6 ATTCTGATTCCCTGGTGGTG

370383-1

ACTGCGAATCTCTTCGGCTA
370383-2 TCCGCTGAGTATCATTGACG
370383-3 CGAAGAGGGTGAAGAGCATTGCGCATCTGCATTGTATCCAC
370383-4 CAGTGCCTCCTCTCAGACAGTAGTGCATTGCTTGGTCAGC
370383-5 AGAGGATCCGCTCGTAAACA
370383-6 TCCAACTCCGTTTCAACTCC

112
CHAPTER VI: Molecular characterization of the biosynthetic gene cluster of a diterpenic
meroterpenoid, 15-deoxyoxalicine B, in Penicillium canescens

6.1 Abstract
Meroterpenoids are a unique class of fungal secondary metabolites (SMs) that consist of
polyketide and terpenoid subunits. The meroterpenoid 15-deoxyoxalicine B, which possesses
activity against the fall armyworm, possesses a unique chemical structure consisting of a rare
pyridinyl-α-pyrone polyketide subunit and a diterpenic subunit. Prior to this study, the
biosynthesis of diterpenic meroterpenoids was not thoroughly understood. We previously
identified the PKS gene involved in 15-deoxyoxalicine by screening a genome-wide polyketide
synthase (PKS) library in Pencillium canescens. Therefore, to further investigate the genetic and
molecular basis for the biosynthesis of 15-deoxyoxalicine B, we generated targeted genes
deletions to identify its biosynthetic gene cluster. Chemical analysis of deletion strains allowed
us to characterize 7 biosynthetic intermediates or shunt products, two of which were novel
compounds. Lastly, we proposed a comprehensive biosynthetic pathway for 15-deoxyoxalicine
B.

113
6.2 Introduction
In Chapter 5, the identification of the polyketide synthase (PKS) involved in the biosynthesis of
the diterpenic meroterpenoid, 15-deoxyoxalicine B, was reported. Meroterpenoids are a class of
secondary metabolites (SMs) that feature immense chemical diversity due to the hybrid nature of
their biosynthesis. The core backbone structures of meroterpenoids are assembled by both PKSs
and terpene cyclases, and further diversified by various tailoring enzymes (Fischbach and Walsh,
2006). Meroterpenoids are produced by a broad range of plants, fungi, and bacteria and have
diverse bioactivities (Geris and J. Simpson, 2009). Examples of known therapeutically useful
meroterpenoids include the acetylcholinesterase inhibitor territrem, which is a potential therapy
for Alzheimer’s disease (Chen et al., 1999), the acyl-CoA cholesterol acetyltransferase inhibitor
pyripyropene, which has the potential to treat and prevent of atherosclerosis (Omura et al., 1993;
Sunazuka et al., 2008), and mycophenolic acid, which is a clinically used immunosuppressive
agent (Colombo et al., 1979, 1982; Muth and Nash, 1975).

Despite the isolation and characterization of many meroterpenoids produced by fungi, the genes
involved in their biosynthesis have been revealed in only a few instances (Guo et al., 2012; Itoh
et al., 2010; Lo et al., 2012), leaving many SMs unmapped to their biosynthetic gene clusters.
Examples include oxalicines A and B, 15-deoxyoxalicines A and B, and decaturins A-F, which
have been isolated from various Penicillium species (Li et al., 2005; Ubillas et al., 1989; Wang et
al., 2013; Zhang et al., 2003a). Interestingly, these SMs belong to a unique skeletal class of
natural products, as their backbone structure is composed of a rare pyridinyl-α-pyrone polyketide
subunit (Figure 6-1, blue) and a diterpenoid subunit (Figure 6-1, red). Further, many of these
SMs have been reported to possess activity against the fall armyworm (Spodoptera frugiperda),
114
which has devastated corn fields and displayed characteristics of insecticide resistance (Sisay et
al., 2018; Yu, 1991; Yu et al., 2003).

Figure 6-1. Structurally related fungal meroterpenoids. The polyketide part is shown in blue, and
the diterpenic terpenoid part in red. Compounds 1-6 were isolated in this study.

Prior to this study, the biosynthesis of diterpenic meroterpenoids had not been thoroughly
investigated. We suspected that the early steps of 15-deoxyoxalicine B biosynthesis would be
similar to that of pyripyropene A, which possesses the rare pyridinyl-α-pyrone polyketide
subunit found in 15-deoxyoxalicine B, and was elucidated in Aspergillus fumigatus (Itoh et al.,
2010). One major difference, however, is the incorporation of a geranylgeranyl prenyl group in
15-deoxyoxalicine B, which distinguishes its final product as a diterpenic meroterpenoid.
Another feature not present in pyripyropenes is the unique asymmetric spiro carbon atom that
connects the polyketide and terpenoid subunits in 15-deoxyalicine B. Therefore, due to the
unique chemical structure and useful bioactivity of 15-deoxyoxalicine B, we generated targeted
gene deletions to molecularly characterize its biosynthetic pathway. We identified 12 contiguous
115
genes involved in the biosynthesis of 15-deoxyoxalicine B, and based on the isolation and
structural elucidation of 7 biosynthetic intermediates, two of which are novel compounds,
proposed a biosynthetic pathway for 15-deoxyoxalicine B.

116
6.3 Results and Discussion

Figure 6-2. HPLC profiles of extracts from (A) parental strain and (B) olcA∆ strain as detected
by UV-Vis at 254 nm and mass spectrometry in positive mode of extracted ion chromatogram
(EIC) at m/z 504.

6.3.1 Identification of genes involved in 15-deoxyoxalicine B biosynthesis
In Chapter 5, a genome-wide PKS and nonribosomal peptide synthetase (NRPS) mutant library
was developed in P. canescens, enabling the identification of the PKS involved in 15-
deoxyoxalicine B biosynthesis as Protein ID 400488 (OlcA) (Figure 6-2). Next, we aimed to
identify the remaining genes in the 15-deoxyoxalicine B biosynthetic gene cluster by evaluating
the putative functions of genes neighboring Protein ID 400488, and by comparing those genes to
potential homologs within the pyripyropene A biosynthetic gene cluster (Table 6-1). We found
that in addition to the pyripyropene A PKS gene, 4 other neighboring genes possessed sequence
homology to genes surrounding Protein ID 400488, including a putative terpene cyclase, an
FAD-dependent monooxygenase, a prenyltransferase, and a CoA ligase. Further, nearby genes
also possessed functions of putative tailoring enzymes, including cytochrome P450,
dehydrogenase, and hydroxylase activity. Lastly, we identified a geranylgeranyl pyrophosphate
117
synthase, which we had suspected would be present in the 15-deoxyoxalicine B biosynthetic
gene cluster due to its unique diterpenic meroterpenoid structure.

Table 6-1. Putative function of genes within the 15-deoxyoxalicine B gene cluster and their
homologs in A. fumigatus

To confirm the involvement of these genes in 15-deoxyoxalicine B biosynthesis, we individually
deleted 13 genes surrounding olcA (Table 6-1). This was accomplished by replacing each gene
with the P. canescens pyrG gene (PcanpyrG) in the P. canescens kus70∆, pyrG∆ background
strain that we developed in Chapter 5. Each of the putative gene cluster deletion strains were
then cultured under 15-deoxyoxalicine B-producing conditions and SM production was analyzed
using LC-MS. Strains harboring deletions of Protein IDs 333321, 437321, 351329, 367480,
393266, 410812, 437327, 333335, 367485, and 351342 revealed complete elimination of 15-
deoxyoxalcine B (1), and were therefore identified as genes involved in 1 biosynthesis. These
genes were designated as olcB-olcL, respectively (Figure 6-3 and Table 6-1).

gene
designation
protein
ID
A.fumigatus
homologs
(Afu6gxxxxx)
Similarity/
Identity
(%)
putative function
410805 cytoskeletal protein adducin
olcB 333321 cytochrome P450 CYP3/CYP5/CYP6/CYP9 subfamilies
olcC 351326 geranylgeranyl pyrophosphate synthase
olcD 437321 13950(pyr4) 57/41 integral membrane protein (terpene cyclase)
olcE 351329 13970(pyr5) 74/60 FAD-dependent monooxygenase
olcF 367480 short chain dehydrogenase
olcG 393266 cytochrome P450 CYP3/CYP5/CYP6/CYP9 subfamilies
olcH 410812 13980(pyr6) 68/52 prenyltransferase
olcA 400488 13930(pyr2) 59/42 PKS
olcI 437327 13920(pyr1) 71/58 CoA ligase
olcJ 333335 cytochrome P450 CYP3/CYP5/CYP6/CYP9 subfamilies
olcK 367485 hydroxylase
olcL 351342 predicted transporter (major facilitator superfamily)
367486 hypothetical protein

118

Figure 6-3. (A) Orientation of the genes surrounding the PKS olcA involved in 15-
deoxyxoalicine B biosynthesis. Each arrow indicates gene size and direction of transcription. On
the basis of a set of deletions we created and analyzed, genes shown in gray are responsible for
15-deoxyoxalicine B while those in white are not. (B) HPLC extracts of strains in the cluster as
detected by UV absorbance at 254 nm. A peak labeled by * appears at the same retention time as
1 in deletion strains. This compound was identified as griseofulvin. Peaks labeled with † denote
compounds that could not be characterized because of poor yield or instability.

6.3.2 Identification of 15-deoxyoxalicine B biosynthetic intermediates

Strains carrying deletions of genes determined to be involved in 15-deoxyoxalicine B
biosynthesis were further examined for production of new metabolites that could be designated
as biosynthetic intermediates or shunt products. While no new compounds were detected in
deletion strains olcC-, olcE-, olcH-, and olcI-, strains olcF-, olcG-, olcJ-, olcK-, and olcL-
revealed the production of intermediates or shunt products in their SM profiles, which were
designated as compounds 2-8 (Figure 6-3). For identification and structural elucidation purposes,
119
the strains producing new compounds were cultured on a large scale and target compounds were
isolated by flash column chromatography and further purified by semi-preparatory high
performance liquid chromatography (HPLC). Using a combination of NMR spectroscopy, high-
resolution electrospray ionization mass spectrometry (HRESIMS), and UV-Vis spectroscopy,
five of the resulting pure compounds were identified as previously described compounds, which
were in good agreement with previously published data. These compounds included 15-
deoxyoxalcine A (2) isolated from olcK- and olcL-, decaturin A (3) isolated from olcB- (Zhang et
al., 2003a), decaturin C (4) isolated from olcK- and olcL- (Li et al., 2005), decaturin D (5)
isolated from olcJ- (Li et al., 2005), and decaturin F (6) isolated form olcF- (Wang et al., 2013).
Additionally, two previously undescribed compounds, 7 and 8, were isolated from olcG and olcJ,
respectively.

The molecular formula of 7 was determined to be C
30
H
39
NO
4
based on HRESIMS spectral data,
representing 12 indices of hydrogen deficiency (IHD). The
1
H NMR and
13
C NMR spectra in
CD
3
OD (Tables S6-1 and S6-2) indicated its structural resemblance to Decaturin E. One main
difference in the
13
C NMR spectra was the shift of C-14 from 100.1 ppm in Decaturin E to 138.3
ppm in 7, which indicated the presence of a double bond. A similar change was observed for C-
17, which shifted from 128.1 ppm in Decaturin E to 35.7 ppm in 7, indicating the absence of the
double bond present in Decaturin E. C-16, the carbon in between C-14 and C-17 exhibited no
change in chemical shift. These observations suggested that 7 was a precursor to Decaturin E,
and that during biosynthetic conversion of 7 to Decaturin E, the C-14/C-16 alkene shifts to the
C-16/C-17 position. This shift is likely a result of hydroxylation at the C-17 position, which then
undergoes a dehydration reaction, driving the formation of the 2,3-dihydrofuran ring. This was
120
further validated by
1
H NMR, which indicated that Decaturin E had possessed one alkene proton
at the H-17 position while 7 possessed two protons at the H-17 position with a more upfield
chemical shift. The
13
C NMR chemical shift of C-11 was obtained from
1
H-
13
C gHMBC data.
Detailed analysis of
1
H-
1
H gCOSY,
1
H-
13
C gHSQC, and
1
H-
13
C gHMBC spectra allowed
complete assignment of 7 and corroborated our proposed structure. Key
1
H-
13
C gHMBC
correlations that allowed structural elucidation of 7 are shown in Figure 6-4A. The relative
stereochemistry of 7 was proposed to be analogous to Decaturin E because Decaturin E is
biosynthesized from 7. J-coupling correlations confirmed that OH-27 was in the β-phase.

Figure 6-4. (A-B) Key gHMBC correlations (C→H) of (A) predecaturin E (7) and (B) decaturin
G (8). (C) Selective 1D NOESY correlations of decaturin G (8).

The molecular formula of 8 was determined to be C
30
H
35
NO
5
based on its HRESIMS spectral
data, representing 14 IHD. The
1
H NMR and
13
C NMR spectra in CD
3
OD (Tables S6-1 and S6-2)
indicated its structural resemblance to 5, which was isolated from the same deletion strain as 8.
One main difference was the presence of a proton [δ
H
= 4.46 (1H, br t, J = 4.8 Hz)], which was
identified at H-18. The
1
H NMR spectrum of 5 had two protons [δ
H
= 2.07 (2H, m)] associated
O
O
N
HO
HO
O
O
N
O
O
HO
A B
C
121
with the H-18 position, suggesting that an additional functional group was added to the C-18
position of 8. This was verified by the fact that H-19 is a double doublet in 5, and a doublet in 8.
The molecular formula of 8 contained one additional oxygen compared to 5, which we reasoned
was from the presence of a hydroxyl group at the C-18 position of 8. The chemical shifts of C-18
and H-18 further validated this observation. To determine the stereochemistry of the
hydroxylated C-18,
1
H 1D NOESY spectroscopy was performed on H-18, which provided
evidence that they hydroxyl is in the β-phase. (Figure 6-4C). The
13
C NMR chemical shifts of C-
3, C-5, and C-27 were obtained from
1
H-
13
C gHSQC and
1
H-
13
C gHMBC data. Detailed analysis
of
1
H-
1
H gCOSY,
1
H-
13
C gHSQC, and
1
H-
13
C gHMBC data allowed complete assignment of 8
and corroborated our proposed structure. Key
1
H-
13
C gHMBC correlations that allowed structural
elucidation of 8 are shown in Figure 6-4B. Based on this information, it was determined that 5 is
the biosynthetic precursor of 8. The relative stereochemistry of the rest of 8 was proposed to be
analogous to 5 because it is biosynthesized from 5.

6.3.3 Elucidation of the biosynthetic pathway of 15-deoxyoxalicine B
Taken together, these data allowed us to propose the biosynthetic pathway of 15-deoxyoxalicine
B in P. canescens (Figure 6-5). To our knowledge, this is the first time that the biosynthesis of a
diterpenic meroterpenoid has been characterized at the molecular and genetic level. Although we
were not able to detect any early biosynthetic intermediates, 15-deoxyoxalicine B (1) possesses a
pyridinyl-α-pyrone polyketide subunit similar to pyripyropene A, for which the early steps of
biosynthesis have been proposed (Itoh et al., 2010). OlcI and OlcA possess high sequence
homology to Pyr1 and Pyr2, respectively, which are the first enzymes involved in pyripyropene
A biosynthesis. We therefore determined that the first steps of 15-deoxyoxalcine B biosynthesis
122
involve conversion of nicotinic acid to nicotinyl-CoA by CoA ligase OlcI, followed by the
condensation of two malonyl-CoA molecules by PKS OlcA to form HPPO.

Figure 6-5. Proposed biosynthetic pathways for 1 and related shunt products. (A) Proposed
biosynthesis pathways leading to the production of 1. Parts of the pathway deduced based on
similarity to the pyripyropene A biosynthetic pathway are indicated with blue arrows. Brackets
indicate hypothetical parts of the pathway. (B) Proposed shunt pathways from decaturin E in
olcJ- strain and decaturin C (4) in olcK- or olcL- strains.

The following step differs from pyripyropene A biosynthesis in that it involves the synthesis of
geranylgeranyl pyrophosphate by OlcC, which is then linked to HPPO by the prenyltransferase
OlcH. Interestingly, we didn’t observe the complete elimination of 1 in olcC-, which suggests
123
that the geranylgeranyl pyrophosphate moiety can be biosynthesized elsewhere in the absence of
OlcC. The next steps involve the epoxidation and cyclization of the terpenoid subunit, which we
propose is catalyzed by OlcE and OlcD due to their homology to Pyr5 and Pyr4, respectively, as
they catalyze those respective reactions in pyripyropene A biosynthesis. This generates the
formation of predecaturin E (7).

Compound 7 was isolated from deletion strain olcG-, which indicates that putative cytochrome
P450 OlcG is the next enzyme in the biosynthetic pathway. We propose that OlcG catalyzes the
allylic carbon oxidation of 7, which undergoes cyclization and loss of an H
2
O molecule to form
decaturin E, followed by conversion to decaturin F (6) by OlcJ. Although we were unable to
isolate decaturin E from deletion strain olcJ-, decaturin D (5) and decaturin G (8) were isolated
from olcJ-, which suggests that decaturin E is fed into a shunt pathway when OlcF is present and
OlcJ is absent to yield 5 and 8 (Figure 6-5B).

Compound 6 was isolated from deletion strain olcF-, which encodes a putative short chain
dehydrogenase. We therefore propose that OlcF catalyzes the oxidation of 6 to generate a 29-
hydroxyl-27-one intermediate, which undergoes spontaneous hemiacetal formation to generate
decaturin C (4). The generation of compound 4 and 15-deoxyoxalcine A (2) was observed in
deletion strains olcK- and olcL-, indicating that both OlcK and OlcL are involved in subsequent
biosynthesis processes following the formation of 4. Decaturin A (3) was isolated from the olcB-
deletion strain. We therefore propose that the conversion of 4 to 3 is catalyzed by OlcK and
OlcL, followed by subsequent conversion of 3 to 15-deoxyoxalcine B (1) by OlcB according to
the oxidative arrangement mechanism displayed in Figure 6-6. Further, we propose that the
124
accumulation of 2 in olcK- and olcL- deletion strains is a shunt process that 4 undergoes when
either OlcK or OlcL is absent and OlcB is present.

Figure 6-6. Proposed mechanism of oxidative rearrangement catalyzed by cytochrome P450
OlcB.

125
6.4 Materials and Methods
Table 6-2. Penicillium canescens strains used in this study.

6.4.1 Strains and Molecular Genetic Manipulations.
The P. canescens wild-type and mutant strains used in this study are listed in Table 6-2. All
DNA insertions into the P. canescens genome were performed using protoplasts and standard
PEG transformation. All deletant strains were generated by replacing each targeted gene with the
P. canescens pyrG gene in the kus70∆, pyrG∆ background strain of P. canescens, whose
generation is described in Chapter 5. Strains harboring the correct gene deletions were identified
using external primers (P1 and P6) from the first round of PCR. The difference in size between
the gene replaced by the selection marker and the native gene allowed us to determine whether
the transformant carried the correct gene replacement (Figure S6-1). For transformants in which
the size of the P1/P6 PCR products are similar to that of the control, additional diagnostic PCRs
were carried out using external primers paired with primers located within the selection marker

Label Genotype
Control ku70::hph
ku70∆, pyrG∆ ku70::hph; pyrG-
410805∆ ku70::hph; pyrG-, ProteinID410805::PcanpyrG
olcA∆ ku70::hph; pyrG-, ProteinID400488::PcanpyrG
olcB∆ ku70::hph; pyrG-, ProteinID333321::PcanpyrG
olcC∆ ku70::hph; pyrG-, ProteinID351326::PcanpyrG
olcD∆ ku70::hph; pyrG-, ProteinID437321::PcanpyrG
olcE∆ ku70::hph; pyrG-, ProteinID351329::PcanpyrG
olcF∆ ku70::hph; pyrG-, ProteinID367480::PcanpyrG
olcG∆ ku70::hph; pyrG-, ProteinID393266::PcanpyrG
olcH∆ ku70::hph; pyrG-, ProteinID410812::PcanpyrG
olcI∆ ku70::hph; pyrG-, ProteinID437327::PcanpyrG
olcJ∆ ku70::hph; pyrG-, ProteinID333335::PcanpyrG
olcK∆ ku70::hph; pyrG-, ProteinID367485::PcanpyrG
olcL∆ ku70::hph; pyrG-, ProteinID351342::PcanpyrG
367486∆ ku70::hph; pyrG-, ProteinID367486::PcanpyrG
126
gene, in which case the deletants yielded PCR products of the expected size whereas no product
would be seen in the non-deletants.

6.4.2 Cultivation and LC-MS Analysis
Wild-type P. canescens ATCC 10419 and mutant strains were cultivated at 26
o
C on Czapek’s
agar plates (complete medium; 3 g NaNO
3
/L, 0.5 g KCl/L, 0.5 g MgSO
4
·7H
2
O/L, 0.01 g
FeSO
4
·7H
2
O/L, 1 g K
2
HPO
4
/L, 30 g sucrose/L, and agar 15 g/L) starting with 1 x 10
7
spores per
Petri dish (D = 10 cm). After 5 days of cultivation, agar was chopped into small pieces and
extracted by 80 ml MeOH followed by 80 ml 1:1 CH
2
Cl
2
/MeOH, each with 1 hour of sonication.
The extract was evaporated in vacuo to yield a water residue, which was suspended in 50 ml H
2
O
and partitioned with 50 ml EtOAc. The EtOAc layer was evaporated in vacuo, re-dissolved in 1
ml of 20% DMSO in MeOH, and a portion (10 μl) was examined by liquid chromatography-
photodiode array detection-mass spectroscopy (HPLC-DAD-MS) analysis.

LC-DAD-MS was carried out using a ThermoFinnigan LCQ Advantage ion trap mass
spectrometer with a RP C18 column (Alltech Prevail C18 3 mm 2.1 x 100 mm) at a flow rate of
125 μl/min. The solvent gradient for HPLC-DAD-MS was 95% MeCN/H
2
O (solvent B) in 5%
MeCN/H
2
O (solvent A), both containing 0.05% formic acid, as follows: 0% solvent B from 0 to
5 min, 0 to 100% solvent B from 5 min to 35 min, 100 to 0% solvent B from 40 to 45 min, and
re-equilibration with 0% solvent B from 45 to 50 min.

127
6.4.3 Isolation and purification of secondary metabolites
For SM isolation and purification, the P. canescens wild-type and mutant strains were cultivated
on ~80 Czapek’s agar plates (~25 mL of medium per plate, D = 10 cm) at 1 x 10
7
spores per
plate at 26
o
C for 6 days. Similar to the method described above, the agar was chopped and
sonicated in MeOH, followed by 1:1 CH
2
Cl
2
/MeOH. The organic material was evaporated and
extracted twice with an equal volume of EtOAc. All EtOAc layers were combined and
evaporated in vacuo.

For isolation of 15-deoxyoxalicine B and its biosynthetic intermediates, the crude extract in the
EtOAc layer (~150 mg) was coated on 2.3 g C
18
silica gel (Cosomil 75C
18
-OPN, Nacalia
Tesque), which was then suspended in MeOH and applied to a silica gel column (32 x 50 mm).
After equilibrating the column to the starting solvent system of 1:9 MeOH-H
2
O, the extract was
eluted with MeOH-H
2
O mixtures of decreasing polarity (fraction A, 1:9, 150 mL; fraction B, 1:1,
150 mL; fraction C, 3:1, 150 mL; fraction D, 1:0, 150 mL). All fractions were analyzed by
HPLC-DAD-MS. Fraction C was subjected to semi-preparative reverse phase HPLC
(Phenomenex Luna 5 μm C18 (2), 250 x 10 mm) with a flow rate of 5.0 ml/min and monitored
by a UV detector at 235 nm. The gradient system was MeCN (solvent B) in 5% MeCN/H
2
O
(solvent A): 30 to 100% solvent B from 0 to 35 min, maintained at 100% from 35 to 38 min, 100
to 30% solvent B from 38 to 39 min, and re-equilibration with 30% solvent B from 39 to 43 min.
Compounds 1 (1.8 mg), 2 (0.9 mg), 3 (1.2 mg), 4 (0.9 mg), 5 (0.8 mg), 6 (1.0 mg), 7 (1.8 mg),
and 8 (0.8 mg) eluted at 9 min, 19 min, 8 min, 15 min, 22 min, 22 min, 20 min, and 14 min,
respectively.

128
6.4.4 Compound structure identification with NMR analysis
Spectral data for all identified compounds are displayed in Figure S6-3. For compound structure
elucidation,
1
H and
13
C spectra were collected on a nuclear magnetic resonance (NMR) Varian
VNMRS-600 and Varian Mercury Plus 400 spectrometers. High-resolution electrospray
ionization mass spectrum (HRESI-MS) was obtained with an Agilent Technologies 1200 series
high-resolution mass spectrometer. Optical rotations were measured with a JascoP-2000
polarimeter operating on the sodium D-line (589 nm), using a 100 mm path-length and are
reported as [α]
T
D
(concentration in g/100 mL, solvent).

129
6.5 Supporting Information
Table S6-1.
1
H-NMR Data for Compounds 7 and 8 (600 MHz in CD
3
OD) compared to
previously published data of structurally related Decaturin E (600 MHz in DMSO-d
6
) (Wang et
al., 2013).
a

7 8: R
1
= O; R
2
= OH
Decaturin E: R
1
= OH; R
2
= H
a
Figures in parentheses are multiplicities and coupling constants (J) in Hz
position Decaturin E 7 8
2
4

5
6
12
15

17
18

19
21

22

23
25

26

27
29
30
31
32
33
9.07 (1H, d, 1.9)
8.23 (1H, ddd, 8.2, 1.9,
1.4)
7.54 (1H, dd, 8.2, 4.7)
8.67 (1H, dd, 4.7, 1.4)
7.36 (1H, s)
2.85 (1H, d, 16.1)
3.01 (1H, d, 16.1)
5.66 (1H, br s)
1.98 (2H, m)

1.58 (1H, m)
1.28 (1H, m)
1.56 (1H, m)
1.41 (1H, m)
1.52 (1H, m)
0.70 (1H, m)
0.91 (1H, m)
1.60 (1H, m)
1.48 (2H, m)

2.98 (1H, m)
0.89 (3H, s)
1.62 (3H, s)
0.87 (3H, s)
0.68 (3H, s)
0.86 (3H, s)
8.98 (1H, d, 1.8)
8.23 (1H, dt, 8.4, 1,8)

7.54 (1H, dd, 8.4, 4.8)
8.59 (1H, dd, 4.8, 1.8)
6.65 (1H, s)
3.25 (1H, d, 16.2)
3.24 (1H, d, 16.2)
2.01-2.12 (2H, m)
1.46-1.51 (1H, m)
1.50-1.60 (1H, m)
1.11 (1H, dd, 12.6, 1.8)
1.34 (1H, td, 13.2, 3.6), H
ax

2.01-2.12 (1H, m), H
eq

1.43 (1H, td, 13.2, 1.8), H
ax

1.50 -1.60 (1H, m), H
eq

0.78 (1H, dd, 12.0, 1.8)
0.96 (1H, td, 13.2, 3.6), H
ax
1.78 (1H, dt, 13.2, 3.6), H
eq

1.59-1.63 (1H, m)
1.63-1.69 (1H, m)
3.13 (1H, dd, 12, 4.8)
0.87 (3H, s)
1.66 (3H, s)
1.03 (3H, s)
0.76 (3H, s)
0.93 (3H, s)
9.06 (1H, dd, 1.8, 0.9)
8.30 (1H, dt, 8.4, 1.8)

7.57 (1H, dd, 8.4, 4.8)
8.64 (1H, dd, 4.8, 1.8)
7.05 (1H, s)
3.07 (1H, d, 16.2)
3.13 (1H, d, 16.2)
5.75 (1H, br d, 4.8)
4.46 (1H, br t, 4.8)

1.70 (1H, d, 4.8)
1.51-1.67 (2H, m)

1.51-1.67 (2H, m)

1.36 (1H, br d, 12)
1.51-1.67 (1H, m)
2.37 (1H, m)
2.37 (1H, m)
2.75 (1H, m)

1.56 (3H, s)
1.78 (3H, s)
1.30 (3H, s)
1.10 (3H, s)
1.05 (3H, s)
130
Table S6-2
13
C-NMR data for compounds 7 and 8 (150 MHz in CD
3
OD) compared to
previously published data of structurally related Decaturin E (150 MHz in DMSO-d
6
) (Wang et
al., 2013).

7 8: R
1
= O; R
2
= OH
Decaturin E: R
1
= OH; R
2
= H

a,b
These assignments may be interchanged.

position Decaturin E 7 8
2
3
4
5
6
7
9
10
11
12
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
146.7
127.2
133.1
124.0
151.4
159.5
170.0
101.2
159.6
94.2
100.1
27.8
130.9
128.1
22.5
47.4
40.3
31.7
17.5
54.5
36.4
37.9
26.9
76.8
38.4
15.4
18.1
15.9
16.0
28.2
147.1
129.8
134.8
125.6
151.5
155.9
167.2
102.7
179.7
106.0
138.3
24.0
129.2
35.7
19.4
57.9
40.7
39.7
b
19.8
56.9
38.5
39.9
b
28.2
79.9
40.1
17.2
21.0
22.0
16.2
28.7
147.8
129.2
135.3
125.6
152.2
161.6
172.8
103.4
163.0
95.8
102.3
29.8
133.5
132.1
65.3
52.0
41.9
33.5
20.6
57.5
39.0
39.6
c
35.1
218.5
39.5
c
17.6
19.0
19.7
22.4
26.9
131

Figure S6-1. Results of diagnostic PCR for deletion strains.

132
Table S6-3. Spectral data of compounds

15-deoxyoxalicine B (1)
White amorphous powder; [α]
25
D
: + 47.3 (c 0.15, CH
2
Cl
2
).
UV/Vis λ
max
(MeOH)
max
: 245, 269, 331 nm.
HRESI-MS, [M + H]
+
m/z found 504.2374 calc. for C
30
H
33
NO
6
: 504.2381.

15-deoxyoxalicine A (2)
White amorphous powder.
UV/Vis λ
max
(MeOH)
max
: 238, 269, 331 nm.
HRESI-MS, [M + H]
+
m/z found 488.2435 calc. for C
30
H
33
NO
5
: 488.2341.

Decaturin A (3)
White amorphous powder; [α]
25
D
: + 73.0 (c 0.10, CH
2
Cl
2
).
UV/Vis λ
max
(MeOH)
max
: 245, 269, 333 nm.
HRESI-MS, [M + H]
+
m/z found 506.2546 calc. for C
30
H
35
NO
6
: 506.2537.

Decaturin C (4)
White amorphous powder; [α]
25
D
: + 86.7 (c 0.08, CH
2
Cl
2
).
UV/Vis λ
max
(MeOH)
max
: 238, 264, 331 nm.
HRESI-MS, [M + H]
+
m/z found 490.2591 calc. for C
30
H
35
NO
5
: 490.2588.

Decaturin D (5)
White amorphous powder; [α]
25
D
: + 85.1 (c 0.07, CH
2
Cl
2
).
UV/Vis λ
max
(MeOH)
max
: 236, 264, 331 nm.
HRESI-MS, [M + H]
+
m/z found 474.2617 calc. for C
30
H
35
NO
4
: 474.2639.

Decaturin F (6)
White amorphous powder; [α]
25
D
: + 92.8 (c 0.08, MeOH).
UV/Vis λ
max
(MeOH)
max
: 238, 269, 331 nm.
HRESI-MS, [M + H]
+
m/z found 492.2752 calc. for C
30
H
37
NO
5
: 492.2751.

Predecaturin E (7)
White amorphous powder; [α]
25
D
: - 48.7 (c 0.15, MeOH).
UV/Vis λ
max
(MeOH)
max
: 238, 324 nm.
HRESI-MS, [M + H]
+
m/z found 478.2962 calc. for C
30
H
39
NO
4
: 478.2952.

Decaturin G (8)
White amorphous powder.
UV/Vis λ
max
(MeOH)
max
: 238, 269, 333 nm.
HRESI-MS, [M + H]
+
m/z found 490.2568 calc. for C
30
H
35
NO
5
: 490.2588.

133
Chapter VII: The big picture
Advances in high-throughput screening technologies have revolutionized our ability to
investigate the characteristics of fungi. This thesis aimed to utilize these techniques to enhance
scientific understanding of fungal adaptation and plasticity methods and explore associated
biotechnological applications. Fungi have already made major contributions to human society
through the production of critical drugs, including widely used antibiotics and anti-cancer agents
(Newman and Cragg, 2012), and the production of enzymes, which are often used for biofuel
production, waste remediation, or the catalyzation of industrial reactions (Coyne et al., 2013;
Karigar and Rao, 2011; Kirk et al., 2002). However, it is more than likely that we have only
scratched the surface in understanding the full potential of this diverse group of microorganisms.
This is compounded by the fact that exposing fungi to new environments can trigger previously
unrealized responses or novel adaptation mechanisms.

7.1 Conclusions and perspectives for fungal space research
The recent revitalization of the space race by the private sector has placed us on the advent of
significant advances in human space exploration. In preparing to travel to Mars and beyond, it is
crucial that we understand the influence of space conditions on the various fungal species that
will, without question, accompany us on our journey. A significant portion of this thesis focused
on investigating characteristics of fungi exposed to the spacecraft environment, using the
International Space Station (ISS) as a research platform. Specifically, Chapter 2 investigated the
‘omic’ responses of Aspergillus nidulans to growth on the ISS, and Chapters 3 and 4 investigated
the ‘omic’ characteristics of an ISS Aspergillus niger isolate. Although these studies utilized the
same genomic, proteomic, and metabolomic analytical techniques, they differed significantly in
134
the experimental design, as one is a carefully controlled experiment and the other evaluates
various features of an isolate. When combined, both types of studies can provide a
comprehensive understanding to how fungi adapt to the spacecraft environment.

The results of the strictly controlled experiment revealed that non-synonymous alterations in
specific regions of the A. nidulans genome confer selective advantage in ISS conditions.
Interestingly, many of these mutations were observed within transposable element genes and a
cluster of uncharacterized genes. Additionally, the same mutation was often observed across
numerous samples, and occurred after growth for only 4 or 7 days. These findings highlight the
enormous adaptation potential of fungi and the minimal time required for genetic adaptation to
commence. In contrast, few differences in protein expression and metabolite production were
observed, especially in wild type samples. Whether this phenomenon is widespread for other
fungal species will require more studies, as most studies investigating ‘omic’ responses to
spacecraft environments have been limited to bacterial species (Huang et al., 2018; Wilson et al.,
2007). Such analyses are crucial to understanding the mechanisms of fungal adaptation to space
environments and to develop appropriate remediation methods.

Examination of the ISS A. niger isolate revealed a distinct molecular phenotype when compared
to a terrestrial laboratory strain that suggests increased resistance to radiation resistance and
oxidative stress, and an enhanced ability to acquire nutrients. Although no perfect control existed
for the ISS isolate, this study provided insight into the characteristics of a fungal strain capable
of inhabiting the ISS despite NASA’s strict microbial remediation protocols. Further, unlike the
strictly controlled A. nidulans experiment, this study enhances our understanding of host-
135
microbe interactions within closed systems, as the ISS-grown A. nidulans samples had no contact
with astronauts also inhabiting the ISS. More studies that characterize the differences of
spacecraft fungal isolates from ‘terrestrial’ strains are required to determine if these observations
remain true for other fungal species.

A key goal of the fungal space research projects was to identify novel biotechnological
applications associated with growth in the spacecraft environment. Accordingly, enhanced
production levels were observed for the pigment asperthecin and the antioxidant pyranonigrin A
in ISS-grown A. nidulans and ISS-isolated A. niger, respectively. Many fungal species have been
reported to produce radiation-absorbing pigments or oxidative stress-minimizing compounds in
response to exposure to high levels of radiation (Gabani and Singh, 2013). We therefore
hypothesized that increased production of these secondary metabolites (SMs) was due to their
ability to confer resistance to the high levels of radiation present on the ISS. We confirmed this
hypothesis in pyranonigrin A, revealing its potential as a radioprotective agent. Additionally, we
successfully identified the biosynthetic gene cluster responsible for biosynthesis of pyranonigrin
A, thereby facilitating product yield optimization or generation of useful second generation
compounds. Unfortunately, due to an inability to produce asperthecin in abundant levels on
Earth, we could not confirm this hypothesis for asperthecin as well. Still, these findings present
various therapeutic and economic applications, as radiation resistant molecules have the potential
to be utilized for cancer therapies and in space programs. More studies should be conducted with
both asperthecin and pyranongrin A to thoroughly evaluate their full biotechnological potential.

136
7.2 Conclusions and perspectives for fungal secondary metabolite research
This thesis also aimed to utilize various culture conditions to alter the metabolome of Pencillium
canescens and explore any associated biotechnological applications. Specifically, Chapter 5
involved screening a genome-wide polyketide synthase (PKS) and nonribosomal peptide
synthetase (NRPS) knockout library under conditions optimized for SM production, which
allowed us to link three SMs, including the diterpenic meroterpenoid 15-deoxyoxalicine B, to the
core genes responsible for their biosynthesis. In Chapter 6, the genetic and molecular basis for
the biosynthesis of 15-deoxyoxalicine B was characterized, as it belongs to a rare class of
compounds for which the biosynthesis was poorly understood. Further, this class of compounds
has been reported to possess activity against the fall armyworm (Zhang et al., 2003b), and
therefore can have various agricultural applications.

Despite reports that P. canescens is a producer of various therapeutically useful SMs (Brian et
al., 1953; Frisvad et al., 2004; Kozlovskiĭ et al., 2013; Nicoletti et al., 2007), none of its SMs had
been linked to their biosynthetic gene clusters prior to these studies. This is largely due to two
reasons. The first is that the most genes involved in the biosynthesis of SMs are either silent or
expressed at very low levels in standard laboratory conditions. We overcame this obstacle
through the utilization of the OSMAC (one strain, many compounds) approach, which involves
the methodical modification of growth temperature and media composition to alter and/or
optimize fungal SM production (Bode et al., 2002; Scherlach and Hertweck, 2006; VanderMolen
et al., 2013). The second reason is the lack of publicly available genome sequences for P.
canescens, which has significantly hindered genetic engineering efforts in this species. This
137
obstacle was overcome in 2013 when the Joint Genome Institute (JGI) sequenced the full
genome of P. canescens and made it publicly available.

In the post-genomic era, large-scale fungal sequencing initiatives, such as JGI’s 1000 Fungal
Genome project, have accelerated our ability to link SMs to their biosynthetic gene clusters.
Further, it enhances our understanding of fungal SM biosynthetic processes and the underpinning
genes that define them. Such knowledge can have enormous economic applications for
pharmaceutical production and industrial processes, as genetic engineering can be used to
optimize SM production levels or to generate useful second generation analogs. However, as
evidenced by these studies, the majority of SMs that P. canescens is capable of producing still
have not been identified or linked to their biosynthetic gene clusters, which remains true for
many other fungal species (Sanchez et al., 2012). Thorough characterization of the fungal
metabolome will require a combination of approaches in addition to OSMAC, such as
heterologous expression techniques or the use of inducible promoters, which were outside the
scope of this thesis.

138
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Abstract (if available)

Abstract Fungi are ubiquitous in nature. This is partially due to their phenotypic plasticity and ability to readily undergo adaptive alterations that enable survival in a vast array of ecological niches. For example, fungi produce various bioactive secondary metabolites (SMs) in response to external stimuli, which confer selective advantage despite not being directly required for survival. Other mechanisms of fungal persistence include melanin production in high radiation environments and the liberation of carbon from cell wall polymers during starvation. Recent advances in ‘omics’ technologies, which include genomic, proteomic, and metabolomic techniques, have revolutionized our ability to characterize the biological state of the cell. Additionally, such studies have the potential to reveal novel biotechnological opportunities due to the various therapeutic and industrial applications of fungal SMs and enzymes. The work herein utilizes environmental and culture conditions to study fungal adaptation and plasticity mechanisms and explore any relevant biotechnological applications. More specifically, it characterizes the ‘omics’ of Aspergillus species exposed to International Space Station (ISS) conditions and harnesses culture conditions to investigate the Penicillium canescens metabolome. ❧ The influence of ISS conditions on fungal ‘omics’ on Aspergillus nidulans is described in Chapter 2. The results revealed that specific and localized genetic alterations bestow selective advantage during growth in the ISS environment. Although the observed proteomic differences were minimal, especially in the wild type strain, alterations in proteins involved in carbohydrate metabolic processes, stress response, and SM biosynthesis were observed. Additionally, increased production of the pigment asperthecin was observed in ISS-grown mutant strains, perhaps to confer resistance to the high levels of radiation present on the ISS. The ‘omics’ of Aspergillus niger isolated from the ISS were investigated in Chapters 3 and 4. The data revealed that although the ISS isolate was within the genetic variance of other sequenced A. niger strains, it exhibited a unique proteome when compared to a terrestrial strain, which suggested an enhanced ability to acquire nutrients and increased radiation and oxidative stress resistance. Additionally, high production levels of the antioxidant pyranonigrin A were observed, which presented a potential biotechnological application for its use as a radiation resistance molecule. To explore this finding, we generated targeted gene deletions to confirm its radiation resistance ability. The Penicillium canescens metabolome is explored in Chapters 5 and 6. Initially, various culture conditions were utilized to optimize yield and diversity of SMs produced. Following condition selection, a genome-wide polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) deletion library was screened, which enabled three SMs to be linked to their core biosynthesis genes. One such compound, 15-deoxyoxalicine B, belongs to a unique class of compounds that exhibits activity against the fall armyworm, which has devastated corn fields. We therefore used targeted gene deletions to elucidate the biosynthetic pathway of 15-deoxyoxalicine B. When taken together, this thesis reveals the significance and various biotechnological applications associated with the characterization of fungal ‘omics’ following growth in or exposure to different conditions.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Romsdahl, Jillian Marie (author)

Core Title Harnessing environmental and culture conditions to alter fungal ‘omics’

School School of Pharmacy

Degree Doctor of Philosophy

Degree Program Molecular Pharmacology and Toxicology

Publication Date 05/10/2019

Defense Date 10/15/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Aspergillus,gene targeting,genomics,International Space Station,OAI-PMH Harvest,Penicillium,proteomics,Secondary metabolites

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Wang, Clay (committee chair), Okamoto, Curtis (committee member), Shen, Wei-Chiang (committee member)

Creator Email jillianromsdahl@gmail.com,romsdahl@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-102843

Unique identifier UC11675737

Identifier etd-RomsdahlJi-6957.pdf (filename),usctheses-c89-102843 (legacy record id)

Legacy Identifier etd-RomsdahlJi-6957.pdf

Dmrecord 102843

Document Type Dissertation

Format application/pdf (imt)

Rights Romsdahl, Jillian Marie

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA